the influence of cognitive, personality, and social ... - OhioLINK ETD

THE INFLUENCE OF COGNITIVE, PERSONALITY, AND SOCIAL VARIABLES: PREDICTING CHANGES IN RISKY BEHAVIORS OVER A TWO-YEAR INTERVAL

A thesis presented to the faculty of the College of Arts and Sciences of Ohio University

In partial fulfillment of the requirements for the degree Master of Science

Melissa T. Buelow August 2005

This thesis entitled THE INFLUENCE OF COGNITIVE, PERSONALITY, AND SOCIAL VARIABLES: PREDICTING CHANGES IN RISKY BEHAVIORS OVER A TWO-YEAR INTERVAL

by MELISSA T. BUELOW

has been approved for the Department of Psychology and the College of Arts and Sciences by

Margret A. Appel Professor of Psychology

Benjamin M. Ogles Interim Dean, College of Arts and Sciences

3 BUELOW, MELISSA T. M.S. August 2005. Psychology The Influence of Cognitive, Personality, and Social Variables: Predicting Changes in Risky Behaviors Over a Two-Year Interval (204 pp.) Director of Thesis: Margret A. Appel The present study examines whether the following variables measured in the Spring of 2002 can be used to predict changes in risky behaviors from 2002 to 2004: sensation seeking, positive outcome expectancy, negative outcome expectancy, peer attachment, parental attachment, family relationships, perceived peer involvement in the behaviors, need for cognition, religiosity, and time perspective. Participants included 51 male and 45 female college students, aged 20 to 29, who also completed the measures at Time 1. At Time 2, participants completed measures of frequency of involvement in various risky behaviors, and multiple stepwise regression analyses were conducted. It was found that the cognitive (outcome expectancies, time perspective, need for cognition) and social variables (parent attachment, peer attachment, family relationship, perceived peer involvement) explained more of the variance in the frequency of future risky behaviors and in the change in frequency over time than did the personality variable (sensation seeking).

Approved: Margret A. Appel Professor of Psychology

TABLE OF CONTENTS Abstract.................................................................................................................................3 Table of Contents...................................................................................................................4 List of Tables.........................................................................................................................9 Introduction.........................................................................................................................12

Cognitive Influences on Risky Behaviors................................................................20

Outcome Expectancy..................................................................................21

Time Perspective.........................................................................................28

Summary of Cognitive Influences...............................................................33

Personality Influences on Risky Behaviors.............................................................34

Sensation Seeking.......................................................................................34

Summary of Sensation Seeking.................................................................. 40

Need for Cognition......................................................................................32

Social Influences on Risky Behaviors.....................................................................41

Peer Attachment..........................................................................................41

Peer Involvement........................................................................................ 44

Family Environment and Parental Attachment...........................................48

Summary of Social Influences....................................................................54

Rationale and Hypotheses...................................................................................................56

Hypothesis 1............................................................................................................56

Hypothesis 2............................................................................................................57

Hypothesis 3........................................................................................................... 60

Method............................................................................................................................... 60

Participants............................................................................................................. 60

Measures.................................................................................................................61

Demographic Questionnaire........................................................................61

Cognitive Appraisal of Risky Events...........................................................61

Peer Frequency of Involvement...................................................................63

National College Health Risky Behavior Survey........................................ 64

Zuckerman-Kuhlman Personality Questionnaire........................................65

Zimbardo Time Perspective Inventory........................................................66

Need for Cognition......................................................................................67

Family Environment Scale..........................................................................67

Inventory of Parent and Peer Attachment....................................................68

Procedure................................................................................................................69 Data Reduction and Analyses..................................................................................70

Results ................................................................................................................................74

Comparison of Time 1 and Time 2 Participants......................................................74

Hypothesis 1: Predicting Time 2 Behavior from Time 1 Variables.........................77 Regression on CARE Frequency of Illicit Drug Use...................................77

Regression on NCHRBS Frequency of Drug Use.......................................79

Regression on CARE Frequency of Risky Sexual Behaviors......................81

Regression on NCHRBS Frequency of Sexual Behaviors...........................81

Regression on CARE Frequency of Heavy Drinking..................................81

Regression on NCHRBS Frequency of Alcohol Use...................................84

Regression on CARE Frequency of Tobacco Use.......................................86

Regression on NCHRBS Frequency of Tobacco Use..................................86

Regression on CARE Frequency of Risky Driving Behaviors....................89 Regression on NCHRBS Frequency of Driving Behaviors.........................91 Regression on CARE Frequency of Aggressive/Illegal Behaviors..............91 Regression on NCHRBS Frequency of Aggressive/Illegal Behaviors.........93 Regression on CARE Frequency of High Risk Sports................................93 Regression on CARE Frequency of Risky Academic/Work Behaviors.......95

Summary of Hypothesis 1 Analyses.......................................................................97

Hypothesis 2: Predicting Changes in Behavior from Time 1 Variables...................97

Regression on Changes in CARE Frequency of Illicit Drug Use................97

Regression on Changes in NCHRBS Frequency of Drug Use....................98

Regression on Changes in CARE Frequency of Risky Sexual Behaviors.............................................................................98 Regression on Changes in NCHRBS Frequency of Risky Sexual Behaviors.......................................................................................100

Regression on Changes in CARE Frequency of Heavy Drinking.............100

Regression on Changes in NCHRBS Frequency of Alcohol Use..............100

Regression on Changes in CARE Frequency of Tobacco Use...................103

Regression on Changes in NCHRBS Frequency of Tobacco Use..............105

Regression on Changes in CARE Frequency of Risky Driving Behaviors.........................................................................105

Regression on Changes in NCHRBS Frequency of Driving Behaviors.........................................................................105 Regression on Changes in CARE Frequency of Aggressive/Illegal Behaviors.........................................................107

Regression on Changes in NCHRBS Frequency of Aggressive/Illegal Behaviors.........................................................107

Regression on Changes in CARE Frequency of High Risk Sports............107

Regression on Changes in CARE Frequency of Risky Academic/Work Behaviors............................................................107

Summary of Hypothesis 2 Analyses.....................................................................109

Hypothesis 3: Relative Influence of the Variables on Risky Behaviors............................. 112 Discussion......................................................................................................................... 114

Predicting Time 2 Behaviors from Time 1 Variables............................................ 114

Cognitive Variables................................................................................... 114

Personality Variable.................................................................................. 116

Social Variables......................................................................................... 117

Predicting Changes in Behavior From Time 1 Variables...................................... 118

Cognitive Variables................................................................................... 118

Personality Variable..................................................................................120

Social Variables.........................................................................................121

Predicting Relative Influence of the Variables.......................................................122

Implications...........................................................................................................123

Limitations............................................................................................................124

Future Research.....................................................................................................126

References.........................................................................................................................128 Appendices Appendix A: Demographic Questionnaire............................................................140

Appendix B: Cognitive Appraisal of Risky Events............................................... 141

Appendix C: Peer Frequency of Involvement........................................................ 147

Appendix D: National College Health Risk Behavior Survey............................... 149 Appendix E: Zuckerman Kuhlman Personality Questionnaire.............................165 Appendix F: Zimbardo Time Perspective Inventory.............................................166 Appendix G: Need for Cognition..........................................................................168 Appendix H: Family Environment Scale............................................................... 170 Appendix I: Inventory of Parent and Peer Attachment.......................................... 172 Appendix J: In-Person Administration Consent Form........................................... 176 Appendix K: In-Person Administration Debriefing Text ...................................... 178 Appendix L: Web-based Administration Consent Form....................................... 179 Appendix M: Web-based Administration Debriefing Text.................................... 181

Appendix N: Correlations Among the Variables.................................................. .182

List of Tables Table 1. Outcome Expectancy and Risky Behaviors....................................................................22 2. Time Perspective and Risky Behaviors..........................................................................29 3. Sensation Seeking and Risky Behaviors.........................................................................35 4. Peer Attachment and Risky Behaviors...........................................................................42 5. Peer Involvement and Risky Behaviors..........................................................................45 6. Family Relationships and Risky Behaviors....................................................................49 7. Summary of Doherty et al. (2004) Findings...................................................................58 8. Demographic Information..............................................................................................62 9. Variables........................................................................................................................ .71 10. Means and Standard Deviations...................................................................................75 11. Summary of Stepwise Regression Analysis for Variables Predicting Log Transformed Frequency of Time 2 CARE Illicit Drug Use (N = 49)......................78 12. Summary of Stepwise Regression Analysis for Variables Predicting Log-Transformed Frequency of Time 2 NCHRBS Drug Use (N = 14)................................................80 13. Summary of Stepwise Regression Analysis for Variables Predicting Log-Transformed Frequency of Time 2 NCHRBS Sexual Behaviors (N = 70)...................................82 14. Summary of Stepwise Regression Analysis for Variables Predicting Log-Transformed Frequency of Time 2 CARE Heavy Drinking (N = 86)..........................................83 15. Summary of Stepwise Regression Analysis for Variables Predicting Log-Transformed Frequency of Time 2 NCHRBS Alcohol Use (N = 88)...........................................85

10 16. Summary of Stepwise Regression Analysis for Variables Predicting Log-Transformed Frequency of Time 2 CARE Tobacco Use (N = 47)................................................87 17. Summary of Stepwise Regression Analysis for Variables Predicting Log-Transformed Frequency of Time 2 NCHRBS Tobacco Use (N = 63)...........................................88 18. Summary of Stepwise Regression Analysis for Variables Predicting Log-Transformed Frequency of Time 2 CARE Risky Driving Behaviors (N = 90)............................90 19. Summary of Stepwise Regression Analysis for Variables Predicting Log-Transformed Frequency of Time 2 CARE Aggressive/Illegal Behaviors (N = 74).......................92 20. Summary of Stepwise Regression Analysis for Variables Predicting Log-Transformed Frequency of Time 2 NCHRBS Aggressive/Illegal Behaviors (N = 14)..................94 21. Summary of Stepwise Regression Analysis for Variables Predicting Log-Transformed Frequency of Time 2 CARE Risky Academic/Work Behaviors (N = 87)...............96 22. Summary of Stepwise Regression Analysis for Variables Predicting Changes in

Frequency of CARE Risky Sexual Behaviors (N = 90)..........................................99

23. Summary of Stepwise Regression Analysis for Variables Predicting Changes in

Frequency of NCHRBS Sexual Behaviors (N = 71).............................................. 101


Frequency of NCHRBS Alcohol Use (N = 88).....................................................102


Frequency of CARE Tobacco Use (N = 91)..........................................................104


Frequency of NCHRBS Driving Behaviors (N = 89)............................................106

11 27. Summary of Stepwise Regression Analysis for Variables Predicting Changes in

CARE Frequency of Risky Academic/Work Behaviors (N = 89).........................108

28. Summary of Time 2 Findings by Variable.................................................................. 110 29. Summary of Time 2 Findings by Type of Variable..................................................... 113 30. Summary of Behavior Changes................................................................................... 119

12 Introduction College is a time of changes in an individual's life. Often for the first time, the individual is away from home, making his or her own decisions. Individuals form new friendships, take new classes, and join new activities. Alcohol and drug use, smoking, and sexual activity are just a few of the behaviors they are exposed to and they must decide whether to participate in or abstain from them. Research into these behaviors has indicated that they are a common occurrence on college campuses and that a number of personality, social, and cognitive factors can influence their occurrence. What is a risky behavior? Researchers have been consistent in their inconsistency about the meaning of this term. According to Bradley and Wildman (2002), risky behaviors are socially approved activities such as water skiing and bungee jumping. In contrast, they defined reckless behaviors as those that are not socially approved and that have a potential for negative consequences. Such reckless behaviors include drug use and dangerous driving habits. In contrast, Benthin, Slovic, and Severson (1993) differentiated between risky behaviors that are socially approved and risky behaviors that are not socially approved, but used the term “risky behavior” for both types of behavior. A number of researchers do not specifically define the term “risky behavior,” but choose to study a number of the same behaviors including dangerous driving, alcohol and other drug use, sexual activities, illegal behaviors, and smoking (e.g., Caspi et al., 1997; Gerrard, Gibbons, Benthin, & Hessling, 1996; Vavrick, 1997). For the current study, the definition of risky behavior follows that used by Doherty, Appel, and Murphy (2004), in which a risky behavior is viewed as a behavior that potentially has negative consequences

13 for one’s health in either the short or long term. The present study will examine the following types of risky behaviors: alcohol use, drug use, tobacco use, high impact sports, unsafe driving, sexual behavior, aggressive behavior, illegal behavior, and irresponsible academic behavior. In 1995, the Centers for Disease Control (CDC) conducted a health risk survey at 148 colleges and universities in the United States. This study was conducted in order to determine the prevalence of and changes over time in risky behaviors that contribute to the main causes of death in young adults. The risky behaviors studied included tobacco use, alcohol and other drug use, risky sexual behaviors, and behaviors that lead to accidents such as risky driving habits and fighting (Douglas et al., 1997). Estimates of risky behaviors were attained over two time periods: involvement in the past 30 days and lifetime involvement. Using the National College Health Risk Behavior Survey, the researchers found that 10.2% of college students rarely or never wore a seatbelt while driving a car and 27.4% had driven a car after drinking alcohol. With regard to sexual activity, 34.5% of college students reported having had six or more sexual partners in their lifetimes (Douglas et al., 1997). Peterson, Oakley, Potter, and Darroch (1998) found that 25% of a sample of college students used birth control pills inconsistently, which weakens the pills’ effectiveness. Studies of high school students indicate similar risky sexual practices. For example, Everett et al., (2000) found that 48% of high-school students they surveyed reported having had sex, risky or not, at least one time (Everett et al., 2000), and Shrier, Emans, Woods, and DuRant (1996)

14 found that 45.8% of their sample reported not using a condom during their last sexual encounter. The prevalence of alcohol and other drug use has also been examined. The CDC researchers found that over one third of students reported smoking cigarettes on a daily basis and 48.7% had tried marijuana at least once in their lives (Douglas et al., 1997). In a later study, Arnett (1998) found that 39% of adults aged 20 through 28 used marijuana at least one time in the past year. Although the percentage of individuals who have tried marijuana was lower in a 1998 study than in the 1995 CDC study, a large proportion of the young adult population is still engaging in this risky behavior. DuRant et al. (1993) found that although there was a low overall incidence of the use of steroids in a sample of high-school students, 25% of students who used steroids were sharing needles and, subsequently, putting their health at risk. The CDC study (Douglas et al., 1997) also found that in the past 30 days, 34.5% of students reported having five or more drinks at a time on at least one occasion, an amount that fits an accepted definition of binge drinking (Wechsler, Lee, Kuo, & Lee, 2000; Wechsler et al., 2002a; Weitzman, Nelson, & Wechsler, 2003). Johnston, O’Malley, & Bachman (2000) found that in 1993, 40.2% of college students had five or more drinks in one sitting on at least one occasion. A 2002 study found that 40% of college students are currently considered binge drinkers (Wechsler et al., 2002a), a number that has remained relatively constant since the 1995 CDC national study. Although many researchers have studied numerous risky behaviors, many of these studies have investigated only one risky behavior at a time. However, risky behaviors do

15 not exist in isolation. Research has shown that alcohol use occurs concurrently with risky sexual practices (Ogletree, Dinger, & Vesely, 2001; Staton et al., 1999) and risky driving behaviors (Arnett, 1998). In a study of college students, Everett, Giovino, Warren, Crossett, and Kann (1998) found that individuals who were smokers at the time of assessment were more likely to drink alcohol and were also more likely to use drugs in their lifetimes. Studies of college students who regularly use drugs have found that, in comparison to students who do not use drugs, they were more likely to exhibit aggressive and delinquent behavior (DuRant, Knight, & Goodman, 1997) and to have risky driving habits that put them at risk for car crashes (Everett, Lowry, Cohen, & Dellinger, 1999). Risky sexual practices, such as not using a condom or having multiple partners, have also been linked to other risky behaviors, such as failing to use a seat belt, fighting, and using tobacco and other drugs (Ogletree et al., 2001; Rome, Rybicki, & DuRant, 1998). Risky behaviors can have a negative effect on one’s health and well-being. Accidents are the leading cause of death among young adults (Wechsler et al., 2002b). Accidental injury is a broad term that includes injuries sustained from driving a car, riding a motorcycle, and participating in sports. In a Harvard School of Public Health survey (Wechsler et al., 2002a), 29% of college students reported that they drove a car after drinking alcohol. Risky behaviors such as alcohol use have been linked to accidental injuries (Wechsler et al., 2002b). The negative effects of risky behaviors on one’s health are not limited to accidental injury. As reported previously, DuRant et al. (1993) found that 25% of 9th grade students who used steroids had shared a needle. Because one of the ways that the Human Immuno-deficiency Virus and the Acquired Immuno-deficiency

16 Syndrome (HIV/AIDS) is transmitted from person to person is by sharing needles, these students are putting themselves at risk for exposure to a life-threatening disease. Unsafe sexual practices, such as not using a condom or having multiple partners, can also result in exposure to HIV/AIDS. Researchers have found that of the 43% of high-school students who reported having had sex, 40% were not using condoms or had multiple partners (Huebner & Howell, 2003). A limitation of this study is that these two types of risky sexual behaviors were not examined separately, in that the researchers combined individuals who had only one partner but did not use a condom with individuals who had multiple partners and used condoms. Longitudinal research is important to the study of risky behaviors because it adds information about how an individual changes over time, as well as whether certain variables can be found to be predictive of later behaviors or changes in behaviors over time. Using a cross-sectional design, researchers have found that among college students who drank alcohol, those who were under the age of 21 were actually less likely to drive a car after drinking alcohol (Wechsler et al., 2002b). To study this phenomenon, the researchers analyzed the data for individuals who drove a car one or more times in the preceding week, however, the availability of a car for individuals from the different age groups was not assessed. Also, because this study compared individuals from two different age groups (ages 18 to 20 and ages 21 to 23), it is not known whether the individuals themselves changed their behaviors as they aged. Other researchers have found that alcohol and other drug use increased over the course of study (Christiansen,

17 Smith, Roehling, & Goldman, 1989; Hix-Small, Duncan, Duncan, & Okut, 2004). Results such as these indicate that involvement in risky behaviors may change over time. One goal of studying risky behaviors is to design prevention programs to decrease these behaviors in school-aged and college-aged students. Programs have been designed for schools, parents, and television with varying degrees of success. Palmgreen, Donohew, Lorch, Hoyle, and Stephenson (2001) assessed the efficacy of an antimarijuana television advertising campaign in Kentucky and Tennessee that targeted the personality trait of sensation seeking by including dramatic and exciting elements, while still conveying the negative effects of marijuana use. Each month, a different sample of 100 high-school students completed measures of sensation seeking and marijuana, alcohol, tobacco, and other drug use in the past 30 days. Results indicated that among students classified as high sensation seekers, marijuana use dropped by about 26% following the campaign, however, cigarette, alcohol, and other drug use did not decline. The television ads ran for 32 months, but no long-term follow-up data were collected to determine if the initial positive results remained after the completion of advertising. Other prevention programs have been designed to increase knowledge of alternatives to risky behaviors. DuRant et al. (1996) compared two programs aimed at reducing fighting behaviors in middle-school students by emphasizing alternatives to fighting and dealing with anger. Measures of violence were obtained through responses to hypothetical situations both before and after the program. Students in both groups showed a decrease in violent responses to these situations following the program, but again, no long-term follow-up data were collected.

18 Turrisi, Jaccard, Taki, Dunnam, and Grimes (2001) examined the efficacy of a different type of prevention program for entering college students. Interested parents were randomly assigned to one of two groups. During the summer, parents in the intervention group received a handbook with information about alcohol use on college campuses, as well as information about strategies regarding communicating with their child in general and about peer pressure. Parents in the control group did not receive this information. Ninety days after beginning college, students completed measures of drinking behavior and attitudes towards drinking. The researchers found that students in the intervention group had less positive attitudes about drinking. Those in the intervention group also drank less alcohol and drank less frequently than students in the control group. However, this study did not measure drinking habits prior to the intervention, so it is not known whether these lower scores were truly a result of the intervention. Prevention programs have been implemented in high schools and on college campuses with the purpose of increasing knowledge of the negative outcomes associated with risky behaviors in order to reduce the incidence of such behaviors. Wechsler et al. (2002a) examined whether alcohol consumption decreased on college campuses as a function of increased sanctions for being caught drinking. The researchers found that although 50% of students surveyed stated that they had received information about alcohol policies, over 40% of students were classified as binge drinkers. A number of elementary and junior-high schools implement the Drug Abuse Resistance Education (DARE) program each year. The DARE program is a 17-week

19 course in which police officers teach students methods to avoid peer pressure to use drugs, provide information about the risks associated with drug use, and educate students about healthier alternatives to drug use. Lynam et al. (1999) investigated whether involvement in the DARE program changed subsequent drug and alcohol use. Students were randomly assigned to either the DARE intervention or to a standard drug-education program. Sixth-grade students completed measures of frequency of involvement in drug and alcohol use before exposure to DARE or to the standard program, after exposure to DARE, and then each year until the age of 20. Students’ reported lifetime substance use in the 6th grade was compared to past-month substance use at age 20. There were no short-term nor long-term benefits to the DARE program: alcohol, cigarette, marijuana, and other drug use did not significantly change as a function of participation in the program. Results such as these indicate the need for effective interventions and prevention programs, as these programs can be expensive to implement and the benefits do not always outweigh the costs. In the case of DARE, the program itself may be inexpensive to implement but students are pulled out of classes in order to participate in a program that does not have demonstrated effects. Lost time in the classroom could negatively influence academic performance. Ashworth, DuRant, Newman, and Gaillard (1992) examined whether providing information about HIV/AIDS resulted in drug-related behavior change. Students in the 11th and 12th grades were randomly assigned to either a control group or an experimental group. Participants in the experimental group attended an HIV/AIDS information group where they received information about the transmission and prevention of the disease. A

20 comparison of pre- and posttest measures indicated that students in the experimental group had greater knowledge of HIV/AIDS than the control group post-intervention, but they did not significantly change their behavior. That is, neither students in the control group nor in the experimental group changed their intravenous drug use and other drug behaviors. Given the prevalence of risky behaviors among college students, an examination of the predictors for the behaviors and their change over time is warranted in order to design effective prevention programs. The present study seeks to examine whether cognitive, personality, and social variables can predict risky behaviors over a two-year interval. At Time 1, in the Spring of 2002, students completed a series of questionnaires regarding outcome expectancy, the need for cognition, time perspective, sensation seeking, perceived peer involvement, peer attachment, and family attachment and environment. They also completed measures of engagement in a number of risky behaviors. Two years later, at Time 2, these students completed the same measures of engagement in risky behaviors. The following sections examine the influences of various cognitive, personality, and social variables on concurrent and future risky behaviors Cognitive Influences on Risky Behaviors Cognitive factors can influence risky behaviors from the initial decision to engage in a behavior to the decision to continue that behavior. The Time 1 cognitive variables that are examined in the present study are outcome expectancy, time perspective, and the need for cognition.

21 Outcome expectancy. Outcome expectancy refers to how an individual perceives the consequences of situations and behaviors. These consequences can be seen as gains or losses or as benefits or risks (Carey, 1995; Highhouse & Yuce, 1996). An individual who looks at the gains or benefits of a risky behavior can be considered to have a high positive outcome expectancy, whereas an individual who focuses on the losses or risks of a behavior can be considered to have a high negative outcome expectancy (Katz, Fromme, & D’Amico, 2000). Positive and negative outcome expectancy are viewed as two separate constructs, not as opposite ends of one construct. Table 1 contains a summary of the literature regarding positive and negative outcome expectancy and risky behaviors. Doherty et al. (2004) administered the frequency of involvement and the positive and negative expectancies sections of the Cognitive Appraisal of Risk Events instrument to 311 undergraduates. The results indicated that positive outcome expectancies predicted concurrent illicit drug use, aggressive/illegal behaviors, heavy drinking, risky academic/work behaviors, tobacco use, and risky driving behaviors. In all of these relationships, a higher positive outcome expectancy predicted higher involvement in the risky behavior. The researchers also found that lower negative outcome expectancies predicted higher illicit drug use. Katz et al. (2000) administered the drug use, heavy drinking, and risky sexual behavior subscales of both the positive and negative consequences sections of the Cognitive Appraisal of Risky Events instrument to 228 undergraduates. The researchers found that positive expectancies were significantly related to drug use and heavy drinking, in that higher positive expectancies were associated with increased drug and alcohol use. They did not find, however, that these

Table 1 �� Researchers

Barkin, Smith, & DuRant (2002)

N

Sex

2676 Male and Female

Age

12-13

Outcome Expectancy Measure Study-Specific Measure (Positive and Negative)

Risky Behavior Measure Study-Specific Measure

Results

Higher positive attitudes were associated with alcohol use Time 1 positive and negative expectancies predicted Time 2 drug and alcohol use

Brown & Cotton (2003)

800

Male and Female

16-45

Study-Specific Measure (Positive and Negative)

Study-Specific Measure

High negative expectancy was associated with less reported instances of speeding

Brown, Flory, Lynam, Leukefeld, & Clayton (2004)

1354

Male and Female

20



Higher positive outcome expectancy was associated with early onset of marijuana use Lower positive outcome expectancy was associated with late onset of marijuana use

Carey (1995)

140

Male and Female

17-38

Adverse Effect Questionnaire (Positive)

Rutgers Alcohol Problem Index

Positive expectancy of sexual enhancement was associated with frequency of alcohol consumption

22

Table 1 (continued) �� Researchers

N

Sex

Age

Outcome Expectancy Measure

Christiansen, Smith, Roehling, & Goldman (1989)

637

Male and Female

11-14

Alcohol Expectancy Questionnaire (Positive and Negative)


Doherty, Appel, & Murphy (2004)

311

Male and Female

18-19

Cognitive Appraisal of Risky Events (Positive and Negative)

Cognitive Appraisal of Positive outcome expectancy Risky Events Frequency predicted alcohol and illicit of Involvement drug use

Gerrard, Gibbons, Benthin, & Hessling (1996)

477

Male and Female

Goldberg, Halpern-Fisher, & Millstein (2002)

395

Male and Female

M = 14 Study-Specific Measure (Negative)

10-16


Risky Behavior Measure

Results

Alcohol expectancies predicted drinking behaviors 2 years later


Outcome expectancy predicted alcohol use 3 months later


Positive alcohol expectancies at Time 1 predicted alcohol and drug use at Time 2 Positive expectancies were a better predictor of future behavior than negative expectancies

Katz, Fromme, & D'Amico (2000)

162

Male and � = 18.5 Cognitive Appraisal of Female Risky Events (Positive and Negative)

Cognitive Appraisal of Positive outcome expectancy Risky Events Frequency predicted future alcohol of Involvement consumption

23


N

Sex

Age

Outcome Expectancy Measure


Results

Mooney, Fromme, Kivlahan, & Marlatt (1987)

325

Male and Female

16-45

Alcohol Expectancy Questionnaire (Positive)

Cahalan's Drinking Habits Questionnaire

Higher positive alcohol expectancy was associated with greater alcohol consumption

Musher-Eizenman, Holub, & Arnett (2003)

432

Male and Female

12-15, 18-22



Outcome expectancies were associated with alcohol and marijuana use in college women Outcome expectancies were not associated with alcohol and marijuana use in college men

Rolison & Scherman (2002)

171

Male and Female

18-21

Risk Involvement and Perception Scale


Perceived risk negatively correlated with involvement in risky behaviors

Rolison & Scherman (2003)

260

Male and Female

18-21



Perceived benefit was a significant predictor of involvement in risky behaviors

24

25 expectancies predicted sexual behaviors. Because the focus of education in some prevention programs is to increase knowledge of the risks and negative consequences associated with sexual behavior, these findings have implications for such prevention programs. Other researchers have found similar results with regard to drug and alcohol use. Barkin, Smith, and DuRant (2002) found that among 7th grade students, self-reports of alcohol use and expected alcohol use, as measured by a study-specific questionnaire, were related to higher positive attitudes towards and expectations from alcohol usage. Carey (1995) used the Adverse Effect Questionnaire to investigate how alcohol consumption was related to positive outcome expectancy. Results indicated that the total positive expectancy score was related to the amount of alcohol consumed on a daily basis. Mooney, Fromme, Kivlahan, and Marlatt (1987) assessed both drinking habits and positive alcohol expectancies (e.g., sexual enhancement) in a group of college students ranging in age from 16 to 45. They found that high positive alcohol expectancies were associated with greater alcohol consumption. Brown, Flory, Lynam, Leukefeld, and Clayton (2004) administered a measure of positive and negative outcome expectancy to students in the 6th grade and obtained measures of marijuana use in both the 6th grade and at age 20. They found that an early onset of marijuana use was significantly associated with higher scores on the positive expectancy measures and lower scores on the negative expectancy measures. A later age of onset of marijuana use was associated with low scores on positive expectancy, but was not related to negative expectancies.

26 Musher, Eizenman, Holub, and Arnett (2003) found that outcome expectancies, both positive and negative, can predict concurrent alcohol and marijuana use in female college students. However, negative expectancies were not associated with these same risky behaviors in college men. The researchers argued that prevention programs focus too much on a factor that was shown not to be significantly related to risky sexual behaviors in both men and women. More information is needed about the cognitive influences on risky sexual behaviors in order to make these prevention programs more effective. In addition to these studies that focused on concurrent alcohol use, research has also shown that outcome expectancies predict future alcohol use. Specifically, negative outcome expectancies have been found to predict increased alcohol consumption three months later (Gerrard, Gibbons, Benthin, & Hessling, 1996), in that low negative expectancies predicted increases in consumption. Christiansen et al. (1989) found that high positive alcohol expectancies predicted alcohol use one year later. Positive outcome expectancies have also been shown to be predictive of both quantity and frequency of alcohol consumption in both men and women (Mooney et al., 1987). Katz et al. (2000) also assessed involvement in alcohol use, drug use, and risky sexual behaviors six months after the initial assessment. They found that positive and negative outcome expectancies measured at Time 1 predicted drug and alcohol use six months later. Specifically, higher positive expectancies and lower negative expectancies predicted frequent alcohol and drug use at Time 2. There are, however, a few shortcomings to this study. At Time 1, students were in the fall semester of the first year

27 of college. When asked to estimate the frequency of involvement in risky behaviors in the previous six months, estimates encompassed months when the students were still in high school. At Time 1 in the present study, freshman and sophomore students were asked to estimate how often they participated in the behaviors in the past 30 days and past 6 months. These periods of time may have included one or two months of the summer before beginning college, however there was no overlap with high school enrollment. Researchers have also investigated the relationship between perceived risks and benefits and risky behaviors. Perceived risks can be viewed as similar to negative outcome expectancies, as researchers have referred to perceived risks as perceptions of losses associated with a certain behavior (Rolison & Scherman, 2003). Rolison and Scherman (2002), using the Risk Involvement and Perception Scales, found that perceived risks were negatively correlated with involvement in various risky behaviors. In a second study using the same measure, Rolison and Scherman (2003) found that higher perceived benefits predicted involvement in risky behaviors. Goldberg, HalpernFisher, and Millstein (2002) investigated whether positive and negative perceptions of alcohol predicted actual drinking and smoking behavior six months later. At the Time 2 administration, students were asked about their drinking and smoking behaviors in the intervening six months. Findings indicated that perceived benefits of alcohol predicted alcohol and cigarette use at Time 2, and perceived benefits were a better predictor of smoking than were perceived risks. Risky driving has also been predicted by the risks and benefits associated with it. Brown and Cotton (2003) found that in 800 Australian individuals between the ages of 16

28 and 50, the perceived risks associated with driving 10 km/h and 20 km/h over the speed limit predicted self-reported estimates of the percentage of time spent speeding. Individuals who perceived a high risk associated with speeding were less likely to report driving over the speed limit. Time perspective. Time perspective refers to how an individual categorizes experiences in order to give meaning and order to these events (Zimbardo & Boyd, 1999). An individual who has a present time orientation responds well to the immediate effect of a situation (Wills, Sandy, & Yaeger, 2004). For example, a present-oriented individual might choose to engage in sexual behaviors for the immediate physical gratification. Conversely, an individual who has a future time orientation responds to the anticipated outcomes of a behavior (Wills et al., 2004; Zimbardo & Boyd, 1999). A future-oriented individual might engage in sexual behaviors to gain the wanted future reward of a child or might not engage in sexual behaviors in order to avoid pregnancy before he or she is ready for a child. The preference for either a future or present time orientation is based on the tendency to consistently choose from one time frame over another. Table 2 contains a summary of research regarding time perspective and risky behaviors. All of these studies have used variations of the Zimbardo Time Perspective Inventory. Numerous studies have shown that time perspective is related to involvement in risky behaviors. Rothspan and Read (1996) had 188 college students complete the Stanford Time Perspective Inventory, an early version of the Zimbardo Time Perspective Inventory, as well as measures of condom use, fear of AIDS, methods used to prevent


N

Sex

Age


311

Male and Female

18-19

Zimbardo Time Perspective Inventory

Cognitive Appraisal of Present time perspective was Risky Events Frequency positively related to alcohol of Involvment and other drug use

Keough, Zimbardo, & Boyd (1999)

2627 Male and Female

14-67


Health and Risk Questionnaire

Present time perspective predicted substance use

Rothspan & Read (1996)

188

17-28

Stanford Time Perspective Inventory


Individuals high in present time perspective were more likely to have had sex in the past 6 months

Male and Female

Time Perspective Measure


Results

Individuals high in future time perspective were more likely to have had fewer sexual partners in the past 6 months Wills, Sandy, & Yaeger (2004)

454

Male and Female

11-12



Future time orientation was inversely related to substance use Present time orientation was positively related to substance use

29


Zimbardo, Keough, & Boyd (1997)

N

Sex

Age

1714

Male and Female

16-50

Time Perspective Measure Zimbardo Time Perspective Inventory

Risky Behavior Measure Health and Risk Questionnaire

Results

Time perspective was a significant predictor of risky driving behaviors. Present time perspective was a better predictor than future time perspective.

30

31 AIDS, and number of sexual partners. The researchers found that individuals who scored high on present time perspective were more likely to have had sex in the past six months. Individuals who scored high on future time perspective were more likely to have had sex with fewer partners both in the past six months and in their lifetimes. Doherty et al. (2004) found that individuals endorsing a present time perspective were involved in more frequent risky sexual, academic/work, and aggressive/illegal behaviors. The researchers also found that having a future orientation was negatively associated with involvement in risky academic and work behaviors but positively associated with involvement in high risk sports. In a study that focused on the relationship between time perspective and substance use, Wills et al. (2004) administered the Zimbardo Time Perspective Inventory to 454 elementary school students and found that having a present time perspective was positively related to alcohol, cigarette, and marijuana use, whereas having a future orientation was inversely related to substance use. Similar studies with college students found that having a present time perspective was positively related to alcohol and other drug use (Doherty et al., 2004; Keough, Zimbardo, & Boyd, 1999). Keough et al. (1999) examined the relationship between time perspective and substance use in 15 samples, including high-school students, college students in three states, and youth offenders. They found that that having a present time perspective consistently predicted substance use, however future time perspective did not consistently predict substance use. Zimbardo, Keough, and Boyd (1997) investigated time perspective as a predictor of risky driving behaviors in college students. Risky driving was defined by participation

32 in such behaviors as speeding, driving under the influence of alcohol, and racing. The researchers administered the Zimbardo Time Perspective Inventory to 1714 students ranging in age from 16 to 50 and found that time perspective was a significant predictor of risky driving behaviors, with present time perspective being a better predictor than future time perspective. Present-oriented individuals were more likely to report engaging in risky driving behaviors than future-oriented individuals. A replication by these researchers found that time perspective remained a significant predictor of driving behaviors even after controlling for sensation seeking, a personality variable that has been shown to significantly relate to risky behaviors and that will be discussed in a later section. The findings from these studies indicate that additional research into the predictive utility of time perspective is needed to clarify its true relationship to risky behaviors. Need for cognition. The need for cognition refers to the extent to which an individual engages in and enjoys thinking (Cacioppo & Petty, 1982; Sadowski & Gulgoz, 1996). Someone who is high in the need for cognition tends to focus on the relevant content of an idea and to organize the information, possibly in order to make a decision. This process can aid the decision-making process, as it allows one to be more able to come to a logical conclusion based on the organized information. Few studies have been conducted that investigate the influence of the need for cognition on risky behaviors. Doherty et al. (2004) found that the need for cognition was associated with heavy drinking behaviors in college students. Specifically, students who scored low on the Need for Cognition, indicating less interest and involvement in

33 thinking activities, were more likely to report heavy drinking behaviors. Therefore, the present study seeks to further research in this area by examining whether the need for cognition, as assessed at Time 1, can be used to predict both behavior at Time 2 and changes in behavior from Time 1 to Time 2. Summary of cognitive influences. Researchers have found that higher negative outcome expectancies are associated with reduced instances of speeding (Brown & Cotton, 2003). Higher positive outcome expectancies are associated with alcohol and other drug use (Barkin et al., 2002; Brown et al., 2004; Carey, 1995). Outcome expectancies have also been found predictive of both concurrent and future alcohol use (Barkin et al., 2002; Christiansen et al., 1989; Gerrard et al., 1996; Goldberg et al., 2002). These studies included a number of different outcome expectancy measures. The results, however, were similar to results found using the Cognitive Appraisal of Risky Events questionnaire (e.g., Doherty et al., 2004). Research has also suggested that having a present time perspective is positively related to alcohol and other drug use (Doherty et al., 2004; Wills et al., 2004), and that having a future time perspective is inversely related to alcohol and other drug use (Keough et al., 1999; Wills et al., 2004; Zimbardo et al., 1997). Low scores on need for cognition were found to be predictive of heavy alcohol use (Doherty et al., 2004). The majority of these studies focused on whether time perspective and outcome expectancy were related to alcohol and other drug use. The present study will expand upon this literature by investigating whether these variables are predictive of a number of different risky behaviors, including alcohol and drug use. The present study also seeks to

34 expand upon previous research by examining the relative influence of these cognitive variables in the presence of other cognitive, social, and personality variables. Personality Influences on Risky Behaviors Personality factors can also influence risky behaviors, in that individuals with certain personality traits enjoy risky activities more than individuals with other personality traits. The Time 1 personality trait that will be examined in the present study is sensation seeking. Sensation seeking. Sensation seeking refers to the tendency to seek new and novel experiences. In this search, one may potentially put oneself in harm's way by taking risks. Researchers have investigated whether sensation seeking, a relatively stable personality trait, is related to various risky behaviors. Risky behaviors may act as the novel experiences that a sensation seeker is looking for (Arnett, 1996). Table 3 is a summary of research regarding sensation seeking and risky behaviors. Sensation seeking has been studied in relation to risky driving behaviors. Bradley and Wildman (2002) administered the Arnett Inventory of Sensation Seeking and a measure of involvement in risky behaviors, including speeding, to a group of Australian undergraduate students. Results indicated that sensation seeking was positively correlated with speeding, and it predicted involvement in that behavior. Arnett, Offer, and Fine (1997) gave the Arnett Inventory of Sensation Seeking to high school seniors who were also asked to keep a log of their driving behaviors. The researchers found that sensation seeking was related to risky driving habits, in that those scoring high on sensation


N

Sex

Age

Sensation Seeking Measure


Results

Arnett (1996)

133

Male and Female

17-18

Arnett Inventory of Sensation Seeking


Sensation seeking was positively associated with risky driving and sexual behaviors

Arnett, Offer, & Fine (1997)

139

Male and Female

17-18


Log of driving behaviors over a 10-day period

Higher levels of sensation seeking were associated with increased instances of risky driving behaviors

Bradley & Wildman (2002)

380

Male and Female

18-25


Risk and Reckless Behavior Questionnaire

Sensation seeking predicted alcohol use and risky sexual and driving behaviors

Brown, Flory, Lynam, Leukefeld, & Clayton (2004)

1354

Male and Female

20

Zuckerman Kuhlman Personality Questionnaire


Higher levels of sensation seeking were associated with an early onset of substance use

Caspi, Begg, Dickson, Harrington, Langley, Moffitt, & Silva (1997)

961

Male and Female

21

Multidimensional Personality Questionnaire

National Survey of Sexual Attitudes and Lifestyles, and a study-specific measure of driving behavior

Individuals scoring low on a measure of harm avoidance at age 18 were more likely to engage in risky behaviors at age 21

35

��

�

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

� ��

��

��

��

36

37 seeking were more likely than those scoring low on sensation seeking to drive after drinking alcohol, to speed, and to race other cars. This finding was replicated with other samples of both college and high school students using the Arnett Inventory of Sensation Seeking Scale (Arnett, 1996; Dahlen, Martin, Ragan, & Kuhlman, 2005), the Multidimensional Personality Questionnaire (Caspi et al., 1997), the Zuckerman Kuhlman Personality Questionnaire (Doherty et al., 2004), and the Sensation Seeking Scale (Zimbardo et al., 1997). Regardless of the inventory used, researchers have concluded the same thing: sensation seeking is related to risky driving behaviors in that those high in sensation seeking participate more frequently in risky driving behaviors than those individuals low in sensation seeking. Sensation seeking has also been studied in connection with risky sexual behaviors. Caspi et al. (1997) found that college students who were high sensation seekers were less likely to use contraceptives during sexual encounters than students who were low sensation seekers, a result also found by Arnett (1996). Researchers have found that sensation seeking was positively associated with risky sexual behaviors in general (Bradley & Wildman, 2002; Zuckerman & Kuhlman, 2000). Hoyle, Fejfar, and Miller (2000) reviewed the literature regarding sensation seeking and risky sexual behaviors, including unprotected sex and sexual encounters with strangers or people an individual does not know very well. They concluded that sensation seeking predicts these sexual behaviors, but cautioned against generalizing the results. The researchers found that risk assessment, a measure of how risky an individual views a behavior after it has occurred,

38 varied directly with sensation seeking, indicating that sensation seeking may operate in conjunction with cognitive variables. Researchers have also investigated the relationship between sensation seeking and involvement in sports. Bradley and Wildman (2002) found that sensation seeking was positively associated with involvement in risky sports. O’Sullivan, Zuckerman, and Kraft (1998) investigated whether college students who were involved with collegiate sports such as football and lacrosse had higher levels of sensation seeking than their undergraduate peers. The researchers found that there were no differences between the athletes and their peers on the measure of sensation seeking. A limitation of this study was that the researchers did not take into account whether the undergraduates spent their free time participating in other types of risky athletic activities such as skiing and hang gliding. In order to fully understand how sensation seeking relates to participation in risky sports, a measure of how often an individual engages in such risky sports is needed. Brown et al. (2004) used the Zuckerman Kuhlman Personality Questionnaire to investigate the relationship between sensation seeking and marijuana use. The students involved in the Lynam et al. (1999) study of the efficacy of the DARE program were the sample for this study. Sensation seeking was only assessed prior to the 6th grade. Marijuana use was assessed each year from the 6th to the 10th grade, as well as at age 20. The researchers found that marijuana use fell into three categories: use that began before the 6th grade, use that began by the 8th or 9th grade, and use that began between the 10th grade and age 20. They found that the early onset of marijuana use was related to high sensation seeking scores, whereas a late onset of marijuana use was related to low

39 sensation-seeking scores. Arnett (1996) also found that sensation seeking, as measured with the Arnett Inventory of Sensation Seeking, was related to the use of marijuana and other drugs. Again, these studies used different measures of sensation seeking, but still concluded that this personality characteristic is related to drug use. Zuckerman and Kuhlman (2000) administered the Zuckerman Kuhlman Personality Questionnaire to 124 undergraduates and concurrently measured frequency of drinking and smoking behaviors. Findings indicated that sensation seeking predicted smoking and drinking behaviors. Using the Sensation Seeking Scale, Jaffe and Archer (1987) found that sensation seeking predicted the use of different types of drugs including alcohol, cigarettes, and marijuana. Sensation seeking has also been studied in conjunction with the cognitive variables previously described. Doherty et al. (2004) examined the relative influence of sensation seeking, outcome expectancy, time perspective, and the need for cognition to predict involvement in various risky behaviors. The findings indicated that whereas sensation seeking explained a significant portion of the variance in frequency of aggressive/illegal behaviors, tobacco use, and risky driving behaviors, the cognitive variables explained the majority of the variance across all types of risky behaviors examined. Brown et al. (2004) found that the early onset of marijuana use was associated with high scores on sensation seeking, high positive expectancies, and low negative expectancies, whereas a late onset of marijuana use was associated with low scores on sensation seeking and positive expectancy. The researchers did not examine whether one of these factors contributed more so than the other. Katz et al. (2000) found that sensation

40 seeking was positively associated with a positive expectancy for drinking and negatively associated with a negative expectancy for drinking and risky sexual behaviors. These researchers stated that sensation seeking and outcome expectancy, though related, were independently related to risky behaviors. The present study seeks to continue this examination of the relative influence of sensation seeking, a personality variable, in the presence of these cognitive variables. Summary of sensation seeking. Previous research has investigated the influence of sensation seeking on risky behaviors. Sensation seeking has been shown to be positively associated with risky driving (Arnett, 1996; Arnett et al., 1997; Doherty et al., 2004; Zimbardo et al., 1997), risky sexual behaviors (Arnett, 1996; Zuckerman & Kuhlman, 2000), and alcohol and other drug use (Bradley & Wildman, 2002; Doherty et al., 2004; Jaffe & Archer, 1987; Zuckerman & Kuhlman, 2000). These studies tended to focus on one type of risky behavior. However, risky behaviors do not always occur in isolation (e.g., Ogletree et al., 2001), and the factors that influence one type of behavior may not be the same factors that influence another type of risky behavior. The Sensation Seeking Scale, used by a number of these researchers, uses various risky behaviors as examples of activities in the statements used to assess sensation seeking. Therefore, there is an overlap between behaviors used to assess the predictor variable and behaviors assessed as the outcome variable. The Zuckerman Kuhlman Personality Questionnaire, a newer version of the Sensation Seeking Scale, took this confound into consideration. The statements used to assess sensation seeking no longer contain these risky behaviors.

41 Social Influences on Risky Behaviors Social factors may influence both the decision to engage in a risky behavior and the decision to continue engaging in the behavior. The Time 1 social variables that will be examined in the present study are peer attachment, perceived peer involvement in risky behaviors, family environment, and parent attachment. Peer attachment. Peer friendships play an enormous role in the life of a young adult. In college, when many students are away from home for the first time, friends are whom they see and interact with on a daily basis. Friends participate in different activities together, some of which may be harmful to their health. Peer friendships can even influence initial involvement in risky behaviors (Dekovic, 1999). Table 4 contains a summary of research regarding attachment to peers and risky behaviors. Arnett, Offer, and Fine (1997) asked 139 senior high school students to keep a log of their driving habits, such as how fast they were driving, whether they were wearing a seatbelt, and who was in the car with them. The researchers found that students drove slower when their parents were in the car, but drove just as fast with a friend in the car as they did when alone. Unfortunately, driving habits such as those found in this study have led to a number of deaths. In just one month in the city of Cincinnati, nine teenagers died from car accidents (Morse, 2004). In many of these accidents, teenagers were riding in a car that was driven by a friend who was speeding and lost control of the car. Tragedies such as these indicate the need to fully understand why individuals engage in risky behaviors.


N

Sex

Age

Peer Measure


Results

Arnett, Offer, & Fire (1997)

139

Male and Female

17-18

Log of driving behaviors, including passenger information

Log of driving behaviors over a 10-day period

Students engaged in risky driving behaviors when alone and with friends in the car

Bradley & Wildman (2002)

380

Male and Female

18-25

Emerging Adult Peer Pressure Inventory

Risk and Reckless Behavior Questionnaire

Peer pressure was associated with concurrent drug use and risky driving and sexual behaviors

Santor, Messervey, & Kusumakar (2000)

145

Male and Female

16-18



High scores on a measure of peer pressure were associated associated with increased alcohol consumption, skipped more classes, and used drugs more often

Weitzman, Nelson, & Wechsler (2003)

1894

Male and Female

17-19

Study-Specific Measure of reasons for drinking alcohol


Students who binge drink in college were more likely to report "fitting in with others" as a reason for drinking alcohol

42

43 Individuals can form close bonds with friends over time. Sometimes an individual feels that this bond is so strong that he or she cannot resist trying something new when a friend asks. For example, Bradley and Wildman (2002) found that Australian college students who engaged in risky behaviors felt pressure from their friends to engage in the activity. Similarly, Weitzman et al. (2003) found that students who began drinking after entering college were more likely than nondrinkers to report reasons such as “because everyone else does” and “fitting in with others” for drinking alcohol (p. 29). The students in this study felt pressure from their friends to engage in a number of risky activities, and this feeling of pressure was a significant predictor of risky behaviors that can be potentially harmful to one’s health. Blanton, Gibbons, Gerrard, Conger, and Smith (1997) surveyed students in the 8th grade and again in the 10th grade with regard to peer attachment, peer alcohol use, parental alcohol use, and own alcohol use. They found that students who made friendships with students who drank alcohol were more likely to drink themselves than were those who did not make friends with students who drank alcohol. The findings of Santor, Messervey, and Kusumakar (2000) indicated that peer pressure and peer conformity, defined by the extent to which an individual participates in behaviors condoned by his or her peer group, also influence academic habits, drug use, and illegal behaviors. They found that high school students who felt a lot of peer pressure skipped classes more often, received poorer grades in classes, used drugs more often, and stole more than students who felt less peer pressure.

44 These studies have shown that peer pressure can influence involvement in risky behaviors. Peer pressure may serve as an indicator of attachment to a peer group, in that the more attached individuals are to their peers, the more likely individuals are to engage in the same behaviors. The present study will examine peer attachment specifically and will investigate whether the results from studies of peer pressure are similar to the results from a study using a measure of peer attachment. Peer involvement. Individuals can be attached to deviant peers as well as to peers who do not engage in risky behaviors. The level of involvement of one’s peers is another factor that may influence the decision to engage in risky behaviors. Table 5 contains a summary of the research regarding peer involvement in risky behaviors. Biglan, Duncan, Ary, and Smolkowski (1995) examined smoking behavior in students aged 14 through 17 as a function of peer relationships and peer smoking behaviors. The results indicated that relationships with deviant peers, as defined by any occurrence of antisocial behavior or substance use, accounted for a significant amount of the variance in an individual’s smoking behavior six months later. Further, the researchers found that the best predictor of smoking behavior was peer smoking behavior over a six-month interval. In a study of sexual behaviors in students aged 14 through 16, Metzler, Noell, Biglan, Ary, and Smolkowski (1994) found that individuals who had friendships with peers who engage in risky behaviors, such as fighting and stealing, were more likely to engage in risky sexual behaviors. In these two studies, the researchers also used the Family Environment Scale to assess the extent to which familial relationships influence involvement in risky behaviors. They both concluded that there is an interaction between parental and peer


N

Sex

Age

Peer Measure

Adalbjarnardottir (2002)

1198

Male and Female

15



Students more likely to drink alcohol if reported peers also drank alcohol

Barkin, Smith, & DuRant (2002)

2646

Male and Female

12-13

Study-Specific Measure of perceived peer involvement in behaviors


Anticipated substance use was positively associated with perceived peer substance use

Biglan, Duncan, Ary, & Smolkowski (1995)

608

Male and Female

14-17

Study-Specific Measure of peer deviant behavior


Peer relationships accounted for a significant amount of the variance in students' smoking 6 months later


311

Male and Female

18-19

Inventory of Parent and Peer Attachment & Frequency of Peer Involvement

Cognitive Appraisal of Perceived peer involvement Risky Events Frequency in risky behaviors was of Involvement associated with risky aggressive and illegal behaviors

Gerrard, Gibbons, Benthin, & Hessling (1996)

477

Male and Female

Metzler, Noell, Biglan, Ary, & Smolkowski (1994)

609

Male and Female

M = 14 Study-Specific Measure of estimated prevalence of the behaviors

14-17

Study-Specific Measure of peer deviant behavior


Results

Time 1 estimates of peer involvement in risky behaviors predicted changes in behaviors over one year

Scale of Sexual RiskTaking

Risky sexual behaviors were associated with friendships with deviant peers

45



Musher-Eizenman, Holub, & Arnett (2003)

N

Sex

Age

432

Male and Female

12-15, 18-22

Peer Measure

Study-Specific Measure of estimated peer substance use

Risky Behavior Measure Study-Specific Measure

Results

Perceived peer alcohol use was associated with individuals own alcohol use

46

47 influences on risky behaviors, which will be discussed in more detail in the following section. Adalbjarnardottir (2002) found that high school students were more likely to consume alcohol if they reported their peers also drank alcohol. Another vein of research has investigated the influence that the perception of peer involvement in risky behaviors has on an individual’s participation in risky behaviors. Doherty et al. (2004) asked students to estimate how often their best friend participated in various risky behaviors in comparison to themselves. Of the types of risky behaviors investigated, the researchers found that a global measure of students’ perceived peer involvement was related to an aggressive and illegal behaviors factor, which included such behaviors as shoving someone, disturbing the peace, and damaging property, and was not related to the other measures of risky behaviors, such as heavy drinking. Barkin et al. (2002) asked over 2000 7th grade students to assess both their own and their friends’ use of alcohol, tobacco, and other drugs. They found that participants’ current and anticipated future use of these substances was positively correlated with perceived peer use. The students in this sample did not know for a fact how often their peer group used alcohol and other drugs, but their perception of peer use was significantly related to their own usage. Similarly, Musher-Eizenman et al. (2003) found that among 7th graders and college students, the amount of cigarettes, alcohol, and marijuana used by an individual could be predicted by the individual’s estimation of peer usage of these substances. Although this study looked at the predictive utility of perceived peer use at the same time that behaviors were assessed, Gerrard et al. (1996) examined whether estimated peer usage measured at one time could predict changes in behavior at a later time. The results

48 indicated that, after controlling for the incidence of risky behaviors at Time 1, high estimates of peer involvement measured at Time 1 predicted increases in the participants’ alcohol and tobacco use and risky driving behavior from Time 1 to Time 2. The results from these studies indicate that there is a relationship between perceived peer involvement in risky behaviors and an individual’s own involvement in these behaviors. However, what is not known is the direction of this relationship. Extent of individuals’ participation in risky behaviors may be a function of their perception of peer participation, or it may be that individuals who engage in risky behaviors inflate their estimates of peer participation. Family environment and parental attachment. In addition to friendships with peers, an individual’s relationship with his or her family can affect involvement in risky behaviors. Perceived conflicts or lack of involvement could lead an individual to feel that his or her family is not accessible, which may in turn lead the individual to participate in risky behaviors. Table 6 contains a summary of research regarding family relationships and risky behaviors. According to current research, an individual’s relationship with his or her parents can influence involvement in risky behaviors. Huebner and Howell (2003) videotaped high school students and their parents discussing a controversial issue. The researchers found that high levels of parental monitoring, such as parents knowing where their children are, whom they are with, and what they are doing, were associated with lower levels of risky sexual behaviors in high school students. Biglan et al. (1995) also


N

Sex

Age

Ary, Duncan, Biglan, Metzler, Noell, & Smolkowski (1999)

608

Male and Female

14-17

Study-Specific Measure of family conflict, involvement, and parental monitoring


Higher levels of perceived family conflict and lower levels of family involvement are associated with risk behaviors

Ary, Duncan, Duncan, & Hops (1999)

196

Male and Female

11-15

Conflict Behavior Questionnaire & Family Environment Scale


Family relationships influence influence involvement in antisocial and risky sexual behaviors

Barrera, Biglan, Ary, & Li (2001)

Biglan, Duncan, Ary, & Smolkowski (1995)

Family Measure


12396 Male and � = 12.3 Study-Specific Measure of Study-Specific Measure Female family conflict, involvement, and parental monitoring

608

Male and Female

14-17

Brown, Flory, Lynam, 1354 Leukefeld, & Clayton (2004)

Male and Female

20

Results

Higher levels of perceived family conflict were associated with risky behaviors

Family Environment Scale


Low levels of parental monitoring were associated with smoking behaviors

Study-Specific Measures of religious activity


Higher levels of church involvement were associated with later onset of marijuana use

49


N

Sex

Age


311

Male and Female

18-19

Family Environment Scale

Cognitive Appraisal of Religion was not Risky Events Frequency significantly associated with of Involvement any of the risky behaviors assessed

DuRant, Cadenhead, Pendergrast, Slavens, & Linder (1994)

225

Male and Female

11-19


Denver Youth Study Self-Reported Delinquency Questionnaire

Religion was not significantly associated with the use of violence

Holder, DuRant, Harris, Henderson, Obeidallah, & Goodman (2000)

141

Male and Female

11-25


Study-Specific Measure of sexual activity

Higher levels of religious activity were associated with decreased sexual behaviors

Huebner & Howell (2003)

2701

Male and 7th - 12th Parental Monitoring Scale Female graders

Study-Specific Measure of sexual activity

Higher levels of parental monitoring were associated with lower levels of risky sexual behaviors

McBride, Paikoff, & Holmbeck (2003)

198

Male and Female

Structured sexual behavior interview

Perceived family conflict predicted the onset of sexual behaviors

9-16

Family Measure

Issues Checklist, Brief Version, & videotaped interactions between students and parents


Results

Observed family conflict predicted an earlier onset of sexual behaviors

50


N

Sex

Age

Family Measure


Results

Metzler, Biglan, Ary, & Li (2001)

221

Male and 5th - 7th Family Environment Scale Female graders

Study-Specific Measures Higher levels of family of substance use and conflict were positively antisocial behavior associated with substance use and antisocial behaviors

Pearce, Jones, Schwab-Stone, & Ruchkin (2003)

1703

Male and Female


11-19

Study-Specific Measure of religious activity

Higher levels of religious activity were associated with a decrease in conduct problems over a 1-year period

51

52 investigated parental monitoring in a high school population and found that parental monitoring interacted with family conflict and lack of involvement as measured by the Family Environment Scale. Specifically, the researchers found that low parental monitoring was associated with a high score on the family conflict subscale and a low score on the involvement subscale. They further found that high school students who experienced low parental monitoring were more likely to associate with peers who smoke, which in turn is a predictor of subsequent smoking habits. Family conflict and cohesion are two other family factors that have received research attention with regard to how they relate to risky behaviors. Family conflict refers to the extent to which the members of a family get along with each other, whereas family cohesion refers to how connected the family members feel as a family unit (Moos & Moos, 1981). Using the conflict and cohesion subscales of the Family Environment Scale, researchers have found that risky sexual activities, drug use, and academic behaviors are related to these two factors. Ary et al. (1999) found that among high school students, family cohesion was significantly related to risky sexual behavior and drug use. Specifically, involvement in these risky behaviors was more likely when students perceived low levels of family involvement. In a similar study, Ary, Duncan, Duncan, and Hops (1999) found that while peer participation was a factor in involvement in risky activities, family relationships also significantly influenced risky behavior participation. Biglan and colleagues (Barrera, Biglan, Ary, & Li, 2001; Metzler, Biglan, Ary, & Li, 1998) found that 7th grade students who perceive high levels of family conflict, as

53 assessed with the Family Environment Scale, also report getting poor grades in school and using alcohol and other drugs. McBride, Paikoff, and Holmbeck (2003) examined whether family conflict was predictive of sexual behaviors. Their results indicated that family conflict predicted the early onset of sexual behaviors and that, after controlling for age, gender, and stage of pubertal development, reported family conflict at Time 1 (age 11) predicted the onset of sexual behavior at Time 2 (age 13). The researchers also observed parent-child interactions while they were discussing a conflictual issue and found that high levels of observed family conflict also predicted the onset of sexual behaviors. Further, researchers have investigated whether religiosity, which is commonly practiced in the family environment, influences risky behaviors. Brown et al. (2004) assessed marijuana use in students in high school and then when they were 20 years old. Measures of family relationships and church involvement were also obtained. The results indicated that the late onset of marijuana use was significantly related to high church involvement. Church involvement and private religious practices were also related to decreased sexual activities and delinquent behaviors in both high school and college students (Holder et al., 2000; Pearce, Jones, Schwab-Stone, & Ruchkin, 2003). Pearce et al. (2003) also found that individuals who indicated a high level of private religious practices, such as praying, showed a decrease in conduct problems over a one-year time period. However, not all research has shown a significant relationship between risky behaviors and religion. Doherty et al. (2004) found that religion, as measured with the Family Environment Scale, was not related to any of the risky behaviors examined.

54 Durant, Cadenhead, Pendergrast, Slavens, and Linder (1994) examined violence in a group of young adults living in projects in a southern city. They concluded that religion did not influence the use of violence in this sample. These contrary findings indicate that it is necessary to examine the effects of religion on a number of risky behaviors before concluding that religion does indeed influence involvement in these activities. Summary of social influences. Previous research has found that peers can influence involvement in risky behaviors such as speeding and alcohol and other drug use (Arnett et al., 1997; Bradley & Wildman, 2002). Perceived peer involvement in risky behaviors has also been shown to be related to individual’s own substance use (Barkin et al., 2002; Musher-Eizenman et al., 2003) and risky academic behaviors (Doherty et al., 2004). Gerrard et al. (1996) found that Time 1 estimates of peer involvement predicted changes in behavior over a one-year interval. Research has also indicated that high levels of family conflict were associated with increased involvement in risky behaviors (Ary et al., 1999; Barrera et al., 2001; Metzler et al., 2001) and that higher levels of religious involvement were associated with decreased conduct problems and risky sexual behaviors (Holder et al., 2000; Pearce et al., 2003). Some studies have examined these social influences in the presence of the previously identified cognitive and personality variables. Gerrard et al. (1996) hypothesized that because students have a high perception of risks associated with risky behaviors, they may in turn increase estimates of peer involvement. Both of these variables, however, influenced subsequent behavior. Arnett (1998) found that sensation seeking and religiosity were both related to risky behaviors, in that individuals who were

55 high in sensation seeking and low in religiosity were more likely to report risky driving habits and alcohol and marijuana use. In contrast, Bradley and Wildman (2002) found that peer pressure was predictive of involvement in reckless behaviors, which included behaviors termed risky behaviors in the context of the present study. These researchers also found that sensation seeking was not predictive of these same behaviors. The results of these studies indicate the need for further research combining these social, personality, and cognitive variables, in order to determine their relative influences on involvement in risky behaviors. Examining these three types of variables in one study is also important given the inconsistencies evidenced in prior research. For example, some researchers have found that outcome expectancies predict alcohol use (e.g., Barkin et al., 2002), however the predictive utility of this variable was not shown for drug use, sexual behaviors, and other behaviors of interest. In contrast, sensation seeking and peer involvement have been shown to be predictive of risky driving, sexual, and substance use behaviors (e.g., Arnett, 1996; Brown et al., 2004; Metzler et al., 1994). One single variable has not been shown to predict every type of risky behavior. Social variables have been shown predictive of risky behaviors that have a social element to them, such as aggressive/illegal behaviors and substance use (e.g., Barkin et al., 2002; Doherty et al., 2004). Cognitive variables have been shown predictive of a number of risky behaviors, but most commonly of alcohol and other substance use (e.g., Doherty et al., 2004). Sensation seeking has been found to be predictive of risky driving behaviors (e.g., Arnett et al., 1997). These studies indicate the importance of studying a number of different predictors of risky behaviors, as

56 one variable might not predict every type of risky behavior. In many of these studies, only one variable was examined, and the relative influence of these predictors was not evaluated. Rationale and Hypotheses The existing literature suggests that personality, cognitive, and social variables can be used to predict engagement in various risky behaviors with the main focus on driving, sexual behaviors, and alcohol and other drug use. The present study sought to expand upon this research by investigating a number of different risky behaviors in addition to risky driving and sexual behaviors and alcohol and other drug use. Certain variables may influence one type of risky behavior more than another, and the current study investigated this. Similarly, specific variables may be more influential than other variables, and these relative associations were also addressed. The present study was a two-year follow-up to Doherty et al.’s (2004) study. The following hypotheses were addressed. Hypothesis 1 Doherty et al. (2004) investigated the relationship between the aforementioned cognitive, personality, and social variables on various types of concurrent risky behaviors assessed with the Cognitive Appraisal of Risky Events involvement scale. They found that positive outcome expectancies and present time perspective predicted alcohol and drug use; perceived peer involvement in risky behaviors was associated with an individual’s own aggressive and illegal behaviors; a lack of parental attachment was associated with increased risky academic and work behaviors; and sensation seeking was

57 positively associated with aggressive behaviors, cigarette use, participation in high-risk sports, and risky driving behaviors. Table 7 contains a summary of these findings. One aspect of the present study will be to examine the predictive utility of the Time 1 cognitive, personality, and social variables for the Time 2 risky behaviors. Previous research has suggested that outcome expectancies and perceptions of peer use predict future smoking and drinking behaviors (Biglan et al., 1995; Christiansen et al., 1989; Gerrard et al., 1996), and that factors influencing concurrent behaviors also influenced future behaviors (e.g., Biglan et al., 1995; Christiansen et al., 1989). Brown et al. (2004) found that sensation seeking was predictive of later marijuana use in young adults. It was hypothesized that these variables (outcome expectancy, perceived peer use, sensation seeking) found to predict behaviors at Time 1 will also predict Time 2 behaviors. There has been no research looking at the prediction of future behaviors from time perspective and need for cognition, therefore no prediction can be made as to their influence on Time 2 behaviors. Hypothesis 2 A second aspect of the present study was to examine whether Time 1 variables predicted changes in behavior from Time 1 to Time 2. Although there is some literature on prediction of concurrent and future risky behaviors, considerably less literature exists that has examined whether cognitive, personality, and social variables are predictive of changes in risky behaviors. Farrow and Arnold (2003) asked female college students to retrospectively rate changes in sexual behaviors since arriving at college and found that

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

�

��

58

59 19% reported a decrease and 37% reported an increase in sexual behaviors. Hix-Small et al. (2004) found that high school students’ alcohol and marijuana use increased over a three-year interval. Katz et al. (2000) found that college students’ alcohol use doubled and drug use tripled over a six-month time period. Christiansen et al. (1989) found that among 7th and 8th grade students, every type of drinking behavior assessed, such as number of drinks on one occasion and instances of drinking in the past year, increased over a one-year interval. Similarly, Gerrard et al. (1996) found that 8th and 10th grade students significantly increased their drinking, smoking, and risky driving behaviors over a two-year interval. Adalbjarnardottir (2002) found that individuals drank more alcohol in one sitting at age 17 than at age 15. The results of these studies indicate that involvement in risky behaviors is not a static process: young adults continually change their level of involvement. Previous research has found that positive expectancies predicted future drug and alcohol use (e.g., Barkin et al., 2002; Goldberg et al., 2002). Christiansen et al. (1989) found that higher scores on a measure of positive alcohol expectancies predicted the onset of alcohol use over a one-year interval. Gerrard et al. (1996) found that health cognitions measured at Time 1 predicted changes in behavior over a two-year interval. The cognitions involved assessing the risks associated with the behavior and having a high concept of the risk of a behavior was associated with increased involvement in the behavior. For the present study, it was hypothesized that positive and negative outcome expectancies would predict changes in frequency of risky behaviors over the two-year interval.

60 Hypothesis 3 The present study also sought to examine the relative influence of the personality, cognitive, and social variables in the prediction of risky behavior. Numerous researchers have found that even in the presence of variables such as sensation seeking, peer attachment, and peer involvement in risky behaviors, the cognitive variable of outcome expectancies remained predictive of concurrent and future risky behaviors (Katz et al., 2000; Brown et al., 2004). Doherty et al. (2004) found that the cognitive variables predicted a greater number of the risky behaviors studied than did the personality and social variables. For the present study, it was hypothesized that the cognitive variables will be more influential than the personality and social variables. Descriptive statistics will be employed to assess this hypothesis Method Participants As part of the initial study, participants were told there would be a follow-up study in two years. Students were invited to provide their email addresses if they would be interested in being contacted about the future study. Providing an email address allowed for contact regarding the present study, but did not require the student to participate. Of the 442 individuals who participated in the initial study, 386 consented to be contacted regarding the follow-up study. Of these 386 individuals, 338 had valid email addresses at Time 2. Participants at Time 2 were 96 (51 males, 45 females) undergraduate students who completed the study either online (n = 87) or in person (n = 9).

61 Demographic information about the participants at Time 1 and the participants at Time 2 is included in Table 8. Measures Demographic questionnaire. A demographic questionnaire (Appendix A) was designed for the initial study (Doherty et al., 2004) and was used in the present study to determine if there were any changes in family or personal information. Cognitive Appraisal of Risky Events (CARE). The Cognitive Appraisal of Risky Events questionnaire (Appendix B) was used to assess outcome expectancies for and frequency of participation in a number of risky behaviors (Fromme, Katz, & Rivet, 1997). The questionnaire has three identical parts, all of which were used in the present study. Each of the 30 items describes a type of risky behavior. Participants responded to each statement according to the expected risk from the behavior, expected benefit from the behavior, and frequency of involvement in the behavior. The expected risk scale measures, on a scale from 1 (“extremely likely”) to 7 (“not at all likely”), the likelihood that a negative consequence will occur following a risky behavior. These consequences include being injured, feeling bad about oneself, losing money, and being embarrassed. The expected benefit scale uses the same 7-point scale to measure how likely a positive consequence is to occur following a risky behavior. These positive consequences include winning money, experiencing pleasure, and feeling good about oneself. For the frequency of involvement scale, participants were asked to estimate how often they participated in each behavior in the past six months, as well as the age at which they first began this behavior.

62

Table 8 Demographic Information Time 1 Demographic

N

Time 2 %

N

%

Age 18 19 20 21 22 23 24 26 29 31 33

110 186 63 2 1 1 0 1 0 1 1

30 51 17 1 .05. Sex of the participant did not account for a significant proportion of the variance (3.9%). Regression on changes in CARE frequency of risky sexual behaviors. A model containing three of the predictors accounted for a unique proportion of the variance in the change in frequency of risky sexual behaviors from Time 1 to Time 2, F(3, 86) = 4.798, p < .01 (Table 22). Sex of the participant did not account for a significant amount of the variance (0.0%). Need for cognition accounted for 9.9% of the variance and future time perspective accounted for an additional 4.4% of the variance. The final model, containing the sex of the participant, need for cognition, and future time perspective, accounted for 14.3% of the variance in the change in risky sexual behaviors from Time 1 to Time 2 as assessed by the CARE. The data indicate that, contrary to what was expected, the tendency to focus on events in the future was associated with an increase in reported sexual behaviors from Time 1 to Time 2. Enjoying and engaging in thinking was associated with a decrease in reported sexual behaviors from Time 1 to Time 2.

99

Table 22 Summary of Stepwise Regression Analysis for Variables Predicting Changes in Frequency of CARE Risky Sexual Behaviors (N = 90) Variable Step 1 Sex

B

SE B

ß

R2

∆R2

-1.079

7.308

-0.016

0

0

Step 2 Sex Need for Cognition

-1.075 7.558

6.975 2.44

-0.016 0.315**

0.1

0.099

3.418 8.497 -11.789

7.17 2.435 5.619

0.05 0.354** -0.223*

0.143

0.044

Step 3 Sex Need for Cognition Future Time Perspective *p < .05; **p < .01

100 Regression on changes in NCHRBS frequency of risky sexual behaviors. A model containing four predictors accounted for a unique proportion of the variance in the change in frequency of risky sexual behaviors from Time 1 to Time 2, F(4, 66) = 7.668, p < .01 (Table 23). Sex of the participant did not account for a significant portion of the variance (0.6%). Present time perspective accounted for an additional 13.6% and the conflict subscale of the FES an additional 11.5% of the variance. Adding the FES control subscale to the model accounted for an additional 6% of the variance. The final model, containing sex of the participant, present time perspective, FES conflict, and FES control, accounted for 31.7% of the variance in the change in risky sexual behaviors from Time 1 to Time 2. High levels of perceived family control and conflict at Time 1 were associated with increased risky sexual behavior from Time 1 to Time 2. Contrary to what was expected, having a present time perspective at Time 1 was associated with decreased risky sexual behavior from Time 1 to Time 2. Regression on changes in CARE frequency of heavy drinking. A significant model was not found to explain a unique proportion of the variance in the change in frequency of heavy drinking from Time 1 to Time 2, F (1, 89) = 3.437, p > .05. Sex of the participant did not account for a significant proportion of the variance (3.7%). Regression on changes in NCHRBS frequency of alcohol use. A model containing three of the predictors accounted for a unique proportion of the variance in the change in frequency of alcohol use from Time 1 to Time 2, F (3, 84) = 7.986, p < .01 (Table 24).

101

Table 23 Summary of Stepwise Regression Analysis for Variables Predicting Changes in Frequency of NCHRBS Sexual Behaviors (N = 71) Variable Step 1 Sex

B

SE B

ß

∆R2

R2

53.167

83.975

0.076

0.006

0.006

Step 2 Sex Present Time Perspective

179.239 217.781

97.423 66.215

0.256* 0.411**

0.136

0.142

184.66 252.757 -57.66

81.951 62.997 17.866

0.264* 0.477** -0.346**

0.115

0.258

161.25 240.391 -44.594

79.781 61.087 18.1

0.230* 0.454** -0.267*

0.06

0.317

-38.015

15.83

-0.258*

Step 3 Sex Present Time Perspective Family Environment Scale: Conflict Subscale Step 4 Sex Present Time Perspective Family Environment Scale: Conflict Subscale Family Environment Scale: Control Subscale *p < .05; **p < .01

102

Table 24 Summary of Stepwise Regression Analysis for Variables Predicting Changes in Frequency of NCHRBS Alcohol Use (N = 88) Variable Step 1 Sex Step 2 Sex Present Time Perspective Step 3 Sex Present Time Perspective Negative Outcome Expectancy *p < .05; **p < .01

B

SE B

ß

∆R2

R2

9.326

8.584

0.116

0.014

0.014

-5.762 -26.010

8.647 6.211

-0.072 -0.452**

0.169

0.182

-6.027 -21.785 5.376

8.486 6.427 2.598

-0.075 -0.378** 0.213*

0.040

0.222

103 Sex of the participant did not account for a significant portion of the variance (1.4%). Present time perspective and a negative outcome expectancy associated with alcohol use accounted for an additional 16.9% and 4.0% of the variance respectively. The final model, containing sex of the participant, present time perspective, and negative outcome expectancy, accounted for 22.2% of the variance in the change in alcohol use from Time 1 to Time 2 as assessed by the NCHRBS. Having a present time perspective at Time 1 was associated with increases in alcohol use from Time 1 to Time 2. Having a high negative expectancy after drinking alcohol at Time 1 was associated with decreases in alcohol use from Time 1 to Time 2. Regression on changes in CARE frequency of tobacco use. A model containing five of the predictors accounted for a unique proportion of the variance in the change in frequency of tobacco use from Time 1 to Time 2, F (5, 85) = 7.723, p > .01 (Table 25). Sex of the participant did not account for a significant amount of the variance (1.0%). Adding peer attachment to the model accounted for an additional 12.9% of the variance. Positive outcome expectancy for tobacco use and having a future time perspective accounted for an additional 6.9% and 4.1% of the variance respectively. Adding present time perspective to the model accounted for an additional 6.3% of the variance. The final model, containing sex of the participant, positive outcome expectancy, peer attachment, future time perspective, and present time perspective accounted for 31.2% of the variance in the change in tobacco use from Time 1 to Time 2 as assessed by the CARE.

104

Table 25 Summary of Stepwise Regression Analysis for Variables Predicting Changes in Frequency of CARE Tobacco Use (N = 91) Variable Step 1 Sex

B

SE B

ß

∆R2

R2

-249.952

265.083

-0.099

0.010

0.010

Step 2 Sex Peer Attachment

37.902 -43.940

260.950 12.103

0.015 -0.377**

0.129

0.139

49.411 -34.670 285.090

251.720 12.148 103.427

0.089 -0.297** 0.275**

0.069

0.208

222.804 -34.761 263.241

259.024 11.895 101.768

0.089 -0.298** 0.254*

0.041

0.249

-414.427

190.346

-0.216*

-13.832 -28.452 302.685

263.409 11.673 98.993

-0.006 -0.244* 0.292**

0.063

0.312

-627.274 -527.857

198.506 189.216

-0.328** -0.302**

Step 3 Sex Peer Attachment Positive Outcome Expectancy Step 4 Sex Peer Attachment Positive Outcome Expectancy Future Time Perspective Step 4 Sex Peer Attachment Positive Outcome Expectancy Future Time Perspective Present Time Perspective *p < .05; **p < .01

105 Perceiving high levels of peer attachment, having a high future time perspective, and having a high present time perspective at Time 1 were all associated with an increase in tobacco use from Time 1 to Time 2. Having a higher positive expectancy for tobacco use at Time 1 was associated with decreased tobacco use from Time 1 to Time 2. Regression on changes in NCHRBS frequency of tobacco use. A significant model was not found to explain a unique proportion of the variance in the change in frequency of tobacco use from Time 1 to Time 2, F (1, 60) = 1.330, p > .05. Sex of the participant did not account for a significant proportion of the variance (2.2%). Regression on changes in CARE frequency of risky driving behaviors. A significant model was not found to explain a unique proportion of the variance in the change in frequency of risky driving behaviors from Time 1 to Time 2. Sex of the participant did not account for a significant proportion of the variance (1.0%). Sensation seeking did account for an additional 4.3% of the variance, however the model containing these two variables was not significant, F (2, 87) = 2.439, p > .05. Regression on changes in NCHRBS frequency of driving behaviors. A model containing two predictors accounted for a unique proportion of the variance in the change in risky driving behaviors from Time 1 to Time 2, F (2, 86) = 7.279, p < .01 (Table 26). Sex of the participant did not account for a significant portion of the variance (0.6%). Adding peer attachment to the model accounted for an additional 13.9% of the variance. The final model, with sex of the participant and peer attachment, accounted for 14.5% of the variance in the change in risky driving behavior from Time 1 to Time 2 as assessed

106

Table 26 Summary of Stepwise Regression Analysis for Variables Predicting Changes in Frequency of NCHRBS Driving Behaviors (N = 89) Variable Step 1 Sex Step 2 Sex Peer Attachment *p < .05; **p < .01

B

SE B

ß

∆R2

R2

0.784

1.090

0.077

0.006

0.006

1.829 -0.182

1.055 0.049

0.179 -0.386**

0.139

0.145

107 by the NCHRBS. Perceiving high levels of peer attachment at Time 1 was associated with increased risky driving behaviors from Time 1 to Time 2. Regression on changes in CARE frequency of aggressive/illegal behaviors. A significant model was not found to explain a unique proportion of the variance in the change in frequency of aggressive/illegal behaviors from Time 1 to Time 2, F (1, 88) = 0.447, p > .05. Sex of the participant did not account for a significant proportion of the variance (0.5%). Regression on changes in NCHRBS frequency of aggressive/illegal behaviors. A significant model was not found to explain a unique proportion of the variance in the change in frequency of aggressive/illegal behaviors from Time 1 to Time 2, F (1, 16) = 0.062, p > .05. Sex of the participant did not account for a significant proportion of the variance (0.4%). Regression on changes in CARE frequency of high risk sports. A significant model was not found to explain a unique proportion of the variance in the change in frequency of involvement in high risk sports from Time 1 to Time 2, F (1, 85) = 0.515, p > .05. Sex of the participant did not account for a significant proportion of the variance (0.6%). Regression on changes in CARE frequency of risky academic/work behaviors. A model containing four predictors accounted for a unique proportion of the variance in the change in frequency of risky academic/work behaviors from Time 1 to Time 2, F (4, 84) = 5.962, p > .01 (Table 27). Sex of the participant did not account for a significant portion of the variance (0.3%). Peer attachment accounted for an additional

108 Table 27 Summary of Stepwise Regression Analysis for Variables Predicting Changes in Frequency of CARE Risky Academic/Work Behaviors (N = 89) Variable Step 1 Sex

B

SE B

ß

∆R2

R2

53.167

83.975

-0.052

0.003

0.003

Step 2 Sex Peer Attachment

179.239 217.781

97.423 66.215

0.051 0.367**

0.124

0.127

Step 3 Sex Peer Attachment Future Time Perspective

184.66 252.757 -57.66

81.951 62.997 17.866

0.118 -0.358** -0.237*

0.051

0.178

161.25 240.391 -44.594 -38.015

79.781 61.087 18.1 15.83

0.044 -0.326** -0.331** -0.245*

0.043

0.221

Step 4 Sex Peer Attachment Future Time Perspective Present Time Perspective *p < .05; **p < .01

109 12.4% of the variance, and adding future time perspective to the model accounted for an additional 5.1% of the variance. Present time perspective accounted for an additional 4.3% of the variance, and the final model, containing sex of the participant, peer attachment, present time perspective, and future time perspective, accounted for 22.1% of the variance in the change in risky academic/work behaviors from Time 1 to Time 2 as assessed with the CARE. Having a present time perspective was associated with increased involvement in risky academic/work behaviors from Time 1 to Time 2, as was having a future time perspective. Perceiving a greater peer attachment at Time 1 was associated with decreased risky academic/work behaviors from Time 1 to Time 2. Summary of Hypothesis 2 Analyses The second hypothesis addressed in the current study dealt with the predictors of behavior change from Time 1 to Time 2. It was hypothesized that outcome expectancies would be predictive of behavior changes. Examining the results, which are summarized in Table 28, indicates that outcome expectancies were only predictive of changes in two of the risky behaviors examined: tobacco use and alcohol use. Having a present time perspective was predictive of four of the changes, and having a future time perspective was predictive of three of the changes. Therefore, Hypothesis 2 received minimal support. Other variables, including future and present time perspective, were also predictive of changes in risky behaviors from Time 1 to Time 2.

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

�

��

�

110

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

�

��

�

111

112 Hypothesis 3: Relative Influence of the Variables on Risky Behaviors The third hypothesis sought to examine the relative influences of the cognitive, personality, and social variables on the various risky behaviors. The hypothesis was that the cognitive variables would be more predictive of the various risky behaviors than would the social variables. This hypothesis was tested by investigating the frequency with which each category of variables predicted the risky behaviors. Table 29 contains a summary of the results, broken down according to the variable categories. The cognitive variables combined were predictive of 11 risky behaviors. The personality variable, sensation seeking, predicted 2 of the behaviors, whereas the social variables predicted 9 of the risky behaviors. Next, the order in which the predictors entered the model was examined in order to determine if the cognitive variables entered the model prior to the personality and social variables for most of the behaviors. The cognitive variables were the first to enter the model, after sex was forced in on the first step, for 8 of the 11 behaviors they predicted. Sensation seeking entered the model first on 1 of the 2 behaviors it predicted, and the social variables entered the model first on 6 of the 9 behaviors it predicted. In contrast to the Time 1 findings (Doherty et al., 2004), in which the cognitive variables entered the model first on the majority of the behaviors studied, the relative influence of the cognitive variables seems comparable to the social variables in the present study. Some risky behaviors, such as aggressive/illegal behaviors, are better predicted by social

113 Table 29 Summary of Time 2 Findings by Type of Variable

Risky Behavior Illicit Drug Use (CARE) Risky Sex (CARE)

Cognitive **

Time 2a Personality

Social

Change Over Time Cognitive Personality

*

**

Heavy Drinking (CARE)

**

Tobacco Use (CARE)

**

Risky Driving (CARE)

*

Aggressive/Illegal (CARE)

**

High Risk Sports (CARE)

*

Risky Academic/Work * ** * (CARE) Drug Use ** * (NCHRBS) Sexual Behaviors ** (NCHRBS) Alcohol Use ** ** (NCHRBS) Risky Driving (NCHRBS) Tobacco Use ** (NCHRBS) Aggressive/Illegal ** (NCHRBS) *regression analysis was significant; **regression analysis was significant and the variable was the first to enter the model a

Social

= regression is on the log-transformed frequency of the variable

**

**

*

**

114 variables, whereas others, such as drug use, are better predicted by cognitive variables. In some cases, cognitive variables predicted Time 2 behaviors but social variables predicted changes in the behaviors. This finding will be discussed further in the next section. Discussion The present study sought to examine the relative influences of cognitive, personality, and social variables on both future risky behaviors and changes in risky behaviors across time. Previous research has examined these predictor variables either individually or in combination in studies of concurrent risky behaviors. However, few studies have examined their relative influence in a longitudinal design. The results are discussed in the context of each of the three hypotheses below. Predicting Time 2 Behaviors from Time 1 Variables The first hypothesis addressed in the current study was that the Time 1 predictors of concurrent risky behaviors would also predict Time 2 risky behaviors. This hypothesis was minimally supported in the current study. Cognitive variables. The cognitive variables accounted for significant portions of the variance in five of the risky behaviors studied at Time 2 (illicit drug use [CARE], heavy drinking [CARE], tobacco use [CARE and NCHRBS], alcohol use [NCHRBS]), as well as accounting for a smaller but still significant portion of the variance in risky academic/work behaviors. With regard to the specific cognitive variables studied, having a high positive outcome expectancy for the specific risky behavior at Time 1 was positively associated with tobacco use, illicit drug use, and alcohol use at Time 2. Previous research has also found that having a high positive outcome expectancy was

115 associated with concurrent alcohol use (Barkin et al., 2002; Doherty et al., 2004) and drug use (Doherty et al., 2004), as well as with future alcohol use (Christiansen et al., 1989; Gerrard et al., 1996; Goldberg et al., 2002). In the current study, negative outcome expectancies were not found to predict any of the Time 2 risky behaviors, supporting Goldberg et al. (2002)’s finding that positive expectancies were better predictors of future behaviors than were negative expectancies. The results suggest that college students’ perceptions of the benefits or gains associated with alcohol and other drug use may be more important predictors of these behaviors than their perceptions of the risks or losses associated with the behaviors. Less prominent cognitive predictors of future behavior included present time perspective and future time perspective. Having a present time perspective at Time 1 was negatively related to illicit drug use (CARE) but positively related to heavy drinking (CARE) at Time 2. Having a future time perspective at Time 1 was negatively related to involvement in risky academic/work behaviors at Time 2. These findings corroborate previous research that has found present time perspective to be positively associated with concurrent alcohol use (Doherty et al., 2004; Wills et al., 2004). Focusing on the immediate effects of a situation or behavior influences future alcohol and drug use. This finding seems related to the previously stated finding that positive outcome expectancies predicted future alcohol and other drug use. Both of these variables incorporate aspects of immediate responses to involvement in a risky behavior, and may combine to more fully explain involvement in risky behaviors.

116 At Time 1, Doherty et al. (2004) found that the cognitive variables predicted the majority of the risky behaviors studied, including illicit drug use, aggressive/illegal behavior, risky sexual behavior, and risky academic/work behaviors. The researchers found that positive outcome expectancies and future time perspective explained a greater portion of the variance than present time perspective, negative outcome expectancy, and the need for cognition. A number of these predictive relationships were no longer evident at Time 2. Personality variable. Doherty et al. (2004) also found that the personality variable of sensation seeking predicted tobacco use, aggressive/illegal behaviors, high risk sports, and risky driving behaviors at Time 1. In the current study, sensation seeking accounted for a significant portion of the variance in Time 2 drug use as measured with the NCHRBS and for a lesser portion of the variance in Time 2 drug use as measured with the CARE instrument. In both cases, sensation seeking was positively associated with future drug use. Individuals scoring high on sensation seeking, indicating that they tend to seek out new and novel experiences, were more likely to endorse future drug use. Perhaps this tendency is what led the individual to try drugs in the first place—for the novelty. Interestingly, although sensation seeking was a significant predictor of drug use at Time 2, it was not found to be a predictor in the Time 1 data. In addition, the results of the present study indicated that sensation seeking, as measured at Time 1, was not predictive of tobacco use, aggressive/illegal behaviors, high risk sports, or risky driving behaviors at Time 2, unlike Time 1.

117 Social variables. The social variables (peer attachment, parent attachment, perceived peer involvement, family environment) accounted for a significant portion of the variance in risky driving behaviors (CARE), aggressive/illegal behaviors (CARE and NCHRBS), risky academic/work behaviors (CARE), and drug use (NCHRBS). Parent attachment at Time 1 was negatively associated with risky academic/work behaviors and aggressive/illegal behaviors at Time 2. Because parent attachment was also found to be predictive of risky academic/work behaviors at Time 1 (Doherty et al., 2004), this provides some support for the hypothesis that variables predicting behavior at Time 1 would also predict behavior at Time 2. However, this was one of only a handful of instances in which the variable predictive of Time 1 behavior was also predictive of Time 2 behavior. Several subscales from the Family Environment Scale were found to be predictive of Time 2 risky behaviors. Specifically, the moral-religious subscale was negatively associated with subsequent risky driving behaviors, the cohesion subscale was negatively associated with drug use, and the conflict subscale was positively associated with aggressive/illegal behaviors. Researchers have also found that higher levels of religious involvement were associated with a later onset of marijuana use (Brown et al., 2004) and decreased sexual behaviors (Holder et al., 2000). In the present study, the moral-religious subscale was not found to be predictive of drug use or sexual behaviors at either Time 1 or Time 2. Peer attachment was found to be predictive of aggressive/illegal behaviors (NCHRBS), in that low levels of peer attachment at Time 1 were associated with high

118 levels of aggressive/illegal behaviors at Time 2. Previous research has found peer attachment to be associated with increased risky driving habits (Arnett et al., 1997; Bradley & Wildman, 2002), and alcohol and other drug use (Bradley & Wildman, 2002; Santor et al., 2000; Weitzman et al., 2003), however the present study did not support these findings. Perceived peer involvement in risky behaviors was predictive of aggressive/illegal behaviors at Time 1. However, perceived peer involvement in risky behaviors was not found to be predictive of any of the Time 2 risky behaviors. As few studies have examined the influence of perceived peer involvement, future research should continue to use this variable in order to determine the relationships between peers’ and one’s own involvement in risky behaviors. Predicting Changes in Behavior From Time 1 Variables The second hypothesis addressed in the current study was that outcome expectancies would predict changes in risky behaviors from Time 1 to Time 2. This hypothesis was also only minimally supported. Table 30 contains a summary of the changes in behaviors from Time 1 to Time 2 for men and women. Cognitive variables. The cognitive variables accounted for a significant portion of the variance in change over time for only two of the risky behaviors studied (risky sexual behaviors [CARE and NCHRBS] and alcohol use [NCHRBS]), as well as accounting for a smaller but significant portion of the variance in change in tobacco use (CARE) and risky academic/work behaviors (CARE). Having a present time perspective predicted the majority of these behaviors, but did so in different ways. Present time perspective was

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

119

120 negatively associated with changes in tobacco use, risky academic/work behaviors, and alcohol use, but was positively associated with changes in risky sexual behaviors. Future time perspective was found to be negatively associated with changes in risky sexual behaviors, tobacco use, and risky academic/work behaviors from Time 1 to Time 2. Previous research has not examined the influence of present and future time perspective on changes in risky behavior over time, therefore more research is needed to determine the nature of these relationships. Outcome expectancies combined to predict changes in only two risky behaviors: alcohol (NCHRBS) and tobacco (CARE) use. High positive expectancies at Time 1 were associated with decreases in tobacco use from Time 1 to Time 2. High negative expectancies were also associated with decreases in alcohol use across this time interval. The data from the current study minimally supports the hypothesis that positive and negative outcome expectancies would predict changes in behavior from Time 1 to Time 2. Outcome expectancies predicted 2 of the 14 risky behaviors studied (CARE and NCHRBS combined). Other cognitive and social variables were found to be more predictive of changes than were outcome expectancies: time perspective predicted 5 of the 14 and peer attachment predicted 3 of the 14. Personality variable. Sensation seeking was not found to be predictive of changes in any of the risky behaviors examined. Previous researchers have found that sensation seeking is predictive of concurrent risky behaviors (e.g., Doherty et al., 2004). Brown et al. (2004) did find that sensation seeking, as assessed in the 6th grade, was associated with marijuana use in that high sensation seekers initiated use earlier than did low sensation

121 seekers. The influence of sensation seeking on changes within a behavior has not been widely studied, however. Future research should examine the predictive utility of sensation seeking on behavior change, as it may have important implications for prevention programs aiming to change risky behaviors in students. Social variables. The social variables were found to be predictive of changes in tobacco use (CARE), risky academic/work behaviors (CARE), sexual behaviors (NCHRBS), and risky driving behaviors (NCHRBS). For each of these behaviors except sexual behaviors, the social variable was the first to enter the regression model. This finding is in contrast to the previous findings by Doherty et al. (2004) that the cognitive variables accounted for more of the variance in concurrent risky behaviors than the social variables. That study, however, did not examine behavior change over time, which may account for the different findings. Peer attachment was predictive of changes in several risky behaviors. High levels of perceived peer attachment at Time 1 were associated with increased involvement in tobacco use and risky driving behaviors. This finding supports Arnett et al.’s (1997) result that students who drove with a friend in the car engaged in more risky driving behaviors than when the same student drove with a parent in the car. Attachment to one’s peers may influence the decision to engage in a risky behavior, as it may be viewed as more exciting to share the experience with another. High levels of peer attachment at Time 1 were associated with decreases in risky academic/work behaviors from Time 1 to Time 2. High scores on the conflict and control subscales of the Family Environment Scale were found related to increases in sexual behaviors from Time 1 to Time 2.

122 Students may have rebelled against family conflict by engaging in increased risky behaviors. Previous researchers have not examined how these social variables relate to changes in risky behaviors over time. Predicting Relative Influence of the Variables The third hypothesis examined in the current study stated that the cognitive variables would be more predictive of risky behaviors and their change over time than the personality and social variables. This hypothesis was partially supported by the results of the current study. The cognitive variables were found to be predictive of 11 of the risky behaviors, but, the social variables were predictive of 9 of the risky behaviors. Sensation seeking, the personality variable, was predictive of only 2 of the risky behaviors studied. At Time 1, the cognitive variables were predictive of all 8 of the risky behaviors studied by Doherty et al. (2004), but the social variables only accounted for the variance in 2 of the risky behaviors. At Time 2, the cognitive and social variables alternated between which entered the model first for a behavior. Cognitive variables entered the model first for 8 of the 11 behaviors they predicted, and social variables entered the model first for 6 of the 9 behaviors they predicted. For most behaviors, either the cognitive or the social variables predicted the behavior or its change over time. The exceptions were sexual behaviors (NCHRBS) and risky academic/work behaviors (CARE). These behaviors were predicted at Time 2 both by the cognitive and social variables, and change over time was also predicted by both sets of variables. In both instances, the social variables entered the model first, followed by the cognitive variables. This finding indicates that for risky

123 academic/work behaviors, social variables including parent and peer attachment were more predictive than time perspective, the cognitive variable. Cognitive and social variables were also both predictive of changes over time in risky sexual behaviors. However, in this case the cognitive variable of present time perspective entered the model before the social variables of family conflict and control. As Doherty et al. (2004) did not examine the predictors of risky behaviors as assessed with the NCHRBS at Time 1, it is not known whether this same relationship was present with regard to concurrent behaviors. Implications The results of the present study indicate the importance of studying the longitudinal course of involvement in risky behaviors. Predictors of concurrent behaviors were not necessarily the same for future behaviors and changes in behavior over time. In fact, the predictors of changes in some of the behaviors were not predictive of that behavior either at Time 1 or at Time 2. For example, present and future time perspective were not significant predictors of tobacco use at either Time 1 or Time 2, yet both were significant predictors of changes in usage from Time 1 to Time 2. The implications of this study are important for designing and improving prevention programs. These programs should be designed keeping cognitive and social variables in mind. A number of researchers who have investigated prevention programs that focus on educating students about the risks associated with involvement in risky behaviors such as alcohol and other drug use have not found significant behavior change (Ashworth et al., 1992; Lynam et al., 1999; Wechsler et al., 2002a). These programs were

124 designed with a focus on the negative consequences of behaviors, which can be thought of as similar to the negative expectancies associated with engagement in the behavior. As the results of this study indicate, positive outcome expectancies are more predictive of subsequent behavior than negative outcome expectancies. Perhaps by exploring the positive outcomes commonly associated with involvement in risky behaviors, prevention programs may better be able to refocus students’ attention on activities other than the specific risky behavior that can lead to the same positive outcomes. One community prevention program has integrated sensation seeking into its design with positive results. Palmgreen et al. (2001) found that marijuana use dropped by 26% following an antimarijuana campaign targeting high sensation seekers. A limitation of this campaign was that cigarette, alcohol, and other drug use did not also decline. Other researchers have found that sensation seeking was predictive of involvement in various risky behaviors (e.g., Zuckerman & Kuhlman, 2000), however, the results of the current study do not support these findings. Sensation seeking, a personality variable, was not as predictive of risky behaviors and changes in involvement over time as the cognitive and social variables. Based on the conflict between these findings and those from prevention programs targeting sensation seeking, more research is needed as to the effectiveness of these programs and the efficacy of sensation seeking as a target variable predictive of behavior change. Limitations A limitation to this study deals with the relatively small sample size. Of the 338 participants from Time 1 who could be contacted at Time 2, 96 actually participated, for

125 a 28% return rate. For some of the behaviors examined, including aggressive/illegal behaviors and illicit drug use as measured by the NCHRBS, few participants endorsed engaging in the behavior. As so many individuals did not engage in the behavior, there was less variation in the responses and the results should be interpreted with caution. With regard to the other behaviors examined, there was more variability in the data as more participants endorsed engaging in the behavior. Differences emerged in the present study depending on the measure used to assess the criterion variables. Depending on the instrument used (CARE or NCHRBS), the predictors for a risky behavior may have been different. For example, positive outcome expectancy, present time perspective, and sensation seeking were found to be predictive of drug use as measured with the CARE instrument. In contrast, sensation seeking and the cohesion subscale of the family environment scale were predictive of drug use when measured with the NCHRBS. Items included in both of these scales included similar questions regarding marijuana and other drug use. In some instances, predictors emerged for a behavior on one scale, but no significant model was found for the same behavior on the other scale. Previous researchers who have used a number of different instruments to examine the predictors of risky behaviors in college students have found results that are similar to each other. The results of the present study run counter to this research, as different predictors were found depending on the instrument used to assess risky behavior involvement. One factor that may account for this is a methodological difference between the two instruments. The CARE instrument assesses frequency of involvement with freeresponse estimates of instances in the past 6 months. In contrast, the NCHRBS

126 instrument uses multiple choice to force responses to estimates of past month and lifetime involvement. Future research Future research should be conducted to further examine the predictors of both future risky behaviors and changes in risky behaviors over time. One finding of the current study was that in some cases, the predictors of risky behaviors at Time 1 and Time 2 were not predictive of changes in behavior over this interval. Conversely, some predictors of behavior change were not predictive of that behavior at Time 1, Time 2, or both. More longitudinal research is needed to provide the field with information about the developmental course of involvement in risky behaviors and the factors that predict them. Prevention programs designed to target predictors of concurrent and future use of drugs and alcohol may not be as effective as programs designed based on the predictors of changes in involvement in these behaviors over time. Although some researchers have examined the influence of outcome expectancies on behavior change (e.g., Gerrard et al., 1996), few have studied the influence of time perspective and sensation seeking on changes in behavior. As time perspective and outcome expectancies were both predictive of a number of the risky behaviors studied at Time 1 and Time 2, their influence on behavior change should also be examined further. The social variables included in this study, such as parent and peer attachment, were similarly predictive of the risky behaviors examined in this study, and therefore future research should include both cognitive and social variables in studies of risky behaviors.

127 As mentioned previously, the predictors of behaviors differed in some cases as a function of the measurement scale used (CARE or NCHRBS). Future research should examine this finding, in order to determine why two scales measuring similar categories of behavior would find different predictors for these behaviors. This line of research has implications for test selection in future studies of risky behaviors in college students, as results may be a function of the type of instrument selected.

128 References Adalbjarnardottir, S. (2002). Adolescent psychosocial maturity and alcohol use: Quantitative and qualitative analysis of longitudinal data. Adolescence, 37, 19-53. Armsden, G. C., & Greenberg, M. T. (1987). The Inventory of Parent and Peer Attachment: Individual differences and their relationship to psychological wellbeing in adolescence. Journal of Youth and Adolescence, 16, 427-454. Arnett, J. J. (1996). Sensation seeking, aggressiveness, and adolescent reckless behavior. Personality and Individual Differences, 20, 693-702. Arnett, J. J. (1998). Risk behavior and family role transitions during the twenties. Journal of Youth and Adolescence, 27, 301-320. Arnett, J. J., Offer, D., & Fine, M. A. (1997). Reckless driving in adolescence: “State” and “trait” factors. Accident Analysis and Prevention, 29, 57-63. Ary, D. V., Duncan, T. E., Biglan, A., Metzler, C. W., Noell, J. W., & Smolkowski, K. (1999). Development of adolescent problem behavior. Journal of Abnormal Child Psychology, 27, 141-150. Ary, D. V., Duncan, T. E., Duncan, S. C., & Hops, H. (1999). Adolescent problem behavior: The influence of parents and peers. Behaviour Research and Therapy, 37, 217-230. Ashworth, C. S., DuRant, R. H., Newman, C., & Gaillard, G. (1992). An evaluation of a school-based AIDS/HIV education program for high school students. Journal of Adolescent Health, 13, 582-588.

129 Ball, S. A. (1995). The validity of an alternative five-factor measure of personality in cocaine abusers. Psychological Assessment, 7, 148-154. Barkin, S. L., Smith, K. S., & DuRant, R. H. (2002). Social skills and attitudes associated with substance use behaviors among young adolescents. Journal of Adolescent Health, 30, 448-454. Barrerra, M., Biglan, A., Ary, D., & Li, F. (2001). Replication of a problem behavior model with American Indian, Hispanic, and Caucasian youth. Journal of Early Adolescence, 21, 133-157. Benthin, A., Slovic, P., & Severson, H. (1993). A psychometric study of adolescent risk perception. Journal of Adolescence, 16, 153-168. Biglan, A., Duncan, T. E., Ary, D. V., & Smolkowski, K. (1995). Peer and parental influences on adolescent tobacco use. Journal of Behavioral Medicine, 18, 315330. Blanton, H., Gibbons, F. X., Gerrard, M., Conger, K. J., & Smith, G. E. (1997). Role of family and peers in the development of prototypes associated with substance use. Journal of Family Psychology, 11, 271-288. Bradley, G., & Wildman, K. (2002). Psychosocial predictors of emerging adults’ risk and reckless behaviors. Journal of Youth and Adolescence, 31, 253-265. Brener, N. D., Collins, J. L., Kann, L., Warren, C. W., & Williams, B. I. (1995). Reliability of the Youth Risk Behavior Survey questionnaire. American Journal of Epidemiology, 141, 575-580.

130 Brener, N. D., McMahon, P. M., Warren, C. W., & Douglas, K. A. (1999). Forced sexual intercourse and associated health-risk behaviors among female college students in the United States. Journal of Consulting and Clinical Psychology, 67, 252-259. Brown, S. L., & Cotton, A. (2003). Risk-mitigating beliefs, risk estimates, and selfreported speeding in a sample of Australian drivers. Journal of Safety Research, 34, 183-188. Brown, T. L., Flory, K., Lynam, D. R., Leukefeld, C., & Clayton, R. R. (2004). Comparing the developmental trajectories of marijuana use of African American and Caucasian adolescents: Patterns, antecedents, and consequences. Experimental and Clinical Psychopharmacology, 12, 47-56. Cacioppo, J. T., & Petty, R. E. (1984). The need for cognition. Journal of Personality and Social Psychology, 41, 116-131. Carey, K. B. (1995). Alcohol-related expectancies predict quantity and frequency of heavy drinking among college students. Psychology of Addictive Behaviors, 9, 236-241. Caspi, A., Begg, D., Dickson, N., Harrington, H., Langley, J., Moffitt, T. E., & Silva, P. A. (1997). Personality differences predict health-risk behaviors in young adulthood: Evidence from a longitudinal study. Journal of Personality and Social Psychology, 73, 1052-1063.

131 Centers for Disease Control and Prevention. (2003, August). National College Health Risk Behavior Survey. Retrieved March 18, 2004, from the Centers for Disease Control and Prevention Web site: http://www.cdc.gov/nccdphp/dash/yrbs/previous_results/college1997.htm. Christiansen, B. A., Smith, G. T., Roehling, P. V., & Goldman, M. S. (1989). Using alcohol expectancies to predict adolescent drinking behavior after one year. Journal of Consulting and Clinical Psychology, 57, 93-99. Dahlen, E. R., Martin, R. C., Ragan, K., & Kuhlman, M. M. (2005). Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving. Accident Analysis and Prevention, 37, 341-348. Dekovic, M. (1999). Risk and protective factors in the development of problem behavior during adolescence. Journal of Youth and Adolescence, 28, 667-685. Doherty, E. A., Appel, M. A., & Murphy, C. E. (2004, March). Risk-taking behavior in college students as a function of personality, social, and cognitive variables. Poster presented at the meeting of the American Psychosomatic Society, Orlando, FL. Douglas, K. A., Collins, J. L., Warren, C., Kann, L., Gold, R., Clayton, S., Ross, J. G., & Kolbe, L. J. (1997). Results from the 1995 National College Health Risk Behavior Survey. Journal of American College Health, 46, 55-66. DuRant, R. H., Cadenhead, C., Pendergrast, R. A., Slavens, G., & Linder, C. W. (1994). Factors associated with the use of violence among urban black adolescents. American Journal of Public Health, 84, 612-617.

132 DuRant, R. H., Knight, J., & Goodman, E. (1997). Factors associated with aggressive and delinquent behaviors among patients attending an adolescent medicine clinic. Journal of Adolescent Health, 21, 303-308. DuRant, R. H., Rickert, V. I., Ashworth, C. S., Newman, C., et al. (1993). Use of multiple drugs among adolescents who use anabolic steroids. New England Journal of Medicine, 328, 922-926. Engels, R. C. M. E., Dekovic, M., & Meeus, W. (2002). Parenting practices, social skills, and peer relationships in adolescence. Social Behavior and Personality, 30, 3-18. Everett, S. A., Giovino, G. A., Warren, C. W., Crossett, L., & Kann, L. (1998). Other substance use among high school students who use tobacco. Journal of Adolescent Health, 23, 289-296. Everett, S. A., Husten, C. G., Kann, L., Warren, C. W., Sharp, D., & Crossett, L. (1999). Smoking initiation and smoking patterns among U.S. college students. Journal of American College Health, 48, 55-60. Everett, S. A., Lowry, R., Cohen, L. R., & Dellinger, A. M. (1999). Unsafe motor vehicle practices among substance-using college students. Accident Analysis and Prevention, 31, 667-673. Everett, S. A., Warren, C. W., Santelli, J. S., Kann, L., Collins, J. L., & Kolbe, L. J. (2000). Use of birth control pills, condoms, and withdrawal among U.S. high school students. Journal of Adolescent Health, 27, 112-118. Farrow, R., & Arnold, P. (2003). Changes in female student sexual behavior during the transition to university. Journal of Youth Studies, 6, 339-355.

133 Fromme, K., Katz, E. C., & Rivet, K. (1997). Outcome expectancies and risk-taking behavior. Cognitive Therapy and Research, 21, 421-442. Gerrard, M., Gibbons, F. X., Benthin, A. C., & Hessling, R. M. (1996). A longitudinal study of the reciprocal nature of risk behaviors and cognitions in adolescents: What you do shapes what you think, and vice versa. Health Psychology, 15, 344354. Goldberg, J. H., Halpern-Fisher, B. L., & Millstein, S. G. (2002). Beyond invulnerability: The importance of benefits in adolescents’ decision to drink alcohol. Health Psychology, 21, 477-484. Highhouse, S., & Yuce, P. (1996). Perspectives, perceptions, and risk-taking behavior. Organizational Behavior and Human Decision Processes, 65, 159-167. Hix-Small, H., Duncan, T. E., Duncan, S. C., & Okut, H. (2004). A multivariate associative finite growth mixture modeling approach examining adolescent alcohol and marijuana use. Journal of Psychopathology and Behavioral Assessment, 26, 255-270. Holder, D. W., DuRant, R. H., Harris, T. L., Daniel, J. H., Obeidallah, D., & Goodman, E. (2000). The association between adolescent spirituality and voluntary sexual activity. Journal of Adolescent Health, 26, 295-302. Hoyle, R. H., Fejfar, M. C., & Miller, J. D. (2000). Personality and sexual risk taking: A quantitative review. Journal of Personality, 68, 1203-1231.

134 Huebner, A. J., & Howell, L. W. (2003). Examining the relationship between adolescent sexual risk-taking and perceptions of monitoring, communication, and parenting styles. Journal of Adolescent Health, 33, 71-78. Jaffe, L. T., & Archer, R. P. (1987). The prediction of drug use among college students from MMPI, MCMI, and sensation seeking scales. Journal of Personality Assessment, 51, 243-253. Johnston, L. D., O’Malley, P. M., & Bachman, J. G. (2000). Monitoring the future: National survey results on drug use, 1975-1999. Volume 2: College students and adults ages 19-40 (National Institutes of Health Publication 00-4802). Bethesda, MD: National Institute on Drug Abuse. Jones, S. E., Oeltmann, J., Wilson, T. W., Brener, N. D., & Hill, C. V. (2001). Binge drinking among undergraduate college students in the United States: Implications for other substance use. Journal of American College Health, 50, 33-38. Katz, E. C., Fromme, K., & D’Amico, E. J. (2000). Effects of outcome expectancies and personality on young adults’ illicit drug use, heavy drinking, and risky sexual behavior. Cognitive Therapy and Research, 24, 1-22. Keough, K. A., Zimbardo, P. G., & Boyd, J. N. (1999). Who’s smoking, drinking, and using drugs? Time perspective as a predictor of substance use. Basic and Applied Social Psychology, 21, 149-164. Lynam, D. R., Milich, R., Zimmerman, R., Novak, S. P., Logan, T. K., Martin, C., Leukefeld, C., & Clayton, R. (1999). Project DARE: No effects at 10-year followup. Journal of Consulting and Clinical Psychology, 67, 590-593.

135 McBride, C. K., Paikoff, R. L., & Holmbeck, G. N. (2003). Individual and familial influences on the onset of sexual intercourse among urban African American adolescents. Journal of Consulting and Clinical Psychology, 71, 159-167. McDaniel, S. R., & Zuckerman, M. (2003). The relationship of impulsive sensation seeking and gender to interest and participation in gambling activities. Personality and Individual Differences, 35, 1385-1400. Metzler, C. W., Biglan, A., Ary, D. V., & Li, F. (1998). The stability and validity of early adolescents’ reports of parenting constructs. Journal of Family Psychology, 12, 600-619. Metzler, C. W., Noell, J., Biglan, A., Ary, D., & Smolkowski, K. (1994). The social context for risky sexual behavior among adolescents. Journal of Behavioral Medicine, 17, 419-438. Mooney, D. K., Fromme, K., Kivlahan, D. R., & Marlatt, G. A. (1987). Correlates of alcohol consumption: Sex, age, and expectancies relate differentially to quantity and frequency. Addictive Behaviors, 12, 235-240. Moos, R. H., & Moos, B. M. (1981). Family environment scale manual. Palo Alto: Consulting Psychologists Press. Morse, J. (2004, April 2). Little Miami mourns for teen: High-speed rural crash kills passenger. Cincinnati Enquirer. Retrieved April 2, 2004, from http://www.enquirer.com/midday/04/04022004_News_mday_teencrashes02.html

136 Musher-Eizenman, D. R., Holub, S. C., & Arnett, M. (2003). Attitude and peer influences on adolescent substance use: The moderating effect of age, sex, and substance. Journal of Drug Education, 33, 1-23. Ogletree, R. J., Dinger, M. K., & Vesely, S. (2001). Associations between number of lifetime partners and other health behaviors. American Journal of Health Behaviors, 25, 537-544. O’Sullivan, D. M., Zuckerman, M., & Kraft, M. (1998). Personality characteristics of male and female participants in team sports. Personality and Individual Differences, 25, 119-128. Palmgreen, P., Donohew, L., Lorch, E. P., Hoyle, R. H., & Stephenson, M. T. (2001). Television campaigns and adolescent marijuana use: Tests of sensation seeking targeting. American Journal of Public Health, 91, 292-296. Pearce, M. J., Jones, S. M., Schwab-Stone, M. E., & Ruchkin, V. (2003). The protective effects of religiousness and parent involvement on the development of conduct problems among youth exposed to violence. Child Development, 74, 1682-1696. Peterson, L. S., Oakley, D., Potter, L. S., & Darroch, J. E. (1998). Women's efforts to prevent pregnancy: Consistency of oral contraceptive use. Family Planning Perspectives, 30, 19-23. Rolison, M. R., & Scherman, A. (2002). Factors influencing adolescents’ decisions to engage in risk-taking behavior. Adolescence, 37, 585-596. Rolison, M. R., & Scherman, A. (2003). College student risk-taking from three perspectives. Adolescence, 38, 689-704.

137 Rome, E. S., Rybicki, L. A., & DuRant, R. H. (1998). Pregnancy and other risk behaviors among adolescent girls in Ohio. Journal of Adolescent Health, 22, 50-55. Rothspan, S., & Read, S. J. (1996). Present versus future time perspective and HIV risk among heterosexual college students. Health Psychology, 15, 131-134. Sadowski, C. J., & Gulgoz, S. (1992). Internal consistency and test-retest reliability of the need for cognition scale. Perceptual and Motor Skills, 74, 610. Sadowski, C. J., & Gulgoz, S. (1996). Elaborative processing mediates the relationship between need for cognition and academic performance. The Journal of Psychology, 130, 303-307. Santor, D. A., Messervey, D., & Kusumakar, V. (2000). Measuring peer pressure, popularity, and conformity in adolescent boys and girls: Predicting school performance, sexual attitudes, and substance abuse. Journal of Youth and Adolescence, 29, 163-182. Shrier, L. A., Emans, J., Woods, E. R., & DuRant, R. H. (1996). The association of sexual risk behaviors and problem drug behaviors in high school students. Journal of Adolescent Health, 20, 377-383. Staton, M., Leukefeld, C., Logan, T. K., Zimmerman, R., Lynam, D., Milich, R., Martin, C., McClanahan, K., & Clayton, R. (1999). Risky sex behavior and substance use among young adults. Health and Social Work, 24, 147-154. Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics. Boston: Allyn & Bacon.

138 Turrisi, R., Jaccard, J., Taki, R., Dunnam, H., & Grimes, J. (2001). Examination of the short-term efficacy of parent intervention to reduce college student drinking tendencies. Psychology of Addictive Behaviors, 15, 366-372. Vavrik, J. (1997). Personality and risk-taking: A brief report on adolescent male drivers. Journal of Adolescence, 20, 461-465. Wechsler, H., Lee, J. E., Kuo, M., & Lee, H. (2000). College binge drinking in the 1990s: A continuing problem: Results of the Harvard School of Public Health 1999 college alcohol study. Journal of American College Health, 48, 199-210. Wechsler, H., Lee, J. E., Kuo, M., Seibring, M., Nelson, T. F., & Lee, H. (2002a). Trends in college binge drinking during a period of increased prevention efforts: Findings from 4 Harvard School of Public Health college alcohol study surveys: 19932001. Journal of American College Health, 50, 203-217. Wechsler, H., Lee, J. E., Nelson, T. F., & Kuo, M. (2002b). Underage college students’ drinking behavior, access to alcohol, and the influence of deterrence policies: Findings from the Harvard School of Public Health college alcohol study. Journal of American College Health, 50, 223-235. Weitzman, E. R., Nelson, T. F., & Wechsler, H. (2003). Taking up binge drinking in college: The influences of person, social group, and environment. Journal of Adolescent Health, 32, 26-35. Wills, T. A., Sandy, J. M., & Yaeger, A. M. (2001). Time perspective and early-onset substance use: A model based on stress-coping theory. Psychology of Addictive Behaviors, 15, 118-125.

139 Zimbardo, P. G., & Boyd, J. N. (1999). Putting time in perspective: A valid, reliable individual-difference metric. Journal of Personality and Social Psychology, 77, 1271-1288. Zimbardo, P. G., Keough, K. A., & Boyd, J. N. (1997). Present time perspective as a predictor of risky driving. Personality and Individual Differences, 23, 1007-1023. Zuckermann, M., & Cloninger, C. R. (1996). Relationships between Cloninger’s, Zuckermann’s, and Eysenck’s dimensions of personality. Personality and Individual Differences, 21, 283-285. Zuckermann, M., & Kuhlman, D. M. (2000). Personality and risk-taking: common biosocial factors. Journal of Personality, 68, 999-1014.

140 Appendix A Demographic Questionnaire Number_________ Are you Male_________ or Female_________?

Age_________

What is your level of education? First year college _________ Second year college _________ Fifth year college or higher _________

Third year college _________ Fourth year college _________ Other _________

What is your approximate GPA? 3.50-4.00 _________ 3.00-3.49 _________ 2.00-2.49 _________ 1.50-1.99 _________ Below 1.00 _________

2.50-2.99 _________ 1.00-1.49 _________

Please check the item below that best describes your ethnic background: Caucasian _________ Black/African-American _________ Hispanic _________ American Indian or Alaska Native _________ Asian or Pacific Islander _________ Other _________ What is your parents’ current marital status? Married _________ Divorced _________ Separated _________ Cohabitating _________ Widowed _________ Other _________

141 Appendix B Cognitive Appraisal of Risky Events RISK OF ACTIVITIES DIRECTIONS: On a scale of 1 (not at all likely) to 7 (extremely likely) how likely is it that you would experience some NEGATIVE CONSEQUENCE (e.g., become sick, be injured, embarrassed, lose money, suffer legal consequences, fail a class, or feel bad about yourself) if you engaged in these activities? Not at all Likely 1. Trying/using drugs other than alcohol or

Moderately Likely

Extremely Likely

1

2

3

4

5

6

7

2. Missing class or work.

1

2

3

4

5

6

7

3. Grabbing, pushing, or shoving someone.

1

2

3

4

5

6

7

4. Leaving a social event with someone I

1

2

3

4

5

6

7

5. Driving after drinking alcohol.

1

2

3

4

5

6

7

6. Making a scene in public.

1

2

3

4

5

6

7

7. Drinking more than 5 alcoholic drinks.

1

2

3

4

5

6

7

8. Not studying for exam or quiz.

1

2

3

4

5

6

7

9. Drinking alcohol too quickly.

1

2

3

4

5

6

7

10. Disturbing the peace.

1

2

3

4

5

6

7

11. Damaging/destroying public property.

1

2

3

4

5

6

7

12. Sex without protection against

1

2

3

4

5

6

7

1

2

3

4

5

6

7

14. Hitting someone with a weapon or object.

1

2

3

4

5

6

7

15. Rock or mountain climbing.

1

2

3

4

5

6

7

16. Sex without protection against sexually

1

2

3

4

5

6

7

1

2

3

4

5

6

7

marijuana.

have just met.

pregnancy. 13. Leaving tasks or assignments for the last minute.

transmitted diseases. 17. Playing non-contact team sports.

142

Not at all Likely

Moderately Likely

Extremely Likely

18. Failing to do assignments.

1

2

3

4

5

6

7

19. Slapping someone.

1

2

3

4

5

6

7

20. Not studying or working hard enough.

1

2

3

4

5

6

7

21. Punching or hitting someone with fist.

1

2

3

4

5

6

7

22. Smoking marijuana.

1

2

3

4

5

6

7

23. Sex with a variety of partners.

1

2

3

4

5

6

7

24. Snow or water skiing.

1

2

3

4

5

6

7

25. Mixing drugs and alcohol.

1

2

3

4

5

6

7

26. Getting into a fight or argument.

1

2

3

4

5

6

7

27. Involvement in sexual activities without

1

2

3

4

5

6

7

28. Playing drinking games.

1

2

3

4

5

6

7

29. Sex with someone I have just met or

1

2

3

4

5

6

7

30. Playing individual sports.

1

2

3

4

5

6

7

31. Smoking tobacco.

1

2

3

4

5

6

7

32. Driving/riding in a car without a seatbelt.

1

2

3

4

5

6

7

33. Using chewing tobacco or snuff.

1

2

3

4

5

6

7

34. Driving over 10 MPH above the speed

1

2

3

4

5

6

7

my consent.

don't know well.

limit.

143

Cognitive Appraisal of Risky Events BENEFIT OF ACTIVITIES DIRECTIONS: On a scale or 1 (not at all likely) to 7 (extremely likely) how likely is it that you would experience some POSITIVE CONSEQUENCE (e.g., pleasure, win money, feel good about yourself, etc.) if you were to engage in these activities?

Not at all Likely 1. Trying/using drugs other than alcohol or

Moderately Likely

Extremely Likely

1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7

4. Leaving a social event with someone I

1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7

1

2

3

4

5

6

7

14. Hitting someone with a weapon or object.

1

2

3

4

5

6

7

15. Rock or mountain climbing.

1

2

3

4

5

6

7

16. Sex without protection against sexually

1

2

3

4

5

6

7

1

2

3

4

5

6

7

marijuana.

have just met.

pregnancy. 13. Leaving tasks or assignments for the last minute.

transmitted diseases. 17. Playing non-contact team sports.

144

Not at all Likely

Moderately Likely

Extremely Likely


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7

29. Sex with someone I have just met or

1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7


1

2

3

4

5

6

7

33. Using chewing tobacco or snuff.

1

2

3

4

5

6

7

34. Driving over 10 MPH above the speed

1

2

3

4

5

6

7

my consent.

don't know well.

limit.

145

Cognitive Appraisal of Risky Events FREQUENCY OF INVOLVEMENT DIRECTIONS: For each of the activities listed below, please indicate how many times you have participated in this activity in the past six (6) months. Then indicate your age when you first engaged in this behavior.

Number of

1. Trying/using drugs other than alcohol or marijuana. 2. Missing class or work. 3. Grabbing, pushing, or shoving someone. 4. Leaving a social event with someone I have just met. 5. Driving after drinking alcohol. 6. Making a scene in public. 7. Drinking more than 5 alcoholic drinks. 8. Not studying for exam or quiz. 9. Drinking alcohol too quickly. 10. Disturbing the peace. 11. Damaging/destroying public property. 12. Sex without protection against pregnancy. 13. Leaving tasks or assignments for the last minute. 14. Hitting someone with a weapon or object. 15. Rock or mountain climbing. 16. Sex without protection against sexually transmitted diseases. 17. Playing non-contact team sports. 18. Failing to do assignments. 19. Slapping someone.

Age when

times in past

first initiated

6 months

this behavior

146

23. Sex with a variety of partners. 24. Snow or water skiing. 25. Mixing drugs and alcohol. 26. Getting into a fight or argument. 27. Involvement in sexual activities without my consent. 28. Playing drinking games. 29. Sex with someone I have just met or don't know well. 30. Playing individual sports. 31. Smoking tobacco. 32. Driving/riding in a car without a seatbelt. 33. Using chewing tobacco or snuff. 34. Driving over 10 MPH above the speed limit.

Number of

Age when

times in past

first initiated

6 months

this behavior

147 Appendix C Frequency of Peer Involvement DIRECTIONS: Please indicate how often your best friend participated in each activity in the past six (6) months. 1 = A lot less often than me 2 = A little less often than me 3 = About as often as me 4 = A little more often than me 5 = A lot more often than me A Lot

A Little About A Little A Lot

Less

Less

the

More

More

Often

Often

Same

Often

Often

1

2

3

4

5


1

2

3

4

5


1

2

3

4

5

4. Leaving a social event with someone

1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5

13. Leaving tasks or assignments for the last

1

2

3

4

5

minute. 14. Hitting someone with a weapon or object. 15. Rock or mountain climbing.

1 1

2 2

3 3

4 4

5 5

1. Trying/using drugs other than alcohol or marijuana.

he/she has just met.

pregnancy.

148

A Lot

A Little

About

A Little

A Lot

Less

Less

the

More

More

Often

Often

Same

Often

Often

1

2

3

4

5

17. Playing non-contact team sports.

1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5

29. Sex with someone he/she has just met or

1

2

3

4

5


1

2

3

4

5


1

2

3

4

5


1

2

3

4

5

33. Using chewing tobacco or snuff. 34. Driving over 10 MPH above the speed limit.

1 1

2 2

3 3

4 4

5 5

16. Sex without protection against sexually transmitted diseases.

my consent.

doesn't know well.

149 Appendix D National College Health Risk Behavior Survey Please answer the following questions about your health behavior. 1. How often do you wear a seat belt when riding in a car driven by someone else? A. Never B. Rarely C. Sometimes D. Most of the time E. Always 2. How often do you wear a seat belt when driving a car? A. I do not drive a car B. Never wear a seat belt C. Rarely wear a seat belt D. Sometimes wear a seat belt E. Most of the time wear a seat belt F. Always wear a seat belt 3. During the past 12 months, how many times did you ride a motorcycle? A. 0 times B. 1 to 10 times C. 11 to 20 times D. 21 to 39 times E. 40 or more times 4. When you rode a motorcycle during the past 12 months, how often did you wear a helmet? A. I did not ride a motorcycle during the past 12 months B. Never wore a helmet C. Rarely wore a helmet D. Sometimes wore a helmet E. Most of the time wore a helmet F. Always wore a helmet 5. During the past 12 months, how many times did you ride a bicycle? A. 0 times B. 1 to 10 times C. 11 to 20 times D. 21 to 39 times E. 40 or more times

150 6. When you rode a bicycle during the past 12 months, how often did you wear a helmet? A. I did not ride a bicycle during the past 12 months B. Never wore a helmet C. Rarely wore a helmet D. Sometimes wore a helmet E. Always wore a helmet 7. During the past 12 months, how many times did you go boating or swimming? A. 0 times B. 1 to 10 times C. 11 to 20 times D. 21 to 39 times E. 40 or more times 8. When you went boating or swimming in the past 12 months, how often did you drink alcohol? A. I did not go boating or swimming in the past 12 months B. Never drank alcohol C. Rarely drank alcohol D. Sometimes drank alcohol E. Most of the time drank alcohol F. Always drank alcohol 9. During the past 30 days, how many times did you ride in a car or other vehicle driven by someone who had been drinking alcohol? A. 0 times B. 1 time C. 2 or 3 times D. 4 or 5 times E. 6 or more times 10. During the past 30 days, how many times did you drive a car or other vehicle when you had been drinking alcohol. A. 0 times B. 1 time C. 2 or 3 times D. 4 or 5 times E. 6 or more times

151 11. During the past 30 days, on how many days did you carry a weapon such as a gun, knife, or club? Do not count carrying a weapon as part of your job. A. 0 days B. 1 day C. 2 or 3 days D. 4 or 5 days E. 6 or more days 12. During the past 30 days, on how many days did you carry a gun? Do not count carrying a gun as part of your job. A. 0 days B. 1 day C. 2 or 3 days D. 4 or 5 days E. 6 or more days 13. During the past 12 months, how many times were you in a physical fight? A. 0 times--Skip to question 16 B. 1 time C. 2 or 3 times D. 4 or 5 times E. 6 or 7 times F. 8 or 9 times G. 10 or more times 14. During the past 12 months, with whom did you fight? (Select all that apply) A. A total stranger B. A friend or someone I know C. A boyfriend, girlfriend, or date D. My spouse or domestic partner E. A parent, brother, sister, or other family member F. Other 15. During the past 12 months, how many times were you in a physical fight in which you were injured and had to be treated by a doctor or nurse? A. 0 times B. 1 time C. 2 or 3 times D. 4 or 5 times E. 6 or more times

152 Sometimes people feel so depressed and hopeless about the future that they may consider attempting suicide, that is, taking some action to end their own life. The next four questions ask about suicide. 16. During the past 12 months, did you ever seriously consider attempting suicide? A. yes B. no 17. During the past 12 months, did you make a plan about how you would attempt suicide? A. yes B. no 18. During the past 12 months, how many times did you attempt suicide? A. 0 times B. 1 time C. 2 or 3 times D. 4 or 5 times E. 6 or more times 19. If you attempted suicide during the past 12 months, did any attempt result in an injury, poisoning, or overdose that had to be treated by a doctor or nurse? A. I did not attempt suicide during the past 12 months B. yes C. no The next eight questions ask about tobacco use. 20. Have you ever tried cigarette smoking, even one or two puffs? A. yes B. no—SKIP to Question 27 21. How old were you when you smoked a whole cigarette for the first time? A. I have never smoked a whole cigarette B. 12 years or younger C. 13 or 14 years old D. 15 or 16 years old E. 17 or 18 years old F. 19 or 20 years old G. 21 to 24 years old H. 25 years old or older

153 22. During the past 30 days, on how many did you smoke cigarettes? A. 0 days B. 1 or 2 days C. 3 to 5 days D. 6 to 9 days E. 10 to 19 days F. 20 to 29 days G. all 30 days 23. During the past 30 days, on the days you smoked, how many cigarettes did you smoke per day? A. I did not smoke cigarettes during the last 30 days B. Less than 1 cigarette per day C. 2 to 5 cigarettes per day D. 6 to 10 cigarettes per day E. 11 to 20 cigarettes per day F. More than 20 cigarettes per day 24. Have you ever smoked cigarettes regularly, that is, at least one cigarette every day for 30 days? A. yes B. no 25. How old were you when you first started smoking cigarettes regularly (at least one cigarette every day for 30 days)? A. I have never smoked cigarettes B. 12 years old or younger C. 13 or 14 years old D. 15 or 16 years old E. 17 or 18 years old F. 19 or 20 years old G. 21 to 24 years old H. 25 years old or older 26. Have you tried to quit smoking cigarettes? A. yes B. no

154 27. During the past 30 days, on how many days did you use chewing tobacco or snuff, such as Redman, Levi Garrett, Beechnut, Skoal, Skoal Bandits, or Copenhagen? A. 0 days B. 1 or 2 days C. 3 to 5 days D. 6 to 9 days E. 10 to 19 days F. 20 to 29 days G. all 30 days The next three questions ask about drinking alcohol. This includes beer, wine, wine coolers, and liquor, such as rum, gin, vodka, or whiskey. For these questions, drinking alcohol does not include a few sips of wine for religious purposes. 28. How old were you when you had your first drink of alcohol other than a few sips? A. I have never had a drink of alcohol other than a few sips—SKIP to Question 31 B. 12 years old or younger C. 13 or 14 years old D. 15 or 16 years old E. 17 or 18 years old F. 19 or 20 years old G. 21 to 24 years old H. 25 years old or older 29. During the past 30 days, on how many days did you have at least one drink of alcohol? A. 0 days B. 1 or 2 days C. 3 to 5 days D. 6 to 9 days E. 10 to 19 days F. 20 to 29 days G. all 30 days 30. During the past 30 days, on how many days did you have 5 or more drinks of alcohol in a row, that is, within a couple of hours? A. 0 days B. 1 or 2 days C. 3 to 5 days D. 6 to 9 days E. 10 to 19 days F. 20 or more days

155 The next three questions ask about marijuana use. 31. During your life, how many times have you used marijuana? A. 0 times—SKIP to Question 34 B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 32. How old were you when you tried marijuana for the first time? A. 12 years old or younger B. 13 or 14 years old C. 15 or 16 years old D. 17 or 18 years old E. 19 or 20 years old F. 21 to 24 years old G. 25 years old or older 33. During the past 30 days, how many times did you use marijuana? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times The next ten questions ask about cocaine and other drug use. 34. During your life, how many times have you used any form of cocaine including powder, crack, or freebase? A. 0 times—SKIP to Question 38 B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times

156 35. How old were you when you tried any form of cocaine, including powder, crack, or freebase, for the first time? A. 12 years old or older B. 13 or 14 years old C. 15 or 16 years old D. 17 or 18 years old E. 19 or 20 years old F. 21 to 24 years old G. 25 years old or older 36. During the past 30 days, how many times did you use any form of cocaine, including powder, crack, or freebase? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 37. During your life, how many times have you used crack or freebase forms of cocaine? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 38. During your life, how many times have you sniffed glue, or breathed the contents of aerosol spray cans, or inhaled any paints or sprays to get high? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times

157 39. During your life, how many times have you taken steroid pills or shots without a doctor’s prescription? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 40. During your life, how many times have you used any other type of illegal drug, such as LSD, PCP, ecstasy, mushrooms, speed, ice, or heroin? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 41. During the past 30 days, how many times have you used any other type of illegal drug, such as LSD, PCP, ecstasy, mushrooms, speed, ice, or heroin? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 42. During the past 30 days, how many times have you used any illegal drugs in combination with drinking alcohol? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times

158 43. During your life, how many times have you used a needle to inject any illegal drug into your body? A. 0 times B. 1 time C. 2 or more times The next fifteen questions ask about sexual behavior. For the purpose of this survey, sexual intercourse is defined as vaginal intercourse, anal intercourse, or oral/genital sex. 44. How old were you when you had sexual intercourse for the first time? A. I have never had sexual intercourse—SKIP to Question 55 B. 12 years old or younger C. 13 or 14 years old D. 15 or 16 years old E. 17 or 18 years old F. 19 or 20 years old G. 21 to 24 years old H. 25 years old or older 45. During your life, with how many females have you had sexual intercourse? A. I have never had sexual intercourse with a female B. 1 female C. 2 females D. 3 females E. 4 females F. 5 females G. 6 or more females 46. During the past 3 months, with how many females have you had sexual intercourse? A. I have never had sexual intercourse with a female B. I have had sexual intercourse with a female, but not during the past 3 months C. 1 female D. 2 females E. 3 females F. 4 females G. 5 females H. 6 or more females

159 47. During your life, with how many males have you had sexual intercourse? A. I have never had sexual intercourse with a male B. 1 male C. 2 males D. 3 males E. 4 males F. 5 males G. 6 or more males 48. During the past 3 months, with how many males have you had sexual intercourse? A. I have never had sexual intercourse with a male B. I have had sexual intercourse with a male, but not during the past 3 months C. 1 male D. 2 males E. 3 males F. 4 males G. 5 males H. 6 or more males 49. During the past 30 days, how many times did you have sexual intercourse? A. 0 times B. 1 time C. 2 or 3 times D. 4 to 9 times E. 10 to 19 times F. 20 or more times 50. During the past 30 days, how often did you or your partner use a condom? A. I have not had sexual intercourse in the past 30 days B. Never used a condom C. Rarely used a condom D. Sometimes used a condom E. Most of the time used a condom F. Always used a condom 51. The last time you had sexual intercourse, did you or your partner use a condom? A. yes B. no 52. Did you drink alcohol or use drugs before you had sexual intercourse the last time? A. yes B. no

160 53. The last time you had sexual intercourse, what method did you or your partner use to prevent pregnancy? (Select all that apply) A. No method was used to prevent pregnancy B. Birth control pills C. Condoms D. Withdraw E. Some other method F. Not sure 54. How many times have you been pregnant or gotten someone pregnant? A. 0 times B. 1 time C. 2 or more times D. Not sure 55. During your life, have you ever been forced to have sexual intercourse against your will? A. yes B. no—SKIP to Question 58 56. How old were you the first time you were forced to have sexual intercourse against your will? A. 4 years old or younger B. 5 to 12 years old C. 13 or 14 years old D. 15 or 16 years old E. 17 or 18 years old F. 19 or 20 years old G. 21 to 24 years old H. 25 years old or older 57. How old were you the last time you were forced to have sexual intercourse against your will? A. 4 years old or younger B. 5 to 12 years old C. 13 or 14 years old D. 15 or 16 years old E. 17 or 18 years old F. 19 or 20 years old G. 21 to 24 years old H. 25 years old or older

161 58. Have you ever had your blood tested for the AIDS virus/HIV infection? A. yes B. no C. not sure The next six questions ask about body weight. 59. How do you describe your weight? A. Very underweight B. Slightly underweight C. About the right weight D. Slightly overweight E. Very overweight 60. Which of the following are you trying to do about your weight? A. Lose weight B. Gain weight C. Stay about the same D. I am not trying to do anything about my weight 61. During the past 30 days, did you diet to lose weight or to keep from gaining weight? A. yes B. no 62. During the past 30 days, did you exercise to lose weight or keep from gaining weight? A. yes B. no 63. During the past 30 days, did you vomit or take laxatives to lose weight or to keep from gaining weight? A. yes B. no 64. During the past 30 days, did you take diet pills to lose weight or keep from gaining weight? A. yes B. no

162 The next seven questions ask about food you ate yesterday. Think about all meals and snacks you ate yesterday from the time you got up until you went to bed. Be sure to include food you ate at home, on campus, in restaurants, or anywhere else. 65. Yesterday, how many times did you eat fruit? A. 0 times B. 1 time C. 2 times D. 3 or more times 66. Yesterday, how many times did you drink fruit juice? A. 0 times B. 1 time C. 2 times D. 3 or more times 67. Yesterday, how many times did you eat green salad? A. 0 times B. 1 time C. 2 times D. 3 or more times 68. Yesterday, how many times did you eat cooked vegetables? A. 0 times B. 1 time C. 2 times D. 3 or more times 69. Yesterday, how many times did you eat hamburger, hot dogs, or sausage? A. 0 times B. 1 time C. 2 times D. 3 or more times 70. Yesterday, how many times did you eat French fries or potato chips? A. 0 times B. 1 time C. 2 times D. 3 or more times

163 71. Yesterday, how many times did you eat cookies, doughnuts, pie, or cake? A. 0 times B. 1 time C. 2 times D. 3 or more times 72. On how many of the past 7 days did you exercise or participate in sports activities for at least 20 minutes that made you sweat and breathe hard, such as basketball, jogging, swimming laps, tennis, fast bicycling, or similar aerobic activities? A. 0 days B. 1 day C. 2 days D. 3 days E. 4 days F. 5 days G. 6 days H. 7 days 73. On how many of the past 7 days did you do stretching exercises, such as toe touching, knee bending, or leg stretching? A. 0 days B. 1 day C. 2 days D. 3 days E. 4 days F. 5 days G. 6 days H. 7 days 74. On how many of the past 7 days did you do exercises to strengthen or tone your muscles, such as push-ups, sit-ups, or weight lifting? A. 0 days B. 1 day C. 2 days D. 3 days E. 4 days F. 5 days G. 6 days H. 7 days

164 75. On how many of the past 7 days did you walk or bicycle for at least 30 minutes at a time? (Include walking or bicycling to or from class or work) A. 0 days B. 1 day C. 2 days D. 3 days E. 4 days F. 5 days G. 6 days H. 7 days 76. During this school year, have you enrolled in a physical education class? A. yes B. no 77. During this school year, on how many college sports teams (intramural or extramural) did you participate? A. 0 teams B. 1 team C. 2 teams D. 3 or more teams The next questions ask about AIDS education and health information. 78. Have you ever been taught about AIDS or HIV infection in your college classes? A. yes B. no C. not sure 79. During this school year, did you receive information about avoiding AIDS or HIV infection on your college campus? A. yes B. no C. not sure

165 Appendix E Zuckerman-Kuhlman Personality Questionnaire Impulsive Sensation Seeking Scale DIRECTIONS: On this page you will find a series of statements that persons might use to describe themselves. Read each statement and decide whether or not it describes you. Then indicate your answer. If you agree with a statement or decide that it describes you, circle TRUE. If you disagree with a statement or feel that it is not descriptive of you, circle FALSE. Answer every statement either TRUE of FALSE even if you are not entirely sure of your answer. TRUE

FALSE

T

F

T

F

3. I often do things on impulse.

T

F

4. I very seldom spend much time on the details of

T

F

T

F

6. Before I begin a complicated job, I make careful plans.

T

F

7. I would like to take off on a trip with no preplanned or

T

F

T

F

9. I like doing things just for the thrill of it.

T

F

10. I tend to change interests frequently.

T

F

11. I sometimes like to do things that are a little

T

F

12. I’ll try anything once.

T

F

13. I would like the kind of life where one is on the move

T

F

14. I sometimes do “crazy” things just for fun.

T

F

15. I like to explore a strange city or section of town by

T

F

16. I prefer friends who are excitingly unpredictable.

T

F

17. I often get so carried away by new and exciting things

T

F

18. I am an impulsive person.

T

F

19. I like “wild” uninhibited parties.

T

F

1. I tend to begin a new job without much planning on how I will do it. 2. I usually think about what I am going to do before doing it.

planning ahead. 5. I like to have new and exciting experiences and sensations even if they are a little frightening.

definite routes or timetables. 8. I enjoy getting into new situations where you can’t predict how things will turn out.

frightening.

and traveling a lot, with lots of change and excitement.

myself, even if it means getting lost.

and ideas that I never think of possible complications.

166 Appendix F Zimbardo Time Perspective Inventory Instructions: Please indicate the extent to which each of the following items is true for you. 1=very untrue 2=somewhat untrue 3=neutral 4=somewhat true 5=very true Very Untrue

……………………………………………… Very True

of me 1. I believe that a person's day

of me

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

should be planned ahead each morning. 2. Thinking about the future is pleasant to me. 3. When I want to achieve something, I set goals and consider specific means of reaching those goals. 4. Meeting tomorrow's deadlines and doing other necessary work comes before tonight's play. 5. It seems to me that my future plans are pretty well laid out. 6. I think that it's useless to plan too far ahead. Things hardly ever come out the way you planned anyway. 7. It upsets me to be late for appointments 8. I tend to lost my temper when I'm provoked. 9. I get irritated at people who keep me waiting when we've agreed to meet at a given time. 10. I complete projects on time by making steady progress. 11. I make lists of things to do.

167

Very Untrue

……………………………………………… Very True

of me 12. I keep working at a difficult

of me

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

17. I try to live one day at a time.

1

2

3

4

5

18. It's fun to gamble when I have

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

21. I get drunk at parties.

1

2

3

4

5

22. I take risks to put excitement

1

2

3

4

5

uninteresting task if it will help get me ahead. 13. I am able to resist temptations when I know there is work to be done. 14. I do things impulsively, making decisions on the spur of the moment. 15. I believe that getting together with friends to party is one of life's important pleasures. 16. If I don't get done on time, I don’t worry about it.

some extra money. 19. I feel that it's more important to enjoy what you are doing than to get the work done on time. 20. I don't do things that will be good for me if they don't feel good now.

into my life.

168 Appendix G Need for Cognition Please rate the following items on how accurate they are at describing you. 1. I would prefer complex to simple problems. -4 -3 -2 -1 0 1 extremely inaccurate

2 3 4 extremely accurate

2. I like to have the responsibility of handling a situation that requires a lot of thinking. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate 3. Thinking is not my idea of fun. -4 -3 -2 -1 extremely inaccurate

0

1


4. I would rather do something that requires little thought than something that is sure to challenge my thinking abilities. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate 5. I try to anticipate and avoid situations where there is a likely chance that I will have to think in depth about something. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate 6. I find satisfaction in deliberating hard and long for hours. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate 7. I only think as hard as I have to. -4 -3 -2 -1 extremely inaccurate

0

1


8. I prefer to think about small, daily projects than long-term ones. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate 9. I like tasks that require little thought once I’ve learned them. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate

169 10. The idea of relying on thought to make my way to the top appeals to me. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate 11. I really enjoy a task that involves coming up with new solutions to problems. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate 12. Learning new ways to think doesn’t excite me very much. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate 13. I prefer my life to be filled with puzzles I must solve. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate 14. The notion of thinking abstractly is appealing to me. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate 15. I would prefer a task that is intellectual, difficult, and important to one that is somewhat important but does not require much thought. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate 16. I feel relief rather than satisfaction after completing a task that required a lot of mental effort. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate 17. It’s enough for me that something gets the job done. I don’t care how or why it works. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate 18. I usually end up deliberating about issues even when they do not effect me personally. -4 -3 -2 -1 0 1 2 3 4 extremely inaccurate extremely accurate

170 Appendix H Family Environment Scale Conflict, Cohesion, Control, and Moral-Religious Subscales INSTRUCTIONS: There are 36 statements on this questionnaire. They are statements about families. You are to decide which of these statements are true of your family and which are false. If you think the statement is True or mostly True of your family, circle T. If you think the statement is False or mostly False of your family, circle F. You many feel that some of the statements are true for some family members and false for others. Mark true if the statement is true for most members. Mark false if the statement is false for most members. If the members are evenly divided, decide what is the overall impression and answer accordingly. Remember, we would like to know what your family seems like to you. So do not try to figure out how other members see your family, but do give us your general impression of your family for each statement. TRUE

FALSE

1. Family members are rarely ordered around.

T

F

2. There are very few rules to follow in our family.

T

F

3. There is one family member who makes most of the decisions.

T

F

4. There are set ways of doing things at home.

T

F

5. There is a strong emphasis on following rules in our family.

T

F

6. Everyone has an equal say in family decisions.

T

F

7. We can do whatever we want in our family.

T

F

8. Rules are pretty inflexible in our household.

T

F

9. You can't get away with much in our family.

T

F

10. Family members attend church, synagogue, or Sunday

T

F

11. We don't say prayers in our family.

T

F

12. We often talk about the religious meaning of Christmas, Passover, or other holidays.

T

F

13. We don't believe in heaven or hell.

T

F

14. Family members have strict ideas about what is right and wrong.

T

F

15. We believe there are some things you just have to take on faith.

T

F

16. In our family each person has different ideas about what is

T

F

T

F

School fairly often.

right and wrong. 17. The Bible is a very important book in our home.

171

TRUE

FALSE

18. Family members believe that if you sin you will be punished.

T

F

19. Family members really help and support one another.

T

F

20. We fight a lot in our family.

T

F

21. We often seem to be killing time at home.

T

F

22. Family members rarely become openly angry.

T

F

23. We put a lot of energy into what we do at home.

T

F

24. Family members sometimes get so angry they throw things.

T

F

25. There is a feeling of togetherness in our family.

T

F

26. Family members hardly ever lose their tempers.

T

F

27. We rarely volunteer when something has to be done at home.

T

F

28. Family members often criticize each other.

T

F

29. Family members really back each other up.

T

F

30. Family members sometimes hit each other.

T

F

31. There is very little group spirit in our family.

T

F

32. If there's a disagreement in our family, we try hard to

T

F

33. We really get along well with each other.

T

F

34. Family members often try to one-up or out-do

T

F

T

F

T

F

smooth things over and keep the peace.

each other. 35. There is plenty of time and attention for everyone in our family. 36. In our family, we believe you don't ever get anywhere by raising your voice.

172 Appendix I Inventory of Parent and Peer Attachment We are interested in your relationships with your parents and with your peers. Below are a number of statements that could describe such relationships. Please read each statement carefully and then decide how much it is true of your relationship with the individuals described in the statement. Then, circle the number corresponding to how true that statement is of your relationship according to the descriptions below. Please answer each item. 1 = Almost never or never true 2 = Seldom true 3 = Sometimes true 4 = Often true 5 = Almost always or always true 1. My parents respect my feelings. 1 2 3 4 5 2. I feel my parents are successful as parents.

1

2

3

4

5

3. I wish I had different parents.

1

2

3

4

5

4. My parents accept me as I am.

1

2

3

4

5

5. I have to rely on myself when I have a problem 1 to solve.

2

3

4

5

6. I like to get my parents’ point of view on things I’m concerned about.

1

2

3

4

5

7. I feel it’s no use letting my feelings show.

1

2

3

4

5

8. My parents sense when I’m upset about something.

1

2

3

4

5

9. Talking over my problems with my parents makes me feel ashamed or foolish.

1

2

3

4

5

10. My parents expect too much from me.

1

2

3

4

5

11. I get upset easily at home.

1

2

3

4

5

12. I get upset a lot more than my parents know about.

1

2

3

4

5

173

13. When we discuss things, my parents consider my point of view.

1

2

3

4

5

14. My parents trust my judgment.

1

2

3

4

5

15. My parents have their own problems, so I don’t bother them with mine.

1

2

3

4

5

16. My parents help me understand to myself better.

1

2

3

4

5

17. I tell my parents about my problems and troubles.

1

2

3

4

5

18. I feel angry with my parents.

1

2

3

4

5

19. I don’t get much attention at home.

1

2

3

4

5

20. My parents encourage me to talk about my difficulties.

1

2

3

4

5

21. My parents understand me.

1

2

3

4

5

22. I don’t know whom I can depend on these days. 1

2

3

4

5

23. When I am angry about something, my parents try to be understanding.

1

2

3

4

5

24. I trust my parents.

1

2

3

4

5

25. My parents don’t understand what I’m going through these days.

1

2

3

4

5

26. I can count on my parents when I need to get something off my chest.

1

2

3

4

5

27. I feel that no one understands me.

1

2

3

4

5

28. If my parents know something is bothering me, they ask me about it.

1

2

3

4

5

174 29. I like to get my friends’ point of view on things I’m concerned about.

1

2

3

4

5

30. My friends sense when I’m upset about something.

1

2

3

4

5

31. When we discuss things, my friends consider my point of view.

1

2

3

4

5

32. Talking over my problems with my friends makes me feel ashamed or foolish.

1

2

3

4

5

33. I wish I had different friends.

1

2

3

4

5

34. My friends understand me.

1

2

3

4

5

35. My friends encourage me to talk about my difficulties.

1

2

3

4

5

36. My friends accept me as I am.

1

2

3

4

5

37. I feel the need to be in touch with my friends more often.

1

2

3

4

5

38. My friends don’t understand what I’m going through these days.

1

2

3

4

5

39. I feel alone or apart when I am with my friends.

1

2

3

4

5

40. My friends listen to what I have to say.

1

2

3

4

5

41. I feel my friends are good friends.

1

2

3

4

5

42. My friends are fairly easy to talk to.

1

2

3

4

5

43. When I am angry about something, my friends try to be understanding.

1

2

3

4

5

44. My friends help me to understand myself better. 1

2

3

4

5

45. My friends are concerned about my well being. 1

2

3

4

5

175 46. I feel angry with my friends.

1

2

3

4

5

47. I can count on my friends when I need to get something off my chest.

1

2

3

4

5

48. I trust my friends.

1

2

3

4

5

176 Appendix J In-Person Administration Consent Form Title of Research: Behaviors Across Time Principal Investigator: Melissa Buelow Department: Ohio University Department of Psychology Federal and university regulations require signed consent for participation in research involving human subjects. After reading the statements on this page, please indicate your consent by providing your Oak email address at the bottom of this page. This study attempts to examine behaviors across time in college students. These behaviors include a wide range of activities, such as drinking and eating habits, sexual activity, and exercise habits. Your participation will involve filling out a number of questionnaires that may contain personal information. You may feel embarrassed or offended by some of the questions, but keep in mind that your answers will remain confidential, away from any identifying information, and you can stop responding at any time without penalty of losing the monetary compensation. This study will provide the behavior field with needed information regarding how behaviors change or remain the same over time in an individual. There are no anticipated risks associated with participation in this study. Participation is entirely voluntary and will take approximately one hour. You will receive $7.00 upon completion of this study. As this project is funded by Ohio University and we need to account for our expenditures, your name and the amount of money received from this study will be shared with Accounts Payable. This will be the only information provided to Accounts Payable, and it will not be linked to your questionnaire responses in any way. Please provide your signature and Oak email address as your consent to participate in this study. Also, in order to compare behaviors across time, your responses in the current administration will be matched to your responses from the Spring of 2002 administration. This will be done using your Oak email address. After the data are matched, a unique number will be given to the data. Your email address will not be stored with your responses, so there will be no identifying information linked to your responses.

177

I certify that I have read and understand this consent form and agree to participate as a subject in the research described. I agree that known risks to me have been explained to my satisfaction and I understand that no compensation is available from Ohio University and its employees for any injury resulting from my participation in this research. I certify that I am 18 years of age or older. My participation in this research is given voluntarily. I understand that I may discontinue participation at any time without penalty or loss of any benefits to which I may otherwise be entitled.

____________________________ Signature

_______________________ Today’s Date

____________________________ Printed Name

_______________________ Oak Email Address

178 Appendix K In-Person Administration Debriefing Text Thank you for your participation. If you have any questions regarding this study, please contact Melissa Buelow at (740) 593-0052, or her advisor, Margret Appel, Ph.D., at (740) 593-1069. If you have any questions regarding your rights as a research participant, please contact Jo Ellen Sherow, Director of Research Compliance, Ohio University, (740)593-0664. In the event that any of the previous questions elicit any feelings of stress, embarrassment, or anxiety, feel free to contact Counseling and Psychological Services, Hudson Health Center 3rd floor (593-1616), or the Psychology and Social Work Clinic, 002 Porter Hall (593-0902).

179 Appendix L Web-based Administration Consent Form Title of Research: Behaviors Across Time Principal Investigator: Melissa Buelow Department: Ohio University Department of Psychology Federal and university regulations require signed consent for participation in research involving human subjects. After reading the statements on the next page, please indicate your consent by providing your Oak email address at the bottom of the screen. This study attempts to examine behaviors across time in college students. These behaviors include a wide range of activities, such as drinking and eating habits, sexual activity, and exercise habits. Your participation will involve filling out a number of questionnaires that may contain personal information. You may feel embarrassed or offended by some of the questions, but keep in mind that your answers will remain confidential, away from any identifying information, and you can stop responding at any time by closing your browser window. This study will provide the behavior field with needed information regarding how behaviors change or remain the same over time in an individual. There are no anticipated risks associated with participation in this study. Participation is entirely voluntary and will take approximately one hour. You will receive $7.00 upon completion of this study. Compensation will be distributed in Porter Hall, Room 311-A, by the principal investigator on the following dates: May 21st from 10am to 12 noon, May 27th from 4 to 8pm, May 28th from 10am to 1pm, June 1st from 4 to 6 pm, and June 2nd from 2 to 5 pm. Please bring your student identification card or an alternate form of picture identification such as a driver’s license to Office A in Room 311 in order to receive the $7.00 compensation. As this project is funded by Ohio University and we need to account for our expenditures, your name and the amount of money received from this study will be shared with Accounts Payable. This will be the only information provided to Accounts Payable, and it will not be linked to your questionnaire responses in any way. Please provide your Oak email address as your consent to participate in this study. Your email address is needed to keep track of participation in the study in order to distribute the monetary compensation following completion of this study. Also, in order to compare behaviors across time, your responses in the current administration will be matched to your responses from the Spring of 2002 administration. This will be done using your Oak email address. After the data are matched, a unique number will be given to the data and your email address will be deleted. Your email address will not be stored with your responses, so there will be no identifying information linked to your responses.

180

I certify that I have read and understand this consent form and agree to participate as a subject in the research described. I agree that known risks to me have been explained to my satisfaction and I understand that no compensation is available from Ohio University and its employees for any injury resulting from my participation in this research. I certify that I am 18 years of age or older. My participation in this research is given voluntarily. I understand that I may discontinue participation at any time without penalty or loss of any benefits to which I may otherwise be entitled.

181 Appendix M Web-based Administration Debriefing Text Thank you for your participation. If you have any questions regarding this study, please contact Melissa Buelow at (740) 593-0052, or her advisor, Margret Appel, Ph.D., at (740) 593-1069. If you have any questions regarding your rights as a research participant, please contact Jo Ellen Sherow, Director of Research Compliance, Ohio University, (740)593-0664. In the event that any of the previous questions elicit any feelings of stress, embarrassment, or anxiety, feel free to contact Counseling and Psychological Services, Hudson Health Center 3rd floor (593-1616), or the Psychology and Social Work Clinic, 002 Porter Hall (593-0902). The $7.00 compensation will be distributed in Porter Hall, room 311A, by Melissa Buelow on the following dates: May 21st from 10am – 12 noon, May 27th from 4 - 8pm, May 28th from 10am - 1pm, June 1st from 4 - 6 pm, and June 2nd from 2 - 5 pm. Please bring your student identification card or an alternate form of picture identification such as a driver’s license. A reminder of these dates and times will be sent to your oak email account.

��

��

� ��

� ��

� ��

� ��

� ��

� ��

� ��

� ��

� ��

��

��

Appendix N Correlations Among the Variables

� � � � � � � � � ��

182

�

�

�

�

�

�

�

�

�

��

��

183

��

�

�

�

�

�

�

�

�

�

��

��

��

184

�

�

�

�

�

�

�

�

�

��

��

��

185

� � � � � � � � � ��

��

��

��

��

��

��

��

��

��

��

��

��

��

186

��

��

��

��

��

��

��

��

��

��

��

��

��

187

��

��

��

��

��

��

��

��

��

��

��

��

188

��

��

��

��

��

��

��

��

��

��

��

��

189

� � � � � � � � � ��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

190

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

191

��

��

��

��

��

��

��

��

��

��

��

��

��

192

��

��

��

��

��

��

��

��

��

��

��

��

193

� � � � � � � � � ��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

194

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

195

��

��

��

��

��

��

��

��

��

��

��

��

196

� � � � � � � � � ��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

197

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

198

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

199

��

��

��

��

��

��

��

��

��

��

��

��

200

� � � � � � � � � ��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

201

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

202

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

203

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

204