December 2018: Psychosocial Factors and Community College Student Success

Background

Research into factors that promote or impede learning comes in many forms. A great deal of research rightly focuses on early learning and young children, and much is known about the sociodemographic and cognitive factors (e.g., phonological awareness for reading, numerosity for mathematics, working memory for both) that influence learning. However, a number of additional factors, including motivation, self-perceptions, and anxiety, can also influence learning; collectively, these are often termed psychological or noncognitive factors (Duckworth & Yeager, 2015; Richardson, Abraham, & Bond, 2012; Stankov & Lee, 2014). The term noncognitive is likely a misnomer in that cognitive processes are likely involved, but the distinction made is between psychometric (task-based, performance, or problem-solving tasks) and all other measures (self-report measures). The literature in this area is quite broad, and it tends to focus on older students. Yet even here, most postsecondary research is focused on 4-year university students, even though, as pointed out by Fong, Davis, Kim, Kim, Marriott, and Kim (2018), half of U.S. postsecondary students are enrolled in community college (Shapiro, Dundar, Yuan, Harrell, & Wakhungu, 2014).

Therefore, Fong and colleagues focused their study on what they call psychosocial (which is highly similar to psychological or noncognitive) factors associated with community college success. Fong and colleagues reviewed several theoretical perspectives on the role of psychosocial factors in learning, focusing on models by Tinto (1975), Bean and Eaton (2000), and Harris and Wood (2014), with a common theme being that students bring various factors to a postsecondary situation (e.g., their cognition, their sociodemographics, their prior achievement), and those factors interact with psychosocial factors to influence actual postsecondary success. The present study is thus an attempt to consolidate known information in this area.

Study Design and Methodology

Due to the broad nature of their study, Fong and colleagues employed a technique called meta-analysis, which is concise way of statistically summarizing all prior studies that have investigated this topic, even when individual studies arise from different theoretical perspectives, vary in size, and use different techniques. A meta-analysis can also evaluate whether the size of any overall effect depends on things such sample characteristics or what outcome was studied.

Fong and colleagues categorized existing studies according to the types of psychosocial factors that were investigated: (a) self-perceptions (e.g., self-concept, self-efficacy); (b) motivation (e.g., intrinsic and extrinsic motivation; mastery and performance goal orientation); (c) attributions (e.g., locus of causality as being either under one’s control, or internal; or outside one’s control, or external); (d) self-regulated learning (e.g., goal setting, monitoring and control of learning); and (e) anxiety. In Table 1 of their article, the authors define each of these factors in more detail and provide sample measures.

The researchers searched multiple databases and identified 703 potential studies, of which 174 were analyzed (studies were excluded mainly because they did not contain the variables studied). Fong and colleagues sought to determine how each of the above categories of psychosocial factors relate to community-college success, for two different outcomes – persistence (e.g., course completion, degree attainment) and achievement (e.g., grades, test scores). For this study, the primary effect size was a correlation coefficient.

Key Findings

Fong and colleagues describe their results according to their different outcomes: (a) persistence and (b) achievement.

• Persistence. Two of the predictor categories of variables were significant and positively related to community college persistence: self-perceptions (r = .10) and motivation (r = .15). These are generally considered small effects (a correlation of .10 is considered small in size and a correlation of .30 is considered to be of medium strength). The other three variables, attributions, self-regulation, and anxiety, were not significantly related with community college persistence.
• Achievement. Four of the five variables were significant and positively related to community college achievement: self-perceptions (r = .13), motivation (r = .17), attributions (r = .14), and self-regulation (r = .18). Only anxiety was unrelated.

Fong and colleagues next determined whether the overall effects above varied according to known factors (called moderators), including whether a study was published versus unpublished, the kind of persistence or achievement outcome studied, the duration of the outcome, and demographic characteristics (gender and minority status). These moderators had little influence for persistence outcomes, but three such moderators had effects for achievement outcomes: (a) published studies showed higher correlations of self-perceptions to achievement, relative to unpublished studies; (b) relations of attributions to achievement outcomes were higher when those outcomes were assessed over a short period of time, relative to longer periods (more than one semester); and (c) in samples with large minority representation, the strength of the relation of anxiety and achievement increased, whereas the strength of the relation of attribution and achievement decreased.

Conclusions and Implications

The relations of psychosocial factors with community college success were, by and large, weak. In cases where they were significant, their actual effects were small. However, even small effects can be important, especially in cases where the effects are malleable and where the population is large, both of which are potentially true for this situation. The present work only studied direct relationships, so it is not clear whether some of these factors interact with one another and/or with demographic factors. The results of the moderator analyses suggest that at least some effects do vary with demographic factors, so more careful delineation in future studies would be relevant. Furthermore, the extent to which the relevance of these factors is maintained (or amplified or ameliorated) in the context of also including cognitive measures is not clear.

In terms of implications, first, Fong and colleagues noted that in summarizing past work, variables such as grit were missing from this literature, and descriptive variables beyond gender and minority status (e.g., socioeconomic status, full versus part-time, working versus nonworking) were underrepresented. These types of variables might be particularly important for community college samples. Second, Fong and colleagues note that although many studies were included, it was unfortunate that so few (17-25%) were published in peer-reviewed journals; most of the works were doctoral dissertations that were never published. For a population so large and relevant, this is indeed unfortunate, and underscores the need for more work in this area.

It is clear that future studies in this area are warranted, particularly those that are targeted to a more specific context (e.g., at-risk students in community college, such as those taking developmental coursework) and to specific achievement domains (e.g., mathematics). Such studies would also do well to not only include careful assessment of these psychosocial domains, but also to combine them with potential demographic and cognitive influences to obtain a more complete understanding of this important population.

For Further Reading

Duckworth, A. L., & Yeager, D. S. (2015). Measurement matters: Assessing personal qualities other than cognitive ability for educational purposes. Educational Researcher, 44, 228–236. doi:10.3102/0013189X15584327

Harris III, F., & Wood, J. L. (2014). The socio-ecological outcomes model: A framework for examining men of color’s experiences and success in community colleges. San Diego, CA: Minority Male Community College Collaborative.

Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. doi:10.1037/a0026838

Shapiro, D., Dundar, A., Yuan, X., Harrell, A., & Wakhungu, P. K. (2014). Completing college: A national view of student attainment rates—Fall 2008 cohort (Signature Report No. 8). Herndon, VA: National Student Clearinghouse Research Center.

Stankov, L., & Lee, J.  (2014). Quest for the best noncognitive predictor of academic achievement. Educational Psychology, 34(1), 1–8. doi:10.1080/01443410.2013.858908