school programs to prevent delinquency and victimization

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DISTINGUISHING CHARACTERISTICS OF EFFECTIVE AND INEFFECTIVE AFTERSCHOOL PROGRAMS TO PREVENT DELINQUENCY AND VICTIMIZATION* DENISE C. GOTTFREDSON AMANDA CROSS The University of Maryland, College Park DAVID A. SOULE´ Maryland Sentencing Commission Research Summary: Using multi-level modeling techniques, this study explores characteristics of 35 after-school programs (ASPs) that criminological research and theory predict should be related to problem behavior outcomes. Controlling for individual-level predictors of problem behavior and for the composition of the participating ASPs, several ASP characteristics were found to be related, as predicted, to victimization, substance use, and delinquent behavior. Policy Implications: This study extended previous findings that providing structured programming and small program size are important for reducing problem behavior through ASPs. Our study also found that two characteristics of the program staff are related to reductions in problem behavior: More highly educated staff and a higher percentage male staff were related to reductions in levels of both delinquent behavior and victimization. The study concludes that program structure, staffing, and size are important in producing more positive behavioral outcomes. * This research was supported in part by a contract (#CCA/AFTER/02-008 A-1) from the Maryland Department of Human Resources (DHR), with oversight from the Maryland Office of Children, Youth and Families (OCYF). We acknowledge the support of DHR staff Linda Heisner, Freda Stevens, and Ann Wallace as well as the support of OCYF staff Charlene Uhl, Lavone Grant, and Susan Walters. We also acknowledge the support of members of the evaluation advisory group (Eric Bruns, Mike Clark, Roe Davis, Karen Finn, Martha Hollerman, Rosemary King Johnston, Colleen Mahoney, Madeline Morey, Stephanie Stone, Jane Sundias, Roanthi Tsakalas, and Kerry Whitacre). We wish to thank Chris Ayers, Penny Beatty, Sara Betsinger, Jessica Blair, Heather Couture, Kelly Felgenhauer, Sarah Parsons, Carlos Rocha, Wayne Sharp, and Leslie Stridiron for research assistance. We also thank Gary Gottfredson, Kun Xiang, and four anonymous reviewers whose comments improved the work.

VOLUME 6

NUMBER 2

2007

PP 289–318

R


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GOTTFREDSON, CROSS, & SOULE´

KEYWORDS: After-School Programs, Delinquency, Delinquency Prevention, Program Effectiveness The after-school period is a time of concern for parents and policy makers as many adolescents are unsupervised during the several hours after school and before parents return from work. Evidence shows that delinquent activity is heightened during this time (Gottfredson et al., 2001; Sickmund et al., 1997; Snyder et al., 1996). After-school programs (ASPs) have the potential to reduce the amount of time students spend in selfcare, thereby promoting socially desirable behavior. They also create a convenient venue for the delivery of intervention programs, such as those that promote learning or discourage substance use and delinquency while providing the opportunity for youths to form supportive relations with adults. Because of the common perception that ASPs have great potential to benefit adolescents and their communities, after-school programming is increasing in the United States with the aid of considerable federal, state, local, and private money. For example, the 21st-Century Community Learning Center Program, which provides after-school enrichment activities for students in low-performing schools, was introduced as a component of the Clinton administration’s effort to help families and communities keep their children “safe and smart.” It is now a major component of the Bush administration’s “No Child Left Behind” Act. The U.S. Department of Education has funded over 6,800 schools in more than 1,400 communities to become community learning centers. In fiscal year 2006, Congress appropriated nearly a billion dollars for the 21st-Century Community Learning Centers. Much research has been conducted on the effect of ASPs on youth outcomes, although many of these studies lack scientific rigor. Ideally, outcome evaluations of ASPs would employ experimental designs. Such evaluations are rare because of perceived practical and ethical difficulties in randomly assigning youths to social services that are presumed to be beneficial. Unfortunately, most non-experimental evaluations fail to account for selection bias, which is a major hurdle in research on ASPs because participation is voluntary. In simple terms, selection bias is likely to occur when youths who capitalize on the opportunity to involve themselves in optional school-oriented activities are also those who are already on track for prosocial development. Delinquency-prone youths, who are often chronic truants, are less likely than their more prosocial peers to voluntarily participate in ASPs (Gottfredson et al., 2001). Similarly, highrisk youths are more likely to drop out of programs, whereas low-risk youths are most likely to remain (Weisman and Gottfredson, 2001). Both


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of these selective participation influences increase the probability of incorrect conclusions about the effectiveness of ASPs for improving youth outcomes, unless selection bias is taken into consideration. Recent evaluations have taken better care to control for selection effects by using comparison groups and sophisticated statistical controls when random assignment to experimental and control groups is not possible. Although important research has been conducted on the effect of ASPs on academic achievement, we restrict ourselves in the review that follows to a consideration of studies that measured social development outcomes that might impact youth safety, criminal behavior, or substance use.

BACKGROUND ASPs AND DELINQUENCY A summary of research conducted in the early 1990s or before concluded that community-based ASPs were a promising approach to crime reduction (Welsh and Hoshi, 2002). This summary was based on three quasi-experimental evaluations comparing areas with and without ASPs that were primarily recreational in nature. Although crime-reduction benefits were reported in all three studies, the authors note that the methodological rigor of the studies was not high. Subsequent studies focused on ASP program effects on individual ASP participants compared with nonparticipants. Hudley (2001) found that an ASP that included the “BrainPower” aggression reduction curriculum was successful in improving the social competence of at-risk 7 to 11 year olds. The treatment group showed decreased inappropriate perceptions of hostile intent and higher self-perceptions of self-control than a comparison group who did not attend the ASP. Similarly, an evaluation of ASPs in Maryland found that participation in an ASP significantly reduced delinquent behavior for middle-school but not for elementary-school youths (Gottfredson, Gerstenblith, et al., 2004). For the older youths, the effect of ASP participation on delinquent behavior was partially mediated by increases in intentions not to use drugs and positive peer associations. Public/Private Ventures evaluated 10 after-school centers in six cities. This evaluation found that higher levels of participation were related to lower rates of initiation of alcohol use and class skipping. Those who attended more were also more likely than the less regular attendees to believe that they handled anger in an appropriate way and to be very proud to belong to their school (Grossman et al., 2002). Experimental studies have also found positive effects of ASPs. Smith and Kennedy (1991) found the “Friendly PEERsuasion” program, which included a major emphasis on developing social skills, reduced the likelihood of initiation of drinking and the incidence of drinking for youths who


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had previously consumed alcohol. In another experiment, the Woodrock Youth Development Project produced positive outcomes (LoSciuto et al., 1999). This program targeted high-risk minority students in middle and elementary school. Compared with controls, participants displayed significantly better outcomes in drug use, race relations and cultural sensitivity, and school attendance. The program did not significantly alter student aggression or attitudes toward drug use. Although the results from this project are positive, it is important to note that the Woodrock program was an unusually comprehensive program, including activities during the school day as well as after school. Other recent evaluations of ASPs failed to find positive results of participation. A quasi-experimental, nationwide evaluation of 21st-Century Community Learning Centers found that middle-school participants engaged in more negative behaviors than nonparticipants. Participants scored higher on a composite measure of problems behaviors, including hitting others and defying teachers. Participants did not perform better academically than nonparticipants, nor were they more likely to complete their homework, despite heavy emphasis on homework assistance in 21stCentury Community Learning Centers. However, elementary students in the treatment group were more likely to report that they felt safe after school and experienced a slight advantage in gains in English grades compared with nonparticipants. Overall, though, this evaluation found that 21st-Century students not only failed to benefit academically but also were to some extent harmed by participation in what had become one of the main sources of pride for the federal government in education reform (James-Burdumy et al., 2005). Weisman et al. (2002), in another quasi-experimental evaluation of 14 ASPs, found that young people attending ASPs displayed greater increases in delinquency, rebelliousness, variety of drug use, and exposure to peers who used drugs from the beginning to the end of the program relative to a comparison group. Similarly, Mahoney et al. (2000) investigated the effects of youth involvement in Swedish youth recreation centers and found evidence linking participation in these centers to higher rates of juvenile offending and persistent offending, even after controlling selfselection factors. Note, however, that these centers operated in the evenings rather than during the after-school hours. Overall, the research findings on the social adjustment benefits of ASPs are mixed. It is possible that the variability in study results is partially explained by variability in the study designs, as the studies producing negative findings all rely on quasi-experimental designs that are less capable of ruling out selection artifacts. It is also likely that characteristics of the programs explain variability in program effectiveness. ASPs are diverse in their content and structure. Identifying the distinguishing characteristics of


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successful versus unsuccessful ASPs may help guide research and practice as educators and community leaders design programs intended to support prosocial development for youths at risk of delinquent behavior. Although detailed studies on characteristics related to effectiveness are uncommon, a small body of recent work has examined this issue. A fiveyear partnership between the State of Maryland and the University of Maryland has facilitated an in-depth study of a large number of ASPs in the state. Results from two different years of this program in which ASP participants were compared with nonparticipants (Gottfredson, Gerstenblith, et al., 2004; Weisman et al., 2002) were summarized above. In both of these studies, the positive effects of ASP participation on delinquent behavior or risk factors for delinquent behavior were larger in the subsets of programs that emphasized social skills or character development instruction and practice. Weisman et al. (2002) also reported that smaller programs (those serving 30 or fewer youths) were more successful than larger programs at reducing rebellious behavior. Gerstenblith et al. (2005) reported results from a different year of the initiative in which characteristics of ASPs were more carefully measured through frequent observations and interviews with providers. That study examined the relation between characteristics of the ASPs and change from pretest to posttest on youth surveys of delinquent behavior and risk factors for such behavior. The researchers capitalized on the differences in the content of ASPs offered across Maryland (e.g., band practice to SAT preparation) to examine which program characteristics were related to positive change. They replicated the Weisman et al. (2002) finding, which showed that a greater emphasis on social skills instruction produced better youth outcomes. Gerstenblith et al. (2005) also found that programs that offered more than three hours of academic enrichment per week were more successful for elementary-school (but not middle-school) students, and that highly structured programming is related to reductions in rebellious behavior and intentions to use drugs. Elements of structure associated with positive outcomes included efficient procedures (organized transitions between activities, orderly opening and closing procedures), high-quality behavior management, highly structured social skills programming (scripted lesson plans), and overall program organization (minimal dead time between activities, all activities planned in advance). In contrast, Gerstenblith et al. (2005) found that increases in youth reports of delinquent behavior, violent crime, and drug-using peers were observed for students attending programs offering more than 2.6 hours per week of leisure activities such as sports, crafts, and field trips. The literature does not paint a complete picture of how ASPs modify adolescent behavior. From the available evidence, it seems clear that participation in ASPs can produce measurable change in youth behavior.


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Sometimes the effect is positive, sometimes it is negative, and sometimes there is no discernable effect. The available evidence suggests that programs that (1) emphasize social skills or character development, (2) are more structured, and (3) are smaller, are more likely to reduce youth problem behavior than programs that do not share these characteristics. Most research on ASPs does not address linkages between ASP outcomes and criminological theories, but the effects of ASPs can be viewed in a theoretical framework. Routine activities theory (Cohen and Felson, 1979; Osgood and Anderson, 2004) is particularly relevant to ASP research. ASPs AND ROUTINE ACTIVITIES Routine activities theory proposes that (1) crime will increase when motivated offenders, suitable crime targets, and the absence of capable guardians converge in space and time; and (2) the likelihood of this occurring is based on the routine activities of an individual (Cohen and Felson, 1979). ASPs may alter criminal opportunities by influencing the routine activities of adolescents, especially the amount of time they spend socializing with peers away from adult guardians. Osgood and Anderson (2004) argue that unsupervised teen socializing provides both opportunity and motivation for delinquency. Lack of adult supervision prevents informal social control of adolescents, while the presence of peers provides an audience for whom delinquents “perform” (Haynie and Osgood, 2005; Osgood et al., 1996). Using rigorous longitudinal designs and large samples, these researchers have demonstrated that the amount of time youths spend socializing in unstructured activities without direct adult supervision predicts delinquent behavior (Haynie and Osgood, 2005; Osgood et al., 1996). Osgood and Anderson (2004) demonstrated using multilevel models that time spent in unstructured socializing with peers has both an individual-level effect and a contextual effect, such that high aggregate levels of unstructured socializing were related to higher rates of offending. They also found that, when parents are not well informed about the activities of adolescents, adolescents spend more time in such unstructured socializing. Although research on routine activities theory emphasizes the situational factors that make crime more likely, social reinforcement and learning of delinquency may also be more likely during times of unstructured socializing. Research by Dishion et al. (e.g., Dishion, 2000; Dishion et al., 1999) suggests that adolescents often reinforce each other’s deviant attitudes and actions in a pattern that Dishion refers to as “deviancy training,” which occurs when peers smile or laugh in reaction to antisocial comments or behavior. The Dishion research shows that increased exposure to such reactions is associated with increased substance use, delinquency, and violence. This form of peer influence may be more likely to occur in the absence of adult supervision.


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ASPs could either increase or decrease the amount of time youths spend in unsupervised peer groups. They have the potential to decrease unstructured socializing if attending youths would otherwise be hanging out with their friends during the after-school hours. They also have the potential to increase unstructured socializing if attending youths would otherwise be in more structured, supervised settings or would not be exposed to peers after school. The current study tests the effects of unsupervised socializing on delinquency, victimization, and substance use within the context of ASP attendance. It elaborates on an earlier evaluation of 37 ASPs in Maryland (Gottfredson, Soule, ´ et al., 2004). The earlier report studied a large number of outcomes, including character development, goal setting, and academic performance using pre- and post-gain scores on these measures as dependent variables. The earlier research found that secondary school students who participated in ASPs increased their decision-making skills and reduced their delinquent behavior compared with nonparticipating youths in comparison groups. The treatment versus comparison analysis of that research also suggested that ASPs were more beneficial for youths in poverty. The initial report from this project (Gottfredson, Soule, ´ et al., 2004) focused on documenting change from the beginning until the end of the ASPs in individual-level outcomes targeted by the programs. The current study improves on the previous report by (1) examining variability across programs in these individual-level program effects and (2) systematically exploring program characteristics related to differential effectiveness. It also focuses on the policy relevant outcomes of delinquency, substance use, and victimization.

METHODS Hierarchical liner modeling (HLM) is used to relate ASP characteristics to changes in delinquency, substance use, and victimization. Programs included in this study were funded in the 2002–2003 school year through the Maryland After-School Opportunity Fund Program (MASOFP), which directed $10 million in federal funds toward providing ASPs for children throughout the state. MASOFP did not dictate the content or structure of the programs funded under the initiative, and as a result, the 258 programs that operated under it were diverse in terms of quality and content of services, frequency of operation, and size. SAMPLE A formal outcome evaluation of 40 funded programs began in September 2002. Evaluators sampled programs from each county in Maryland,


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favoring the stronger, more stable programs. The evaluation team targeted programs that operated more than 30 days during the school year, served at least 25 students in grades 4 through 12, had higher attendance, and had lower drop-out rates than did the remaining programs. This study uses data from 351 of those programs, 9 of which provided comparison groups. The sample (N = 497) included all secondary school youths whose parents or guardians consented to study participation and who completed both a pretest and a posttest. Of these youths, 389 were in the ASP treatment group and 108 were in the comparison condition. The average age of participants was 12.4, the majority were female (61%), about half were African American (49%), and nearly half were eligible to receive free or reduced lunch (47%). COMPARISON GROUP Comparison group youths were recruited by county administrators, who were asked to identify a group of non-ASP-attending youths demographically similar to the after-school participant group. Youths in the comparison group were selected at the county as opposed to the program level. In most instances, comparison youths attended the same school as the MASOFP participants. Appendix A compares the preintervention measures of demographic characteristics, delinquent behavior, victimization, and drug use for comparison and treatment youths for whom postintervention data were also available. These groups were significantly different in terms of ethnicity, free lunch status, and drug use. On average, the comparison group youths were significantly less likely to be African American or to receive free or reduced lunch than youths in the treatment group, and they were more likely to report using drugs. The groups were similar in terms of gender, age, delinquent behavior, and victimization experiences. ATTRITION The attrition rate from pretest to the posttest was 41% for the treatment group and 31% for the comparison group, despite efforts to recover attrited youths. An analysis, including all pretested treatment and comparison youths, revealed that comparison youths were significantly more likely to complete the posttest than were treatment youths. Gender, age, race, free lunch status, time 1 drug use, delinquency, and victimization were not significantly related to posttest completion. Interactions between 1. Five of the 40 evaluation programs were dropped from the current analysis. Three were disqualified because they only served elementary students, and two middleschool programs were dropped because they did not provide postintervention information.


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group membership and ethnicity, free lunch status, and time 1 drug use did significantly predict attrition. Table 1 presents the means of these variables for the attrited and retained portions of the treatment and control groups. Although attrition from the treatment group seems to have been random with regard to race and free lunch status, those who used more drugs at pretest were significantly more likely to drop out. In contrast, the subjects retained in the comparison condition were significantly less likely to be African American and to be receiving free lunch than those who were lost, whereas pretest substance use did not differ significantly between attrited and retained youth. These differences in the attrition patterns increased the dissimilarity of the groups.

TABLE 1. ATTRITION ANALYSIS FOR TREATMENT AND CONTROL GROUPS Treatment Group (N = 389) % African American % free lunch Last month drug use* Control Group (N = 108) % African American* % free lunch* Last month drug use

Attrited

Retained

All

51 45 0.09

54 52 0.06

53 49 0.08

Attrited

Retained

All

51 54 0.07

31 30 0.14

37 36 0.12

*Difference between attrited and retained is significant; p < .05.

MEASURES Individual-level (level 1) variables include demographic characteristics and a pretreatment measure of each outcome variable. Program-level (level 2) variables include measures of time expenditure, program structure, and program management as well as measures of the average population characteristics of the ASP participants. These measures will be described in the following section. LEVEL 1 VARIABLES We chose to limit our analysis to the most policy relevant outcomes: drug use, delinquency, and victimization. These self-reported behaviors and experiences2 were derived from a 173-item questionnaire administered once near the beginning of each program and once near the end. 2. Self-report measures are frequently used to assess the extent of the participation of adolescents in antisocial or illegal behavior. Some question the validity of data


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The lag period between pretest and posttest was not consistent across sites. Two main reasons for this inconsistency exist: (1) Programs ran for different lengths of time during the school year, and (2) researchers and program staff at several sites experienced difficulties scheduling questionnaire administrations. The mean number of days elapsed between pretest and posttests was 130 days (minimum = 37, maximum = 207, S.D. = 55). Mean days elapsed between pretest and posttest was included in statistical models when appropriate. Substance use. The drug use measure was derived from a 5-item scale that assessed how frequently within the last month participants had used tobacco, alcohol, marijuana, hallucinogens, or “other” drugs (a = 0.77). Possible responses on each item ranged from 0 to 3. A score of 3 indicated that the student consumed all the referenced drugs every day, and a score of 0 indicated no use. The five items were averaged to create a scale measuring frequency of substance use. Delinquent behavior.3 Delinquent behavior was assessed on a 13-item scale (a = 0.85). The items asked students whether they had engaged in a variety of delinquent acts (theft, vandalism, assault, gang involvement, weapon carrying, breaking and entering, and robbery). Youths responded “yes” or “no” to each item. The items were averaged to create a scale ranging from 0 to 1, in which a score of 1 indicated that the student engaged in all 13 behaviors within the last year (pretest) or the last 3 months (posttest). Victimization. Victimization was measured on a 7-item scale (a = 0.72) that assessed whether the student had been the subject of theft, robbery, destruction of property, assault, or threatened assault. Youths responded “yes” or “no” to each item. The items were averaged to create a scale ranging from 0 to 1, in which a score of 1 indicated that the student had generated from self-reports because they find it hard to believe that young people will honestly report delinquency or drug use. Considerable research has examined the reliability and validity of self-reports by correlating data from self-reports with data on the same behaviors obtained from official records (Farrington et al., 1996). Studies have illustrated that young people are willing to report accurate information on both minor and serious delinquent activities (Espiritu et al., 2001). Furthermore, self-report data on victimization experiences is generally considered preferable to official police reports because a large proportion of victimization, especially juvenile victimization, goes unreported to the police. Concerns will continue to arise regarding the validity of self-report data, but the general conclusion from studies evaluating self-report measures is that they compare favorably with other accepted social science indicators (Hindelang et al., 1981). The self-report measures of behavior employed in the current research are therefore likely to be valid measures of actual behavior. 3. Although official arrest records were available to researchers, this measure was not analyzed because no participants were arrested during the study period.


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experienced all 7 forms of victimization within the last year (pretest) or the last 3 months (posttest). The distribution of each dependent variable was highly skewed with a modal value of 0. Therefore, time 2 measures of each dependent variable were log transformed4 to reduce the skew in their distributions. The transformed variables were then multiplied by 100 for ease of interpretation in the final models. Raw scores of the outcome measures are presented on Table 2 along with the transformed variables. Time 1 measures of these variables, used as controls, were the raw time 1 scores. ASP participation. This dummy variable is coded 1 if the youth was enrolled in one of the ASP programs and 0 if he or she was a member of one of the comparison groups. Control variables. The time 1 untransformed measure of each outcome was employed as a control for preexisting differences, as were the age, gender, and race of individuals. Demographic information was also gathered from the youth questionnaire. Correlations among level 1 variables are displayed on Appendix B. LEVEL 2 VARIABLES Data on program characteristics were collected through in-person interviews with program directors, attendance logs maintained by program staff, provider information forms completed by staff members, and program observations by University of Maryland researchers. Eighty-nine percent of programs were observed on two occasions. The remaining programs were observed once. Scores were averaged across the two observations for those programs observed twice. As mentioned, the MASOFP programs were dissimilar in style, content, size, and duration. The amount of data collected was too great to address comprehensively in a single paper. Because of the large number of variables available for this research and the relatively small number of programs, data reduction was necessary. We therefore eliminated redundant variables, those that lacked variability, and those that were ambiguously defined. Within these constraints, we attempted to include in the analysis measures of program characteristics that had been shown in previous research to be related to the outcomes of interest and to the measures of theoretical interest. We focus on five kinds of program characteristics: characteristics of the students who attended, characteristics of the organizational structure of the program, program management variables, time spent in recreation activities, and aggregate level of unsupervised socializing. Program-level 4. The original variables were transformed by adding “1” and then taking the natural log of the sum.


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TABLE 2. DESCRIPTION OF STUDY VARIABLES Individual-Level Predictor Variables Male Age African American Substance use T1 Delinquency T1 Victimization T1 Two-parent home Free lunch Outcome Variables (raw scores) Substance use T2 Delinquency T2 Victimization T2 Outcome Variables (log transformed Substance use T2 Delinquency T2 Victimization T2 Program-Level Population Variables Caucasian % Male % Attendance % Program Organization Variables Days elapsed from pretest to posttest Male staff % Structure and efficiency Reward use Program Management Variables Use of published curriculum Total hours of operation Days of training provided to staff % of staff with a BA degree or more Number of youths Program Time Expenditure Recreation % Unsupervised Socializing Friends alone days

N

Mean

497 497 492 489 493 491 495 464

0.39 12.36 0.49 0.08 0.11 0.18 0.44 0.47

491 0.11 492 0.10 489 0.11 and multiplied 491 8.86 492 8.62 489 9.10 35 35 35

S.D. 0.49 1.26 0.50 0.20 0.15 0.21 0.50 0.50 0.25 0.15 0.18 by 100) 18.06 12.54 14.16

Minimum Maximum Source 0 10.00 0 0 0 0 0 0

1 17.00 1 0.92 0.98 0.87 1 1

YQ YQ YQ YQ YQ YQ YQ YQ

0 0 0

1.14 0.63 0.69

YQ YQ YQ

0 0 0

76.23 48.92 52.37

YQ YQ YQ

43.64 41.02 66.41

36.63 18.35 16.58

0 0 35.03

100.00 100.00 94.94

YQ YQ AL

35 131.00 35 23.87 35 0.63 35 1.04

55.00 16.67 0.23 0.27

37.00 0 0.17 0.50

207.00 75.00 1.00 1.63

PID PF O PDI

35 0.71 0.46 35 342.72 175.57

0 56.00

1 728.00

PDI O, AL

35

3.44

1.77

0

35 35

45.03 29.78

30.77 19.10

35

12.31

8.39

0

35

2.37

1.12

0.33

0 9.00

5.00

PF

100.00 88.00

PF O

30.00

PDI

5.10

YQ

ABBREVIATIONS: AL = Attendance log; O = Observation; PDI = Program director interview; PF = Provider form; PID = Program Information Database; YQ = Youth questionnaire.

student characteristics used in analysis were percent of students white, percent of students male, and average attendance rate. Organizational structure variables were days elapsed between pretest and posttest, percent of staff members who were male, structure and efficiency of program activities (measured on a continuous scale from scored from 0 to 1, where high scores indicated adherence to a comprehensive schedule), and use of rewards to reinforce positive behavior (measured on a continuous scale


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from 0 to 2, where 2 indicated frequent use of all eight types of possible rewards, including informal, formal, social, or material awards and 0 indicated no rewards used). Program management variables were use of published curriculum to direct activities (coded 1 if program director responded “yes” when asked if any type of published curriculum was used in programming, 0 otherwise), total hours of program operation (calculated by multiplying the number of hours the program was open each day by the number of days it was open), amount of training provided to the average staff member (response categories ranged from 0 to 5, where “0” indicated no on-the-job training and “5” indicated four or more days), percentage of staff members who had a bachelor’s degree or more advanced level of education, and number of youths present on the day of observation. The remaining two categories of program characteristics each contain a single variable representing different measures of unstructured time use. First, we include the percent of time dedicated to recreation because previous research has suggested that the loose structure of sports activities may provide opportunities for engagement in antisocial behavior. Second, following Osgood and Anderson (2004), we include the average number of days per week students reported spending time with their friends without adults present. Alone time with friends was included as a program characteristic5 because many programs intend to decrease the amount of time youth spend in this activity. Students enrolled in ASPs should report spending very few days this way. Correlations among the program-level variables are displayed in Appendix C. Dependent variables and program characteristics were examined for outliers and missing data. The time 2 distribution of raw delinquency scores contained 13 outliers, the time 2 distribution of raw substance scores contained 14 outliers, and the victimization distribution contained 0 outliers. One program characteristic variable also contained an outlier. All outliers were trimmed to within 3 standard deviations of the mean. Missing data in the level 1 file were rare (see Table 2) and were handled through the use of listwise deletion.

ANALYSIS STRATEGY AND RESULTS HLM is appropriate for our data because the individual outcomes are nested within ASPs. The first stage of the analysis compared outcomes of the comparison and treatment groups. This model is the only one to include the comparison group. In all subsequent analyses, we explained 5. Significant between-program variability was observed for this measure, which suggests that, although it is based on youth self-reports, it does indeed measure a program characteristic.


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variability across ASPs in posttest scores, controlling for pretest scores, using only data from the treatment group. The alternative strategy of modeling variability in treatment-control differentials across programs was not an option because only 9 of 35 treatment sites included comparison groups. We used a liberal critical value for significance tests of p < .10 in these analyses, because of the small number of programs available for program-level analyses. Descriptive information for all study variables is presented in Table 2. This table shows considerable differences across programs in program characteristics. For example, on average, programs operated for 343 hours during the school year, whereas one program was open only 56 hours and another was open 728 hours. Thirty youths were observed at the average program, 9 at the smallest and 88 at the largest. The typical student attended his or her program on about 66% of the days it was open. But the average attendance was much lower for some programs, with one program averaging only 35% attendance. This evidence also shows that low attendance is avoidable, as one program achieved 95% attendance. A more in-depth look at the demographic makeup of programs (not tabled) showed that they were fairly racially homogeneous. At 15 programs, more than 90% of students were members of minority groups, whereas at 5 programs, less than 10% were.6 At the average program 59% of participants were girls and two programs served only girls. No programs served boys exclusively, but four programs served more than 75% boys. Table 3 shows the effect of ASP participation on all three outcomes, controlling for time 1 measures of the same outcome. All variables in these analyses were measured at the individual level. Independent variables were centered on their respective group mean for this analysis, and all coefficients in the model were allowed to vary across programs. The fixed effect analysis for each dependent variable shows that, on average, the measure of prior behavior is a significant predictor of the outcome. ASP treatment cases report significantly lower delinquency, on average, but the levels of victimization and substance use are similar for the ASP and control cases. Examination of the random effects portion of the model shows that the intercept, or mean level of problem behavior in each project, varies significantly across ASPs. The coefficients relating ASP participation and prior problem behavior to the outcomes also vary significantly across ASPs. The variation in program effectiveness across programs, which makes interpretation of the fixed effect ambiguous, is the focus of 6. The lack of ethnic heterogeneity within programs precluded analysis of withinprogram ethnicity effects.


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the remainder of the article. The level 2 models investigate which characteristics of programs or their populations are related to differences in outcomes. The first step of the examination of variability in the effectiveness of ASPs examines the “unconditional models” of the outcomes for the treatment group in order to determine the extent of variability that lies between programs. These models predict each individual-level outcome variable from a model that contains only intercepts and random disturbance terms at both levels 1 and 2. The random effects segments of these models show that significant variability occurs (p < 0.01 for substance use and delinquency; p < 0.05 for victimization) in the outcomes across projects. The intraclass correlations (the percentage of total variance in the outcomes that lies between programs) ranges from 5% to 7%, which is comparable with intraclass correlations for these outcomes reported between schools (Gottfredson et al., 2005). Next, individual-level characteristics were entered as predictors in the level 1 models.7 These models were estimated first with group-mean centering to test for variability across programs in the level 1 coefficients. This analysis shows that the intercepts for all outcome variables continues to vary significantly across ASPs (p < 0.01) for all three outcomes after controlling for the level 1 variables. The level 1 effects, for the most part, do not vary significantly across projects. The exceptions are the random effects components of prior behavior for delinquency and African American for substance use. These slopes were freed in subsequent analysis, whereas other slopes were fixed. The models were rerun using grand-mean centering. In these models, the intercept term from level 1 can be interpreted as the adjusted group mean for each outcome. Table 4 shows that prior behavior is a significant predictor of subsequent behavior for all outcomes. Surprisingly, gender does not predict any outcomes in this sample. Older students are more likely to report substance use, and African Americans are less likely. Older students also reported more delinquency. Nonsignificant level 1 variables were dropped in subsequent models. Next, program characteristics were entered into the level 2 equations to attempt to understand which program characteristics were related to the remaining between-program variability in the intercepts from the level 1 equations. These analyses were conducted in a stepwise fashion and used 7. We also attempted to include as level 1 control variables single-parent and free lunch status. These variables were moderately correlated with one another and with ethnicity. Their correlations with the outcome variables were small and not significantly different from 0 (see Appendix B). To simplify the models, they were excluded.

8.42 56.52a 0.88a

1.15 4.16 3.06

0.00** 0.00** 0.77

8.34 46.62a –5.38a

a

0.76 5.24 1.75

Delinquency (ln*100) Coefficient Robust S.E. 0.00** 0.00** 0.01**

p

8.35 18.66a 0.26a

a

0.76 3.52 1.31

Victimization (ln*100) Coefficient Robust S.E.

0.00** 0.00** 0.84

p

unknown

NOTE: Predictor variables are group mean centered. N of level 1 cases = 483–488; N of level-2 cases = 35. † p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001. a Coefficient varies significantly across programs, p < 0.10.

Intercept Prior behavior Treatment

a

Substance Use (ln*100) Coefficient Robust S.E. p

304

TABLE 3. LEVEL 1 MODEL OF ASP PARTICIPATION EFFECTS ON PROBLEM BEHAVIOR OUTCOMES

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7.20 49.83 −1.53 2.22 −3.05a

0.56 5.86 1.24 0.70 1.10

.00** .00** .22 .00** .01**

7.89 47.16a 0.90 0.75 0.59

a

0.84 5.20 1.27 0.47 1.06

Delinquency (ln*100) Coefficient Robust S.E. .00** .00** .43 .02* .50

p

8.35 18.93 −0.17 0.20 −0.74

a

0.83 3.86 1.56 0.51 1.35

Victimization (ln*100) Coefficient Robust S.E.

.00** .00** .91 .70 .59

p

unknown

NOTE: Predictor variables are grand mean centered. N of level 1 cases = 383–388; N of level 2 cases = 35. † p < 0.10; *p < 0.05; **p < 0.01. a In group-mean centered analysis, coefficient varies significantly across programs, p < 0.10.

Intercept Prior behavior Male Age African American

a

Substance Use (ln*100) Coefficient Robust S.E. p

TABLE 4. LEVEL 1 MODELS OF OUTCOME VARIABLES



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305


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grand-mean centering of level 1 variables. The five categories of independent variables were entered into the equation in blocks. Any variable in the block that significantly contributed to the outcome measure was maintained in subsequent steps, and those that were nonsignificant were dropped. All models include significant variables from the level 1 analysis, although these variables are not presented in the tables below. Model 1 introduces program-level student characteristics.8 Model 2 introduces the characteristics of the organizational structure of the program, model 3 includes program management variables, model 4 includes programming time spent in recreation activities, and model 5 includes aggregate level of unsupervised socializing. The results of the level 2 analysis for substance use are displayed on Table 5, results for delinquency are on Table 6, and results for victimization are on Table 7. Few program-level study variables are significantly related to substance use. No doubt, this is in part because most between-program variation in substance use was explained by previous use, age, and race. Nevertheless, several program management variables are significantly related to drug use. Programs that use published curriculum to guide programming and that employ a higher proportion of college-educated staff members are more successful at preventing drug use, net of individual predictors of substance use. Total hours of operation is statistically significant in model 3, which indicates that programs that are open more frequently are associated with increased drug use for participants. When recreation time and friends-alone time were added, this variable loses its statistical significance. More study variables predict delinquent behavior and victimization. Table 6 shows that a higher percentage of Caucasian program participants consistently predicts less delinquency. Although percentage of students male is not related to delinquency, the percent of staff male is related. The higher percentage of staff members male variable, the lower the delinquent behavior. Use of published curriculum and staff education have similar effects for delinquency as for substance use, but published curriculum does not retain its significance in the final delinquency model. Number of youth present is also significantly related to higher levels of delinquency and this effect remains significant even when all other significant programlevel variables are controlled. The final outcome of interest is victimization. As with the delinquency outcome, programs with more male staff and fewer participants are more 8. Staff reports of program location as urban or nonurban were available but could not be included in the multivariate analysis because of high collinearity between this measure and percent Caucasian (r = -0.473). In analyses not presented, however, all models were run excluding percent Caucasian and including urban location. Urban location was not a significant predictor of any outcome.


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307

TABLE 5. LEVEL 2 MODELS OF SUBSTANCE USE Student Characteristics % Caucasian % male % attendance Program Organization Days pretest to posttest Use of rewards Structure and efficiency % male staff Program Management Published curriculum Total hours operated Amount of staff training % staff holding a BA Number of youth Time Expenditure % of time in recreation Unsupervised Socializing Friends alone days

Model 1

Model 2

Model 3

Model 4

Model 5

–1.30 –0.26 –6.00

— — —

— — —

— — —

— — —

0.00 1.09 0.92 –2.33

— — — —

— — — —

— — — —

–3.43** 0.01† 0.23 –0.03* 0.04

–2.69* 0.01 — –0.05* —

–2.73* 0.01 — –0.04* —

0.07

— 0.24

NOTE: Robust standard errors used to determine p-levels. Outcome variable is the natural log multiplied by 100. † p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001.

successful at reducing victimization. Time spent in recreation and time spent alone with friends are both significant in the victimization analysis. Recreation time operates in the direction opposite to what we predicted, but the effect is reduced to nonsignificance when an alternative measure of unstructured socializing is introduced. Increased number of days that youth spent with friends away from adult supervision is associated with increased victimization. Neither measure of unstructured time use are statistically significant predictors in the models for substance use or delinquent behavior. These results contradict previous research on recreation time in ASPs and unsupervised socializing. This topic will be revisited in the discussion section. URBAN VERSUS NONURBAN PROGRAMS The effects of ASPs may differ by neighborhood type. Vandell and Shumow (1999) suggested that self-care may be more problematic for youths living in high-crime areas. ASPs located in more dangerous areas may have more potential to protect youths from victimization that might occur in the community than ASPs located in safer areas. On the other


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TABLE 6. LEVEL 2 MODELS OF DELINQUENCY Model 1 Model 2 Model 3 Model 4 Model 5 Student Characteristics % Caucasian % male % attendance Program Organization Days pretest to posttest Use of rewards Structure and efficiency % male staff Program Management Published curriculum Total hours operated Amount of staff training % staff holding a BA degree Number of youth Time Expenditure % of time in recreation Unsupervised Socializing Friends alone days

–2.63† –2.17 –2.93

–2.12 — —

–3.69** — —

–3.83** — —

–3.21* — —

0.01 2.13 –2.94 –4.63*

— — — –3.91†

— — — –4.20†

— — — –5.00*

–1.62* 0.00 0.23 –0.04* 0.04*

–1.11 — — –0.04** 0.03†

–0.62 — — –0.03* 0.03†

0.00

— 0.56


hand, Sherman (1997) suggested that ASPs located in urban areas might increase criminal activity by providing more opportunity for youths to interact with other crime-prone youths. Sherman noted community centers located in gang-ridden neighborhoods have the potential to increase opportunities for crime when they bring together victims and offenders in time and space. Unfortunately, our data do not allow a detailed examination of neighborhood context, primarily because of the small number of urban programs (10) in our sample. However, an exploratory analysis of differences across program locations in ASP program characteristics revealed five significant differences between urban and nonurban programs. Students at urban programs were about 8.5 months older on average than students at nonurban programs. On average, urban programs served a greater proportion of minority students (83% vs 46%), were open for 100 fewer total hours, had 48 fewer days in between pretest and posttests, and were rated 20% lower on structure and efficiency. Nevertheless, urban location was not a significant predictor of any study outcome (see footnote 9). Future researchers may wish to investigate potential differences in ASP effectiveness for different community contexts.


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TABLE 7. LEVEL 2 MODELS OF VICTIMIZATION Model 1 Model 2 Model 3 Model 4 Model 5 Student Characteristics % Caucasian % Male % Attendance Program Organization Days pretest to posttest Use of rewards Structure and efficiency % male staff Program Management Published curriculum Total hours operated Amount of staff training % staff holding a BA degree Number of youth Time Expenditure % of time in recreation Unsupervised Socializing Friends alone days

— — —

— — —

— — —

— — —

— — —

0.00 — — — –2.23 — — — –3.76 — — — –10.24** –9.25** –8.15** –14.20*** –0.60 0.00 0.06 –0.02 0.06†

— — — — 0.06* –0.16*

— — — — 0.06* –0.06 2.08***


DISCUSSION Prior research on the characteristics of effective ASPs examined correlates of success in models that did not control for potentially confounding characteristics of the individuals who happened to attend the programs. This study examined correlates of program success in reducing substance use, delinquency, and victimization experiences in models that control both for individual-level correlates of these outcomes and for programlevel average population characteristics. The results of this study confirm the importance of smaller size and greater structure in ASPs and suggest that certain characteristics of program staff are also important. These results confirm the findings of Weisman et al. (2002) regarding the importance of program size. Our results suggest that students who attended larger programs experienced more delinquency and victimization. The current results also accord with the Gerstenblith et al. (2005) finding that structured programming is important. Our direct measure of program structure was related to lower levels of delinquency and victimization, although not significantly. However, we


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found that the use of published curricula, an alternative measure of program structure, produced significant reductions in substance use. Similarly, higher amounts of youth time spent in unstructured socializing also increased victimization experiences. The current findings, however, contradict the Gerstenblith et al. finding about the negative effect of recreation time. In general, the amount of recreation time at programs did not affect our outcomes, and in the single instance in which it was significantly related to a dependent variable, the relationship was in direction opposite to that expected. The study highlights the importance of program staff in producing positive behavioral outcomes. Higher aggregate staff education was related to both decreased delinquency and victimization levels. An unexpected finding was that the percentage staff male was also related to lower levels of delinquent behavior and victimization. Although additional research will be required to understand this finding, one possibility is that male staff provide greater actual or perceived levels of guardianship than do female staff. The results of this study partially confirm the Osgood and Anderson (2004) research relating unstructured time usage to delinquency. The current analysis finds that aggregate unstructured socializing is related to higher rates of victimization. Previous work on the routine activities of adults has shown that spending time away from home is related to increased victimization (Forde and Kennedy, 1997), but this work has yet to be extended to juvenile populations. Future research might further explore the relation of unstructured socializing to victimization. Our study did not replicate the Osgood and Anderson (2004) finding that aggregate unsupervised socializing is related to delinquent behavior. We also found that this measure of time use was not significantly related to drug use. Although the zero-order correlation between aggregate unstructured time use and delinquency show the expected significant negative correlation (see Appendix C), the two are not significantly related in the multivariate model. An analysis not shown demonstrated that age explained all of the effect of alone time with friends on delinquency. This finding suggests an intuitive post hoc explanation for the difference between the current results and the Osgood and Anderson results; one that jibes well with basic differences in the current sample and the Osgood and Anderson sample. Our subjects are younger than the high-school students and young adults Osgood and Anderson studied—61% of our sample was 12 years old or younger. It is possible that the negative effects of unsupervised socializing with peers do not emerge until later adolescence. Our findings also showed that programs that hired more college graduates and used published curricula to guide program activities were more successful in preventing delinquency and drug use. The curriculum most


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commonly used by site directors was Botvin’s (Botvin et al., 1995) Life Skills Training (LST), although site directors reported using a large variety of curricula. LST focuses on drug resistance, self-management, and general social skills. Although this study is not a conclusive replication of previous research that suggests that social skills instruction is a beneficial component of ASPs, it does imply that incorporating planned activities based on established instructional methods is effective in preventing delinquency and victimization. LIMITATIONS Conclusions from this study are limited by the small number of programs and by the small number of individuals within some programs. Larger samples would have added power and would have allowed us to conduct analyses of within-program effects. We are further limited by the potential omitted variable bias, as we could not include, for a variety of reasons discussed above, relevant variables such as urbanicity, free lunch status, family structure, and emphasis on social skills instruction. Additionally, selecting the strongest programs (those that were open the longest and had the highest attendance rates) may have obscured some potential influences on effectiveness that are more distinctly lacking in disorganized or short-term programs. This research could have been strengthened if comparison cases were available for all programs. Comparison cases were recruited for only nine projects included in the analysis. This article did not focus on the effectiveness of the programs but on what characteristics were related to differing levels of the outcomes across programs. We could control for pretest measures of the outcome, which restricted the influence of selection effects in our results; yet the current study is correlational in nature and cannot establish causality. Despite these limitations, the study provides some insights into how ASPs can be structured and managed to increase their influence on problem behavior outcomes. The study confirms the importance of structure, staffing, and size in producing more positive behavioral outcomes. Smaller programs, programs that make use of structured programming (particularly, published curricula), and programs that employ more highly educated staff and higher percentages of male staff produced reductions in one or more problem behavior. We hope that designers of ASPs will consider using the results of our study to modify their programs designs, and that future research on the effectiveness of ASPs for reducing delinquency will be guided by these findings.


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REFERENCES Botvin, Gilbert J., Eli Baker, Linda Dusenbury, Elizabeth M. Botvin, and Tracy Diaz 1995 Long-term follow-up results of a randomized drug abuse prevention trial in a white middle-class population. Journal of the American Medical Association 273:1106–1112. Cohen, Lawrence E. and Marcus Felson 1979 Social change and crime rate trends: A routine activities approach. American Sociological Review 44:588–608. Dishion, Thomas J. 2000 Cross-setting consistency in early adolescent psychopathology: Deviant friendships and problem behavior sequelae. Journal of Personality 68:1109–1127. Dishion, Thomas J., Joan McCord, and Fran¸cois Poulin 1999 When interventions harm: Peer groups and problem behavior. American Psychologist 54:255–266. Espiritu, Rachele C., David Huizinga, Anne Crawford, and Rolf Loeber 2001 Epidemiology of self reported delinquency. In Rolf Loeber and David P. Farrington (eds.), Child Delinquents: Development, Intervention, and Service Need. Thousand Oaks, Calif.: Sage. Farrington, David P., Rolf Loeber, Magda Stouthamer-Loeber, Welmoet B. Van Kammen, and Laura Schmidt 1996 Self-reported delinquency and a combined delinquency seriousness scale based on boys, mothers, and teachers: Concurrent and predictive validity for African-Americans and Caucasians. Criminology 34:493–517. Forde, David R. and Leslie W. Kennedy 1997 Risky lifestyles, routine activities, and the general theory of crime. Justice Quarterly 14:265–294. Gerstenblith, Stephanie A., David A. Soule, ´ Denise C. Gottfredson, Shaoli Lu, Melissa A. Kellstrom, Shannon C. Womer, and Sean L. Bryner 2005 ASPs, antisocial behavior, and positive youth development: An exploration of the relationship between program implementation and changes in youth development. In Joseph L. Mahoney, Reed W. Larson, and Jacquelynne S. Eccles (eds.), Organized Activities as Contexts of Development: Extracurricular Activities, After-School and Community Programs. Mahwah, N.J.: Lawrence Erlbaum. Gottfredson, Denise C., Gary D. Gottfredson, and Stephanie A. Weisman 2001 The timing of delinquent behavior and its implications for ASPs. Criminology & Public Policy 1:61–80. Gottfredson, Denise C., David A. Soule, ´ and Amanda Cross 2004 A Statewide Evaluation of the Maryland After School Opportunity Fund Program. Final Report to the Maryland Department of Human Resources. (Technical report available from the authors.) Gottfredson, Denise C., Stephanie A. Gerstenblith, David A. Soule, ´ Shannon C. Womer, and Shaoli Lu 2004 Do ASPs reduce delinquency? Prevention Science 5:253–266.


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Gottfredson, Gary D., Denise C. Gottfredson, Allison A. Payne, and Nisha C. Gottfredson 2005 School climate predictors of school disorder: Results from the national study of delinquency prevention in schools. Journal of Research in Crime and Delinquency 42:412–444. Grossman, Jean B., Marilyn L. Price, Veronica Fellerath, Linda Z. Jucovy, Lauren J. Kotloff, Rebecca Raley, and Karen E. Walker 2002 Multiple Choices After School: Finding from the Extended Service School Initiative. Philadelphia, Pa.: Public/Private Ventures. Haynie, Dana L. and D. Wayne Osgood 2005 Reconsidering peers and delinquency: How do peers matter? Social Forces 84:1109–1130. Hindelang Michael J., Travis Hirschi, and Joseph G. Weis 1981 Measuring Delinquency. Thousand Oaks, Calif.: Sage. Hudley, Cynthia 2001 Perceived Academic and Behavioral Competence in Middle Childhood: Influences of a Community-Based Youth Development Program. ERIC Clearinghouse on Elementary and Early Childhood Education. James-Burdumy, Susanne, Mark Dynarsky, Mary Moore, John Deke, Wendy Mansfield, and Carol Pistorino 2005 When Schools Stay Open Late: The National Evaluation Of The 21st Century Community Learning Centers Program: Final Report. U.S. Department of Education, Institute of Education Sciences. Available online: http://www.ed.gov/ies/ncee. LoSciuto, Leonard, Susan M. Hilbert, Margaretta M. Fox, Lorraine L. Porcellini, and Alden Lanphear 1999 A two-year evaluation of the Wood Rock Youth Development Project. Journal of Early Adolescence 19:488–507. Mahoney, Joseph L., Hakan Stattin, and David Magnusson 2000 Youth recreation centre participation and criminal offending: A 20-year longitudinal study of Swedish boys. International Journal of Behavioral Development 24:1–12. Osgood, D. Wayne and Amy L. Anderson 2004 Unstructured socializing and rates of delinquency. Criminology 42:519–550. Osgood, D. Wayne, Janet K. Wilson, Patrick M. O’Malley, Jerald G. Bachman, and Lloyd D. Johnston 1996 Routine activities and individual deviant behavior. American Sociological Review 61:635–655. Sherman, Lawrence W. 1997 Communities and crime prevention. In Lawrence W. Sherman, Denise C. Gottfredson, Doris MacKenzie, John Eck, Peter Reuter, and Shawn Bushway (eds.), Preventing Crime: What Works, What Doesn’t, What’s Promising: A Report to the United States Congress. Washington, D.C.: U.S. Department of Justice Office of Justice Programs.


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Sickmund, Melissa, Howard Snyder, and Eileen Poe-Yamagata 1997 Juvenile Offenders and Victims: 1997 Update On Violence–Statistics Summary. Washington, D.C.: Office of Juvenile Justice and Delinquency Prevention. Snyder, Howard, Melissa Sickmund, and Eileen Poe-Yamagata 1996 Juvenile Offenders and Victims: 1996 Update On Violence. Washington, D.C.: Office of Juvenile Justice and Delinquency Prevention. Smith, Christine and Stephen D. Kennedy 1991 Final Impact Evaluation of the Friendly Peersuasion Program for Girls Incorporated. New York: Girls Incorporated. Vandell, Deborah and Lee Shumow 1999 After-school child care programs. The Future of Children: When School is Out 9:64–80. Weisman, Stephanie A. and Denise C. Gottfredson 2001 Attrition from ASPs: Characteristics of students who drop out. Prevention Science 2:201–205. Weisman, Stephanie A., Shannon C. Womer, Melissa A. Kellstrom, Sean L. Bryner, Amy Kahler, Lee Slocum, and Denise C. Gottfredson 2002 Maryland After School Community Grant Program. Report on the 20012002 School Year Evaluation of the Phase 3 After School Programs. (Technical report available from the authors.) Welsh, Brandon C. and Akemi Hoshi 2002 Communities and crime prevention. In Lawrence W. Sherman, David P. Farrington, Brandon C. Welsh, and Doris L. MacKenzie (eds.), EvidenceBased Crime Prevention. London, U.K.: Routledge.

Denise C. Gottfredson is a professor in the Department of Criminology and Criminal Justice at the University of Maryland. She received a Ph.D. degree in social relations from The Johns Hopkins University, where she specialized in sociology of education. Her research interests include delinquency and delinquency prevention and particularly the effects of school environments on youth behavior. She has recently completed randomized experiments to test the effectiveness of the Baltimore City Drug Treatment Court and the Strengthening Families Program in Washington, D.C. She is currently directing a randomized trial of the effects of after-school programs on the development of problem behavior. Amanda Cross graduated from New College of Florida in 2001 and received a Master of Arts degree from the University of Maryland in 2005. Her undergraduate concentration was developmental psychology with an emphasis on the precursors to deviance and antisocial behavior in adolescence and adulthood. Her interest in juvenile delinquency led her to seek graduate education in criminology. As a graduate student in the Department of Criminology and Criminal Justice at the University of Maryland, Amanda has worked in collaboration with the state of Maryland, investigating methods to improve the effectiveness of after-school programs. She currently manages a randomized trial of the effects of after-school programs on the development of problem behavior.


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David A. Soule´ is the Executive Director of the Maryland State Commission on Criminal Sentencing Policy. His research interests include sentencing, delinquency prevention, and program evaluation. He earned his Ph.D. degree in criminology and criminal justice from the University of Maryland in 2003.


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APPENDIX A. DESCRIPTIVE INFORMATION AND PRETEST SCORES FOR RETAINED TREATMENT AND CONTROL CASES

Demographic Characteristics % male Average age (years) % receiving free lunch* % African American* Mean Pretest Score Last-month drug use* Delinquency Victimization * Difference is significant p < .05.

Treatment N = 360–389

Control N = 104–108

38 12.3 52 54

41 12.5 30 31

0.06 0.11 0.17

0.14 0.12 0.16

Drug use (ln) Delinquency (ln) Victimization (ln) Drug use T1 Delinquency T1 Victimization T1 Age Male African American Two-parent home Free lunch

— 0.53** 0.20** 0.60** 0.41** 0.21** 0.23** –0.02 –0.04 –0.07 0.00 — 0.43** 0.45** 0.62** 0.34** 0.12* 0.05 0.16** –0.09 0.04

2

— 0.13** 0.32** 0.32** 0.08 0.03 0.01 –0.05 –0.04

3

— 0.53** 0.19** 0.14** –0.02 0.02 –0.05 0.10

4

— 0.40** 0.05 0.01 0.18** –0.08 0.11*

5

— 0.06 0.07 0.09 –0.06 0.11*

6

— 0.13** 0.25** –0.08 0.03

7

— −0.07 0.01 0.02

8

— –0.19** 0.31**

9

— –0.25**

10

—

11

unknown

* p < .05 ** p < .01

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

1

APPENDIX B. CORRELATION MATRIX FOR STUDENT-LEVEL VARIABLES, TREATMENT GROUP

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— 0.25 –0.20 0.21 –0.18 0.53** –0.31 –0.18 0.09 –0.38* 0.14 0.09 0.22 –0.42* 0.03 –0.34** –0.52** — –0.09 –0.16 –0.12 0.02 0.04 –0.22 0.04 0.07 0.17 0.02 –0.03 0.19 –0.03 –0.17 –0.10 — –0.12 –0.19 0.12 0.28 0.15 –0.23 –0.26 0.12 0.34* 0.13 –0.25 –0.49** –0.18 0.07

3

— –0.07 0.26 0.13 0.24 0.58** –0.13 –0.18 0.00 0.15 –0.05 0.09 –0.21 –0.18

4

— –0.18 0.04 0.08 0.04 0.43** –0.06 –0.12 –0.28 0.20 0.10 0.22 –0.01

5

— –0.21 0.17 0.11 –0.21 0.24 0.02 0.10 –0.46** –0.24 –0.43** –0.22

6

— –0.09 –0.12 –0.02 0.12 0.07 0.21 0.26 –0.04 0.04 –0.18

7

9

10

— 0.19 — 0.42* 0.23 — –0.30 –0.07 –0.28 –0.07 –0.16 –0.34* –0.27 0.09 –0.22 –0.14 0.21 0.28 –0.24 0.38* 0.00 0.09 0.06 0.32 0.06 0.13 0.11

8

— 0.21 0.21 –0.27 –0.21 –0.33 –0.13

11

13

— 0.10 — 0.02 –0.20 0.01 0.04 0.00 –0.37* 0.16 –0.41*

12

— 0.42* 0.57** 0.51**

14

— 0.46** 0.10

15

— 0.60**

16

—

17

unknown

NOTE: N = 35. *p < 0.05; **p < 0.01.

% Caucasian % male % attendance Days pretest to posttest Use of rewards Structure and efficiency % male staff Published curriculum Total hours operated Amount of staff training % staff holding a BA degree Number of youth % of time in recreation Friends alone days Mean drug use (ln) Mean delinquency (ln) Mean victimization (ln)

2

318

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.

1

APPENDIX TABLE C. CORRELATION MATRIX FOR PROGRAM-LEVEL VARIABLES



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