The effects of adolescent prosocial behavior interventions: a meta‑analytic review

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https://doi.org/10.1007/s12564-021-09691-z

The effects of adolescent prosocial behavior interventions:

a meta‑analytic review

Jongho Shin¹ · ByungYoon Lee¹

Received: 7 September 2020 / Revised: 25 March 2021 / Accepted: 30 March 2021 / Published online: 21 April 2021

Abstract

This meta-analytic review examined the effectiveness of the interventions for promoting adolescents’ prosocial behavior, and aimed to explain variability in the effectiveness. Thirty-three studies that used interventions for prosociality targeting adolescents and compared with a control group or had a pre-post comparison were included in the analyses. Across all the studies, the prosocial behavior interventions for adolescents had a small, beneficial effect on promoting prosocial behavior (g = 0.442; 95% CI [0.240, 0.644]). The results of moderator analyses showed that the interventions designed to increase social competence had a larger effect than those seeking to prevent problem behavior, and that the interventions that used reports by others had a larger effect than those using self-reports. These findings indicated that the prosocial behavior interventions promoted adolescents’ prosocial behavior, but with a small effect, and that the effectiveness varied depending on the objectives of intervention and the measurement types. However, this result seemed to be slightly influenced by publication bias, thus the interpretations of the results should be careful. Directions for future research and implications for educators implementing prosocial behavior interventions during adolescence are discussed.

Keywords Prosocial behavior · Adolescent · Meta-analysis · Prosocial behavior interventions

Introduction

Prosocial behavior is an umbrella term that describes any voluntary behaviors intended to benefit others including sharing, caring, helping, and donating (Padilla-Walker &

Carlo, 2014, p. 6). Here, “voluntary” behaviors are one’s spontaneous actions toward someone in need of help, not an action of professional help (Trommsdorff et al., 2007). While prosocial behavior differs depending on the actor’s motivation (Persson, 2005) and the action’s cost (Eisenberg & Spin- rad, 2014), one common description of prosocial behavior is that it helps to form healthy interpersonal relationship and to integrate into society (Fehr & Fischbacher, 2003). Proso- cial behavior, often, varies from culture to culture. Some studies have suggested that people in individualistic culture displayed more prosocial behavior than people in collectivist

societies (e.g., Trommsdorff et al., 2007; Yamagishi, 1988).

However, finding which culture promotes more prosociality is not the focal point. Some scholars have highlighted that it is more important to figure out what causes the differences in prosocial behavior in each culture; for example, in the individualistic culture, autonomy and generalized trust lead to prosociality, whereas social duties and institutional trust cre- ate it in the collectivistic culture (Irwin, 2009; Miller, 1997).

Prosocial behavior during adolescence has been found to be associated with high self-esteem, academic success, and positive interpersonal relationship (Van der Graaff et al., 2018). Moreover, intervention programs to promote adolescents’ prosocial behavior have recently received much attention (e.g., Caprara et al., 2015; Mesurado, Distefano, et al., 2019). Despite such ongoing research efforts, a lack of prosociality among adolescents still appear across the countries. For instance, a number of Korean adolescents have suffered from online bullying (The Ministry of Educa- tion, 2018), and only 18% of Canadian youth reported that they would help a victim and interrupt the bullying dynamic (Trach et al., 2010). Such less prosociality among adolescents has been found to show short-term and long-term negative effects in peer group membership (Brook et al., 2013)

* ByungYoon Lee bylee14@snu.ac.kr Jongho Shin jshin21@snu.ac.kr

1 Seoul National University, Seoul, South Korea

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and occupational attainment in their adulthood (Carter, 2019). Thus, delving into the implementation of interventions that foster adolescents’ prosocial behavior is essential.

Even with some successful intervention programs (e.g., Caprara et al., 2015; Mesurado, Distefano, et al., 2019), studies reveal substantial variability in the effectiveness of the intervention programs promoting prosociality of adolescents (e.g., O’Hare et al., 2015; Truskauskaite-Kuneviciene, 2016). More clarity is desirable about the effectiveness of the intervention programs for adolescent prosocial behavior. One way to do so is to conduct a meta-analytic review.

A recent meta-analysis (Mesurado, Guerra, et al., 2019) provided the effectiveness of the intervention programs for prosocial behavior. However, their meta-analytic review included a small number of studies, and the detailed contents of interventions (e.g., different age groups, the assessment instruments) were not taken into consideration. A meta-analytic review including a wide scope of individual studies and a consideration of possible sources in variation is needed to verify the effectiveness of the interventions fostering prosociality in adolescents.

It is important to take another step to identify key components that influenced the effectiveness of numerous intervention programs (Nelson & McMaster, 2018). For example, intervention characteristics and participant characteristics are often added to explain the differences between various intervention programs (Bolier et al., 2013; Masi et al., 2011;

Yoon et al., 2014). Most of the interventions for prosocial behavior have a shared goal—to improve one’s prosociality—but how to reach the goal seems to be different. Some of the programs focused on an addition of positive attitudes as a way of fostering prosociality (e.g., Caprara et al., 2014) while others used programs that help to remove one’s negative behavior (e.g., Muratori et al., 2015). In addition to the intervention characteristics, the participant pool varied, such as the grade level and the sample type (e.g., at-risk adolescents). This heterogeneity in intervention characteristics and participant characteristics might have affected the effectiveness of the intervention programs. Thus, testing the moderating effects of intervention characteristics and participant characteristics were added in this meta-analytic review.

In sum, the purpose of this study is to analyze the effectiveness of intervention programs for adolescents’ prosocial behavior and to further identify the moderating variables with an increasing number of interventions for promoting prosocial behavior in the field of psychology and youth education (Caprara et al., 2015; Ellis et al., 2016). This meta-analytic review, therefore, was conducted to draw conclusions regarding the magnitude of the intervention effectiveness for prosocial behavior and, at the same time, to advance the existing programs helping positive youth development (Damon, 2004; Lee et al., 2014). The research questions of this study are as follows.

Research Question 1: What is the overall mean effect of prosocial behavior interventions on promoting prosocial behavior in adolescents?

Research Question 2: How do the intervention effects differ across the intervention characteristics and the participant characteristics?

Literature review

Importance of adolescence in promoting prosocial behavior

Adolescence is often referred to be a central period for the prosociality development (Eisenberg et al., 2016). Adoles- cents experience many changes inside and outside them- selves, and one of the notable changes is that they become more sensitive toward their sociocultural development (Blakemore & Mills, 2014). This sensitivity among adolescents often makes it difficult for them to act prosocially (Espelage et al., 2003) due to the increased complexity in social behavior (Lerner & Steinberg, 2004). At the same time, adolescence is also viewed as a time in life when prosocial behavior should be more boosted. This particular period plays an important role in developing perspective taking (Van der Graaff et al., 2014); in moral reasoning (Malti et al., 2014); and in the cognitive and affective development that makes them think and act more prosocially (Carlo, Crockett, et al., 2011). Adolescents also play social roles in their social context, which helps them to build intimate circles with friends, and friendship made within the circles provides adolescents with emotional intimacy and opportunities for supporting each other (Goldstein et al., 2015).

Therefore, adolescence can be an important time for them to become more prosocial to others with educational assistance from adequate programs.

Engaging in prosocial behavior has been shown to promote various beneficial outcomes in adolescents’ lives. First, prosocial behavior itself is often referred as a protective factor against problem behaviors (Carlo et al., 2014), and is found to play a protective role in the domain of aggressive behavior (Card et al., 2008). A longitudinal study (Padilla- Walker et al., 2015) highlighted that prosocial adolescents engaged less in aggressive behavior in their adulthood. Act- ing in a prosocial manner is helpful, particularly, for high- risk students who had problems with substance abuse (Carlo, Mestre, et al., 2011), severe delinquency (Padilla-Walker et al., 2015), and victimization from bullying (Wang et al., 2015).

Secondly, prosocial behavior during adolescence is positively related to social competence (Eisenberg et al., 2016).

With adolescence being a sensitive period about social relationships with peers and family members (Steinberg, 2011),

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the development of prosociality during this period is essential in promoting adolescents’ social skills including peer attachment and acceptance (Dirks et al., 2018). Moreover, adolescents’ behaving prosocially can lead to their contributions to the broader community; for example, adolescents who had volunteering experience showed positive civic engagement including civic efficacy and civic knowledge (Schmidt et al., 2007). Prosocial experience during adolescence could help students to improve appreciation of diver- sity, to increase their responsibility towards the community, and to gain a sense of positive contribution to society in the future (Yates & Youniss, 1998).

Not only the socio-affective outcomes, but also academic achievement has been observed among prosocial adolescents. Wentzel (1993) found that prosociality of middle school students was positively related to their GPA and standardized test scores as well as teachers’ preferences for students. Caprara et al., (2000) have also demonstrated prosocial behavior as a predicting variable for academic achievement in a longitudinal research. In their study, third graders’ prosocial behavior had a strong positive effect on academic achievement five years later. In light of these findings, adolescents with social mobility and academic capaci- ties seem to have more opportunities to engage in prosocial behavior (Fabes et al., 1999). Thus, a high quality of prosocial behavior intervention can shed light on the promotion of prosocial behavior in adolescence.

Factors affecting the effectiveness of prosocial behavior interventions

Objectives of intervention programs

Some of the prosocial behavior intervention programs focus on directly teaching students strategies that are conducive to prosocial behavior, such as perspective-taking skills (e.g., Caprara et al., 2014, 2015). In such intervention programs, socially appropriate behavioral instructions were added to improve adolescents’ prosocial behavior. On the other hand, the main purposes of other interventions were to manage anger, to resolve dispute, and to deal with impulse control (e.g., McMahon & Washburn, 2003; Yeager et al., 2013).

These interventions attempted to remove negative attitudes in adolescents, by doing so, they were found to foster prosocial behavior. Positive behavioral patterns were sup- plemented in the former interventions whereas negative ones were eliminated through the latter interventions. However, the intervention objective has not been further examined as a moderator when examining the effectiveness of intervention programs. This study explored the moderating effects of the intervention objective on the effectiveness of prosocial behavior intervention programs.

Duration of intervention programs

The variability in the effectiveness of interventions for youth behavior is often explained by the differences in program duration (Granski et al., 2020; Kriemler et al., 2011).

Generally, ineffective interventions seemed to be the ones with shorter duration (Limbos et al., 2007). Mixed effects, however, regarding intervention length were found when it comes to adolescence. January and colleagues (2011) highlighted that greater duration of intervention led to larger positive effects of adolescents’ social skills whereas intervention length was found to be unrelated to the effectiveness of interventions targeting adolescents’ behavior (Hynynen et al., 2016). Accordingly, it was hypothesized that the effectiveness of interventions would differ depending on intervention duration.

Measurement methods of prosocial behavior

Self-report, behavioral ratings, and observations have been the general measurements of prosocial behavior. Accord- ing to El Mallah (2019), various measurements of prosocial behavior have been an important issue when studying adolescents’ prosociality. She also provided some strengths and limitations of the most often-used measurements of prosocial behavior. For example, self-report questionnaires are practical and easy to administer, but they often increase the social desirability bias and the described situations of the questionnaires are too hypothetical. Behavioral ratings by others, such as peers, teachers, and parents often become susceptible to halo effect although they provide diverse perspectives on one’s behavior in different contexts. Therefore, it would be interesting to examine whether the different measuring methods would affect the general effectiveness of prosocial behavior interventions.

Use of standardized measures for prosocial behavior In addition to the different methods for measuring prosocial behavior, whether or not standardized measures were used in individual studies can be an important moderator. While some studies use well-validated, self-reported measures (e.g., Caprara et al., 2005), others measure prosocial behavior by asking oneself how many times he/she performed a behavior (e.g., Bosworth et al., 1998). Lack of using standardized measures not only allows researchers to search for and choose the most appropriate measurement “for their a priori hypotheses” (Elson et al., 2014; Ferguson, 2015), but also it causes statistical differences between the obtained data (Cho & Lim, 2003). Poorly standardized measures also can lead to an unstable effect size (Ferguson, 2007). Thus, this study attempted to find the moderating effect of the use

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of standardized measures for prosocial behavior on the effectiveness of the intervention programs.

Participants’ grade levels and intervention programs Generally, prosocial behavior has been found to increase with age (Eisenberg et al., 2006; Matsumoto et al., 2016).

However, some studies have demonstrated that stable levels of self-reported prosocial behavior decreased from early to late adolescence (Luengo Kanacri et al., 2013). While greater effects of prosocial behavior interventions were reported mainly for elementary school students (Caprara et al., 2014; Webster-Stratton et al., 2004), prosocial behavior seemed to be more salient across middle school years (Wentzel et al., 2007). With such mixed results regarding the grade level and adolescents’ prosociality, the current study added participants’ grade level as a moderator to find out when in adolescence is the most effective in promoting prosocial behavior.

Participant types and intervention programs

Most of the studies targeting the promotion of positive behavior in adolescents by implementing interventions have focused on students in the general population (Gansle, 2005). However, proper intervention programs for prosocial behavior are also required for students with emotional or behavioral disorders (Cheney et al., 2008). Among various interventions for at-risk students, positive behavior support has emerged as an influential intervention for their social/emotional development (Walker et al., 2005);

and their aggressive behavior was replaced with prosocial skills as one of the positive outcomes from participating in an intervention program (Kellner et al., 2008). Thus, the participant type was added as a moderator to examine the potential differences in the effectiveness of prosocial behavior interventions.

Method

Search process

A comprehensive search for relevant studies from 1988 to 2019 was conducted. Most of the studies were identified electronically through Google Scholar and ProQuest Digi- tal Dissertation databases. Korean studies were searched through a database called RISS (Research Information Shar- ing Service). In this meta-analysis, peer-reviewed journals and unpublished thesis/dissertations were searched and included, but conference proceedings and research reports were excluded. The search terms used in this study were

as follows: adolescen*, youth, student, prosocial behavior, prosociality, intervention, and program.

After the initial search with the search terms, approximately 2,800 articles were found. In the first stage, only the full-access articles at the time of search, the articles written in English or Korean, and the articles that actually used an intervention program were selected (n = 105). The intervention programs selected for this study were not limited to school-based ones. Although an intervention is often viewed as a supplement to general education curriculum at school (Nelson & McMaster, 2018), interventions can play an important role in promoting positive behavior no mat- ter where it is held. Thus, both in-school and out-of-school programs (e.g., research labs, youth welfare institutions, and psychiatry facilities) were included. In addition, articles that measured prosocial behavior as a sum or one of the related behaviors (e.g., helping, sharing, and cooperating) were included. The second stage consisted of reviewing and selecting articles (n = 48) whose participants going to elementary school, middle school, or high school. Studies that provided only the age of participants, not the school level, were also selected after reviewing that the age corresponded to a school level. Neither preschoolers (kindergartners) nor college students were included due to the purpose of this meta-analysis. This study also included the study design containing not only (quasi-) experimental studies, but also longitudinal studies looking for long-term effects of an intervention. In case of longitudinal design, studies in which the final year of the intervention implementation ended in either elementary, middle, or high school were included. Finally, the authors reviewed the individual articles and selected those that had sufficient information on every moderator.

The whole search process found a total of 33 studies including 32 peer-reviewed journals and one unpublished master’s thesis. These 33 studies made up of 38 separate observations were examined in this meta-analysis because three studies (Cheon et al., 2018; Garaigordobil, 2008; Schonert-Reichl, 2012) consisted of more than two measurements. In order to code the measurement type, all of the data reported using each measurement tool from these three studies were added as a separate observation.

Coding procedures Intervention characteristics

The intervention characteristics that were coded in this study were as follows: Country of intervention held, program name, and the information on each moderator. The country was coded as a place where the intervention was held, not the country that the authors came from. In order to code the intervention objective, the information about each program was coded beforehand, then, the coders categorized them

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into—to increase social competence or to prevent problem behavior. The duration of each intervention was provided in different units (e.g., weeks, month, or year). In order to run a moderator analysis, the duration was categorized into less than one year or more than one year. The measurement methods used in the included studies all varied, ranging from teacher observation to self-report, thus they were categorized into others-report or self-report. We coded the studies by whether they used standardized measurements or not. We coded the studies as standardized if the measurements were validated in their studies or if they used validated measures. Unstandardized measurements were those without references for the measurement. The components of prosocial behavior also varied. Some studies used prosocial behavior or a single related behavior (e.g., helping, sharing) as one outcome variable, while others measured a sum of the related behaviors. In the latter case, the average of each related behavior was calculated and coded.

Participant characteristics

Adolescents in this study were coded into their grade levels ranging from elementary (1st to 5th), middle (6th to 8th), and high (9th to 12th) school. When the grade of participants overlapped in two levels, the grade level that more participants included was selected (Kim, 2016). The participant type—at-risk students vs. regular students—was coded as well.

Data analysis

The Comprehensive Meta-Analysis (CMA) Program was used to carry out all the meta-analyses. To conduct descriptive data, Statistical Package for Social Sciences (SPSS) was used.

Effect size calculations

Since all the outcome data were continuous, the standardized mean differences were calculated. For some studies without means and standard deviations, a t test, F test, or p value were calculated for effect sizes (Nelson & McMaster, 2018).

Hedge’s g was applied in this study because it avoids effect size underestimation (Field, 2001), and it is more standardized than the other indices, such as Cohen’s d or delta (Ferguson, 2009). In addition, a random-effects model was used in this analysis because the studies showed heterogeneity in sample, intervention, and outcome. All studies were weighted so that studies with a larger sample size carried more weight.

Statistical heterogeneity

An overall Q test and I² calculation were carried out to obtain statistical heterogeneity across the studies. A significant Q value (p < 0.05) suggests the proportion of variability among the effect sizes (Borenstein et al., 2009), and I² indicates the percentage of the heterogeneity between the studies (Higgins & Thompson, 2002). The heterogeneity analysis turned out to be significant, therefore it was able to run moderator analyses to search for what factors have resulted in such variations. All (categorical) moderator analyses were conducted in the CMA program.

Publication bias

Even though a meta-analytic review is a useful research tool to synthesize multiple studies, its validity is often questioned due to potential publication bias resulted from “the underre- porting of non-significant results or disconfirming evidence”

(Gurevitch et al., 2018). Publication bias frequently occurs in meta-analyses because only the studies with statistically significant results are selected while the studies with negative findings are not submitted for publication, which leads to an artificially favorable direction of the meta-analytic results (Lin, 2020). One intuitive method to find out publication bias is to examine the symmetry of a funnel plot.

When an asymmetric funnel plot usually indicates the presence of publication bias, other methods (e.g., regression test and the trim-and-fill method) are assessed. The funnel plot of this study provided a visual analysis of a possible publication bias, thus the other methods were used to inspect the publication bias of this meta-analysis. The results of the publication bias analyses were elaborated in the Results part.

Results

Descriptive results

The descriptive results of this meta-analysis are as follows.

First, the included studies were published from 1988 to 2019 (Mpublcation year = 2009.5; SD = 8.78 years). The sample size ranged from 5 to 1,824 (Msample size = 310.12; SD = 365.42).

More than half of the studies included North American adolescents (54.5%), followed by European (30.3%), Asian (9.1%), and South American adolescents (6.1%). Approxi- mately a half of the studies (51.5%) were conducted with elementary school students, 27.3% with middle school students, and 21.2% with high school students. Out of 33 studies, five studies dealt with prosocial behavior of at-risk adolescents. In terms of intervention characteristics, the purpose of increasing social competence (72.7%) and the purpose of preventing problem behavior (27.3%) accounted for the

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whole study. There are more interventions implemented for less than a year (63.6%) than those that ran for more than a year (36.4%). 19 studies used self-report measurement, and 11 studies used others-report measurements. Three studies used a mixed measurement. Most of the studies (n = 28) used standardized measures for prosocial behavior while five studies used unstandardized measures.

Meta‑analysis results Intervention effects

The weighted random mean effect size (Hedge’s g = 0.442, SE = 0.103) of this meta-analysis was small, and the 95%

CI did not include zero [0.240, 0.644]. Most of the studies seemed to favor the intervention condition, except for the three studies directing negative effects. The test of heterogeneity was significant (Q(37) = 1636.687; I² = 97.74%;

p = 0.000), indicating that the effect sizes showed statisti- cally significant variation. After an inspection of the funnel plot, it displayed a sign of the presence of publication bias.

In order to measure the asymmetry provided by the funnel plot, Egger’s regression test was used (Egger et al., 1997). As a result, the intercept was 0.13 (SE = 1.86, t = 0.07, p = 0.94).

Due to its power issue, Duval and Tweedie’s (2000) trim-and fill-method was added to identify the impact of the observed

asymmetry on the overall results. It indicated that the effect size corrected by adding 10 studies to the right side of the distribution increased from 0.442 (95% CI: 0.240–0.644) to 0.603 (95% CI: 0.425–0.781). This result suggests that the current study would have some publication bias. Thus, the results need to be interpreted with caution, and it might be difficult to claim that the intervention programs for prosocial behavior have “practical significance” (Ferguson et al., 2007).

Moderator analyses

Table 1 describes the mean effect sizes, standard errors, 95% CI, and Q values for each moderator. First, the Q test results of the intervention objective were found to be statistically significant (p(Q) < 0.05). The objective of increasing social competence showed the larger effect (g = 0.488;

SE = 0.133) than the objective of preventing problem behav- ior (g = 0.205; SE = 0.054). Secondly, the between-group difference for the measurement type was also significant (p(Q) < 0.05). Studies using reports by others were found to have larger effect (g = 0.538; SE = 0.157) than those using self-report (g = 0.189; SE = 0.057). However, the duration of intervention, the use of standardized measure, participants’

grade level, and the sample type did not yield significant moderating effects.

Table 1 Effect sizes of

moderators Moderators n g SE 95% CI Between groups p(Q)

Lower Upper Q df(Q)

Objective of intervention 3.902 1 0.048

To prevent problem behavior 9 0.205 0.054 0.100 0.310 To increase social competence 29 0.488 0.133 0.228 0.748

Duration of intervention 0.259 1 0.611

Less than 1 year 22 0.489 0.181 0.135 0.843

More than 1 year 16 0.385 0.096 0.198 0.572

Reported by 4.375 1 0.036

Others 25 0.538 0.157 0.231 0.845

Self 13 0.189 0.057 0.078 0.301

Standardized measure 1.737 1 0.188

Unstandardized 5 0.640 0.143 0.360 0.921

Standardized 33 0.401 0.111 0.183 0.620

Grade level 0.507 2 0.776

Elementary school 21 0.349 0.082 0.189 0.510

Middle school 9 0.323 0.089 0.148 0.498

High school 8 0.609 0.396 − 0.167 1.385

Sample type 1.948 1 0.163

At-risk population 5 0.661 0.146 0.375 0.947

Regular population 33 0.407 0.110 0.192 0.621

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Discussion

The effects of adolescents prosocial behavior interventions

The purpose of this meta-analysis was to provide a comprehensive review of studies regarding the prosocial behavior interventions for adolescents, to evaluate the effectiveness of the interventions, and to determine possible causes in variation of the effectiveness. Previous meta-analyses dealt with multiple behavioral outcomes similar to prosocial behavior, such as positive youth development programs (Ciocanel et al., 2017), social and emotional learning interventions (Durlak et al., 2011), and social skills interventions (January et al., 2011). In those studies, prosocial behavior was just one of the multiple outcomes. In addition, the effectiveness of intervention on adolescents’

prosocial behavior was already found in a recent meta- analysis (Mesurado, Distefano, et al., 2019; Mesurado, Guerra, et al., 2019). However, the current meta-analytic review was conducted with more studies, and found possible sources in variation of the effectiveness.

This meta-analysis demonstrated that the prosocial behavior interventions for adolescents had a small effect.

There are several factors that can affect effect sizes, such as sampling (Ferguson, 2009). For example, there were studies that did not randomly assign the participants, and that had relatively a small number of participants. Most of such studies targeted students with emotional and behavioral problems. Since one of our goals in this meta-analysis was to conduct a moderating analysis with the different sample types, studies with a small number at-risk adolescents were added. The large variation in the number of participants and uncontrolled research settings of some studies might have led to an unstable effect size of the intervention programs as a whole. Thus, the interpretations should be careful considering the small effect found in this study.

The analysis of publication bias showed that the results of this study seemed to be affected by publication bias.

Thus, it would be difficult to claim that prosocial behavior interventions for adolescents yielded a practically significant effect on their prosocial behavior. One possible explanation might be related to the number of the studies that failed to show statistical significance in promoting prosociality. Prosocial behavior has long been known for its positive aspects on adolescents, such as academic success (Wentzel, 1993) and intimate relationship with others (Padilla-Walker & Carlo, 2014). As prosocial behavior is such an important, must-have construct to be fostered, studies with a failure to show practically significant effects may be difficult to be published. The result of this study,

however, showed that the confidence interval of the effect size did not cross the zero point, which could weight to the finding that these prosocial behavior programs have positive effects for adolescents. Yet, the effectiveness of prosocial behavior interventions for adolescents needs to be interpreted with caution.

In sum, the current results suggest that the prosocial behavior programs for adolescents produce an effect that is positive and statistically significant, but practically looks weak. Even with a careful search for the studies that examined the effectiveness of prosocial behavior programs for this meta-analysis, there is a possibility that some articles have not been searched for reasons (e.g., language and publication status). Thus, a more thorough and unbiased screening of literature including unpublished articles such as dissertations and reports is needed for a future meta-analysis (Polanin et al., 2019). In addition, coding other aspects (e.g., contexts, participants, and methods) of the included studies is needed for moderator analyses, which can reflect other potential reasons for the effect size variation across the included studies (Pigott & Polanin, 2020). Therefore, future meta-analyses should increase the number of individual studies as well as the possible moderators through a systematic and comprehensive search for eligible studies (Kugley et al., 2017).

Differential effectiveness

The second research question aimed to examine the possible sources of variation in the overall mean effect depending on the intervention characteristics (objective, duration, measurement type, and the use of standardized measurements) and the participant characteristics (grade level and participant type). Only the intervention objective and the measurement type yielded significant between-groups variations while the rest did not.

The interventions conducted to increase social competence seemed to be more effective than those conducted to prevent problem behavior. Commonly, programs designed to prevent problem behaviors have smaller effects compared to those structured to improve positive behaviors (Menting et al., 2013). It is because a large number of participants who did not actually need behavioral interventions were included in the individual studies and the severity of the initial behavior was low (Bennett et al., 1998). Moreover, adding positive attitudes can be more effective than removing negative ones because the programs seeking to increase one’s social skills also improve other components of social skills including positive peer relations, awareness of emotions in others, and handling interpersonal conflicts (January et al., 2011).

Thus, it implies that intervening one’s prosocial behavior by adding positive stance (e.g., social competence) can lead to a larger positive change in adolescents than intervening by preventing a negative behavior.

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Second, the moderating effect of the measurement type indicated that the interventions that used ratings by others were found to be more effective than those that used self- report measurements. Given that adolescents often show a higher incidence of incomplete and inconsistent responses in self-reported measurements (Keefer et al., 2013; Soto et al., 2008), they might have been unable to accurately assess their own prosocial behavior when self-reports were used. Moreover, there is a chance of adolescents’ misun- derstanding of the concept of prosocial behavior. Accord- ing to Duckworth and Yeager (2015), literacy is a common concern of using self-report questionnaires when assessing adolescents’ constructs; it is not just a vocabulary issue, but more an understanding of “the intended idea” of the questionnaire items. For example, adolescents often face difficulties in the structure of prosocial behavior and they view the word “prosocial” and “good” as the same construct (Keefer, 2015), which can affect them to underestimate their prosociality. Reports by others yielded a larger effect than self-reports because these others include teachers, peers, or parents who may provide diverse perspectives on the target’s behavior in different environments (Noland & McCallum, 2000). Additionally, half of the others-report measurements in this meta-analysis were composed of teachers. Teach- ers reach a judgment on a student with their own standards based on a long-term observation at school (Duckworth &

Yeager, 2015). In other words, teachers in the included studies might have assessed students based on their previous experience with regard to the students’ prosocial behavior rather than as a result of the intervention program. It is, therefore, important to pay more attention to the person that measures an adolescent’s prosocial behavior and the close- ness with the target in future research.

On the other hand, the effects did not differ depending on the intervention duration. The duration of each program in the included studies varied from two weeks to five years because one-time research and longitudinal studies were all included for this meta-analysis. The way of dividing the duration into either less or more than one year might have been naïve. Another important issue regarding the program duration and the program effectiveness was that the included programs did not disclose the session period. Not only the total duration of each program, but also how long each session takes can have critical consequences of behavioral changes. However, the information on the length of each session was not provided in some of the included studies.

Thus, a future research with the intervention session can give more valid information on this regard.

The use of standardized measurement for prosocial behavior did not work as a moderator. Even though it was not statistically significant, the studies with unstandardized measures showed a larger effect than those with standardized measures. We analyzed the rationale behind this pattern

with the following findings. First, most of the unstandardized measures included in this study were observation by others (e.g., peers, teachers, and facilitator). Second, the studies that used others-reported measures showed a larger effect than those that used self-reported ones. The method of others’ carefully observing the change in the target’s behavior can be powerful and intuitive, particularly, when measuring someone else’s change in the behavior (Small et al., 1983). However, most of the unstandardized observation measures may lack the statistical significance because this method has many practical difficulties to provide accu- rate or valid assessments for prosocial behavior (Larrieu &

Mussen, 1986), which might have led to the larger effect of unstandardized measures, but not a significant difference.

With regard to the grade level of participants, no moderating effect was found. This result can be related to the mixed results of the literature regarding age and prosociality. There are mounting evidence suggesting that there were larger positive effects on social skills when participants exposed to the interventions as early as possible (e.g., Ramey & Ramey, 1998; Webster-Stratton et al., 2004). On the other hand, many studies have reported a gradual increase in prosocial development from childhood to early adulthood (e.g., Croc- etti et al., 2016; Luengo Kanacri et al., 2013). Moreover, some studies have reported an increase in prosociality during adolescence and a slight decrease in high school years (e.g., Van der Graaff, 2018). Adolescence is a period with diverse changes happening to adolescents, such as physical changes (Carlo et al., 2012) and changes in empathy and self-regulation (Padilla-Walker & Christensen, 2011), which can affect one’s prosocial behavior in positive or negative ways. This natural change in adolescence might have led to the inconsistencies in the relationship between age and prosocial behavior as well as no moderating effects of grade level in this study.

The effectiveness did not differ according to the participant type. The nature of the intervention programs for at-risk adolescents and regular adolescents might be quite different from each other. Students diagnosed with behavioral disorders participate in a therapy or treatment more often than an intervention program (Kellner et al., 2008; Nitkowski et al., 2009), and approximately 85% of at-risk population tended to successfully respond to the trainings that provide systematic reinforcement of target social behavior (Reinke et al., 2014). Compared to such programs, it is possible that the intervention programs for regular adolescents were less therapeutic. In addition, the administrator of each program can be another reason of this no-differential effect. All of the interventions targeting the at-risk adolescents in this meta- analysis were administered by experts, such as child psychol- ogists, therapeutic specialist, and psychotherapists working in youth welfare institutions. Although the interventions for the regular population were administered by teachers who

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received adequate training process, at-risk population may display stronger effectiveness due to proven expertise of the program administrator.

Limitations and future directions

This study has several limitations that should be addressed in future research. An important limitation is that the major- ity of the individual studies were from North America and Europe. Cultural differences have played an important role in one’s manifestation of prosocial behavior (Eisenberg, 1992; Feygina & Henry, 2015). Since prosocial behavior often involves evaluations based on cultural values and different socialization beliefs (Trommsdorff et al., 2007), each program may reflect the cultural values of their countries or societies with which the researchers are familiar. Thus, more studies with various programs that were created from different parts of the world should be included to carry broader implications for adolescents’ prosocial behavior in a future review. Another limitation is about the conceptualization of prosocial behavior, which was broader than intended. The studies included in this meta-analysis differed on the opera- tional definition of prosocial behavior ranging from prosocial behavior itself to other related behaviors (e.g., helping, sharing, and cooperation). The absence of consensus in a definition of adolescent prosocial behavior may lead to a lack of accuracy in construct operationalization (El Mallah, 2019). Thus, future studies should analyze the effectiveness of interventions depending on how prosocial behavior was viewed: either one construct or a sum of other related behaviors. Furthermore, it is not clear in what contexts prosocial behavior was measured in each study. Without a clarification of context in which prosocial behavior was measured, it is possible that some of the participants might have misunder- stood the concept of prosocial behavior. Future syntheses, therefore, should investigate situations in which prosocial behavior is manifested. Lastly, there are other interesting moderators that were not able to be included in this meta- analysis. For example, the program operator, especially the level of their expertise, can be an important factor. This potential moderator was not able to be added in this review due to the absence of the desired information in the individual studies, which is a common limitation of a meta-analytic review (Menting et al., 2013). Thus, future research could provide more information regarding additional intervention characteristics.

Conclusion

Despite the perceived limitations, this meta-analytic review synthesized thirty-three studies that examined prosocial behavior interventions for adolescents from different parts

of the world (e.g., Europe, North and South America, and Asia), and yielded a small effect in the effectiveness of the programs. Moreover, the effectiveness of the interventions differed depending on the objective of intervention and the measurement type. This is an interesting result given that these two factors are the current issues of studies regarding adolescent prosocial behavior (El Mallah, 2019). This meta- analytic review highlighted that the objective of intervention and the measurement type are significant when developing intervention programs that promote prosocial behavior of adolescents, and provided empirical evidence supporting the effectiveness of the individual interventions. It is hoped that this meta-analysis can provide useful information for researchers when creating and developing programs for fostering adolescents’ prosocial behavior as well as for practi- tioners and teachers when selecting an adequate program for their target students.

Authors’ contributions Shin had the idea for the article, Shin and Lee performed the literature search and data analysis, and Lee drafted and revised the work.

Declarations

Conflict of interest The author declare that they have no conflict interest.

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