In the United States, gender gaps in science interest widen during the middle school years. Recent research on adults shows that gender gaps in some academic fields are associated with mindsets about ability and gender-science biases. In a sample of 529 students in a U.S. middle school, we assess how explicit boy-science bias, science confidence, science possible self (belief in being able to become a scientist), and desire to be a scientist vary by gender. Guided by theories and prior research, we use a series of multivariate logistic regression models to examine the relationships between mindsets about ability and these variables. We control for self-reported science grades, social capital, and race/ethnic minority status. Results show that seeing academic ability as innate (“fixed mindsets”) is associated with boy-science bias, and that younger girls have less boy-science bias than older girls. Fixed mindsets and boy-science bias are both negatively associated with a science possible self; science confidence is positively associated with a science possible self. In the final model, high science confident and having a science possible self are positively associated with a desire to be a scientist. Facilitating growth mindsets and countering boy-science bias in middle school may be fruitful interventions for widening participation in science careers.

Student-centered instruction is featured in reforms that aim to improve excellence and equity in mathematics education. Although research on stereotype threat suggests that student-centered instruction may have differential effects on racial minority students, the relationship between student-centered mathematics instruction and student engagement remains understudied. This study examined the relationship between student-centered mathematics instruction and adolescents’ behavioral, cognitive, emotional, and social engagement in mathematics and whether the relationship differed by ethnicity. The authors used a multilevel path analysis with data from 3,883 sixth- to 12th-grade students (52.1% girls, 38.2% eligible for free/reduced lunch, 66.1% Caucasian, 23.8% African American, 7.2% multiracial, and 2.9% Asian American). The results showed that student-centered instruction was positively related to all dimensions of mathematics engagement. However, the positive association between student-centered mathematics instruction and student engagement was weaker for African American students. This study provides empirical evidence for the benefits of student-centered instruction while suggesting differential effects based on students’ ethnicity.

When randomized control trials are not available, regression discontinuity (RD) designs are a viable quasi-experimental method shown to be capable of producing causal estimates of how a program or intervention affects an outcome. While the RD design and many related methodological innovations came from the field of psychology, RDs are underutilized among psychologists even though many interventions are assigned on the basis of scores from common psychological measures, a situation tailor-made for RDs. In this tutorial, we present a straightforward way to implement an RD model as a structural equation model (SEM). By using SEM, we both situate RDs within a method commonly used in psychology, as well as show how RDs can be implemented in a way that allows one to account for measurement error and avoid measurement model misspecification, both of which often affect psychological measures. We begin with brief Monte Carlo simulation studies to examine the potential benefits of using a latent variable RD model, then transition to an applied example, replete with code and results. The aim of the study is to introduce RD to a broader audience in psychology, as well as show researchers already familiar with RD how employing an SEM framework can be beneficial. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

This article examines ethical concerns surrounding research on minority issues. Specifically, it addresses whether researchers have an obligation to consider (a) the impact that minority research can have on vulnerable populations and (b) their own backgrounds before conducting or assessing minority research for publication. The article argues for such an obligation mainly from a consequentialist perspective and then explores possible strategies for assessing and meeting that obligation. Finally, it explores the possible negative secondary effects of those strategies and censorship concerns.

The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science1–3. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts’ choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, where for one claim per study, at least five re-analysts independently re-analysed the original data. The statistical appropriateness of the re-analyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent re-analyses yielded the same result (within a tolerance region of +/- 0.05 Cohen’s d) as the original report; with a four times broader tolerance region, this indicator rose to 57%. Regarding the conclusions drawn, 74% of analyses were reported to arrive at the same conclusion as in the original investigation; 24% to no effects/inconclusive result, and 2% to the opposite effect as in the original investigation. This exploratory study suggests that the common single-path analyses in social and behavioural research should not simply be assumed to be robust to alternative analyses4. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.

Using COVID Pulse Data collected by the U.S. Census Bureau I establish that there are weak to nocorrelational relationships between a household reporting a child attending virtual or in-person school andvarious outcomes including expectations of loss of employment, child hunger, anxiety. Due to the coarsenessof the data, it is unclear if this is an artifact of the data or a reflection of the lack of underlying causalrelationships between mode of schooling and the outcomes. Therefore, these results should not be used tomake policy decisions or draw substantive conclusions about the decision to reopen schools and are reportedonly to avoid the file-drawer effect.