This meta-analytic review summarizes obesity prevention programs and their effects and investigates participant, intervention, delivery, and design features associated with larger effects. A literature search identified 64 prevention programs seeking to produce weight gain prevention effects, of which 21% produced significant prevention effects that were typically pre-to post effects. Larger effects emerged for programs that targeted children and adolescents (vs. preadolescents) and females, programs that were relatively brief, programs that solely targeted weight control versus other health behaviors (e.g., smoking), programs evaluated in pilot trials, and programs wherein participants must have self-selected into the intervention. Other factors, including mandated improvements in diet and exercise, sedentary behavior reduction, delivery by trained interventionists, and parental involvement, were not associated with significantly larger effects. Keywords: obesity, prevention, meta-analysis, moderators Obesity in adulthood results in an increased risk for future death from all causes, coronary heart disease, atherosclerotic cerebrovascular disease, and colorectal cancer, as well as serious medical problems including hyperlipidemia, hypertension, gallbladder disease, and diabetes mellitus Unfortunately, successful treatments for obesity have been elusive. For adults, the current treatment of choice only results in about a 10% reduction in body weight, and virtually all patients regain this weight within a few years of treatment . Obesity treatments for children and adolescents have yielded similar effects, though behavioral family-based interventions have produced more persistent weight loss effects Studies have evaluated four major types of interventions that were expected to produce weight gain prevention effects. These include (a) multifocus cardiovascular disease prevention programs that targeted obesity along with other risk factors for cardiovascular disease (e.g., hypertension and smoking), (b) prevention programs that focused solely on the prevention of obesity or weight gain, (c) interventions designed to solely increase physical activity, and (d) eating disorder prevention programs that promoted use of healthy weight-management skills. Although numerous evaluations of weight gain prevention programs have been conducted, their results have not been comprehensively reviewed and analyzed with meta-analytic procedures. Several excellent narrative reviews exist (e.g., We are very grateful to Amy Greenwold, Krista Heim, and David Huh for their assistance with the literature search and article preparation. Correspondence , Vol. 132, No. 5, 667-691 0033-2909/06/$12.00 DOI: 10.1037/0033-2909 667 to systematically consider the moderators associated with interventions that produced the largest effects. The third aim is to discuss promising directions for future research in light of the findings from completed trials. Putative Moderators of Intervention Effects A unique feature of meta-analyses is that they permit empirical examination of factors associated with variation in effect sizes. Elucidating factors that moderate prevention program effects is informative because it highlights aspects of the participants, intervention, program delivery, and research design that are associated with stronger intervention effects. This information should increase the yield of future prevention efforts by identifying the conditions under which optimal prevention effects occur. As well, this information might identify particular subgroups of individuals for whom alternative obesity prevention programs need to be developed. Analyses of moderators of intervention effects should also advance general theories regarding effective routes to alter maladaptive health behaviors and attitudes. Accordingly, we investigated several potential moderators of intervention effects that were selected on the basis of theory, prior findings, and previous literature reviews. Participant Features Participant Age Researchers have hypothesized that obesity prevention programs are more effective when they are delivered to middle school or high school students versus grade school students Participant Gender Results from prior trials suggest that obesity prevention programs that promoted a healthier lower calorie diet Participant Ethnicity There is also reason to believe that ethnicity might moderate obesity prevention effects. On the one hand, there is evidence that Black and Hispanic individuals show elevated rates of overweight and obesity as well as greater increases in weight over development, relative to other ethnic groups (e.g., Risk Status of Participants More generally, we have hypothesized Intervention Features Intervention Duration Previous meta-analyses of prevention programs for other problem behaviors have suggested that longer duration multisession interventions produced more superior effects than very brief interventions Parental Involvement It has also been suggested that parental involvement leads to more favorable results in obesity prevention, as the family is thought to be key to developing a psychosocial environment that is conducive to healthy eating and physical activity Psychoeducational Content Because research has suggested that psychoeducational content is ineffective in producing behavioral change Dietary Improvement One implication from the energy balance model of obesity is that a reduction in fat and sugar intake and an increase in fruit and vegetable intake will decrease the risk for future weight gain Increased Activity Another implication from the energy balance model of obesity is that increased physical activity will decrease risk for future weight gain Reduced Sedentary Behavior A third implication of the energy balance model of obesity is that interventions that reduce sedentary behavior, such as TV viewing and video game use, should also decrease risk for future weight gain. Indeed, it has been theorized that more effective obesity prevention programs focused on reducing sedentary behavior Number of Behavior Targets Our review of the literature suggested that the number of health behaviors targeted in an intervention was inversely related to the magnitude of intervention effects for obesity. Specifically, it appeared that interventions that attempted to change a broad array of health behaviors, such as body weight, blood pressure, cholesterol, and smoking, were less effective than programs that focused solely on body weight. Our clinical experience from designing and evaluating prevention programs also suggests that interventions focusing on a few concepts are more effective than those focusing on a broader array of concepts. It may be that the greater the complexity of the message relayed by the intervention, the more difficult it is for participants to process, store, and retrieve information presented in the programs. Consistent with this general impression, a review of school-based cardiovascular disease prevention trials concluded that broad-based programs targeting multiple health behaviors aimed at reducing risks for cardiovascular disease have not been effective for reducing obesity in children OBESITY PREVENTION PROGRAMS Delivery Features Teachers Versus Professional Interventionists Researchers have suggested that obesity prevention programs are more effective when delivered by dedicated interventionists versus classroom teachers Didactic Versus Interactive Format Meta-analytic reviews of substance abuse Design Features Pilot Study Our review of the prevention and treatment literature for obesity and eating disorders suggested that larger intervention effects were often observed for pilot trials of a new intervention relative to large demonstration trials. Such a pattern of effects might occur because interventionists are more passionate about new prevention programs or because demonstration trials are more methodologically rigorous and are therefore more immune to experimenter effects (e.g., because they more often use blinded assessors and minimal intervention control conditions). Thus, we hypothesized that intervention effects would be significantly larger for pilot evaluations of new interventions. Recruitment Method Our experience suggests that intervention effects are often larger when prevention programs are delivered solely to participants who have actively self-selected into trials in response to recruitment efforts, such as media advertisements, relative to when prevention programs are offered to all individuals in a defined population (e.g., a particular school). Presumably this is because the former strategy recruits individuals who are more motivated to achieve weight gain prevention effects and therefore engage more effectively in the prevention program. Thus, we hypothesized that intervention effects would be significantly larger for selfpresenting volunteers than for participants recruited through population-based recruitment efforts. Random Assignment We theorized that trials that randomly assigned participants to condition might produce larger intervention effects than trials that used alternative approaches to allocating participants to treatment condition, such as matching. We reasoned that because random assignment is the best approach to generating groups that are equivalent on any potential confounding variables at baseline (with sufficiently large sample sizes), it should therefore minimize the chances that any of these confounding variables are correlated with treatment condition, which should thus maximize the ability to detect intervention effects if they really occur (i.e., randomization maximizes the signal-to-noise ratio reflected in inferential tests of the intervention effects). Accordingly, we hypothesized that intervention effects may be greater for trials that used random assignment relative to other approaches to assigning participants to condition. However, because the proper analysis of intervention effects involves tests of differential change across conditions, which adjusts for any initial differences at baseline on the outcome, we suspected that this effect might not reach statistical significance. Consistent with this expectation, random assignment did not emerge as a significant moderator of effects sizes in our meta-analysis of eating disorder prevention programs Nested Data Modeled Incorrectly Virtually all parametric inferential tests, such as repeated measures analysis of variance, growth curve, and survival models, used to test for intervention effects within randomized trials assume independence of errors. However, when participants are nested within schools, classes, or group-based interventions, the assumption of independence may not hold Potential Artifacts We also investigated three variables that might produce artifacts for the effect sizes and bias our estimates of effect size moderators, with the goal of including these variables as covariates in the models if necessary. First, our review of the eating disorder prevention field suggested that interventions tend to produce larger effect sizes when they are compared with assessment-only or waitlist control conditions relative to when they are compared with active interventions that are credible and structurally matched to the intervention in terms of contact hours Method Sample of Studies Following the recommendations of Lipsey and Wilson Preventive Medicine, Journal of Pediatrics, Health Education Quarterly). Third, we consulted narrative reviews of the obesity prevention field to search for additional citations of relevance. Fourth, the reference sections of all identified articles were examined. Finally, established obesity prevention researchers were contacted and asked for copies of unpublished articles (under review or in press) describing prevention trials. Inclusion and Exclusion Criteria The defining feature of a successful obesity prevention program is that it results in significantly less weight gain or risk for obesity onset than observed in the control group. Thus, we only included trials that used some type of proxy measure of body fat as an outcome. Most trials used the body mass index (BMI ϭ Kg/M 2 ) as the primary proxy measure of body fat, but a few studies, particularly older ones, used skinfold thickness. It is important to note that BMI is not a direct measure of body fat. Although this proxy measure tends to show high correlations with the most precise measures of body fat (r ϭ .80 -.90), such as dual energy x-ray absorptiometry (DEXA; Dietz & Robinson, 1998), it has been found to show lower agreement with DEXA measures in large epidemiology samples (r ϭ .71; Ellis, As noted previously, we included trials that were primarily conceptualized as evaluations of obesity prevention programs, as well as trials that evaluated other interventions that were expected to result in less weight gain or risk for obesity onset but that were not primarily conceptualized as obesity prevention programs (e.g., certain physical activity interventions, eating disorder prevention programs, and psychoeducational interventions). A prior meta-analysis indicated that certain eating disorder prevention programs and psychoeducational interventions produced significant weight gain prevention effects This meta-analysis focused solely on effect sizes for weight gain prevention effects, as assessed by differential change in body fat measures. We did not include effect sizes for changes in self-reported dietary intake or physical activity, because numerous trials have found significant intervention effects for self-reported dietary intake and physical activity, but no significant effects for weight change (e.g., We focused exclusively on prevention programs that were evaluated in controlled trials. We included trials in which participants were randomly assigned to an intervention; to active interventions that were not focused on weight gain prevention (e.g., a general parent training intervention); or to usual-programming (e.g., standard physical education classes), waitlist, or assessment-only control conditions. We also included trials in which some relevant comparison group was used (e.g., matched controls) in a quasiexperimental design. Random assignment to condition is optimal because it is the best approach to generating comparison groups that are equated on any potential confounding variables at baseline We also focused exclusively on studies that tested whether the change in the outcomes over time was significantly greater in the intervention group versus the control group. This could take the form of a Time ϫ Condition interaction in a repeated-measures analysis of variance model, an analysis of covariance model that controlled for initial levels of the outcome variable, or a growth curve model that controlled for initial levels of the outcome (e.g., the effects were conditional upon the intercept value of the dependent variable coded to reflect the level of the outcome at baseline; We excluded trials that were described as obesity treatment programs by the authors because the purpose of the present report was to provide a meta-analytic review of programs that sought to prevent future weight gain or obesity onset. Nonetheless, we included evaluations of programs that sought to prevent future weight gain in overweight or obese samples if they were not referred to as treatment programs by the authors. More generally, we did not exclude studies solely because the average BMI of participants fell above conventional cutoffs for overweight or obese (e.g., over 25 or 30 for young adult samples). We also restricted our focus to trials that targeted children and adolescents because of our interest in determining whether effective interventions have been designed for developing individuals. We believe that obesity prevention programs should be implemented before most individuals will show onset of obesity. However, we used a broad view of adolescence and included trials with a mean participant age of up to 22 years because this captured college-based obesity prevention programs. College-aged individuals are still developing self-regulation skills, particularly with regard to dietary and exercise behaviors. In addition, many developmental psychologists consider adolescence to span from approximately age 12 through age 24 because most individuals in the United States have not settled into adult roles by their early 20s Effect Size Estimation Procedures We calculated effect sizes for tests of differential change in BMI and risk for obesity onset across the intervention and control conditions because virtually all of the prevention trials included BMI as a primary outcome. Although other proxy measures of adiposity were used in several trials, such as skinfold thickness and waist-to-hip ratios, these latter outcomes were operationalized inconsistently and were collected in only a subset of the trials. We considered averaging the effect sizes from these various adiposity proxy measures, but we noted that the intervention effects for these various outcomes were often contradictory and were concerned that averaging across diverse measures would introduce unnecessary error variance into the analyses. Furthermore, the measurement error is considerably lower for the BMI relative to alternative proxy body fat measures, including waist circumference, triceps skinfold, and subscapular skinfold measures The correlation coefficient (r) was selected as the index of effect size because of its similar interpretation across different combinations of interval, ordinal, and nominal variables (Pearson's r, Spearman's rho, and point biserial; 1 If effect sizes were reported in Cohen's (1988) d, we converted them to r with the formula provided on page 20 of We were able to use the methods described previously to generate effect sizes or estimates of effect sizes for all trials that reported significant intervention effects and for most trials that reported nonsignificant effects. However, for the two trials that reported nonsignificant effects and did not provide any other data with which to estimate the effect size Operationalization and Coding of Effect Size Moderators 2 It might be noted that only 55% of the trials that did not use random assignment to condition used matching to create the groups, suggesting that the variable reflecting random assignment was not simply a surrogate for matching, which would have complicated the interpretation of the former moderator. STICE, SHAW, AND MARTI One aspect of our coding system was constrained by the distribution of a certain moderator across studies. Specifically, although we were interested in testing whether the intervention effects were significantly larger for females than males, only 33% of the trials that we located reported effect sizes separately for the sexes (and only 21% provided a direct test of whether sex moderated the intervention effects). Accordingly, we tested whether interventions offered solely to females were more effective than those offered solely to males or those offered to both sexes. We took this approach because (a) this variable emerged as a significant predictor of eating disorder prevention program effects There were also a number of other potential moderators that we were unable to code because insufficient information was provided in the articles and reports. We were unable to code average attendance because only 44% of the studies reported this variable. We were unable to code the socioeconomic status of the sample because parallel information (e.g., average parental income) was reported in only 35% of studies. We were unable to code the method of handling missing data (e.g., listwise deletion [completer analysis], last observation carried forward, full information maximum likelihood estimation imputation) because less than 40% of the studies reported this information. We used a consensus approach to coding the effect size moderators. Eric Stice and Heather Shaw were each responsible for coding certain moderators but consulted with each other when questions regarding the coding of particular studies arose. Although this approach allowed for a refinement of the coding system and served to increase interrater agreement, we did not use the consensus approach on all data points or double code all studies. Thus, we examined intercoder agreement by having Eric Stice and Heather Shaw code all of the moderators for a randomly selected 30% of the trials examined in this meta-analytic review. Results Descriptive Statistics The literature search identified 46 trials that met the inclusion criteria, in which 61 different obesity prevention programs were evaluated (12 trials evaluated more than 1 prevention program, and 3 prevention programs were evaluated in 2 trials), resulting in a total of 64 effect sizes for this review. Of these 64 prevention programs, 30 were universal, and 34 were selected. The majority focused on both males and females (n ϭ 48), but 14 focused solely on females, and 2 focused solely on males. The majority of these interventions were school-based programs (84%). A total of 51 of the 64 prevention programs used random assignment to condition, of which 13% were randomized at the participant level, 2% were randomized at the group level, and 85% were randomized at the school level. Brief descriptions of the samples, program content, and intervention effects are provided in To assess interrater agreement between the two coders responsible for abstracting effect sizes and moderators, we calculated the interclass correlation coefficient for continuous variables and kappa () coefficients for nominal variables (see Average Effect Size and Effect Size Heterogeneity Analyses were conducted on the effect size for change in BMI in the intervention condition versus the control condition. We first converted Pearson's rs to z scores to avoid problematic standard error estimates The average effect size across all studies was very small (r ϭ .04) but was significantly larger than zero (z ϭ 2.94, p Ͻ .01). The rs for the effect sizes ranged from Ϫ.24 to .50. Only 13 of these interventions (1 of which was evaluated in two trials), or 21% of the 61 programs evaluated, found significant positive intervention effects based on an alpha level of .05 There was significant heterogeneity in effect sizes (Q ϭ 204.41, p Ͻ .001), indicating that there was variability across the effect sizes produced by the interventions (i.e., that effects were not equivalent across trials). The heterogeneity in the effects suggests that there may be participant, intervention, delivery, and design features that account for the variability in effect sizes. Moderator Analyses Two moderators could not be examined because of severe restrictions in range; because only two studies used credible active control conditions, and because we located only two unpublished reports, we did not consider type of control condition or publication status 3 further. Two potential confounding variables were not examined because they did not show significant relations to effect sizes: preliminary univariate analyses indicated that length of follow-up (z ϭ 1.58, p ϭ .11, ␤ ϭ 0.18) and the age range of participants in the trials (z ϭ .80, p ϭ .42, ␤ ϭ 0.10) were not significantly related to effect size magnitude. Within this context, it should be noted that preliminary analyses also indicated that 3 Even though there were only two unpublished trials included in the present meta-analysis, we confirmed that there was no evidence that the unpublished studies had significant different effect sizes relative to published studies (z ϭ .03, p ϭ .82, ␤ ϭ 0.03).

The authors assess sex differences in the importance of 10 basic values as guiding principles. Findings from 127 samples in 70 countries (N 77,528) reveal that men attribute consistently more importance than women do to power, stimulation, hedonism, achievement, and self-direction values; the reverse is true for benevolence and universalism values and less consistently for security values. The sexes do not differ on tradition and conformity values. Sex differences are small (median d .15; maximum d .32 [power]) and typically explain less variance than age and much less than culture. Culture moderates all sex differences and sample type and measurement instrument have minor influences. The authors discuss compatibility of findings with evolutionary psychology and sex role theory and propose an agenda for future research.

The authors propose new procedures for evaluating direct, indirect, and total effects in multilevel models when all relevant variables are measured at Level 1 and all effects are random. Formulas are provided for the mean and variance of the indirect and total effects and for the sampling variances of the average indirect and total effects. Simulations show that the estimates are unbiased under most conditions. Confidence intervals based on a normal approximation or a simulated sampling distribution perform well when the random effects are normally distributed but less so when they are nonnormally distributed. These methods are further developed to address hypotheses of moderated mediation in the multilevel context. An example demonstrates the feasibility and usefulness of the proposed methods.

Abstract This study examined the developmental trajectories of three frequently postulated executive function (EF) components, Working Memory, Shifting, and Inhibition of responses, and their relation to performance on standard, but complex, neuropsychological EF tasks, the Wisconsin Card Sorting Task (WCST), and the Tower of London (ToL). Participants in four age groups (7-, 11-, 15-, and 21-year olds) carried out nine basic experimental tasks (three tasks for each EF), the WCST, and the ToL. Analyses were done in two steps: (1) analyses of (co)variance to examine developmental trends in individual EF tasks while correcting for basic processing speed, (2) confirmatory factor analysis to extract latent variables from the nine basic EF tasks, and to explain variance in the performance on WCST and ToL, using these latent variables. Analyses of (co)variance revealed a continuation of EF development into adolescence. Confirmatory factor analysis yielded two common factors: Working Memory and Shifting. However, the variables assumed to tap Inhibition proved unrelated. At a latent level, again correcting for basic processing speed, the development of Shifting was seen to continue into adolescence, while Working Memory continued to develop into young-adulthood. Regression analyses revealed that Working Memory contributed most strongly to WCST performance in all age groups. These results suggest that EF component processes develop at different rates, and that it is important to recognize both the unity and diversity of EF component processes in studying the development of EF.

The authors identified several specific problems with the measurement of achievement goals in the current literature and illustrated these problems, focusing primarily on A. J. Elliot and H. A. McGregor’s (2001) Achievement Goal Questionnaire (AGQ). They attended to these problems by creating the AGQ-Revised and conducting a study that examined the measure’s structural validity and predictive utility with 229 (76 male, 150 female, 3 unspecified) undergraduates. The hypothesized factor and dimensional structures of the measure were confirmed and shown to be superior to a host of alternatives. The predictions were nearly uniformly supported with regard to both the antecedents (need for achievement and fear of failure) and consequences (intrinsic motivation and exam performance) of the 4 achievement goals. In discussing their work, the authors highlight the importance and value of additional precision in the area of achievement goal measurement.

Effects of early child care on children’s functioning from 412 years through the end of 6th grade (M age5 12.0 years) were examined in the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (n5 1,364). The results indicated that although parenting was a stronger and more consistent predictor of children’s development than early child-care experience, higher quality care predicted higher vocabulary scores and more exposure to center care predicted more teacher-reported externalizing problems. Discussion focuses on mechanisms responsible for these effects, the potential collective consequences of small child-care effects, and the importance of the ongoing follow-up at age 15. Large numbers of children in the United States ex-perience routine nonmaternal child care during their infant, toddler, and preschool years. In 1999, 9.8 million American children under the age of five years were in child care for 40 or more hours a week (Committee on Family andWork Policies, 2003), with many beginning in the first year of life (U.S. Bureau of the Census, 1999). Questions about possible long-term effects of early child care on school-aged children’s functioning are of great interest to parents, educators, and policymakers, especially as heated debate has often characterized discussion of child-care effectsFboth before the onset of the work pre-

Abstract Multiple imputation (MI) and full information maximum likelihood (FIML) are the two most common approaches to missing data analysis. In theory, MI and FIML are equivalent when identical models are tested using the same variables, and when m, the number of imputations performed with MI, approaches infinity. However, it is important to know how many imputations are necessary before MI and FIML are sufficiently equivalent in ways that are important to prevention scientists. MI theory suggests that small values of m, even on the order of three to five imputations, yield excellent results. Previous guide-lines for sufficient m are based on relative efficiency, which involves the fraction of missing information (γ) for the parameter being estimated, and m. In the present study, we used a Monte Carlo simulation to test MI models across several scenarios in which γ and m were varied. Standard errors and p-values for the regression coefficient of interest varied as a function of m, but not at the same rate as relative efficiency. Most importantly, statistical power for small effect sizes diminished as m became smaller, and the rate of this power falloff was much greater than predicted by changes in relative efficiency. Based our findings, we recommend that researchers using MI should perform many more imputations than previously considered sufficient. These recommendations are based on γ, and take into consideration one’s tolerance for a preventable power falloff (compared to FIML) due to using too few imputations. Keywords Multiple imputation. Number of imputations. Full information maximum likelihood.Missing data. Statistical power Since Rubin’s (1987) classic book on the subject, multiple imputation has enjoyed a steady growth in popularity and usefulness. Technical articles, books, and multiple imputa-tion software abound (e.g., Collins et al. 2001; Graham et al.

The authors evaluated the efficacy of a computer-delivered personalized normative feedback intervention in reducing alcohol consumption among heavy-drinking college students. Participants included 252 students who were randomly assigned to an intervention or control group following a baseline assessment. Immediately after completing measures of reasons for drinking, perceived norms, and drinking behavior, participants in the intervention condition were provided with computerized information detailing their own drinking behavior, their perceptions of typical student drinking, and actual typical student drinking. Results indicated that normative feedback was effective in changing perceived norms and alcohol consumption at 3- and 6-month follow-up assessments. In addition, the intervention was somewhat more effective at 3-month follow-up among participants who drank more for social reasons. Social norms approaches to prevention of high-risk drinking are being increasingly implemented on college campuses. One of these approaches involves providing heavy-drinking students with personalized normative feedback designed to correct misperceptions of descriptive drinking norms. Despite the fact that this approach is frequently included as one component in multicomponent interventions,

This article urges counseling psychology researchers to recognize and report how missing data are handled, because consumers of research cannot accurately interpret findings without knowing the amount and pattern of missing data or the strategies that were used to handle those data. Patterns of missing data are reviewed, and some of the common strategies for dealing with them are described. The authors provide an illustration in which data were simulated and evaluate 3 methods of handling missing data: mean substitution, multiple imputation, and full information maximum likelihood. Results suggest that mean substitution is a poor method for handling missing data, whereas both multiple imputation and full information maximum likelihood are recommended alternatives to this approach. The authors suggest that researchers fully consider and report the amount and pattern of missing data and the strategy for handling those data in counseling psychology research and that editors advise researchers of this expectation.

Encouraging students to share and compare solution methods is a key component of reform efforts in mathematics, and comparison is emerging as a fundamental learning mechanism. To experimentally evaluate the effects of comparison for mathematics learning, the authors randomly assigned 70 seventh-grade students to learn about algebra equation solving by either (a) comparing and contrasting alternative solution methods or (b) reflecting on the same solution methods one at a time. At posttest, students in the compare group had made greater gains in procedural knowledge and flexibility and comparable gains in conceptual knowledge. These findings suggest potential mechanisms behind the benefits of comparing contrasting solutions and ways to support effective comparison in the classroom.