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      Adaptation of a Self-Regulated Practice Behavior Scale for Chinese Music Majors

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          Abstract

          In this study, we evaluated the validity and reliability of a Chinese adaptation of the Self-Regulated Practice Behavior (SRPB) scale developed by Miksza. Tasks included supplementing, altering, and translating items to create a viable adaptation for Chinese music majors and evaluating the construct validity and reliability of the Chinese-adapted Self-Regulated Practice Behavior (C-SRPB) scale. Confirmatory factor analysis and exploratory structural equation modeling (ESEM) were used to analyze responses provided by Chinese music majors ( N = 880) from various music conservatories and universities. Results indicated that an adjusted six-factor ESEM model was the best fit to the data. Internal consistency reliability coefficients for the subscales ranged from good to excellent (αs = .77–.86). Significant correlations between six subscales of C-SRPB and practice habits (i.e., daily duration, self-rated efficiency, and percentage of time spent on informal practice) provided criterion validity evidence. Overall, the findings suggested that the C-SRPB is a valid and reliable tool for measuring the self-regulated music practice of music majors in China. The results also suggest that music majors’ understanding of self-regulated practice vary according to individualistic and collectivist culture contexts.

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          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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              Is Open Access

              Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer

              Scale development and validation are critical to much of the work in the health, social, and behavioral sciences. However, the constellation of techniques required for scale development and evaluation can be onerous, jargon-filled, unfamiliar, and resource-intensive. Further, it is often not a part of graduate training. Therefore, our goal was to concisely review the process of scale development in as straightforward a manner as possible, both to facilitate the development of new, valid, and reliable scales, and to help improve existing ones. To do this, we have created a primer for best practices for scale development in measuring complex phenomena. This is not a systematic review, but rather the amalgamation of technical literature and lessons learned from our experiences spent creating or adapting a number of scales over the past several decades. We identified three phases that span nine steps. In the first phase, items are generated and the validity of their content is assessed. In the second phase, the scale is constructed. Steps in scale construction include pre-testing the questions, administering the survey, reducing the number of items, and understanding how many factors the scale captures. In the third phase, scale evaluation, the number of dimensions is tested, reliability is tested, and validity is assessed. We have also added examples of best practices to each step. In sum, this primer will equip both scientists and practitioners to understand the ontology and methodology of scale development and validation, thereby facilitating the advancement of our understanding of a range of health, social, and behavioral outcomes.
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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                Journal of Research in Music Education
                Journal of Research in Music Education
                SAGE Publications
                0022-4294
                1945-0095
                October 2023
                January 16 2023
                October 2023
                : 71
                : 3
                : 343-365
                Affiliations
                [1 ]Faculty of Education, East China Normal University, Shanghai, China
                [2 ]Department of Psychology/Analytics\Assessment Research Centre/Centre for Psychosocial Health, The Education University of Hong Kong, Hong Kong, China
                [3 ]Department of Cultural and Creative Arts, The Education University of Hong Kong, Hong Kong, China
                Article
                10.1177/00224294221147008
                c1f6717d-e723-4b82-8392-e88052e4f116
                © 2023

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

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