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O R I G I N A L P A P E R

A Meta-Analysis of After-School Programs That Seek to Promote Personal and Social Skills in Children and Adolescents

Joseph A. Durlak • Roger P. Weissberg •

Molly Pachan

Published online: 19 March 2010

� Society for Community Research and Action 2010

Abstract A meta-analysis of after-school programs that

seek to enhance the personal and social skills of children

and adolescents indicated that, compared to controls,

participants demonstrated significant increases in their

self-perceptions and bonding to school, positive social

behaviors, school grades and levels of academic achieve-

ment, and significant reductions in problem behaviors. The

presence of four recommended practices associated with

previously effective skill training (SAFE: sequenced,

active, focused, and explicit) moderated several program

outcomes. One important implication of current findings is

that ASPs should contain components to foster the personal

and social skills of youth because youth can benefit in

multiple ways if these components are offered. The second

implication is that further research is warranted on identi-

fying program characteristics that can help us understand

why some programs are more successful than others.

Keywords After-school � Meta-analysis � Social competence � Social skills � Youth development

Introduction

What is known about the impact of after-school programs

(ASPs)? Considerable attention has focused on the academic

benefits of ASPs. The results of two large-scale evaluations

of twenty-first Century Community Learning Centers (21

CCLCs) that is, centers that received federal funding

through No Child Left Behind legislation have generated

controversy. Neither the evaluation of centers serving ele-

mentary (James-Burdumy et al. 2005) or middle school

students (Dynarski et al. 2004) found any significant gains in

achievement test scores, although there were some gains in

secondary outcomes such as parental involvement in school

and student commitment to homework. These findings led

some to suggest drastic reductions in the levels of federal

financial support for ASPs, which had reached one billion

dollars a year by 2002 (Mahoney and Zigler 2006).

However, researchers have discussed several methodo-

logical issues that limit the interpretation of the results

of the national evaluations of 21 CCLCs (Kane 2004;

Mahoney and Zigler 2006). Depending on the age group in

question, these include the lack of initial group equivalence,

high attrition among respondents, low levels of student

attendance, and the possible nonrepresentativeness of

evaluated programs. There is also the problem of treating

centers as though they provided a uniform approach to

academic assistance when they clearly did not. While some

21 CCLCs provided students with intensive small group

instruction or individual tutoring, others merely asked stu-

dents to work independently on homework.

Instead of focusing on the results of only two evalua-

tions out of many, a well-done meta-analysis that evaluates

a broad sample of relevant studies carefully can assess the

magnitude of change on different outcomes and identify

some of the important characteristics of programs associ-

ated with more positive results. For example, the meta-

analysis of 35 outcome studies by Lauer et al. (2006) led to

the conclusion that ASPs ‘‘…can have positive effects on the achievement of academically at-risk students’’ (p. 303).

J. A. Durlak (&) � M. Pachan Department of Psychology, Loyola University Chicago,

6525 N. Sheridan Road, Chicago, Il 60626, USA

e-mail: [email protected]

R. P. Weissberg

Department of Psychology, University of Illinois at Chicago

& Collaborative for Academic, Social, and Emotional Learning

(CASEL), Chicago, Il, USA

123

Am J Community Psychol (2010) 45:294–309

DOI 10.1007/s10464-010-9300-6

Significant gains in reading or math achievement, or in

both areas, were observed for elementary, middle, and high

school students, and the latter group showed the most

improvement in both areas. Although the results of a meta-

analysis are never definitively conclusive, Lauer et al.’s

(2006) results begin to clarify which program participants

might be more likely to derive academic benefits from

ASPs.

What About Personal and Social Benefits?

The recent focus on the academic benefits of ASPs tends to

overlook the fact that many ASPs were initially created

based on the idea that young people’s participation in

organized activities after school would be beneficial for

their personal and social growth. While other factors have

influenced the growth of ASPs in the United States, one of

the goals of many current programs is to foster youths’

personal and social development through a range of adult-

supervised activities. Moreover, substantial developmental

research suggests that opportunities to connect with sup-

portive adults, and participate with peers in meaningful and

challenging activities in organized ASPs can help youth

develop and apply new skills and personal talents (Eccles

and Templeton 2002; Mahoney et al. in press; National

Research Council and Institute of Medicine 2002). In other

words, ASPs can be a prime community setting for

enhancing young people’s development.

Nevertheless, studies evaluating the personal and social

benefits of ASPs have produced inconsistent findings that

are further complicated by variations in the designs, par-

ticipants, and types of outcomes assessed across studies

(Harvard Family Research Project 2003; Mahoney et al. in

press; Riggs and Greenberg 2004). Just as the meta-anal-

ysis by Lauer et al. (2006) sought to clarify the nature and

extent of some of the academic benefits of ASPs, the cur-

rent study applied meta-analytic techniques in an effort to

examine the personal and social benefits of participation in

ASPs. No previous meta-analysis has systematically

examined the outcomes of ASPs that attempt to enhance

youths’ personal and social skills in order to describe the

nature and magnitude of the gains from such programs, and

to identify the features that characterize more effective

programs. These are the two primary goals of the current

review.

All the programs in the current review were selected

because they included within their overall mission the

promotion of youth’s personal and social development.

Although some ASPs offer a mix of activities that include

academic, social, cultural, and recreational pursuits, the

current review concentrates on those aspects of each pro-

gram that are devoted to developing youths’ personal and

social skills.

Impact of Skill Training

There is extensive evidence from a wide range of promo-

tion, prevention, and treatment interventions that youth can

learn personal and social skills (Collaborative for Aca-

demic, Social, and Emotional Learning [CASEL] 2005;

Commission on Positive Youth Development 2005; Lösel

and Beelman 2003). Programs that enhance children’s

social and emotional learning (SEL) skills cover such areas

as self-awareness and self-management (e.g., self-control,

self-efficacy), social awareness and social relationships

(e.g., problem solving, conflict resolution, and leadership

skills) and responsible decision-making (Durlak et al.

2009). Our first hypothesis was that ASPs attempting to

foster participants’ SEL skills would be effective and that

youth would benefit in multiple ways. We examined out-

comes in three general areas: feelings and attitudes, indi-

cators of behavioral adjustment, and school performance.

Positive outcomes have been obtained in these three areas

for school-based SEL interventions that target youths’

personal and social skills (Durlak et al. 2009), and we

hypothesized that a similar pattern of findings would

emerge for successful ASPs.

Recommended Practices for Effective Skill Training

Several authors have offered recommendations regarding

the procedures to be followed for effective skill training.

For instance, there is broad agreement that staff are likely

to be effective if they use a sequenced step-by-step training

approach, emphasize active forms of learning so that youth

can practice new skills, focus specific time and attention on

skill training, and clearly define their goals (Arthur et al.

1998; Bond and Hauf 2004; Durlak 1997, 2003; Dusenbury

and Falco 1995; Gresham 1995; Ladd and Mize 1983;

Salas and Cannon-Bowers 2001). Moreover, these features

are viewed as important in combination with each other

rather than as independent contributing factors. For

example, sequenced training will not be as effective if

active forms of learning are not used, and the latter will not

be as helpful unless the skills that are to be learned are

clearly specified.

Although the above recommendations are drawn from

skill training interventions that have primarily occurred in

school and clinical settings, we expected them to be sim-

ilarly important in ASPs. Therefore, we coded for the

presence of the four above features using the acronym

SAFE (Sequenced, Active, Focused and Explicit). We

hypothesized that staff that followed all four of these fea-

tures when they tried to promote personal and social skills

would be more effective than staff that did not incorporate

all four during skill development.

Am J Community Psychol (2010) 45:294–309 295

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For example, new skills cannot be acquired immedi-

ately. It takes time and effort to develop new behaviors and

more complicated skills must be broken down into smaller

steps and sequentially mastered. Therefore, a coordinated

sequence of activities is required that links the learning

steps and provides youth with opportunities to connect

these steps. Usually, this occurs through lesson plans or

program manuals, particularly if programs use or adapt

established curricula. Gresham (1995) has noted that it is

‘‘…important to help children learn how to combine, chain and sequence behaviors that make up various social skills’’

(p. 1023).

Youth do have different learning styles, and some can

learn through a variety of techniques, but evidence from

many educational and psychosocial interventions indicates

that the most effective and efficient teaching strategies for

many youth emphasize active forms of learning. Young

people often learn best by doing. Salas and Cannon-Bowers

(2001) stress that ‘‘It is well documented that practice is a

necessary condition for skill acquisition’’ (p. 480).

Active forms of learning require youth to act on the

material. That is, after youth receive some basic instruction

they should then have the opportunity to practice new

behaviors and receive feedback on their performance. This

is typically accomplished through role playing and other

types of behavioral rehearsal strategies, and the cycle of

practice and feedback continues until mastery is achieved.

These hands-on forms of learning are much preferred over

exclusively didactic instruction, which rarely translates into

behavioral change (Durlak 1997).

Sufficient time and attention must be devoted to any task

for learning to occur (Focus). Therefore, staff should des-

ignate time that is primarily directed at skill development.

Some sources discuss this feature in terms of training being

of sufficient dosage or duration. Exactly how many training

sessions are needed is likely to depend on the type and

nature of the targeted skills, but implicit in the notion of

dosage or duration is that specific time, effort, and attention

should be devoted to skills training. We coded programs on

focus because of its relevance to the current meta-analysis.

Although all reviewed programs indicated their intention to

develop youths’ personal and social skills, some did not

mention any specific program components or activities that

were specifically devoted to skill development. We

examined how program duration related to outcomes in a

separate analysis.

Finally, clear and specific learning objectives are pre-

ferred over general ones (Explicit). Youth need to know

what they are expected to learn. Therefore, staff should not

target personal and social development in general terms,

but identify explicitly what skills in these areas youth are

expected to learn (e.g., self-control, problem-solving skills,

resistance skills, and so on).

In sum, the current meta-analysis of ASPs that attempt

to foster the personal and social skills of program partici-

pants was conducted with the expectation that such pro-

grams would yield significant effects across a range of

outcomes, and that the application of four recommended

practices during the skill development components of ASPs

would moderate program outcomes.

Method

An ASP in this meta-analysis was defined as an organized

program offering one or more activities that: (a) occurred

during at least part of the school year; (b) happened outside

of normal school hours; and (c) was supervised by adults.

In addition to meeting this definition, the ASP had to meet

the inclusion criterion of having as one of its goals the

development of one or more personal or social skills in

young people between the ages of 5 and 18. The personal

and social skills could include any one or a combination of

skills in areas such as problem-solving, conflict resolution,

self-control, leadership, responsible decision-making, or

skills related to the enhancement of self-efficacy or self-

esteem. Included reports also had to have a control group,

present sufficient information so that effect sizes could be

calculated, and appear by December 31, 2007. Although it

was not a formal criterion, all the included reports descri-

bed programs conducted in the United States.

Evaluations that only focused on academic performance

or school attendance and only reported academic outcomes

were excluded, as were reports on adventure education and

Outward Bound programs, extra-curricular school activi-

ties, and summer camps. These types of programs have

been reviewed elsewhere (Bodilly and Beckett 2005;

Cason and Gillis 1994; Harvard Family Research Project

2003).

Locating Relevant Studies

The major goal of the search procedures was to secure a

nonbiased representative sample of studies by conducting a

systematic search for published and unpublished reports.

Four primary procedures were used to locate reports: (a)

computer searches of multiple databases (ERIC, PsycInfo,

Medline, and Dissertation Abstracts) using variants of the

following search terms, after-school, out-of-school-time,

school, students, social skills, youth development, children,

and adolescents (b) hand searches of the contents of three

journals publishing the most outcome studies (American

Journal of Community Psychology, Journal of Community

Psychology, and Journal of Counseling Psychology),

(c) inspection of the reference lists of previous ASP

reviews and each included report, and (d) inspection of the

296 Am J Community Psychol (2010) 45:294–309

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database on after-school research maintained by the

Harvard Family Research Project (2009) from which many

unpublished reports were identified and obtained. The dates

of the literature search ranged from January 1, 1980 to

December 31, 2007. Although no review can be absolutely

exhaustive, we feel that the study sample is a representative

group of current program evaluations.

Study Sample

Results from 75 reports evaluating 69 different programs

were evaluated. Several reports presented data on separate

cohorts involved in different ASPs, each with its own

control group, and these interventions were treated as

separate programs. In the 75 evaluations, 68 assessed

outcomes at post; 8 also collected some follow-up infor-

mation, and 7 only contained follow-up data. Post effects

were based on the endpoint of the youths’ program par-

ticipation. That is, on those occasions when two reports

were available on the same participants and one contained

results after 1 year of participation while the second

offered information after 2 years of participation, only the

latter data were evaluated. The final study sample con-

tained examples of 21st CCLCs, programs conducted by

Boys and Girls and 4-H Clubs, and a variety of local ini-

tiatives developed and supported by various community

and civic organizations.

Index of Effect

The index of effect was a standardized mean difference

(SMD) that was calculated whenever possible by sub-

tracting the mean of the control group from the mean of the

after school group at post (and at follow-up if relevant) and

dividing by the pooled standard deviation of the two

groups. If means and standard deviations were not avail-

able, then effects were estimated using procedures descri-

bed by Lipsey and Wilson (2001). When results were

reported as nonsignificant and no other information was

available, the effect size for that outcome measure was set

at zero. There were 38 imputed zero effects and these

values were not significantly associated with any coded

variables.

Each effect was corrected for small sample bias and

weighted by the inverse of its variance prior to any analysis

(Hedges and Olkins 1985). Larger effects are desired and

reflect a stronger positive impact on the after-school group

compared to controls. Whenever possible, we adjusted for

any pre-intervention differences between groups on each

outcome measure by first calculating a pre SMD and then

subtracting this pre SMD from the obtained post SMD.

This strategy has been used in other meta-analyses (Derzon

2006; Wilson et al. 2001).

The consistent strategy in treating SMDs was to calcu-

late one effect size per study for each analysis. In other

words, for the first analysis of the overall effects from all

68 programs at post, we averaged all the effect sizes within

each study so that each study yielded only one effect. For

the subsequent analyses by outcome category, if there were

multiple measures from a program for the same outcome

category, they were averaged so that each study contributed

only one effect size for that type of outcome. For example,

if SMDs from measures of self-esteem and self-concept

were available in the same study, the data were averaged to

produce a single effect reflecting self-perceptions.

A random effects model was used in the analyses. A

random effects model assumes that variation in SMDs across

studies is the result of both sampling error and unique but

random features of each study, and the use of such a model

permits a broader range of generalization of the findings. A

two-tailed .05 probability level was used throughout the

analyses. Mean effects for different study groupings are

reported along with .05 confidence intervals (CI). Moreover,

homogeneity analyses were conducted to assess whether

mean SMDs estimate the same population effect. Homo-

geneity analyses were based on the Q statistic which is

distributed as a chi-square with k - 1 degrees of freedom,

where k = the number of studies. For example, when studies

are divided for analysis to assess possible moderator vari-

ables, Q statistics assess the statistical significance of the

variability in effects that exists within and between study

groups. In addition, we also used the I2 statistic (Higgins

et al. 2003) which indicates the degree rather than the sta-

tistical significance of the variability of effects (heteroge-

neity) among a set of studies along a 0–100% scale.

Coding

A coding system was developed to capture basic study

features, methodological aspects of the program evalua-

tion, and characteristics of the ASP, participants, and out-

comes. The coding of most of the variables is

straightforward and only a few variables are described

below.

Methodological Features

Two primary methodological features were coded as

present or absent: use of a randomized design, and use of

reliable outcome measures. The reliability of an outcome

measure was considered acceptable if its alpha coefficient

was C0.70, or an assessment of inter-judge agreement for

coded or rated variables was C.70 (for kappa, C.60). We

coded reliability in a dichotomous fashion because several

reports offered no information on reliability. A third

method variable, attrition, was measured on a continuous

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basis as the percentage of the initial sample that was

retained in the final analyses (possible range 0–100%).

Outcome Categories

Outcome data were grouped into eight categories. Two of

these assessed feelings and attitudes (child self-perceptions

and bonding to school); three were indicators of behavioral

adjustment (positive social behaviors, problem behaviors,

and drug use), and three assessed aspects of school per-

formance (achievement test scores, grades, and school

attendance).

Self-perceptions

Self-perceptions included measures of self-esteem, self-

concept, self-efficacy and in a few cases (four studies)

racial/cultural identity or pride. School bonding assessed

positive feelings and attitudes toward school or teachers

(e.g., liking school, or reports that the school/classroom

environment or teachers are supportive). Positive social

behaviors measured positive interactions with others.

These are behavioral outcomes assessing such things as

effective expression of feelings, positive interactions with

others, cooperation, leadership, assertiveness in social

contexts or appropriate responses to peer pressure or

interpersonal conflict. Problem behaviors assessed diffi-

culties that youth demonstrated in controlling their

behavior adequately in social situations, and included dif-

ferent types of acting-out behaviors such as noncompli-

ance, aggression, delinquent acts, disciplinary referrals,

rebelliousness, and other types of conduct problems. Drug

use primarily consisted of youth self-reports of their use of

alcohol, marijuana, or tobacco. Achievement test scores

reflected performance on standardized school achievement

tests typically assessing reading or mathematics. School

grades were either drawn from school records or reported

by youth and reflected performance in specific subjects

such as reading, mathematics or social studies, or overall

grade point average. School attendance assessed the fre-

quency with which students attended school.

SAFE Features

The presence of the four recommended practices for skill

training was coded dichotomously on a yes/no basis.

Sequenced: Does the program use a connected and coor-

dinated set of activities to achieve their objectives relative

to skill development? Active: Does the program use active

forms of learning to help youth learn new skills? Focused:

Does the program have at least one component devoted to

developing personal or social skills? Explicit: Does the

program target specific personal or social skills? Programs

that met all four criteria were designated as SAFE pro-

grams while those not meeting all four criteria were called

Other programs.

Reliability of Coding

Reliability was estimated by randomly selecting 25% of the

studies that were then coded independently by the first

author and trained graduate student assistants who worked

at different time periods. Kappa coefficients corrected for

change agreement were acceptable across all codes (0.70–

0.95, average = 0.85) and disagreements in coding were

resolved through discussion. The product moment corre-

lations for coding continuous items including the calcula-

tion of effects were all above 0.95.

Results

Table 1 summarizes several features of the 68 studies with

post data. Sixty-seven per cent of the studies appeared after

2000, and the majority were unpublished technical reports

or dissertations (k = 51, or 68%). Nearly half of the pro-

grams served elementary students (46%), over a third

served students in junior high (37%), and a few involved

high school students (9%; six evaluations did not report the

age of participants). In terms of methodological features,

35% employed a randomized design, mean attrition was

10%, and reliability was reported and was acceptable for

73% of the outcome measures.

Twenty-five studies did not specify the ethnicity of the

participants at post, and the remaining 43 reported this

information in various ways. Among the latter studies,

participating youth were predominantly ([90%) African American in ten studies; Latino in six studies, Asian or

Pacific Islander in three studies, and American Indian in

one study. There was no information on the socioeconomic

status of the participants’ families in nearly half of the

reports (k = 31, or 46%). Based on the way information

was reported in the remaining studies, 17 studies primarily

served a low-income group (25%) and 13 studies (19%)

served youth from both low- and middle-income levels.

Overall Impact at Post

First, we inspected the distribution of effects and Winsor-

ized three values that were C3 standard deviations from the

mean (i.e., reset these values to three standard deviations

from the mean). The Winsorized study level effects, which

ranged in value from -0.16 to ?0.85, had an overall mean

of ?0.22 (CI = 0.16–0.29), which was significantly dif-

ferent from zero. These data indicate that ASPs have an

overall positive and statistically significant impact on

298 Am J Community Psychol (2010) 45:294–309

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participating youth. However, there was statistically sig-

nificant variability in the distribution of effects based on

the Q statistic (Q = 306.42, p \ .001), and a high degree of variability according to the I

2 value (78%) suggesting

the need to search for moderator variables that might

explain this variability in program impact.

In What Ways Do Youth Change?

Table 2 presents the mean effects obtained for the eight

outcome categories, their confidence intervals, and the

number of studies contributing data for each category.

Significant mean effects ranged in magnitude from 0.12

(for school grades) to 0.34 for child self-perceptions (i.e.,

increased self-confidence and self-esteem). The mean

effects for school attendance (0.10) and drug use (0.10)

were the only outcomes that failed to reach statistical

significance. In other words, ASPs were associated with

significantly increased participants’ positive feelings and

attitudes about themselves and their school (child self-

perceptions and school bonding), and their positive social

behaviors. In addition, problem behaviors were signifi-

cantly reduced. Finally, there was significant improvement

in students’ performance on achievement tests and in their

school grades. These data support our first hypothesis.

Participation in ASPs is associated with multiple benefits

that pertain to youths’ personal, social, and academic life.

Moderator Analysis

There were 41 SAFE programs evaluated at post that fol-

lowed all four recommended skill training practices; 27

Other programs did not use all four practices. Table 3

contains the mean SMDs for SAFE and Other Programs

overall and within each of the eight outcome categories

along with Q and I2 values. The use of I2 aids in inter-

pretation because the Q statistic has low power when the

number of studies is small and conversely may be statis-

tically significant when there are a large number of studies,

even though the amount of heterogeneity might be low

(Higgins et al. 2003). When studies are grouped according

Table 1 Descriptive characteristics of reviewed studies at post

k %

Publication features

Date of report

1979–1990 3 4.4

1991–2000 19 27.9

2001–2008 46 67.6

Source of report

Published article 24 35.3

Unpublished report 44 64.7

Methodological features

Experimental design

Randomized 24 35.3

Quasi-experimental design 44 64.7

Reliability of outcome measures

Acceptable reliability 270 73.2

Unknown/unacceptable 99 26.8

Mean per cent of attrition 10

Characteristics of participants

Mean educational level

Elementary school (K-5) 31 45.6

Middle school (6–8) 25 36.8

High school (9–12) 6 8.8

Did not report 6 8.8

Presenting problems

None (universal intervention) 61 89.7

Some presenting problems 7 10.3

Predominant ethnicity of participants

[90% African-American 10 14.5 [90% Latino 6 8.8 [90% Asian/Pacific Islander 3 4.4 [90% American Indian 1 1.5 Did not report ethnicity 25 36.8

Socioeconomic status

Predominately low income 17 25.0

Mixed income 13 19.1

Did not report SES 31 45.6

Program features

Duration

Less than 1 year 45 66.2

1–2 years 12 17.6

More than 2 years 11 16.2

The percentages do not always add to 100% due to missing data

Table 2 Mean effects for 68 studies at post in each outcome area

Outcomes SMD k 95% Confidence interval

Feelings and attitudes

Child self-perceptions 0.34* 23 {0.23, 0.46}

School bonding 0.14* 28 {0.03, 0.25}

Indicators of behavioral adjustment

Positive social behaviors 0.19* 36 {0.10, 0.29}

Problem behaviors 0.19* 43 {0.10, 0.27}

Drug use 0.10 28 {0.00, 0.20}

School performance

Achievement test scores 0.17* 20 {0.06, 0.29}

School grades 0.12* 25 {0.01, 0.23}

School attendance 0.10 21 {-0.01, 0.20}

* Denotes mean effect is significantly different from zero at the .05

level

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to hypothesized moderators, there should be low hetero-

geneity within groups (reflected in low I2 values and non-

significant Q statistics) but high and statistically significant

levels of heterogeneity between groups (reflected by

corresponding high I2 values and statistically significant

Q-between values). Benchmarks for I 2

suggest that values

under 15% indicate negligible heterogeneity, from 15 to

24% reflect a mild degree of heterogeneity, between 25 and

50% a moderate degree, and values C75% a high degree of

heterogeneity (Higgins et al. 2003).

The data in Table 3 indicate that whereas SAFE pro-

grams are associated with significant mean effects for all

outcomes (mean SMDs between 0.14 and 0.37), Other

programs do not yield significant mean effects for any

outcome. There is empirical support for moderation for

four outcomes in terms of significant Q-between statistics

and correspondingly high (74–93%) I2 values (positive

social behaviors, problem behaviors, achievement test

scores, and grades). However, the Q-between statistics

were not significant and the I2 values were generally low

for the other four outcomes (self-perceptions, school

bonding, drug use and school attendance). Furthermore,

there is a moderate degree of within group variability

among SAFE programs (I2 values between 34 and 76%) for

four outcomes (problems behaviors, drug use, test scores

and grades) suggesting the possibility of additional mod-

erators that might improve the model fit.

Although we required that program staff had to follow

all four SAFE practices, there was some relationship

between the absolute number of practices used and out-

comes. The mean study-level ESs for staff using none, one,

two, or four of the SAFE practices (three practices were not

present in any report) were 0.02 (k = 4), 0.07 (k = 7), 0.10

(k = 16), and 0.31 (k = 0.31), respectively.

Ruling out Rival Explanations

To examine other potential explanations for the results we

first compared the effects in each outcome category for

studies grouped according to each of the following vari-

ables: randomization (yes or no), use of a reliable outcome

measure (yes or no), presence of an academic component in

the ASP (yes or no), and the educational level (elementary,

middle, or high school) and gender of the participants. We

also computed product moment correlations between SMDs

and sample size, program duration, and per cent of attrition.

There were too few data on participants’ ethnicity and

socioeconomic status to examine these variables ade-

quately. Setting was strongly associated with the presence

of an academic component so we only examined the latter

variable (i.e., school-based programs were more likely to

offer some form of academic assistance).These procedures

resulted in 64 analyses (eight variables crossed with eight

outcome categories). For these analyses, significant effects

emerged in only two cases, which would be expected by

chance. The use of randomized designs was associated with

higher levels of positive social behaviors (Q-between =

4.80, p \ .05), and there was a significant positive

Table 3 Outcomes for the use of recommended skill training practices as a moderator (SAFE Criteria)

SAFE programs at post Other programs at post Between groups

SMD k 95%CI Q-within I2 values SMD k 95%CI Q-within I2 values Q-between I2 values

All programs 0.31* 41 {0.24, 0.38} 48.07 17 0.07 27 {-0.01, 0.16} 11.94 0 17.69** 94

Feelings and attitudes

Child Self-

perceptions

0.37* 21 {0.24, 0.50} 21.22 6 0.13 2 {-0.33, 0.59} 0.69 0 0.69 0

School bonding 0.25* 13 {0.08, 0.41} 14.65 18 0.03 15 {-0.12, 0.19} 6.86 0 3.33 70

Indicators of behavioral adjustment

Positive social

behaviors

0.29* 19 {0.21, 0.37} 15.27 0 0.06 17 {-0.03, 0.15} 23.75 33 13.97** 93

Problem

behaviors

0.30* 22 {0.17, 0.42} 31.74 34 0.08 21 {-0.05, 0.20} 17.01 0 5.85* 82

Drug use 0.16* 12 {0.05, 0.27} 17.86 38 0.03 16 {-0.08, 0.13} 16.36 8 2.94 66

School performance

Achievement

test scores

0.20* 10 {0.13, 0.27} 36.93** 76 0.02 10 {-0.04, 0.07} 1.75 0 15.14** 93

Grades 0.22* 9 {0.07, 0.36} 19.71 60 0.05 16 {-0.04, 0.13} 10.71 0 3.91* 74

School

attendance

0.14** 9 {0.05, 0.24} 10.69 25 0.07 12 {0.01, 0.13} 10.52 0 1.65 39

* Denotes mean effect is significantly different from zero at the .05 level

** Denotes mean effect is significantly different from zero at the .01 level

300 Am J Community Psychol (2010) 45:294–309

123

correlation between female gender and higher test scores

(r = 0.69, p \ .01). Overall, these analyses suggest that the above variables do not serve as an alternative explanation

for the positive findings obtained by SAFE programs.

Additional comparisons indicated that SAFE and Other

programs did not differ significantly on any of the above

variables.

We also examined publication source and found that

published reports (k = 24) yielded significantly higher

study-level ESs than unpublished (k = 44) reports

(respective mean SMDs = 0.34 and 0.10). Upon further

examination, this effect was restricted to Other programs.

Whereas 25 unpublished Other studies yielded a nonsig-

nificant mean SMD of 0.05 (CI = -0.03, 0.13), the two

published studies of Other programs had a mean SMD of

0.69 (CI = 0.21, 1.17). In contrast, there was no SMD

mean difference between the 19 unpublished and 22 pub-

lished reports of SAFE programs (mean SMDs = 0.31 and

0.30, respectively).

Sensitivity Analyses

We conducted several sensitivity analyses of study-level

effects in consideration of different features of program

evaluations. Among the 68 evaluations at post, eight were

conducted by researchers who had apparently developed

the skills content of the ASP and might have a vested

interest in its positive outcomes; there were 17 studies in

which investigators failed to confirm the pre-intervention

equivalence of program and control groups; there were four

cases of differential attrition occurring between program

and control groups; and in eight cases, some criterion

related to attendance was used when composing the inter-

vention sample (e.g., only children who attended a certain

percentage of available times were assessed). On the other

hand, in 22 studies an intent-to-treat analysis was con-

ducted in which all youth assigned to the ASP were

assessed regardless of whether or not they attended fre-

quently or not at all. In two cases, the same ASP was

evaluated in two separate reports. Separate analyses

removing the studies with each of above features did not

change the main outcome findings.

Finally, although published and unpublished SAFE

studies yielded similar results, we also conducted a trim

and fill analysis (Duval and Tweedie 2000) to estimate the

possibility of publication bias on study-level effect sizes

(i.e., to determine if additional but missing unpublished

studies would change the main finding). This procedure

suggested the trimming and filling of four studies and

resulted in an adjusted mean estimate for SAFE programs

that remained statistically significant from zero (mean

ES = 0.22, p \ .05).

The Impact of Pre SMDs

The impact of computing pre SMDs is reflected in the

mean comparisons between the 81 outcomes in which it

was possible to calculate such SMDs (group 1) versus the

remaining 334 outcomes in which these data could not be

calculated due to lack of information (group 2). While the

post mean SMDs are similar for both groups (0.20 and

0.18, respectively), the mean pre SMD for group 1 was

-0.10. Subtracting the pre SMD from the post SMD to

create an adjusted post SMD for group 1 produced a sig-

nificant mean difference at post favoring group 1 (0.29

versus 0.18, respectively, p \.01). The values of the pre and post mean SMDs for group 1 indicate that on 20% of

the outcomes the after-school group started at a docu-

mented disadvantage compared to controls, but overcome

this disadvantage over time and were superior to the con-

trol group at post. Including pre SMDs increased the

overall mean effect by 61% on these outcomes. (0.29 vs.

0.18). Pre SMDs were not more likely for some outcome

categories than others, nor were they associated with other

coded variables except for SAFE and Other programs. The

former were more likely to have pre SMD which might be

one factor contributing to their larger effects.

Putting Current Findings into Context

It may seem customary to view the effects achieved in this

review (i.e., mean SMDs in the 0.20 and 0.30s) as ‘‘small’’

in magnitude. However, methodologists now stress that

instead of simply resorting to Cohen’s (1988) conventions

regarding the size of obtained effects, findings should be

interpreted in the context of prior research and, whenever

possible, in terms of their practical value (Vacha-Haase and

Thompson 2004). If one does so, the impact for ASP

programs achieves more prominence.

For example, Table 4 compares the mean SMDs

achieved by the 41 effective SAFE programs to the results

reported in meta-analyses of other interventions for school-

aged youth. The SMDs of SAFE programs are similar to or

better than those produced by several other community-

and school-based interventions for youth assessing out-

comes such as self-perceptions, positive social behaviors,

problem behaviors, drug use, and school performance

(DuBois et al. 2002; Durlak and Wells 1997; Haney and

Durlak 1998; Lösel and Beelman 2003; Tobler et al. 2000;

Wilson et al. 2001, 2003). For these comparisons, we used

the findings from other meta-analyses regarding universal

interventions wherever possible because the vast majority

of effective ASPs in our review did not involve youth with

identified problems.

Of particular note, the mean SMD obtained by SAFE

programs on achievement test scores (0.31) is not only

Am J Community Psychol (2010) 45:294–309 301

123

larger than the effects obtained in reviews of primarily

academically-oriented ASPs and summer school programs

(Cooper et al. 2000; Lauer et al. 2006), but is comparable

to the results of 87 meta-analyses of school-based educa-

tional interventions (Hill et al. 2008).

It is possible to convert a mean SMD into a percentile

using Cohen’s U3 index to reflect the average difference

between the percentile rank of intervention and control

groups (Institute for Education Sciences 2008a). A mean

effect of 0.31 translates into a percentile difference of 12%.

Put another way, the average member of the control group

would demonstrate a 12 percentile increase in achievement

if they had participated in a SAFE after-school program.

Results at Follow-up

The 15 reports containing follow-up data collected infor-

mation on different outcome categories. The cell sizes at

follow-up ranged from zero for school attendance to nine

for self-perceptions (mean ES = 0.19; p \ .05). Unfortu- nately, there is too little information at follow-up to offer

any conclusions about the durability of changes produced

by ASPs.

Discussion

This is the first meta-analysis to evaluate the outcomes

achieved by ASPs that seek to promote youths’ personal

and social skills. This review included a large number of

ASPs (k = 75), and is the first time many of these reports

have been scrutinized. Two-thirds of the evaluated reports

appeared after 2000. As a result, this review yields an up-

to-date perspective on a rapidly growing research literature.

Current data indicate that ASPs had an overall positive

and statistically significant impact on participating youth.

Desirable changes occurred in three areas: feelings and

attitudes, indicators of behavioral adjustment, and school

performance. More specifically, there were significant

increases in youths’ self-perceptions, bonding to school,

positive social behaviors, school grades, and achievement

test scores. Significant reductions also appeared for prob-

lem behaviors. Finally, SAFE programs were associated

with practical gains in participants’ test scores suggesting

an average difference of 12 percentile points between the

after-school and control group, and achieved results in this

and several other areas that were similar to or better than

those obtained by many other evidence-based psychosocial

interventions for school-aged populations. The implication

of current findings is that ASPs merit support and recog-

nition as an important community setting for promoting

youths’ personal and social well-being and adjustment.

An important qualification is that not all ASPs were

effective. Only the group of SAFE programs yielded sig-

nificant effects on any outcomes. Commenting on the

results of our review as well as several others, Granger

(2008) noted that although some ASPs achieve positive

results, many others do not, indicating that there is much

room for improvement among current programs. As we

discuss, below, this has important implications for future

research and practice.

Several steps were taken to increase the credibility of

the findings. We searched carefully and systematically for

relevant reports to obtain a representative sample of

Table 4 Comparing the mean effects of SAFE programs to the results of other universal interventions for children and adolescents

Outcomes Current review Other reviews

Mean effects

Feelings and attitudes

Self-perceptions 0.37 0.19 a

School bonding 0.25 –

Indicators of behavioral adjustment

Positive social behaviors 0.29 0.15 b , 0.39

c

Problem behaviors 0.30 0.21 b , 0.27

c , 0.09

d , 0.17

e 0.30

f

Drug use 0.16 0.11 b , 0.05

e , 0.15

g

School performance

Achievement test scores 0.20 0.11 b , 0.30

f , 0.24

h

Grades 0.22 –

School attendance 0.14 –

Results from other meta-analyses are from outcome categories most comparable to those in the current review and resulting from weighted

random effects analyses whenever possible a

Haney and Durlak (1998), b

DuBois et al. (2002), c

Lösel and Beelman (2003), d

Wilson et al. (2003), e

Wilson et al. (2001), f

Durlak and

Wells (1997), g

Tobler et al. (2000), h

Hill et al. (2008)

302 Am J Community Psychol (2010) 45:294–309

123

published and unpublished evaluations, and are confident

that our sample of studies is an unbiased representation of

evaluations of ASPs meeting our inclusion criteria that

have appeared by the end of 2007. We also examined and

were able to rule out some plausible rival explanations for

our main findings. Furthermore, the current review under-

estimates the true impact of ASPs for at least two reasons.

One has to do with the nature of the control groups used in

current evaluations; the second has to do with the dosage of

the intervention received by many program youth.

Control Groups

The intent of this review was to compare outcomes for

youth attending a particular ASP to those not attending the

program, but this does not mean that comparison youth

constituted a true no intervention control group. For

example, it is well known that in any one time period not

only do many youth spend their out-of-school time in

different pursuits (e.g., in ASPs, extra-curricular school

activities and church groups, as well as hanging out with

friends, and being alone some of the time), but also they

may change their level of participation across activities

over time (Mahoney et al. 2006, in press). In five reviewed

reports, authors noted that youth in their control condition

were participating in alternative ASPs or other types of

potentially beneficial out-of-school time activities (Brooks

et al. 1995; Philliber et al. 2001; Rusche et al. 1999; Tebes

et al. 2007; Weisman et al. 2003). It is recommended that

evaluators monitor the types of alternative services that are

received by control groups, so a truer estimate of the

impact of intervention can be made.

Program Dosage

It is axiomatic that recipients must receive a sufficient

dosage for an intervention to have an effect. However, it

appears this did not happen in several of the reviewed

programs, which may be an explanation for the poor results

obtained for some programs. Although each report did not

contain specific data on program attendance, when some

information was presented it was apparent that attendance

was a problem for several programs. For example, youths’

attendance ranged from 15 to 26% in 11 evaluations (Baker

and Witt 1996; Dynarski et al. 2004; James-Burdumy et al.

2005; LaFrance et al. 2001; Lauver 2002; Maxfield et al.

2003, two cohorts; Philliber et al. 2001; Prenovost 2001,

three cohorts).

Moreover, analyses conducted in some reports indicated

that attendance was positively related to youth outcomes.

This occurred in six of the seven studies that examined this

issue, although significant differences did not always

emerge on every outcome measure (Baker and Witt 1996;

Fabiano et al. 2005; Lauver 2002; Morrison et al. 2000;

Prenovost 2001; Vandell, et al. 2005; Zief 2005). Reviews

of other ASPs have also reported a significant positive

relationship between attendance and positive outcomes

(Simpkins et al. 2004, but also see Roth et al. 2010).

Furthermore, attendance is only one aspect of partici-

pation. Information is also needed on the breadth of youth

activities within any program and their level of engagement

in each activity. For example, studies suggest that youths’

level of engagement predicts positive social and academic

outcomes (Mahoney et al. 2007; Shernoff 2010). In sum,

the receipt of alternative after-school activities by control

groups and the low attendance achieved in some programs

worked against finding positive outcomes. The next sec-

tions discuss several other issues suggested by the current

findings.

Elements of Effective ASPs

As hypothesized, the use of four recommended training

practices (i.e., SAFE) moderated several outcomes and

distinguished between ASPs that were or were not asso-

ciated with multiple positive outcomes. Moreover, there is

convergent evidence from numerous other sources on the

importance of SAFE features. Although the terminology

may differ, others have mentioned the importance of one or

more SAFE features in ASP programs (Gerstenblith et al.

2005; Granger and Kane 2004; Mahoney et al. 2001, 2002;

Miller 2003, National Research Council and Institute of

Medicine 2002). For example, Granger (2008) noted that

our data were consistent with a developing consensus in the

after-school field that ‘‘being explicit about program goals,

implementing activities focused on these goals, and getting

youth actively involved are practices of effective pro-

grams’’ (p. 11). We recommend that future research should

continue to examine the value of these features in ASPs.

Fortunately, SAFE practices can be applied to a wide

variety of intervention approaches.

Gains in Achievement Test Scores

SAFE ASPs yielded significant improvement in partici-

pants’ standardized test scores and at a magnitude (i.e.,

SMD of 0.31), which is over two times larger than that

found in the previous meta-analysis of academically-ori-

ented ASPs (Lauer et al. 2006). Why were current pro-

grams so effective in the academic realm?

There are several possible explanations. First, it should

come as no surprise that programs promoting skill devel-

opment can also improve school performance. There is

now a growing body of research indicating that interven-

tions that promote SEL skills also result in improved aca-

demic performance (Collaborative for Academic, Social,

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123

and Emotional Learning [CASEL] 2005; Weissberg and

Greenberg 1998; Zins et al. 2004). We have obtained a

mean SMD of similar magnitude (i.e., 0.27) for school-

based interventions promoting students’ personal and

social skills (Durlak et al. 2009).

Second, current results are based on a set of recent

evaluations of ASPs, only a few of which have ever been

part of any previous review. Although we did not code the

academic components of ASPs, it is possible that devel-

opers of newer ASPs may have used strategies that would

strengthen their impact. For example, others have sug-

gested that gains in academic achievement are more likely

to occur if staff are well-trained and supervised, use evi-

dence-based instructional strategies, are supportive and

reinforcing to youth during learning activities, conduct pre-

assessments to ascertain learners’ strengths and academic

needs, and coordinate their teaching or tutoring with school

curricula (e.g., Birmingham et al. 2005; Southwest Edu-

cational Development Laboratory 2006). A recent multi-

site evaluation indicated that ASP participants do manifest

academic progress if evidence-based instructional strate-

gies are used and are well-implemented (Sheldon et al. in

press). More research needs to analyze how different fea-

tures of the academic components of future ASPs con-

tribute to outcomes. Third, it must be acknowledged that

only 20 programs collected outcome data on academic

achievement, so current results need replication in more

programs to confirm their generality.

Limitations and Directions for Future Research

There are four important limitations in our review that

suggest directions for future research.

1. Current conclusions rest upon outcome research that

should be improved in several ways. Many reports lacked

data on the racial and ethnic composition or the socio-

economic status of participants, so we could not relate

outcomes to these participant characteristics. Missing sta-

tistical data at pre or post limited the number of effects that

could be directly calculated. At a minimum, future program

evaluations should provide complete information on the

demographic characteristics of participants, their pre and

post scores on all outcomes, and, if pertinent, their prior

academic achievement, and any presenting problems youth

might have. The goals, procedures and contents of each

program component should be specified and described, and

data on levels of participation and breadth and degree of

engagement in different activities should be included.

Reliable and valid outcome measures should be used and,

whenever possible, data should be collected using multiple

methodologies (e.g., from school records, questionnaires,

and behavioral observations) and from multiple informants

(e.g., youth, parents, teachers, and ASP staff).

Future evaluations should also be aware of the analytic

procedures that should be used for nested designs. That is,

when an intervention is conducted in a group context or

setting such as in an ASP, participant data are not inde-

pendent and analyses treating individual data as indepen-

dent can greatly increase Type I error rates. Unfortunately,

virtually all the reviewed reports employed one interven-

tion and one control group so that appropriate corrections

for nested data could not be made (Baldwin et al. 2005).

Guidelines are available for the appropriate analyses of

nested data (Institute for Education Sciences 2008b;

Raudenbush and Bryk 2002).

Care is also needed in designating program participants.

Eight studies only analyzed data from participants who had

attended a certain number of program activities using

unique criteria in each circumstance. This method con-

founds the impact of intervention with dosage. A preferred

strategy used in some studies (e.g., Philliber et al. 2001;

Weisman et al. 2001; Zief 2005) is an intent-to-treat

analysis in which all participants’ data are evaluated

regardless of their program dosage. Additional analyses

can then be conducted to examine the relationship between

program attendance and outcomes.

Current findings illustrate how the impact of interven-

tion can be more completely portrayed by including pre

SMDs in the final calculation of effects. On 19% of the

outcomes, the after-school group started at a disadvantage

(mean pre SMD = -0.10) but overcame this disadvantage

over time (mean post SMD = 0.20). Incorporating pre

SMDs increased the final SMD for these outcomes by 69%

(0.29 versus 0.20). More journals are now requiring authors

to report SMDs for individual studies (Durlak 2009) and

future researchers should consider calculating adjusted

SMDs that take into consideration any initial differences

between groups.

2. Although the four SAFE features we assessed did

distinguish between more and less effective programs, it is

important to put these findings in context. First, authors

have noted additional aspects of skill training that are

important, such as the trainer’s interpersonal skills, sensi-

tivity to the learner’s developmental abilities and cultural

background, and the importance of helping youth gener-

alize their newly-developed skills to everyday settings

(Dusenbury and Falco 1995; Gresham 1995). Unfortu-

nately, information on these additional recommended ele-

ments was not available.

Second, although previous authors have stressed that the

four features we assessed work in combination with each

other, their relative influence might nevertheless vary not

only in relation to youths’ developmental level and cultural

background, but also on the nature and number of targeted

skills. For example, younger children will likely need more

practice than older youth when attempting to master more

304 Am J Community Psychol (2010) 45:294–309

123

complex skills. The relative influence of different training

procedures on eventual skill development also deserves

attention in future research.

Third, it would be preferable to evaluate SAFE practices

as continuous rather than dichotomous variables. That is,

program staff can be compared in terms of how much they

focus on skill development and use of active learning

techniques instead of viewing these practices as all-or-none

phenomena. Observational systems have now been devel-

oped to record the use of SAFE practices in ASPs as

continuous variables (Pechman et al. 2008).

Fourth, based on Q and I2 values there was stronger

empirical support for SAFE practices as moderators for

some outcomes over others (e.g., for positive social

behaviors, problem behaviors, test scores, and grades) and

it was possible to calculate pre SMDs for more SAFE than

Other programs. Therefore, current data are just a begin-

ning in exploring the ‘‘black box’’ of ASPs, that is, in

understanding all the structures and processes that consti-

tute an effective program. Current data are correlational in

nature and we cannot conclude that SAFE features caused

the positive changes in program participants. Because the

current meta-analysis focused only on the skill-building

components of ASPs, it is possible that additional program

variables play a role in the effectiveness of ASPs. For

example, program quality is one feature that comes to

mind, and has been emphasized in the operation of ASPs

(Birmingham et al. 2005; Granger 2008; High/Scope

Educational Research Foundation 2005; Miller 2003;

Vandell et al. 2004).

Several independent groups have focused on six core

features that contribute to the quality of a youth develop-

ment program (Yohalem et al. 2007). In addition to skill-

building opportunities, these include the characteristics of

interpersonal relationships (between staff and youth and

among youth), the program’s psychosocial environment,

the level and nature of youths’ engagement in activities,

social norms, and program routine and structure. In turn,

these features are related to such variables as staff behav-

ior, program policies, youth initiative, and issues related to

community partnerships and support. Information on these

variables were not available in reviewed reports, and future

researchers should explore their influence.

Two additional, important foci for future research

involve the creation and evaluation of effective staff

development and training programs and data on program

implementation. What are the most efficient ways for staff

to learn new techniques and implement them effectively?

Some authors stress the importance of less structured

activities that might stimulate youth initiative and foster

heightened leadership skills and autonomy. The South

Baltimore Youth Center (Baker et al. 1995) which did not

follow SAFE practices used an empowering strategy by

having participants assume responsibility for all major

Youth Center activities. This strategy was associated with

impressive significant improvement in adolescents’ self-

reported delinquent behavior and drug use (SMDs of 1.10

and 0.82, respectively). Data from other studies also con-

firm the value of empowering strategies in ASPs (Hirsch

and Wong 2005; Hansen and Larson 2007), but more

controlled outcome data are needed.

Nevertheless, the findings on the value of structured skill

development practices do not necessarily contradict the

value of less structured activities for three reasons. First,

alternative strategies can lead to similar outcomes. There

are many possible ways to get from Point A to Point B and

some competencies may be better promoted via one strat-

egy than another. Studies directly comparing the relative

benefits of different strategies on different skills and

adjustment outcomes would be helpful. Second, most ASPs

contain multiple components so more structured approa-

ches can be used some of the time and less structured ones

at other times. Third, empowerment strategies can be used

within structured components, for example, by asking more

skilled youth to be role models, trainers, or co-group

leaders for others. Assuming such roles could promote

youths’ leadership skills and sense of self-efficacy.

Future research that can clarify how different aspects of

program quality influence different youth outcomes will be

extremely helpful in improving ASPs. Because program

quality is a multi-dimensional construct, assessing quality

across its dimensions and relating these to a range of youth

outcomes can provide an empirical basis for understanding

the processes within ASPs that lead to different results. As

research on this topic accumulates, it will be possible to

develop a clearer understanding of what constitutes a high

quality program and in what respects current programs can

be improved.

3. Unfortunately, few reports have collected any follow-

up data, so we cannot offer any conclusions about the long-

term effects of ASPs. Hopefully, future evaluations will

contain follow-up information to determine the durability

of any gains emanating from program participation.

4. Although the initial study sample seems sufficiently

large (68 studies with post data), dividing studies first

according to outcome categories, and then according to

other potentially important variables reduced the statistical

power of the analyses. Therefore, the failure to obtain

statistically significant findings for some of the variables

examined here should be viewed cautiously.

As more ASP evaluations appear, researchers will have

more power to detect the influence of potentially important

variables. At the individual level, we need information on

how gender, race/ethnicity, age, income status, and the

presence of academic or behavioral problems are related to

participants’ participation, engagement and different types

Am J Community Psychol (2010) 45:294–309 305

123

of outcomes. At the ecological level we need to understand

how family, school, and neighborhood characteristics and

resources are associated with consistent and active partic-

ipation in ASPs, and interact with various program

processes and structures to influence youth outcomes

(Mahoney et al. 2007; Weiss et al. 2005). Such data would

help us maximize the fit between program features and

local needs to increase the reach and benefits of ASPs.

Notwithstanding the above limitations, the current

review offers empirical support for the notion that ASPs

can be successful in achieving their historical mission of

fostering the personal and social development of young

people. Although not conclusive, current findings should

stimulate more interest in investigating and understanding

how ASPs programs affect youth, and what can be done to

enhance their effectiveness.

Acknowledgments This article is based on a grant from the William T. Grant Foundation (grant #2212) awarded to the first and

second authors. We wish to express our appreciation to David

DuBois, Mark Lipsey, Robert Granger, and Nicole Yohalem who

provided helpful comments on an earlier draft of this manuscript. We

offer additional thanks to Mark Lipsey and David Wilson for pro-

viding the macros used for calculating effects from each relevant

outcome and conducting the statistical analyses. Finally, we wish to

thank Heather Weiss and Chris Wimer from the Harvard Family

Research Project who supplied copies of relevant reports that we were

unable to obtain.

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