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      Factor structure and measurement invariance of the Basic Psychological Needs Scale in three South African samples: A bifactor exploratory structural equation modelling approach

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      Psychological Reports
      SAGE Publications

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          Abstract

          The Basic Psychological Needs Scale (BPNS) is still being used but validation studies that applied confirmatory factor analysis (CFA) revealed that the scale has inadequate psychometric properties. CFA is based upon restrictive statistical assumptions that may result in biased parameter estimates. There are statistical developments that overcome these limitations. This study explored the factorial validity of the scale in three South African student samples who completed the English ( n = 326), Afrikaans ( n = 478), or Setswana ( n = 260) version of the BPNS. CFA, bifactor CFA, exploratory structural equation modelling (ESEM) and bifactor ESEM were applied to the data. The three-factor bifactor ESEM model yielded the best fit, but model fit was inadequate for the English and Setswana versions, and almost adequate for the Afrikaans version. After removal of problematic items based on substantive reasons, high modification indices, and high expected parameter change values, reduced bifactor ESEM models displayed adequate fit. The general factor showed sufficient reliability scores for all language versions. Subscales exhibited insufficient reliability scores, except for the Competence and Relatedness subscales of the BPNS-Afrikaans. A reduced three-factor bifactor ESEM model was partially metric invariant for the English and Afrikaans groups. The BPNS-Afrikaans showed potential for use, but alternative measures of basic psychological needs should be considered for the English and Setswana groups in the current context. The cross-cultural application of basic psychological needs in a South African context is questioned. An emic approach to exploring and conceptualising basic psychological needs in African contexts is recommended.

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

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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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              Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance

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                Author and article information

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                Journal
                Psychological Reports
                Psychol Rep
                SAGE Publications
                0033-2941
                1558-691X
                June 26 2021
                : 003329412110252
                Affiliations
                [1 ]Africa Unit for Transdisciplinary Health Research (AUTHeR), Faculty of Health Sciences, North-West University, South Africa
                Article
                10.1177/00332941211025275
                48cd094a-edfe-451f-add5-ca24de0f4806
                © 2021

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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