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      Measuring Research Mentors’ Cultural Diversity Awareness for Race/Ethnicity in STEM: Validity Evidence for a New Scale

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          Abstract

          Research mentors are reticent to address, and sometimes unaware of how, racial or ethnic differences may influence their mentees’ research experiences. Increasing research mentors’ cultural diversity awareness (CDA) is one step toward improving mentoring effectiveness, particularly with mentees from underrepresented racial/ethnic groups in science, technology, engineering, and mathematics fields. The indicators of CDA for research mentors are not yet known. Thus, we developed a scale to assess CDA related to race/ethnicity (CDA–R/E) in research mentoring relationships informed by multicultural counseling theory and social cognitive theory. The validation process was guided by classical test theory and item response theory and involved qualitative data, cognitive interviews, and an iterative series of item testing with national samples of mentors and mentees. Confirmatory factor analysis evidenced validity for a three-factor mentor scale assessing attitudes, behavior, and confidence, and a two-factor mentee scale assessing attitudes and behavior. The mentee version captures mentees’ perception of the relevance of culturally aware mentoring (“Attitudes”) and their perception of the frequency of mentor’s culturally aware mentoring behaviors (“Behaviors”). Implications for use of the CDA–R/E scale in practice, such as assessing alignment between mentor and mentee CDA scores, and use in future studies are discussed.

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          The Scree Test For The Number Of Factors

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            Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis

            Exploratory factor analysis (EFA) is a complex, multi-step process. The goal of this paper is to collect, in one article, information that will allow researchers and practitioners to understand the various choices available through popular software packages, and to make decisions about “best practices” in exploratory factor analysis. In particular, this paper provides practical information on making decisions regarding (a) extraction, (b) rotation, (c) the number of factors to interpret, and (d) sample size.
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              Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety.

              Determining sample size requirements for structural equation modeling (SEM) is a challenge often faced by investigators, peer reviewers, and grant writers. Recent years have seen a large increase in SEMs in the behavioral science literature, but consideration of sample size requirements for applied SEMs often relies on outdated rules-of-thumb. This study used Monte Carlo data simulation techniques to evaluate sample size requirements for common applied SEMs. Across a series of simulations, we systematically varied key model properties, including number of indicators and factors, magnitude of factor loadings and path coefficients, and amount of missing data. We investigated how changes in these parameters affected sample size requirements with respect to statistical power, bias in the parameter estimates, and overall solution propriety. Results revealed a range of sample size requirements (i.e., from 30 to 460 cases), meaningful patterns of association between parameters and sample size, and highlight the limitations of commonly cited rules-of-thumb. The broad "lessons learned" for determining SEM sample size requirements are discussed.
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                Author and article information

                Contributors
                Role: Monitoring Editor
                Journal
                CBE Life Sci Educ
                CBE Life Sci Educ
                CBE-LSE
                lse
                CBE Life Sciences Education
                American Society for Cell Biology
                1931-7913
                Summer 2021
                : 20
                : 2
                : ar15
                Affiliations
                []Center for Women’s Health Research and Department of Medicine, University of Wisconsin–Madison, Madison, WI 53715
                []Wisconsin Institute for Science Education and Community Engagement, University of Wisconsin-Madison, Madison, WI 53715
                Author notes
                *Address correspondence to: Angela Byars-Winston ( ambyars@ 123456wisc.edu ).
                Article
                CBE.19-06-0127
                10.1187/cbe.19-06-0127
                8734392
                33734868
                793b0558-0779-4b06-b591-a2ec278ad23e
                © 2021 A. Byars-Winston. CBE—Life Sciences Education © 2021 The American Society for Cell Biology. “ASCB®” and “The American Society for Cell Biology®” are registered trademarks of The American Society for Cell Biology.

                This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License.

                History
                : 03 July 2019
                : 04 January 2021
                : 12 January 2021
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                Education
                Education

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