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      Testing models of reciprocal relations between social influence and integration in STEM across the college years

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          Abstract

          The present study tests predictions from the Tripartite Integration Model of Social Influences (TIMSI) concerning processes linking social interactions to social integration into science, technology, engineering, and mathematics (STEM) communities and careers. Students from historically overrepresented groups in STEM were followed from their senior year of high school through their senior year in college. Based on TIMSI, we hypothesized that interactions with social influence agents (operationalized as mentor network diversity, faculty mentor support, and research experiences) would promote both short- and long-term integration into STEM via social influence processes (operationalized as science self-efficacy, identity, and internalized community values). Moreover, we examined the previously untested hypothesis of reciprocal influences from early levels of social integration in STEM to future engagement with social influence agents. Results of a series of longitudinal structural equation model-based mediation analyses indicate that, in the short term, higher levels of faculty mentorship support and research engagement, and to a lesser degree more diverse mentor networks in college promote deeper integration into the STEM community through the development of science identity and science community values. Moreover, results indicate that, in the long term, earlier high levels of integration in STEM indirectly influences research engagement through the development of higher science identity. These results extend our understanding of the TIMSI framework and advance our understanding of the reciprocal nature of social influences that draw students into STEM careers.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Missing data: our view of the state of the art.

            Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.
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              The Search for R&D Spillovers

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Writing – original draft
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – original draft
                Role: Writing – review & editing
                Role: Formal analysisRole: Writing – original draft
                Role: Data curationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Funding acquisitionRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                16 September 2020
                2020
                : 15
                : 9
                : e0238250
                Affiliations
                [1 ] Department of Teaching, Learning, and Culture, Texas A&M University, College Station, Texas, United States of America
                [2 ] Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut, United States of America
                [3 ] Department of Social and Behavioral Sciences, University of California San Francisco, San Francisco, California, United States of America
                [4 ] Department of Learning Sciences, West Virginia University, Morgantown, West Virginia, United States of America
                Aalborg University, DENMARK
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-4063-357X
                Article
                PONE-D-20-08469
                10.1371/journal.pone.0238250
                7494109
                32936827
                72e050af-74de-4591-967f-134f79332d8e
                © 2020 Hernandez et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 24 March 2020
                : 12 August 2020
                Page count
                Figures: 4, Tables: 2, Pages: 30
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: 1R01GM107707
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: 1R01GM107707
                Award Recipient :
                C. Park and M. Williams. This project was funded was provided by National Institute of General Medical Sciences (Grant No. 1R01GM107707). https://www.nigms.nih.gov/ The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Psychology
                Social Psychology
                Social Influence
                Social Sciences
                Psychology
                Social Psychology
                Social Influence
                Social Sciences
                Economics
                Labor Economics
                Employment
                Careers
                People and Places
                Population Groupings
                Educational Status
                Undergraduates
                Computer and Information Sciences
                Network Analysis
                Network Reciprocity
                Science Policy
                Science and Technology Workforce
                Careers in Research
                Biology and Life Sciences
                Psychology
                Personality
                Social Sciences
                Psychology
                Personality
                Research and Analysis Methods
                Research Design
                Longitudinal Studies
                Research and Analysis Methods
                Research Design
                Survey Research
                Surveys
                Custom metadata
                All data and meta-data files are available from the online Dryad database (doi: 10.5061/dryad.k98sf7m3k.)

                Uncategorized
                Uncategorized

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