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PurposeThis study examined differences related to gender and racial/ethnic identity among academic researchers participating in the National Science Foundation’s “Innovation-Corps” (NSF I-Corps) entrepreneurship training program. Drawing from prior research in the fields of technology entrepreneurship and science, technology, engineering and mathematics (STEM) education, this study addresses the goal of broadening participation in academic entrepreneurship. Design/methodology/approachUsing ANOVA and MANOVA analyses, we tested for differences by gender and minoritized racial/ethnic identity for four variables considered pertinent to successful program outcomes: (1) prior entrepreneurial experience, (2) perceptions of instructional climate, (3) quality of project team interactions and (4) future entrepreneurial intention. The sample includes faculty (n = 434) and graduate students (n = 406) who completed pre- and post-course surveys related to a seven-week nationwide training program. FindingsThe findings show that group differences based on minoritized racial/ethnic identity compared with majority group identity were largely not evident. Previous research findings were replicated for only one variable, indicating that women report lower amounts of total prior entrepreneurial experience than men, but no gender differences were found for other study variables. Originality/valueOur analyses respond to repeated calls for research in the fields of entrepreneurship and STEM education to simultaneously examine intersecting minoritized and/or under-represented social identities to inform recruitment and retention efforts. The unique and large I-Corps national dataset offered the statistical power to quantitatively test for differences between identity groups. We discuss the implications of the inconsistencies in our analyses with prior findings, such as the need to consider selection bias.
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The LOOP Estimator: Adjusting for Covariates in Randomized Experiments
Background:When conducting a randomized controlled trial, it is common to specify in advance the statistical analyses that will be used to analyze the data. Typically, these analyses will involve adjusting for small imbalances in baseline covariates. However, this poses a dilemma, as adjusting for too many covariates can hurt precision more than it helps, and it is often unclear which covariates are predictive of outcome prior to conducting the experiment. Objectives:This article aims to produce a covariate adjustment method that allows for automatic variable selection, so that practitioners need not commit to any specific set of covariates prior to seeing the data. Results:In this article, we propose the “leave-one-out potential outcomes” estimator. We leave out each observation and then impute that observation’s treatment and control potential outcomes using a prediction algorithm such as a random forest. In addition to allowing for automatic variable selection, this estimator is unbiased under the Neyman–Rubin model, generally performs at least as well as the unadjusted estimator, and the experimental randomization largely justifies the statistical assumptions made.
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- Award ID(s):
- 1646108
- PAR ID:
- 10547162
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Evaluation Review
- Volume:
- 42
- Issue:
- 4
- ISSN:
- 0193-841X
- Format(s):
- Medium: X Size: p. 458-488
- Size(s):
- p. 458-488
- Sponsoring Org:
- National Science Foundation
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