Using data from the Educational Longitudinal Study of 2002-2006 (ELS:02/06), this study investigated the effects of advanced math course taking on math achievement and college enrollment and how such effects varied by socioeconomic status (SES) and race/ethnicity. Results from propensity score matching and sensitivity analyses showed that advanced math course taking had positive effects on math achievement and college enrollment. Results also demonstrated that the effect of advanced math course taking on math achievement was greater for low SES students than for high SES students, but smaller for Black students than for White students. No interaction effects were found for college enrollment. Limitations, policy implications, and future research directions are discussed.

Increasing the correlation between the independent variable and the mediator ( coefficient) increases the effect size () for mediation analysis; however, increasing by definition increases collinearity in mediation models. As a result, the standard error of product tests increase. The variance inflation due to increases in at some point outweighs the increase of the effect size () and results in a loss of statistical power. This phenomenon also occurs with nonparametric bootstrapping approaches because the variance of the bootstrap distribution of approximates the variance expected from normal theory. Both variances increase dramatically when exceeds the coefficient, thus explaining the power decline with increases in . Implications for statistical analysis and applied researchers are discussed.

Evaluating the structural quality of arguments is a skill important to students' ability to comprehend the arguments of others and produce their own. The authors examined college and high school students' ability to evaluate the quality of 2-clause (claim-reason) arguments and tested a tutorial to improve this ability. These experiments indicated that college and high school students had difficulty evaluating arguments on the basis of their quality. Experiments 1 and 2 showed that a tutorial explaining skills important to overall argument evaluation increased performance but that immediate feedback during training was necessary for teaching students to evaluate the claim-reason connection. Using a Web-based version of the tutorial, Experiment 3 extended this finding to the performance of high-school students. The study suggests that teaching the structure of an argument and teaching students to pay attention to the precise message of the claim can improve argument evaluation.

Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3 variance-covariance structures. The results from a full-crossed simulation design suggest that traditional repeated measures have significantly higher power than do hierarchical multivariate linear models for main effects, but they have significantly lower power for interaction effects in most situations. Significant power differences are also exhibited when power is compared across different covariance structures.

Mentoring, particularly same-gender and same-race mentoring, is increasingly seen as a powerful method to attract and retain more women and racial minorities into science, technology, engineering, and mathematics (STEM) education and careers. This study examines elements of a mentoring dyad relationship (i.e., demographic and perceived similarity) that influence the quality of mentorship, as well as the effect of mentorship on STEM career commitment. A national sample of African American undergraduates majoring in STEM disciplines were surveyed in their senior year. Overall, perceived similarity, rather than demographic similarity, was the most important factor associated with protégé perceptions of high quality mentorship and high quality mentoring was in turn associated with higher commitment to STEM careers. We discuss the implications for mentoring underrepresented students and broadening participation in STEM.

Personal relevance goes by many names in the motivation literature, stemming from a number of theoretical frameworks. Currently these lines of research are being conducted in parallel with little synthesis across them, perhaps because there is no unifying definition of the relevance construct within which this research can be situated. In this paper we propose a new framework to synthesize existing research on relevance and provide a common platform for researchers to communicate and collaborate. In light of this new framework we review the role of relevance in three prominent theories in the motivation literature: the four-phase model of interest development (Hidi & Renninger, 2006), expectancy-value theory (Eccles et al., 1983), and self-determination theory (Deci & Ryan, 1985). We then explore eight relevance constructs commonly used in the literature and the educational interventions that derive from them. Finally, we offer a synthesis of these constructs and suggest some directions for future research.

Low-income preschoolers have lower average performance on measures of early numerical skills than middle-income children. The present study examined the effectiveness of numerical card games in improving children's numerical and executive functioning skills. Low-income preschoolers (N=76) were randomly assigned to play a numerical magnitude comparison card game, a numerical memory and matching card game, or a shape and color matching card game across four 15-minute sessions. Child who played either of the numerical games improved their numeral identification skills, while only children who played the numerical magnitude comparison game improved their symbolic magnitude comparison skills. These improvements were maintained eight weeks later. The results suggest that a brief, low-cost intervention can successfully improve the numerical skills of low-income children.

The end of college is a key transition point when students prepare for the workforce or graduate school, and when competence beliefs that have been shaped throughout college play a particularly important role in decision-making processes. This study examined the roles of two competence beliefs, self-efficacy for scientific tasks and science academic self-efficacy, during the final year of college. A structural equation model was used to examine science research self-efficacy and science academic self-efficacy as predictors of post-graduation science career intentions and life satisfaction; prior achievement was also included as a predictor of competence beliefs and post-graduation outcomes. Findings indicated that both types of self-efficacy predicted career intentions and life satisfaction. To better understand the processes that contribute to gender gaps in certain science careers, gender differences in mean levels of self-efficacy and in the structural relations among the variables of interest were examined using multi-group analyses. Females reported lower academic self-efficacy, despite having similar levels of prior achievement and outcomes; structural relations also appeared to vary by gender. Results extend theoretical understanding of the roles of two distinct forms of self-efficacy and the potential mechanisms explaining gender gaps in science fields.

Many of the analytical models commonly used in educational research often aim to maximize explained variance and identify variable importance within models. These models are useful for understanding general ideas and trends, but give limited insight into the individuals within said models. Data envelopment analysis (DEA), is a method rooted in organizational management that makes such insights possible. Unlike models alluded to above, DEA does not explain variance. Instead, it explains how efficiently an individual utilizes their inputs to produce outputs, and identifies which input is not being utilized optimally. This paper provides a history and usages of DEA from fields outside of education, and describes the math and processes behind it. This paper then extends DEA's usage into the educational field using a study on child reading ability. Using students from the Project KIDS dataset (), DEA is demonstrated using a simple view of reading framework, identifying individual efficiency levels in using reading-based skills to achieve reading comprehension, determining which skills are being underutilized, and classifying new subsets of readers. New subsets of readers were identified using this method, with implications for more targeted interventions.