Bayesian analysis in educational psychology research: An example of gender differences in achievement goals

Abstract

Much research in educational psychology concerns group differences. In this study, we argue that Bayesian estimation is more appropriate for testing group differences than is the traditional null hypothesis significance testing (NHST). We demonstrate the use of Bayesian estimation on gender differences in students' achievement goals. Research findings on gender differences in achievement goals have been mixed. We explain how Bayesian estimation of mean differences is more intuitive, informative, and coherent in comparison with NHST, how it overcomes structural and interpretive problems of NHST, and how it offers a way to achieve cumulative progress toward increasing precision in estimating gender differences in achievement goals. We provide an empirical demonstration by comparing a Bayesian and a traditional NHST analysis of gender differences in achievement goals among 442 7 th-grade students (223 girls and 219 boys). Whereas findings from the two analyses indicate comparable results of higher endorsement of mastery goals among girls and higher endorsement of performance-approach and avoidance goals among boys, it is the Bayesian analysis rather than the NHST that is more intuitively interpreted. We conclude by discussing the perceived disadvantages of Bayesian estimation, and some ways in which a consideration of Bayesian probability can aid interpretations of traditional analytical methods.

A latent profile analysis of undergraduates’ achievement motivations and metacognitive behaviors, and their relations to achievement in science.

“…However, the results of many studies on gender differences in achievement goals have been inconsistent, and some studies demonstrated there was no significant difference in achievement goals (Schunk et al, 2012). For example, female students had lower scores on both of mastery and performance goals (Preckel, Goetz, Pekrun, & Kleine, 2008), or on mastery goals but greater scores on performance goals (Peterson & Kaplan, 2016). In another study (King, 2016), there was no difference in performance goals between males and females, but females had greater mastery approach goals than males.…”

Same mindset, different goals and motivational frameworks: Profiles of mindset-based meaning systems

“…Regarding achievement goals, however, small but consistent gender differences have been found, and the pattern of gender variations are tied to specific subject domains (Wirthwein et al, 2020). When goals are assessed regarding a verbal domain or school motivation in general, girls tend to report more mastery goals (Martin, 2004;Peterson & Kaplan, 2016), but this tendency often diminishes or disappears in maths-related domains (Butler, 2008;Friedel et al, 2007). In contrast, boys tend to prioritise the goals of validating competence or avoiding displays of incompetence (i.e., performance-approach andavoidance goals; Peterson & Kaplan, 2016;Yu & McLellan, 2019).…”

“…When goals are assessed regarding a verbal domain or school motivation in general, girls tend to report more mastery goals (Martin, 2004;Peterson & Kaplan, 2016), but this tendency often diminishes or disappears in maths-related domains (Butler, 2008;Friedel et al, 2007). In contrast, boys tend to prioritise the goals of validating competence or avoiding displays of incompetence (i.e., performance-approach andavoidance goals; Peterson & Kaplan, 2016;Yu & McLellan, 2019). Studies on goal profiles similarly show that girls are overrepresented in profiles with dominant mastery goals, whereas boys are overrepresented in profiles with dominant performance goals (Luo et al, 2011;Schwinger et al, 2016).…”

Developmental Level of Moral Judgment Influences Behavioral Patterns During Moral Decision-Making

“…While performing statistical analyses, we employed Bayesian inference in addition to classical inference. According to Peterson and Kaplan (2016), results from Bayesian inference can be more intuitively interpreted compared with those from classical inference in the studies of educational research. More specifically, the result of classical inference, a Pvalue, does not directly indicate whether an effect is statistically significant, or whether a null BEHAVIORAL PATTERNS OF MORAL JUDGMENT 8 or alternative hypothesis should be rejected.…”