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      Predicting Research Productivity in STEM Faculty: The Role of Self-determined Motivation

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          Abstract

          How are university faculty members in STEM disciplines motivated to conduct research, and how does motivation predict their success? The current study assessed how multiple types of self-determined motivation predict research productivity in a sample of 651 faculty from 10 US institutions. Using structural equation modeling, the basic psychological needs of autonomy and competence predicted autonomous motivation (enjoyment, value) that, in turn, was the strongest predictor of self-reported research productivity. Using negative binomial regression, autonomous motivation was the strongest predictor of faculty publications and citations, with a one-standard deviation increase in autonomous motivation (approximately a half response option on a 1–5 Likert scale) corresponding to an 11.63% increase in publications and a 22.57% increase in citations over a three-year period. Occupational and social-environmental background variables (e.g., research percentage on contract, career age, balance, collegiality), as well as controlled motivation (guilt, rewards), had comparatively limited predictive effects. These results are of relevance to higher education institutions aiming to support scholarly productivity in STEM faculty in identifying specific beneficial and detrimental aspects of faculty motivation that contribute to measurable gains in research activity.

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          Most cited references64

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          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

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            lavaan: AnRPackage for Structural Equation Modeling

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              Comparative fit indexes in structural models.

              Normed and nonnormed fit indexes are frequently used as adjuncts to chi-square statistics for evaluating the fit of a structural model. A drawback of existing indexes is that they estimate no known population parameters. A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models. Two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes. CFI avoids the underestimation of fit often noted in small samples for Bentler and Bonett's (1980) normed fit index (NFI). FI is a linear function of Bentler and Bonett's non-normed fit index (NNFI) that avoids the extreme underestimation and overestimation often found in NNFI. Asymptotically, CFI, FI, NFI, and a new index developed by Bollen are equivalent measures of comparative fit, whereas NNFI measures relative fit by comparing noncentrality per degree of freedom. All of the indexes are generalized to permit use of Wald and Lagrange multiplier statistics. An example illustrates the behavior of these indexes under conditions of correct specification and misspecification. The new fit indexes perform very well at all sample sizes.
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                Author and article information

                Contributors
                Robert.Stupnisky@und.edu
                Journal
                Res High Educ
                Res High Educ
                Research in Higher Education
                Springer Netherlands (Dordrecht )
                0361-0365
                1573-188X
                3 October 2022
                : 1-24
                Affiliations
                [1 ]GRID grid.266862.e, ISNI 0000 0004 1936 8163, College of Education and Human Development, , University of North Dakota, ; 213 Centennial Drive Stop 7189, Grand Forks, ND 58202 USA
                [2 ]GRID grid.14848.31, ISNI 0000 0001 2292 3357, School of Library and Information Science, , University of Montreal, ; Montreal, QC Canada
                [3 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Department of Educational and Counselling Psychology, , McGill University, ; Montreal, QC Canada
                Author information
                http://orcid.org/0000-0002-5391-7205
                Article
                9718
                10.1007/s11162-022-09718-3
                9528871
                36213330
                ea016267-4de8-4c0e-a6b8-613cd57003a6
                © The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 2 September 2021
                : 24 August 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100005717, SBE Office of Multidisciplinary Activities;
                Award ID: 1853969
                Award Recipient :
                Categories
                Article

                Education
                faculty,motivation,research productivity,bibliometrics,stem
                Education
                faculty, motivation, research productivity, bibliometrics, stem

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