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      Validation of an instrument to measure community capacity building for prevention

      , ,
      Journal of Public Health
      Springer Science and Business Media LLC

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          Abstract

          Aim

          In Germany, the CTC-EFF (Effectiveness of the Community Prevention System Communities That Care) study, a replication study of the US Community Youth Development Study (CYDS), was rolled out in 2021. This article aims to examine the validity of a translated and adapted version of an instrument that measures five constructs of community capacity for prevention.

          Subject and methods

          Twenty-two a priori-matched intervention and comparison communities participated in the study. Two to ten community key informants ( n = 182) were interviewed for each community. Exploratory (EFA) and confirmatory factor analyses (CFA) were conducted to assess structural validity. Reliability was assessed at the individual level using McDonald’s omega and at the community level using a generalizability coefficient (GC). Inter-rater agreement was measured using an intraclass correlation coefficient (ICC). Concurrent construct validity was assessed using bivariate correlations and mean comparisons.

          Results

          CFA showed good model fit (CFI = 0.964) for the hypothesized five constructs model. Reliability analyses showed good internal consistency at the individual level (omega = 0.86–0.87) and insufficient to moderate reliability at the community level (CG = 0.00–0.62). Inter-rater agreement ranged from insufficient to substantial (ICC = 0.02–0.32). Consistent with theory, three constructs correlated positively at the individual and community level ( r = 0.24–0.42). There are no associations between these constructs and interviewer ratings about the respondents’ cooperativeness, trustworthiness, and understanding.

          Conclusion

          Three of the five constructs were measured with good validity. Two constructs show insufficient reliability. These will be revised until the next CTC-EFF data collection wave. Further validation is indicated.

          Trial registration

          This study was registered with the German Clinical Trial Register, no. DRKS00022819, on August 18, 2021.

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          Most cited references36

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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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            Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification.

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              Alternative Ways of Assessing Model Fit

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                Author and article information

                Contributors
                Journal
                Journal of Public Health
                J Public Health (Berl.)
                Springer Science and Business Media LLC
                2198-1833
                1613-2238
                April 17 2023
                Article
                10.1007/s10389-023-01905-5
                630fb183-121d-4ea2-bc1e-917bb5fe69a9
                © 2023

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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