Abstract This mixed‐methods study examines the experience of the impostor phenomenon in a racially/ethnically and sexually diverse sample of undergraduates in majors related to science, technology, engineering, and math (STEM). Guided by an intersectionality framework, we examined whether experiences of the impostor phenomenon differ at the intersection of race, ethnicity, and sexual identity. Survey data from undergraduate STEM students ( N = 735), consisting of 559 (76%) racial/ethnic minorities, 169 (23%) lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ) individuals, and 119 (16.2%) racialized LGBTQ individuals, revealed racial/ethnic and LGBTQ differences in the impostor phenomenon. Findings also suggest that STEM students with at least one mentor reported lower levels of impostor phenomenon. This finding was not moderated by race/ethnicity or sexual identity, suggesting that mentoring may be a form of social support that offers equivalent benefits to students from diverse STEM educational backgrounds. To supplement the quantitative findings, we collected open and closed‐ended data from participants. Thematic analysis of these data provides insight into the range of coping strategies STEM students use when experiencing the impostor phenomenon. Implications and recommendations for research, theory, and policy are further discussed.

Identifying the best model using fit indices is a crucial step in cognitive diagnosis (CD) data analysis. Previous research has examined the performance of popular relative and absolute model fit indices in the context of cognitive diagnosis models (CDMs). However, the effectiveness of these indices under uncommon conditions, such as small sample sizes, remains unclear. Therefore, the primary goal of this study is to investigate the impact of sample size on commonly used model fit indices in CD. Four relative model fit indices, including AIC, BIC, CAIC, and SABIC, and three absolute model fit indices, SRMSR, RMSEA 2, and M 2, were considered. The study also investigates the performance of two general approaches in CD, the generalized DINA (G-DINA) model and the general nonparametric classification (GNPC) method, in contrast to the studied model fit indices. The goal of this part of the study is to explore whether these two general approaches can serve as effective alternatives when the model fit indices do not perform well. Practical guidelines that would be useful for researchers and practitioners were provided based on the simulation results under various conditions. Additionally, a real data analysis was conducted to demonstrate the practical utility of the study.