Psychosocial Factors and Community College Student Success

Abstract

This meta-analysis examined the relationship between psychosocial factors and community college student success. Informed by college persistence models and motivational theory, we statistically integrated past research on five psychosocial categories (motivation, self-perceptions, attributions, self-regulation, and anxiety), examining their relationship with two student success outcomes: community college persistence (58 samples, N = 23,372) and achievement (186 samples, N = 56,095). Results indicated that psychosocial factors had small but meaningful relationships with community college persistence and achievement. Correlations were larger overall for motivation and self-perceptions, and when outcomes were more proximally related with student engagement. Implications for research and practice are discussed.

Keywords

psychosocial variables noncognitive factors community college 2-year college meta-analysis

Community colleges, also known as junior colleges, are typically 2-year public higher education institutions that award an associate in art or science as its highest degree, along with certificates, vocational training, or the possibility to transfer to 4-year colleges and universities (Cohen & Brawer, 2003; Fong, Zientek, Yetkiner Ozel, & Phelps, 2015). Community college enrollment has rapidly expanded over the past century, affording greater participation in higher education, especially for individuals with limited opportunities (Goldrick-Rab, 2010; Kirst & Stevens, 2015; Zientek, Yetkiner Ozel, Fong, & Griffin, 2013). Although the growth of community college enrollment has recently slowed down compared to the boom in the early 2000s (Juszkiewicz, 2015), almost half of U.S.’s postsecondary students are enrolled in community colleges (Shapiro, Dundar, Yuan, Harrell, & Wakhungu, 2014), and because of growing attention toward free community college, online education, and greater interest in national graduation rates, this number is expected to grow.

The term community college was popularized by Bogue (1948), who described the purpose of the community college as enriching the quality of community living. Additionally, the community descriptor implies the local influence such institutions should impart to their surrounding area. More broadly, the community college also represents the inclusive culture of learning and democratization of education, attracting those underserved by traditional higher education (Cohen & Brawer, 2003). The main intent of the community college is to facilitate access and opportunity to higher education for all students, whether constrained by financial limitations, academic preparation, or work and familial obligations. In fact, compared to their 4-year college student counterparts, community college students are more likely to be first-generation (36%; 4-year: 25%), non-White (50%; 4-year: 33%), and low-income (Berkner & Choy, 2008; Goldrick-Rab, 2010; Juszkiewicz, 2015). Moreover, 60% of community students are employed full- or part-time, and many are considered nontraditional at an average age of 28 (Juszkiewicz, 2015). Thus, the success of community college students is critical when considering issues of equality and diversity in higher education.

Similar to other higher education institutions, community colleges face their own set of challenges. First, students often enter community college without the requisite skills to enroll in college credit–bearing courses and are required to take remedial coursework, better known as developmental education. Previous research has indicated that over half of community college students require remediation in mathematics, and one-third of students take developmental coursework to improve their reading skills (Bailey, Jeong, & Cho, 2010). Second, statistics have consistently shown a low degree of completion rates and associate’s degree attainment (Fike & Fike, 2008) or transfer to a 4-year university for community college students (Monaghan & Attewell, 2015). In fact, only one-third of community college students earned a credential within six years (Calcagno, Bailey, Jenkins, Kienzl, & Leinbach, 2006), and some reports indicated that almost half of students dropped out within their first year (Xu & Jaggars, 2011).

The Role of Psychosocial Factors in Community College Student Success

The preponderance of the literature examining factors affecting community college students’ academic success and persistence has tended to concentrate on variables such as first-generation status, socioeconomic status, and prior school achievement (e.g., Arbona & Nora, 2007; Crisp & Nora, 2010; Halpin, 2000; Hawley & Harris, 2005). This trend is also reflective of the field of education as a whole. In a sweeping metareview of over 800 meta-analyses, Hattie (2009) found 139 meta-analyses that focused on student-related influences compared to the disproportionately more prevalent effects of home, school, teacher, and curricula on achievement. Moreover, only 28 of the student-related meta-analyses measured the impact of attitudes and dispositions, which have been shown to be relatively malleable and under students’ personal control to develop (Yeager & Walton, 2011). Although it is critical to identify such background and environmental factors, it is also important to use prescriptive measures that assess students’ cognitive, motivational, and behavioral variables affecting access, success, and retention (Bean & Eaton, 2001; Melguizo, 2011; Nora, 2003; Wang, 2012; Weinstein & McCombs, 1998). By identifying variables that can be enhanced through educational interventions, such as students’ self-regulated learning, educators and practitioners can design and implement interventions to help students improve in these areas.

Variables of this nature, also called psychosocial or noncognitive factors (Duckworth & Yeager, 2015; Farrington et al., 2012), have been shown to be highly predictive of postsecondary student success (Richardson, Abraham, & Bond, 2012). Decades of research on psychosocial variables have demonstrated that college students’ persistence, engagement, effort, and learning approaches were influenced by their perceived needs, motivation, and beliefs (Robbins, Allen, Casillas, Peterson, & Le, 2006; Schunk, Meece, & Pintrich, 2014). Examples of key constructs in the motivation literature are goals, self-efficacy, attributions, and self-regulation, spanning myriad psychology theories. In a large meta-analytic review of psychosocial factors and college student achievement and retention, Robbins et al. (2004) aggregated the positive contributions of psychosocial predictors, even when controlling for traditional predictors of college student success such as socioeconomic status, standardized test scores, and high school grade point average (GPA). Additionally, Richardson et al. (2012) conducted a similar review to Robbins et al. (2004) with an updated sample of studies and corroborated the importance of self-efficacy and effort regulation, among other motivational and self-regulatory predictors, for university students’ academic performance. These two reviews form the basis for the current study; however, these findings of both meta-analyses have been synthesized for only students attending 4-year colleges and universities, and there remains a gap in the literature regarding the evidence for community college students.

Although meta-analytic work in the community college is fairly scant, there has been early synthesis research on the effectiveness of remedial or developmental education programs and courses, which are pervasive in community colleges (Burley, 1993; Kulik, Kulik, & Shwalb, 1983; Kulik, Cohen, & Ebeling, 1980). Together, the evidence regarding these programs displayed a moderate degree of effectiveness; however, the focus of these college-based programs is on cognitive or content-related aspects of student learning, with less attention to noncognitive or psychosocial aspects. Napoli and Wortman (1996) synthesized the relationship between academic/social integration and community college student success. However, their synthesis included only six studies, thereby providing relatively tentative support for academic integration as a positive predictor of student persistence.

Regarding primary studies, there is a growing literature base of work that has examined the predictive relationship between psychosocial variables and community college success but hardly any synthesis or review work on the topic. Researchers in the 1970s began publishing work on the topic; since then, over 200 dissertations and peer-reviewed articles have further developed the field of community college research and the influence of psychosocial factors. For instance, Robbins et al. (2006) investigated the predictive role of motivation, study skills, and self-perceptions with over 5,000 community college students. Other research has found that study approaches and time management were predictive of achievement, especially for part-time community college students (MacCann, Fogarty, & Roberts, 2012). Additionally, qualitative work has supported the importance of cultivating motivation, self-confidence, and other psychological characteristics in fostering community college student achievement and transfer (Barbatis, 2010; Suarez, 2003; Wirth & Padilla, 2008).

Moreover, psychosocial factors have been posited to particularly influence the achievement of community college students enrolled in developmental coursework, which remediates students’ lack of requisite skills in mathematics, reading, and writing (Fong, Zientek, et al., 2015; Zientek et al., 2013). High achievement and persistence in developmental education allow students to advance toward credit-bearing coursework and either degree attainment or transfer; however, many students often repeat developmental courses and are unable to effectively progress through course streams (Bahr, 2008). Due to a lack of academic preparation and a history of academic failure and setback, developmental students may potentially reap greater benefits from higher levels of motivated and self-regulated dispositions.

Although an abundance of research has accumulated in this area, a single study can be thought of as just a piece of the puzzle, whereas meta-analysis can reveal a more complete picture (Dent & Koenka, 2015). Also, due to the interdisciplinary nature and scope of the topic, reconciling theoretical perspectives and conceptualizations of psychosocial factors is necessary to influence policy and advance theory. To aggregate the existing research together, the present study is a systematic review and meta-analysis of the psychosocial predictors of community college student persistence and achievement. To our knowledge, no such review has been conducted, and we argue that synthesizing the past research on this topic will be important for scholars and practitioners to consider for the success of community college students.

Integrating Across Perspectives on Psychosocial Variables

For the purposes of this study, we integrated the literature and theories that span the field of psychosocial development. We believe that it is beyond the scope to fully discuss the range of contemporary motivational and psychological theories in this article; however, our aim is to distill and highlight the key motivational constructs that both reflect the field of community college research and meaningfully capture the array of constructs in practical categories. Broadly, we conceived of five umbrella categories that seemed to best fit existing psychological theories: self-perceptions, motivation, attributions, self-regulated learning, and anxiety. First, we examined similarities within these categories, defined each category, and assigned predictor variables from included studies of the meta-analysis into each category, where they might belong. Once constructs were distributed, we assessed construct definitions and how they were measured to verify category membership. We now briefly discuss each of these categories, their theoretical underpinnings, and empirical evidence for their influence on student success.

Self-Perceptions

Researchers in social psychology and education have been long interested and invested in understanding students’ self-perceptions and perceived competency beliefs. A wealth of research supports a strong reciprocal relationship and the importance of these perceptions for student success (Hansford & Hattie, 1982). Two well-studied and established constructs related to self-perceptions in achievement settings are self-concept and self-efficacy. Self-concept, or the composite view of one’s self (Marsh et al., 2015), has been consistently identified as a positive predictor of academic success (Valentine, DuBois, & Cooper, 2004). Self-efficacy is described by Bandura (1986) in social-cognitive theory as the personal factor that determines motivation and action from a person’s abilities. Bandura (1997) defined perceived self-efficacy as “beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments” (p. 3). Self-efficacy has been positively linked with academic achievement and higher educational attainments for university students in recent meta-analytic work (Honicke & Broadbent, 2016).

In the field of community college research, many studies have assessed the influence of self-perceptions on student success. With a sample of 427 community college students, Nakajima, Dembo, and Mossler (2012) found a small positive association between self-efficacy and cumulative GPA (r = .23) but no significant relationship with persistence to the subsequent semester. In a large, diverse sample of 475 community college students, Edman and Brazil (2009) found that self-efficacy was positively related with GPA but only for Asian and Hispanic students (rs = .17-.24) and not for White or African American students. Although there is ample evidence that self-perceptions are positively related with community college success, the exact parameters of this relationship regarding particular outcomes and subgroups are unclear.

Motivation

Although a wide range of definitions for motivation exist in the literature, a definition we borrow to capture this category is the processes of both initiating and sustaining behavior (Linnenbrink-Garcia & Patall, 2016; Schunk et al., 2014). Key motivation constructs in the field are intrinsic and extrinsic motivation, goals, and task value.

Self-determination theory (Deci & Ryan, 1985) posits two types of behavioral regulation or the motivations behind behavior: extrinsic versus intrinsic. Intrinsic motivation is defined as interest or enjoyment of a task for its own sake, without any external incentive (Ryan & Deci, 2000), whereas extrinsic motivation refers to engagement motivated by external pressures or influences. Although intrinsic motivation has been linked with other adaptive outcomes such as improved learning, performance, and well-being, Ryan and Deci (2000) argued that understanding extrinsic motivation is also critical. Given the difficulty of instilling intrinsic motivation in learning, instructors often rely on extrinsic motivation as an essential strategy in teaching. Moreover, the propensity to be intrinsically motivated becomes increasingly curtailed over time, especially during adolescence (Wigfield, Eccles, Mac Iver, Reuman, & Midgley, 1991). In an urban community college setting, Liao, Edlin, and Ferdenzi (2014) found that extrinsic motivation (r = .283) was more strongly related with persistence than intrinsic motivation (r = .054) within a sample of 310 students. Although this is unexpected given the stigma of using external pressures and rewards to motivate according to psychological theory (Deci, Koestner, & Ryan, 1999), the motivations of community college students may be more complex as they juggle multiple responsibilities and social obligations enrolled at school (Fong, Krause, Acee, & Weinstein, 2015).

There has been an abundance of empirical work on achievement goal theory in the past three decades, mainly in K–12 settings and 4-year universities (Hulleman, Schrager, Bodmann, & Harackiewicz, 2010; Livengood, 1992). Goal theory has generally focused on two facets of goal-oriented strivings: mastery goal orientation (i.e., learning or task orientation) and performance goal orientation (i.e., ego orientation). Both goal orientations represent two approaches toward competence (Pintrich, 2000): Mastery goal orientation is generally focused on developing competence as well as learning and understanding, whereas performance goal orientation is focused on demonstrating competence in relation to others (Dweck & Leggett, 1988). Mastery goal orientation has been hypothesized to describe students who pursue learning for the intrinsic value of the activity. On the other hand, performance goal-oriented learners pursue tasks with emphasis on the outcome or grade earned. In a sample of 322 developmental community college students, Ray, Garavalia, and Murdock (2003) confirmed theoretical predictions and observed how students’ mastery-oriented goals were positively related with achievement (r = .168), whereas performance-oriented goals negatively predicted achievement (r = −.026). Although some studies have shown performance-related goals to be maladaptive, meta-analytic research has shown the potential of both performance and mastery goals to be beneficial for student performance (Hulleman et al., 2010).

Another cognitive motivational theory is expectancy-value theory (Eccles et al., 1983). In this theory, motivation is explained by a learner’s beliefs about how well he or she will do on a task (expectancy) and the extent to which he or she values the task (value). Wigfield and Eccles (2000) defined expectancies for a learner’s success as beliefs about how well he or she will do on upcoming tasks, similar to the construct of self-efficacy. Achievement values are understood as the combination of attainment value, intrinsic value, utility value, and cost; these encompass the importance of doing well on a task, the level of enjoyment derived from the activity, the utility for future plans, and the required amount of effort and emotional cost, respectively (Guo et al., 2016). Research has shown that achievement values strongly predicted students’ future course enrollment intentions (Bong, 2001) as well as academic performance (Hulleman, Durik, Schweigert, & Harackiewicz, 2008). For instance, in a dissertation on community college students, Parlett (2012) examined a small sample of 47 developmental reading students and found a strong influence of utility value on academic achievement (r = .442). In contrast, other dissertation research indicated that achievement values negatively predicted course grades, but they were positively associated with course completion in a sample of 59 students enrolled in online community college courses (Menager-Beeley, 2001).

Attributions

Weiner’s (1985) attribution theory explained the functional significance of cognitive processes during the behaviorist movement in psychology. Investigating individuals’ perception of causality when faced with success or failure, attribution theorists studied how people evaluated why a particular incident occurred and how attributions toward the outcome of that incident guided subsequent behavior (Bar-Tal, 1978). Specifically in education, Weiner (1985, 1992) highlighted the important implications of causal biases when interpreting successes or failures while learning. The key dimensions of the attributional model are locus of causality, stability, and controllability (Weiner, 1992). Locus of causality, which originates from an earlier construct called locus of control (Rotter, 1954), refers to the perception that outcomes of behavior are a consequence of one’s own doing (internal) or forces outside of one’s control, such as luck, fate, or powerful others (external). The notion of stability arises from the fact that some causes can fluctuate (e.g., mood) or stay constant (e.g., ability). Controllability (sometimes known as volitional control) involves the distinction that a student can increase or decrease effort expenditure. A related construct to the attributional model and controllability is perceived behavioral control—the level of ease or difficulty concerned with executing a particular behavior (Ajzen & Madden, 1986; Zhou, 2016). Believing one has the opportunities and resources to perform an activity are likely to have strong intentions to engage in it.

In the attributional model, Weiner (1985) outlined the range of attributions that can result along internal–external and controllable–uncontrollable dimensions. First, if a learner attributes success to internal causes such as high ability and effort, there is more personal investment when succeeding in tasks and greater feelings of pride and increased likelihood of initiating achievement activities. Second, when faced with failure, internally oriented students with high controllability ascribe a lack of effort to their poor achievement and are more inclined to continue striving toward unattained goals with greater intensity and persistence. On the other hand, more externally oriented students may attribute their successes and failures to reasons outside of their control, thereby avoiding the personal investment of greater effort. Previous meta-analytic work has supported the positive influence of more internal, controllable attributions on academic achievement (Findley & Cooper, 1983). In line with this synthesis, Dille and Mezack (1991) examined the attributions of 151 community college students and found that high external locus of control was significantly more characteristic of unsuccessful students in online courses.

Self-Regulated Learning

Self-regulated learning strategies relate to the active, constructive process of setting goals and monitoring and controlling cognitive, motivational, and behavioral aspects of learning (Zimmerman, 1986, 2002). Cognitively, students self-regulate by checking their understanding from their use of strategies to monitor how much information they have processed and attempt to pinpoint mistakes or areas for improvement (Weinstein & McCombs, 1998). After students employ the motivational strategy of setting goals, self-regulated learning strategies are required to help students monitor their progress toward a particular goal. Self-regulated learning involves either modifying that goal or creating a new action plan to attain that goal (Krause & Fong, 2012). Behaviorally, students can self-regulate their study habits, skill development, and anxiety during exams. Recent meta-analytic work has shown the positive impact of self-regulated learning (specifically, metacognitive and cognitive strategies) on academic achievement (Dent & Koenka, 2015) in K–12 settings. In the community college setting, using a large sample of 788 students across 10 U.S. states, Allen and Robbins (2010) found that students’ self-regulation of their effort and studying was predictive of cumulative GPA but not of associate’s degree attainment.

Anxiety

As the most widely studied academic emotion in the educational literature (Zeidner, 1998), anxiety is not only conceptually distinct as a psychological factor but also highly prevalent in today’s college campus culture and student population. Although some degree of academic stress can indicate a healthy interest in the task and a response to appropriate task difficulty, many students experience overwhelming amounts of anxiety that ultimately affect their performance. These emotional responses may be even more impactful for high-risk students, who may already be primed to be fearful of difficult academic tasks. In some respects, anxiety is related to a threat of evaluation (Martin & Marsh, 2008), such as the worries students experience when taking exams, giving presentations, or writing research papers. To reduce the deleterious effects of anxiety on performance, anxiety requires management through a process of awareness, reflection, and control for students to analyze how their affective reactions to learning are manifesting and hindering their performance. Anxiety has been investigated for its unmotivating, debilitating effects on achievement (Ashcraft & Moore, 2009; Ma & Xu, 2004). Previous meta-analytic work has found overall negative associations between anxiety and academic performance (r = −.21, Seipp, 1991; r = −.29, Ma, 1999). In the community college setting, research has also shown a similarly negative effect of anxiety on the success of 75 high-risk students enrolled in developmental education (r = −.28; Ochroch & Dugan, 1986).

Integration Across Motivational Constructs

Recent psychological studies on motivation have integrated multiple perspectives to examine interactive and distinctive influences of particular constructs (Linnenbrink-Garcia & Patall, 2016). Combining theories and using multiple forms of motivation have led to new advances in student engagement and learning. For example, in a sample of urban community college students, Liao, Ferdenzi, and Edlin (2012) measured both intrinsic and extrinsic forms of motivation along with self-regulated learning and their relationships with achievement and persistence. Other research has begun studying profiles of motivational characteristics and adaptive combinations of motivation (Conley, 2012; Shell & Husman, 2008). Because of the advances in integrating motivation perspectives in the literature, the use of multiple categories of psychosocial factors in our meta-analysis has both theoretical and empirical support.

Psychosocial Factors in Educational Persistence Models

Vincent Tinto’s (1975) integration model is perhaps the most influential model for understanding student retention. For many decades, it has contributed to our understanding of the complex factors and processes higher education institutions face when fostering college student success. However, since its conception, many other scholars have critiqued the model (see Braxton, 2000; Wolf-Wendel, Ward, & Kinzie, 2009), citing its inadequacies to accommodate subpopulations of postsecondary students, such as commuter students and community college students (Pascarella, Smart, & Ethington, 1986), as well as failing to include important factors and explanatory mechanisms. We first review Tinto’s model, and then discuss Bean and Eaton’s (2000) model, which incorporates psychological mechanisms. Next, we explain a more recent higher education model of student success, the socioecological outcomes model (Harris & Wood, 2014), which is focused on community college students, and more specifically, men of color.

Tinto’s Integration Model

Tinto’s (1975) integration model incorporates preentry characteristics of students, institutional characteristics, and the academic and social integration of students. Understood as a longitudinal process, college persistence is a series of interactions between the student and the academic and social systems within the institution. These interactions together with the experiences of social and academic integration lead to modifying goal and institutional commitment toward dropping out or persisting in college. Although not explicitly mentioned, the presence of psychosocial factors is well situated with the integration model. Tinto argued that students begin this integration process with preentry characteristics, aspects he termed as individual attributes. Beyond demographic characteristics and levels of ability, students enter college with personal qualities, motivations, and perceptions that influence their drive and goals to succeed (Bers & Smith, 1991; Napoli & Wortman, 1998).

Bean and Eaton’s Psychological Model

Bean and Eaton’s (2000) psychological model departs from Tinto’s (1975) integration model by emphasizing the psychological mechanisms that underlie the process of academic and social integration. The flow of their psychological model begins with students entering college with a variety of psychological attributes such as self-efficacy assessments, attributions, and their past behavior (Bean & Eaton, 2001). After interactions within the institution, students employ strategies to become socially and academically integrated. Some of these strategies are coping behaviors, self-assessments, and locus of control, which lead to the development of students’ positive attitudes and intentions toward greater persistence.

Socioecological Outcomes Model

Although Harris and Wood (2014) originally conceived the model for community college men of color, the socioecological outcomes model incorporates the salient factors that influence all students and their interactions with their society, environment, personality, and campus. The model posits that students matriculate into community colleges with a variety of inputs ranging from background and defining factors such as primary language and generation status to societal factors such as economic conditions and capital identity. The first of the socioecological domains, the noncognitive domain, has direct ties to the literature on psychosocial factors. It comprises constructs such as self-efficacy, locus of control (or attributions), interest, and value (Xiong & Wood, 2015). The noncognitive domain has fluid and dynamic relationships with other domains: academic, environmental, and campus ethos. Together, they inform student success in the areas of persistence, achievement, and labor outcomes.

Summary and Research Questions

Prominent higher education theories and models of college student persistence infuse concepts that stem from the psychological literature. Moreover, empirical support for the influence of psychosocial factors on community college student success exist, yet a lack of consensus remains regarding which constructs are the most critical for student success, the extent to which these variables are important for student success, and for whom are they most beneficial. Therefore, we conducted a research synthesis and meta-analysis, guided by the following research questions:

Research Question 1: What is the overall relationship between psychosocial factors (motivation, self-perceptions, attributions, self-regulation, and anxiety) and community college student achievement and persistence?

Research Question 2: If significant variation exists across studies, to what extents do study characteristics (i.e., type of outcome, outcome duration, and sample characteristics) influence the magnitude of this relationship?

Method

Research syntheses primarily focus on empirical studies and seek to summarize past research by drawing overall conclusions from multiple, separate investigations that address related or identical topics (for the encouragement of the use of meta-analysis in higher education, see Bowman, 2012). In the following sections, we briefly outline the methodological and analytic approaches used in our meta-analysis (Cooper, Hedges, & Valentine, 2009).

Literature Search and Inclusion Criteria

Studies were collected from multiple sources and included exhaustive search strategies meant to uncover both published and unpublished research. We searched ERIC, PsycINFO, and Proquest Dissertation and Theses Full Text electronic databases using a broad array of subject terms within the domains of psychosocial variables, community college, and academic outcomes. Once the search strategy was employed, and all citations had been retrieved, abstracts for these studies were judged for relevance, resulting in a pool of studies that would possibly meet the inclusion criteria. The full texts of these potentially codeable studies were reviewed and evaluated with the inclusion criteria. We conducted ancestry searches by reviewing the reference sections of all relevant studies retained for coding.

To be included, a study must have met the following criteria: the inclusion of (a) a community college sample, (b) a psychosocial predictor, (c) a student success outcome, and (d) a correlational relationship between predictor and outcome. Regarding the sample criteria, students in the study must have been currently enrolled in a public or private community college, 2-year vocational school, or 2-year technical school. If the school information was not provided but the students were reported as pursuing associate’s degrees, we coded this as meeting our inclusion criteria.

Based on the pool of included studies, we divided the types of predictors in categories based on motivational theory and previous synthesis work on psychosocial factors (Robbins et al., 2004). The five types of predictors were motivation, self-perceptions, attributions, self-regulation, and anxiety (see Table 1 for descriptions and representative measures of each predictor category). Motivation consisted of variables dealing with interest and goals. Self-perceptions included self-concept, self-esteem, self-efficacy, or any self-evaluation of abilities or worth. Attributions referred to causal reasons students cite for their success and failure along two dimensions: controllable or uncontrollable, and internal or external. Self-regulation encompassed learning strategies, study skills, and time management skills. Last, anxiety included stress and both test and generalized anxiety.

Table 1

Psychosocial categories, definitions, and their representative measures

Psychosocial category	Definition and measures
Attributions	Definition: Causal explanations for success and failure that vary along internal–external and controllable–uncontrollable dimensions
	Representative measures: Adult Nowicki-Strickland Internal-External Locus of Control Scale (Nowicki & Duke, 1974), Rotter’s Internal-External Locus of Control Scale (Rotter, 1966), Self-Mastery Scale (Pearlin & Schooler, 1978), Locus of Control Scale (Levenson, 1974)
Self-regulation	Definition: Strategies that manage the learning process including cognitive, motivational, and metacognitive strategies, such as time management and study skills
	Representative measures: Learning and Study Strategies Inventory (Weinstein, Schulte, & Palmer, 1987), MSLQ (Pintrich et al., 1991), Study Attitudes and Methods Survey (Michael, Michael, & Zimmerman, 1972), Self-Regulated Learning Strategies (Corno, Collins, & Capper, 1982), Student Readiness Inventory (Le, Casillas, Robbins, & Langely, 2005)
Anxiety	Definition: Negative affect influencing student performance through worry and stress
	Representative measures: Fennema-Sherman Mathematics Attitude Scale (Fennema & Sherman, 1976), Learning and Study Strategies Inventory (Weinstein et al., 1987), Manifest Anxiety Scale (Taylor, 1953), MSLQ (Pintrich et al., 1991), IPAT Anxiety Scale (Krug, Scheier, & Cattell, 1976), State-Trait Anxiety Inventory (Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983), Test Anxiety Scale (Benson & El-Zahhar, 1994)
Motivation	Definition: Interests, goals, and values that initiate and sustain behavior
	Representative measures: Achievement Goals Questionnaire (Elliot & Church, 1997), Achievement Motivation Scale (Meharbian, 1968), MSLQ (Pintrich et al., 1991), PALS (Midgley et al., 2000)
Self-perceptions	Definition: Perceptions toward the self that relate to one’s overall view of self (self-concept, self-esteem) or academic competence (self-efficacy)
	Representative measures: College Self-Efficacy Inventory (Solberg et al., 1993), MSLQ (Pintrich et al., 1991), Motivation to Read Profile (Gambrell, Palmer, Codling, & Mazzoni, 1996), Nursing Academic Self-Efficacy Scale (Harvey & McMurray, 1994), RSES (Rosenberg, 1965), Self-Concept of Ability (Brookover et al., 1962), Self-Description Questionnaire II (Shavelson, Hubner, & Stanton, 1976)

Note. MSLQ = Motivated Strategies for Learning Questionnaire, PALS = Patterns of Adaptive Learning Survey, RSES = Rosenberg Self-Esteem Scale. References for representative measures are available in Appendix A, available as supplemental material in the online version of the journal.

The outcomes of the review consisted of both persistence- and achievement-related outcomes. Persistence outcomes were operationalized to include course completion, dropping out, reenrolling the following semester, or degree completion. Achievement-related outcomes consisted of course grades, GPA, and test scores such as standardized achievement measures. Outcome variables were coded so that larger values reflected greater persistence or achievement. Last, studies needed to have reported enough data for us to calculate a correlation between predictor and outcome. We queried authors of studies conducted within the past five years when any of the information needed to determine inclusion was missing. Six out of the 15 authors, who were queried, responded with the requested data.

Information Retrieval and Coding

Numerous characteristics of each study were coded directly from the research report. In some instances, some inference was necessary, such as using preestablished definitions to code ambiguous characteristics. Codes comprised four broad categories: research report, sample, predictor variable, and outcome variable.

Research Report Characteristics

First, we coded for characteristics of the report, including author name, year of the report, and type of report (journal article, dissertation, thesis, conference paper, or institutional report). We also categorized reports by publication status, published (journal articles) and unpublished (dissertations, theses, conference papers, or reports), allowing us to assess publication bias (Polanin, Tanner-Smith, & Hennessy, 2016).

Sample Characteristics

Second, we coded for sample characteristics in each report. We coded for the community college setting as well as demographic characteristics such as age, gender, ethnicity, and socioeconomic status. We also coded for educational characteristics related to the sample, including program/major, full-time status, and hours worked per week.

Predictor Characteristics

Third, we coded aspects of the predictor variable such as the type of psychosocial variable and study authors’ descriptions of the predictor. We also noted if there was a scale or instrument name, reliability of the measure, and domain. Note that we reversed the sign of the correlation if the predictor was negatively valenced, such as anxiety, if greater levels of anxiety represented more of a maladaptive psychosocial characteristic. We did the same for external, uncontrollable attributions, which have been shown to be detrimental to students’ motivation and achievement.

Outcome Characteristics

Fourth, we coded characteristics of the outcome. Namely, we were interested in student success outcomes, which comprise either retention in community college, degree attainment, and course completion or achievement-related outcomes such as grades, GPA, or tests. For the persistence outcome, the majority of studies (k = 35) reported a correlation between persistence and psychosocial factors. When study designs measured persistence as a dichotomous outcome (persisters and nonpersisters; k = 23), effect sizes were computed as a d and converted to r:

r = \frac{d}{d^{2} + a}; a = \frac{(n_{1} + n_{2})}{n_{1} n_{2}} .

Similar for negatively valenced predictors, if the outcome was framed in a negative direction such as dropping out, then we reversed the direction of the effect size. Last, we coded for time duration of the outcome: whether it referred to achievement or persistence after one semester or beyond one semester.

Coder Reliability

All reports were coded independently by trained coders. The coders had experience coding for meta-analysis and were extensively trained on each code using the previously mentioned coding frame. As a reliability check, all pairs of codes for each study were compared for agreement between the two coders. We calculated a reliability measure between coding by dividing the number of matched codes by the total number of codes. Disagreements were noted and resolved by another coder. Coder interrater reliability was high, with an agreement rate of 96%.

Effect Size Calculation and Data Integration

Effect sizes were computed as Pearson’s r (see Fritz, Morris, & Richler, 2012; McGrath & Meyer, 2006, for benefits of using r as an effect size metric). When possible, we extracted correlations and sample sizes from correlation tables or text in the reports. If data were only available from means, standard deviations, and sample sizes of two groups (i.e., persisters and nonpersisters), we estimated a correlation. When this information was not reported in a study, corresponding inference test statistics (t statistic, F statistic, chi-square) were used to derive an effect size. If only statistical significance was denoted, a conservative effect size was derived with an assumed p value of .05. As suggested by Cooper et al. (2009), we employed Fisher’s z transformations on raw correlations to stabilize variance and normalize the sampling distribution. We transformed them back into correlations to present average effect sizes and confidence intervals. Transformations were defined as

z = \frac{1}{2} \ln (\frac{1 + r}{1 - r}) .

We used a shifting-unit-of-analysis approach (Cooper, 1998) to deal with the issue of determining what constitutes an independent estimate of effect. This approach involves coding as many effect sizes from each study that exist as a result of variations within the study but averaging effects appropriately in analyses to prevent violating the assumption of independent data points. Accounting for sample size, weighted procedures were used to calculate average effect sizes across all comparisons in which each independent effect size is first multiplied by the inverse of its variance and then the sum of these products is then divided by the sum of the inverses (see Cooper et al., 2009). Analyses were conducted using random error assumptions (rather than fixed error assumptions), in which a study-level variance component also is assumed to be an additional source of random variation.

Due to the possibility of not obtaining all the studies due to either failure on the part of the meta-analyst to retrieve all relevant reports or censoring on the part of authors, we employed Duval and Tweedie’s (2000) trim-and-fill procedure to assess whether the effect size distribution differed from normally distributed estimates. This trim-and-fill method imputes missing values that would be present to approximate a normal distribution of effect sizes; this estimation indicates the impact of data censoring on the observed effect size distribution.

Moderator Analyses

Effect sizes may vary even if they estimate the same underlying population value. Therefore, homogeneity analyses were needed to determine whether sampling error alone accounted for this variance compared to the observed variance caused by features of the studies (Cooper et al., 2009). Consequently, possible moderators were tested using homogeneity analyses. We tested homogeneity of the observed set of effect sizes using a within-class goodness-of-fit statistic (Q_w). A significant Q_w statistic suggests that sampling variation alone could not adequately explain the variability in the effect size estimation, and it follows that moderator variables should be examined (Cooper et al., 2009). Similarly, homogeneity analyses can be used to determine whether multiple groups of average effect sizes vary more than predicted by sampling error. In this case, statistical differences among different categories of studies were tested by computing the between-class goodness-of-fit statistic, Q_b. A significant Q_b statistic indicates that average effect sizes vary between categories of the moderator variables more than predicted by sampling error alone. For continuous moderators such as percentage of females or minority composition in the sample, we used metaregression to assess moderation of continuous variables on the correlation between psychosocial factors and persistence. Metaregression equations were as follows:

(female composition) E S_{i} = β_{0} + β_{f e m a l e} + u_{i} + e_{i} .

(minority composition) E S_{i} = β_{0} + β_{m i n o r i t y} + u_{i} + e_{i} .

Results

After employing our search for relevant studies, we uncovered 11,832 unique reports of studies. On reviewing the titles and abstracts of the reports, we selected 703 studies for full-text retrieval to evaluate the inclusion criteria. Our final pool included 174 studies spanning 1971 to 2014 (see Table 2 for description of studies). We excluded reports for the following reasons: no psychosocial variable (k = 172); a non-U.S., noncommunity college student sample (k = 49); an experimental design without pretest correlations (k = 54); no relevant outcome (k = 59); not enough available data (k = 170); a duplicate data set (k = 5); or the full-text could not be retrieved after every exhaustive strategy was attempted (k = 20). Table S1 (the online supplement Table S1 is available as supplemental material in the online version of the journal) provides characteristics of the included studies and associated effect sizes.

Table 2

Characteristics of included studies

Characteristic	k	%
Publication type
Peer-reviewed journal article	31	17.3
Doctoral dissertation	137	76.8
Master’s thesis	1	1.0
Conference paper	5	2.7
Institutional report	5	2.7
Publication year
1960s	2	1.1
1970s	30	16.8
1980s	44	24.6
1990s	36	20.1
2000s	39	21.9
2010s	28	15.7
Region
Northeast	37	20.7
South	64	35.8
West	33	18.4
Midwest	36	20.1
Multiple regions	3	1.6
Not reported	6	3.3

Because of both theoretical and practical differences in persistence outcomes and achievement-related outcomes, we opted to conduct multilevel analyses, categorizing studies into two outcomes groups (achievement and persistence) for separate analyses (see Tables 3 and 4 for characteristics regarding samples and effect sizes by outcome). To identify outliers, we used Grubbs’s test within the set of effect sizes. We detected one significant outlier from Dille and Mezack (1991), which reported a very high correlation between attributions and course completion (r = .88). We converted this value to its nearest neighbor (r = .44) in the distribution of effect sizes (this process is called Winsorization).

Table 3

Samples and effect sizes from included studies for community college persistence outcomes

Characteristic	Self-perceptions	Motivation	Attributions	Self-regulation	Anxiety
Sample
No. of samples	30	23	17	16	13
Average sample size	182.87	185.7	112.8	398.1	138.4
Range of sample sizes	13–744	13–1,488	13–209	6–1,488	6–439
Effect size
No. of effects	44	33	22	31	18
Range of effects	−.28 to .42	−.04 to .43	−.19 to .44	−.10 to .42	−.11 to .25
Outliers	0	0	1	0	0

Table 4

Samples and effect sizes from included studies for community college achievement outcomes

Characteristic	Self-perceptions	Motivation	Attributions	Self-regulation	Anxiety
Sample
No. of samples	95	92	42	50	41
Average sample size	173.04	172.2	112.1	320.2	96.7
Range of sample sizes	4–2,068	4–2,384	4–552	4–3,144	4–1,878
Effect size
No. of effects	163	183	70	178	61
Range of effects	−.57 to .58	−.55 to .60	−.42 to .63	−.42 to .63	−.55 to .56

Overall Analysis of the Relationship Between Psychosocial Factors and Persistence

First, we examined the overall relationships between psychosocial factors and community college student persistence. Table 5 presents the results of the meta-analysis for the psychosocial variables and persistence, including number of samples, average correlations, 95% confidence intervals, and heterogeneity statistics. Overall, relationships between two psychosocial variables (self-perceptions and motivation) and community college persistence were positive. The averaged correlations for self-perceptions and motivation were .10 and .15, respectively. Using a binomial effect size display (Rosenthal & Rubin, 1982), we can translate the self-perceptions and persistence effect size of .15 in more practical terms. This display uses the correlation as the difference in outcome rages between two groups and, in this case, persisters and nonpersisters. Of the half of students with greater motivation, 57.5% belong to half of the group with greater persistence. In contrast, in the group with lower motivation, 42.5% belong to persisting group. Likewise, for self-perceptions, for the half with more positive self-perceptions, 55% belong to the persisters, whereas in the group with more negative self-perceptions, 45% were persisters. In addition, we found it somewhat surprising that other attributions, self-regulation, and anxiety were not related with community college persistence. In fact, the weighted average correlations between each of these predictors and persistence were not significantly different from zero.

Table 5

Overall meta-analysis results of psychosocial factors and community college student persistence and achievement

	Persistence				Achievement
Predictor	k	r	95% Confidence interval	Q_w	k	r	95% Confidence interval	Q_w
Self-perceptions	30 (33)	.10*** (.12)	.04, .16 (.06, .18)	111.12* (133.86)*	95 (108)	.13* (.09*)	.09, .17 (.05, .14)	426.88* (786.05*)
Motivation	23	.15***	.09, .21	58.68***	92 (106)	.17* (.21*)	.12, .21 (.17, .25)	489.48* (639.51*)
Attributions	17 (20)	.05 (.09)	−.04, .13 (−.002, .17)	45.7* (71.84*)	42 (52)	.14* (.18*)	.08, .19 (.12, .23)	97.29* (132.28*)
Self-regulation	16 (18)	.03 (.02)	−.02, .08 (−.03, .07)	34.5 (41.44)	50 (57)	.18* (.20*)	.13, .22 (.16, .25)	246.55* (346.41*)
Anxiety	13	.02	−.03, .07	6.49	41 (50)	.03 (−.003)	−.03, .09 (−.6, .6)	65.84 (85.87)

Note. Values in parentheses represent the point estimates and confidence intervals derived from trim-and-fill procedures. For persistence outcomes, trimmed studies for self-perceptions and attributions were to the right of the mean; trimmed studies for self-regulation were to the left of the mean. For achievement outcomes, trimmed studies were to the right of the mean, except for the self-perceptions and anxiety analyses, which were to the left of the mean.

p < .01. ***p < .001.

Next, we tested for heterogeneity among the effect size distributions. The tests of the distribution of the effect sizes revealed that we can reject the hypothesis that the effects were estimating the same underlying population value, for all the psychosocial factors, except for anxiety; therefore, there was sufficient heterogeneity among the effect sizes that may be explained by moderator variables. Stem-and-leaf plots are presented in Figure 1 to display variability among effect sizes for persistence and psychosocial variables.

Figure 1.

Stem-and-leaf plot of effect sizes for psychosocial factors and community college persistence.

We also searched for possible missing effects in the distribution of effect sizes, those that would influence the size of the average correlations for the psychosocial factors on persistence. From trim-and-fill analyses under a random-error model, there was evidence that effect sizes might have been missing for self-perceptions, attributions, and self-regulation (see Table 5 for imputed values from trim-and-fill procedures within the parentheses). Altogether, when accounting for possible data censoring, the influence of these psychosocial factors on community college persistence was either unchanged or slightly stronger.

Overall Analysis of the Relationship Between Psychosocial Factors and Achievement

Next, we examined the overall relationship between psychosocial factors and community college student achievement. Table 6 presents the results of the meta-analysis for the psychosocial variables and achievement. Overall, relationships between four psychosocial variables (self-perceptions, motivation, attributions, and self-regulation) and community college achievement were positive. The averaged correlations for self-perceptions, motivation, attributions, and self-regulation were .13, .17, .14, and .18, respectively. Similar to the analyses for persistence, anxiety was not significantly associated with achievement. Interpreting the results for self-regulation, using the binomial effect size display, we found that of the half of students with greater self-regulation, 59% belong to half of the group with greater achievement. In contrast, in the group with lower self-regulation, 41% belong to the high-achieving group.

Table 6

Results of the moderator analyses for psychosocial factors and persistence

	Self-perceptions			Motivation			Attributions			Self-regulation			Anxiety
Moderator	k	r	Q_b or 95% CI	k	r	Q_b or 95% CI	k	r	Q_b or 95% CI	k	r	Q_b or 95% CI	k	r	Q_b or 95% CI
Publication status			2.38			1.06			1.26			0.25			—
Published	5	.03	−.02, .09	4	.10	−.03, .22	5	.11	−.10, .31	5	.05	.001, .09	0	—	—
Unpublished	25	.11**	.03, .18	19	.17***	.11, .23	12	−.01	−.08, .05	11	.02	−.06, .10	13	.02	−.03, .07
Outcome duration			1.50			.03			1.77			0.23			0.01
1 Semester	11	.12**	.05, .20	11	.12**	.05, .20	6	.13	−.04, .30	6	.02	−.07, .11	5	.02	−.06, .11
More than 1 semester	10	.06	−.02, .13	5	.14	−.05, .32	7	.01	−.07, .09	8	.04	−.01, .10	6	.02	−.05, .59
Persistence type			1.07			1.12			2.42			0.06			1.04
Course completion	7	.19	−.03, .39	5	.22*	.02, .40	3	.21	−.08, .47	3	.04	−.02, .11	3	.11	−.07, .28
Reenrollment	27	.07*	.01, .13	18	.11**	.03, .18	14	−.02	−.08, .03	11	.04*	.01, .06	10	.01	−.04, .06

Note. CI = confidence interval. All anxiety and persistence effect sizes came from unpublished sources; thus moderator tests could not be conducted.

p < .05. **p < .01. ***p < .001.

In addition, the tests of the distribution of the effect sizes revealed a significant degree of heterogeneity among all psychosocial factors and achievement. Stem-and-leaf plots are presented in Figure 2 to display variability among effect sizes for achievement and psychosocial variables. Through trim-and-fill procedures, we found evidence that each of the distributions were missing effect sizes, under random effects. Imputing these values would increase the correlation between psychosocial factors and achievement for all predictors except self-perceptions and anxiety, which had lower correlations after imputation.

Figure 2.

Stem-and-leaf plot of effect sizes for psychosocial factors and community college achievement.

Moderator Analyses

Next, we conducted moderator analyses to explain the variability among effect sizes and test both methodological and theoretical hypotheses regarding when and how psychosocial factors can most influence community college student persistence and achievement. Using a random-effects model, we examined two categories of moderators: categorical moderators, which included publication status (published or unpublished), type of persistence outcome (course completion or student retention), type of achievement outcome (course grades, GPA, or test scores), and time length of persistence outcome (one semester, more than one semester), and continuous moderators, which included percentage female composition and percentage minority student composition.

Publication Status

We first tested if there was a significant difference in average correlations from published and unpublished studies. For persistence studies, there was no evidence for a publication bias for any of the psychosocial variables. However, for studies with achievement outcomes and self-perceptions as predictors, there was a significant difference between published, r = .21, k = 24, and unpublished studies, r = .10, k = 71, Q = 4.98, p < .05, suggesting some evidence of a publication bias. For the other predictors and achievement, there was no other significant moderation of publication status.

Type of Outcome

We found two types of persistence in our pool of included studies. The first was the more traditional sense of persistence, that is, student retention to the next semester or beyond at the enrollment level. The second type of persistence was at the course level, that is, whether students successfully completed a course. One study (Kelley, 1999) measured both types of outcomes and contributed two separate effect sizes. Under random effects, moderator analyses revealed no significant differences between the two types of persistence outcomes for any of the psychosocial variables (Table 6). However, we detected a trend that favored stronger correlations for the course completion outcome. To further examine this possibility, we aggregated all the psychosocial variables into a combined factor to increase statistical power for detecting a significant difference. When, combining the variables together, the correlation for the course completion outcome (r = .20) was higher than those for the retention outcome, r = .06, Q(1) = 5.01, p = .025. This finding suggests that psychosocial factors are more related to persistence at the course level (more proximal) rather than at the enrollment level (more distal). We also categorized achievement into three types of outcomes: course grades, GPA, and test scores. However, moderator analyses revealed no significant differences among achievement outcome categories across psychosocial categories (see Table 7).

Table 7

Results of the moderator analyses for psychosocial factors and achievement

	Self-perceptions			Motivation			Attributions			Self-regulation			Anxiety
Moderator	k	r	Q_b or 95% CI	k	r	Q_b or 95% CI	k	r	Q_b or 95% CI	k	r	Q_b or 95% CI	k	r	Q_b or 95% CI
Publication status			4.98*			2.81			0.47			0.19			0.20
Published	24	.21***	.13, .28	14	.25***	.14, .34	3	.24	−.07, .51	9	.19***	.12, .25	11	.01	−.10, .12
Unpublished	71	.10***	.06, .15	78	.15***	.10, .20	39	.13***	.08, .18	41	.17***	.11, .23	30	.04	−.03, .10
Outcome duration			0.90			0.09			4.03*			2.39			0.36
1 Semester	67	.14***	.09, .19	72	.17***	.12, .22	31	.16***	.09, .23	36	.15***	.10, .20	32	.02	−.05, .09
More than 1 semester	38	.11***	.06, .15	27	.16***	.08, .23	13	.07*	.01, .13	20	.22***	.14, .29	12	.06	−.05, .16
Achievement type			1.12			4.72			2.77			4.72			1.41
Course grade	23	.15***	.10, .21	23	.10**	.04, .16	4	.31	−.03, .58	15	.13**	.04, .22	16	−.04	−.18, .11
GPA	65	.12***	.07, .17	53	.17***	.11, .23	29	.10***	.06, .13	35	.20***	.15, .25	24	.04	−.01, .10
Test score	17	.12**	.03, .20	23	.20***	.12, .28	12	.18*	.04, .14	17	.08	−.02, .19	5	.12	−.13, .37

Note. CI = confidence interval.

p < .05. **p < .01. ***p < .001.

Time Duration of Outcome

We were also interested in whether the time length of the persistence and achievement outcomes influences the magnitude and directions of the averaged correlations. Specifically, we assessed whether there were differences among outcomes with durations of one semester and beyond one semester. Although there was not an overall difference among time durations for persistence outcomes, we detected one significant difference between effect sizes for the time duration groups for attributions and achievement. The averaged effect size for the one-semester duration, r = .16, k = 31, was higher than those beyond one semester, r = 0.07, k = 20, Q(1) = 4.03, p < .05.

Demographic Characteristics

For the moderators treated continuously, we conducted a set of metaregression analyses to measure the influence of demographic characteristics on the correlation between psychosocial factors and student success, under random effects (method of moments). Due to incomplete reporting and inconsistent operationalization of demographic information, we were limited in the types of moderator analyses that could be conducted. Specifically, although socioeconomic status, hours worked, and age would be important variables to consider in this meta-analysis, we could not assess these variables as moderators without a substantial reduction in sample size and power. That being said, we did code the percentages of minorities and female students for each sample as effect sizes could not be calculated for each gender and ethnicity group separately.

For persistence outcomes, because of the few number of studies in each psychosocial category, we opted to not perform metaregression analyses. For achievement-related outcomes, we found no significant moderation of gender for self-perceptions (β = −.03, p = .80), anxiety (β = .01, p = .96), attributions (β = −.10, p = .39), self-regulation (β = −.15, p = .69), and motivation (β = .21, p = .08). However, for anxiety and attributions, minority status was a significant moderator. Specifically, the relationship between anxiety and achievement increases as the percentage of minority students in the sample increases (β = .31, p = .02). This finding suggests that the influence of anxiety on community college achievement was greater for samples with larger percentages of minority students. In other words, it appeared that samples with a larger proportion of minority students and higher levels of anxiety achieved at higher levels. In addition, minority status also significantly moderated the influence of attributions on achievement (β = −.17, p = .04). For samples with larger percentages of minority students, the influence of attributions on community college achievement was weaker. There was no significant moderation of minority status on self-perceptions (β = .03, p = .63), self-regulation (β = −.15, p = .69), and motivation (β = .01, p = .89).

Discussion

In sum, the results of our meta-analysis indicated that psychosocial factors are positively related with community college student success. Given the complexity of improving community college persistence and achievement as evidenced by the chronically low rates of degree completion that plague U.S. 2-year colleges, identifying positive factors that can potentially bolster student retention is surely worthwhile. Not accounting for other background characteristics, we argue that psychosocial factors should not be easily dismissed, and encourage future scholarship in this area.

Although the correlational effect sizes in our study are deemed small according to guidelines set by Cohen (1992; small: .10, medium: .30, large: .50), we argue that the effects of psychosocial factors are still meaningful to consider. Moreover, Cohen contextualized his guidelines by citing two important issues. First, when interpreting the magnitude of effect sizes, researchers should consider how effects could vary by the academic discipline. For instance, the small effect sizes found in our meta-analysis are comparable in magnitude to a number of observed effects found in a systematic review of college student research by Pascarella and Terenzini (2005). Second, because the psychosocial predictor variables in the included studies were self-reported, the effects with student success outcomes could be attenuated due to measurement error, underestimating the true effects. In addition, myriad factors can influence students’ persistence and achievement, and even small effects can accumulate over time (Abelson, 1985) and affect important outcomes such as baccalaureate attainment.

Of the five categories of psychosocial factors, it appears that motivation and self-perceptions were the most influential predictors for both achievement and persistence outcomes. This finding is in line with other community college research that highlights the importance of motivation for persistence (Fong, Krause, et al., 2015), and the theoretical underpinnings of Bean and Eaton’s (2000) and Harris and Wood’s (2014) models. Self-perceptions were also substantially related with student success but not as strongly as one would predict based on previous meta-analytic findings on self-efficacy and achievement (Valentine et al., 2004) and other reviews in higher education (Richardson et al., 2012; Robbins et al., 2004).

Interestingly, regarding self-regulation as a predictor, there was a weak correlation associated with persistence but a stronger correlation with achievement, which was in line with previous meta-analytic results (Dent & Koenka, 2015; Richardson et al., 2012; Robbins et al., 2004). A similar pattern emerged for attributions as a psychosocial predictor: Greater internality and controllability in students’ attributions were strongly related with achievement but not with persistence. These findings suggest that self-regulated learning and attributions may be important for community college students’ achievement at the course level (stronger proximal impact), but they seem weakly related with persisting and reenrolling in the future (weaker distal impact). Furthermore, moderator tests revealed that attributions were more influential when achievement was measured after one semester, compared to outcomes beyond one semester such as cumulative GPA.

One explanation stems from the nature of self-regulated learning and attributions being focused on strategies and experiences in the classroom. For example, students’ regulation of their time and strategy usage tend to be more directly tied to their coursework, as are the attributions toward success and failure such as those experienced when receiving exam scores. On the other hand, constructs such as self-perceptions and motivation can have broader impacts and global influences beyond the course level and influence intentions and behaviors related to persistence and reenrollment. Surprisingly, anxiety seemed unrelated to either student success outcomes. Because of ample heterogeneity in effect sizes of anxiety and success outcomes, we observed that stress can at times be positive and negative (with simply a mean effect averaging out to zero). This result is in keeping with previous research finding small differences in GPA between college students high and low in anxiety (Chapell et al., 2005).

One notable finding was the differential impact psychosocial variables seem to have on persistence versus achievement outcomes, as evidenced also in Robbins et al.’s (2004) meta-analysis. Compared to effects on persistence, the significantly larger association between psychosocial variables and achievement suggests that the power of psychological qualities students possess were more influential at the course level in relation to their grades, GPA, and test scores, compared with broader level outcomes such as reenrollment. This finding is in line with previous researchers who discussed student retention as a series of persistence decisions (King, 2003), leading to students choosing to ultimately stay in college and persist until degree completion. Students’ personal choices to study, regulate their learning, and complete a course are the daily decisions they make that can potentially encourage them toward the broader goal of persistence and degree attainment. In support of many of the higher education persistence theories (Bean & Eaton, 2000; Tinto, 1975), our results underscore the role of psychological mechanisms as a crucial component in models that describe student’s academic integration.

Furthermore, the series of findings of the nonsignificant metaregressions of female percentage and minority percentages in the samples was a promising sign, in our opinion. Overall, the fact that these demographic characteristics did not moderate the relation between psychosocial factors and success substantiated that the influences of psychosocial factors hold across samples of varying minority status and female composition. This finding supports Bean and Eaton’s (2000) psychological model, which posits that psychological processes operate regardless of ethnicity or gender.

However, we observed a few exceptions of significant moderation of minority status for two predictor variables: anxiety and attributions. Some of these results appear rather surprising and counterintuitive. Regarding anxiety, main effects indicated nonsignificant results, but metaregressions with minority status as moderator revealed that within samples with greater percentages of minority students, anxiety had a stronger positive influence on achievement. Although we do not have clear explanations for why anxiety positively influenced achievement—in fact, Ma’s (1999) meta-analysis on math anxiety indicated no moderation in mixed ethnic samples—there are empirical and theoretical reasons why anxiety can be framed positively as discussed earlier (McGonigal, 2015; Perkins & Corr, 2005). For example, academic stress can be viewed as either a threat appraisal, which elicits negative affect, or a challenge appraisal, which can result in positive coping behaviors. Threat appraisals might lead to procrastination, whereas challenge appraisals of stress may lead to studying harder and preparing for coursework more effectively (Zajacova, Lynch, & Espenshade, 2005).

Regarding attributions, we found that for samples with greater percentages of minorities, internal attributions had a negative influence on community college achievement. Although this finding may appear counterintuitive, given the well-established link between internal attributions and academic outcomes overall (Findley & Cooper, 1983), some research and theory on Black students’ motivation may illuminate our unexpected findings. Graham (1994) reviewed the African American empirical literature on motivation and found that internal attributions may uniquely influence Black students’ achievement behavior. Internal causality exists at multiple levels such as generally how the world operates as well as personally. Previous research on Black college students found that high internality regarding personal control was associated with better achievement, but at a more global level, internality did not significantly relate with academic outcomes (Gurin, Gurin, Lao, & Beattie, 1969). Graham also cited three studies in her review that found no significant relationships between Black students’ internal attributions and their achievement. Perhaps minority students at community colleges may have high degree of internal attributions at a global level, which does not readily translate into positive academic outcomes. We note that these metaregression results were based on relatively small sample sizes, and clearly, further research that disentangles some of the moderating effects is needed.

Comparing Results With Existing Reviews

To further interpret the findings of the meta-analysis, we compare our results to two meta-analytic reviews (Richardson et al., 2012; Robbins et al., 2004), which examined the influence of psychosocial factors on university and college student success. Regarding persistence, the magnitudes of the self-perceptions and motivation predictors were comparable to the effects found by Robbins et al. (2004). The motivation–persistence correlation of .15 in our meta-analysis was similar to Robbins et al.’s effect sizes for motivational constructs of academic goals (r = .21) and achievement motivation (r = .11). Likewise, the self-perceptions–persistence correlation (r = .10) fell in between the magnitudes of the mean correlations between retention and self-concept (r = .06) and academic self-efficacy (r = .26) from Robbins et al. Surprisingly, although the strong effect on retention in the Robbins et al.’s meta-analysis was academic-related skills (r = .298), the correlation between self-regulation (analogous to academic-related skills) and persistence was nonsignificant. This result suggests that self-regulated learning may relate differentially for 2-year college student persistence compared to that of 4-year college student persistence. Allen and Robbins (2010), when comparing 2-year and 4-year students, argued that perhaps 2-year community college students are more likely to have life situations that mitigate the benefit of self-regulatory skills on achievement within their 2-year sample.

Regarding psychosocial factors and community college student achievement, our findings largely support those of Robbins et al. (2004) and Richardson et al. (2012). Small associations between achievement and most of the psychosocial variables (self-perceptions, attributions, motivation, and self-regulation) were also found in these prior reviews. For instance, the present meta-analysis found an average correlation of .14 between attributions and achievement, which closely mirrors the corresponding correlation of .13 in the Richardson et al. meta-analysis. Furthermore, Richardson et al. observed GPA correlations of self-efficacy and self-esteem of .31 and .09, respectively, which brackets our findings with a similar correlation of .13. We observed some differences as well between our review and Richardson et al.’s. Anxiety and stress were found to be consistently negatively associated with college achievement by Richardson et al.; however, in the present meta-analysis, there were no significant relationships between anxiety and achievement.

Implications for Research and Practice

Results from this meta-analysis point to important implications for both theory and practice in higher education. First, regarding implications for future research, our systematic synthesis revealed gaps in the literature. For example, our search through the literature uncovered a number of motivational predictors that have been unexamined in the community college literature. After coding and categorizing studies based on psychosocial predictors, we observed that variables such as grit (see Bowman, Hill, Denson, & Bronkema, 2015) were missing from the extant literature that we retrieved. Grit is defined as the combination of consistency of interest and perseverance of effort (Duckworth, Peterson, Matthews, & Kelly, 2007), and it seems particularly relevant for community college students who face a range of obstacles and need to regulate a complex set of academic, social, and life goals. Primary studies that investigate contemporary motivation constructs are encouraged to extend the field of community college student success. In addition, studies that clearly delineate the relationship of psychosocial factors and success across gender, ethnicity, socioeconomic status, full-time versus part-time students, and working versus nonworking students are missing from the literature (which prevented us from assessing critical moderator tests). Although a subset of studies reported such details regarding demographic characteristics, there was a lack of consistent and complete reporting within the included reports to meaningfully assess them in the present synthesis. A call for future research that explores additional psychosocial factors and across multiple demographic groups is warranted.

In addition, reviewing the literature of community college students and psychosocial factors revealed some important aspects of the corpus of research in this area. One of these observations was the large representation of unpublished or grey literature (e.g., dissertations, theses, and conference papers). Only a small fraction of the included studies in this review were published in peer-reviewed journals, and moderator tests showed some evidence of publication bias (self-perceptions and achievement). Publishing and disseminating findings is imperative to further the field of higher education, namely, the community college area, in order to not only encourage research in postsecondary education (see Crisp, Carales, & Nuñez, 2016) but also to illuminate small or nonsignificant findings to avoid an overestimation bias.

With regard to practice, by affirming the positive relationship between psychosocial factors and community college success, this study offers administrators and practitioners accumulated and synthesized evidence of the value of cultivating psychosocial qualities in students. In particular, our findings can be used to build programs, initiatives, orientations, success courses, and trainings that focus on bolstering psychosocial development in community college students (see Lazowski & Hulleman, 2016). For example, in a recent intervention study, Bol, Campbell, Perez, and Yen (2015) measured the impact of self-regulated learning modules in developmental mathematics courses. They found that students who participated in the psychosocial intervention had higher time management skills, metacognitive skills, course grades, and course persistence. Similarly, implementing a student success course or orientation for entering community college students may improve students’ interests, goal-setting behaviors, and self-perceptions and, in turn, positively influence their achievement and persistence.

In a practical sense, the results of this meta-analysis point to the need for psychosocial factors to be cultivated to improve community college student outcomes. In our meta-analysis, averaged correlations as large as .16 can be translated (using a binomial effect size display) into a 16% point difference between highly motivated, self-efficacious students, and their less motivated counterparts. Fortunately, implementing educational interventions that cultivate psychosocial qualities such as mind-set and achievement values have been shown to be surprisingly efficient and cost-effective (Yeager & Walton, 2011). As limited financial resources and curricular bandwidth continue to plague higher education institutions, a sophisticated understanding of how to develop psychosocial factors in community college students is critical.

Limitations to Generalizability

The first limitation of this synthesis is that meta-analyses in general consist of synthesis-generated evidence, which should not be interpreted as supporting causal relationships (see Cooper, 1998). A synthesis can establish only an association between a moderator variable and the outcome but not causality or directionality. Therefore, when significant differences were found when comparing groups of effect sizes within a research synthesis, results should be interpreted and used to direct future research of these factors in a controlled design to appropriately appraise causal impact. It is also important to note that some of the findings were based on small numbers of effect sizes, making it difficult to place a great deal of confidence in the direction and magnitude of the estimated effects.

In addition, there were some important variables that could not be examined as moderators due to incomplete reporting in the included studies. Sample characteristics such as age, full-time status, hours working, and distance versus traditional learning modes could not be assessed. Future research is encouraged to systematically examine these factors to see their moderating influence on the relationship between psychosocial factors and community college student success. Finally, one caveat to our review is our intention to not rule out structural elements at the community college setting that may influence student success, such as institutional factors, faculty–student interactions, or campus climate but to particularly focus on the personal qualities students possess. We argue that these factors together inform student’s integration and success at 2-year community colleges and beyond.

Footnotes

Notes

Authors

CARLTON J. FONG is currently a postdoctoral research fellow at The University of Texas at Austin, 1912 Speedway D5400, Austin, TX 78712-1604; email: carlton.fong@utexas.edu . He received his Doctorate in Educational Psychology and Master’s in Program Evaluation from The University of Texas at Austin. In particular, he examines the psychosocial and motivation factors that influence achievement and persistence.

COREEN W. DAVIS is a doctoral student in the Program in Higher Education Leadership at The University of Texas at Austin, 1912 Speedway D5400, Austin, TX 78712-1604; email: corwdavis@gmail.com .

YUGHI KIM is a doctoral student in the Program in Higher Education Leadership at The University of Texas at Austin, 1912 Speedway D5400, Austin, TX 78712-1604; email: ykim467@utexas.edu .

YOUNG WON KIM is a graduate research assistant in Educational Psychology at The University of Texas at Austin, 1 University Station, D5800, Austin, TX 78712-0383; email: youngwonkim@utexas.edu .

LAUREN MARRIOTT is a graduate research assistant in Higher Education at The University of Texas at Austin, 1912 Speedway D5400, Austin, TX 78712-1604; email: laurenmarriott@gmail.com .

SOOYEON KIM is a graduate research assistant in Higher Education at The University of Texas at Austin, 1912 Speedway D5400, Austin, TX 78712-1604; email: sooyeon@utexas.edu .

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