by Submitted to the Graduate School of Wayne State ...

INVESTIGATING THE COGNITIVE PROCESSES THAT MEDIATE PROTECTION MOTIVATION THEORY: A PARALLEL-PROCESS LATENT GROWTH MODELING ANALYSIS

by MATTHEW L. COLE DISSERTATION

Submitted to the Graduate School of Wayne State University, Detroit, Michigan in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY 2008

Major: PSYCHOLOGY (CASPAL) Approved By:

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ACKNOWLEDGEMENTS I am grateful for the continued guidance of my dissertation chair, Ty Partridge, Ph.D., on this project. He served as a valued mentor and advisor throughout this time of professional and personal growth. I am extremely indebted to his initial suggestion to obtain the Mplus software and learn its application to latent growth modeling. This research would not have been possible without the patient support and guidance of Bonita Stanton, M.D., and Xiaoming Li, Ph.D., and all the members of the Pediatric Prevention Research Center team who believed in me from the start. I am thankful for the contributions and insight made by Antonia Abbey, Ph.D., specifically, that I look at the Vietnamese control group. I am thankful to all the researchers and support staff who participated in the data collection and data input over several years in this project. I am also thankful to all the youth and parents who served as participants in these studies and provided the data I have analyzed in this dissertation. Finally, I would like to thank my wife, Dana, and our daughter, Ava. Dana’s proofreading and editorial contributions were invaluable, and her encouragement, love, and steadfast support with Ava helped me remained focused and committed to completing this dissertation.

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PREFACE This research was funded by Fogarty International/AIDSFIRCA Grant 1R03TW05699-01, awarded to Linda M. Kaljee, Ph.D., and grant R01MH54983 from the National Institute of Mental Health, Bethesda, MD, awarded to Bonita Stanton, M.D.

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TABLE OF CONTENTS ACKNOWLEDGEMENTS............................................................................................................ ii PREFACE ...................................................................................................................................... iii TABLE OF CONTENTS............................................................................................................... iv LIST OF TABLES....................................................................................................................... viii LIST OF FIGURES ....................................................................................................................... xi CHAPTER 1—Introduction............................................................................................................ 1 Purpose of Dissertation and Specific Aims ................................................................................ 2 Overview of Health Behaviors and Cognitive Factors ............................................................... 3 The Role of Social Cognition Models in Health Behavior ......................................................... 4 Overview of Protection Motivation Theory................................................................................ 5 Overview of Measurement Invariance...................................................................................... 10 Overview of Longitudinal Growth Modeling ........................................................................... 13 Growth Modeling With Parallel Processes ........................................................................... 15 Restatement of Purpose and Main Research Questions............................................................ 16 CHAPTER 2—Method................................................................................................................. 18 Overview of Existing Data........................................................................................................ 18 FOK Delivered to American Youth.......................................................................................... 19 Introduction........................................................................................................................... 19 American FOK Content and Delivery .................................................................................. 20 Participants and Research Site .............................................................................................. 21 Measures ............................................................................................................................... 23 Administration of the YHRBI............................................................................................... 27 iv

FOK Delivered to Vietnamese Youth....................................................................................... 27 Introduction........................................................................................................................... 27 Vietnamese FOK Content and Delivery ............................................................................... 29 Participants and Research Site .............................................................................................. 30 Measures ............................................................................................................................... 31 Administration of the Vietnamese YHRBI........................................................................... 32 Data Analysis ............................................................................................................................ 33 Overview of the Data Analysis ............................................................................................. 33 Procedures Addressing Missing Data and Cluster Randomization ...................................... 33 Exploratory and Confirmatory Factor Analysis.................................................................... 34 Tests of Measurement Invariance ......................................................................................... 36 Single-Process and Parallel-Process Latent Growth Modeling ............................................ 37 CHAPTER 3—Results.................................................................................................................. 40 Introduction............................................................................................................................... 40 Characteristics of the Demographic Covariates and Behavioral Outcome Variables .............. 41 Demographic Covariates in American Youth....................................................................... 41 Demographic Covariates in Vietnamese Youth.................................................................... 47 Behavioral Outcome Variables in American Youth ............................................................. 51 Behavioral Outcome Variables in Vietnamese Youth .......................................................... 58 Cultural Differences in Demographic Covariates and Behavioral Outcome Variables ....... 61 Determination of Risk Behavior and Condom Use PMT Models ............................................ 64 Exploratory Factor Analysis of Risk Behavior PMT Items.................................................. 64 Confirmatory Factor Analysis of Risk Behavior PMT Items ............................................... 68

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Exploratory Factor Analysis of Condom Use PMT Items.................................................... 70 Confirmatory Factor Analysis of Condom Use PMT Items ................................................. 74 Measurement Invariance of the Risk Behavior and Condom Use PMT Models...................... 76 Internal Consistency Reliability Estimates of the Risk Behavior PMT Models................... 76 Configural Invariance of the Risk Behavior PMT Models via Single Group CFA.............. 78 Configural, Metric and Intercept Invariance of the Risk Behavior PMT Models via Multiple Group CFA............................................................................................................................ 81 Internal Consistency Reliability Estimates of the Condom Use PMT Models..................... 82 Configural Invariance of the Condom Use PMT Models via Single Group CFA................ 82 Configural, Metric and Intercept Invariance of the Condom Use PMT Models via Multiple Group CFA............................................................................................................................ 90 Parallel-Process Latent Growth Modeling Analyses ................................................................ 91 Observed Growth of the Risk Behavior PMT Constructs .................................................... 91 Observed Growth of the Condom Use PMT Constructs ...................................................... 93 Latent Growth of the Risk Behavior PMT Constructs........................................................ 101 Latent Growth of the Condom Use PMT Constructs.......................................................... 113 The Effects of Age, Gender, Educational Level, and Intervention Type on Coping- and Threat-Appraisal Trajectories ............................................................................................. 127 Relationship between the Coping- and Threat-Appraisal Processes and Self-Reported Risk and Protective Behavior...................................................................................................... 132 CHAPTER 4—Discussion.......................................................................................................... 158 Overview of Primary Findings................................................................................................ 159 Implication of Results to Behavioral Interventions and Prevention Research ....................... 162

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Potential Limitations of Current Study ................................................................................... 167 Suggestions for Future Research ............................................................................................ 168 Conclusion .............................................................................................................................. 169 REFERENCES ........................................................................................................................... 171 ABSTRACT................................................................................................................................ 192 AUTOBIOGRAPHICAL STATEMENT................................................................................... 194

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LIST OF TABLES Table 1. Distribution of the American Youth over Five Assessment Periods ............................... 23 Table 2. List of Behaviors Assessed by The YHRBI...................................................................... 25 Table 3 Frequency of American Youth across the Study’s Five Assessment Periods .................. 26 Table 4. Frequency of the Vietnamese Youth across the Study’s Five Assessment Periods......... 30 Table 5. Frequency of Demographic Variables in American Youth............................................. 42 Table 6. Frequency of Demographic Variables in Vietnamese Youth.......................................... 48 Table 7. Frequency of Self-Reported Behaviors in American Youth ............................................ 53 Table 8. Frequency of Self-Reported Behaviors in Vietnamese Youth ......................................... 59 Table 9. Frequency of Demographic Covariates and Behavioral Outcome Variables in American Youth Sexually-Active at Baseline and Vietnamese Youth in the Treatment Group............. 62 Table 10. Seven-Factor Oblimin Solution for Risk Behavior PMT Items in American Youth at Baseline................................................................................................................................. 66 Table 11. Six-Factor Oblimin Solution for Condom Use PMT Items in American Youth SexuallyActive at Baseline and All Vietnamese Youth at Baseline .................................................... 72 Table 12. Internal Consistency Reliability Estimates of the Risk Behavior PMT Models across Time....................................................................................................................................... 77 Table 13. Goodness of Fit Indices of CFAs of the Risk Behavior PMT Models........................... 78 Table 14. Factor Loadings of CFAs of the Risk Behavior PMT Models ...................................... 79 Table 15. Tests of Measurement Invariance of the Risk Behavior PMT Model Using MGCFA.. 81 Table 16. Internal Consistency Reliability Estimates of the Condom Use PMT Models across Time....................................................................................................................................... 83 Table 17. Goodness of Fit Indices of CFAs of the Condom Use PMT Models............................. 84 viii

Table 18. Factor Loadings of CFAs of the Condom Use PMT Models........................................ 85 Table 19. Tests of Measurement Invariance of the Condom Use PMT Model Using MGCFA.... 90 Table 20. Observed Means of Risk Behavior PMT Constructs across Time in American Youth Sexually-Active and Abstinent at Baseline............................................................................ 94 Table 21. Observed Means of Risk Behavior PMT Constructs across Time in Three American FOK Intervention Groups ..................................................................................................... 96 Table 22. Observed Means of Condom Use PMT Constructs across Time in American and Vietnamese Youth.................................................................................................................. 98 Table 23. Observed Means of Condom Use PMT Constructs across Time in American Intervention Groups ............................................................................................................ 100 Table 24. Parameter Estimates for Single-Process and Parallel-Process Multiple-Group Models of Risk Behavior PMT Constructs in American Youth Sexual and Abstinent at Baseline.. 104 Table 25. Parameter Estimates for Parallel-Process Multiple-Group Model of Risk Behavior PMT Constructs in American Intervention Groups ............................................................ 105 Table 26. Parameter Estimates for Single-Process and Parallel-Process Multiple Group Models of Condom Use PMT Constructs in Vietnamese Youth ...................................................... 116 Table 27. Parameter Estimates for Single-Process and Parallel-Process Multiple Group Models of Condom Use PMT Constructs in American and Vietnamese Youth............................... 117 Table 28. Parameter Estimates for Parallel-Process Multiple-Group Model of Condom Use PMT Constructs in American Intervention Groups ..................................................................... 118 Table 29. Parameter Estimates for PPLGM of Risk Behavior PMT Constructs With TimeInvariant Covariates in American Youth ............................................................................ 129

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Table 30. Parameter Estimates for PPLGM of Condom Use PMT Constructs With TimeInvariant Covariates in American and Vietnamese Youth.................................................. 131 Table 31. Parameter Estimates for PPLGM of Risk Behavior PMT Constructs and Unprotected Sexual Behavior, Alcohol Use and Cigarette Use in American Youth Sexual and Abstinent at Baseline........................................................................................................................... 137 Table 32. Parameter Estimates for PPLGM of Risk Behavior PMT Constructs and Unprotected Sexual Behavior, Alcohol Use and Cigarette Use in American Youth in the Intervention Groups................................................................................................................................. 141 Table 33. Parameter Estimates for PPLGM of Condom Use PMT Constructs and Sexual Behavior, Alcohol Use and Cigarette Use in Vietnamese Youth ........................................ 145 Table 34. Parameter Estimates for PPLGM of Condom Use PMT Constructs and Unprotected Sexual Behavior, Alcohol Use and Cigarette Use in American Youth in the Intervention Groups................................................................................................................................. 149 Table 35. Parameter Estimates for Parallel-Process Latent Growth Model of Risk Behavior PMT Constructs and Condom Use Behavior in American Youth in the FOK Intervention Groups ............................................................................................................................................. 153 Table 36. Parameter Estimates for Parallel-Process Latent Growth Model of Condom Use PMT Constructs and Condom Use Behavior in American Youth in the FOK Intervention Groups ............................................................................................................................................. 153 Table 37. Parameter Estimates for Parallel-Process Latent Growth Model of Condom Use PMT Constructs and Condom Use Behavior in Vietnamese Youth............................................. 154

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LIST OF FIGURES Figure 1. A schematic representation of the cognitive mediating processes of PMT (from McMath & Prentice-Dunn, 2005) ........................................................................................... 7 Figure 2. PPLGM with time-invariant covariate.......................................................................... 39 Figure 3. Second-order CFA of risk behavior YHRBI items in American youth at baseline...... 69 Figure 4. Second-order CFA of condom use YHRBI items in American and Vietnamese youth at baseline ................................................................................................................................. 75 Figure 5. Developmental trajectories of coping- and threat-appraisal processes from PPLGM of risk behavior PMT constructs in the sexual- and abstinent-at-baseline American youth... 106 Figure 6. Interrelationships between the parameter estimates for PPLGM of risk behavior coping- and threat-appraisal processes in American youth abstinent at baseline............... 107 Figure 7. Interrelationships between the parameter estimates for PPLGM of risk behavior coping- and threat-appraisal processes in American youth sexually-active at baseline ..... 108 Figure 8. Developmental trajectories of coping- and threat-appraisal processes from PPLGM of risk behavior PMT constructs in the three intervention groups of American youth........... 109 Figure 9. Interrelationships between the parameter estimates for PPLGM of risk behavior coping- and threat-appraisal processes in American youth in the FOK group ................... 110 Figure 10. Interrelationships between the parameter estimates for PPLGM of risk behavior coping- and threat-appraisal processes in American youth in the FOK + ImPACT group 111 Figure 11. Interrelationships between the parameter estimates for PPLGM of risk behavior coping- and threat-appraisal processes in American youth in the FOK + ImPACT + Booster group ................................................................................................................................... 112

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Figure 12. Developmental trajectories of coping- and threat-appraisal processes, from PPLGM of condom use PMT constructs in American youth sexually-active at baseline and Vietnamese youth................................................................................................................ 119 Figure 13. Developmental trajectories of coping- and threat-appraisal processes, from PPLGM of condom use PMT constructs in American youth in the intervention groups ................. 120 Figure 14. Interrelationships between the parameter estimates for PPLGM of condom use coping- and threat-appraisal processes in Vietnamese control group youth....................... 121 Figure 15. Interrelationships between the parameter estimates for PPLGM of condom use coping- and threat-appraisal processes in Vietnamese treatment group youth................... 122 Figure 16. Interrelationships between the parameter estimates for PPLGM of condom use coping- and threat-appraisal processes in American youth sexually-active at baseline ..... 123 Figure 17. Interrelationships between the parameter estimates for PPLGM of condom use coping- and threat-appraisal processes in American youth in the FOK group ................... 124 Figure 18. Interrelationships between the parameter estimates for PPLGM of condom use coping- and threat-appraisal processes in American youth in the FOK + ImPACT group 125 Figure 19. Interrelationships between the parameter estimates for PPLGM of condom use coping- and threat-appraisal processes in American youth in the FOK + ImPACT + Booster group ................................................................................................................................... 126 Figure 20. Developmental trajectories of coping- and threat-appraisal processes and self-reported unprotected sex in American youth sexual and abstinent at baseline, from PPLGM of risk behavior PMT constructs .................................................................................................... 138

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Figure 21. Developmental trajectories of coping- and threat-appraisal processes and self-reported alcohol use in American youth sexual and abstinent at baseline, from PPLGM of risk behavior PMT constructs .................................................................................................... 139 Figure 22. Developmental trajectories of coping- and threat-appraisal processes and self-reported cigarette use in American youth sexual and abstinent at baseline, from PPLGM of risk behavior PMT constructs .................................................................................................... 140 Figure 23. Developmental trajectories of coping- and threat-appraisal processes and self-reported unprotected sex in American youth in the FOK intervention groups, from PPLGM of risk behavior PMT constructs .................................................................................................... 142 Figure 24. Developmental trajectories of coping- and threat-appraisal processes and self-reported alcohol use in American youth in the FOK intervention groups, from PPLGM of risk behavior PMT constructs .................................................................................................... 143 Figure 25. Developmental trajectories of coping- and threat-appraisal processes and self-reported cigarette use in American youth in the FOK intervention groups, from PPLGM of risk behavior PMT constructs .................................................................................................... 144 Figure 26. Developmental trajectories of coping- and threat-appraisal processes and self-reported sexual activity in Vietnamese youth in the Control and Treatment Groups, from PPLGM of condom use PMT constructs............................................................................................... 146 Figure 27. Developmental trajectories of coping- and threat-appraisal processes and self-reported alcohol use in Vietnamese youth in the Control and Treatment groups, from PPLGM of condom use PMT constructs............................................................................................... 147

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Figure 28. Developmental trajectories of coping- and threat-appraisal processes and self-reported cigarette use in Vietnamese youth, in the Control and Treatment groups, from PPLGM of condom use PMT constructs............................................................................................... 148 Figure 29. Developmental trajectories of coping- and threat-appraisal processes and self-reported unprotected sex in American youth in the FOK intervention groups, from PPLGM of condom use PMT constructs............................................................................................... 150 Figure 30. Developmental trajectories of coping- and threat-appraisal processes and self-reported alcohol use in American youth in the FOK intervention groups, from PPLGM of condom use PMT constructs............................................................................................................. 151 Figure 31. Developmental trajectories of coping- and threat-appraisal processes and self-reported cigarette use in American youth in the FOK intervention groups, from PPLGM of condom use PMT constructs............................................................................................................. 152 Figure 32. Developmental trajectories of coping- and threat-appraisal processes and self-reported condom use in American youth in the FOK intervention groups, from PPLGM of risk behavior PMT constructs .................................................................................................... 155 Figure 33. Developmental trajectories of coping- and threat-appraisal processes and self-reported condom use in American youth in the FOK intervention groups, from PPLGM of condom use PMT constructs............................................................................................................. 156 Figure 34. Developmental trajectories of coping- and threat-appraisal processes and self-reported condom use in Vietnamese youth in the Control and Treatment groups, from PPLGM of condom use PMT constructs............................................................................................... 157

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1

CHAPTER 1—Introduction Two important public health concerns in the United States are substance use and sexual risk-taking behavior. The initiation of sexual behavior and involvement with alcohol and other drugs often begins in adolescence and the consequences of substance use and unsafe sex during adolescent development are serious (Johnston, O'Malley, & Bachman, 2003; Kann, 2001). For example, adults receiving a diagnosis of acquired immunodeficiency syndrome (AIDS) frequently report contracting the human immunodeficiency virus (HIV) as an adolescent (Tapert, Aarons, Sedlar, & Brown, 2001). Additionally, sexually active adolescents have higher acquisition rates of sexually transmitted infections (STIs) than any other age group, with 8 million cases of STIs occurring yearly in people under age 25 (Centers for Disease Control and Prevention, 2006b). Since the use of alcohol, marijuana, cocaine, or other illicit drugs by adolescents is associated with increased rates of sexual risk-taking behavior, such as early onset of sexual activity, more sexual partners, and inconsistent use of condoms (Bailey, Pollock, Martin, & Lynch, 1999; Lowry, Holtzman, Truman, & Kann, 1994), it is imperative that prevention research explore not only the relationship between substance use and sexual risktaking behavior, but also the mechanisms of efficacious interventions. An intervention that has been shown to be effective in both reducing risk-taking behavior and increasing protective behavior is Focus on Kids (FOK) (Stanton et al., 1996; Stanton, Li, Galbraith, Feigelman, & Kaljee, 1996). FOK is a behavioral intervention that uses concepts from a social cognitive model, protection motivation theory (PMT), to educate adolescents about how behavioral intentions are formed in response to threats. Briefly, PMT posits that when environmental and personal factors combine to pose a potential threat, two cognitive mediating processes, threat appraisal and coping appraisal, combine to form protection motivation—the

2 intention to respond to a potential threat in either an adaptive or maladaptive manner (Rogers, 1983). FOK teaches youth that they have the option of responding to threats in either adaptive or maladaptive ways by providing values clarification and goal setting. In addition, FOK provides pedagogical exercises regarding HIV, AIDS, STIs, sexual risk-taking behaviors, contraception, human development, and condom skills training (Galbraith, Ricardo, Stanton, & Black, 1996). Purpose of Dissertation and Specific Aims The purpose of this dissertation is to investigate the cognitive processes that are postulated to mediate PMT, which is the social cognitive model used as the theoretical foundation for the FOK HIV prevention program. Longitudinal data from American and Vietnamese adolescents who received FOK and provided self-reported protective condom use behavior, and sexual and drug risk-taking behavior, were analyzed. This dissertation focused on three specific aims: (1) Expanding on previous analyses using these data (Kaljee, Genberg, Riel et al., 2005; Stanton et al., 2004), the stability of PMT constructs within and between cultures (United States and Viet Nam), intervention (FOK treatment and control groups), and across time (the repeated measures of longitudinal data), were examined using confirmatory factory analysis to demonstrate measurement invariance (Vandenberg & Lance, 2000); (2) Parallel-process latent growth modeling (Muthén & Curran, 1997) was conducted to examine developmental trajectories of PMT’s coping- and threat-appraisal processes, as well as individual (i.e., gender, age and grade) and contextual (culture and intervention type) predictors of within- and between-person differences in growth trajectories;

3 (3) An examination of the interrelationships between trajectories of coping- and threatappraisal and the following self-reported risk and protective behaviors: sexual behavior (i.e. vaginal intercourse), cigarette use, alcohol use, and protective condom use behavior. Overview of Health Behaviors and Cognitive Factors Risk-taking behaviors contribute to a substantial proportion of deaths in Western societies and industrialized countries through individual actions and the affects these actions may have on the health of others (Conner & Norman, 2005). For example, unprotected sex may contribute to health problems in the individual (e.g. STI/HIV infection) and health problems in others via the potential transmission of STIs or HIV. Fortunately, the behavioral repertoires that comprise risk-taking and maladaptive behavior are modifiable, as evidenced, when individuals adopt specific health-promoting behaviors (e.g. condom use), avoid health-threatening maladaptive behaviors (e.g. smoking and drinking), and engage in preventative actions that support adaptive health behaviors (e.g., education on correct condom use). Since the 1980’s, research aimed at identifying the intrinsic factors that mediate both maladaptive and adaptive health behaviors has steadily gained momentum within psychology and other behavioral health sciences (e.g., Adler & Matthews, 1994; Winett, 1985). Research targeting such intrinsic factors as sociodemographic characteristics, personality traits, cognitive processes, and social support mechanisms, has been motivated by an interest in both understanding the reasons why individuals perform adaptive health-promoting or maladaptive health-threatening behaviors and in designing interventions to adjust the prevalence of these behaviors to improve individuals’ and populations’ health (Conner & Norman, 1996; Conner & Norman, 2001). Of the intrinsic factors that have received the most attention from psychologists, cognitive processes are one of the most important proximal determinants of whether or not health

4 behaviors will occur (Conner & Norman, 2005). For example, beliefs, attitudes, perceptions, appraisals and knowledge have been central to the design of a small number of widely used models of health behavior. Such models have been termed social cognition models (SCMs) due to their use of cognitive processes for understanding and predicting health behaviors. The Role of Social Cognition Models in Health Behavior SCMs describe key cognitions and their role in the regulation and prediction of behavior. Two categories of SCMs have been developed and applied in health psychology to explain health-related behaviors and predict future health-related treatment outcomes. The first type of SCMs are attribution models, which are concerned with individuals’ causal explanations of health-related events and their reaction to a health threat. For example, research based on Leventhal’s self-regulation model (Leventhal, Nerenz, & Steele, 1984), which seeks to examine individuals’ reactions to a health threat (e.g., cancer, heart disease), falls into this category (e.g., Moss-Morris et al., 2002). In this model, individuals’ representation of their illness or health threat have a central role in determining coping efforts and therefore serve as the causal explanation for the health threat. The second type of SCMs are primarily concerned with various aspects of an individual’s cognitions to predict health behaviors and outcomes (Conner & Norman, 2005). These models include the Health Belief Model (Becker, 1974; Janz & Becker, 1984); the Theory of Reasoned Action/Theory of Planned Behavior (Ajzen, 1991; Fishbein & Ajzen, 1975); Social Cognitive Theory (Bandura, 1982; Bandura, 2000); and Protection Motivation Theory (PMT; e.g. Maddux & Rogers, 1983; Prentice-Dunn & Rogers, 1986; Rogers & Prentice-Dunn, 1997). Collectively, SCMs have been successfully applied to predicting behavioral intentions to engage in various maladaptive behaviors, such as smoking, drinking, and unprotected sex, among adults and

5 adolescents. By focusing on such cognitions as appraisal of the maladaptive response, SCMs have demonstrated that examining attitudes about adaptive health responses are required for a thorough conceptualization of health-threatening behavior. For example, smoking cessation research indicates that smokers’ intentions to quit smoking cigarettes are related to perceptions about the benefits of not smoking (Ho, 1992; Ho, 1994; Sutton, Marsh, & Matheson, 1990). Since this project is concerned with examining the cognitive processes that mediate PMT in particular, a detailed description of this SCM is given. Overview of Protection Motivation Theory PMT is one of a select group of SCMs that attempts to explain and predict the motivation to change health behavior. Sharing the Health Belief Model’s emphasis on the cognitive processes that mediate attitudes and behavioral change (Prentice-Dunn & Rogers, 1986), PMT describes factors that contribute to an individual’s motivation to perform or not perform health behavior, and purports that how people respond to threat is primarily a function of various cognitive appraisals. Initially, PMT focused on the conditions under which fear appeals influence attitudes and behavior (Rogers, 1975). Fear appeals are messages that describe the unfavorable consequences that might occur from failure to adopt the communicator’s recommendations (Leventhal, 1970). The conception of fear appeals was based on two influential models of fear: the fear-drive model, which proposes that fear acts as a driving force to motivate behavior (Janis & Feschbach, 1953), and the fear as motivational intervening variable model, which proposes that fear is inferred from stimulus-response conditions and variables that motivate an organism to respond (Mowrer, 1939). Early fear appeals research proposed three main stimulus variables in a fear appeal, (a) magnitude of an aversive or noxious event (e.g., great bodily harm during an automobile

6 accident if a seat belt is not used), (b) probability the event will occur if no protective behavior is adopted or existing behavior modified, and (c) efficacy of a recommended coping response to reduce or eliminate the aversive event (Hovland, Janis, & Kelley, 1953). In developing PMT, Rogers (1975) sought to provide conceptual clarity to fear appeals research by identifying key variables and their cognitive mediational effects. For example, the magnitude of an aversive event was conceptualized as initiating perceptions of severity, the probability of event occurrence was conceptualized as initiating perceptions of vulnerability, and the availability of an effective coping response was conceptualized as initiating perceptions of response efficacy (Norman, Boer, & Seydel, 2005). As originally proposed, the primary focus of PMT is on cognitive processes related to fear appeals (Rogers & Mewborn, 1976), and four central factors that are also the primary components of the Health Belief Model (Rosenstock, 1974), originally defined PMT: severity (individual’s perceived severity of a health threat), vulnerability (individual’s perceived risk of a health threat), response-efficacy (individual’s perceived efficacy of a protective coping response), and self-efficacy (individual’s expectation that a healthy coping response will have an effect and expectation that the individual can, in fact, perform the coping response). These four factors further define the two main cognitive processes proposed to mediate protection motivation: threat appraisal process and coping appraisal process. Subsequent research, aimed at using the original PMT model to predict health behavior, led to the inclusion of three additional factors that culminate in a seven-factor conceptualization of the threat and coping appraisal processes that mediate protection motivation to engage in a coping response (see Figure 1). This revision added intrinsic and extrinsic rewards (i.e., perception of the internal and external benefits of maladaptive responses), and response cost (i.e.,

7 perception of the barriers that inhibit performance of the adaptive behavior). The new description of the model proposes that various contextual and intrapersonal sources of information (e.g., fear appeals and personality, respectively) have the potential to initiate the threat and coping appraisal processes (Rogers, 1983; Rogers & Prentice-Dunn, 1997). As the intention to respond to a potential threat in either an adaptive or maladaptive manner, protection motivation (PM) is a positive function of perceptions of self-efficacy, response efficacy, severity, and vulnerability, and a negative function of perceptions of response costs and intrinsic and extrinsic rewards (Norman et al., 2005). While threat appraisal focuses on the source of the threat as a function of the algebraic summation of severity plus vulnerability minus rewards, coping appraisal focuses on the adaptive responses the individual can use to deal with the threat via the algebraic summation of efficacy minus response costs. Cognitive Mediating Processes

Severity Maladaptive Response Vulnerability

Intrinsic rewards Extrinsic rewards

=

Threat appraisal

Protection Motivation

Adaptive Response

Self efficacy Response Efficacy

-

Response costs

=

Behavior

Coping appraisal

Figure 1. A schematic representation of the cognitive mediating processes of PMT (from McMath & Prentice-Dunn, 2005) As shown in Figure 1, the coping- and threat-appraisal processes are postulated to interact and guide PM. Specifically, the motivation and intention to behave in an adaptive, healthy manner arises from an interaction between the coping- and threat-appraisal processes. The threat-

8 appraisal process evaluates potentially harmful responses or contextual health threats, and the coping-appraisal process evaluates the expectation that the individual has the ability to cope with any assessed circumstances, or has the potential to adopt behaviors that both reduce the likelihood or severity of a harmful event and overcome perceived barriers (Tanner, Hunt, & Eppright, 1991; Weinstein, 1993). Furthermore, PM is maximized when severity and vulnerability of the health threat and efficacy of the adaptive response are high, and when rewards associated with the maladaptive behavior and costs associated with the adaptive behavior are small (Prentice-Dunn & Rogers, 1986). PMT has been used to predict the motivation to change a wide range of health behaviors including exercise (Wurtele & Maddux, 1987), parental adherence to eye patching in children with amblyopia (Norman, Searle, Harrad, & Vedhara, 2003), sun protective behavior among individuals with a family history of melanoma (Azzarello, Dessureault, & Jacobsen, 2006), safety behavior in parents with toddlers (Beirens et al., 2007), cigarette smoking (Ho, 1992), breast cancer testing (Helmes, 2002), and sexual risk-taking behaviors (Bengel, Belz-merk, & Farin, 1996). For example, in a study aimed at identifying the factors that predict sexual risktaking and protective behaviors in heterosexual adult men recruited in Germany and at vacation spots in Spain, PMT guided the conceptualization of self-protective behaviors as a function of the severity of contracting HIV/AIDS in conjunction with the availability and effectiveness of coping responses, such as communication and condom use. In addition to identifying motivating factors and variables that predict behavioral change, PMT has been used as the guiding theoretical foundation for a wide range of behavioral intervention and risk prevention programs. For example, PMT has guided behavioral interventions designed to increase exercise behavior (Milne, Orbell, & Sheeran, 2002), increase

9 breast self-examination (Fry & Prentice-Dunn, 2006), increase asthma medication compliance (Schaffer & Tian, 2004), decrease smoking (Maddux & Rogers, 1983), and increase condom use behaviors (Stanton et al., 1996; Stanton, Kim, Galbraith, & Parrott, 1996; Stanton et al., 2004; Stanton et al., 2007). As mentioned in the specific aims, this dissertation is concerned with examining not only the inter-relationships shown in the PMT model in Figure 1, but also the developmental trajectory of the PMT constructs, over time, by investigating the shape of the coping- and threatappraisal process growth curves using parallel-process latent growth modeling. Use of this analytical strategy allows the growth curves of these processes to be modeled and analyzed simultaneously. Several studies that have investigated the relationships between the coping- and threat-appraisal processes have found that, in general, these processes interact. For example, when coping-appraisal processes are high (e.g., high response- or self-efficacy), high levels of threat-appraisal processes (e.g., severity and vulnerability) have been found to contribute to high PM and the strong intention to adopt the recommended adaptive response (Rogers & Mewborn, 1976). Alternatively, if coping-appraisal processes are low, increases in severity and vulnerability actually contribute to weakened intentions to adopt the adaptive response (i.e., the “boomerang” effect) (Sturges & Rogers, 1996). This same interaction effect between coping and threat appraisal has been observed when public service announcements, intended to moderate drinking (Kleinot & Rogers, 1982; Self & Rogers, 1990) or increase condom use (Witte, 1992), may actually lead to increased message rejection and decreased intention to modify behavior, particularly if the message is perceived as overly threatening by individuals who lack assurance of their coping skills.

10 To address the potential interaction between the coping- and threat-appraisal processes empirically, this dissertation examined data from a PMT-based HIV intervention program delivered to American and Vietnamese youth (Kaljee, Genberg, Riel et al., 2005; Stanton et al., 2004).

The Youth Health Risk Behavioral Inventory (YHRBI) (Stanton et al., 1995), a

questionnaire comprised of PMT-based items arranged according to the seven constructs that underlie PMT (see Chapter 2 for a detailed description of the YHRBI), was used to gather data to determine the program efficacy. Program efficacy was defined as decreases in youth risk-taking behavior and changes in YHRBI scores from baseline to post-intervention follow-up assessments reflecting changes in youth attitudes and perceptions on the 7 PMT constructs in support of the predicted behavioral change (e.g., increases in self-efficacy and decreases in response cost). Since it is imperative that scores on the YHRBI are reflective of meaningful constructs, methodological issues related to the measurement and analysis of constructs are reviewed in the next section. Overview of Measurement Invariance Suppose a researcher has a scale made up of multiple observed/manifest variables (e.g., items on a questionnaire) that are intended to measure a specified construct. The construct is essentially a latent variable, which is a term used by researchers to refer to a variable that is not present in the data set (i.e., it is unobserved), but that is presumed to be the source of the manifest variables (Bollen, 2002). In the language of factor analysis, the items are manifest variables, xi , that serve as indicators of a factor in a common factor model,η j . Specifically, the value of each xi score is conceptualized as being due to the value of the underlying η j construct, plus error ε i , with the strength of the relationship between η j and xi , given by the factor loading

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λij (Cheung & Rensvold, 1998). Researchers typically generate models of xi and η j by first using exploratory factor analysis (EFA) to find the number and type of latent variables in a plausible model. Once a plausible model is identified using EFA, a single-group measurement model can be confirmed using a structural equation modeling (SEM) procedure known as confirmatory factor analysis (CFA), in which a covariance structure implied by the model, Σ(γ ) ,

is compared to the covariance structure of the sample data matrix, S (Schumacker & Lomax, 2004). A goodness-of-fit index (GFI) is used to evaluate the comparison between Σ(γ ) and S, and several GFIs are used by researchers to evaluate model fit of CFA models. The GFIs that will be used to evaluate model fit in this dissertation are presented in the data analysis section below. Furthermore, suppose that the researcher now administers the scale to samples or groups from two or more distinct populations (e.g., genders, cultural groups, age groups, etc.), and wants to test the difference between group means, or analyze the scale score at multiple time points (i.e., longitudinal data), or examine the relationship of the scale to other variables in the groups (i.e. covariates). When scales are used in this nature, there is a critical assumption that the scale is measuring the same construct in all of the groups. If that assumption holds, then comparisons and analyses of scale scores yield meaningful interpretations.

Alternatively, if groups are

compared based on a scale that is not measuring the same construct in all groups, then inference problems may occur, and comparisons and analyses are unlikely to yield meaningful results. This uncertainty regarding the assumption of what is being measured is the general issue of measurement invariance (MI), and questions that can be addressed via tests of MI and that are not directly addressable through traditional classical test theory evaluation of true and error scores (such as estimates of scale reliability via Cronbach’s coefficient alpha) include the

12 following: Do respondents from different contexts interpret a given measure in a conceptually similar manner? Do gender, ethnic, or other within-person differences preclude responding to instruments in similar ways? Does the very process that is of substantive interest (i.e., an intervention or experimental manipulation) affect the conceptual frame of reference from which a group responds to a measure over time? (Vandenberg & Lance, 2000). Measurement invariance is critically important when comparing groups, for if MI cannot be established, then any findings of between-group differences will be ambiguous (Cheung & Rensvold, 2002). The same issues of MI are often relevant in longitudinal research. When a scale is administered over repeated occasions to the same sample of people, the question of MI involves the issue of whether the scale is measuring the same construct on different occasions. If tests of MI have not been conducted prior to interpreting results, the researcher does not know if score differences are due to true differences in construct endorsement, or to differences in the psychometric responses to the scale items (i.e., systematic error due to characteristics of the scale). The problem of not conducting ME/I analyses was presented by (Horn & McArdle, 1992) over a decade ago The general question of invariance of measurement is one of whether or not, under different conditions of observing and studying phenomena, measurements yield measures of the same attributes. If there is no evidence indicating presence or absence of measurement invariance…then the basis for drawing scientific inference is severely lacking: findings of differences between individuals and groups cannot be unambiguously interpreted. (p.117). The most frequently used technique for testing MI is a Multigroup Confirmatory Factor Analysis (MGCFA) that involves comparing factor structures and loadings (Alwin & Jackson, 1981). The degree of invariance is most frequently assessed by examining differences in χ2 between two nested models via the likelihood-ratio test (LR), also known as the chi-square difference test (Bollen, 1989). The LR is calculated as Δχ 2 = χ c2 − χ uc2 where χ c2 and χ uc2 are the

13 values for the constrained model and the unconstrained (or less constrained) model, respectively. Significance is evaluated with Δdf degrees of freedom, where Δdf = df c − df uc (Cheung & Rensvold, 2002).

MI can be established in MGCFA according to the following hierarchical

structure of tests that seek to maximize interpretability (Chen, Sousa, & West, 2005): (1) Configural invariance—the most basic level of MI, with the central requirement that the same manifest items xi must be indicators of the same latent factor η j in each group (Horn & McArdle, 1992). Configural invariance proposes that participants in different groups conceptualize constructs in the same way (Widaman & Reise, 1997); (2) Metric loading invariance—the second level of MI, requiring that there is the same pattern of factor loadings λij across groups. When λij loadings are equal in groups, the unit of measurement of the underlying factor can be considered to be identical (Bollen, 1989; Bollen, 2002); (3) Intercept variance—when this level of invariance is achieved, factor loadings and intercepts are equal in groups. Intercept variance is required for comparing latent mean differences across longitudinal time points (Widaman & Reise, 1997). Overview of Longitudinal Growth Modeling With the attainment of MI, and stability of the PMT constructs between groups (United States and Viet Nam) and across time, growth modeling to examine developmental trajectories can occur. The current research will be conducted with latent growth modeling (LGM) using Mplus software (Muthén & Muthén, 1998-2006). According to the statistical methodology adopted by Mplus, LGM integrates hierarchical linear modeling (HLM)/multilevel and structural equation modeling (SEM) approaches into a multivariate, latent variable approach to evaluate the

14 fit of general growth patterns to the data by averaging trends (linear slopes) and levels (intercepts) using maximum likelihood (ML) estimation procedures. This is accomplished by conceptualizing intercepts and linear slopes as factors (i.e., latent variables) that are proposed to mediate the growth of some observed outcome variable (e.g., the coping appraisal process) over time. Two main equations capture the essence of the latent variable approach to analyzing growth as used by Mplus: (1) yit = η 0i + η1i xt + ε it (2a) η 0i = α 0 + γ 0 wi + ζ 0i (2b) η1i = α1 + γ 1 wi + ζ 1i In HLM/multilevel terms, equation (1) represents level 1 growth within subjects, where

y = outcome, x = timescore, η 0 = baseline level growth factor (intercept), η1 = trend growth factor (linear slope), ε i = within subjects error, i = individual participant, and t = time point. Subscripts 0 and 1 represent the intercept and linear slope growth factors, respectively. From the HLM/multilevel perspective, equations (2a) and (2b) represent level 2 between subjects mean baseline level ( α 0 ) and between subjects mean trend ( α1 ), respectively. Thus, given the coping or threat appraisal process of a given individual ( yit ) at an observed time point ( xt ) would depend on that participant’s appraisal process status at baseline (η 0i ), rate of change in appraisal process (η1i ), and error ( ε it ). Furthermore, an individual’s intercept (η 0i ) and linear slope (η1i ) factors will be affected by the between subjects mean baseline rate of appraisal ( α 0 ) and between subjects mean appraisal trend ( α 1 ), as well as any covariates ( wi ) included in the model (e.g., gender, culture, etc.), along with unmeasured sources of residual error ( ζ i ). In SEM terms, equation (1) is a measurement part, describing the relationship between the outcomes and the

15 factors, and equations (2a) and (2b) are the structural part, describing how the intercept and linear slope latent variables, η 0 and η1 , respectively, vary across individuals according to their variance and covariance. Analyzing growth using the latent variable approach of Mplus offers several advantages over either a strictly multilevel/HLM approach or a conventional SEM approach, and is useful in bridging together different modeling ideas. For example, the latent variable approach provides for flexible curve shape, models growth in relation to baseline starting point (i.e., ability to regress among random effects, such as regressing linear slope on the intercept), and the ability to model multiple processes in parallel.

Growth Modeling With Parallel Processes Latent growth modeling with parallel processes allows a researcher to model two developmental processes concurrently, as well as investigate the interrelationships between the growth parameters via their covariance (cf. Muthén & Curran, 1997). From the HLM/multilevel framework, parallel processes would be referred to as bivariate outcomes, and in the latent variable, multivariate, single-level framework (which is the conceptual foundation used in this study), a total of 10 outcome variables were analyzed during each parallel-processes analysis: 5 dependent variables for process one, and 5 dependent variables for process two. Essentially, two single-process growth models were run concurrently within a mean vector and covariance framework during each parallel-process run. Although latent growth modeling with parallel processes is a relatively novel procedure, it is starting to gain the interest of researchers. For example, parallel-processes growth modeling has recently been used to examine concurrent developmental trajectories of physical activity and smoking, substance use and delinquency, inattention/hyperactivity and aggression, resistive

16 efficacy and number of sexual partners, parenting practices and substance use, and behavioral control and resiliency (Audrain-McGovern, Rodriguez, & Moss, 2003; Dembo, Wareham, & Schmeidler, 2007; Jester et al., 2005; Mitchell, Kaufman, Beals, & Pathways of Choice and Healthy Ways Project Team, 2005; Simons-Morton, 2007; Wong et al., 2006). In this dissertation, parallel-processes latent growth modeling was used to model concurrently the development of the PMT coping- and threat-appraisal processes. Additionally, the interrelationship between the growth parameters of each process and self-reported risk and protected behaviors was examined.

Restatement of Purpose and Main Research Questions The purpose of this dissertation study is to increase the understanding of the cognitive processes that mediate PMT for the (a) prevention of high risk sexual and drug behavior, and (b) increase in condom use behavior in American and Vietnamese youth. Specifically, the present dissertation describes the interrelationship between the coping- and threat-appraisal processes in health behavior by using PMT as a theoretical framework for investigating the unique variance of these appraisal processes as cognitive factors in the model of protection motivation (PrenticeDunn & Rogers, 1986). Data from American and Vietnamese youth who participated in longitudinal studies on the effects of an HIV risk reduction intervention based on PMT constructs were analyzed using CFA and parallel-process latent growth modeling (PPLGM). The data were organized into risk behavior PMT constructs and condom use PMT constructs according to the two types of questionnaires that the American and Vietnamese youth completed (see Chapter 2), and CFA was used to investigate measurement invariance and the stability of these two broad categories of PMT constructs within- and between-participants and time. PPLGM is an advanced statistical strategy that allowed for the simultaneous modeling of the

17 developmental trajectories of the PMT coping- and threat-appraisal processes, and their relationship with time-invariant covariates (e.g., gender, age and grade) and self-reported sexual and drug risk behaviors (e.g., vaginal intercourse, alcohol and cigarette use) and protective behaviors (e.g., condom use) (Muthén, 1996; Muthén, 2002). Presently, there is considerable research interest in the behavioral sciences to characterize and understand the variability in developmental pathways that lead to risky behaviors in adolescents and young adults. The results of this study are well-suited to inform both the research and clinical communities concerning the developmental trajectories of the coping- and threat-appraisal processes, and their role in mediating PMT as a significant predictor for risky and protective health behavior later in life. This study addressed the following three primary questions by examining longitudinal data from American and Vietnamese adolescents who received the Focus on Kids (FOK) behavioral intervention and provided self-reports of risk-taking and protective condom use behavior: (1) What is the stability of the risk behavior and condom use PMT constructs within and between cultures and across time using confirmatory factor analysis (CFA) to demonstrate measurement invariance? (2) What are the developmental trajectories and interrelationships among PMT’s coping- and threat-appraisal processes for risk behavior and condom use using parallel-process latent growth modeling analysis, and are these growth parameters differentially expressed across gender, age, grade, culture and intervention type? (3) What are the developmental trajectories of self-reported sexual and drug risk behavior and protective condom use behavior among youth, and how are these growth parameters related with PMT’s coping- and threat-appraisal processes for risk behavior and condom use using parallel-process latent growth modeling analysis?

18

CHAPTER 2—Method

This chapter details the methodology that was used to collect the data needed to address the research questions developed for this study. This chapter also describes analytical steps that were used to answer the dissertation research questions.

Overview of Existing Data Data used in the present study are taken from existing research on the efficacy of the Focus on Kids (FOK) HIV risk reduction program delivered to American and Vietnamese youth (Kaljee, Genberg, Riel et al., 2005; Stanton et al., 2004). Based on PMT, FOK is a communitybased HIV/AIDS behavioral intervention program targeting low-income, inner city, AfricanAmerican adolescents (Galbraith et al., 1996; Stanton et al., 1996; Stanton et al., 1996). Recognized as a “Program That Works” (1999) and an Effective Program by the Centers for Disease Control and Prevention (Centers for Disease Control and Prevention, 2006a), FOK incorporates pedagogical and hands-on materials to help youth learn how intentions are formed to respond to threats in either adaptive or maladaptive ways. FOK is presented in a small group setting over 8 sessions; its main objectives are the reduction of adolescent sex and drug risktaking behaviors, and increases in the occurrence of protective behaviors (e.g., condom use). Members of the FOK research team have replicated the original FOK intervention in the original Baltimore inner city setting (Stanton et al., 1997; Stanton et al., 2007; Wu et al., 2003; Wu et al., 2005), and have adapted FOK for implementation and evaluation in other contexts, including Africa (Stanton et al., 1998), The Bahamas (Cole et al., 2007), Viet Nam (Kaljee, Genberg, Riel et al., 2005), and West Virginia (Stanton et al., 2005; Stanton et al., 2006). Members of the FOK research team have also investigated the effects of adding different components to the original

19 FOK program, including parental (Stanton et al., 2000; Yang et al., 2006) and peer (Fang, Stanton, Li, Feigelman, & Baldwin, 1998) intervention components. Finally, members of the FOK research team have used PMT as a guiding model for the prediction of risky and protective health behavior in such contexts as Africa (Stanton et al., 1999), Baltimore (Feigelman, Li, & Stanton, 1995; X. Li et al., 1994; X. Li, Stanton, & Feigelman, 1999; X. Li et al., 2001; Stanton, Li, Cottrell, & Kaljee, 2001; Stanton et al., 2002; Wu, Stanton, Li, Galbraith, & Cole, 2005), China (X. Li et al., 2004; Yang et al., 2006), Viet Nam (Kaljee, Genberg, Minh et al., 2005), and West Virginia (Cottrell et al., 2005). FOK is based on PMT, and this dissertation project was concerned with the cognitive processes that are proposed to mediate PMT. These cognitive processes were characterized by examining the interrelationships between the PMT constructs coping and threat appraisal in American and Vietnamese youth who received FOK. Furthermore, the interrelationships between the coping- and threat-appraisal processes and sexual and drug risk-taking and protective behaviors were examined in the American and Vietnamese youth.

FOK Delivered to American Youth Introduction The United States of America is a country with one of the largest number of HIV infections in the world (UNAIDS/WHO, 2007). While men accounted for most of the HIV or AIDS diagnoses (74%) among adults and adolescents in the country in 2005, AIDS was the fourth leading cause of death among African Americans aged 25–44 years in the United States in 2004 (Anderson, Mosher, & Chandra, 2006). Within the United States, the Baltimore Metropolitan Area ranks fifth per capita in AIDS incidence behind New York, Miami, San Francisco, and Fort Lauderdale, with a rate of 39.3 per 100,000 (Centers for Disease Control and

20 Prevention, 2007). In Baltimore, the HIV/AIDS epidemic primarily impacts African-Americans, injection drug users and sexually active populations, and involves significantly more AfricanAmerican females compared to national averages (Blattner & Brown, 2005). Finally, while HIV incidence trends are downward since the mid-1990’s due to various preventative interventions, African-American men and women continue to have 10-fold higher rates of HIV infection compared to Whites (Maryland Department of Health and Mental Hygiene, 2007). Data used in the present study from American youth are from a randomized, longitudinal trial delivered to an initial population of 817 African-American youth (and their parents) living in the Baltimore Metropolitan Area, who were followed semi-annually through 24 months postintervention (Stanton et al., 2004). Youth were recruited over 3 waves in 1999-2000 and were randomized at the level of 35 community sites (recreation centers, churches, housing developments, etc.) in and around low-income urban areas of Baltimore. Youth were randomized to receive either the FOK risk reduction intervention, FOK plus a one-session parentalmonitoring intervention, Informed Parents and Youth Together (ImPACT) (Stanton et al., 2000), or FOK plus the ImPACT parenting component plus four FOK booster sessions (Stanton et al., 1997). For the purposes of this dissertation study, parent data will not be included. The research was approved by the Institutional Review Boards at the University of Maryland and Wayne State University. Written, informed consent/assent was obtained from parents and youths.

American FOK Content and Delivery All 817 youth received the FOK intervention, which is an 8-session (each approximately 1.5 hours in duration) risk reduction intervention that uses games, discussions, homework assignments, and videos delivered in the youth’s home or at a designated community site to groups of 5 to 10 youths by a pair of interventionists. The intervention provides pedagogy to

21 emphasize health behaviors through decision-making, goal-setting, communication, negotiating, consensual relationships, abstinence and safe sex. Information was also provided regarding drug and alcohol use, and drug selling and delivery. Those youth who also received the parentalmonitoring intervention were shown, in their home, a 20-minute video (made in and for the targeted communities in Baltimore) that emphasized several concepts of parental monitoring and communication. In addition to viewing the movie, youth engaged in an interactive role-play that described a confrontational scenario and a correct condom use demonstration. Those youth who also received the booster sessions were presented at 7, 10, 13 and 16 months post-intervention with a variety of review activities from the primary FOK sessions, and were presented with new activities reviewing the material from the original FOK program. Content included information on decision making, sexual abuse, sexual responsibility, drug use and drug selling. These youth also engaged in goal-setting games that focused on the negative consequences of substance abuse, including faulty decision-making, such as unprotected sex.

Participants and Research Site Youth ages 13 to 16 years and living in or around 35 community sites located in Baltimore, Maryland, were eligible for inclusion in the FOK risk reduction intervention. Inclusion criteria also included absence of any recognized psychiatric disorder and mental retardation. Youth were eligible to enroll even if their parent or guardian was not willing to participate in the study, however, all invited parents did participate in their assigned interventions. Youth from 13 of the 35 sites (n=321) were randomized to receive the FOK risk reduction intervention; youth from the remaining 22 sites (n=496) were randomized to receive FOK and, along with their parents, ImPACT. These 496 youth were further separated into two groups: n=258 youth who received FOK + ImPACT, and n=238 youth who received FOK +

22 ImPACT + FOK booster sessions. The average number of youth in the 35 sites (i.e., the average cluster size) was 24.03 (range 4 to 58), the median value was 24, and the standard deviation was 10.55. The median age of the 817 youth at baseline was 14 years, and 58% were female; intervention groups were similar with respect to gender and age at baseline. Follow-up assessments were conducted at 6, 12, 18, and 24 months after intervention. To adequately address the research questions of this dissertation, n = 19 youth who did not provide baseline PMT data were excluded from the population of 817 American youth to yield a final sample size of N = 797 at baseline. A detailed description of the American youth in terms of their demographic characteristics and frequency of self-reported behaviors at baseline and over the four follow-up assessments is presented in Chapter 3. The median age of the youth at baseline was 14, and 58.3% were female. Table 1 presents an overview of the distribution of sample sizes for the American youth at baseline and follow-up periods 1 to 4. By working with all available American youth at each time point, the distribution of sample sizes for the American youth at baseline and follow-up periods 1 to 4 are N = 797, N = 587, N = 571, N = 520 and N = 480, respectively. Since the pattern of missingness for the American youth across the five waves of data is missing at random (MAR; i.e., the researchers did not systematically exclude a specific number of participants at each wave), the Full Information Maximum Likelihood (FIML) approach to missing data estimation was used (Muthén & Curran, 1997). When the pattern of missingness is random (as it is in this instance), FIML is preferred over other methods for handling missing data, such as imputation (Duncan, Duncan, & Strycker, 2006). The FIML approach is described in more detail below.

23 Table 1. Distribution of the American Youth over Five Assessment Periods Assessment Perioda Sample size (% of baseline) Baseline 797 (100) Follow-up 1 587 (73.7) Follow-up 2 571 (71.6) Follow-up 3 520 (65.2) Follow-up 4 480 (60.2) a Follow up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post intervention, respectively, for American youth.

Measures Data from the American youth are comprised of self-report responses to the Youth Health Risk Behavior Inventory (YHRBI) (Stanton et al., 1995; Stanton et al., 2004). The YHRBI was originally developed for urban African American youth in Baltimore over a 3-phase process (Stanton et al., 1995). In phase I, ecological validity (i.e., ethnographic investigation and translation of PMT into the local culture) was addressed via focus group discussions and individual Q-methodology pile-sorting (Block, 1978; Stanton, Aronson, Borgatti, & Galbraith, 1993; Stanton, Black, Kaljee, & Ricardo, 1993). Phase II involved initial questionnaire construction and testing of the preliminary instrument. Finally, instrument finalization and evaluation of psychometric properties occurred in phase III. Cronbach’s alpha coefficient (Cronbach, 1951) was used to estimate instrument reliability, and confirmatory factor analysis (Jöreskog & Sörbom, 1993) was used to assess construct validity. The final instrument contained 116 items arranged according to 4 groups of the 7 PMT constructs (self-efficacy, response efficacy, response cost, intrinsic rewards, extrinsic rewards, severity, vulnerability), producing 28 scales. The four groups represented the following risky and protective health behaviors: initiate sex, condom use, drug using, and drug trafficking.

24 The YHRBI that produced the data from the American adolescents is a 286-item questionnaire that assessed (a) demographic characteristics, (b) prevalence and perceptions of a variety of truant, sexual, and substance-use risk and protective behaviors during the past six months, (c) knowledge of HIV/AIDS, (d) perceptions of peer- and family-involvement in risk and protective behaviors, (e) social desirability, and (f) intentions or expectations for future risk and protective behavior. The first section of the YHRBI (4 items) assessed demographic characteristics of the youth (e.g., gender, age, and grade), and the second section (57 items) assessed behavioral history during the previous 6 months of a variety of behaviors organized into three broad categories: delinquency, substance abuse, and sexuality. Dichotomous responses (0 =No, 1 =Yes) were used for a majority of these items, and Table 2 presents the complete list of behaviors assessed by the YHRBI. In the next section of the questionnaire (115 items), youth were queried along a 5-point Likert scale about their attitudes and perceptions of risk and protective behaviors according to the seven constructs of PMT (102 items) and about their attitudes and perceptions related to social desirability (13 items). The 102 PMT items were arranged into an a priori structure of two broad categories of items based on sexual activity at baseline: items assessing attitudes, perceptions and beliefs regarding sexual, drug and delinquency risk behavior (71 items), and items assessing specifically attitudes, perceptions and beliefs regarding condom use (31 items). While all 797 youth completed the 71 risk behavior items, only those American youth who reported sexual activity during their initial baseline assessment (i.e., youth reported ever engaging in vaginal intercourse) completed the 31 condom use items and continued to provide responses to these condom use items across the study waves (see Table 3). As shown in Table 3, the frequency of youth who were sexually-active at baseline, and therefore completed the PMT

25 condom use items, decreased from 347 youth at baseline to 182 youth at the final assessment period due to random attrition. Power estimation research in growth modeling designs with five time points has found that sample sizes of at least 150 yield power of .80 (Muthén & Curran, 1997). Therefore, an ample number of American youth were available in this study to provide data for latent growth modeling of condom use PMT constructs and corresponding behavioral data across five time points, and the FIML approach to data MAR was used.

Table 2. List of Behaviors Assessed by The YHRBI Delinquency # of missed school days over last 4 weeks Missed school due to suspension Missed school due to truancy # of grade repeated # of times suspended (not including summer) Carry knife or razor to use as weapon Carry gun to use as weapon Carry bat or stick to use as weapon Been in a physical fight with friend Beaten someone up with friends Substance Abuse Smoked a cigarette Had a drink of beer, wine or liquor Tried marijuana Tried any form of cocaine, including crack Tried any other type of illegal drug Used a needle to shoot up/inject drugs Been asked to run or sell drugs Sold any drugs Delivered drugs

Sexuality Talked with family or adults about HIV/AIDS Kissed or hugged a boy/girl for a long time Boys held hands with another boy Boys kissed another boy Felt breasts/had breasts felt under clothes Felt penis/had penis felt over or under clothes Girls let a boy put his penis in your rectum You used a condom during anal sex Partner used a condom during anal sex Been molested or raped Have boyfriend/girlfriend Age of boyfriend/girlfriend Engaged in sexual activity # of sexual partners Frequency of sexual activity Age of most recent sexual partner Asked sexual partner about sexual history Currently living with sexual partner Asked sexual partner if they always use condoms Frequency of condom use Used birth control pills Used condom Used withdrawal method Gotten pregnant or gotten a girl pregnant Had a STI/HIV/AIDS Received treatment for STI/HIV/AIDS

26 Table 3 Frequency of American Youth across the Study’s Five Assessment Periods Assessment Perioda

All Youth (% baseline)b

Youth Youth in FOK Youth in Youth in FOK sexually-active group FOK + + ImPACT + at baseline (% total) ImPACT Booster c (% total) (% total) (% total) Baseline 797 (100) 347 (43.5) 310 (38.9) 255 (32.0) 232 (29.1) Follow-up 1 587 (73.7) 247 (42.1) 232 (39.5) 195 (33.2) 160 (27.3) Follow-up 2 571 (71.6) 234 (41.0) 232 (40.6) 182 (31.9) 157 (27.5) Follow-up 3 520 (65.2) 200 (38.4) 209 (40.2) 166 (31.9) 145 (27.9) Follow-up 4 480 (60.2) 182 (38.0) 192 (40.0) 152 (31.7) 136 (28.3) Youth Sexually-Active at Baseline Baseline 347 (100) 143 (41.2) 109 (31.4) 95 (27.4) Follow-up 1 247 (71.2) 101 (40.9) 85 (34.4) 61 (24.7) Follow-up 2 234 (67.4) 100 (42.7) 75 (32.1) 59 (25.2) Follow-up 3 200 (57.6) 86 (43.0) 64 (32.0) 50 (25.0) Follow-up 4 182 (52.4) 76 (41.8) 55 (30.2) 51 (28.0) a Follow-up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post intervention, respectively, for American youth. bThe total sample size of American youth at each assessment period completed the set of 71 YHRBI items related to sexual, drug and delinquency risk behavior. cThis subset of American youth who reported ever engaging in vaginal intercourse at baseline also completed the set of 31 YHRBI items related to condom use at each assessment period. The YHRBI also asked youth to rate their perceptions of parental monitoring and communication along a 5-point Likert scale (26 items), and whether peers or family members are involved in the risk and protective behaviors according to a 3-point Likert scale (34 items). To assess intentions for future behavior, 12 items asked youth to rate their intention or expectation to engage in the following sexual and drug risk-taking and protective behaviors in the next 6 months: smoke marijuana, deal drugs, deliver drugs, get HIV infection, drink alcohol, get an STI, use cocaine, get pregnant or get a girl pregnant, sniff glue, have sex, use a condom, and have a baby. Finally, the YHRBI assessed youth knowledge of HIV/AIDS via 38 true/false items. For the current study, YHRBI items on perceptions of parental monitoring and communication, perceptions of peer or family involvement in risky and protective health behaviors, and intentions for future behavior were excluded.

27

Administration of the YHRBI The YHRBI was administered at baseline (i.e., before intervention) and on four consecutive semi-annual follow-up assessments (6, 12, 18 and 24 months after intervention) in the respondents’ homes or at community centers via a talking Macintosh computer over the course of approximately 45 minutes (Romer et al., 1997). Hypercard 2.1 and Macromedia were used to program the YHRBI, thereby providing (a) audio-visual presentation of instructions and questions via a monitor and headphones (one male voice and one female voice were recorded to present the YHRBI to boys and girls, respectively), (b) complex branching and skip patterns depending on responses, and (c) storage of respondents’ answers. Researchers gave children a brief overview of how to use the computer and helped them enter some basic demographic information to acquaint them with the procedure. For most questions, children simply used a mouse to "click on" answers displayed (as icons) on the monitor (e.g., Yes or No). For some questions, children typed their answers using the keyboard. A special "Say It Again" icon was always present to allow respondents to repeat a question if it was not fully comprehended on the first exposure. Respondents could choose not to answer questions by selecting a "Next Question" icon. Researchers were constantly present to answer questions when they arose, but otherwise they left respondents alone to complete the interview.

FOK Delivered to Vietnamese Youth Introduction Viet Nam has one of the fastest growing economies in the world, and its recent appointment to the World Trade Organization, nomination to the Security Council, and its November 2006 hosting of the Asia-Pacific Economic Cooperation Summit, underscores Viet

28 Nam’s goal of attaining middle income status (UNDP, 2008). Along with this rapid social and economic progress, the estimated number of people living with HIV in Viet Nam has more than doubled between 2000 and 2005 from 120,000 to 260,000 (WHO, 2005). Although the ratio of HIV infection prevalence in 2005 was estimated to be 2 to 1, males to females, the number of infected females compared with males is rising each year due to increased heterosexual transmission among injecting drug users who use contaminated injecting equipment, and among couples where the male has engaged in unprotected sex with non-regular partners or sex workers (Tuan et al., 2007) The Vietnamese data used in the current study were collected as part of a randomizedcontrolled effectiveness trial of the Vietnamese Focus on Kids (VFOK) HIV prevention program for adolescents in Khanh Hoa Province, located in South Central Viet Nam (Kaljee, Genberg, Riel et al., 2005). Longitudinal data were collected over five time points from September 2001 to July 2003, with randomization of participants occurring at the level of eight rural communes. In contrast to the American youth, who all received the FOK intervention after baseline assessment, half of the Vietnamese youth (n = 240) received the intervention immediately after baseline (i.e., the treatment group), and the remaining 240 youth received the intervention after collection of the 18-month follow-up data (i.e., the control group). Previous research has compared the effectiveness of VFOK in the control and intervention youth and this dissertation study replicates and expands on those results (Kaljee, Genberg, Riel et al., 2005). Therefore, data from the 240 youth who received VFOK after baseline (i.e., the treatment group) and the 240 control group youth were used. The research was approved by the Institutional Review Board at the University of Maryland, and the Khanh Hoa Provincial Health Service Ethical

29 Review Board (Nha Trang City). Written, informed consent/assent was obtained from youth and parents/guardians.

Vietnamese FOK Content and Delivery Vietnamese youth who provided data for this dissertation received the VFOK intervention, an 8-session program (each session approximately 2 hours in length) designed to teach youth new skills for decision-making and communication, as well as pedagogy regarding HIV/AIDS and other STIs, birth control and condom use. The program was delivered weekly to groups of 10 same-gender youth, with one facilitator per group, in local schools. The program is similar to the American FOK program described above; however, the materials have been translated into Vietnamese, and all of the stories, scenarios, and role-plays have been contextualized for the Vietnamese youth by incorporating differences in verbal and non-verbal styles. Additionally, results of a pilot HIV study in Khanh Hoa Province, indicating that single youth have poor knowledge regarding HIV and sexual risk-taking and protective behaviors, and have low motivation to carry and use condoms (Kaljee, Minh, Thoa, & Tho, 2003), were influential in making the following changes to the VFOK: modification of the condom demonstration to include information about those birth control methods readily available in Khanh Hoa Province; modification of the pedagogy to provide a greater emphasis on basic knowledge about HIV/AIDS and other STIs, as well as puberty and adolescent development; and inclusion of a section on the negative effects of alcohol use on relationships and sexual risktaking behaviors.

30

Participants and Research Site Adolescents between the ages of 14 and 21 years, and living in the Khanh Hoa Province in South Central Viet Nam were included. In 2003, there were an estimated 1,200 HIV positive individuals in the Khanh Hoa Province, approximately 60% of whom live in the provincial capital of Nha Trang(Kaljee, Genberg, Minh et al., 2005). Approximately 80% of the residents of Khanh Hoa live in rural areas, and of the documented cases of HIV in Khanh Hoa Province, approximately 40% are individuals under 29 years of age. At baseline, 60 youth were selected from each of 8 study communes: 4 from within the Nha Trang City limits and 4 in the Dien Khanh District, which is approximately 10 km outside of Nha Trang. This sampling of youth produced a total of 480 participants with equal numbers of male and female respondents. These youth had a median age at baseline of 17 years, and 50% were female (a detailed description of the demographic characteristics and behavioral frequencies of the Vietnamese youth are presented in Chapter 3). Follow-up assessments were conducted at immediate post-intervention, 6, 12, and 18 months after intervention. The number of youth randomly lost due to attrition from follow-up 1 through 4 was n = 14, 17, 29, and 34, respectively (see Table 4). Table 4. Frequency of the Vietnamese Youth across the Study’s Five Assessment Periods Assessment Perioda

Sample size Control Group n Treatment Group n (% baseline) (% baseline) (% baseline) Baseline 480 (100) 240 (100) 240 (100) Follow-up 1 466 (97.1) 232 (96.7) 234 (97.5) Follow-up 2 463 (96.5) 234 (97.5) 229 (95.4) Follow-up 3 451 (94.0) 232 (96.7) 219 (91.3) Follow-up 4 446 (92.9) 222 (92.5) 224 (93.3) a Follow-up assessment periods 1-4 occurred at immediate, 6, 12, and 18 months post intervention, respectively, for Vietnamese youth..

31 The Vietnamese participants were used to examine questions regarding PMT constructs and self-reported protective condom use behavior across 5 time points, similar to the American youth who were sexually-active at baseline. In keeping with power estimation research that suggests sample sizes ≥ 150 are required to obtain power of .80 in growth modeling designs with 5 time points (Muthén & Curran, 1997), the sample sizes were acceptable in the Vietnamese control and treatment groups across time, and the FIML approach to the analysis of data MAR was used (Duncan et al., 2006).

Measures Data from the Vietnamese youth are comprised of self-report responses to the 154-item Vietnamese Youth Health Risk Behavior Inventory (V-YHRBI). For the V-YHRBI, items were modified to reflect the cultural differences between Vietnamese and American adolescents. Original YHRBI items assessing attitudes, perceptions and prevalence of drug use and drug trafficking were deleted from the V-YHRBI because of ethical concerns about youth revealing illegal information. Additional items that were modified included items assessing attitudes and perceptions regarding benefits of education for future employment, filial responsibility, alcohol use, pregnancy and abortion, government responsibility for preventing HIV/AIDS, commercial sex workers, and media influences on adolescent behavior. The V-YHRBI does not assess all of the risk-taking behaviors assessed in the original YHRBI. Specifically, truancy, drug use and drug trafficking are not assessed. However, the questionnaire does contain the following sections from the original YHRBI tool, plus an additional section on communication within a malefemale relationship: (a) demographics—7 items assessing gender, age, religion, in-school/out-ofschool, and employment status; (b) violence and weapon carrying—4 dichotomous items assessing prevalence of fighting and carrying a stick, bat, or knife; (c) substance use—4

32 dichotomous items assessing prevalence of tobacco and alcohol use; (d) sexual activity—28 dichotomous items assessing, and prevalence of sexual risk and protective behaviors, including vaginal, oral, and anal sex, as well as use of condoms and other contraceptives; (e) gender relations—20 Likert scale items assessing past and current relationships; (f) condom access and general attitudes—49 Likert scale items based on PMT assessing attitudes and perceptions regarding sexuality, HIV/AIDS, contraceptives, condom use, and alcohol consumption; (g) peer activities—7 Likert scale items assessing perceptions of peer engagement in alcohol use, and sexual risk and protective activities; (h) intentions—8 Likert scale items assessing future probability to engage in various risk and protective behaviors, including alcohol consumption, fighting, dropping out of school, finding employment, having sex, becoming infected with a STI, and using a condom; and (i) HIV/AIDS knowledge—25 true/false items assessing knowledge. Additionally, 2 true/false items asked respondents if they had ever talked to an adult about HIV/AIDS, and whether they knew anyone who had HIV/AIDS. For the current study, VYHRBI items on perceptions of gender relations and perceptions of peer involvement in risky and protective health behaviors are excluded.

Administration of the Vietnamese YHRBI The V-YHRBI was self-administered at baseline (i.e., before intervention) and on four consecutive follow-up assessments (immediate post-intervention, 6, 12, and 18 months after intervention) usually in the respondents’ school during after school hours. Youth were divided into same-gender groups of ten, and an interviewer reviewed procedures and was available to answer questions and provide assistance as necessary reading items, however, this was necessary for fewer than 10 youth. Youth completed the instrument in approximately 30 minutes. Youth were given a small stipend (~US$3) after completion of each evaluation.

33

Data Analysis Overview of the Data Analysis After an initial data clean-up and coding phase, four phases of data analysis occurred. Microsoft Excel and SPSS-Windows version 16.0 were used for data clean-up and coding, which included defining missing values and coding of a sexual risk variable. The sexual variable was created by combining the variables “sexual intercourse in the past six months” and “condom use during last sexual episode”. In the first analysis phase, SPSS was used to investigate the covariates and behavioral outcome variables used in this study. While covariates were comprised of the demographic variables gender, age, and grade of education, the behavioral outcome variables were comprised of vaginal intercourse with and without condom, cigarette use and alcohol use. Chi-square analysis for nominal and dichotomous variables was used to investigate the relationships between demographic variables, behavioral variables, and gender, culture, and group (control vs. intervention in the Vietnamese youth, and FOK vs. FOK + ImPACT vs. FOK + ImPACT + Booster for American youth). In the second analysis phase, SPSS and Mplus version 5.0 (Muthén & Muthén, 1998-2006) were used to define the PMT constructs using exploratory factory analysis (EFA) followed by confirmatory factor analysis (CFA). Next, Mplus was used to assess measurement invariance (MI) using single group CFA and multiple group CFA. In the final analysis phase, Mplus was used for latent growth modeling (LGM) and parallel-process latent growth modeling (PPLGM) of PMT constructs and behaviors.

Procedures Addressing Missing Data and Cluster Randomization The American and Vietnamese samples contained data that were randomly missing due to attrition. This pattern of missingness is termed missing at random (MAR), and classical

34 approaches to handling attrition based on listwise deletion or single imputation strategies are generally considered inadequate (Newman, 2003). A contemporary approach to handling attrition that many researchers consider preferable over either listwise deletion or mean or regression based estimation is maximum likelihood estimation (Little & Rubin, 2002; Schafer & Graham, 2002). Mplus provides full information maximum likelihood estimation (FIML) under MAR that imputes the missing data via an estimation maximization algorithm. FIML estimates unbiased parameters when all available data from respondents are analyzed, regardless of the completeness of longitudinal response patterns (Feng, Silverstein, Giarrusso, McArdle, & Bengtson, 2006; McArdle & Hamagami, 1992). Because participants from the same cluster (i.e., 35 communities for United States data set and 8 provinces for Viet Nam data set) were included in this study, the assumption of independence was violated. To estimate the effect of this nonindependence and determine if the data warranted a multilevel analysis (cf. Duncan et al., 1997), the design effect d was estimated as d = 1 + p(c – 1), where p is the intraclass correlation and c is the average cluster size (Muthén, 2000). According to results from monte carlo simulation, a design effect of < 2 is considered acceptable and small enough to be ignored (Muthén & Satorra, 1995). A preliminary analysis found that the d values for all of the PMT constructs in American and Vietnamese samples were < 2, suggesting clustering had minimal effect in either the American or Vietnamese sample. Therefore, further multilevel analysis was not required.

Exploratory and Confirmatory Factor Analysis Exploratory and confirmatory factor analysis was used to address the first primary question of this dissertation—What is the stability of the PMT constructs within and between cultures (United States and Viet Nam) and across time (the repeated measures of longitudinal

35 data) using CFA to demonstrate measurement invariance? EFA was conducted as a precursor to CFA to parcel all of the YHRBI and V-YHRBI items into a select group of items that appear to fit the risk behavior and condom use PMT constructs. EFA was conducted in SPSS using principle axis factoring as the extraction method, followed by direct oblimin rotation, which is a nonorthogonal rotation method (Tabachnick & Fidell, 2001). Direct oblimin was selected as the method for rotation since the PMT constructs are interrelated into the higher order coping and threat appraisal constructs. Determination of the number of factors to retain was based on eigenvalues > 1 and factor loadings > .30 (Comrey & Lee, 1992). CFA was then run until acceptable model fit was obtained. CFA was conducted in Mplus using maximum likelihood estimation. CFA is the measurement model part of structural equation modeling, and is essentially a multivariate regression model that describes the relationships between observed dependent variables and latent variables (Muthén & Muthén, 1998-2006). As initially described in Chapter 1, CFA compares the model fit between the covariance structure of the observed dependent variables (referred to as factor indicators), and the covariance structure of the implied latent variables (referred to as factors). Evaluating model fit in CFA proceeds unlike traditional statistical methods (Shah & Goldstein, 2006). Historically, the most popular goodness-of-fit index (GFI) has been the χ2-statistic, wherein a nonsignificant value of χ2 indicates failure to reject the null hypothesis that Σ(γ ) is identical to S, and adequate fit is usually implied (Cheung & Rensvold, 2002). Although the χ2-statistic tends to be the starting point for evaluating model fit, sample sizes larger than N = 200 often preclude a non-significant χ2 value (Newcomb, 1990). As such, the ratio of the chi-square statistic to the degrees of freedom (χ2/df), the comparative fit index (CFI), and the root mean square error of approximation (RMSEA) are appropriate indexes for

36 evaluating model fit under conditions when samples are greater than N = 200 (Bentler, 1990; Loehlin, 1998). Models that are good representations of the data have a χ2/df ratio of less than 2 to 1, a CFI value ≥ .90, and a RMSEA that is less than .08. Additionally, Bentler recently argued that best practices for evaluating model fit include reporting results from only a few well-chosen fit statistics rather than using the full battery of tests (Bentler, 2007).

Tests of Measurement Invariance After acceptable model fit of the baseline data was found using CFA, MI of the PMT models was established (Vandenberg & Lance, 2000; Vandenberg, 2002). First, traditional classical test theory evaluation of MI occurred via Cronbach’s coefficient alpha estimate of scale reliability. As one of the most frequently used methods of estimating internal consistency reliability (Cronbach, 1951),

Cronbach’s coefficient alpha (α) tests whether items are

sufficiently interrelated to justify their combination in a scale, and reliability estimates ≥ .70 are deemed acceptable for clinical significance (Cicchetti, 1994). Since reliability is related to test length, when estimating the reliability of scales with fewer than six items, coefficient α frequently underestimates the reliability of test scores (Charter, 2003). Under circumstances where the scale has few items, investigators have used the Spearman-Brown prophecy formula to adjust α values (Dimitrov, 2002): ρ x∗x′ =

2 ρ xx′ where ρ x∗x′ = predicted reliability and ρ xx′ = 1 + ρ xx′

current reliability. Internal consistency reliability estimates of the risk behavior and condom use PMT models were examined in all samples across time. Next, three invariance tests were conducted using single and multiple group CFA (MGCFA) to investigate overall measurement level invariance (Cheung & Rensvold, 2002). The first test examined configural invariance, which is the most basic level of MI. The central

37 requirement for configural invariance is that the same manifest items xi are indicators of the same latent factor η j in each sample and in each group (Meredith, 1993; Vandenberg & Lance, 2000). As such, single group CFAs of the risk behavior and condom use PMT models in all samples across time were investigated, followed by MGCFAs using only the baseline data. The MGCFA of the risk behavior PMT model in the American youth used gender as the grouping variable; the MGCFA of the condom use PMT model in the combined sample of sexually-active American youth and all the Vietnamese youth used culture as the grouping variable. The second test examined metric loading invariance, which requires factor loading parameters to be equal across groups (Bollen, 1989; Bollen, 2002). The tests of metric loading invariance used the likelihood-ratio test (LR) to compare the difference in χ2 between the configural invariance models and a model in which factor loadings were constrained to be equal (Bollen, 1989). The final invariance test examined intercept variance by using the likelihood-ratio test (LR) to compare the difference in χ2 between the configural invariance models and a model in which factor loadings and intercepts were constrained to be equal between groups. (Widaman & Reise, 1997) notes that intercept invariance is required for comparing latent mean differences across longitudinal time points (i.e., latent growth modeling).

Single-Process and Parallel-Process Latent Growth Modeling After MGCFA was used to demonstrate MI among the PMT constructs, latent growth modeling (LGM) (Muthén, 1997; Muthén, 2002) was used to identify the form of each latent trajectory class that best fit the data. Parallel-processes growth modeling is a complex model. Since the goal is model convergence, the plan of analysis must proceed systematically or else there may be difficulties in determining where there is model misspecification. B. Muthén

38 recommends the following analysis plan for

parallel-processes

modeling

(personal

communication, May 7, 2007): (1) Determine the shape of each growth curve, (2) Fit each model without covariates, (3) Modify each model as necessary to attain acceptable model fit, (4) Run joint analysis of both processes, and (5) Include covariate(s) in parallel-process growth model. A diagram of a typical parallel-process growth model with a time-invariant covariate (e.g., culture) and the three latent growth factors (intercept, linear slope, and a quadratic growth factor) is presented in Figure 2. Model fit for growth models was evaluated using the same criteria used for CFA (see above), i.e., χ2, the CFI, and RMSEA. CFI values > 0.9, and RMSEA values < 0.08 were considered satisfactory (Bentler, 1990; Loehlin, 1998).

39

y11

y12

y13

y14

i1

s1

q1

i2

s2

q2

y15

x

y21

y22

y23

Figure 2. PPLGM with time-invariant covariate

y24

y25

40

CHAPTER 3—Results

Introduction As outlined in the Chapter 2 analytical plan, the data analysis for this study occurred in four phases, after a preliminary data clean-up and coding process. The four analytical phases are presented in the following four sections of results. In the first section, the characteristics of the demographic covariates and behavioral outcome variables in the American and Vietnamese samples at baseline and over the four follow-up assessments are presented. Results are presented for all youth in each sample and each group. The American youth are organized in two sets of groups: sexually active at baseline vs. abstinent at baseline (since only the sexually-active youth completed the condom use YHRBI items), and FOK vs. FOK + ImPACT vs. FOK + ImPACT +

Booster (to investigate the effect of intervention); the Vietnamese youth are organized into control vs. intervention groups to investigate the effect of intervention. In the second section, results of the EFA and CFA are described and the final risk behavior and condom use PMT models, determined using baseline data, are presented. Then, the third section presents the following four measurement invariance (MI) tests: internal consistency reliability estimates of the risk behavior and condom use PMT models, in all samples across time; single CFA and multiple CFA tests of configural invariance; multiple group CFA test of metric invariance; and multiple group CFA test of intercept invariance. Finally, section four provides parallel-process latent growth models (PPLGM) of the coping- and threat-appraisal processes in each sample. The concurrent analysis of these higher-order PMT constructs and the behavioral outcome variables, with and without covariates are included (i.e., gender, age, grade, culture, and group).

41

Characteristics of the Demographic Covariates and Behavioral Outcome Variables Demographic Covariates in American Youth Table 5 presents the demographic characteristics of the 797 American youth from baseline through follow-up 4. The specific demographic variables that were used as covariates in the current study have been selected from the complete set of demographics of the original study. Overall, the gender composition of the American youth remained consistent through the study, with a male to female ratio of approximately 2:3, in the total sample, in each FOK group, and the youth who were abstinent at baseline. In contrast, the group of youth, who were sexually-active at baseline, is comprised of an approximately equal number of males and females. The significant difference in gender makeup between the sexual and abstinent youth remained in effect at followup 1 and 4. For all the youth, the median age at baseline was 14, while at 24 months post intervention (follow-up assessment 4) the median age was 16. Similar to the significant withingroup difference in gender that was found, a significant within-group difference in the distribution of age was found. Throughout the study, each FOK group and abstinent group were comprised of significantly younger youth, while the youth who were sexual at baseline were older. The difference in age between the sexual and abstinent youth remained a median difference of 2 years. Data about the educational level show that the median grade increased from a baseline grade of 9, to a grade of 11 at follow-up 4. While there was no between-group difference in grade for the different FOK groups from baseline through follow-up 3, youth who were sexual at baseline remained approximately 1 grade higher than abstinent youth. At follow-up 4, more youth in the Booster group were in grade 12 compared the other groups. Additionally, beginning at follow-up 1 and continuing throughout the remainder of the study, both the sexual at baseline and the FOKonly groups contained more youth who dropped out of school than the other groups.

Table 5. Frequency of Demographic Variables in American Youth Baseline (N = 797) Sexual at Abstinent at FOK FOK + ImPACT FOK + ImPACT Baseline Baseline + Booster Gender Male 332 (41.7)** 169 (48.7)++ 163 (36.2)** 136 (43.9)*+ 98 (38.4)** 98 (42.2)* Female 465 (58.3) 178 (51.3) 287 (63.8) 174 (56.1) 157 (61.6) 134 (57.8) Age Median/Mean 14/14.18 15/14.64++ 13/13.84 14/14.18 14/14.16 14/14.21 ** ** ** * ** 13 299 (37.5) 71 (20.5) 228 (50.7) 121 (39.0) 98 (38.4) 80 (34.5)** 14 185 (23.2) 80 (23.1) 105 (23.3) 65 (21.0) 64 (25.1) 56 (24.1) 15 180 (22.6) 100 (28.8) 80 (17.8) 71 (22.9) 46 (18.0) 63 (27.2) 16 133 (16.7) 96 (27.7) 37 (8.2) 53 (17.1) 47 (18.4) 33 (14.2) Grade Dropped Out 2 (0.3)** 1 (0.3)**++ 1 (0.2)** 1 (0.3)** 0 (0.0)** 1 (0.1)** 6 27 (3.4) 4 (1.2) 23 (5.1) 8 (2.6) 11 (4.3) 8 (3.4) 7 108 (13.6) 31 (8.9) 77 (17.1) 50 (16.1) 32 (12.5) 26 (11.2) 8 225 (28.2) 69 (19.9) 156 (34.7) 85 (27.4) 79 (31.0) 61 (26.3) 9 237 (29.7) 127 (36.6) 110 (24.4) 80 (25.8) 69 (27.1) 88 (37.9) 10 124 (15.6) 63 (18.2) 61 (13.6) 54 (17.4) 42 (16.5) 28 (12.1) 11 60 (7.5) 43 (12.4) 17 (3.8) 23 (7.4) 19 (7.5) 18 (7.8) 12 13 (1.6) 9 (2.6) 4 (0.9) 8 (2.6) 3 (1.2) 2 (0.9) Graduated/GED 1 (0.1) 0 (0.0) 1 (0.2) 1 (0.3) 0 (0.0) 0 (0.0) Note: Data are number of American youth (percent total or percent group). * p < .05 ** p < .01 χ 2 difference within variable. + p < .05 ++ p < .01 χ 2 difference between groups (sexual vs. abstinence or FOK vs. FOK + ImPACT vs. FOK + ImPACT + Booster).

42

Variable

Total

Table 5. Continued Follow-Up 1 (N = 587) Sexual at Abstinent at FOK FOK + ImPACT FOK + ImPACT Baseline Baseline + Booster Gender Male 239 (40.7)** 116 (47.0)++ 123 (36.2)** 98 (42.2)* 77 (39.5)** 64 (40.0)* Female 348 (59.3) 131 (53.0) 217 (63.8) 134 (57.8) 118 (60.5) 96 (60.0) Age Median/Mean 15/15.12 16/15.56++ 14/14.80 15 (15.07) 15 (15.14) 15 (15.17) ** ** ** ** 14 231 (39.4) 57 (23.1) 174 (51.2) 100 (43.1) 77 (39.5) 54 (33.8)** 15 142 (24.2) 56 (22.7) 86 (25.3) 48 (20.7) 49 (25.1) 45 (28.1) 16 125 (21.3) 72 (29.1) 53 (15.6) 51 (22.0) 33 (16.9) 41 (25.6) 17 89 (15.2) 62 (25.1) 27 (7.9) 33 (14.2) 36 (18.5) 20 (12.5) Grade Dropped Out 7 (1.2)** 4 (1.6)**++ 3 (0.9)** 4 (1.7)** 2 (1.0)** 1 (0.6)** 6 2 (0.3) 0 (0.0) 2 (0.6) 0 (0.0) 1 (0.5) 1 (0.6) 7 32 (5.5) 8 (3.2) 24 (7.1) 12 (5.2) 10 (5.1) 10 (6.2) 8 81 (13.8) 19 (7.7) 62 (18.2) 41 (17.7) 18 (9.2) 22 (13.8) 9 195 (33.2) 72 (29.1) 123 (36.2) 75 (32.3) 74 (32.9) 46 (28.8) 10 147 (25.0) 75 (30.4) 72 (21.2) 48 (20.7) 48 (24.6) 51 (31.9) 11 95 (16.2) 53 (21.5) 42 (12.4) 41 (17.7) 32 (16.4) 22 (13.8) 12 23 (3.9) 14 (5.7) 9 (2.6) 9 (3.9) 8 (4.1) 6 (3.8) Graduated/GED 5 (0.9) 2 (0.8) 3 (0.9) 2 (0.9) 2 (1.0) 1 (0.6) Note: Data are number of American youth (percent total or percent group). * p < .05 ** p < .01 χ 2 difference within variable. ++ p < .01 χ 2 difference between groups (sexual vs. abstinence, FOK vs. FOK + ImPACT vs. FOK + ImPACT + Booster).

43

Total

Table 5. Continued Follow-Up 2 (N = 571) Sexual at Abstinent at FOK FOK + ImPACT FOK + ImPACT Baseline Baseline + Booster Gender Male 226 (39.6)**++ 110 (47.0) 116 (34.4)** 98 (42.2)* 65 (35.7)** 63 (40.1)* Female 345 (60.4) 124 (53.0) 221 (65.6) 134 (57.8) 117 (64.3) 94 (59.9) Age Median/Mean 15/15.20 16/15.59++ 14/14.86 15/15.06 15/15.10 15/15.16 ** ++ ** ** ** 14 230 (40.3) 48 (20.5) 182 (54.0) 98 (42.2) 76 (41.8) 56 (35.7)** 15 137 (24.0) 57 (24.4) 80 (23.7) 53 (22.8) 44 (24.2) 40 (25.5) 16 122 (21.4) 72 (30.8) 50 (14.8) 51 (22.0) 30 (16.5) 41 (26.1) 17 82 (14.4) 57 (24.4) 25 (7.4) 30 (12.9) 32 (17.6) 20 (12.7) Grade Dropped Out 10 (1.8)** 8 (3.4)**++ 2 (0.6)** 7 (3.0)** 2 (1.1)** 1 (0.6)** 6 1 (0.2) 0 (0.0) 1 (0.3) 0 (0.0) 1 (0.5) 0 (0.0) 7 26 (4.6) 4 (1.7) 22 (6.5) 9 (3.9) 12 (6.9) 5 (3.2) 8 58 (10.2) 11 (4.7) 47 (13.9) 30 (12.9) 11 (6.0) 17 (10.8) 9 158 (27.7) 53 (22.6) 105 (31.2) 72 (31.0) 39 (21.4) 47 (29.9) 10 149 (26.1) 58 (24.8) 91 (27.0) 65 (28.0) 53 (29.1) 31 (19.7) 11 116 (20.3) 58 (24.8) 58 (17.2) 32 (13.8) 40 (22.0) 44 (28.0) 12 47 (8.2) 38 (16.2) 9 (2.7) 17 (7.3) 21 (11.5) 9 (5.7) Graduated/GED 6 (1.1) 4 (1.7) 2 (0.6) 0 (0.0) 3 (1.6) 3 (1.9) Note. Data are number of American youth (percent total or percent group). * p < .05 ** p < .01 χ 2 difference within variable. ++ p < .01 χ 2 difference between groups (sexual vs. abstinence, FOK vs. FOK + ImPACT vs. FOK + ImPACT + Booster).

44

Total

Table 5. Continued Follow-Up 3 (N = 520) Sexual at Abstinent at FOK FOK + ImPACT FOK + ImPACT Baseline Baseline + Booster Gender Male 198 (38.1)** 84 (42.0)* 114 (35.6)** 83 (39.7)** 56 (33.7)** 59 (40.7)* Female 322 (61.9) 116 (58.0) 206 (64.4) 126 (60.3) 110 (66.3) 86 (59.3) Age Median/Mean 16/16.11 17/16.61++ 15/15.80 16/16.05 16/16.11 16/16.19 ** ** ** ** 15 207 (39.8) 44 (22.0) 163 (50.9) 91 (43.5) 68 (41.0) 48 (33.1)** 16 120 (23.1) 41 (20.5) 79 (24.7) 44 (21.1) 39 (23.5) 37 (25.5) 17 122 (23.5) 65 (32.5) 57 (17.8) 46 (22.0) 31 (18.7) 45 (31.0) 18 71 (13.7) 50 (25.0) 21 (6.6) 28 (13.4) 28 (16.9) 15 (10.3) Grade Dropped Out 19 (3.7)** 15 (7.5)**++ 4 (1.2)** 10 (4.8)** 7 (4.2)** 2 (1.4)** 6 1 (0.2) 0 (0.0) 1 (0.3) 1 (0.5) 0 (0.0) 0 (0.0) 7 8 (1.5) 4 (2.0) 4 (1.2) 6 (2.9) 1 (0.6) 1 (0.7) 8 35 (6.7) 6 (3.0) 29 (9.1) 13 (6.2) 13 (7.8) 9 (6.2) 9 101 (19.4) 26 (13.0) 75 (23.4) 52 (24.9) 26 (15.7) 23 (15.9) 10 130 (25.0) 36 (18.0) 94 (29.4) 51 (24.4) 44 (26.5) 35 (24.1) 11 135 (26.0) 61 (30.5) 74 (23.1) 38 (18.2) 44 (26.5) 53 (36.6) 12 67 (12.9) 35 (17.5) 32 (10.0) 28 (13.4) 25 (15.1) 14 (9.7) Graduated/GED 24 (4.6) 17 (8.5) 7 (2.2) 10 (4.8) 6 (3.6) 8 (5.5) Note. Data are number of American youth (percent total or percent group). * p < .05 ** p < .01 χ 2 difference within variable. ++ p < .01 χ 2 difference between groups (sexual vs. abstinence, FOK vs. FOK + ImPACT vs. FOK + ImPACT + Booster).

45

Total

Table 5. Continued Follow-Up 4 (N = 480) Sexual at Abstinent at FOK FOK + ImPACT FOK + ImPACT Baseline Baseline + Booster Gender Male 186 (38.8)** 81 (44.5)+ 105 (35.2)** 73 (38.0)** 58 (38.2)** 55 (40.4)* Female 294 (61.2) 101 (55.5) 193 (64.8) 119 (62.0) 94 (61.8) 81 (59.6) Age Median/Mean 16/16.15 17/16.69++ 15/15.82 16/16.08+ 16/16.13 16/16.28 ** ** ** ** 15 185 (38.5) 34 (18.7) 151 (50.7) 84 (43.8) 60 (39.5) 41 (30.1) 16 109 (22.7) 40 (22.0) 69 (23.2) 38 (19.8) 39 (25.7) 32 (23.5) 17 114 (23.8) 56 (30.8) 58 (19.5) 41 (21.4) 26 (17.1) 47 (34.6) 18 72 (15.0) 52 (28.6) 20 (6.7) 29 (15.1) 27 (17.8) 16 (11.8) Grade Dropped Out 16 (3.3)** 15 (8.2)**++ 1 (0.3)** 10 (5.2)**++ 2 (1.3)** 4 (2.9)** 6 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 7 3 (0.6) 0 (0.0) 3 (1.0) 2 (1.0) 1 (0.7) 0 (0.0) 8 20 (4.2) 5 (2.7) 15 (5.0) 7 (3.6) 9 (5.9) 4 (2.9) 9 64 (13.3) 16 (8.8) 48 (16.1) 34 (17.7) 14 (9.2) 16 (11.8) 10 104 (21.7) 22 (12.1) 82 (27.5) 47 (24.5) 34 (22.4) 23 (16.9) 11 122 (25.4) 45 (24.7) 77 (25.8) 44 (22.9) 45 (29.6) 33 (24.3) 12 93 (19.4) 46 (25.3) 47 (15.8) 24 (12.5) 27 (17.8) 42 (30.9) Graduated/GED 58 (12.1) 33 (18.1) 25 (8.4) 24 (12.5) 20 (13.2) 14 (10.3) Note. Data are number of American youth (percent total or percent group). * p < .05 ** p < .01 χ 2 difference within variable. + p < .05 ++ p < .01 χ 2 difference between groups (sexual vs. abstinence or FOK vs. FOK + ImPACT vs. FOK + ImPACT + Booster).

46

Total

47

Demographic Covariates in Vietnamese Youth Characteristics of the demographic covariates in the Vietnamese youth from baseline through follow-up 4 are presented in Table 6. Similar to the American sample, gender, age, and grade are the only demographic covariates that are described. As shown, the Vietnamese sample was comprised of a relatively equal number of males and females, and the proportion of males-to-females remained equal in the control and intervention groups throughout the study. In contrast, there was significant variability in the age of the Vietnamese youth. The median age of all youth at baseline was 17, and by follow-up 4, the median age was 18.5. Within-group variability of age was significant in all groups throughout the study, since the ages of youth ranged from 14-21 at baseline and 15-22 at follow-up 4. Youth in the control group were significantly older than youth in the intervention group at baseline and follow-up assessments 2 and 3 (mean difference approximately 0.4 years of age). Similar to the distribution of age, there was significant within-group variability in educational level among the Vietnamese youth. The median grade at baseline among the 332 youth who either did not graduate or did not drop out was 11; at follow-up 4, the median grade among the 224 who did not graduate or drop out was 12. Finally, no difference in educational level was found between the control and intervention groups.

48

Table 6. Frequency of Demographic Variables in Vietnamese Youth Baseline (N = 480) Variable Total Control Treatment Gender Male 240 (50.0) 120 (50.0) 120 (50.0) Female 240 (50.0) 120 (50.0) 120 (50.0) Age Median/Mean 17/17.07 17/17.24+ 17/16.90 14 7 (1.5)** 2 (0.8)** 5 (2.1)** 15 86 (17.9) 39 (16.2) 47 (19.6) 16 114 (23.8) 52 (21.7) 62 (25.8) 17 88 (18.3) 41 (17.1) 47 (19.6) 18 83 (17.3) 49 (20.4) 34 (14.2) 19 55 (11.5) 31 (12.9) 24 (10.0) 20 40 (8.3) 22 (9.2) 18 (7.5) 21 7 (1.5) 4 (1.7) 3 (1.2) 22 ---Grade Dropped Out 142 (29.6)** 78 (32.5)** 64 (26.7)** 6 11 (2.3) 6 (2.5) 5 (2.1) 7 2 (0.4) -2 (0.8) 8 13 (2.7) 4 (1.7) 9 (3.8) 9 41 (8.5) 17 (7.1) 24 (10.0 10 97 (20.2) 56 (23.3) 41 (17.1) 11 95 (19.8) 41 (17.1) 54 (22.5) 12 73 (15.2) 37 (15.4) 36 (15.0) Graduated/GED 6 (1.2) 1 (0.4) 5 (2.1) Note. Data are number of Vietnamese youth (percent total or percent group). ** p < .01 χ 2 difference within variable. + p < .05 χ 2 difference between groups (control vs. treatment).

Follow-up 1 (N = 466) Total Control Treatment 232 (49.8) 115 (49.6) 117 (50.0) 234 (50.2) 117 (50.4) 117 (50.0) 17/17.57 18/17.71 17/17.43 1 (0.2)** 0 (0.0)** 1 (0.4)** 34 (7.3) 11 (4.7) 23 (9.8) 110 (23.6) 58 (25.0) 52 (22.2) 101 (21.7) 41 (17.7) 60 (25.6) 85 (18.2) 48 (20.7) 37 (15.8) 60 (12.9) 35 (15.1) 25 (10.7) 59 (12.7) 30 (12.9) 29 (12.4) 15 (3.2) 8 (3.4) 7 (3.0) 1 (0.2) 1 (0.4) 0 (0.0) 149 (32.0)** 82 (35.3)** 67 (28.6)** 11 (2.4) 6 (2.6) 5 (2.1) 3 (0.6) 2 (0.9) 1 (0.4) 8 (1.7) 1 (0.4) 7 (3.0) 33 (7.1) 14 (6.0) 19 (8.1) 93 (20.0) 53 (22.8) 40 (17.1) 94 (20.2) 41 (17.7) 53 (22.6) 53 (11.4) 26 (11.2) 27 (11.5) 22 (4.7) 7 (3.0) 15 (6.4)

49

Table 6. Continued Follow-up 2 (N = 463) Variable Total Control Treatment Gender Male 229 (49.5) 115 (49.1) 114 (49.8) Female 234 (50.5) 119 (50.9) 115 (50.2) Age Median/Mean 18/17.97 18/18.13+ 17/17.80 14 ---** ** 15 15 (3.2) 6 (2.6) 9 (3.9)** 16 87 (18.8) 41 (17.5) 46 (20.1) 17 107 (23.1) 45 (19.2) 62 (27.1) 18 96 (20.7) 47 (20.1) 49 (21.4) 19 58 (12.5) 38 (16.2) 20 (8.7) 20 56 (12.1) 36 (15.4) 20 (8.7) 21 34 (7.3) 18 (7.7) 16 (7.0) 22 10 (2.2) 3 (1.3) 7 (3.1) Grade Dropped Out 124 (26.8)** 64 (27.4)** 60 (26.2)** 6 5 (1.1) 4 (1.7) 1 (0.4) 7 6 (1.3) 3 (1.3) 3 (1.3) 8 6 (4.5) 1 (0.4) 5 (2.2) 9 21 (15.8) 8 (3.4) 13 (5.7) 10 73 (16.0) 41 (17.5) 32 (14.0) 11 74 (21.6) 45 (19.2) 29 (12.7) 12 100 (11.7) 41 (17.5) 59 (25.8) Graduated/GED 54 (26.8) 27 (11.5) 27 (11.8) Note. Data are number of Vietnamese youth (percent total or percent group). ** p < .01 χ 2 difference within variable. + p < .05 χ 2 difference between groups (control vs. treatment).

Follow-up 3 (N = 451) Total Control Treatment 223 (49.4) 114 (49.1) 109 (49.8) 228 (50.6) 118 (50.9) 110 (50.2) + 18/18.53 19/18.73 18/18.32 ---** ** 1 (0.2) 0 (0.0) 1 (0.5)** 40 (8.9) 12 (5.2) 28 (12.8) 109 (24.2) 57 (24.6) 52 (23.7) 104 (23.1) 45 (19.4) 59 (26.9) 66 (14.6) 42 (18.1) 24 (11.0) 53 (11.8) 33 (14.2) 20 (9.1) 52 (11.5) 30 (12.9) 22 (10.0) 26 (5.8) 13 (5.6) 13 (5.9) 118 (26.2)** 59 (25.4)** 59 (26.9)** 4 (0.9) 4 (1.7) 0 (0.0) 4 (0.9 2 (0.9) 2 (0.9) 7 (1.6) 3 (1.3) 4 (1.8) 10 (2.2) 3 (1.3) 7 (3.2) 34 (7.5) 15 (6.5) 19 (8.7) 100 (22.2) 60 (25.9) 40 (18.3) 94 (20.8) 43 (18.5) 51 (23.3) 80 (17.7) 43 (18.5) 37 (16.9)

50

Table 6. Continued Follow-Up 4 (N = 446) Variable Total Control Treatment Gender Male 217 (48.7) 107 (48.2) 110 (49.1) Female 229 (51.3) 115 (51.8) 114 (50.9) Age Median/Mean 18.5 (18.92) 19/19.06 18/18.78 14 ---15 1 (0.2)** 0 (0.0)** 1 (0.4)** 16 12 (2.7) 2 (0.9) 10 (4.5) 17 88 (19.7) 46 (20.7) 42 (18.8) 18 122 (27.4) 54 (24.3) 68 (30.4) 19 68 (15.2) 33 (14.9) 35 (15.6) 20 57 (12.8) 33 (14.9) 24 (10.7) 21 49 (11.0) 30 (13.5) 19 (8.5) 22 49 (11.0) 24 (10.8) 25 (11.2) Grade Dropped Out 123 (27.6)** 57 (25.7)** 66 (29.5)** 6 3 (0.7) 3 (1.4) 0 (0.0) 7 1 (0.2) 1 (0.5) 0 (0.0) 8 1 (0.2) 1 (0.5) 0 (0.0) 9 10 (2.2) 3 (1.4) 7 (3.1) 10 19 (4.3) 10 (4.5) 9 (4.0) 11 71 (15.9) 39 (17.6) 32 (14.3) 12 119 (26.7) 56 (25.2) 63 (28.1) Graduated/GED 99 (22.2) 52 (23.4) 47 (21.0) Note. Data are number of Vietnamese youth (percent total or percent group). ** p < .01 χ 2 difference within variable.

51

Behavioral Outcome Variables in American Youth Frequency analysis of self-reported sexual intercourse (vaginal; with and without a condom), cigarette use, and alcohol use among the American youth, for the measured time periods, is presented in Table 7. For vaginal intercourse (with or without condom use), the percentage of total youth who reported sexual behavior increased throughout the study from 43.5% (n = 347) youth at baseline, to 58.5% (n = 281) youth at follow-up 4. While the percentage of total youth who reported sexual intercourse without a condom rose from 11.0% (n = 88) to 15.8% (n = 76), so did the percentage of total youth who reported engaging in sexual intercourse using a condom (32.5%, n = 259, to 42.7%, n = 205). There was no gender effect for sexual intercourse with or without a condom among the total sample of American youth from baseline through follow-up assessment 2. However, by follow-up periods 3 and 4, significantly more females than males reported engaging in sexual intercourse and condom use (~ ratio of 3:2 females to males). In contrast, beginning at baseline and continuing throughout the study, there were significantly more females than males in the total sample who reported abstaining from sexual activity (percentage of females:males = 63.8:36.2 at baseline and 67.8:32.2 at follow-up 4). Ironically, by follow-up assessment 4, approximately 20% more females than males reported engaging in sexual intercourse without a condom. As predicted, the frequency of sexual behaviors, among the 347 youth who reported ever engaging in sexual intercourse at baseline, remained significantly higher than among the youth who reported having never engaged in sexual intercourse at baseline. Surprisingly, the rate of sexual activity among the 450 youth who reported abstinence at baseline, grew from the baseline level of 0% to 24.9% (n = 117) at followup 4. From baseline through follow-up 3, a significant decrease in the frequency of sexual activity across the three FOK intervention groups was found. At follow-up 4, the percentage of

52 sexual activity in the Booster group was still lower than either the FOK or the ImPACT groups. Similar to the decline in frequency of sexual activity across FOK groups, was a decline in their frequency of unprotected sex (i.e., sexual activity without a condom) throughout the study, with the difference significant at baseline and follow-up assessment period 1. For cigarette and alcohol use, the frequency of self-reported behavior, in all youth across time, occurred in a Ushaped fashion, as the percentage for both of these risk behaviors decreased from baseline through follow-up 2 (27.8, 15.8 and 15.6 for cigarette use, and 43.4, 27.3, and 25.4 for alcohol use), and then increased slightly over follow-up periods 3 and 4 (17.5 and 17.3 for cigarette use, and 26.7 and 27.3 for alcohol use). The frequency of cigarette and alcohol use remained significantly higher in the youth who were sexual at baseline vs. the youth who were abstinent throughout the study, with both behaviors tending to decrease over time in both groups. In contrast, the pattern of cigarette and alcohol use, between the FOK groups across time, occurred in a quasi U-shaped fashion in the ImPACT and Booster groups, but developed in a quasi inverted U-shaped fashion in the FOK group across time. For example, the percentage of cigarette use from baseline through follow-up 4, within the FOK group, was 40.4, 48.4, 47.2, 44.0, and 54.2. A similar pattern was observed across time in the FOK group for alcohol use (36.7, 42.5, 48.3, 39.6., and 39.7). Regarding the relationship between gender and behavior in the American youth, the patterns followed the general ratio of females:males in the sample (more females engaged in the behavior compared to males), with the exception of the Booster group, in which more males compared to females reported cigarette use at follow-up periods 1, 3 and 4.

Table 7. Frequency of Self-Reported Behaviors in American Youth Risk Behavior

53

Sexually activea Abstinent Sex w/condom Sex w/o condom Use Cigarettes Use Alcohol

All Youth (N = 797) Total Male 347 (43.5) 169 (48.7) 450 (56.5) 163 (36.2)** 259 (32.5) 128 (49.4) 88 (11.0) 41 (46.6) 218 (27.8) 91 (41.7)** 341 (43.4) 143 (41.9)

Female 178 (51.3) 287 (63.8) 131 (50.6) 47 (53.4) 127 (58.3) 198 (58.1)

Baseline Sexual at Baseline (n = 347) Total Male 347 (43.5)++ 169 (48.7)** 0 (0.0)++ 0 (0.0) 259 (32.5)++ 128 (49.4)* 88 (11.0)++ 41 (46.6) 147 (18.4)++ 67 (45.6) 217 (27.2)++ 98 (45.4)

Female 178 (51.3) 0 (0.0) 131 (50.6) 47 (53.4) 80 (54.4) 119 (54.6)

Abstinent at Baseline (n = 450) Total Male Female 0 (0.0) 0 (0.0) 0 (0.0) 450 (56.5) 163 (36.2)** 287 (63.8) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 72 (9.0) 24 (33.3)** 48 (66.7) 128 (16.1) 46 (35.9)** 82 (64.1)

FOK (n = 310) FOK + ImPACT (n = 255) FOK + ImPACT + Booster (n = 232) Total Male Female Total Male Female Total Male Female Sexually active 143 (41.2)++ 78 (54.5) 65 (45.5) 109 (31.4) 42 (38.5)* 67 (61.5) 95 (27.4) 49 (51.6) 46 (48.4) Abstinent 167 (37.1) 58 (34.7)** 109 (65.3) 146 (32.4) 56 (38.4)** 90 (61.6) 137 (30.4) 49 (35.8)** 88 (64.2) Sex w/condom 102 (39.4) 57 (55.9) 45 (44.1) 87 (33.6) 36 (41.4) 51 (58.6) 70 (27.0) 35 (50.0) 35 (50.0) Sex w/o condom 41 (46.6)+ 21 (51.2) 20 (48.8) 22 (25.0) 6 (27.3)* 16 (72.7) 25 (28.4) 14 (56.0) 11 (44.0) Use Cigarettes 88 (40.4)+ 42 (47.7) 46 (52.3) 73 (33.5) 26 (35.6) 47 (64.4) 57 (26.1) 23 (40.4) 34 (59.6) Use Alcohol 125 (36.7) 62 (49.6) 63 (50.4) 114 (33.4) 34 (29.8)** 80 (70.2) 102 (29.9) 47 (46.1) 55 (53.9) Note. Data are number of American youth (percent total or percent group) reporting engaging in behavior in the past six months. a Youth reporting engaging in vaginal intercourse in the past six months. ** p < .01 or * p < .05 χ 2 difference between gender. ++ p < .01 or + p < .05 χ 2 difference between group (sexual at baseline vs. abstinent at baseline, FOK vs. FOK + ImPACT vs. FOK + ImPACT + Booster).

Table 7. Continued Risk Behavior

54

Sexually active Abstinent Sex w/condom Sex w/o condom Use Cigarettes Use Alcohol

All Youth (N = 587) Total Male 295 (50.3) 141 (47.8) 292 (49.7) 98 (33.6)** 230 (39.2) 107 (46.5) 65 (11.1) 34 (52.3) 93 (15.8) 49 (52.7) 160 (27.3) 73 (45.6)

Female 154 (52.2) 194 (66.4) 123 (53.5) 31 (47.7) 44 (47.3) 87 (54.4)

Follow-Up 1 Sexual at Baseline (n = 247) Total Male 210 (35.8)++ 99 (47.1) 37 (6.3) ++ 17 (45.9) 162 (27.6)++ 71 (43.8) 48 (8.2)++ 28 (58.3) 52 (8.9)++ 29 (55.8) ++ 101 (17.2) 48 (47.5)

Female 111 (52.9) 20 (54.1) 91 (56.2) 20 (41.7) 23 (44.2) 53 (52.5)

Abstinent at Baseline (n = 340) Total Male Female 85 (14.5) 42 (49.4)** 43 (50.6) 255 (43.4) 81 (31.8)** 174 (68.2) 68 (11.6) 36 (52.9)** 32 (47.1) ** 17 (2.9) 6 (35.3) 11 (64.7) 41 (7.0) 20 (48.8) 21 (51.2) 59 (10.1) 25 (42.4) 34 (57.6)

FOK (n = 232) FOK + ImPACT (n = 195) FOK + ImPACT + Booster (n = 160) Total Male Female Total Male Female Total Male Female Sexually active 118 (40.0)++ 59 (50.0) 59 (50.0) 110 (37.3) 48 (43.6) 62 (56.4) 67 (22.7) 34 (50.7) 33 (49.3) Abstinent 114 (39.0) 39 (34.2)** 75 (65.8) 85 (29.1) 29 (34.1)** 56 (65.9) 93 (31.8) 30 (32.3)** 63 (67.7) Sex w/condom 89 (38.7) 41 (46.1) 48 (53.9) 82 (35.7) 37 (45.1) 45 (54.9) 59 (25.7) 29 (49.2) 30 (50.8) Sex w/o condom 29 (44.6)++ 18 (62.1) 11 (37.9) 28 (43.1) 11 (39.3) 17 (60.7) 8 (12.3) 5 (62.5) 3 (37.5) Use Cigarettes 45 (48.4)++ 21 (46.7) 24 (53.3) 30 (32.3) 15 (50.0) 15 (50.0) 18 (19.4) 13 (72.2)** 5 (27.8) + Use Alcohol 68 (42.5) 32 (47.1) 36 (52.9) 53 (33.1) 23 (43.4) 30 (56.6) 39 (24.4) 18 (46.2) 21 (53.8) Note. Data are number of American youth (percent total or percent group) reporting engaging in behavior in the past six months. a Youth reporting engaging in vaginal intercourse in the past six months. ** p < .01 or * p < .05 χ 2 difference between gender. ++ p < .01 or + p < .05 χ 2 difference between group (sexual at baseline vs. abstinent at baseline, FOK vs. FOK + ImPACT vs. FOK + ImPACT + Booster).

Table 7. Continued Risk Behavior

55

Sexually active Abstinent Sex w/condom Sex w/o condom Use Cigarettes Use Alcohol

All Youth (N = 571) Total Male 309 (54.1) 136 (44.0) 262 (45.9) 90 (34.4)** 221 (38.7) 99 (44.8) 88 (15.4) 37 (42.0) 89 (15.6) 37 (41.6) 145 (25.4) 69 (47.6)

Female 173 (56.0) 172 (65.6) 122 (55.2) 51 (58.0) 52 (58.4) 76 (52.4)

Follow-Up 2 Sexual at Baseline (n = 234) Total Male 197 (34.5)++ 86 (43.7) 37 (6.5)++ 24 (64.9)* 136 (23.8)++ 61 (44.9) 61 (10.7)++ 25 (41.0) 49 (8.6)++ 22 (44.9) 82 (14.4)++ 44 (53.7)

Female 111 (56.3) 13 (35.1) 75 (55.1) 36 (59.0) 27 (55.1) 38 (46.3)

Abstinent at Baseline (n = 337) Total Male Female 112 (19.6) 50 (44.6) 62 (55.4) 225 (39.4) 66 (29.3)** 159 (70.7) 85 (14.9) 38 (44.7) 47 (55.3) 27 (4.7) 12 (44.4) 15 (55.6) 40 (7.0) 15 (37.5) 25 (62.5) 63 (11.0) 25 (39.7) 38 (60.3)

FOK (n = 232) FOK + ImPACT (n = 182) FOK + ImPACT + Booster (n = 157) Total Male Female Total Male Female Total Male Female Sexually active 128 (41.4)++ 60 (46.9) 68 (53.1) 107 (34.6) 43 (40.2)* 64 (59.8) 74 (23.9) 33 (44.6) 41 (55.4) Abstinent 104 (39.7) 38 (36.5)** 66 (63.5) 75 (28.6) 22 (29.3)** 53 (70.7) 83 (31.7) 30 (36.1)* 53 (63.9) Sex w/condom 93 (42.1) 41 (44.1) 52 (55.9) 74 (33.5) 31 (41.9) 43 (58.1) 54 (24.4) 27 (50.0) 27 (50.0) Sex w/o condom 35 (39.8) 19 (54.3) 16 (45.7) 33 (37.5) 12 (36.4) 21 (63.6) 20 (22.7) 6 (30.0) 14 (70.0) Use Cigarettes 42 (47.2)+ 18 (42.9) 24 (57.1) 25 (28.2) 10 (40.0) 15 (60.0) 22 (24.7) 9 (40.9) 13 (59.1) ++ Use Alcohol 70 (48.3) 34 (48.6) 36 (51.4) 41 (28.3) 16 (39.0) 25 (61.0) 34 (23.4) 19 (55.9) 15 (44.1) Note. Data are number of American youth (percent total or percent group) reporting engaging in behavior in the past six months. a Youth reporting engaging in vaginal intercourse in the past six months. ** p < .01 or * p < .05 χ 2 difference between gender. ++ p < .01 or + p < .05 χ 2 difference between group (sexual at baseline vs. abstinent at baseline, FOK vs. FOK + ImPACT vs. FOK + ImPACT + Booster).

Table 7. Continued Risk Behavior

56

Sexually active Abstinent Sex w/condom Sex w/o condom Use Cigarettes Use Alcohol

All Youth (N = 520) Total Male 297 (57.1) 123 (41.4)** 223 (42.9) 75 (33.6)** 206 (39.6) 86 (41.7)* 91 (17.5) 37 (40.7) 91 (17.5) 39 (42.9) 139 (26.7) 54 (38.8)**

Female 174 (58.6) 148 (66.4) 120 (58.3) 54 (59.3) 52 (57.1) 85 (61.2)

Follow-Up 3 Sexual at Baseline (n = 200) Total Male 177 (34.0)++ 72 (40.7)* 23 (4.4)++ 12 (52.2) 116 (22.3)++ 51 (44.0) 61 (11.7)++ 21 (34.4)* 51 (9.8)++ 21 (41.2) ++ 78 (15.0) 31 (39.7)

Female 105 (59.3) 11 (47.8) 65 (56.0) 40 (65.6) 30 (58.8) 47 (60.3)

Abstinent at Baseline (n = 320) Total Male Female 120 (23.1) 51 (42.5) 69 (57.5) 200 (38.5) 63 (31.5)** 137 (68.5) 90 (17.3) 35 (38.9)* 55 (61.1) 30 (5.8) 16 (53.3) 14 (46.7) 40 (7.7) 18 (45.0) 22 (55.0) 61 (11.7) 23 (37.7) 38 (62.3)

FOK (n = 209) FOK + ImPACT (n = 166) FOK + ImPACT + Booster (n = 145) Total Male Female Total Male Female Total Male Female Sexually active 123 (41.4)++ 52 (42.3) 71 (57.7) 95 (32.0) 37 (38.9)* 58 (61.1) 79 (26.6) 34 (43.0) 45 (57.0) Abstinent 86 (38.6) 31 (36.0)* 55 (64.0) 71 (31.8) 19 (26.8)** 52 (73.2) 66 (29.6) 25 (37.9)* 41 (62.1) Sex w/condom 83 (40.3) 35 (42.2) 48 (57.8) 67 (32.5) 27 (40.3) 40 (59.7) 56 (27.2) 24 (42.9) 32 (57.1) Sex w/o condom 40 (44.0) 17 (42.5) 23 (57.5) 28 (30.8) 10 (35.7) 18 (64.3) 23 (25.3) 10 (43.5) 13 (56.5) Use Cigarettes 40 (44.0)+ 14 (35.0) 26 (65.0) 30 (33.0) 12 (40.0) 18 (60.0) 21 (23.1) 13 (61.9)* 8 (38.1) * Use Alcohol 55 (39.6) 22 (40.0) 33 (60.0) 43 (30.9) 14 (32.6) 29 (67.4) 41 (29.5) 18 (43.9) 23 (56.1) Note. Data are number of American youth (percent total or percent group) reporting engaging in behavior in the past six months. a Youth reporting engaging in vaginal intercourse in the past six months. ** p < .01 or * p < .05 χ 2 difference between gender. ++ p < .01 or + p < .05 χ 2 difference between group (sexual at baseline vs. abstinent at baseline, FOK vs. FOK + ImPACT vs. FOK + ImPACT + Booster).

Table 7. Continued Risk Behavior

57

Sexually active Abstinent Sex w/condom Sex w/o condom Use Cigarettes Use Alcohol

All Youth (N = 480) Total Male 281 (58.5) 122 (43.4)* 199 (41.5) 64 (32.2)* 205 (42.7) 92 (44.9)* 76 (15.8) 30 (39.5)* 83 (17.3) 40 (48.2) 131 (27.3) 54 (41.2)

Female 159 (56.6) 135 (67.8) 113 (55.1) 46 (60.5) 43 (51.8) 77 (58.8)

Follow-Up 4 Sexual at Baseline (n = 182) Total Male 164 (34.2)++ 74 (45.1) 18 (3.8)++ 7 (38.9) 110 (22.9)++ 53 (48.2) 54 (11.2)++ 21 (38.9) 51 (10.6)++ 29 (56.9)* + 61 (12.7) 32 (52.5)

Female 90 (54.9) 11 (61.1) 57 (51.8) 33 (61.1) 22 (43.1) 29 (47.5)

Abstinent at Baseline (n = 298) Total Male Female 117 (24.4) 48 (41.0) 69 (59.0) 181 (37.7) 57 (31.5)** 124 (68.5) 95 (19.8) 39 (41.1) 56 (58.9) 22 (4.6) 9 (40.9) 13 (59.1) 32 (6.7) 11 (34.4) 21 (65.6) ** 70 (14.6) 22 (31.4) 48 (68.6)

FOK (n = 192) FOK + ImPACT (n = 152) FOK + ImPACT + Booster (n = 136) Total Male Female Total Male Female Total Male Female Sexually active 111 (39.5) 52 (46.8) 59 (53.2) 89 (31.7) 36 (40.4) 53 (59.6) 81 (28.8) 34 (42.0) 47 (58.0) Abstinent 81 (40.7) 21 (25.9)** 60 (74.1) 63 (31.7) 22 (34.9)* 41 (65.1) 55 (27.6) 21 (38.2) 34 (61.8) Sex w/condom 79 (38.5) 37 (46.8) 42 (53.2) 65 (31.7) 28 (43.1) 37 (56.9) 61 (29.8) 27 (44.3) 34 (55.7) Sex w/o condom 32 (42.1) 15 (46.9) 17 (53.1) 24 (31.6) 8 (33.3) 16 (66.7) 20 (26.3) 7 (35.0) 13 (65.0) Use Cigarettes 45 (54.2)++ 19 (42.2) 26 (57.8) 20 (24.1) 10 (50.0) 10 (50.0) 18 (21.7) 11 (61.1) 7 (38.9) * Use Alcohol 52 (39.7) 22 (42.3) 30 (57.7) 42 (32.1) 14 (33.3) 28 (66.7) 37 (28.2) 18 (48.6) 19 (51.4) Note. Data are number of American youth (percent total or percent group) reporting engaging in behavior in the past six months. a Youth reporting engaging in vaginal intercourse in the past six months. ** p < .01 or * p < .05 χ 2 difference between gender. ++ p < .01 or + p < .05 χ 2 difference between group (sexual at baseline vs. abstinent at baseline, FOK vs. FOK + ImPACT vs. FOK + ImPACT + Booster).

58

Behavioral Outcome Variables in Vietnamese Youth Table 8 presents the frequency analysis of self-reported sexual intercourse (vaginal; with and without a condom), cigarette use, and alcohol use among the Vietnamese youth for the measured time periods. The frequency of sexual activity among the total sample was less than 2% at baseline, and remained at this low level, throughout the study. With the exception of follow-up 1, there were no between-group differences in any of the sexual behaviors; at followup 1, there was both a significant increase in the percentage of youth reporting sexual activity in the control vs. treatment groups (0 vs. 1.1), and a significant decrease in the percentage of youth reporting abstinence in the control vs. treatment groups (49.8 vs. 49.1). For cigarette and alcohol use, the pattern of behavior, among the total sample of youth, progressed in a U-shaped pattern throughout the study, with the percentage of cigarette and alcohol use at baseline (16.3 and 29.8, respectively) dropping at follow-up 1 (14.4 and 20.4, respectively), but then increasing over follow-up periods 2-4 (cigarette use percentage = 19.1 at follow-up 4; alcohol use percentage = 35.7 at follow-up 4). Throughout the study, there remained a significant gender effect on behavior as the ratio of males:females that reported substance use remained at least 4:1.

Table 8. Frequency of Self-Reported Behaviors in Vietnamese Youth Risk Behavior Sexually active Abstinent Sex w/condom Sex w/o condom Use Cigarettes Use Alcohol

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Risk Behavior Sexually active Abstinent Sex w/condom Sex w/o condom Use Cigarettes Use Alcohol

Total (n = 480) Total Male 8 (1.7) 4 (50.0) 472 (98.3) 236 (50.0) 3 (0.6) 2 (66.7) 5 (1.1) 2 (40.0) 78 (16.3) 63 (80.8)** 143 (29.8) 100 (69.9)**

Female 4 (50.0) 236 (50.0) 1 (33.3) 3 (60.0) 15 (19.2) 43 (30.1)

Total (n = 466) Total Male 5 (1.1) 4 (80.0)* 461 (98.9) 228 (49.5) 4 (0.9) 3 (75.0) 1 (0.2) 1 (100.0) 67 (14.4) 63 (94.0)** 95 (20.4) 81 (85.3)**

Baseline Control (n = 240) Total Female 2 (0.4) 2 (100.0) 238 (49.6) 118 (49.6) 0 (0.0) 0 (0.0) 2 (0.5) 2 (100.0) 35 (7.3) 33 (94.3)** 75 (15.6) 58 (77.3)**

Female 0 (0.0) 120 (50.4) 0 (0.0) 0 (0.0) 2 (5.7) 17 (22.7)

Treatment (n = 240) Total Male 6 (1.2) 2 (33.3) 234 (48.8) 118 (50.4) 3 (0.6) 2 (66.7) 3 (0.6) 0 (0.0) 43 (9.0) 30 (69.8)** 68 (14.2) 42 (61.8)*

Female 4 (66.7) 116 (49.6) 1 (33.3) 3 (100.0) 13 (30.2) 26 (38.2)

Follow-Up 1 (Immediate Post Intervention) Control (n = 232) Female Total Male Female + 1 (20.0) 0 (0.0) 0 (0.0) 0 (0.0) 233 (50.5) 232 (49.8)+ 115 (49.6) 117 (50.4) 1 (25.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) ** 4 (6.0) 36 (7.7) 34 (94.4) 2 (5.6) 14 (14.7) 57 (12.2)+ 50 (87.7)** 7 (12.3)

Treatment (n = 234) Total Male 5 (1.1) 4 (80.0)* 229 (49.1) 113 (49.3) 4 (0.9) 3 (75.0) 1 (0.2) 1 (100.0) 31 (6.7) 29 (93.5)** 38 (8.2) 31 (81.6)**

Female 1 (20.0) 116 (50.7) 1 (25.0) 0 (0.0) 2 (6.5) 7 (18.4)

Follow-Up 2 (6 Months Post Intervention) Risk Behavior Total (n = 463) Control (n = 234) Treatment (n = 229) Total Male Female Total Male Female Total Male * Sexually active 6 (1.2) 6 (100.0) 0 (0.0) 4 (0.9) 4 (100.0) 0 (0.0) 2 (0.4) 2 (100.0) Abstinent 457 (98.8) 223 (48.8) 234 (51.2) 230 (49.7) 111 (48.3) 119 (51.7) 227 (49.0) 112 (49.3) Sex w/condom 3 (0.6) 3 (100.0) 0 (0.0) 3 (0.6) 3 (100.0) 0 (0.0) 0 (0.0) 0 (0.0) Sex w/o condom 3 (0.6) 3 (100.0) 0 (0.0) 1 (0.2) 1 (100.0) 0 (0.0) 2 (0.4) 2 (100.0) Use Cigarettes 80 (17.2) 78 (97.5)** 2 (2.5) 46 (9.9) 46 (100.0)** 0 (0.0) 34 (7.3) 32 (94.1)** Use Alcohol 123 (26.6) 109 (88.6)** 14 (11.4) 66 (14.3) 56 (84.8)** 10 (15.2) 57 (12.3) 53 (93.0)** Note: Data are number of Vietnamese youth (percent total or percent group) reporting engaging in behavior in the past 6 months. ** + p < .01 or * p < .05 χ 2 difference between gender. p < .05 χ 2 difference between group.

Female 0 (0.0) 115 (50.7) 0 (0.0) 0 (0.0) 2 (5.9) 4 (7.0)

Table 8. Continued Risk Behavior

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Sexually active Abstinent Sex w/condom Sex w/o condom Use Cigarettes Use Alcohol

Total (n = 451) Total Male 7 (1.6) 7 (100.0)* 444 (98.4) 216 (48.6) 4 (0.9) 4 (100.0) 3 (0.7) 3 (100.0) 84 (18.6) 83 (98.8)** 139 (30.8) 115 (82.7)**

Follow-Up 3 (12 Months Post Intervention) Control (n = 232) Female Total Male Female 0 (0.0) 3 (0.7) 3 (100.0) 0 (0.0) 228 (51.4) 229 (50.8) 111 (48.5) 118 (51.5) 0 (0.0) 3 (0.7) 3 (100.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (1.2) 49 (10.9) 49 (100.0)** 0 (0.0) 24 (17.3) 77 (17.1) 63 (81.8)** 14 (18.2)

Treatment (n = 219) Total Male 4 (0.9) 4 (100.0) 215 (47.7) 105 (48.8) 1 (0.2) 1 (100.0) 3 (0.7) 3 (100.0) 35 (7.8) 34 (97.1)** 62 (13.7) 52 (83.9)**

Follow-Up 4 (18 Months Post Intervention) Risk Behavior Total (n = 446) Control (n = 222) Treatment (n = 224) Total Male Female Total Male Female Total Male * Sexually active 7 (1.6) 7 (100.0) 0 (0.0) 2 (0.4) 2 (100.0) 0 (0.0) 5 (1.1) 5 (100.0)* Abstinent 439 (98.4) 210 (47.8) 229 (52.2) 220 (49.3) 105 (47.7) 115 (52.3) 219 (49.1) 105 (47.9) * Sex w/condom 4 (0.9) 4 (100.0) 0 (0.0) 2 (0.4) 2 (100.0) 0 (0.0) 2 (0.4) 2 (100.0) Sex w/o condom 3 (0.7) 3 (100.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 3 (0.7) 3 (100.0) ** ** Use Cigarettes 85 (19.1) 84 (98.8) 1 (1.2) 45 (10.1) 45 (100.0) 0 (0.0) 40 (9.0) 39 (97.5)** Use Alcohol 159 (35.7) 127 (79.9)** 32 (20.1) 77 (17.3) 66 (85.7)** 11 (14.3) 82 (18.4) 61 (74.4)** Note: Data are number of Vietnamese youth (percent total or percent group) reporting engaging in behavior in the past 6 months. ** + p < .01 or * p < .05 χ 2 difference between gender. p < .05 χ 2 difference between group.

Female 0 (0.0) 110 (51.2) 0 (0.0) 0 (0.0) 1 (2.9) 10 (16.1)

Female 0 (0.0) 114 (52.1) 0 (0.0) 0 (0.0) 1 (2.5) 21 (25.6)

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Cultural Differences in Demographic Covariates and Behavioral Outcome Variables As described in Chapter 2, YHRBI items, assessing condom use, were completed by the Vietnamese youth and the American youth who were sexually-active at baseline. Table 9 presents the demographic covariates and behavioral outcome variables across time, for the American youth who were sexually active at baseline and the Vietnamese youth in the treatment group, in an effort to closely examine the similarities and differences between the American and Vietnamese samples. As shown, both groups were comprised of equal proportions of males and females at baseline. Throughout the study, the ratio of females:males grew in the American sample but remained similar in the Vietnamese sample. American youth were younger than the Vietnamese youth at baseline (median age 15 vs. 17), and were two median grades lower at baseline than the Vietnamese youth (median grade 9 vs. 11). However, there were significantly more Vietnamese youth who reported dropping out of school at baseline than the American youth (26.7% vs. 0.3%). The between-sample differences in the frequency of sexual variables remained significant throughout the study, presumably due to both the low rates of sexual activity in the Vietnamese youth throughout the study, and the requirement that only the American youth who were sexual at baseline were given the condom use YHRBI items. In contrast to the large difference in frequency of sexual behaviors between cultures, differences in substance use behaviors were not as vast. Specifically, at baseline, the American youth reported using significantly more cigarettes and alcohol than the Vietnamese youth (42.4% vs. 17.9% for cigarette use, and 62.5% vs. 23.3% for alcohol use). By follow-up assessment 2, the frequency of cigarette use was similar between the American and Vietnamese samples (20.9% vs. 14.8%), and by follow-up assessment 4, the frequency of alcohol use was also similar between the American and Vietnamese samples (33.5% vs. 36.6%).

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Table 9. Frequency of Demographic Covariates and Behavioral Outcome Variables in American Youth Sexually-Active at Baseline and Vietnamese Youth in the Treatment Group Baseline Follow-Up 1 Follow-Up 2 American Vietnamese American Vietnamese American Vietnamese Youth Youth Youth Youth Youth Youth Gender Male 169 (48.7) 120 (50.0) 116 (47.0) 117 (50.0) 110 (47.0) 114 (49.8) Female 178 (51.3) 120 (50.0) 131 (53.0) 117 (50.0) 124 (53.0) 115 (50.2) ** ** ** Age Median 15 17 15 17 16 17 Mean 14.64 (1.09) 16.90 (1.63) 15.06 (1.10) 17.43 (1.63) 15.59 (1.07) 17.80 (1.72) Grade Dropped Out 1 (0.3)** 64 (26.7) 4 (1.6)** 67 (28.6) 8 (3.4)** 60 (26.2) Median 9 11 10 11 10 11 Mean 9.10 (1.25) 10.34 (1.41) 9.78 (1.15) 10.34 (1.36) 10.21 (1.23) 10.78 (1.34) Graduated/GED 0 (0.0)** 5 (2.1) 2 (0.8) 15 (6.4) 4 (1.7) 27 (11.8) Risk Behaviors Vaginal sex 347 (100)** 6 (2.4) 210 (85.0)** 5 (2.1) 197 (84.2)** 2 (0.9) Abstinent 0 (0.0) 234 (97.6) 37 (15.0) 229 (97.9) 37 (15.8) 227 (99.1) Vaginal w/ condom 259 (74.6)** 3 (1.2) 162 (65.6)** 4 (1.7) 136 (58.1)** 0 (0.0) Vaginal w/o condom 88 (25.4)** 3 (1.2) 48 (19.4) 1 (0.4) 61 (26.1) 2 (0.9) Smoke cigarettes 147 (42.4)** 43 (17.9) 52 (21.1)* 31 (13.2) 49 (20.9) 34 (14.8) Drink beer/wine/liquor 217 (62.5)** 68 (23.3) 101 (40.9)** 38 (16.2) 82 (35.0)* 57 (24.9) Note. Data are number of youth (percent culture); age and grade are expressed as median and mean (SD). Follow-up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post intervention, respectively, for American youth, and at immediate, 6, 12, and 18 months post intervention, respectively, for Vietnamese youth. ** p < .01 or * p < .05 χ2 difference between cultures for dichotomous and nominal variables.

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Table 9. Continued Follow-Up 3 Follow-Up 4 American Vietnamese American Vietnamese Youth Youth Youth Youth Gender Male 84 (42.0)* 109 (49.8) 81 (44.5) 110 (49.1) Female 116 (58.0) 110 (50.2) 101 (55.5) 114 (50.9) Age Median 17** 18 17** 18 Mean 16.11 (1.09) 18.32 (1.74) 16.69 (1.08) 18.78 (1.72) Grade Dropped Out 15 (7.5)** 59 (26.9) 15 (8.2)** 66 (29.5) Median 11 11 11 12 Mean 10.48 (1.22) 10.98 (1.17) 10.83 (1.14) 11.36 (.88) Graduated/GED 17 (8.5) 37 (16.9) 33 (18.1) 47 (21.0) ** ** Risk Behaviors Vaginal sex 177 (88.5) 4 (1.8) 164 (90.1) 5 (2.2) Abstinent 23 (11.5) 215 (98.2) 18 (9.9) 219 (97.8) ** ** Vaginal w/ condom 116 (58.0) 1 (0.4) 110 (60.4) 2 (0.9) Vaginal w/o condom 61 (30.5)** 3 (1.4) 54 (29.7) 3 (1.3) * * Smoke cigarettes 51 (25.5) 35 (16.0) 51 (28.0) 40 (17.9) Drink beer/wine/liquor 78 (39.0)* 62 (28.3) 61 (33.5) 82 (36.6) Note. Data are number of youth (percent culture); age and grade are expressed as median and mean (SD). Follow-up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post intervention, respectively, for American youth, and at immediate, 6, 12, and 18 months post intervention, respectively, for Vietnamese youth. ** p < .01 or * p < .05 χ2 difference between cultures for dichotomous and nominal variables.

64

Determination of Risk Behavior and Condom Use PMT Models As the first step in running parallel-process latent growth models, EFA and CFA were run to determine stable risk behavior and condom use PMT constructs, and to answer the first research question of this dissertation: What is the stability of the risk behavior and condom use PMT constructs within and between cultures and across time using confirmatory factor analysis (CFA) to demonstrate measurement invariance? EFA was conducted to aid in the initial structuring of the PMT constructs and to parcel the YHRBI and V-YHRBI items into a select group of items, followed by confirmatory models to investigate construct goodness of fit. EFA was conducted in SPSS using principle axis factoring with direct oblimin rotation; Mplus was used for the CFA.

Exploratory Factor Analysis of Risk Behavior PMT Items Principal axis factor analysis was conducted on a set of 50 YHRBI items assessing attitudes, perceptions and beliefs regarding sexual, drug and delinquency risk behavior in the American youth. This set of 50 items was culled from the full set of 71 YHRBI items assessing risk behavior. The 21 items that were excluded did not clearly assess risk behavior PMT constructs (e.g. “It’s important to me that my friends respect me”). Because PMT is comprised of constructs that are intercorrelated (e.g., self efficacy, response efficacy and response cost are interrelated), the nonorthogonal rotation method, direct oblimin, was performed on the extracted factors to facilitate interpretation. In the initial EFA, seven factors with eigenvalues greater than 1.0 were extracted, accounting for 44% of the variance of the original items. As the aim of the EFA was to develop an initial structure of the PMT constructs for the subsequent CFAs, further

65 EFAs were not conducted. Correlations between the seven factors ranged from -.245 to .232. Factor loadings for retained and nonretained items are presented in Table 10, with loadings greater than .30 in bold (Comrey & Lee, 1992).

66

Table 10. Seven-Factor Oblimin Solution for Risk Behavior PMT Items in American Youth at Baseline F1 F2 F3 F4 F5 F6 F7 h2 SE1. Even if all my friends were having sex, I would not feel I had to -.025 .003 .104 .204 .107 -.015 .423 .309 SE2. I would be able to say no if I didn't want to have sex -.070 .228 .316 .246 .251 -.089 .360 .533 SE3. If I was going out with a drug user, I would not have to use drugs .019 -.024 .005 -.006 -.084 .039 .535 .275 SE4. If all my friends were drinking I wouldn't have to .002 -.068 -.038 .084 -.180 .072 .557 .296 SE5. If someone asked me to deliver drugs I could say no -.063 .069 .084 -.032 .038 .005 .405 .223 SE6. If a friend asked me to deliver drugs I could say no -.043 .351 .249 -.097 .075 -.086 .364 .464 SE7. If my friends start dealing drugs I would not hang out with them -.069 .578 -.002 .274 -.101 .032 -.052 .455 SE8. If my friends start using drugs I would not hang out with them -.069 .556 -.051 .262 -.129 -.008 -.030 .431 SE9. If a relative asked me to deliver drugs I could say no -.074 .214 .215 .052 .070 -.063 .373 .352 SE10. I can refuse to deliver drugs and dealers will not bother me .042 -.009 .025 -.092 .011 .021 .404 .181 RE1. A guy and a girl can go together and not have sex -.054 .178 .289 .178 .176 .343 -.060 .397 RE2. If my friends take drugs and I don't they will think it's ok .054 -.068 .061 -.021 .049 .435 .016 .203 RE3. If kids have interesting activities to do they won't sell drugs .012 .402 .171 -.082 .002 .477 .019 .274 RE4. Kids do not have to keep delivering drugs -.002 .010 -.047 -.042 .139 .305 -.086 .129 RE5. A person who decides to stop using drugs can stop -.054 .091 -.085 -.035 -.066 .335 -.005 .136 RC1. Boys think its important to have sex to feel like a man -.046 .130 .068 -.309 .216 .111 .121 .245 RC2. Girls think its important to have sex to feel like a woman .036 .159 -.063 -.381 .121 .198 .051 .315 RC3. Sometimes sex just happens and you really can't control it .045 .092 -.125 -.404 .073 .166 .026 .299 RC4. A guy my age who has never had sex is probably scared -.034 .027 .036 .010 -.029 -.818 .038 .648 RC5. A girl my age who has never had sex is probably scared -.041 -.031 .074 .051 .039 -.789 .066 .590 RC6. Boys needs to sell drugs for their girlfriend .078 -.027 -.475 -.123 .175 .028 .026 .350 RC7. I would get beat up if I didn't sell drugs .004 .047 -.542 .082 .109 .000 .003 .320 RC8. If the police do catch a kid dealing drugs not much will happen -.058 .007 .140 .000 -.201 -.047 .055 .093 Note. This set of 50 YHRBI items is compiled from a larger set of 84 items. Bold indicates pattern matrix factor loading values above .30, extraction method: Principal Axis Factoring, rotation method: direct oblimin (Δ = 0) with Kaiser normalization, h2 = communality.

67

Table 10. (Continued)

F1 F2 F3 F4 F5 F6 F7 h2 IR1. I feel good about having sex .441 -.134 .177 -.487 -.025 -.022 .062 .542 IR2. I feel good about getting HIV infection .646 .098 -.218 .279 -.074 .037 -.035 .510 IR4. I feel good about getting pregnant or getting a girl pregnant .616 -.019 -.007 -.118 -.137 -.005 .049 .416 IR5. I feel good about having a baby .535 -.052 .008 -.206 -.085 -.003 .016 .376 IR6. I feel good about smoking marijuana .613 -.028 .131 -.096 .093 -.021 .002 .418 IR7. I feel good about drinking alcohol .643 -.087 .130 -.143 .162 -.084 .061 .530 IR8. I feel good about using cocaine .643 .081 -.236 .267 -.073 .075 -.011 .522 IR9. I feel good about dealing drugs .771 -.030 .054 .069 .092 .014 -.015 .603 IR10. I feel good about delivering drugs .824 .002 .027 .141 .140 -.005 -.028 .688 ER1. Getting burned (an STD) proves that a boy is a man -.013 .118 -.533 .060 .048 .090 -.052 .347 ER2. Someone my age would want to have sex to see how it feels .030 .136 .023 -.388 .160 .073 .118 .271 ER3. Troubles don't seem bad when you're high on drugs .040 -.040 -.165 -.037 .614 .017 -.027 .462 ER4. Sex feels better when you are high on drugs -.013 -.162 -.169 .043 .577 .042 .024 .414 ER5. Being high on a drug makes a person feel good .061 -.051 .036 -.085 .462 .211 -.066 .328 ER6. Delivering or selling drugs is exciting .076 -.042 -.246 -.110 .398 -.020 -.078 .309 SEV1. People who use drugs are not good parents -.021 .290 -.087 .030 .034 -.007 .072 .108 SEV2. People who use drugs become addicts -.023 .515 .131 -.051 .037 .093 .022 .316 SEV3. People who use drugs get AIDS -.015 .477 -.159 .004 -.043 .039 -.025 .259 SEV4. Bad things happen to families of kids who sell drugs .015 .560 -.074 -.033 -.006 .016 -.004 .318 SEV5. People who use drugs die early -.021 .508 -.112 -.162 -.053 -.046 -.015 .288 SEV6. People who use drugs spend all of their money on drugs .004 .559 .119 -.197 -.038 -.004 -.033 .350 SEV7. Kids who sell drugs will get hurt .014 .645 .037 .008 .013 .005 -.083 .396 VUL1. If I am horny I can't control what happens .032 .024 -.227 -.233 .076 .110 -.061 .186 VUL2. I can go with a person for a long time and have sex with them .116 -.100 -.104 -.287 -.166 .053 -.319 .300 VUL3. If my friends use drugs then I will use drugs .182 .049 -.370 .004 .125 .056 -.131 .287 VUL4. If I use drugs, I will not be able to stop using them -.038 .036 .261 .161 .037 -.002 -.302 .160 VUL5. My friends expect me to take drugs -.020 .015 -.610 -.004 .131 .057 -.012 .433 Note: This set of 50 YHRBI items is compiled from a larger set of 84 items. Bold indicates pattern matrix factor loading values above .30, extraction method: Principal Axis Factoring (PAF), rotation method: direct oblimin (Δ = 0) with Kaiser normalization, h2 = communality.

68

Confirmatory Factor Analysis of Risk Behavior PMT Items After the EFA, a series of CFAs using maximum likelihood estimation were conducted in

Mplus. The CFA models were based on the seven factor structure of the 50-item EFA analysis. The seven factors correspond to the seven PMT constructs: self-efficacy, response efficacy, response cost, intrinsic rewards, extrinsic rewards, severity and vulnerability. Of the original 50 items, those with the highest EFA pattern matrix loading values and no cross-loadings were retained for the initial CFA. CFAs continued from this starting point in an exploratory fashion until acceptable model fit was obtained. Models that were deemed acceptable had a χ2/df ratio of less than 2 to 1, a CFI value ≥ .90, and a RMSEA that is less than .08. The CFA model with the best model fit is presented in Figure 3. This higher-order CFA model had the following goodness 2 = 720.031, p < .001, of fit indices, which met many of the requirements for an acceptable fit: χ 240

CFI = .863, RMSEA = .050 (.046-.055). Although the χ 2 -statistic suggests the model may have some limitations, sample sizes larger than N = 200 often preclude a non-significant χ 2 value (Newcomb, 1990). The CFI value, however, is approaching .90, and the RMSEA value of .05 (with 90% confidence interval of .046-.055) suggests a model with excellent fit (Bentler, 2007). Figure 3 shows that all of the items loaded on the first-order PMT constructs ≥ .30, with the exception of item RE 5 (“A person who decides to stop using drugs can stop”) which had a factor loading value of .27. Figure 3 also shows that the first-order PMT constructs loaded on the coping- and threat-appraisal higher-order PMT constructs in a pattern that is fairly consistent with the original PM model (see Figure 1). Specifically, PM is proposed to be a positive function of self-efficacy, response efficacy, severity and vulnerability (note the positive loadings of these factors on the coping- and threat-appraisal factors), and a negative function of response costs,

69 intrinsic rewards, and extrinsic rewards (note the negative loading of response cost on coping appraisal).

.54

Even if all my friends were having sex, I would not feel I had to

.43

If I was going out with a drug user, I would not have to use drugs

.43

If someone asked me to deliver drugs I could say no

.18

Self Efficacy

.35

I can refuse to deliver drugs and dealers will not bother me

.93

A guy and a girl can go together and not have sex

.53 .40

If my friends take drugs and I don't they will think it's ok

1.06

Coping Appraisal

.27

A person who decides to stop using drugs can stop .33

Response Efficacy

-.11

Boys think its important to have sex to feel like a man

.39 .54

Girls think its important to have sex to feel like a woman

.65 .30

Sometimes sex just happens and you really can't control it A girl my age who has never had sex is probably scared

Response Cost

-.46 .36

I feel good about having sex I feel good about drinking alcohol I feel good about dealing drugs

.37 .57 .86

I feel good about delivering drugs Troubles don't seem so bad when you are high on drugs .40

Intrinsic Rewards

Sex feels better when you are high on drugs

.94 .52 .48

Bad things happen to families of kids who sell drugs People who use drugs die early If I am horny I can't control what happens If my friends use drugs then I will use drugs My friends expect me to take drugs

Extrinsic Rewards

.67 Threat Appraisal

.70

Dealing or selling drugs is exciting People who use drugs get AIDS

.35

.59 .53

.07 Severity

1.07

.56 .37 .55

Vulnerability

.55

2 χ 240 = 720.031, p < .001, CFI = .863, RMSEA = .050 (.046-.055).

Figure 3. Second-order CFA of risk behavior YHRBI items in American youth at baseline

70

Exploratory Factor Analysis of Condom Use PMT Items Principal axis factor analysis was conducted on a set of 26 YHRBI items assessing attitudes, perceptions and beliefs regarding condom use and 2 HIV/AIDS knowledge composites in the American youth sexually-active at baseline and all the Vietnamese youth (N = 827 at baseline). The set of 26 items was extracted from a larger set of 31 YHRBI items in the American youth and 49 V-YHRBI items in the Vietnamese youth that assess attitudes and perceptions regarding sexuality, HIV/AIDS, contraceptives, condom use, and alcohol consumption; the 2 HIV/AIDS knowledge composites were compiled from a subset of 8 of the HIV/AIDS knowledge true/false items (38 from the American sample and 25 from the Vietnamese sample). In a previous study using the Vietnamese sample, the HIV/AIDS knowledge true/false items were combined into one composite and used to define the severity construct (Kaljee, Genberg, Riel et al., 2005). In the current study, preliminary analyses that attempted to use the individual HIV/AIDS knowledge dichotomous items combined with the 5point Likert scale condom use items displayed considerable measurement error and failed to converge (i.e., the analysis was not capable of occurring). However, in order to include the HIV/AIDS knowledge items in the EFA analysis and subsequent CFA analysis, two severity composites were developed from 3 and 5 of the HIV/AIDS knowledge items, respectively. The first composite, SEVGCU, is comprised of the following 3 items: “Anybody can get AIDS”, “You can get AIDS from sharing needles”, and “A negative result can happen even if someone is infected with AIDS”. This composite is measuring the general severity of HIV/AIDS. The second composite, SEVSCU, is comprised of the following 5 items: “You can get AIDS the first time you have sex”, “You can get AIDS via anal intercourse”, “You can get AIDS via oral sex”, “A person can get AIDS in one sexual contact”, and “Using a condom during sex is a way to

71 protect yourself from getting AIDS”. This composite is measuring the severity of AIDS as a function of sexual risk behavior. The 3 items from the American sample and the 18 items from the Vietnamese sample, not included in the final set of 28 condom use items, were excluded because they were either present in one sample but not the other (e.g., the item “I could carry a condom, keep a condom with me” was only present in the V-YHRBI; the item “Condoms break often” was only present in the original YHRBI). In the initial EFA, six factors with eigenvalues greater than 1.0 were extracted, accounting for 56% of the variance of the original items. Correlations between the six factors ranged from -.509 to .280. Factor loadings for retained and nonretained items are presented in Table 11, with loadings greater than .30 in bold. As shown, the condom use items corresponding to intrinsic rewards loaded with extrinsic rewards instead of on its factor. Similar to the risk behavior PMT items, factor loadings from this initial run were used to develop the subsequent CFAs.

72

Table 11. Six-Factor Oblimin Solution for Condom Use PMT Items in American Youth Sexually-Active at Baseline and All Vietnamese Youth at Baseline F1 F2 F3 F4 F5 F6 h2 SE1CU. I could get condoms if I wanted to .093 -.680 -.006 .020 .000 -.059 .518 SE2CU. I could put a condom on correctly .155 -.561 .058 .012 -.036 -.056 .453 SE3CU. I could convince the person I am having sex with we should use a condom .021 -.706 .063 .028 .013 .035 .531 SE4CU. I could ask for condoms in a pharmacy/store -.063 -.734 -.021 .075 .076 -.084 .475 SE5CU. I could ask for condoms at the Commune Health Center/clinic -.007 -.747 -.008 .010 .014 .038 .541 SE6CU. I could ask the person I am having sex with about past sexual relationships .039 -.508 .002 -.022 -.215 .177 .408 SE7CU. I could refuse to have sex if the other person will not use a condom -.101 -.560 .073 -.196 -.034 .014 .413 RE1CU. If you have sex, condoms are an important way to prevent a pregnancy .057 -.061 .598 -.111 .058 -.018 .409 RE2CU. If you have sex, condoms are the best way to prevent getting an STD .025 .025 .801 .000 .037 -.032 .631 RE3CU. If you have sex, condoms are an important way to prevent getting AIDS -.071 -.030 .702 .046 .079 .012 .498 RC1CU. If a girls carries condoms, people will think she is having sex .034 .004 .157 .110 -.154 .049 .082 RC2CU. Condoms make sex hurt for a girl -.234 .094 .050 .378 -.041 .071 .244 RC3CU. Condoms take away the feeling a boy has during sex .098 -.099 -.037 .668 .054 -.035 .416 RC4CU. Girls and boys in a serious relationship don't use condoms .123 .047 -.051 .478 -.057 .022 .266 IR1CU. I want others my age to think I am having sex .063 .095 -.038 .155 -.324 .111 .189 IR2CU. I want others my age to think I am not a virgin .155 .030 -.060 -.064 -.221 .236 .176 ER1CU. Someone my age would want to have sex to see how it feels .266 -.066 .046 .012 -.581 -.016 .612 ER2CU. Boys think its important to have sex to feel like a man -.006 -.160 -.041 -.020 -.758 .051 .634 ER3CU. Girls think its important to have sex to feel like a man -.035 -.051 -.073 .091 -.781 -.033 .616 ER4CU. Sex feels good for girls .174 .006 .078 .015 -.592 .017 .731 ER5CU. Sex feels good for boys .198 -.056 .033 .018 -.599 .117 .787 ER6CU. Sex feels better when you are high on drugs/alcohol -.109 .052 .057 .226 -.057 -.086 .095 Note. This set of 26 YHRBI items is compiled from a larger set of 31 items in American Youth, and 49 items in Vietnamese youth; the 2 severity composites are compiled from 8 of the full set of HIV/AIDS knowledge true/false items (38 in the American sample and 25 in the Vietnamese sample). Bold indicates factor loading values above .30, extraction: PAF, rotation: direct oblimin (Δ = 0) with Kaiser normalization, h2 = communality.

Table 11. (Continued)

73

F1 F2 F3 F4 F5 F6 h2 SEVGCU. Composite of three items on the general severity of AIDS. .088 -.178 .141 -.020 -.133 -.525 .137 SEVSCU. Composite of five items on the severity of AIDS related to sexual behavior. .171 -.045 .109 -.128 -.225 -.536 .188 VUL1CU. Most of my close friends are having sex .776 -.047 .026 -.018 -.166 .075 .814 VUL2CU. Most of the boys I know are having sex .721 -.099 .059 -.058 -.212 .077 .833 VUL3CU. Most of the girls I know are having sex .719 -.117 .059 -.044 -.162 .059 .781 VUL4CU. Most of my friends are drinking alcohol .509 -.018 .008 .086 .085 -.079 .236 Note. This set of 26 YHRBI items is compiled from a larger set of 31 items in American Youth, and 49 items in Vietnamese youth; the 2 severity composites are compiled from 8 of the full set of HIV/AIDS knowledge true/false items (38 in the American sample and 25 in the Vietnamese sample). Bold indicates factor loading values above .30, extraction: PAF, rotation: direct oblimin (Δ = 0) with Kaiser normalization, h2 = communality.

74

Confirmatory Factor Analysis of Condom Use PMT Items A series of CFAs using maximum likelihood estimation were conducted in Mplus on the combined sample of 347 American youth sexually-active at baseline and 480 Vietnamese youth in the control and treatment groups (total combined sample size = 827 youth at baseline). The CFA models were based on the results of the 28-item EFA analysis (26 condom use items and 2 HIV/AIDS knowledge composites). As noted in the EFA analysis, the two intrinsic rewards items loaded on the extrinsic rewards factor to produce a six-factor pattern matrix corresponding to the following six PMT constructs: self-efficacy, response efficacy, response cost, extrinsic rewards, severity and vulnerability. Of the original set of 26 condom use items, those with the highest EFA pattern matrix loading values and no cross-loadings were retained for the initial CFA; the 2 severity composites were always included on each CFA run. CFAs continued from this starting point, in an exploratory fashion, until acceptable model fit was obtained. The CFA 2 = 462.766, p < .001, CFI = .952, model with the best model fit is presented in Figure 4: χ160

RMSEA = .050 (.044-.055). The CFI value of .952 and the RMSEA value of .05 (with 90% confidence interval of .044-.055) suggest a model with excellent fit (Bentler, 2007). Figure 4 shows that all items loaded on the first-order PMT constructs ≥ .42, and similar to the risk behavior model, these first-order PMT constructs loaded on the coping- and threat-appraisal higher-order PMT constructs in a pattern that is fairly consistent with the original conceptualization of the PM model (see Figure 1). Specifically, self-efficacy, response efficacy, severity and vulnerability had positive loadings on the coping- and threat-appraisal factors, and response cost had a negative loading on coping appraisal.

75

I could get condoms if I wanted to I could put a condom on correctly

.73

I could convince my sexual partner that we should use a condom

.77

.69

I could ask for condoms in a pharmacy/store

.30

Self Efficacy .60 1.24

.63

I could ask for condoms at the Commune Health Center/clinic .61

If you are going to have sex, condoms are an important way to prevent pregnancy

.79

If you are going to have sex, condoms are the best way to prevent an STD

Response Efficacy

.71

If you are going to have sex, condoms are an important way to prevent AIDS Condoms make sex hurt for a girl

.42

Condoms take away the feeling a boy has during sex

.62

Coping Appraisal

.32

-.18 Response Cost

.47

When a girl and a boy are in a serious relationship, they don't use condoms

.45

.41

Boys think its important to have sex to feel like a man Girls think its important to have sex to feel like a man

.71 .68

Extrinsic Rewards

.67 55

Sex feels good for girls

.73

.78

Sex feels good for boys Anybody can get AIDS + You can get AIDS from sharing needles + A negative result can happen even if someone is infected with AIDS

.47

You can get AIDS the first time you have sex + You can get AIDS via anal intercourse + You can get AIDS via oral sex + A person can get AIDS in one sexual contact + Using a condom during sex is a way to protect yourself from getting AIDS

.64

Severity

.64

Threat Appraisal

.96 Most of my close friends are having sex Most of the boys I know are having sex Most of the girls I know are having sex

.89 .92

Vulnerability .89

2 χ160 = 462.766, p < .001, CFI = .952, RMSEA = .050 (.044-.055).

Figure 4. Second-order CFA of condom use YHRBI items in American and Vietnamese youth at baseline

76

Measurement Invariance of the Risk Behavior and Condom Use PMT Models The final precursor to running the latent growth models using the PMT constructs was demonstration of MI of both the risk behavior and condom use PMT models in all samples and at all time points. MI tests started with internal consistency reliability estimates of each PMT model in all samples across time, followed by tests of configural invariance using single group CFA, and concluded with tests of configural, metric and intercept invariance using multiple group CFA.

Internal Consistency Reliability Estimates of the Risk Behavior PMT Models Results of coefficient alpha (α) tests of the risk behavior PMT model, in the total sample of American youth and in the samples of American youth sexually-active and abstinent at baseline, are presented in Table 12. Since each construct in the risk behavior PMT model is comprised of fewer than six items, the Spearman-Brown prophecy formula was used to adjust α values (Dimitrov, 2002): ρ x∗x′ =

2 ρ xx′ where ρ x∗x′ = predicted reliability and ρ xx′ = current 1 + ρ xx′

reliability. Reliability estimates of the risk behavior PMT models were examined in all samples across the five time points. Table 12 shows that the unadjusted α values ranged from a low of .307 for the response efficacy construct in the abstinent youth at baseline, to a high of .810 for the intrinsic rewards construct in the abstinent youth at follow-up 3. The corrected alphas ranged from a low of .470 to a high of .895, exceeded .70 for 78% of the constructs, and were below .60 for only 5 of the 105 tests conducted. The weakest construct was response efficacy. Generally, all estimates increased across time even though sample sizes decreased.

Table 12. Internal Consistency Reliability Estimates of the Risk Behavior PMT Models across Time

77

Sampleb

Vulnerability Severity Extrinsic Intrinsic Response Response Self(3 items) (3 items) Rewards Rewards Cost Efficacy Efficacy (3 items) (4 items) (4 items) (3 items) (4 items) Baseline .529c .692d .397 .569 .590 .742 .770 .870 .680 .810 .570 .726 .460 .630 All .579 .733 .307 .470 .571 .727 .741 .851 .673 .805 .531 .694 .498 .665 Abstinent .467 .637 .397 .568 .586 .739 .761 .864 .673 .805 .618 .764 .406 .578 Sexually-Active Follow-Up 1 All .650 .788 .480 .649 .580 .734 .770 .870 .660 .795 .550 .710 .570 .726 .650 .788 .510 .675 .520 .684 .780 .876 .620 .765 .600 .750 .540 .701 Abstinent Sexually-Active .660 .795 .430 .601 .640 .780 .730 .844 .700 .824 .460 .630 .590 .742 Follow-Up 2 All .660 .795 .470 .639 .640 .780 .740 .851 .680 .810 .580 .734 .590 .742 .660 .795 .500 .667 .630 .773 .770 .870 .700 .824 .590 .742 .590 .742 Abstinent Sexually-Active .660 .795 .420 .592 .630 .773 .700 .824 .650 .788 .560 .718 .570 .726 Follow-Up 3 All .740 .851 .490 .658 .580 .734 .790 .883 .710 .830 .610 .758 .590 .742 .730 .844 .500 .667 .521 .685 .810 .895 .680 .810 .600 .750 .610 .758 Abstinent Sexually-Active .760 .864 .460 .630 .650 .788 .750 .857 .740 .851 .610 .758 .530 .693 Follow-Up 4 All .710 .830 .560 .718 .660 .795 .780 .876 .660 .795 .600 .750 .570 .726 .710 .830 .530 .693 .660 .795 .760 .864 .700 .824 .670 .802 .590 .742 Abstinent Sexually-Active .710 .830 .590 .742 .660 .795 .780 .876 .620 .765 .460 .630 .540 .701 a Follow-up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post intervention, respectively, for American youth. b American youth organized into youth who were abstinent at baseline vs. youth who were sexually-active as baseline. See Table 7 for sample sizes at each assessment period. c Cronbach’s coefficient alpha (α). d Adjusted alpha for a 6-item construct according to the Spearman-Brown prophecy formula: 2 ρ xx′ where ρ x∗x′ = predicted reliability and ρ xx′ = current reliability. ρ x∗x′ = 1 + ρ xx′ Assessment Perioda

78

Configural Invariance of the Risk Behavior PMT Models via Single Group CFA

As noted, the central requirement for configural invariance is that the same manifest items xi are indicators of the same latent factor η j in each sample and in each group across each assessment period (Meredith, 1993; Vandenberg & Lance, 2000). To investigate configural invariance, CFAs of the risk behavior PMT model were conducted across time in the total sample of American youth and in the sexually-active and abstinent at baseline samples. Tables 13 and 14 present the goodness of fit indices and factor loadings for each of the CFAs, respectively. As shown in Table 13, each CFA displayed acceptable model fit, as demonstrated by the low RMSEA values. The first-order and second-order factor loadings, displayed in Table 14, are relatively consistent across samples and time. Table 13. Goodness of Fit Indices of CFAs of the Risk Behavior PMT Models CFId TLI RMSEA Sample χ df Size (90% C.I.) All youth 797 720.03 240 .863 .843 .050 (.046-.055) Sexual at baseline 347 482.73 240 .841 .817 .054 (.047-.061) Abstinent at baseline 450 420.86 240 .902 .888 .041 (.035-.048) Follow up #1 All youth 587 601.79 239 .891 .874 .051 (.046-.056) Sexual at baseline 247 495.65 240 .831 .806 .066 (.057-.074) Abstinent at baseline 340 463.08 240 .888 .871 .052 (.045-.059) Follow up #2 All youth 571 755.04 239 .850 .827 .061 (.057-.066) Sexual at baseline 234 530.60 239 .795 .764 .072 (.064-.080) Abstinent at baseline 337 613.04 239 .828 .801 .068 (.062-.075) Follow up #3 All youth 520 699.17 239 .873 .853 .061 (.056-.066) Sexual at baseline 200 501.76 241 .823 .797 .074 (.065-.083) Abstinent at baseline 320 502.83 239 .888 .871 .059 (.052-.066) Follow up #4 All youth 480 785.62 239 .839 .814 .069 (.064-.074) Sexual at baseline 182 477.25 239 .810 .780 .074 (.064-.084) Abstinent at baseline 298 626.76 239 .826 .799 .074 (.067-.081) Note. Goodness of fit indices for the second-order confirmatory factor analysis model shown in Figure 3. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. a Follow up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post-intervention, respectively. bp values for all chi-square tests of model fit were < .001. Assessment Perioda Baseline

Modelb

2b

79 Table 14. Factor Loadings of CFAs of the Risk Behavior PMT Models YHRBI Baselinec Follow up #1c Follow up #2c b d e f d e f Item N=797 n=347 n=450 N=587 n=247 n=340 N=571d n=234e .539 .457 .554 .657 .716 .628 .612 .662 SE1 .426 .461 .406 .453 .508 .420 .472 .533 SE3 .425 .335 .528 .613 .534 .654 .609 .506 SE5 .346 .321 .434 .422 .424 .414 .408 .431 SE10 .528 .451 .535 .615 .657 .633 .640 .568 Response RE1 .400 .415 .440 .487 .353 .549 .479 .438 efficacy RE2 .267 .429 .142 .352 .433 .326 .248 .288 RE5 .386 .552 .259 .337 .515 .208 .381 .716 Response RC1 .839 .635 .329 .643 .689 .603 .577 .702 cost RC2 .653 .468 1.079 .512 .535 .473 .707 .487 RC3 .299 .365 .185 .265 .326 .199 .378 .300 RC5 .369 .333 .308 .231 .146 .325 .141 .080 Intrinsic IR1 .574 .544 .555 .481 .489 .569 .418 .391 rewards IR7 .857 .854 .819 1.018 .926 .892 1.230 1.360 IR9 .936 .965 .918 1.072 .959 .956 1.232 1.330 IR10 .518 .547 .517 .512 .544 .490 .329 .272 Extrinsic ER3 .479 .543 .432 .451 .557 .373 .405 .305 rewards ER4 .701 .628 .735 .565 .637 .500 .891 .963 ER6 .592 .610 .575 .518 .580 .492 .509 .374 Severity SEV3 .525 .602 .462 .526 .428 .606 .523 .612 SEV4 .555 .573 .529 .558 .417 .653 .645 .690 SEV5 .370 .384 .333 .370 .386 .337 .304 .222 VulnerVUL1 .554 .422 .674 .682 .708 .665 .581 .591 ability VUL3 .550 .599 .539 .682 .665 .668 .564 .503 VUL5 .934 1.487 .974 .917 .871 .970 .964 .960 Coping SE 1.057 .688 1.058 1.047 .916 1.088 1.036 1.151 appraisal RE -.105 .175 -.195 -.433 -.309 -.507 -.131 .263 RC .351 .419 .280 .350 .433 .370 .369 .330 Threat IR .673 .939 .559 .807 .905 .840 .422 .391 appraisal ER .073 .033 .174 -.050 .226 -.159 -.033 .033 SEV 1.067 .732 1.242 .963 .855 .975 1.167 1.252 VUL Note. Factor loadings of the second-order confirmatory factor analysis model shown in Figure 3. a Protection motivation theory (PMT) constructs Self efficacy-Severity are first-order, and Coping and Threat appraisal are second-order. b See Table 10 for a description of the YHRBI risk behavior items. c Follow up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post-intervention, respectively. d Sample size for all American youth. e Sample size for American youth sexually-active at baseline. f Sample size for American youth abstinent at baseline. PMT Constructa Self efficacy

n=337f .586 .435 .685 .389 .713 .489 .211 .314 .573 .742 .385 .191 .432 1.163 1.203 .329 .438 .922 .634 .475 .593 .345 .568 .587 .936 .986 -.283 .373 .408 -.023 1.143

80 Table 14. Continued YHRBI Follow up #3c Follow up #4c b d e f Item N=520 n=200 n=320 N=480d n=182e n=298f .683 .627 .690 .676 .712 .659 SE1 .520 .430 .572 .502 .484 .511 SE3 .708 .849 .655 .701 .732 .683 SE5 .581 .777 .514 .534 .520 .540 SE10 .660 .521 .695 .623 .651 .630 Response RE1 .441 .398 .464 .615 .602 .610 Efficacy RE2 .379 .505 .348 .343 .399 .305 RE5 .149 .603 .104 .374 .383 .420 Response RC1 .465 .728 .480 .686 .571 .783 Cost RC2 .748 .399 .718 .627 .743 .554 RC3 .381 .444 .249 .450 .449 .453 RC5 .235 .158 .273 .243 .222 .268 Intrinsic IR1 .597 .617 .637 .551 .604 .563 Rewards IR7 1.020 .900 1.010 .969 .953 .869 IR9 1.077 .970 1.041 .993 1.029 .859 IR10 .525 .652 .503 .462 .360 .559 Extrinsic ER3 .451 .499 .471 .432 .380 .507 Rewards ER4 .714 .687 .644 .672 .635 .658 ER6 .616 .740 .563 .625 .595 .635 Severity SEV3 .585 .536 .580 .542 .281 .688 SEV4 .549 .436 .591 .576 .566 .597 SEV5 .380 .361 .373 .312 .317 .302 VulnerVUL1 .669 .470 .784 .782 .742 .823 Ability VUL3 .731 .803 .696 .665 .574 .708 VUL5 .825 .335 .823 1.082 1.040 .988 Coping SE .979 2.414 .934 .951 .942 1.059 Appraisal RE -.301 .249 -.433 -.131 -.267 .013 RC .468 .400 .463 .425 .475 .292 Threat IR .650 .845 .649 .655 .668 .433 Appraisal ER .449 -.155 .156 -.074 .276 .033 SEV .916 1.021 .837 1.040 .827 1.522 VUL Note. Factor loadings of the second-order confirmatory factor analysis model shown in Figure 3. a Protection motivation theory (PMT) constructs Self efficacy-Severity are first-order, and Coping Appraisal and Threat Appraisal are second-order. b See Table 10 for a description of the YHRBI risk behavior items. c Follow up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post-intervention, respectively. d Sample size for all American youth. e Sample size for American youth sexually active at baseline. f Sample size for American youth abstinent youth. PMT Constructa Self Efficacy

81 Configural, Metric and Intercept Invariance of the Risk Behavior PMT Models via Multiple Group CFA

Table 15 displays the results of three invariance tests using multiple group CFA, conducted in the total sample of American youth at baseline, to examine overall measurement level invariance (Cheung & Rensvold, 2002). For all tests gender was used as the grouping variable, and the likelihood-ratio test (LR) was used to compare the difference in χ2 between the configural invariance model and the metric and intercept invariance models (Bollen, 1989). In the first test, model fit of the MGCFA with freely estimated factor loadings (i.e., test of configural invariance) was acceptable according to the low RMSEA value of .06 (90% CI = .056-.065). In the second test, metric invariance, model fit of the MGCFA with factor loadings constrained to be equal between groups was also found to be acceptable due to the low RMSEA value .06 (.056-.065). In the final test, intercept invariance, model fit of the MGCFA with both factor loadings and intercepts constrained to be equal between groups was acceptable (RMSEA = .06, 90% CI = .055-.066). As shown in Table 15, the LR test found a non significant difference in χ2 between the configural invariance model and the metric and intercept invariance models. Taken together, the results demonstrate MI of the risk behavior PMT model was established, and subsequent latent growth modeling analyses on the PMT constructs could be investigated. Table 15. Tests of Measurement Invariance of the Risk Behavior PMT Model Using MGCFA Model Comparison

Δdf χ Δχ 2 Model RMSEA df Configural invariance 1212.68 496 .06 .80 ---Metric invariance 1215.40 497 .06 .80 2 vs. 1 2.72ns 1 Intercept invariance 1208.87 495 .06 .80 2 vs. 1 3.81ns 1 Note. Multiple group confirmatory factor analysis of the first-order model shown in Figure 3, with gender as the grouping variable in American youth (N = 797 at baseline). ns Chi-square difference is not significant according to likelihood-ratio test (critical value for χ2 distribution with 1 df = 3.84). 2

CFI

82 Internal Consistency Reliability Estimates of the Condom Use PMT Models

Results of coefficient alpha (α) tests of the condom use PMT model in the sample of American youth who were sexually-active at baseline and in Vietnamese youth in the control and treatment groups are presented in Table 16. Similar to the risk behavior PMT model, each construct in the condom use PMT model is comprised of fewer than six items, therefore, the Spearman-Brown prophecy formula was used to adjust α values (Dimitrov, 2002). Reliability estimates of the condom use PMT models were examined in all samples across the five time points. Unadjusted α values over the 125 tests ranged from a low of .130 for the severity construct in the Vietnamese control group at follow-up 3, to a high of .930 for the vulnerability construct in the American and Vietnamese sample at follow-up 4. The corrected alphas ranged from a low of .230 to a high of .964, exceeded .70 for 90% of the constructs, and were below .60 for only 24 of the 125 tests conducted. The weakest construct was severity. Configural Invariance of the Condom Use PMT Models via Single Group CFA

Configural invariance of the condom use PMT model was evaluated by investigating the goodness of fit indices and factor loadings of CFAs conducted across time, in the American youth who were sexually-active at baseline and in all of the Vietnamese youth. Tables 17 and 18 present the goodness of fit indices and factor loadings for each of the CFAs, respectively. As shown in Table 17, all of the CFAs displayed acceptable model fit according to their low RMSEA values. Similar to the results of the CFAs of the risk behavior PMT models, both the first-order and second-order factor loadings, displayed in Table 18, are relatively consistent across samples and time, and therefore support configural invariance of the condom use PMT model.

Table 16. Internal Consistency Reliability Estimates of the Condom Use PMT Models across Time SelfResponse Response Extrinsic Efficacy Efficacy Cost Rewards Severity Vulnerability a (5 items) (3 items) (3 items) (4 items) (2 items) (3 items) Sample American Sexual & All Vietnamese .820 .901 .750 .857 .500 .667 .836 .911 .460 .630 .926 .962 .815 .898 .731 .845 .565 .722 .710 .830 .400 .571 .622 .767 American Sexual .775 .873 .755 .860 .424 .596 .799 .888 .300 .462 .787 .881 Vietnamese Control & Treatment .769 .869 .697 .821 .424 .596 .804 .891 .280 .438 .785 .880 Vietnamese Control .778 .875 .778 .875 .397 .568 .795 .886 .311 .474 .785 .880 Vietnamese Treatment Follow-Up 1 American Sexual & All Vietnamese .790 .883 .700 .824 .460 .630 .810 .895 .400 .571 .930 .964 .850 .919 .650 .788 .570 .726 .710 .830 .460 .630 .870 .930 American Sexual .780 .876 .710 .830 .398 .569 .760 .864 .300 .462 .790 .883 Vietnamese Control & Treatment .750 .857 .760 .864 .410 .582 .800 .889 .270 .425 .830 .907 Vietnamese Control .710 .830 .620 .765 .370 .540 .710 .830 .320 .485 .750 .857 Vietnamese Treatment Follow-Up 2 American Sexual & All Vietnamese .810 .895 .730 .844 .480 .649 .820 .901 .450 .621 .920 .958 .820 .901 .730 .844 .580 .734 .690 .817 .570 .726 .850 .919 American Sexual .800 .889 .730 .844 .400 .571 .770 .870 .340 .507 .800 .889 Vietnamese Control & Treatment .750 .857 .780 .876 .500 .667 .810 .895 .260 .413 .840 .913 Vietnamese Control .850 .919 .620 .765 .320 .485 .720 .837 .420 .592 .750 .857 Vietnamese Treatment Follow-Up 3 American Sexual & All Vietnamese .830 .907 .760 .864 .560 .718 .800 .889 .400 .571 .910 .953 .900 .947 .760 .864 .610 .758 .660 .795 .620 .765 .860 .925 American Sexual .810 .895 .760 .864 .550 .710 .760 .864 .270 .425 .790 .883 Vietnamese Control & Treatment .810 .895 .820 .901 .550 .710 .790 .883 .130 .230 .800 .889 Vietnamese Control .800 .889 .720 .837 .550 .710 .730 .844 .410 .582 .780 .876 Vietnamese Treatment Follow-Up 4 American Sexual & All Vietnamese .820 .901 .750 .857 .500 .667 .790 .883 .430 .601 .930 .964 .930 .964 .760 .864 .550 .710 .700 .824 .570 .726 .820 .901 American Sexual .800 .889 .740 .851 .490 .658 .740 .851 .370 .540 .870 .930 Vietnamese Control & Treatment .780 .876 .810 .895 .470 .639 .790 .883 .360 .529 .890 .942 Vietnamese Control .820 .901 .660 .795 .500 .667 .690 .817 .380 .551 .850 .919 Vietnamese Treatment a See Table 17 for description of the follow-up assessments in each sample and sample sizes at each assessment period. cCronbach’s coefficient alpha (α). dAdjusted alpha for a 6-item construct according to the Spearman-Brown prophecy formula (see Table 12).

83

Assessment Perioda Baseline

84 Table 17. Goodness of Fit Indices of CFAs of the Condom Use PMT Models CFId TLI RMSEA Sample χ df Size (90% C.I.) All youth 827 462.77 160 .952 .943 .050 (.044-.055) American youth 347 344.17 160 .880 .857 .058 (.049-.066) Vietnamese youth 480 328.12 159 .936 .923 .048 (.041-.055) Vietnamese control 240 275.66 163 .913 .898 .055 (.043-.066) Vietnamese treatment 240 270.41 163 .924 .912 .053 (.042-.064) Follow up #1 All youth 713 407.76 160 .949 .940 .049 (.043-.055) American youth 247 314.49 160 .884 .863 .062 (.052-.073) Vietnamese youth 466 335.85 160 .931 .918 .049 (.041-.056) Vietnamese control 232 275.08 161 .921 .906 .055 (.044-.066) Vietnamese treatment 234 215.92 160 .943 .932 .040 (.025-.053) Follow up #2 All youth 697 448.02 162 .940 .930 .053 (.048-.059) American youth 234 260.18 160 .918 .903 .052 (.040-.063) Vietnamese youth 463 334.59 161 .935 .924 .049 (.042-.057) Vietnamese control 234 280.09 161 .924 .910 .058 (.046-.069) Vietnamese treatment 229 283.74 161 .903 .886 .058 (.047-.069) Follow up #3 All youth 651 386.24 160 .954 .945 .047 (.041-.053) American youth 200 293.02 160 .906 .888 .071 (.058-.083) Vietnamese youth 451 271.32 161 .961 .954 .039 (.031-.047) Vietnamese control 232 213.96 161 .966 .960 .038 (.023-.051) Vietnamese treatment 219 228.76 162 .950 .941 .044 (.030-.056) Follow up #4 All youth 628 424.52 161 .940 .930 .054 (.053-.078) American youth 182 284.46 159 .912 .895 .066 (.064-.084) Vietnamese youth 446 309.20 161 .947 .937 .045 (.038-.053) Vietnamese control 222 282.97 161 .921 .907 .058 (.047-.070) Vietnamese treatment 224 325.29 162 .888 .868 .067 (.056-.078) Note. Goodness of fit indices for the second-order confirmatory factor analysis model shown in Figure 4. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. a Follow up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post intervention, respectively, for American youth, and at immediate, 6, 12, and 18 months post intervention, respectively, for Vietnamese youth. b p values for all chi-square tests of model fit were < .001. Assessment Perioda Baseline

Model

2b

85 Table 18. Factor Loadings of CFAs of the Condom Use PMT Models YHRBI Baseline Assessmentc b Item N=827d n=347e N=480f n=240g n=240h .767 .650 .658 .640 .651 SE1CU .733 .720 .523 .478 .540 SE2CU .685 .647 .614 .591 .660 SE3CU .595 .649 .711 .770 .636 SE4CU .632 .745 .592 .640 .550 SE5CU .613 .570 .690 .876 .898 Response RE1CU .790 .776 .689 .433 .619 Efficacy RE2CU .714 .726 .691 .522 .528 RE3CU .425 .350 .681 .506 .751 Response RC2CU .615 .696 .524 .515 .590 Cost RC3CU .467 .625 .262 .427 .163 RC4CU .712 .138 .463 .370 .388 Extrinsic ER2CU .674 .239 .481 .473 .353 Rewards ER3CU .671 1.567 .756 .962 .880 ER4CU .730 1.585 .766 .784 .936 ER5CU .341 1.000 .789 .794 Severity SEVGCU .464 .638 .774 .202 .204 .276 SEVSCU .888 .539 .687 .722 .636 VulnerVUL1CU .682 .743 .746 .718 Ability VUL2CU .921 .590 .818 .768 .906 VUL3CU .889 1.242 .892 2.058 1.162 1.176 Coping SE .319 .486 .191 .204 .346 RE -.186 -.077 -.178 -.455 -.145 RC .782 .227 .654 .266 .316 Threat ER .636 .416 .180 1.073 .062 SEV .960 .827 .609 .122 1.120 VUL Note. Factor loadings of the second-order confirmatory factor analysis model shown in Figure 4. a Protection motivation theory constructs Self efficacy-Severity are first-order, and Coping and Threat appraisal are second-order. b See Table 22 for a description of the YHRBI condom use items. c Follow up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post-intervention, respectively, for American youth, and at immediate, 6, 12, and 18 months post intervention, respectively, for Vietnamese youth. d Sample size for American youth sexually active at baseline + Vietnamese youth. e Sample size for American youth sexually active at baseline. f Sample size for Vietnamese youth. g Sample size for Vietnamese youth in the control group. h Sample size for Vietnamese youth in the treatment group PMT Constructa Self Efficacy

86 Table 18. Continued YHRBI Follow up #1c b Item N=713d n=247e N=466f n=232g n=234h .697 .589 .696 .718 .639 SE1CU .654 .772 .595 .535 .439 SE2CU .641 .800 .610 .485 .679 SE3CU .604 .659 .624 .748 .489 SE4CU .624 .800 .593 .508 .567 SE5CU .538 .375 .584 .642 .428 Response RE1CU .821 .939 .777 .772 .810 Efficacy RE2CU .638 .583 .665 .745 .559 RE3CU .718 .504 .977 .375 .912 Response RC2CU .547 .761 .416 .632 .560 Cost RC3CU .220 .471 .096 .388 .039 RC4CU .664 .571 .337 .477 .184 Extrinsic ER2CU .651 .362 .446 .584 .279 Rewards ER3CU .599 .482 .718 .671 .957 ER4CU .601 .640 .699 .611 .965 ER5CU .547 .608 .385 2.368 Severity SEVGCU .508 .528 .632 .311 .432 .086 SEVSCU .898 .845 .681 .736 .617 VulnerVUL1CU .841 .834 .867 .816 Ability VUL2CU .930 .793 .733 .767 .725 VUL3CU .901 1.797 .902 .847 1.028 2.150 Coping SE .236 .287 .609 .359 .242 RE .171 -.222 -.319 -.303 -.063 RC .751 .236 .670 .603 .262 Threat ER .361 .564 .471 .388 .322 SEV .925 .106 .576 .789 .197 VUL Note. Factor loadings of the second-order confirmatory factor analysis model shown in Figure 4. a Protection motivation theory constructs Self efficacy-Severity are first-order, and Coping Appraisal and Threat Appraisal are second-order. b See Table 22 for a description of the YHRBI condom use items. c Follow up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post-intervention, respectively, for American youth, and at immediate, 6, 12, and 18 months post intervention, respectively, for Vietnamese youth. d Sample size for American youth sexually active at baseline + Vietnamese youth. e Sample size for American youth sexually active at baseline. f Sample size for Vietnamese youth. g Sample size for Vietnamese youth in the control group. h Sample size for Vietnamese youth in the treatment group PMT Constructa Self Efficacy

87 Table 18. Continued YHRBI Follow up #2c b Item N=697d n=234e N=463f n=234g n=229h .733 .765 .738 .715 .710 SE1CU .687 .744 .653 .525 .807 SE2CU .661 .762 .632 .567 .725 SE3CU .602 .601 .620 .582 .636 SE4CU .644 .579 .654 .597 .653 SE5CU .595 .632 .580 .609 .490 Response RE1CU .720 .638 .747 .822 .608 efficacy RE2CU .731 .766 .725 .814 .615 RE3CU .668 .580 .751 .762 .683 Response RC2CU .666 .675 .636 .766 .608 cost RC3CU .235 .459 .115 .211 .062 RC4CU .762 .474 .290 .200 1.411 Extrinsic ER2CU .703 .180 .366 .218 1.511 rewards ER3CU .586 .669 .871 1.725 .173 ER4CU .646 .867 .900 1.864 .145 ER5CU .626 .600 .953 .413 Severity SEVGCU .570 .556 .745 .356 .154 .691 SEVSCU .827 .721 .647 .746 .774 VulnerVUL1CU .882 .993 .949 .750 ability VUL2CU .947 .836 .639 .690 .817 VUL3CU .827 1.144 1.269 .930 1.093 1.052 Coping SE .364 .334 .494 .340 .504 appraisal RE -.240 -.214 -.265 -.255 -.232 RC 1.075 .585 .279 .253 .792 Threat ER .427 .285 1.013 .592 -.124 SEV .717 .684 .182 .400 .174 VUL Note. Factor loadings of the second-order confirmatory factor analysis model shown in Figure 4. a Protection motivation theory constructs Self efficacy-Severity are first-order, and Coping Appraisal and Threat Appraisal are second-order. b See Table 22 for a description of the YHRBI condom use items. c Follow up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post-intervention, respectively, for American youth, and at immediate, 6, 12, and 18 months post intervention, respectively, for Vietnamese youth. d Sample size for American youth sexually active at baseline + Vietnamese youth. e Sample size for American youth sexually active at baseline. f Sample size for Vietnamese youth. g Sample size for Vietnamese youth in the control group. h Sample size for Vietnamese youth in the treatment group PMT Constructa Self efficacy

88 Table 18. Continued YHRBI Follow up #3c b Item N=651d n=200e N=451f n=232g n=219h .731 .739 .719 .773 .613 SE1CU .635 .835 .572 .537 .672 SE2CU .660 .899 .629 .630 .646 SE3CU .675 .697 .689 .676 .645 SE4CU .714 .857 .697 .716 .545 SE5CU .648 .606 .654 .762 .569 Response RE1CU .774 .804 .747 .808 .695 Efficacy RE2CU .742 .751 .724 .753 .691 RE3CU .701 .703 .774 .853 .703 Response RC2CU .711 .544 .754 .758 .792 Cost RC3CU .348 .480 .281 .290 .263 RC4CU .677 .424 .254 .386 .879 Extrinsic ER2CU .594 .411 .321 .489 .965 Rewards ER3CU .591 .907 .843 .762 .187 ER4CU .607 .604 .961 .783 .240 ER5CU .519 .581 .712 .416 Severity SEVGCU .430 .670 .947 .305 .089 .706 SEVSCU .843 .765 .675 .634 .763 VulnerVUL1CU .844 .839 .901 .759 Ability VUL2CU .893 .897 .725 .735 .679 VUL3CU .886 1.504 .847 .976 1.063 1.189 Coping SE .312 .449 .514 .442 .501 RE -.210 -.481 -.318 -.380 -.192 RC .683 .308 .280 .514 1.218 Threat ER .276 .268 .935 .519 -.233 SEV 1.008 .807 .329 .693 .167 VUL Note. Factor loadings of the second-order confirmatory factor analysis model shown in Figure 4. a Protection motivation theory constructs Self efficacy-Severity are first-order, and Coping Appraisal and Threat Appraisal are second-order. b See Table 22 for a description of the YHRBI condom use items. c Follow up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post-intervention, respectively, for American youth, and at immediate, 6, 12, and 18 months post intervention, respectively, for Vietnamese youth. d Sample size for American youth sexually active at baseline + Vietnamese youth. e Sample size for American youth sexually active at baseline. f Sample size for Vietnamese youth. g Sample size for Vietnamese youth in the control group. h Sample size for Vietnamese youth in the treatment group PMT Constructa Self Efficacy

89 Table 18. Continued YHRBI Follow up #4c b Item N=628d n=182e N=446f n=222g n=224h .713 .793 .713 .676 .729 SE1CU .667 .812 .620 .521 .730 SE2CU .589 .869 .588 .565 .623 SE3CU .685 .812 .699 .717 .674 SE4CU .682 .905 .684 .631 .723 SE5CU .586 .598 .586 .649 .534 Response RE1CU .894 .899 .863 .859 .846 Efficacy RE2CU .663 .692 .671 .811 .569 RE3CU .724 .612 .754 .841 .581 Response RC2CU .659 .729 .613 .704 .682 Cost RC3CU .285 .330 .270 .207 .332 RC4CU .675 .368 .256 .199 .461 Extrinsic ER2CU .617 .281 .299 .254 .386 Rewards ER3CU .579 .838 .975 1.665 .852 ER4CU .586 .969 .715 1.527 .845 ER5CU .844 .467 .886 .433 Severity SEVGCU .428 .739 .549 .537 .262 .649 SEVSCU .866 .659 .765 .780 .744 VulnerVUL1CU .891 .905 .911 .909 Ability VUL2CU .934 .793 .800 .840 .762 VUL3CU .900 1.272 .910 .832 1.225 .757 Coping SE .292 .397 .544 .326 .620 RE -.223 -.130 -.211 -.205 -.147 RC .706 .245 .281 .281 .126 Threat ER .328 .272 .942 .649 .871 SEV .913 1.060 .216 .264 .129 VUL Note. Factor loadings of the second-order confirmatory factor analysis model shown in Figure 4. a Protection motivation theory constructs Self efficacy-Severity are first-order, and Coping Appraisal and Threat Appraisal are second-order. b See Table 22 for a description of the YHRBI condom use items. c Follow up assessment periods 1-4 occurred at 6, 12, 18, and 24 months post-intervention, respectively, for American youth, and at immediate, 6, 12, and 18 months post intervention, respectively, for Vietnamese youth. d Sample size for American youth sexually active at baseline + Vietnamese youth. e Sample size for American youth sexually active at baseline. f Sample size for Vietnamese youth. g Sample size for Vietnamese youth in the control group. h Sample size for Vietnamese youth in the treatment group PMT Constructa Self Efficacy

90 Configural, Metric and Intercept Invariance of the Condom Use PMT Models via Multiple Group CFA

Table 19 presents the results of configural, metric and intercept invariance tests using MGCFA conducted in the combined sample of American youth sexually-active at baseline and all the Vietnamese youth. For these tests culture was used as the grouping variable and the likelihood-ratio test (LR) was used to compare the difference in χ2 between the configural invariance model and the metric and intercept invariance models. Results of tests were used to evaluate overall measurement level invariance (Bollen, 1989). In the first test, model fit of the MGCFA with freely estimated factor loadings (i.e., test of configural invariance) was acceptable, according to the low RMSEA value of .063 (90% CI = .056-.070). In the second test, metric invariance, model fit of the MGCFA with factor loadings constrained to be equal between groups, was also found to be acceptable due to the low RMSEA value .063 (.056-.071). In the final test, intercept invariance, model fit of the MGCFA with both factor loadings and intercepts constrained to be equal between groups, was acceptable (RMSEA = .063, 90% CI = .056-.070). Table 19 shows the non significant difference in χ2 between the configural invariance model and the metric and intercept invariance models according to the LR test. Taken together, the results demonstrate MI of the condom use PMT model was established, and subsequent latent growth modeling analyses on the PMT constructs were investigated. Table 19. Tests of Measurement Invariance of the Condom Use PMT Model Using MGCFA Model Comparison

Δdf χ Δχ 2 Model RMSEA df Configural invariance 674.49 328 .063 .88 ---Metric invariance 675.73 329 .063 .88 2 vs. 1 1.24ns 1 Intercept invariance 673.65 327 .063 .88 2 vs. 1 0.84ns 1 Note. MGCFA factor analysis of the first-order model shown in Figure 4, with culture as the grouping variable in a joint sample of sexually-active American youth and all the Vietnamese youth (N = 827 at baseline). nsChi-square difference is not significant 2

CFI

91 Parallel-Process Latent Growth Modeling Analyses

Following establishment of MI in the risk behavior and condom use PMT models, the second research question of this dissertation was explored: What are the developmental trajectories and interrelationships among PMT’s coping- and threat-appraisal processes for risk behavior and condom use using parallel-process latent growth modeling analysis, and are these growth parameters differentially expressed across gender, age, grade, culture and intervention type? To this end, the developmental trajectories and interrelationships among the coping- and threatappraisal processes were examined in each sample. First, the observed means of the risk behavior and condom use PMT constructs are presented, and any group differences (culture or intervention type) are noted. Next, results of single- and parallel-process latent growth models without covariates of the risk behavior and condom use coping- and threat-appraisal processes in each sample are presented. The final set of results display the parameter estimates and latent growth curves of the coping- and threat-appraisal process and the behavioral outcome variables, with and without time-invariant covariates. Observed Growth of the Risk Behavior PMT Constructs

To facilitate the latent growth modeling of the PMT constructs, the observed growth of each PMT construct across samples and time were investigated (excluding demographic covariates). Each of the seven first-order PMT constructs is expressed as the total raw score of their respective number of items (e.g., self-efficacy is comprised of the sum total of 4 items, and therefore has a maximum score of 20). Based on PMT, the second-order constructs, coping appraisal and threat appraisal, are expressed as the algebraic sum of self-efficacy plus response efficacy minus response cost, and severity plus vulnerability minus intrinsic rewards and

92 extrinsic rewards minus, respectively. To remove any negative values and shift the lowest score to zero, a linear transformation was conducted by adding a constant to each participant’s copingand threat-appraisal score (c = 29). Linear transformations of this sort have no effect on correlation coefficients of any order or on the proportions of variance that their squares yield (Cohen, Cohen, West, & Aiken, 2003). Table 20 presents the observed means of the risk behavior PMT constructs in the sexually-active and abstinent at baseline American youth, and Table 21 shows the observed means of these PMT constructs in the FOK, ImPACT, and Booster groups. As presented in Table 20, growth of the first-order PMT constructs self-efficacy and response efficacy, dropped in both groups, from a starting mean of 15.57 at baseline for the abstinent youth and 15.82 for the sexually-active youth, to an end mean of 14.83 and 14.75 at follow-up 4. In contrast, development of response cost, intrinsic rewards, extrinsic rewards and vulnerability, either increased in a linear fashion, from baseline through follow-up 4 in both groups, or grew in a nonlinear inverted U-shape function (i.e., rise and fall). The mean scores on several of these constructs were significantly higher in the sexual youth. For example, response cost, intrinsic rewards, and vulnerability were higher in the sexual youth as compared to the abstinent youth. These results indicate sexual youth have more barriers to adaptive behaviors (response cost), more rewards for the maladaptive behaviors (intrinsic rewards), and more perceptions of vulnerability to engage in maladaptive behaviors (vulnerability). The betweengroup differences in first-order construct means translate into differences in means of the higherorder constructs. Specifically, and as predicted by PMT, sexual youth had lower coping- and threat-appraisal throughout the study compared to abstinent youth. In the analysis of the differences in observed risk behavior construct means between the three intervention groups in the American youth (see Table 21), the means of the self-efficacy and response efficacy

93 constructs were higher in the Booster group compared to either the FOK or the ImPACT groups. Significant differences between the FOK groups occurred in the coping appraisal higher-order constructs at follow-up assessments 2 and 3. At these time periods, the Booster group mean was higher than means for either the FOK only group or the ImPACT group. Observed Growth of the Condom Use PMT Constructs

Tables 22 and 23 present the observed means of the condom use PMT constructs in the treatment and control Vietnamese youth ,compared to the sexually-active American youth (Table 22), and across the three FOK groups in the sexually-active American youth (Table 23). Similar to the risk behavior PMT constructs, these second-order constructs were exposed to a linear transformation by adding a constant to each participant’s coping- and threat-appraisal score to remove any negative values and shift the lowest score to zero (c = 17). There were several between-group differences in the mean condom use PMT constructs. For example, Vietnamese youth in the treatment group had a significantly higher mean coping appraisal score than the control group beginning at the first post intervention assessment (follow-up 1) and continuing throughout the final assessment. Although there was a significant difference in observed threat appraisal mean between the treatment and control groups at baseline such that the treatment mean was lower than the control mean, threat appraisal was higher in the treatment group immediately after the intervention (i.e., follow up 1). Mean scores were significantly lower on the coping appraisal construct after the intervention and higher on the threat appraisal construct at all assessments in American youth compared to Vietnamese youth. Among the sexually-active American youth organized into the three FOK groups, coping appraisal scores in the ImPACT group were higher than the other two groups at follow ups 1 and 2, and along with threat appraisal were the highest in the Booster group at follow up 4 (see Table 23).

94 Table 20. Observed Means of Risk Behavior PMT Constructs across Time in American Youth Sexually-Active and Abstinent at Baseline Constructa

Assessmentb

Abstinent at Baseline Sexual at Baseline Mean SD Mean SD Self 15.57 3.11 15.82 3.07 Baseline 2.92 15.26 2.97 Efficacy Follow Up 1 15.38 2.82 15.01 2.84 Follow Up 2 15.23 15.21 3.04 14.83 3.28 Follow Up 3 3.25 14.75 3.17 Follow Up 4 14.83 11.60 2.07 11.57 2.37 Response Baseline 2.02 11.13 2.01 Efficacy Follow Up 1 11.46 * 2.03 10.91 2.00 Follow Up 2 11.09 11.03 2.00 10.76 2.12 Follow Up 3 2.04 10.76 2.26 Follow Up 4 10.99 ** 3.10 12.93 3.29 12.07 Response Baseline ** 2.59 12.65 2.91 Cost Follow Up 1 12.10 * 2.80 12.75 2.71 Follow Up 2 12.27 12.02 2.50 12.18 2.87 Follow Up 3 2.76 12.07 2.65 Follow Up 4 12.13 ** 2.70 8.69 3.51 6.30 Intrinsic Baseline ** 3.16 8.81 3.32 Rewards Follow Up 1 7.41 ** 3.29 9.11 3.01 Follow Up 2 8.22 ** 3.37 9.55 3.27 8.27 Follow Up 3 ** 3.29 9.71 3.48 Follow Up 4 8.47 ** 2.34 7.36 2.56 6.85 Extrinsic Baseline 1.90 8.24 2.23 Rewards Follow Up 1 7.98 * 2.00 8.54 1.94 Follow Up 2 8.14 8.09 1.90 7.99 2.27 Follow Up 3 1.93 8.01 2.01 Follow Up 4 8.12 10.01 2.22 9.71 2.44 Severity Baseline 2.06 9.62 1.97 Follow Up 1 9.91 2.06 9.70 2.06 Follow Up 2 9.86 9.52 1.98 9.20 2.17 Follow Up 3 2.12 9.39 1.88 Follow Up 4 9.54 ** 2.06 5.88 2.21 5.29 VulnerBaseline ** 2.22 6.91 2.59 Ability Follow Up 1 6.25 ** 2.32 7.18 2.41 Follow Up 2 6.47 ** 6.33 2.41 7.09 2.35 Follow Up 3 2.35 6.67 2.30 Follow Up 4 6.56 a PMT second-order constructs Coping- and Threat Appraisal are comprised of Efficacy minus Response Costs, and Severity plus Vulnerability minus Rewards, respectively, plus a constant (c = 29); items comprising each construct are shown in Table 10 and Figure 3. bSee Table 7 for description of the assessment periods and corresponding sample sizes. ** p < .01 or * p < .05 mean difference between abstinent and sexual at baseline groups via independent samples t test.

95 Table 20. Continued Constructa

Assessmentb

Abstinent at Baseline Sexual at Baseline Mean SD Mean SD 44.10 5.38 43.46 5.22 Coping Baseline * 5.38 42.74 5.40 Appraisal Follow Up 1 43.73 * 43.05 5.15 42.17 4.76 Follow Up 2 5.04 42.41 5.29 Follow Up 3 43.22 5.19 42.45 5.87 Follow Up 4 42.69 4.67 28.56 5.70 31.15** Threat Baseline ** 4.81 28.45 4.93 Appraisal Follow Up 1 29.77 * 29.03 4.39 28.25 4.57 Follow Up 2 4.86 27.74 4.42 Follow Up 3 28.47 * 4.57 27.36 4.93 Follow Up 4 28.49 a PMT second-order constructs Coping- and Threat Appraisal are comprised of Efficacy minus Response Costs, and Severity plus Vulnerability minus Rewards, respectively, plus a constant (c = 29); items comprising each construct are shown in Table 10 and Figure 3. bSee Table 7 for description of the assessment periods and corresponding sample sizes. ** p < .01 or * p < .05 mean difference between abstinent and sexual at baseline groups via independent samples t test.

96 Table 21. Observed Means of Risk Behavior PMT Constructs across Time in Three American FOK Intervention Groups Constructa

Assessmentb

FOK FOK + ImPACT FOK + ImPACT+Booster Mean SD Mean SD Mean SD Self 15.72 3.19 15.47 3.08 15.84 2.96 Baseline 2.88 15.12 2.87 15.55 3.08 Efficacy Follow Up 1 15.35 2.76 14.89 2.94 15.56 2.76 Follow Up 2 15.06 ** 14.75 3.09 14.86 3.40 15.76 2.78 Follow Up 3 3.22 14.95 3.14 15.14 3.26 Follow Up 4 14.45 11.69 2.25 11.48 2.15 11.58 2.21 Response Baseline 1.91 11.17 2.09 11.52 2.08 Efficacy Follow Up 1 11.31 2.02 10.79 2.09 11.23 1.92 Follow Up 2 11.04 10.79 2.00 10.83 2.29 11.22 1.80 Follow Up 3 2.08 11.01 2.17 11.07 2.14 Follow Up 4 10.69 12.55 3.31 12.36 3.29 12.41 3.00 Response Baseline 2.80 12.49 2.54 12.27 2.87 Cost Follow Up 1 12.24 2.84 12.32 2.55 12.15 2.88 Follow Up 2 12.79 12.15 2.70 12.23 2.61 11.81 2.60 Follow Up 3 2.53 12.46 2.57 11.69 3.07 Follow Up 4 12.13 7.36 3.45 7.21 3.17 7.44 3.22 Intrinsic Baseline 3.25 8.14 3.38 7.78 3.28 Rewards Follow Up 1 8.03 3.16 8.63 3.68 8.49 2.64 Follow Up 2 8.63 9.00 3.55 8.72 3.52 8.44 2.96 Follow Up 3 3.63 8.54 3.40 8.81 3.06 Follow Up 4 9.33 ** 2.57 6.68 2.39 7.26 2.30 7.26 Extrinsic Baseline 1.96 8.24 2.05 7.77 2.13 Rewards Follow Up 1 8.18 ** 1.94 8.50 2.10 7.95 1.87 Follow Up 2 8.39 8.16 1.95 8.20 1.94 7.72 2.28 Follow Up 3 2.00 8.17 1.95 7.96 1.93 Follow Up 4 8.08 9.91 2.29 9.78 2.48 9.95 2.19 Severity Baseline * 1.98 9.47 1.99 9.89 2.12 Follow Up 1 9.97 * 2.10 9.42 2.00 9.94 2.02 Follow Up 2 10.00 9.43 2.03 9.27 2.15 9.50 2.00 Follow Up 3 2.03 9.52 2.00 9.32 2.06 Follow Up 4 9.56 5.61 2.34 5.41 1.97 5.62 2.06 VulnerBaseline 2.44 6.65 2.37 6.26 2.38 Ability Follow Up 1 6.61 * 2.36 7.01 2.53 6.30 2.16 Follow Up 2 6.86 ** 6.90 2.35 6.71 2.46 6.11 2.39 Follow Up 3 2.38 6.68 2.42 6.25 2.12 Follow Up 4 6.79 a PMT second-order constructs Coping- and Threat Appraisal are comprised of Efficacy minus Response Costs, and Severity plus Vulnerability minus Rewards, respectively, plus a constant (c = 29); items comprising each construct are shown in Table 10 and Figure 3. bSee Table 7 for description of the assessment periods and corresponding sample sizes. ** p < .01 or * p < .05 mean difference between FOK intervention groups via independent samples t test.

97 Table 21. Continued Constructa

Assessmentb

FOK FOK + ImPACT FOK + ImPACT + Booster Mean SD Mean SD Mean SD 43.86 5.59 43.59 5.02 44.00 5.26 Coping Baseline 5.46 42.79 5.12 43.79 5.63 Follow Up 1 43.42 * 42.31 4.80 42.36 5.14 43.65 5.06 Follow Up 2 ** 5.02 42.45 5.04 44.17 5.26 Follow Up 3 42.39 * 5.25 42.50 5.44 43.53 5.66 Follow Up 4 42.01 29.91 5.29 30.30 5.36 29.88 5.26 Threat Baseline 4.68 28.71 5.04 29.55 5.03 Follow Up 1 29.42 28.94 4.04 28.30 5.13 28.84 4.27 Follow Up 2 4.70 28.05 4.69 28.44 4.74 Follow Up 3 28.13 4.72 28.40 4.79 27.78 4.70 Follow Up 4 27.87 a PMT second-order constructs Coping- and Threat Appraisal are comprised of Efficacy minus Response Costs, and Severity plus Vulnerability minus Rewards, respectively, plus a constant (c = 29); items comprising each construct are shown in Table 10 and Figure 3. bSee Table 7 for description of the assessment periods and corresponding sample sizes. ** p < .01 or * p < .05 mean difference between FOK intervention groups via independent samples t test.

98 Table 22. Observed Means of Condom Use PMT Constructs across Time in American and Vietnamese Youth Vietnamese Youth American Youth Control Group Treatment Group Sexual at Baseline Mean SD Mean SD Mean SD Self 17.40* 5.12 16.46 5.04 21.35++ 3.58 Baseline ** 5.04 22.23 3.92 21.57 3.41 Efficacy Follow Up 1 17.94 ** 4.78 21.04 5.02 21.51 2.82 Follow Up 2 18.62 ** 5.12 21.56 4.61 21.42 3.64 Follow Up 3 19.14 ** 4.84 21.55 4.56 21.31 3.62 Follow Up 4 19.30 2.16 11.85 2.69 12.90++ 2.36 12.80** Response Baseline ** ++ 2.56 13.32 2.08 12.14 2.24 Efficacy Follow Up 1 12.34 ** 2.55 13.06 2.04 12.28++ 2.16 Follow Up 2 12.44 ++ 2.31 12.85 2.51 12.11 2.29 Follow Up 3 12.66 ++ 2.61 2.42 12.71 2.37 11.79 Follow Up 4 12.41 8.47 1.81 8.61 1.80 8.27 2.63 Response Baseline ** 1.71 7.94 2.16 8.43+ 2.44 Cost Follow Up 1 8.50 1.80 8.15 1.95 8.37 2.46 Follow Up 2 8.32 * 1.85 7.90 2.11 8.26 2.30 Follow Up 3 8.30 * 1.74 8.00 2.02 8.26 2.37 Follow Up 4 8.42 3.28 9.14 3.08 9.35 3.01 13.80++ Extrinsic Baseline ++ 2.85 3.26 9.51 2.98 13.59 Rewards Follow Up 1 9.82 2.55 2.82 9.25 2.60 13.54++ Follow Up 2 9.68 ++ 2.66 2.71 9.25 2.80 13.25 Follow Up 3 9.59 ++ 2.50 2.71 9.76 2.50 13.08 Follow Up 4 9.74 1.24 5.32 1.49 5.17 1.59 6.64++ Severity Baseline ** 1.40 6.17 1.41 6.51+ 1.61 Follow Up 1 5.40 1.69 1.38 5.86 1.56 6.54++ Follow Up 2 5.63 ** 1.25 6.22 1.52 6.51 1.71 Follow Up 3 5.76 ** ++ 1.41 6.19 1.53 6.63 1.66 Follow Up 4 5.74 4.64 2.43 4.24 2.15 12.70++ 2.48 VulnerBaseline ++ 3.43 2.42 4.46 2.26 11.69 Ability Follow Up 1 4.50 3.46 2.63 4.63 2.36 11.19++ Follow Up 2 4.90 ++ 3.37 4.83 2.56 11.66 3.48 Follow Up 3 5.31 ++ 3.22 2.83 5.10 2.68 11.89 Follow Up 4 5.24 a PMT second-order constructs Coping- and Threat Appraisal are comprised of Efficacy minus Response Costs, and Severity plus Vulnerability minus Rewards, respectively, plus a constant (c = b 17); items comprising each construct are shown in Table 11 and Figure 4. See Table 8 for ** description of the assessment periods and corresponding sample sizes. p < .01 or * p < .05 difference between control and treatment groups via independent samples t test. ++ p < .01 or + p < .05 difference between Vietnamese treatment and American sexual at baseline groups via independent samples t test. Constructa

Assessmentb

99 Table 22. Continued Vietnamese Youth American Youth Construct Assessment Control Group Treatment Group Sexual at Baseline Mean SD Mean SD Mean SD ** ++ 38.73 6.80 36.70 6.70 43.00 5.73 Coping Baseline ** 6.76 44.65 5.86 42.29++ 5.46 Appraisal Follow Up 1 38.84 ** 6.64 42.96 6.76 42.46 5.13 Follow Up 2 39.74 ** 7.16 43.50 6.75 42.30 6.03 40.50 Follow Up 3 ** + 6.55 43.36 6.51 41.84 5.69 Follow Up 4 40.34 * ++ 17.86 3.55 17.12 3.44 22.57 3.93 Threat Baseline ** 3.63 18.07 3.43 21.60++ 4.41 Appraisal Follow Up 1 17.11 ++ 4.22 3.61 18.25 3.40 21.23 Follow Up 2 17.87 ++ 4.66 18.47 3.96 18.79 3.80 21.90 Follow Up 3 ++ 4.38 3.55 18.61 3.71 22.44 Follow Up 4 18.28 a PMT second-order constructs Coping- and Threat Appraisal are comprised of Efficacy minus Response Costs, and Severity plus Vulnerability minus Rewards, respectively, plus a constant (c = b 17); items comprising each construct are shown in Table 11 and Figure 4. See Table 8 for ** description of the assessment periods and corresponding sample sizes. p < .01 or * p < .05 difference between control and treatment groups via independent samples t test. ++ p < .01 or + p < .05 difference between Vietnamese treatment and American sexual at baseline groups via independent samples t test. a

b

100 Table 23. Observed Means of Condom Use PMT Constructs across Time in American Intervention Groups Constructa

Assessmentb

FOK FOK + ImPACT FOK + ImPACT+Booster Mean SD Mean SD Mean SD Self 21.59 3.66 21.00 3.99 21.38 2.94 Baseline 3.46 22.16 3.11 21.35 3.70 Efficacy Follow Up 1 21.14 2.73 21.65 2.91 21.50 2.88 Follow Up 2 21.39 21.53 3.33 21.05 4.03 21.74 3.69 Follow Up 3 4.18 21.41 3.63 22.00 2.47 Follow Up 4 20.76 12.80 2.46 13.02 2.25 12.89 2.33 Response Baseline 2.49 12.12 2.11 12.58 1.96 Efficacy Follow Up 1 11.90 2.23 12.74 2.19 12.31 1.88 Follow Up 2 11.89 11.90 2.38 12.41 2.10 12.10 2.39 Follow Up 3 2.68 11.73 2.55 12.12 2.59 Follow Up 4 11.60 8.51 2.74 7.97 2.62 8.26 2.47 Response Baseline 2.57 8.21 2.54 8.25 1.98 Cost Follow Up 1 8.77 * 2.60 8.00 2.60 7.91 1.74 Follow Up 2 8.93 8.34 2.21 8.16 2.20 8.24 2.62 Follow Up 3 2.40 8.45 2.20 7.84 2.53 Follow Up 4 8.40 13.74 3.42 13.70 3.30 13.99 3.06 Extrinsic Baseline 3.17 13.99 2.66 13.46 2.51 Rewards Follow Up 1 13.34 2.63 13.16 2.43 13.82 2.57 Follow Up 2 13.67 13.11 2.72 13.36 3.02 13.36 2.02 Follow Up 3 2.63 13.05 2.18 13.36 2.66 Follow Up 4 12.91 6.60 1.41 6.65 1.09 6.69 1.13 Severity Baseline 1.75 6.49 1.56 6.81 1.38 Follow Up 1 6.35 * 1.63 6.09 1.99 6.84 1.18 Follow Up 2 6.71 6.36 1.79 6.41 1.71 6.88 1.53 Follow Up 3 * 1.90 6.56 1.82 7.22 0.70 Follow Up 4 6.28 12.44 2.48 12.70 2.65 13.06 2.26 VulnerBaseline 3.40 12.06 3.45 11.93 3.42 Ability Follow Up 1 11.24 3.32 11.27 3.77 11.54 3.28 Follow Up 2 10.92 11.40 3.49 11.63 3.87 12.14 2.92 Follow Up 3 3.22 11.85 3.44 12.64 2.85 Follow Up 4 11.42 Note. American youth who were sexually-active at baseline completed the condom use YHRBI items. aPMT second-order constructs Coping- and Threat Appraisal are comprised of Efficacy minus Response Costs, and Severity plus Vulnerability minus Rewards, respectively, plus a constant (c = 17); items comprising each construct are shown in Table 10 and Figure 3. bSee Table 7 for description of the assessment periods and Table 3 for corresponding sample sizes. ** p < .01 or * p < .05 mean difference between FOK intervention groups via independent samples t test.

101 Table 23. Continued Constructa

Assessmentb

FOK FOK + ImPACT FOK + ImPACT+Booster Mean SD Mean SD Mean SD 42.99 6.10 43.05 5.99 42.95 4.88 Coping Baseline * 5.61 43.16 5.29 42.69 5.30 Follow Up 1 41.22 * 41.38 5.19 43.46 5.42 42.89 4.29 Follow Up 2 5.63 42.30 6.46 42.72 6.27 Follow Up 3 42.08 * 6.40 41.69 5.45 43.28 4.61 Follow Up 4 40.97 22.43 4.04 22.63 4.04 22.71 3.66 Threat Baseline 4.86 21.57 3.96 22.22 4.18 Follow Up 1 21.27 21.00 4.23 21.16 4.38 21.71 4.01 Follow Up 2 5.02 21.63 4.86 22.65 3.66 Follow Up 3 21.66 * 4.40 22.30 4.66 23.55 3.86 Follow Up 4 21.80 Note. American youth who were sexually-active at baseline completed the condom use YHRBI items. aPMT second-order constructs Coping- and Threat Appraisal are comprised of Efficacy minus Response Costs, and Severity plus Vulnerability minus Rewards, respectively, plus a constant (c = 17); items comprising each construct are shown in Table 10 and Figure 3. bSee Table 7 for description of the assessment periods and Table 3 for corresponding sample sizes. ** p < .01 or * p < .05 mean difference between intervention groups via independent samples t test.

Latent Growth of the Risk Behavior PMT Constructs

Results from the observed means of the risk behavior coping- and threat-appraisal PMT constructs informed the starting shape of the latent growth curves that were modeled using Mplus. For example, both of the second-order constructs developed in a non-linear, U-shape

fashion, and a quadratic slope factor was therefore added to model both growth trajectories. Results of the multiple group single- and parallel-process latent growth models are displayed in Table 24. Model fit for growth models was evaluated using the same criteria used for CFA, i.e., χ2 probability > .05, the CFI ≥ .900, and RMSEA < 0.08 (Bentler, 1990; Loehlin, 1998). As presented in the top panel of Table 24, model fit was excellent for both coping appraisal (χ2= 18.63, df=12, p=.10, CFI= 0.985, TLI = 0.975, RMSEA=.037) and threat appraisal (χ2= 4.21, df=12, p=.98, CFI= 1.000, TLI = 1.053, RMSEA=.000) using a quadratic growth function. The significant coping appraisal mean intercept parameter for the abstinent and sexual youth (44.10

102 vs. 43.45, respectively) indicates significant variation across these youth in the baseline coping appraisal score; the initial coping-appraisal process was higher in abstinent youth (see observed mean in Table 20). The linear slope for the coping-appraisal process decreased more rapidly across the study in the youth who were sexually-experienced. In contrast, the positive quadratic growth trend demonstrates that coping appraisal rose by the end of the study for both groups. Similar to the coping-appraisal process, the intercept for the threat-appraisal process was significant in both groups and was higher in the abstinent youth. The linear slope for the threatappraisal process decreased initially in both groups, and then grew by the end of the study in the abstinent youth (quadratic slope = 0.20**). Combining both of the single-process multiple-group latent growth models into a single parallel-process multiple-group growth model allows for improved interpretability of the analysis. Figure 5 displays the developmental trajectories of the coping- and threat-appraisal processes in the abstinent and sexually-active youth as estimated by the model shown in the bottom panel of Table 24. As shown, the coping-appraisal process started higher in the abstinent youth, and decreased in a similar fashion across the study in both groups until the final assessment during which coping appraisal grew slightly in the sexually-experienced youth. Similarly, the threat-appraisal process started higher in the abstinent youth compared to the sexual youth (31.44** vs. 28.60**), but grew more rapidly in the abstinent youth by the final assessment. Plotting the various covariance estimates presented in the lower panel of Table 24 provides yet another visual representation of the parallel-process model to aid model interpretation. Figure 6 shows the interrelationships between the coping- and threat-appraisal processes from the PPLGM analysis in the youth who were abstinent at baseline, and Figure 7 shows the interrelationships in the youth who were sexually-active at baseline. In abstinent

103 youth, the significant positive covariance between the intercept of the threat-appraisal process and the linear slope of the coping-appraisal process (2.63*), and the significant negative covariance between the threat-appraisal intercept and the coping-appraisal quadratic (-0.82*) indicate youth who have higher starting levels of threat appraisal may have significant upward linear growth of the coping-appraisal process that will level off over time. Finally, PPLGM of the risk behavior coping- and threat-appraisal processes between FOK intervention groups was examined, and estimated growth parameters and growth curves across groups are presented in Table 25 and Figure 8, respectively. Results indicate excellent model fit for the parallel-process multiple-group model (RMSEA = .029). For the copingappraisal process, there were similar negative linear trajectories for the FOK and ImPACT groups (-0.88** and -0.80*, respectively), with the Booster group having both the highest intercept and the least amount of negative growth across time. In contrast, for the threat-appraisal process, the ImPACT group had the highest intercept, and grew the most by the end of the study after an initial negative linear growth across all groups (quadratic growth parameter for the ImPACT group = 0.29**). Examining the interrelationships between the coping- and threatappraisal processes across groups (see Figures 9-11), we notice the positive covariance between the threat-appraisal intercept and the coping-appraisal linear slope, and the negative covariance between the threat-appraisal intercept and the coping-appraisal quadratic trend. We also notice the negative covariance between the coping-appraisal intercept and the threat-appraisal linear slope, and the positive covariance between the coping-appraisal intercept and the threat-appraisal quadratic. Together, these results indicate higher starting levels of threat appraisal are related to upward linear growth of the coping-appraisal process that will level off over time, while higher initial coping appraisal is related to declining threat-appraisal growth that levels off over time.

104 Table 24. Parameter Estimates for Single-Process and Parallel-Process Multiple-Group Models of Risk Behavior PMT Constructs in American Youth Sexual and Abstinent at Baseline Single Process Multiple Group Models Coping Appraisal Model Threat Appraisal Model a Parameter Baseline Abstinent Baseline Sexual Baseline Abstinent Baseline Sexual Mean Intercept 44.10 (0.25)** 31.14 (0.22)** 43.45 (0.27)** 28.60 (0.30)** ** Mean Linear -0.45 (0.24) -1.47 (0.23) -0.13 (0.32) -0.96 (0.30)** ** * Mean Quadratic 0.02 (0.06) 0.20 (0.06) -0.05 (0.08) 0.17 (0.08) I variance 8.97 (3.31)** 10.94 (2.85)** 2.51 (4.04) 9.37 (4.63)* 4.11 (2.54) L variance -1.07 (2.82) -2.60 (3.97) 4.63 (4.25) Q variance 0.05 (0.16) 0.21 (0.14) 0.04 (0.22) 0.25 (0.22) -3.16 (2.44) I - L covariance 1.96 (2.77) 4.23 (3.65) -3.94 (4.14) I - Q covariance -0.55 (0.55) 0.44 (0.48) -0.59 (0.73) 0.82 (0.83) L - Q covariance 0.10 (0.62) -0.88 (0.56) 0.26 (0.88) -1.03 (0.93) 2 2 χ = 18.63, df=12, p=.10, CFI= 0.985, χ = 4.21, df=12, p=.98, CFI=1.000, TLI = 0.975, RMSEA=.037 (.000-.069) TLI=1.053, RMSEA=.000 (.000-.000) Parallel Process Models Abstinent at Baseline Sexual at Baseline Coping Appraisal Threat Appraisal Coping Appraisal Threat Appraisal ** ** ** Mean Intercept 44.10 (0.25) 31.14 (0.22) 43.44 (0.27) 28.60 (0.30)** Mean Linear -0.46 (0.24) -0.95 (0.30)** -1.45 (0,23)** -0.10 (0.32) * ** Mean Quadratic 0.02 (0.06) 0.17 (0.08) -0.06 (0.08) 0.19 (0.06) I variance 9.03 (3.32)** 14.79 (3.95)** 2.29 (4.05) 9.45 (4.61)* L variance -0.81 (2.81) 10.15 (4.12)* -2.67 (3.95) 4.60 (4.23) 0.05 (0.22) Q variance 0.07 (0.16) 0.63 (0.23)** 0.24 (0.22) I - L covariance 1.89 (2.77) -3.92 (2.45) 4.47 (3.65) -4.04 (4.12) I - Q covariance -0.54 (0.54) 0.57 (0.48) -0.64 (0.73) 0.84 (0.83) L - Q covariance 0.04 (0.62) -1.02 (0.56) 0.26 (0.88) -1.01 (0.93) -1.00 (1.13) 2.15 (1.50) ICoping - IThreat cov -0.17 (1.12) -0.92 (1.71) LCoping - LThreat cov 0.00 (0.07) -0.09 (0.10) QCoping - QThreat cov Coping Threat -L cov -0.20 (1.21) I -2.42 (1.65) 0.06 (0.31) 0.68 (0.40) ICoping - QThreat cov IThreat - LCoping cov 2.63 (1.09)* -0.23 (1.67) -0.82 (0.27)** -0.28 (0.43) IThreat - QCoping cov Threat Coping L -Q cov 0.24 (0.28) 0.51 (0.44) Coping Threat -Q cov -0.13 (0.29) 0.20 (0.41) L 2 =72.57, df =56, p =.07, CFI = 0.977, TLI = .963, RMSEA = .027 (.000-.044) χ Note: See Table 10 and Figure 3 for description of the coping- and threat-appraisal risk behavior PMT constructs. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. a American youth organized into abstinent at baseline (n = 347) vs. sexually-active at baseline (n = 450). ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error).

105

Table 25. Parameter Estimates for Parallel-Process Multiple-Group Model of Risk Behavior PMT Constructs in American Intervention Groups FOKa FOK + ImPACT FOK + ImPACT + Booster Parameter Coping Appraisal Threat Appraisal Coping Appraisal Threat Appraisal Coping Appraisal Threat Appraisal Mean Intercept 43.94 (0.31)** 43.54 (0.30)** 43.97 (0.34)** 29.93 (0.30)** 30.26 (0.32)** 28.89 (0.34)** ** * Mean Linear -0.88 (0.27) -0.80 (0.35) -0.18 (0.35) -0.56 (0.30) -1.60 (0.36)** -0.46 (0.33) ** Mean Quadratic 0.09 (0.07) 0.13 (0.09) 0.01 (0.09) -0.00 (0.07) 0.29 (0.09) -0.03 (0.08) I variance 12.91 (4.23)** 16.14 (4.20)** -3.49 (4.10) 5.63 (4.68) 6.22 (4.83) 15.07 (4.65)** * L variance -3.21 (3.47) 7.35 (3.77) -5.54 (4.42) 1.00 (4.12) -0.05 (4.56) 5.48 (3.64) Q variance -0.18 (0.18) 0.18 (0.19) -0.14 (0.26) 0.39 (0.23) 0.02 (0.26) 0.45 (0.18)* * * 2.18 (4.11) I - L covariance 0.58 (3.52) -8.48 (3.77) 9.25 (3.82) 0.70 (4.10) -5.28 (3.86) I - Q covariance -0.47 (0.70) 1.47 (0.76)* -1.70 (0.78)* -0.18 (0.82) -0.25 (0.81) 0.94 (0.75) L - Q covariance 0.84 (0.75) -1.24 (0.81) 0.91 (1.01) -0.03 (1.03) -0.77 (0.89) -1.43 (0.76) Coping Threat -I cov 1.16 (1.65) 0.43 (1.51) 1.12 (1.76) I -1.66 (1.42) 1.08 (1.95) -1.17 (1.66) LCoping - LThreat cov Coping Threat -Q cov -0.08 (0.76) -0.02 (0.12) -0.02 (0.10) Q -1.41 (1.68) ICoping - LThreat cov -2.78 (1.72) -1.23 (1.71) 0.33 (0.40) 0.65 (0.43) 0.49 (0.43) ICoping - QThreat cov 2.91 (1.48)* 0.39 (1.78) 2.45 (1.85) IThreat - LCoping cov -0.95 (0.38)* -0.48 (0.45) -0.80 (0.48) IThreat - QCoping cov Threat Coping -Q cov 0.53 (0.36) 0.20 (0.49) 0.49 (0.42) L 0.29 (0.33) -0.27 (0.49) -0.06 (0.40) LCoping - QThreat cov 2 χ = 102.43, df=84, p=.08, CFI=0.976, TLI=.961, RMSEA = .029 (.000-.046) Note: See Table 10 and Figure 3 for the coping- and threat-appraisal risk behavior PMT model. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. a American youth organized into FOK (n = 310), FOK + ImPACT (n = 255), and FOK + ImPACT + Booster (n = 232) groups. ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error).

Abstinent at Baseline: Coping

Abstinent at Baseline: Threat

Sexual at Baseline: Coping

Sexual at Baseline: Threat

45

40

Estimated Mean Appraisal

106

35

30

25

20

15

10

5

0

Baseline

Follow Up #1

Follow Up #2

Follow Up #3

Follow Up #4

Figure 5. Developmental trajectories of coping- and threat-appraisal processes from PPLGM of risk behavior PMT constructs in the sexual- and abstinent-at-baseline American youth

follow up #1

baseline

1

1

1

follow up #2

1 1

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Process 1: 1.89

i1

Coping Appraisal

.04

s1 2.63*

-.54

q1

-.13 -.82**

-.17

.00

107

-1.00 .06 -.20

Process 2:

1

baseline

-3.92

i2

Threat Appraisal

1 1

1

1

follow up #1

.57

.24

-1.02

s2 0

1

4 2

3

follow up #2

q2 0 1

4

follow up #3

9

16

follow up #4

Figure 6. Interrelationships between the parameter estimates for PPLGM of risk behavior coping- and threat-appraisal processes in American youth abstinent at baseline

follow up #1

baseline

1

1

1

follow up #2

1 1

0

2

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follow up #3

4

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Process 1: 4.47

i1

Coping Appraisal

.26

s1 -.23

-.64

q1

.20 -.28

-.92

-.09

108

2.15 .68 -2.42

Process 2:

1

baseline

-4.04

i2

Threat Appraisal

1 1

1

1

follow up #1

.84

.51

-1.01

s2 0

1

4 2

3

follow up #2

q2 0 1

4

follow up #3

9

16

follow up #4

Figure 7. Interrelationships between the parameter estimates for PPLGM of risk behavior coping- and threat-appraisal processes in American youth sexually-active at baseline

FOK Group: Coping Appraisal FOK Group: Threat Appraisal

ImPACT Group: Coping Appraisal ImPACT Group: Threat Appraisal

Booster Group: Coping Appraisal Booster Group: Threat Appraisal

45

40

Estimated Mean Appraisal

109

35

30

25

20

15

10

5

0

Baseline

Follow Up #1

Follow Up #2

Follow Up #3

Follow Up #4

Figure 8. Developmental trajectories of coping- and threat-appraisal processes from PPLGM of risk behavior PMT constructs in the three intervention groups of American youth

follow up #1

baseline

1

1

1

follow up #2

1 1

0

2

1

follow up #3

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3

follow up #4

9 16

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Process 1: .58

i1

Coping Appraisal

.84

s1 2.91*

-.47

q1

.29 -.95*

-1.66

-.08

110

1.16 .33 -1.41

Process 2: Threat Appraisal 1

baseline

-8.48*

i2 1 1

1

1

follow up #1

1.47*

.53

-1.24

s2 0

1

4 2

3

follow up #2

q2 0 1

4

follow up #3

9

16

follow up #4

Figure 9. Interrelationships between the parameter estimates for PPLGM of risk behavior coping- and threat-appraisal processes in American youth in the FOK group

follow up #1

baseline

1

1

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follow up #2

1 1

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Process 1: 9.25*

i1

Coping Appraisal

.91

s1 .39

-1.70*

q1

-.27 -.48

1.08

-.02

111

.43 .65 -2.78

Process 2:

1

baseline

.70

i2

Threat Appraisal

1 1

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follow up #1

-.18

.20

-.03

s2 0

1

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3

follow up #2

q2 0 1

4

follow up #3

9

16

follow up #4

Figure 10. Interrelationships between the parameter estimates for PPLGM of risk behavior coping- and threat-appraisal processes in American youth in the FOK + ImPACT group

follow up #1

baseline

1

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follow up #2

1 1

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Process 1: 2.18

i1

Coping Appraisal

-.77

s1 2.45

-.25

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-.06 -.80

-1.17

-.02

112

1.12 .49 -1.23

Process 2:

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baseline

-5.28

i2

Threat Appraisal

1 1

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.94

.49

-1.43

s2 0

1

4 2

3

follow up #2

q2 0 1

4

follow up #3

9

16

follow up #4

Figure 11. Interrelationships between the parameter estimates for PPLGM of risk behavior coping- and threat-appraisal processes in American youth in the FOK + ImPACT + Booster group

113

Latent Growth of the Condom Use PMT Constructs

The observed means for the condom use PMT constructs between the Vietnamese control and treatment groups, and within the sample of American youth sexually-active at baseline, informed the starting shape of the coping- and threat-appraisal single-process growth models. Specifically, Table 22 shows the growth of the coping-appraisal process in the Vietnamese treatment group grew rapidly from baseline to follow-up 1, and then started to decrease throughout the remainder of the study. This inverted U-shape function suggests a quadratic slope factor was indicated for the coping-appraisal process. Similarly, the threat appraisal scores developed in a non-linear, inverted U-shape fashion in the treatment group, and a quadratic slope factor was therefore added to the threat-appraisal process growth model. As presented in the top panel of Table 26, coping and threat appraisal were both modeled using a quadratic growth function, however, the intercept for the coping-appraisal score at follow up 1 was allowed to be a freely estimated parameter in the treatment group, thereby improving model fit. Results found that both models had acceptable model fit, with the growth parameters for the treatment group displaying greater support for PMT than the control group. Specifically, the linear slope was higher in the treatment group for both processes indicating strong growth of the coping- and threat-appraisal processes after the intervention. Combining each of the coping- and threatappraisal single-process models into a parallel-process model produced acceptable goodness of fit (see bottom panel of Table 26, RMSEA = .060), and provides for the concurrent visual display of the coping- and threat-appraisal developmental trajectories in the Vietnamese control and treatment youth (see Figure 12). As shown, both the coping- and threat-appraisal curves start at relatively similar baseline intercepts in the control and treatment groups and grow more throughout the study in the treatment group, with the growth of the coping-appraisal process in

114 the treatment group growing significantly more than the threat-appraisal process. Specifically, there is rapid linear growth of the coping-appraisal process in the treatment group after the intervention, a slight downward trend from follow up 1 to 2 (i.e., the negative quadratic trend = 0.68**), and remaining moderate growth through the end of the study. The top panel of Table 27 presents the results of the single-process latent growth modeling between the Vietnamese treatment group and the American youth who were sexuallyactive at baseline, and model fit was excellent for both processes. By combining the singleprocess runs into the parallel-process multiple-group model shown in the bottom panel of Table 27 and Figure 12, we notice the striking between-culture differences in the intercepts for both processes. Specifically, the coping-appraisal process intercepts for the American and Vietnamese youth are 42.89** vs. 36.74**, respectively; the intercepts for the threat-appraisal process are 22.55** vs. 17.17**, respectively. Furthermore, the developmental trajectory of the copingappraisal process grows in the Vietnamese treatment youth but decreases in the American youth. Alternatively, the threat-appraisal process in the American youth drops initially through follow up 2, and then grows through the end of the study (quadratic = 0.30**), whereas treat appraisal grows in the Vietnamese youth from baseline through follow up 3, and then drop slightly through the end of the study (quadratic = -0.11**). An investigation of the interrelationships between the growth parameters of the condom use PMT constructs in the sexually-experienced American youth (see Figure 16) displays several significant covariances compared to the risk behavior PMT constructs in these youth (see Figure 7). Specifically, the significant covariance between the coping- and threat-appraisal intercept and linear slope growth parameters indicates high initial level and growth of the coping-appraisal process are related to high initial level and growth of the threat-appraisal process.

115 The developmental trajectories of the condom use coping- and threat-appraisal processes across the three FOK intervention groups were explored and are displayed in Table 28 and Figure 13. The parallel-process growth model across the three groups fit the data extremely well: χ 2 = 82.66, df=84, p=.52, CFI=1.000, TLI=1.012, RMSEA = .000 (.000-.050). This suggests the nonlinear growth from the full sample of sexually-experienced American youth was retained in each intervention group. Results of the growth parameters suggest there was relatively little difference in the trajectory of the coping-appraisal process across groups, although the visual display of the growth curves shows the ImPACT and Booster groups had greater coping appraisal than the FOK-only group. Figure 13 also shows that coping appraisal in the Booster group started to grow by the end of the study (quadratic = 0.12). While the threatappraisal process started to grow in all three groups after follow up 2 (as demonstrated by the significant quadratic growth parameter across all three groups), threat appraisal remained consistently higher in the Booster group. Finally, Figure 18 presents the significant interrelationships between the coping- and threat-appraisal linear and quadratic growth parameters in the ImPACT group. Specifically, the negative covariance between these growth parameters indicates decreasing initial linear growth of either cognitive process is related to subsequent increasing growth in either process as the study progresses for youth in the ImPACT group.

116 Table 26. Parameter Estimates for Single-Process and Parallel-Process Multiple Group Models of Condom Use PMT Constructs in Vietnamese Youth Single Process Multiple Group Models Coping Appraisal Model Threat Appraisal Model a Parameter Control Group Tx Group Control Group Tx Group Mean Intercept 38.54 (0.43)** 17.65 (0.23)** 36.74 (0.42)** 17.17 (0.21)** Mean Linear 0.84 (0.33)* -0.02 (0.22) 4.31 (0.37)** 0.86 (0.22)** ** Mean Quadratic -0.10 (0.08) 0.05 (0.05) -0.67 (0.08) -0.12 (0.05)* I variance 30.48 (5.13)** 4.87 (1.91)* 17.56 (5.37)** -0.53 (1.80) ** ** L variance 9.11 (3.32) 1.66 (1.58) 9.96 (3.83) -0.30 (1.66) Q variance 0.57 (0.17)** 0.16 (0.08) 0.41 (0.19)* 0.01 (0.09) I - L covariance -2.97 (3.50) -0.23 (1.55) -2.33 (3.99) 2.18 (1.53) I - Q covariance 0.36 (0.69) -0.03 (0.30) 0.57 (0.77) -0.39 (0.30) -0.44 (0.34) L - Q covariance -2.11 (0.71)** -1.90 (0.80)* 0.05 (0.36) χ 2 =26.03, df = 11, p=.01, CFI=0.986, χ 2 =19.18, df=12, p=.08, CFI=0.980, TLI = 0.975, RMSEA=.075 (.038-.113) TLI = 0.967, RMSEA=.050 (.000-.090) Parallel Process Models Control Group Treatment Group Coping Appraisal Threat Appraisal Coping Appraisal Threat Appraisal ** ** ** Mean Intercept 38.53 (0.43) 17.65 (0.23) 36.74 (0.42) 17.17 (0.21)** Mean Linear 0.83 (0.33)* -0.02 (0.22) 4.32 (0.37)** 0.83 (0.22)** ** Mean Quadratic -0.10 (0.08) 0.05 (0.05) -0.68 (0.08) -0.11 (0.05)* I variance 30.10 (5.06)** 4.55 (1.86)* 17.58 (5.37)** -0.67 (1.84) ** ** L variance 8.69 (3.29) 1.44 (1.55) 10.02 (3.82) -0.41 (1.64) Q variance 0.56 (0.17)** 0.16 (0.08) 0.42 (0.19)* 0.00 (0.09) I - L covariance -2.60 (3.46) 0.01 (1.51) -2.39 (3.99) 2.28 (1.54) I - Q covariance 0.28 (0.69) -0.07 (0.29) 0.59 (0.76) -0.39 (0.30) -0.41 (0.33) -1.93 (0.80) L - Q covariance -2.04 (0.70)** 0.07 (0.36) Coping Threat ** -I cov 5.58 (1.52) 1.46 (1.30) I 0.03 (1.08) 1.49 (1.25) LCoping - LThreat cov 0.03 (0.06) 0.09 (0.06) QCoping - QThreat cov Coping Threat -L cov -0.89 (1.41) I 1.45 (1.38) 0.16 (0.33) -0.26 (0.32) ICoping - QThreat cov IThreat - LCoping cov 0.20 (1.13) 0.06 (1.18) -0.27 (0.26) 0.08 (0.25) IThreat - QCoping cov Threat Coping L -Q cov 0.11 (0.25) -0.33 (0.27) Coping Threat -Q cov -0.15 (0.25) -0.36 (0.29) L 2 =102.11, df =55, p =.00, CFI= 0.970, TLI = 0.950, RMSEA = .060 (.041-.078) χ Note: See Table 11 and Figure 4 for description of the coping- and threat-appraisal condom use PMT constructs. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. a Vietnamese youth organized into control and treatment groups. ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error).

117 Table 27. Parameter Estimates for Single-Process and Parallel-Process Multiple Group Models of Condom Use PMT Constructs in American and Vietnamese Youth Single Process Multiple Group Models Coping Appraisal Model Threat Appraisal Model a Parameter American Youth Vietnam Youth American Youth Vietnam Youth Mean Intercept 42.89 (0.31)** 22.55 (0.21)** 36.74 (0.42)** 17.17 (0.21)** Mean Linear -0.32 (0.37) -1.19 (0.27)** 0.86 (0.22)** 4.31 (0.37)** ** ** Mean Quadratic 0.01 (0.09) 0.29 (0.07) -0.12 (0.05)* -0.67 (0.08) I variance 4.45 (6.04) 1.91 (2.91) -0.53 (1.80) 17.56 (5.37)** ** L variance 2.79 (5.45) 1.26 (3.01) 9.96 (3.83) -0.30 (1.66) Q variance 0.39 (0.29) 0.11 (0.17) 0.41 (0.19)* 0.01 (0.09) I - L covariance 0.19 (5.26) 0.30 (2.65) -2.33 (3.99) 2.18 (1.53) I - Q covariance 0.11 (1.03) -0.01 (0.52) 0.57 (0.77) -0.39 (0.30) -0.34 (0.68) L - Q covariance -0.97 (1.18) 0.05 (0.36) -1.90 (0.80)* χ 2 =16.19, df=11, p=.13, CFI=0.991, χ 2 =5.41, df=12, p=.94, CFI=1.000, TLI =0.983, RMSEA=.040 (.000-.079) TLI=1.06, RMSEA=.000 (.000-.011) Parallel Process Models American Youth Vietnam Youth Coping Appraisal Threat Appraisal Coping Appraisal Threat Appraisal ** ** ** Mean Intercept 42.89 (0.31) 22.55 (0.21) 36.74 (0.42) 17.17 (0.21)** Mean Linear -0.38 (0.37) -1.20 (0.28)** 4.31 (0.37)** 0.83 (0.22)** ** ** Mean Quadratic 0.02 (0.09) 0.30 (0.07) -0.68 (0.08) -0.11 (0.05)* I variance 4.14 (5.98) 1.64 (2.87) -0.67 (1.84) 17.58 (5.37)** ** L variance 2.67 (5.34) 1.39 (2.98) -0.41 (1.64) 10.02 (3.82) * Q variance 0.38 (0.28) 0.13 (0.17) 0.00 (0.09) 0.42 (0.19) I - L covariance 0.67 (5.18) 0.39 (2.62) -2.39 (3.99) 2.28 (1.54) I - Q covariance -0.01 (1.02) -0.02 (0.52) 0.59 (0.76) -0.39 (0.30) L - Q covariance -0.94 (1.16) -0.40 (0.67) 0.07 (0.36) -1.93 (0.80)* Coping Threat ** I -I cov 3.33 (1.20) 1.46 (1.30) 4.40 (1.85)* 1.49 (1.25) LCoping - LThreat cov Coping Threat * Q -Q cov 0.25 (0.11) 0.09 (0.06) Coping Threat I -L cov -0.58 (1.58) 1.45 (1.38) ICoping - QThreat cov 0.04 (0.40) -0.26 (0.32) Threat Coping I -L cov -2.71 (1.43) 0.06 (1.18) 0.67 (0.36) 0.08 (0.25) IThreat - QCoping cov LThreat - QCoping cov -1.04 (0.46)* -0.33 (0.27) Coping Threat * L -Q cov -0.94 (0.46) -0.36 (0.29) 2 =71.01, df =55, p =.07, CFI=0.981, TLI = 0.969, RMSEA = .031 (.000-.051) χ Note: See Table 11 and Figure 4 for description of the coping- and threat-appraisal condom use PMT constructs. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. a Youth organized into American youth sexually-active at baseline (n = 347) and Vietnamese youth in the treatment group (n = 240). ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error).

118

Table 28. Parameter Estimates for Parallel-Process Multiple-Group Model of Condom Use PMT Constructs in American Intervention Groups FOKa FOK + ImPACT FOK + ImPACT + Booster Parameter Coping Appraisal Threat Appraisal Coping Appraisal Threat Appraisal Coping Appraisal Threat Appraisal Mean Intercept 42.69 (0.53)** 43.12 (0.57)** 42.94 (0.48)** 22.37 (0.34)** 22.62 (0.39)** 22.72 (0.38)** Mean Linear -0.62 (0.62) 0.15 (0.67) -0.61 (0.63) -1.12 (0.43)* -1.27 (0.46)** -1.06 (0.56) * ** Mean Quadratic 0.02 (0.15) -0.10 (0.17) 0.12 (0.15) 0.25 (0.11) 0.30 (0.11) 0.32 (0.15)* I variance 12.16 (11.84) -0.99 (4.82) 4.78 (5.18) -2.62 (12.90) 0.27 (4.63) 5.74 (9.03) L variance 8.36 (9.54) -1.02 (5.28) 2.40 (9.38) -1.65 (9.64) 1.05 (5.06) 4.27 (5.04) Q variance 0.54 (0.46) 0.02 (0.32) 0.50 (0.53) 0.16 (0.35) 0.09 (0.28) 0.30 (0.30) I - L covariance -10.53 (10.14) 3.32 (4.43) 3.98 (8.10) 5.07 (10.40) -2.55 (4.78) 0.10 (4.11) I - Q covariance 2.78 (2.00) -0.62 (0.88) -1.46 (1.57) 0.64 (0.93) -0.47 (1.91) 0.03 (0.82) L - Q covariance -2.00 (2.00) 0.09 (1.22) -0.98 (2.09) -0.21 (1.11) -0.13 (1.98) -1.13 (1.17) Coping Threat -I cov 3.96 (2.12) 4.01 (2.28) 1.75 (1.88) I 1.31 (3.08) 8.61 (3.23)** 4.97 (3.45) LCoping - LThreat cov Coping Threat ** -Q cov 0.04 (0.19) 0.57 (0.20) 0.33 (0.21) Q 0.99 (2.74) ICoping - LThreat cov -2.30 (2.62) -1.03 (3.01) -0.44 (0.70) 0.48 (0.63) 0.33 (0.79) ICoping - QThreat cov -3.21 (2.47) -4.86 (2.73) 0.41 (2.13) IThreat - LCoping cov 0.99 (0.63) 1.26 (0.67) -0.33 (0.50) IThreat - QCoping cov -0.15 (0.76) -2.35 (0.81)** -1.22 (0.81) LThreat - QCoping cov -0.21 (0.77) -1.93 (0.77)* -1.26 (0.87) LCoping - QThreat cov 2 χ = 82.66, df=84, p=.52, CFI=1.000, TLI=1.012, RMSEA = .000 (.000-.050) Note: See Table 11 and Figure 4 for the coping- and threat-appraisal condom use PMT model. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. a American youth organized into FOK (n = 143), FOK + ImPACT (n = 109), and FOK + ImPACT + Booster (n = 95) groups. ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error).

Vietnam Treatment: Coping Appraisal Vietnam Control: Coping Appraisal

Vietnam Treatment: Threat Appraisal Vietnam Control: Threat Appraisal

American Sexual: Coping Appraisal American Sexual: Threat Appraisal

45 40

Estimated Mean Appraisal

119

35 30 25 20 15 10 5 0 Baseline

Follow Up #1

Follow Up #2

Follow Up #3

Follow Up #4

Figure 12. Developmental trajectories of coping- and threat-appraisal processes, from PPLGM of condom use PMT constructs in American youth sexually-active at baseline and Vietnamese youth

FOK Group: Coping Appraisal FOK Group: Threat Appraisal

ImPACT Group: Coping Appraisal ImPACT Group: Threat Appraisal

Booster Group: Coping Appraisal Booster Group: Threat Appraisal

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120

35

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5

0

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Figure 13. Developmental trajectories of coping- and threat-appraisal processes, from PPLGM of condom use PMT constructs in American youth in the intervention groups

follow up #1

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.03

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5.58

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Figure 14. Interrelationships between the parameter estimates for PPLGM of condom use coping- and threat-appraisal processes in Vietnamese control group youth

follow up #1

baseline

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.59

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-.36 .08

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1.46 -.26 1.45

Process 2:

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-.33

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Figure 15. Interrelationships between the parameter estimates for PPLGM of condom use coping- and threat-appraisal processes in Vietnamese treatment group youth

follow up #1

baseline

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-.94

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4.40* **

.25*

123

3.33

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-.02

3

follow up #2

q2 0 1

4

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Figure 16. Interrelationships between the parameter estimates for PPLGM of condom use coping- and threat-appraisal processes in American youth sexually-active at baseline

follow up #1

baseline

1

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-2.00

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124

3.96 -.44 .99

Process 2:

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-.62

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Figure 17. Interrelationships between the parameter estimates for PPLGM of condom use coping- and threat-appraisal processes in American youth in the FOK group

follow up #1

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Process 1: 3.98

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.57**

125

4.01 .48

Process 2: -2.55

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Figure 18. Interrelationships between the parameter estimates for PPLGM of condom use coping- and threat-appraisal processes in American youth in the FOK + ImPACT group

follow up #1

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1.75 .33 -1.03

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Figure 19. Interrelationships between the parameter estimates for PPLGM of condom use coping- and threat-appraisal processes in American youth in the FOK + ImPACT + Booster group

127

The Effects of Age, Gender, Educational Level, and Intervention Type on Coping- and ThreatAppraisal Trajectories

The next set of models assessed the effects of the time-invariant covariates gender, age, educational level, and intervention type (i.e., type of FOK group for the American youth) on the parallel-process growth parameters for risk behavior and condom use PMT constructs. These results are presented in Tables 29 and 30. Results revealed that among the total sample of 797 American youth, the trajectories of the risk behavior coping- and threat-appraisal processes were significantly associated with gender, age and educational level (see top panel of Table 29). Specifically, boys had lower coping-appraisal trajectories (intercept = -1.82**), and older youth and youth with higher educational levels had lower threat-appraisal trajectories (intercepts = -1.04** and -0.65**, respectively) that grew more than other youth (linear slopes = 0.78** and 0.52**, respectively. Among youth who were abstinent at baseline, gender and age were the covariates found to be associated with the risk behavior PMT higher-order constructs. As displayed in the middle panel of Table 29, abstinent boys had lower coping-appraisal trajectories than girls (intercept = -1.04**), and older youth had lower threat-appraisal trajectories that grew more than younger youth (linear slope = 0.56*). For the youth who were sexually-active at baseline, gender, age and educational level were associated with the coping- and threat-appraisal trajectories (see bottom panel of Table 29). For example, older boys who entered the study having ever engaged in sexual intercourse, and who had high educational levels, were likely to have lower coping-appraisal trajectories, and lower threat-appraisal trajectories that grew more than other youth. Results of the PPLGM of condom use coping- and threat-appraisal processes with timeinvariant covariates indicate that among the Vietnamese youth in the control group, older youth

128 with high educational levels were likely to have higher coping-appraisal trajectories that grew more rapidly than other youth (age intercept = 0.56*, grade linear slope = 0.16*), and older youth were likely to have higher threat-appraisal trajectories (intercept = 0.34*). In the sample of Vietnamese youth who received the V-FOK treatment, those with high educational level had high coping-appraisal trajectories (grade intercept = 0.47**). In contrast, the treatment youth who were boys had lower initial threat-appraisal trajectories than girls (intercept for boys = -0.81*). Finally, among the American youth who completed the condom use PMT items (i.e., the youth who were sexually-active at baseline), older youth with high educational level were likely to have higher coping-appraisal trajectories than other youth (age intercept = 1.22**, grade intercept = 0.63**), and older girls with higher education were likely to have higher threat-appraisal trajectories (boys intercept = -0.95*, age intercept = 0.72**, grade intercept = 0.43**).

129 Table 29. Parameter Estimates for PPLGM of Risk Behavior PMT Constructs With TimeInvariant Covariates in American Youth Coping Appraisal Threat Appraisal Intercept Linear Quadratic Intercept Linear Quadratic All Youth (n = 797) Meana 43.82 (0.18)** -0.67 (0.19)** 0.08 (0.05) 30.03 (0.18)** -0.86 (0.19)** 0.09 (0.05) χ 2 =35.47, df = 28, p = .16, CFI = 0.990, TLI = 0.983, RMSEA=.018 (.000-.035) -0.43 (0.37) -0.31 (0.39) 0.02 (0.10) Boys -1.82 (0.36)** -0.13 (0.38) 0.01 (0.10) χ 2 =38.42, df = 32, p = .20, CFI = 0.992, TLI = 0.986, RMSEA=.016 (.000-.032) -0.16 (0.17) 0.06 (0.04) -1.04 (0.16)** 0.78 (0.17)** -0.14 (0.04)** Age -0.18 (0.16) χ 2 =39.22, df = 32, p = .18, CFI = 0.991, TLI = 0.984, RMSEA=.017 (.000-.033) b -0.01 (0.13) -0.09 (0.14) 0.06 (0.04) -0.65 (0.13)** 0.52 (0.14)** -0.08 (0.04)* Grade χ 2 =42.42, df = 32, p = .10, CFI = 0.986, TLI = 0.976, RMSEA=.020 (.000-.035) -0.01 (0.22) 0.34 (0.23) -0.03 (0.06) -0.01 (0.23) -0.00 (0.24) 0.01 (0.06) Groupc 2 χ =36.62, df = 32, p = .26, CFI = 0.994, TLI = 0.989, RMSEA=.013 (.000-.031) Youth Abstinent at Baseline (n = 450) Mean 44.10 (0.25)** -0.46 (0.24) 0.02 (0.06) 31.14 (0.22)** -1.45 (0.23)** 0.19 (0.06)** χ 2 =39.07, df = 28, p = .08, CFI = 0.977, TLI = 0.963, RMSEA=.030 (.000-.050) -0.16 (0.49) -0.03 (0.12) 0.76 (0.45) -0.78 (0.48) 0.12 (0.12) Boys -1.04 (0.51)* χ 2 =40.07, df = 32, p = .16, CFI = 0.984, TLI = 0.972, RMSEA=.024 (.000-.044) 0.14 (0.25) 0.02 (0.06) -0.83 (0.22)** 0.56 (0.24)* -0.09 (0.06) Age -0.47 (0.25) 2 χ =46.43, df = 32, p = .05, CFI = 0.971, TLI = 0.950, RMSEA=.032 (.003-.050) 0.16 (0.19) 0.01 (0.05) -0.44 (0.17)** 0.29 (0.19) -0.03 (0.05) Grade -0.15 (0.19) χ 2 =45.81, df = 32, p = .05, CFI = 0.972, TLI = 0.952, RMSEA=.031 (.000-.050) Group -0.33 (0.30) 0.51 (0.29) -0.07 (0.07) 0.09 (0.27) 0.10 (0.28) -0.02 (0.07) 2 χ =44.34, df = 32, p = .07, CFI = 0.975, TLI = 0.956, RMSEA=.029 (.000-.049) Note: See Table 10 and Figure 3 for description of the risk behavior coping- and threat-appraisal protection motivation theory constructs. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. a Mean intercept, linear and quadratic growth parameters are for the parallel-process latent growth model without covariate. b See Table 5 for the frequency distribution of grades across time in American youth; youth who dropped out of school were assigned a value of 0, and youth who graduated or completed the GED were assigned a value of 13. c Group variable coded 0, 1 and 2, for FOK, FOK + ImPACT, and FOK + ImPACT + Booster, respectively. ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error).

130 Table 29. Continued. Coping Appraisal Threat Appraisal Intercept Linear Quadratic Intercept Linear Quadratic Youth Sexually-Active at Baseline (n = 347) Mean 43.44 (0.27)** -0.95 (0.30)** 0.17 (0.08)* 28.60 (0.30)** -0.10 (0.32) -0.06 (0.08) χ 2 =33.50, df = 28, p = .22, CFI = 0.978, TLI = 0.964, RMSEA=.024 (.000-.050) -0.01 (0.15) -1.14 (0.59) -0.11 (0.65) -0.04 (0.16) Boys -2.65 (0.52)** 0.11 (0.60) 2 χ =35.92, df = 32, p = .29, CFI = 0.987, TLI = 0.977, RMSEA=.019 (.000-.045) -0.33 (0.28) 0.06 (0.07) -0.61 (0.27)* 0.79 (0.30)** -0.14 (0.07)* Age 0.38 (0.25) χ 2 =43.76, df = 32, p = .08, CFI = 0.955, TLI = 0.922, RMSEA=.033 (.000-.055) 0.30 (0.20) -0.28 (0.22) 0.09 (0.06) -0.42 (0.22) 0.57 (0.24)* -0.10 (0.06) Gradeb 2 χ =39.87, df = 32, p = .16, CFI = 0.969, TLI = 0.947, RMSEA=.027 (.000-.050) c 0.39 (0.33) 0.13 (0.37) -0.00 (0.09) -0.29 (0.36) -0.08 (0.40) 0.02 (0.10) Group χ 2 =34.81, df = 32, p = .34, CFI = 0.989, TLI = 0.980, RMSEA=.016 (.000-.044) Note: See Table 10 and Figure 3 for description of the risk behavior coping- and threat-appraisal protection motivation theory constructs. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. a Mean intercept, linear and quadratic growth parameters are for the parallel-process latent growth model without covariate. b See Table 5 for the frequency distribution of grades across time in American youth; youth who dropped out of school were assigned a value of 0, and youth who graduated or completed the GED were assigned a value of 13. c Group variable coded 0, 1 and 2, for FOK, FOK + ImPACT, and FOK + ImPACT + Booster, respectively. ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error).

131 Table 30. Parameter Estimates for PPLGM of Condom Use PMT Constructs With TimeInvariant Covariates in American and Vietnamese Youth Coping Appraisal Threat Appraisal Intercept Linear Quadratic Intercept Linear Vietnamese Youth in the Control Group (n = 240) Meana 38.53 (0.43)** 0.83 (0.33)* -0.10 (0.08) 17.64 (0.23)** -0.02 (0.22) χ 2 =58.96, df = 28, p = .00, CFI = 0.964, TLI = 0.943, RMSEA=.068 (.043-.092) 0.21 (0.66) -0.09 (0.15) -0.51 (0.45) -0.11 (0.43) Boys 0.44 (0.86) 2 χ =68.26, df = 32, p = .00, CFI = 0.958, TLI = 0.929, RMSEA=.069 (.046-.091) -0.33 (0.20) 0.04 (0.05) 0.34 (0.14)* -0.18 (0.13) Age 0.56 (0.26)* χ 2 =63.45, df = 32, p = .00, CFI = 0.964, TLI = 0.938, RMSEA=.064 (.040-.087) 0.04 (0.09) 0.16 (0.07)* -0.03 (0.02) -0.06 (0.05) 0.03 (0.04) Gradeb 2 χ =63.90, df = 32, p = .00, CFI = 0.964, TLI = 0.938, RMSEA=.064 (.041-.087) Mean Boys Age Grade

Vietnamese Youth in the Treatment Group (n = 240) 36.74 (0.42)** 4.32 (0.37)** -0.68 (0.08)** 17.17 (0.21)** 0.83 (0.22)** χ 2 =43.15, df = 27, p = .03, CFI = 0.976, TLI = 0.961, RMSEA=.050 (.018-.077) 0.32 (0.74) -0.07 (0.16) -0.81 (0.41)* 0.50 (0.44) -0.97 (0.80) 2 χ =49.67, df = 31, p = .02, CFI = 0.973, TLI = 0.952, RMSEA=.050 (.021-.075) -0.27 (0.23) 0.04 (0.05) -0.12 (0.13) 0.10 (0.13) 0.08 (0.25) χ 2 =44.91, df = 31, p = .19, CFI = 0.980, TLI = 0.964, RMSEA=.043 (.000-.069) -0.02 (0.08) 0.01 (0.02) 0.05 (0.04) 0.03 (0.05) 0.47 (0.08)** 2 χ =48.27, df = 31, p = .03, CFI = 0.977, TLI = 0.958, RMSEA=.048 (.018-.073)

Quadratic 0.05 (0.05) -0.01 (0.10) 0.04 (0.03) -0.01 (0.01)

-0.11 (0.05)* -0.08 (0.10) 0.00 (0.03) -0.01 (0.01)

American Youth Sexually-Active at Baseline (n = 347) 42.89 (0.31)** -0.38 (0.37) 0.02 (0.09) 22.55 (0.21)** -1.20 (0.28)** 0.30 (0.07)** χ 2 =27.85, df = 28, p = .47, CFI = 1.000, TLI = 1.001, RMSEA=.000 (.000-.041) -1.32 (0.72) 0.23 (0.18) -0.95 (0.42)* 0.10 (0.55) -0.05 (0.14) Boys -1.16 (0.61) 2 =29.97, df = 32, p = .57, CFI = 1.000, TLI = 1.017, RMSEA=.000 (.000-.036) χ -0.72 (0.33)* 0.15 (0.08) 0.72 (0.19)** -0.29 (0.25) 0.01 (0.19) Age 1.22 (0.27)** 2 χ =28.93, df = 32, p = .63, CFI = 1.000, TLI = 1.026, RMSEA=.000 (.000-.034) -0.17 (0.27) 0.07 (0.07) 0.43 (0.16)** 0.13 (0.20) -0.05 (0.05) Grade 0.63 (0.23)** 2 =30.85, df = 32, p = .53, CFI = 1.000, TLI = 1.010, RMSEA=.000 (.000-.038) χ 0.16 (0.37) 0.21 (0.46) -0.01 (0.12) 0.17 (0.26) -0.05 (0.34) 0.05 (0.09) Groupc 2 χ =31.82, df = 32, p = .48, CFI = 1.000, TLI = 1.002, RMSEA=.000 (.000-.039) Note: See Table 11 and Figure 4 for description of the condom use coping- and threat-appraisal PMT constructs. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. aMean intercept, linear and quadratic growth parameters are for the parallel-process latent growth model without covariate. bSee Tables 5 and 6 for the frequency distribution of grades in American and Vietnamese youth, respectively; youth who dropped out of school were assigned a value of 0, and youth who graduated or completed the GED were assigned a value of 13. cGroup variable coded 0, 1 and 2, for FOK, FOK + ImPACT, and FOK + ImPACT + Booster, respectively. ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error). Mean

132 Relationship between the Coping- and Threat-Appraisal Processes and Self-Reported Risk and Protective Behavior

In this section of the parallel-process latent growth modeling analyses, the third research question of the dissertation was examined: What are the developmental trajectories of self-reported sexual behavior and drug risk behavior and protective condom use behavior among youth, and how are these growth parameters related with PMT’s coping- and threat-appraisal processes for risk behavior and condom use using parallel-process latent growth modeling analysis? Tables 31-37 and Figures 20-34 show the results of PPLGM in which trajectories of the risk behavior and condom use coping- and threat-appraisal processes and self-reported behaviors were modeled concurrently. Trajectories were determined for the following self-reported risk behaviors in each sample: unprotected sexual intercourse in the American youth (i.e., sex without a condom), sexual intercourse with or without a condom in the Vietnamese youth (the combined variable was necessary due to the low rates of sexual activity in these youth), and alcohol and cigarette use in all youth. Trajectories were also determined for self-reported protective condom use behavior in all youth. The trajectories for the self-reported behaviors are plotted as the estimated mean percent group (i.e., the frequency of youth in each group engaging in the behavior), whereas the trajectories for the coping- and threat-appraisal processes continue to be plotted as the estimated mean. The growth parameter estimates of the PPLGM of the coping- and threat-appraisal risk behavior PMT constructs and self-reported risk behaviors (unprotected sex, alcohol use, cigarette use) in the American youth who were sexually-active and abstinent at baseline are presented in Table 31, and the respective developmental trajectories are plotted in Figures 20-22. The trajectory of the unprotected sex curve in the youth who were abstinent-at-baseline is predictive

133 of the increase in sexual activity among these youth (see Figure 20). The growth of unprotected sex in the youth who started the study abstinent decelerates by follow-up periods 3 and 4 (quadratic slope = -0.95**), and although the growth curve displays an acceleration of unprotected sex from follow-up periods 2-4 in the youth who started the study sexually-active, the linear slope of the behavior is actually negative (-1.17), albeit nonsignificant. As shown in Figures 21 and 22, the trajectories of alcohol and cigarette use, respectively, begin significantly higher in the sexual youth compared to the abstinent youth and decline through follow-up 2. Alcohol use begins to accelerate in both groups after follow-up 2 (quadratic slope = 2.20** and 3.09** in the abstinent and sexual youth, respectively). In contrast, cigarette use begins to accelerate after follow-up 2 only in the sexual youth (quadratic slope = 0.41 and 3.87** in the abstinent and sexual youth, respectively). Table 32 and Figures 23-25 display the results of these PPLGM in the American youth across the three FOK intervention groups. Model fit for the three models was excellent, with RMSEA values no larger than .040. As shown in Figure 23, the growth of unprotected sex was the lowest in the Booster group, although all groups reached a similar peak by the end of the study. As shown in Figures 24 and 25, respectively, both alcohol and cigarette use declined across time and across groups from baseline through follow-up 2, with the greatest decline in the Booster group. From follow-up 2 through the end of the study, however, both alcohol and cigarette use accelerated in all three groups. While alcohol use reached the same level across groups by the end of the study, cigarette use grew to a lower level in either the Booster or ImPACT groups compared to the FOK-only group by the end of the study. Examining the results of the PPLGM of the coping- and threat-appraisal condom use PMT constructs and self-reported risk behaviors in the Vietnamese youth, we notice acceptable

134 model fit across the three models (see Table 33), with RMSEA values = .089, .040, and .056 for the sexual intercourse, alcohol use, and cigarette use models, respectively. As presented in Figure 26, the trajectory of sexual activity begins higher in the treatment group vs. the control group (intercept = 2.55* vs. 0.46), declines in the treatment group vs. the control group through follow-up 3 (linear slope = -1.37 vs. 0.44), but accelerates in the treatment group vs. the control group from follow-up 3 through the end of the study (quadratic slope = 0.34* vs. -0.08). The drop in sexual activity in the treatment group at follow up 1 mirrors the rapid rise in coping appraisal at this assessment period. Figure 27 shows the trajectory of alcohol use in the treatment and control groups, plotted concurrently with the coping- and threat-appraisal processes. As shown, alcohol use starts lower in the treatment group compared to the control group, remains lower in the treatment group through follow-up 2, and then accelerates rapidly from follow-up 2 through follow-up 4, culminating in an end-of-study level in the treatment group that is higher than the control group (quadratic slope in treatment vs. control groups = 2.43** vs. 0.84, respectively). In contrast to the alcohol use trajectory, the trajectory of cigarette use begins higher in the treatment group vs. the control group (intercept = 17.13** vs. 14.07**), and declines in the treatment group (linear slope = -2.57), but grows in the control group (linear slope = 3.79*). By the end of the study, cigarette use has started to accelerate from its initial downward trend in the treatment group (quadratic slope = 0.75), culminating in a level in the treatment group that is lower than the control group (cf. Table 8, percent of total cigarette use at follow-up 4 for the treatment vs. control groups = 9.0 vs. 10.1). Table 34 and Figures 29-31 display the results of these PPLGM in the American youth who were sexually-active at baseline and organized into the three FOK intervention groups. Model fit for the three models was acceptable; RMSEA values were no larger than .064 (see Table 34).

The growth of unprotected sex across the three FOK

135 intervention groups was similar to the full sample of American youth stratified into the three FOK intervention groups, with the Booster group displaying the lowest growth in unprotected sex that reaches a similar peak by the end of the study (see Figure 29). The apparent intervention effect seen in the full sample of American youth stratified across FOK groups remained in this subset of American youth, as illustrated in the growth of alcohol and cigarette use observed in Figures 30 and 31, respectively. Specifically, both alcohol and cigarette use declined across time and across groups from baseline through follow-up 2, with the greatest decline in the Booster group. From follow-up 3 through the end of the study, however, both alcohol and cigarette use accelerated in all three groups with the least amount of growth in the Booster group. Protective condom use growth was modeled in concert with the trajectories of risk behavior and condom use coping- and threat-appraisal processes in the American youth, and in concert with the trajectories of condom use coping- and threat-appraisal processes in the Vietnamese youth. Table 35 and Figure 32 presents the results from PPLGM of risk behavior PMT constructs in all of the American youth, stratified across the three FOK intervention groups. The model fit the data extremely well (RMSEA = .034), as condom use started relatively high across groups, with the highest initial level observed in the ImPACT group (intercept = 34.43**), and grew in all groups from baseline through follow-up 2. From follow-up 2 through the end of the study both the FOK and ImPACT groups displayed a decelerating quadratic slope (-0.69 and -0.79, respectively), whereas the Booster group displayed an accelerating quadratic slope (0.21) indicating an intervention effect that promoted protective behavior. Condom use was also modeled with the coping- and threat-appraisal trajectories in the subset of American youth who completed the condom use PMT items (i.e., youth who were sexually-active at baseline); Table 36 shows the acceptable model fit (RMSEA=.081). Condom use was understandably quite high

136 across the three intervention groups at baseline in these sexually-experience youth, and declined throughout the study. However, condom use decreased the least in the Booster group, and grew the most from follow up 3 to follow up 4 (see Figure 33). Finally, condom use and coping- and threat-appraisal trajectories were modeled in the Vietnamese youth. Table 37 displays the model fit and growth parameter estimates, and results indicate model fit was acceptable (RMSEA .066). In contrast to the apparent effect of the V-FOK intervention on the coping-appraisal process, and on alcohol and cigarette risk behavior, there was no significant growth in protective condom use behavior as modeled in either the treatment or control groups.

137

Table 31. Parameter Estimates for PPLGM of Risk Behavior PMT Constructs and Unprotected Sexual Behavior, Alcohol Use and Cigarette Use in American Youth Sexual and Abstinent at Baseline Parallel-Process Model Abstinent at Baseline (n = 450) Sexually-Active at Baseline (n = 347) Intercept Linear Quadratic Intercept Linear Quadratic Coping Appraisal 44.10 (0.25)** -0.45 (0.24) 0.02 (0.06) 43.44 (0.27)** -0.95 (0.30)** 0.17 (0.08)** ** ** ** ** Threat Appraisal 31.13 (0.22) -1.44 (0.23) 0.19 (0.06) 28.60 (0.30) -0.10 (0.32) -0.06 (0.08) Unprotected Sex 0.00 (0.00) 5.73 (1.12)** -0.95 (0.31)** 23.76 (2.25)** -1.17 (2.81) 0.75 (0.69) 2 χ = 170.66, df = 132, p=.01, CFI=0.956, TLI = 0.930, RMSEA=.027 (.013-.038) -0.45 (0.24) 0.02 (0.06) 43.44 (0.27)** -0.93 (0.30)** 0.16 (0.08)** Coping Appraisal 44.10 (0.25)** Threat Appraisal 31.14 (0.22)** -1.44 (0.23)** 0.19 (0.06)** 28.59 (0.30)** -0.07 (0.32) -0.07 (0.08) ** ** ** Alcohol Use 27.03 (2.00) -9.51 (2.30) 2.20 (0.55) 60.77 (2.55)** -18.74 (2.87)** 3.09 (0.71)** χ 2 = 211.55, df = 132, p=.00, CFI=0.930, TLI = 0.888, RMSEA=.039 (.029-.048) -0.46 (0.24) 0.02 (0.06) 43.44 (0.27)** -0.94 (0.30)** 0.16 (0.08)** Coping Appraisal 44.10 (0.25)** ** ** ** ** Threat Appraisal 31.14 (0.22) -1.46 (0.23) 0.20 (0.06) 28.58 (0.30) -0.06 (0.32) -0.08 (0.08) ** ** ** Cigarette Use 15.50 (1.68) -2.69 (1.72) 0.41 (0.39) 38.78 (2.52) -17.31 (2.70) 3.87 (0.68)** χ 2 = 195.59, df = 132, p=.00, CFI=0.947, TLI = 0.915, RMSEA = .035 (.024-.045) Note: See Table 10 and Figure 3 for description of the risk behavior coping and threat appraisal protection motivation theory constructs. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error).

Abstinent at Baseline: Coping Appraisal Abstinent at Baseline: Threat Appraisal Abstinent at Baseline: Unprotected Sex

Sexual at Baseline: Coping Appraisal Sexual at Baseline: Threat Appraisal Sexual at Baseline: Unprotected Sex

Appraisal (Estimated Mean)/Behavior (Estimated Percent Group)

138

45

40

35

30

25

20

15

10

5

0

Baseline

Follow Up #1

Follow Up #2

Follow Up #3

Follow Up #4

Figure 20. Developmental trajectories of coping- and threat-appraisal processes and self-reported unprotected sex in American youth sexual and abstinent at baseline, from PPLGM of risk behavior PMT constructs

Abstinent at Baseline: Coping Appraisal Abstinent at Baseline: Threat Appraisal Abstinent at Baseline: Alcohol Use

Sexual at Baseline: Coping Appraisal Sexual at Baseline: Threat Appraisal Sexual at Baseline: Alcohol Use

Appraisal (Estimated Mean)/Behavior (Estimated Percent Group)

139

60 55 50 45 40 35 30 25 20 15 10 5 0

Baseline

Follow Up #1

Follow Up #2

Follow Up #3

Follow Up #4

Figure 21. Developmental trajectories of coping- and threat-appraisal processes and self-reported alcohol use in American youth sexual and abstinent at baseline, from PPLGM of risk behavior PMT constructs

Abstinent at Baseline: Coping Appraisal Abstinent at Baseline: Threat Appraisal Abstinent at Baseline: Cigarette Use

Sexual at Baseline: Coping Appraisal Sexual at Baseline: Threat Appraisal Sexual at Baseline: Cigarette Use

Appraisal (Estimated Mean)/Behavior (Estimated Percent Group)

140

45

40

35

30

25

20

15

10

5

0

Baseline

Follow Up #1

Follow Up #2

Follow Up #3

Follow Up #4

Figure 22. Developmental trajectories of coping- and threat-appraisal processes and self-reported cigarette use in American youth sexual and abstinent at baseline, from PPLGM of risk behavior PMT constructs

141

Table 32. Parameter Estimates for PPLGM of Risk Behavior PMT Constructs and Unprotected Sexual Behavior, Alcohol Use and Cigarette Use in American Youth in the Intervention Groups Parallel-Process Model FOK (n = 310) FOK + ImPACT (n = 255) Intercept Linear Quadratic Intercept Linear Quadratic Coping Appraisal 43.94 (0.31)** -0.87 (0.27)** 0.09 (0.07) 43.54 (0.30)** -0.79 (0.35)* 0.13 (0.09) ** ** ** Threat Appraisal 29.94 (0.30) -0.54 (0.30) -0.01 (0.07) 30.25 (0.32) -1.59 (0.36) 0.28 (0.09)** Unprotected Sex 12.59 (1.87)** 1.15 (2.24) 0.08 (0.54) 8.64 (1.71)** 6.84 (2.29)** -1.21 (0.57)* χ 2 = 280.97, df = 198, p=.00, CFI=0.922, TLI = 0.875, RMSEA=.040 (.028-.050) -0.87 (0.27)** 0.09 (0.07) 43.55 (0.30)** -0.81 (0.35)* 0.14 (0.09) Coping Appraisal 43.94 (0.31)** Threat Appraisal 29.94 (0.30)** -0.56 (0.30) 0.00 (0.07) 30.25 (0.32)** -1.58 (0.36)** 0.28 (0.09)** ** ** * ** ** Alcohol Use 39.09 (2.61) -8.29 (2.84) 1.41 (0.69) 43.56 (2.99) -17.22 (3.11) 3.47 (0.74)** χ 2 = 278.22, df = 198, p=.00, CFI=0.936, TLI = 0.898, RMSEA=.039 (.028-.049) -0.89 (0.27)** 0.09 (0.07) 43.53 (0.30)** -0.78 (0.35)* 0.13 (0.09) Coping Appraisal 43.94 (0.31)** ** ** Threat Appraisal 29.93 (0.30) -0.57 (0.30) 0.00 (0.07) 30.25 (0.32) -1.60 (0.36)** 0.29 (0.09)** Cigarette Use 27.70 (2.44)** -9.42 (2.40)** 2.15 (0.59)** 26.99 (2.74)** -10.35 (2.74)** 1.93 (0.63)** χ 2 = 271.32, df = 198, p=.00, CFI=0.944, TLI = 0.910, RMSEA = .037 (.025-.048) FOK + ImPACT + Booster (n = 232) Intercept Linear Quadratic Coping Appraisal 43.96 (0.34)** -0.13 (0.35) 0.00 (0.09) ** Threat Appraisal 29.90 (0.34) -0.50 (0.33) -0.01 (0.08) Unprotected Sex 8.38 (1.91)** -0.73 (2.47) 0.64 (0.62) Coping Appraisal Threat Appraisal Alcohol Use

43.97 (0.34)** 29.87 (0.34)** 42.94 (3.26)**

-0.22 (0.35) 0.03 (0.09) -0.41 (0.33) -0.04 (0.08) -17.91 (3.54)** 3.64 (0.88)**

Coping Appraisal Threat Appraisal Cigarette Use

43.96 (0.34)** 29.89 (0.34)** 20.31 (2.67)**

-0.20 (0.36) -0.46 (0.33) -5.67 (2.77)*

0.02 (0.09) -0.02 (0.08) 1.07 (0.65)

Note: See Table 10 and Figure 3 for description of the risk behavior coping and threat appraisal protection motivation theory constructs. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error).

FOK: Coping Appraisal FOK: Threat Appraisal FOK: Unprotected Sex

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Figure 23. Developmental trajectories of coping- and threat-appraisal processes and self-reported unprotected sex in American youth in the FOK intervention groups, from PPLGM of risk behavior PMT constructs

FOK: Coping Appraisal FOK: Threat Appraisal FOK: Alcohol Use

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Figure 24. Developmental trajectories of coping- and threat-appraisal processes and self-reported alcohol use in American youth in the FOK intervention groups, from PPLGM of risk behavior PMT constructs

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Figure 25. Developmental trajectories of coping- and threat-appraisal processes and self-reported cigarette use in American youth in the FOK intervention groups, from PPLGM of risk behavior PMT constructs

145

Table 33. Parameter Estimates for PPLGM of Condom Use PMT Constructs and Sexual Behavior, Alcohol Use and Cigarette Use in Vietnamese Youth Parallel-Process Model Control Group (n = 240) Treatment Group (n = 240) Intercept Linear Quadratic Intercept Linear Quadratic Coping Appraisal 38.53 (0.43)** 0.83 (0.33)* -0.10 (0.08) 36.74 (0.42)** 4.33 (0.37)** -0.68 (0.08)** ** ** ** Threat Appraisal 17.65 (0.23) -0.02 (0.21) 0.05 (0.05) 17.18 (0.21) 0.82 (0.22) -0.11 (0.05)* Sexual Intercourse 0.46 (0.50) 0.44 (0.66) -0.08 (0.15) 2.55 (1.00)* -1.37 (0.90) 0.34 (0.18)* χ 2 = 377.92, df = 131, p=.00, CFI=0.888, TLI = 0.820, RMSEA = .089 (.078 -.099) 0.83 (0.33)* -0.10 (0.08) 36.74 (0.42)** 4.31 (0.37)** -0.67 (0.08)** Coping Appraisal 38.53 (0.43)** Threat Appraisal 17.64 (0.23)** -0.02 (0.22) 0.06 (0.05) 17.17 (0.21)** 0.82 (0.22)** -0.11 (0.05)* ** ** * Alcohol Use 28.55 (2.84) -1.55 (2.28) 0.84 (0.51) 25.67 (2.82) -6.67 (2.84) 2.43 (0.64)** χ 2 = 182.52, df = 131, p=.00, CFI=0.978, TLI = 0.964, RMSEA = .040 (0.25-.054) 0.84 (0.33)* -0.10 (0.08) 36.74 (0.42)** 4.31 (0.37)** -0.67 (0.08)** Coping Appraisal 38.53 (0.43)** ** ** ** Threat Appraisal 17.65 (0.23) -0.01 (0.21) 0.05 (0.05) 17.17 (0.21) 0.83 (0.22) -0.11 (0.05)* Cigarette Use 14.07 (2.17)** 3.79 (1.88)* -0.47 (0.45) 17.13 (2.45)** -2.57 (1.94) 0.75 (0.40) 2 = 229.41, df = 131, p =.00, CFI=0.962, TLI = 0.939, RMSEA = .056 (.044-.068) χ Note: See Table 11 and Figure 4 for description of the risk behavior coping and threat appraisal protection motivation theory constructs. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error).

Coping Appraisal: Treatment Group Coping Appraisal: Control Group

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Figure 26. Developmental trajectories of coping- and threat-appraisal processes and self-reported sexual activity in Vietnamese youth in the Control and Treatment Groups, from PPLGM of condom use PMT constructs

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Figure 27. Developmental trajectories of coping- and threat-appraisal processes and self-reported alcohol use in Vietnamese youth in the Control and Treatment groups, from PPLGM of condom use PMT constructs

Coping Appraisal: Treatment Group Coping Appraisal: Control Group

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Figure 28. Developmental trajectories of coping- and threat-appraisal processes and self-reported cigarette use in Vietnamese youth, in the Control and Treatment groups, from PPLGM of condom use PMT constructs

149

Table 34. Parameter Estimates for PPLGM of Condom Use PMT Constructs and Unprotected Sexual Behavior, Alcohol Use and Cigarette Use in American Youth in the Intervention Groups Parallel-Process Model FOK (n = 143) FOK + ImPACT (n = 109) Intercept Linear Quadratic Intercept Linear Quadratic Coping Appraisal 42.71 (0.52)** -0.68 (0.61) 0.04 (0.15) 43.12 (0.57)** 0.24 (0.68) -0.13 (0.17) ** ** * ** ** Threat Appraisal 22.37 (0.34) -1.15 (0.43) 0.27 (0.11) 22.63 (0.39) -1.30 (0.47) 0.31 (0.12)** Unprotected Sex 28.40 (3.61)** -4.95 (4.42) 1.36 (1.08) 18.85 (3.78)** 6.63 (4.70) -0.87 (1.13) 2 χ = 293.02, df = 198, p=.00, CFI=0.714, TLI = 0.545, RMSEA=.064 (.048-.080) -0.81 (0.62) 0.06 (0.16) 43.20 (0.57)** 0.04 (0.67) -0.07 (0.17) Coping Appraisal 42.72 (0.53)** Threat Appraisal 22.38 (0.34)** -1.06 (0.44)* 0.23 (0.11)* 22.62 (0.39)** -1.22 (0.47)** 0.29 (0.12)* ** ** ** Alcohol Use 55.04 (4.09) -8.10 (4.30) 0.45 (1.03) 62.99 (4.48) -23.78 (5.24) 4.98 (1.32)** χ 2 = 251.59, df = 198, p=.01, CFI=0.856, TLI = 0.770, RMSEA=.048 (.027-.066) -0.67 (0.62) 0.03 (0.15) 43.12 (0.57)** 0.18 (0.68) -0.11 (0.17) Coping Appraisal 42.69 (0.53)** ** * * Threat Appraisal 22.38 (0.34) -1.09 (0.43) 0.24 (0.11) 22.62 (0.39)** -1.28 (0.46)** 0.31 (0.11)** Cigarette Use 38.70 (3.91)** -15.73 (4.13)** 3.58 (1.09)** 42.53 (4.64)** -19.17 (5.05)** 4.09 (1.21)** χ 2 = 253.89, df = 198, p=.01, CFI=0.866, TLI = 0.788, RMSEA = .049 (.029-.066) Parallel-Process Model FOK + ImPACT + Booster (n = 95) Intercept Linear Quadratic Coping Appraisal 42.91 (0.49)** -0.56 (0.63) 0.11 (0.16) ** Threat Appraisal 22.72 (0.38) -1.08 (0.56) 0.33 (0.15)* Unprotected Sex 20.92 (4.28)** -6.39 (5.77) 2.08 (1.39) Coping Appraisal Threat Appraisal Alcohol Use

42.98 (0.48)** 22.69 (0.38)** 66.22 (4.75)**

-0.91 (0.64) 0.21 (0.16) -1.09 (0.56) 0.34 (0.14)* -28.85 (5.15)** 4.97 (1.30)**

Coping Appraisal Threat Appraisal Cigarette Use

42.92 (0.48)** 22.68 (0.38)** 33.78 (4.53)**

-0.60 (0.63) 0.11 (0.15) -1.12 (0.56)* 0.32 (0.14)* ** -16.04 (4.85) 3.70 (1.21)**

Note: See Table 11 and Figure 4 for description of the risk behavior coping and threat appraisal protection motivation theory constructs. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error).

FOK: Coping Appraisal FOK: Threat Appraisal FOK: Unprotected Sex

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Figure 29. Developmental trajectories of coping- and threat-appraisal processes and self-reported unprotected sex in American youth in the FOK intervention groups, from PPLGM of condom use PMT constructs

FOK: Coping Appraisal FOK: Threat Appraisal FOK: Alcohol Use

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Figure 30. Developmental trajectories of coping- and threat-appraisal processes and self-reported alcohol use in American youth in the FOK intervention groups, from PPLGM of condom use PMT constructs

FOK: Coping Appraisal FOK: Threat Appraisal FOK: Cigarette Use

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Figure 31. Developmental trajectories of coping- and threat-appraisal processes and self-reported cigarette use in American youth in the FOK intervention groups, from PPLGM of condom use PMT constructs

153 Table 35. Parameter Estimates for Parallel-Process Latent Growth Model of Risk Behavior PMT Constructs and Condom Use Behavior in American Youth in the FOK Intervention Groups Intercept Linear Quadratic FOK Coping Appraisal 43.94 (0.31)** -0.91 (0.27)** 0.10 (0.07) ** Threat Appraisal 29.94 (0.30) -0.56 (0.30) 0.00 (0.07) Condom Use 33.53 (2.58)** 5.06 (2.91) -0.69 (0.73) FOK + ImPACT Coping Appraisal 43.54 (0.30)** -0.79 (0.35)* 0.13 (0.09) ** Threat Appraisal 30.27 (0.32) -1.58 (0.36)** 0.27 (0.09)** Condom Use 34.43 (2.96)** 4.76 (3.03) -0.79 (0.72) FOK + ImPACT + Booster Coping Appraisal 43.98 (0.34)** -0.26 (0.35) 0.03 (0.09) Threat Appraisal 29.89 (0.34)** -0.48 (0.33) -0.02 (0.08) ** Condom Use 30.46 (2.98) 3.12 (3.12) 0.21 (0.78) χ 2 =257.42, df = 198, p=.00, CFI=0.957, TLI = 0.932, RMSEA=.034 (.020-.045) Note: See Table 10 and Figure 3 for description of the risk behavior coping and threat appraisal protection motivation theory constructs. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error). Table 36. Parameter Estimates for Parallel-Process Latent Growth Model of Condom Use PMT Constructs and Condom Use Behavior in American Youth in the FOK Intervention Groups Intercept Linear Quadratic FOK Coping Appraisal 42.68 (0.52)** -0.93 (0.61) 0.10 (0.15) ** ** Threat Appraisal 22.38 (0.34) -1.22 (0.43) 0.28 (0.11)** Condom Use 70.40 (3.76)** -8.05 (5.02) 1.39 (1.24) FOK + ImPACT Coping Appraisal 43.13 (0.57)** 0.07 (0.68) -0.09 (0.17) ** ** Threat Appraisal 22.63 (0.39) -1.32 (0.47) 0.32 (0.12)** Condom Use 80.54 (3.77)** -13.91 (4.94)** 1.89 (1.22) FOK + ImPACT + Booster Coping Appraisal 42.93 (0.48)** -0.73 (0.63) 0.15 (0.15) Threat Appraisal 22.72 (0.38)** -1.05 (0.57) 0.31 (0.15)* ** * Condom Use 75.39 (4.23) -14.02 (6.04) 2.70 (1.51) 2 =348.71, df = 198, p =.00, CFI=0.624, χ TLI = 0.402, RMSEA=.081 (.067-.095) Note: See Table 11 and Figure 4 for description of the condom use coping and threat appraisal protection motivation theory constructs. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error).

154 Table 37. Parameter Estimates for Parallel-Process Latent Growth Model of Condom Use PMT Constructs and Condom Use Behavior in Vietnamese Youth Intercept Linear Quadratic Control Group (n = 240) Coping Appraisal 38.52 (0.43)** 0.84 (0.33)* -0.10 (0.08) ** Threat Appraisal 17.65 (0.23) -0.02 (0.21) 0.05 (0.05) Condom Use 0.00 (0.00) 0.36 (0.27) -0.07 (0.08) Treatment Group (n = 240) Coping Appraisal 36.74 (0.42)** 4.32 (0.37)** -0.67 (0.08)** ** ** Threat Appraisal 17.17 (0.21) 0.83 (0.22) -0.11 (0.05)* Condom Use 1.59 (0.83) -0.81 (0.55) 0.15 (0.12) 2 =265.98, df = 131, p =.00, CFI=0.949, χ TLI = 0.918, RMSEA=.066 (.054-.077) Note: See Table 11 and Figure 4 for description of the risk behavior coping and threat appraisal protection motivation theory constructs. CFI: Comparative fit index. TLI: Tucker–Lewis coefficient. RMSEA: root mean square error of approximation. a Group variable coded 0 and 1, for Control and Treatment, respectively. ** p < .01 or * p < .05 unstandardized growth parameter estimate (standard error).

Appraisal (Estimated Mean)/Behavior (Estimated Percent Group)

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Figure 32. Developmental trajectories of coping- and threat-appraisal processes and self-reported condom use in American youth in the FOK intervention groups, from PPLGM of risk behavior PMT constructs

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Figure 33. Developmental trajectories of coping- and threat-appraisal processes and self-reported condom use in American youth in the FOK intervention groups, from PPLGM of condom use PMT constructs

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Figure 34. Developmental trajectories of coping- and threat-appraisal processes and self-reported condom use in Vietnamese youth in the Control and Treatment groups, from PPLGM of condom use PMT constructs

158

CHAPTER 4—Discussion

This dissertation set out to increase our understanding of the cognitive processes that mediate protection motivation theory (PMT) for the prevention of high risk sexual and drug behavior, and for the promotion of protective condom use behavior in American and Vietnamese youth. To this end, three primary questions were examined using existing longitudinal data from American and Vietnamese adolescents who, pursuant to the Focus on Kids (FOK) behavioral intervention they received, provided self-reports of their attitudes, perceptions and beliefs regarding risk-taking and protective condom use behavior, as well as estimates of the occurrence of those behaviors: (1)

What is the stability of the risk behavior and condom use PMT constructs within and between cultures (United States and Viet Nam) and across time using confirmatory factor analysis (CFA) to demonstrate measurement invariance?

(2)

What are the developmental trajectories and interrelationships among PMT’s copingand threat-appraisal processes for risk behavior and condom use using parallelprocess latent growth modeling analysis (PPLGM), and are these growth parameters differentially expressed across gender, age, grade, culture and intervention type?

(3)

What are the developmental trajectories of self-reported sexual and drug risk behavior and protective condom use behavior among youth, and how are these growth parameters related with PMT’s coping- and threat-appraisal processes for risk behavior and condom use using parallel-process latent growth modeling analysis?

159 Overview of Primary Findings

First, CFA analyzed measurement invariance and stability of the two broad categories of risk behavior and condom use PMT constructs within- and between-participants and time. These two broad categories of PMT constructs were modeled according to the two second-order constructs of PMT: coping appraisal and threat appraisal. According to PMT, coping appraisal is based on variables that pertain to perceptions of response efficacy and the perceived ability to emit a recommended response (i.e., self-efficacy), in addition to perceptions of barriers that may inhibit the likelihood of choosing the adaptive response (i.e., response costs). PMT postulates that the other second-order construct, threat appraisal, is associated with the maladaptive health response (e.g., unprotected sexual intercourse) and is affected by perceptions pertaining to the severity of, and personal vulnerability to, the proposed threat. Furthermore, while rewards associated with continuing to engage in the maladaptive response reduce the likelihood of an adaptive or protective response (e.g., condom use), high perceptions of severity and vulnerability increase the probability that the adaptive response will occur. Since protection motivation is determined by the sum of variables that increase the probability of an adaptive response minus those variables that decrease the likelihood of an adaptive choice, PMT stipulates a threatappraisal score would be derived from the algebraic summation of severity plus vulnerability minus rewards, whereas a coping-appraisal score would be derived from the algebraic summation of efficacy minus response costs (McMath & Prentice-Dunn, 2005). The results of CFA for the risk behavior and condom use PMT constructs indicate construct validation for the coping-appraisal process as efficacy and response cost loaded onto coping appraisal positively and negatively, respectively. In contrast, each of the first-order constructs loaded positively onto threat appraisal even though the model predicts opposite

160 loadings between severity/vulnerability and rewards. This observation, however, is not necessarily surprising given that there is considerable overlap between items and their correspondence to threat-appraisal constructs. Specifically, results of meta-analysis underscore the wide variability that can be found in threat appraisal outcomes, and heterogeneity in the PMT dependent variables that appears to come from many directions (Floyd, Prentice-Dunn, & Rogers, 2000). Despite the apparent lack of construct validity in the current study for the threatappraisal process, there was strong measurement invariance for the risk behavior and condom use PMT constructs, thereby facilitating interpretation of group differences because consistent factor structures were observed within- and between cultures and time. Thus, different factor structures were not confounded with group differences on the latent variables coping- and threatappraisal (Millsap, 1997). Given the extensive methodological rigor that was put into developing the YHRBI (Stanton et al., 1995) and the V-YHRBI (Kaljee, Genberg, Riel et al., 2005), it is not surprising that both of the PMT models fit the data extremely well within- and between-cultures. However, the initial items were just a starting a point and iterative processes using EFA followed by CFA were required to generate the final PMT model. Researchers involved in assessment should aim to establish measurement invariance among their constructs and scales, and use advanced techniques, such as multiple-group CFA to establish measurement invariance. Following demonstration of measurement invariance via multiple-group CFA, PPLGM was conducted, allowing for the simultaneous modeling of the developmental trajectories of the PMT coping- and threat-appraisal processes, and their relationship with self-reported sexual and drug risk behaviors. An overview of the key findings indicate the coping- and threat-appraisal processes were higher in American youth who were abstinent at baseline compared to youth who were sexually-experienced, and were higher in youth in the FOK intervention groups that added

161 a parental component and a booster component compared to youth in the FOK-only group. American boys had lower risk behavior coping-appraisal trajectories than girls, and older youth with high educational levels had lower risk behavior threat-appraisal trajectories that grew more than other youth over the course of the study. In contrast, American youth who completed the condom use PMT variables, older youth with educational level were likely to have higher coping-appraisal growth curves than other youth, and older, more educated girls were likely to have higher threat-appraisal trajectories. In Vietnamese youth, the FOK intervention had a strong effect on PMT as the coping-appraisal process grew significantly higher in the treatment group compared to the control group. Additionally, Vietnamese treatment adolescents with high educational level had high coping-appraisal trajectories. The final series of analyses examined the concurrent growth of risk- and protectivebehavior and PMT processes using multiple-group PPLGM. An overview of the main findings reveal American youth in the parental and booster groups had the greatest initial reduction in unprotected sex, and alcohol and cigarette use over the first 12 months of the study, and the lowest subsequent growth of these risk behaviors over the next 12 months of the study. In Vietnamese youth, those in the treatment group had significantly greater reductions in risk behavior compared to control youth. Interestingly, the trajectories of alcohol and cigarette risk behavior over the final 12 months of the study for all youth grew after the initial drop from baseline through follow up 2, even though coping- and threat appraisal also grew during the final year of assessment. This finding, however, is consistent with research indicating adolescent substance use ordinarily increases from sixth grade through high school (Dishion, Capaldi, Spracklen, & Li, 1995; Kandel, 1996).

162 Finally, condom use was modeled, and while protective condom use developed the most in the American youth in the Booster group, condom use behavior did not change in the Vietnamese youth from baseline through 18 months post-intervention. Culturally, Vietnamese youth have a stigma of sexual relations outside of marriage that is dangerous to their character and how they are perceived by others, and this helps Vietnamese youth maintain abstinence (Kaljee et al., 2007). For American youth, in contrast, there are a variety of different social norms for looking at sex, compared to the one social norm in Viet Nam that sex is just for marriage. Sex for American youth is a right of passage—sexual behavior says that you’re cool, and therefore abstinence has little significance. The idea of sex has very little to do with protecting one’s character in America, but in Viet Nam, sex contributes to one’s character and social standing. Thus, information about sex that might influence reductions in sexual behavior might be more respected in Viet Nam due to these contextual factors. Implication of Results to Behavioral Interventions and Prevention Research

Although several studies have examined PMT and its application to a wide range of health behaviors, including cigarette smoking (Ho, 1992; Maddux & Rogers, 1983), and sexual risk-taking and condom use behaviors (Bengel et al., 1996; Stanton et al., 1996; Stanton et al., 1996; Stanton et al., 2004; Stanton et al., 2007), the specific questions addressed here have received minimal attention in prior research, in large part because of methodological demands. Specifically, to address research question #1, which is concerned with the measurement invariance of a PMT model between cultures and across time, researchers would need to administer similar PMT-based questionnaires to two or more different samples, and obtain follow-up assessments in the same samples at multiple time points. Data used in the present study satisfy these demands, as they are comprised of self-report responses from American and

163 Vietnamese youth who completed the YHRBI and a culturally-adapted Vietnamese version, respectively, over four follow up assessment periods (Kaljee, Genberg, Riel et al., 2005; Stanton et al., 2004). Assuming that a researcher is able to obtain data that will allow them to establish crosscultural measurement invariance of PMT constructs across time, to address research questions #2 and #3 (which are concerned with examining the developmental trajectories and interrelationships among PMT constructs and self-reported behaviors), a robust analytical procedure that can model growth in two or more processes simultaneously would need to be adopted. Latent growth modeling with parallel-processes is just such a procedure as it helps a researcher model two or more developmental processes concurrently, by estimating the parameters of the latent growth curves (e.g., intercepts, slopes) of each construct, and by investigating the interrelationships between growth parameters via their covariance (Muthén & Curran, 1997; Muthén, 2002). The ability to readily model more than one trajectory simultaneously permits consideration of a wide range of hypotheses and tests for interrelationships among constructs that may develop or “move” (Schulenberg & Maggs, 2001; Schulenberg, Maggs, Steinman, & Zucker, 2001). Latent growth modeling with parallelprocesses is a relatively novel procedure; however, it is starting to gain popularity among researchers. For example, PPLGM has recently been used to examine concurrent developmental trajectories

of

physical

activity

and

smoking,

substance

use

and

delinquency,

inattention/hyperactivity and aggression, resistive efficacy and number of sexual partners, parenting practices and substance use, and behavioral control and resiliency (Audrain-McGovern et al., 2003; Dembo et al., 2007; Jester et al., 2005; Mitchell et al., 2005; Simons-Morton, 2007; Wong et al., 2006). In this dissertation, PPLGM modeled the concurrent development of the

164 PMT coping- and threat-appraisal processes in all samples, and examined the interrelationship between the growth parameters of each process and the developmental trajectories of selfreported sexual and drug risk behaviors and protective condom use behaviors as a function of gender, age, educational level, culture, and type of intervention. This study has three broad implications for current and future research. The first implication is that PMT is an important model for not only designing behavioral interventions and prevention programs, but also for explaining the cognitive appraisal processes that underlie the decision to adopt or not adopt risky or protective health behaviors (Ho, Davidson, & Ghea, 2005). The development of health risk behaviors in adolescence involves a complex set of interacting factors from multiple contexts (Davison & Birch, 2001). Health behaviors are consequences or outcomes of personal and social factors that embrace familial, societal and cultural influences within a system of continual dynamic interrelationships (Gochman, 1997). Therefore, intervention and prevention programs should be theoretically based on social cognition models (SCMs) that attempt to explain and predict motivation to change health behavior. SCMs based on PMT focus on the various cognitions and appraisals that contribute to the production of risky maladaptive responses and healthy coping responses. Research of the type contained in the present study has demonstrated that examining attitudes about adaptive health responses is required for a thorough conceptualization of health-threatening and adaptive behavior. Furthermore, the social-cognitive concept of reciprocal determinism proposes that behavior is a function of aspects of the environment and of the person, all of which are in constant reciprocal interaction (Baronowski, Perry, & Parcel, 2002). The second implication from this study involves the utility of the PPLGM as an analytical tool. PPLGM provided for the simultaneous modeling of both the coping- and threat-appraisal

165 processes, and the modeling of the PMT constructs and ongoing behavior. If it were not for this model, the interrelationships between the growth processes could not have been investigated. This study illustrates the strength of PPLGM in identifying the underlying mechanisms concerning how the FOK intervention program achieves its effects. In other words, if PMT has been correctly modeled, changes in the causal variables, such as coping- and threat-appraisal processes, will reduce risk behavior or increase protective behavior (Cheong, MacKinnon, & Khoo, 2003). As observed in the present study, modeling the change in the coping-appraisal process trajectory in the American Booster group and the Vietnamese treatment group, and modeling the concurrent change in the developmental trajectories of the risk behaviors allows for a more thorough evaluation of the FOK intervention than if only the change in either outcome variable alone were modeled (Donaldson, Graham, & Hansen, 1994). Few studies have examined whether the impact of PMT-based prevention programs and behavioral interventions on health-protective behaviors and intentions are, in fact, mediated by PMT variables (Norman et al., 2005; Rippetoe & Rogers, 1987). Thus, there is a clear need for research to investigate mediational processes by PMT variables on the developmental trajectories of risk and protective behaviors. For example, PPLGM could be used to model mediation, estimate the growth parameters, and describe structural relations between the FOK intervention program and the PMT mediator process and the risk and protective behavioral outcome process. This would be accomplished by regressing the growth rate factor of the outcome process on both the intervention program and the intercept and growth factor of the mediator process (cf. Cheong et al., 2003; Mitchell, Beals, & Kaufman, 2006). The final implication from this study concerns differences in adolescent sexual and drug risk behaviors in Viet Nam and the United States. By simply addressing the issue of the global

166 HIV/AIDS crisis, this study is contributing to global awareness. The United States of America is one of the countries with the largest number of HIV and other sexually transmitted infections in the world (Panchaud, Singh, Feivelson, & Darroch, 2000), and based on data from the 33 states and four dependent territories with long-term, confidential name-based HIV reporting, men accounted for most of the HIV or AIDS diagnoses (74%) among adults and adolescents in the country in 2005 (UNAIDS/WHO, 2007). Among African Americans aged 25-55 year, AIDS was the fourth leading cause of death in the United States in 2004 (Anderson et al., 2006). Racial and ethnic minorities continue to be disproportionately affected by the HIV epidemic in the United States, and although African Americans represent about 13% of the population, they accounted for 48% of new HIV or AIDS diagnoses in 2005 (Centers for Disease Control and Prevention, 2007). In Baltimore, the proportion of HIV cases in 2002 that resulted from heterosexual exposure had surpassed injection drug use (Blattner & Brown, 2005). In some neighborhoods in Baltimore, HIV rates in certain age groups rival those of some of the hardest hit regions of subSaharan Africa, and the disenfranchisement expressed among adolescents and young adults in the high-risk Pimlico-West (The Ranch) and Broadway-East areas where a sense of hopelessness translates into behaviors that continue to fuel new HIV infections (Maryland Department of Health and Mental Hygiene, 2007). Viet Nam is facing a rapidly growing HIV epidemic that is beginning to extend beyond initial concentrations in networks of injecting drug users and sex workers (Tuan et al., 2007). The number of people living with HIV doubled between 2000 and 2005, from approximately 122 000 to 263 000, and the adult HIV prevalence is estimated to be 0.5% at the national level and exceeds 1% in several provinces (WHO, 2005). Although the ratio of HIV infection prevalence in 2005 was estimated to be 2 to 1, males to females, the number of infected females compared

167 with males is increasing each year due to increased heterosexual transmission among injecting drug users who use contaminated injecting equipment, and among couples where the male has engaged in unprotected sex with non-regular partners or sex workers (Tuang et al., 2007). Potential Limitations of Current Study

This study contained several limitations. The first major limitation concerns the use of only 347 of the American youth to provide data on condom use PMT items. Though it makes conceptual sense to not include youth who have not yet had any sexual experiences, their inclusion in the study may have provided interesting data concerning the cognitive appraisal of intention to use condoms. A second limitation of this study concerns the absence of a clear control group for the American youth that would be similar to the control group that was available for the comparisons in the Vietnamese youth. Another study limitation concerns the differences in age between the American and Vietnamese youth. Since the Vietnamese youth were approximately 2 years older than the American youth, the differences in self-reported behaviors and appraisal growth trajectories between the two cultures cannot exclude the differences in age as a causal variable. Finally, the low rate of condom use and sexual activity in the Vietnamese youth made for difficulties in the interpretations of any differences in condom use and sexual activity. Although low rates of condom use among the Vietnamese youth are to be expected because it remains the least used form of contraception (Kaljee et al., 2007), the low rates made cross-cultural comparison of sexual risk (i.e., sexual intercourse without a condom) unavailable.

168 Suggestions for Future Research

There are several suggestions for future research using these data. First, the behavioral intention data that was provided by all the youth (but was not presented in the current study) should be analyzed and included in the PPLGM as a distal outcome. In other words, the relationship between coping- and threat- appraisal, ongoing behavior, and intention or expectation for future behavior should be investigated. Since one of the primary purposes of PMT-based interventions is to persuade people to follow the communicator’s recommendations, analyzing behavioral intentions would allow for the evaluation of the effectiveness of any attempted persuasions (Rogers & Prentice-Dunn, 1997). Including the intentions data would also provide for modeling PMT by intentions. The second suggestion for future research is to include data of some of the other risk behaviors that were obtained in the current study. Specifically, developmental trajectories of delinquency and truancy behaviors, such as fighting, suspensions from school, and using weapons should be modeled in concert with the PMT constructs using PPLGM since violence and delinquency have been found to covary with other risk behaviors, such as risky sexual behavior and drug use (Caspi et al., 1997). The final suggestion is for future research to use growth mixture modeling, which is a growth modeling strategy that adds a categorical latent variable to the model in an attempt to identify unobservable heterogeneity in the samples (Lubke & Muthén, 2005). Some of the growth models contained significant intercept variance parameters suggesting there may be subsets of youth beyond either the three FOK intervention groups, or the two control and treatment groups. Growth mixture modeling is one statistical approach that may be helpful in

169 classifying youth in diagnostic categories based not only on their sexual and drug behaviors, but also on their coping- and threat-appraisal trajectories. Conclusion

There is considerable research interest in the behavioral sciences to characterize and understand the variability in developmental pathways that lead to risky behaviors in adolescents and young adults. This study has provided results that will inform both the research and clinical communities regarding the developmental trajectory of PMT in concert with risky and protective behavior. In American youth overall, both the risk behavior and the condom use coping-appraisal processes decreased over time. The threat-appraisal process grew in a U-shaped manner for the risk behavior and condom use items. Across the FOK intervention groups, the Booster group showed higher coping appraisal for both the risk behavior and condom use items; for threat appraisal, the Booster group was highest for condom use items, but lowest for risk behavior items. In Vietnamese youth, threat-appraisal was similar for both the control and treatment groups, but did grow across time in both groups. In contrast, there was a pronounced treatment effect for the coping-appraisal process.

Cross-culturally, the coping-appraisal process was

similar in intensity between the American and Vietnamese youth, but the threat-appraisal process was lower in the Vietnamese youth relative to the American youth. Behaviorally, there appeared to be a strong intervention effect in the American youth, whereby the addition of ImPACT and Booster sessions contributed to decreasing growth in risk behavior across time. Risk behaviors decreased over time to a larger degree for Vietnamese youth in the treatment group compared to the control group. Sexual behaviors were very low in the Vietnamese youth compared to the American youth. Culturally, Vietnamese youth have a stigma of sexual relations outside of marriage, and this contextual variable may motivate these youth to maintain abstinence (which

170 was reflected in their low rates of sexual behavior). This may also contribute to their low threat appraisal (since the youth do not have a history of contacting risky sexual behavior), and the high coping appraisal in the treatment group (since the youth are starting to learn about new behaviors). For American youth, in contrast, there are a variety of different social norms that promote sex and sexual activity. Sex for American youth is a right of passage—sexual behavior says that you’re cool, and therefore abstinence has little significance. The differences in copingand threat-appraisal growth trajectories between the American and Vietnamese youth, as well as the cultural differences in rates of sexual engagement and condom use, underscores the vast differences in current and past experiences by these youth. While American youth are exposed to a barrage of sexual messages in media that may have lead to youth becoming more sensitized to threats, and conversely, less concerned with coping skills, Vietnamese youth are exposed to a different set of messages that focus more precisely on the growing HIV/AIDS epidemic in VietNam. Therefore, Vietnamese youth may be more focused on coping-appraisal processes than American youth, and threat appraisal, while still an important motivating variable to Vietnamese youth, is not as strong in these youth as it is in American youth. In conclusion, there was a profound treatment effect in the Vietnamese youth, as the coping-appraisal curves grew rapidly in the treatment group compared to the control group. In the American youth, addition of either a parental or booster component to the FOK-only intervention increased the coping-appraisal process and produced the greatest accelerating growth in the threat-appraisal process after a slight initial drop. Overall, this study illustrates the strength of parallel-process latent growth modeling in identifying both the cognitive processes that mediate PMT and the mechanisms concerning how the FOK intervention program may achieves its effects.

171

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192

ABSTRACT INVESTIGATING THE COGNITIVE PROCESSES THAT MEDIATE PROTECTION MOTIVATION THEORY: A PARALLEL-PROCESS LATENT GROWTH MODELING ANALYSIS

by MATTHEW L. COLE

May 2008 Advisor: Ty Partridge Major:

Psychology

Degree:

Doctor of Philosophy

This dissertation set out to increase our understanding of cognitive processes that mediate protection motivation theory (PMT) for the prevention of high risk sexual and drug behavior, and the increase in condom use behavior in American and Vietnamese youth. Longitudinal data from American and Vietnamese youth who participated in the Focus On Kids (FOK) HIV risk reduction intervention based on PMT constructs were analyzed using confirmatory factor analysis (CFA) and parallel-process latent growth modeling (PPLGM). The data were organized into risk behavior and condom use PMT constructs, and CFA demonstrated measurement invariance and the stability of these two broad categories of PMT constructs within- and between-participants and time. PPLGM allowed for the simultaneous modeling of the developmental trajectories of the PMT coping- and threat-appraisal processes, and their relationship with self-reported sexual and drug risk behaviors. Results indicate the coping- and threat-appraisal processes were higher in American youth who were abstinent at baseline compared to youth who were sexually-experienced, and were higher in youth in the FOK intervention groups that added a parental component and a booster component compared to

193 youth in the FOK-only group. In Vietnamese youth, the FOK intervention had a strong effect on PMT as the coping-appraisal process grew significantly higher in the treatment group compared to the control group. Examining the concurrent growth of behavior and PMT processes, American youth in the parental and booster groups had the greatest initial reduction in unprotected sex, and alcohol and cigarette use over the first 12 months of the study, and the lowest subsequent growth of these risk behaviors over the next 12 months of the study. In Vietnamese youth, those in the treatment group had significantly greater reductions in risk behavior compared to control youth. While protective condom use developed the most in the American youth in the Booster group, condom use behavior did not change in the Vietnamese youth from baseline through 18 months post-intervention. This study illustrates the strength of parallel-process latent growth modeling in identifying both the cognitive processes that mediate PMT and the mechanisms concerning how the FOK intervention program may achieves its effects.

194

AUTOBIOGRAPHICAL STATEMENT

Matthew Cole’s varied academic and professional career began in 1985 when he received a Bachelor of Music degree in Trumpet Performance from Cleveland Institute of Music at Case Western Reserve. With degree in hand, Matthew moved to New York City and spent the next ten years trying his hand at a career in the music industry, utilizing his entrepreneurial spirit to create Soundtrack City, his music and video production business. A life altering moment happened while playing guitar and piano for older adults (as a music therapist) at a local hospital: Matthew was introduced to the gratifying world of clinical psychology. Having grown up in Southfield, he returned home to Michigan to pursue specialized undergraduate and graduate training in psychology at Eastern Michigan University. In 1997 he completed a Bachelor of Science degree in Psychology, and in 1999 completed a Master of Science degree in Clinical Behavioral Psychology. Upon graduation he worked as a limited licensed psychologist performing neuropsychological evaluations and offering cognitivebehavioral therapy to children, adults and older adults with traumatic brain injury, developmental disabilities and chemical dependency. He had the opportunity to use and develop his clinical skills for Ann Arbor Rehabilitation Centers, Inc. and the Wayne County Juvenile Assessment Center. As Matthew continued his clinical work in neuropsychology, he quickly developed a passion for learning more about the brain and behavioral neuroscience. He decided to continue his graduate studies at Wayne State University when he was offered a National Institute on Drug Abuse predoctoral training grant to investigate the behavioral effects of opioid tolerance and dependence in the behavioral pharmacology laboratory of Alice M Young. He was also awarded a graduate teaching assistantship to run the undergraduate learning and memory laboratory. In 2002, he was awarded a Master of Arts degree in Biopsychology, with a minor in Pharmaceutical Sciences. With the blending of his clinical and experimental skills, Matthew continued graduate studies at WSU under the mentorship of his psychology advisor, Ty Partridge, and completed a research assistantship at the Pediatric Prevention Research Center from 2003-2005, under the supervision of Bonita Stanton, professor and chair of the Department of Pediatrics, at the Wayne State University School of Medicine. It was during this time that he began teaching as an adjunct professor of psychology for WSU and Lawrence Technological University (LTU), and was awarded the Heberlien Award for teaching excellence, in 2005, by the Wayne State University Department of Psychology. In 2006, Matthew joined LTU’s department of Humanities, Social Sciences, and Communication as senior lecturer and director of the psychology program, teaching Behavioral Neuroscience, Cognitive Psychology, Sensation and Perception, Drugs and Behavior, Research Methods, and Animal Behavior. Under his direction, the psychology program has enjoyed tremendous success as one of the fastest growing programs in the College of Arts and Sciences. Matthew remains involved on multiple research projects at the Prevention Center in collaboration with Dr. Stanton and colleagues. His continued allegiance with the Prevention Center has been critical for the success of several research presentations and publications on adolescent sex and drug risk behaviors as found in the Journal of Adolescent Health, Archives of Pediatrics and Adolescent Medicine, AIDS Education and Prevention, Preventive Medicine, Social Behavior and Personality, and Journal of Pediatric Psychology.