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      Development of a Short and ICD-11 Compatible Measure for DSM-5 Maladaptive Personality Traits Using Ant Colony Optimization Algorithms

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          Abstract

          While Diagnostic and Statistical Manual of Mental Disorders–Fifth edition ( DSM-5) Section III and ICD-11 (International Classification of Diseases 11th–Revision) both allow for dimensional assessment of personality pathology, the models differ in the definition of maladaptive traits. In this study, we pursued the goal of developing a short and reliable assessment for maladaptive traits, which is compatible with both models, using the item pool of the Personality Inventory for DSM-5 (PID-5). To this aim, we applied ant colony optimization algorithms in English- and German-speaking samples comprising a total N of 2,927. This procedure yielded a 34-item measure with a hierarchical latent structure including six maladaptive trait domains and 17 trait facets, the “Personality Inventory for DSM-5, Brief Form Plus” (PID5BF+). While latent structure, reliability, and criterion validity were ascertained in the original and in two separate validation samples ( n = 849, n = 493) and the measure was able to discriminate personality disorders from other diagnoses in a clinical subsample, results suggest further modifications for capturing ICD-11 Anankastia.

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

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            Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research.

            Measurement invariance assesses the psychometric equivalence of a construct across groups or across time. Measurement noninvariance suggests that a construct has a different structure or meaning to different groups or on different measurement occasions in the same group, and so the construct cannot be meaningfully tested or construed across groups or across time. Hence, prior to testing mean differences across groups or measurement occasions (e.g., boys and girls, pretest and posttest), or differential relations of the construct across groups, it is essential to assess the invariance of the construct. Conventions and reporting on measurement invariance are still in flux, and researchers are often left with limited understanding and inconsistent advice. Measurement invariance is tested and established in different steps. This report surveys the state of measurement invariance testing and reporting, and details the results of a literature review of studies that tested invariance. Most tests of measurement invariance include configural, metric, and scalar steps; a residual invariance step is reported for fewer tests. Alternative fit indices (AFIs) are reported as model fit criteria for the vast majority of tests; χ(2) is reported as the single index in a minority of invariance tests. Reporting AFIs is associated with higher levels of achieved invariance. Partial invariance is reported for about one-third of tests. In general, sample size, number of groups compared, and model size are unrelated to the level of invariance achieved. Implications for the future of measurement invariance testing, reporting, and best practices are discussed.
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              Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares.

              In confirmatory factor analysis (CFA), the use of maximum likelihood (ML) assumes that the observed indicators follow a continuous and multivariate normal distribution, which is not appropriate for ordinal observed variables. Robust ML (MLR) has been introduced into CFA models when this normality assumption is slightly or moderately violated. Diagonally weighted least squares (WLSMV), on the other hand, is specifically designed for ordinal data. Although WLSMV makes no distributional assumptions about the observed variables, a normal latent distribution underlying each observed categorical variable is instead assumed. A Monte Carlo simulation was carried out to compare the effects of different configurations of latent response distributions, numbers of categories, and sample sizes on model parameter estimates, standard errors, and chi-square test statistics in a correlated two-factor model. The results showed that WLSMV was less biased and more accurate than MLR in estimating the factor loadings across nearly every condition. However, WLSMV yielded moderate overestimation of the interfactor correlations when the sample size was small or/and when the latent distributions were moderately nonnormal. With respect to standard error estimates of the factor loadings and the interfactor correlations, MLR outperformed WLSMV when the latent distributions were nonnormal with a small sample size of N = 200. Finally, the proposed model tended to be over-rejected by chi-square test statistics under both MLR and WLSMV in the condition of small sample size N = 200.
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                Author and article information

                Journal
                Assessment
                Assessment
                ASM
                spasm
                Assessment
                SAGE Publications (Sage CA: Los Angeles, CA )
                1073-1911
                1552-3489
                28 December 2020
                April 2022
                : 29
                : 3
                : 467-487
                Affiliations
                [1 ]Freie Universität Berlin, Berlin, Germany
                [2 ]Goethe University Frankfurt, Frankfurt am Main, Germany
                [3 ]University of Kassel, Kassel, Germany
                [4 ]University of Pittsburgh, Pittsburgh, PA, USA
                [5 ]University Medical Center Rostock, Rostock, Germany
                [6 ]University of Minnesota, Minneapolis, MN, USA
                Author notes
                [*]André Kerber, Department of Education and Psychology, Division of Clinical–Psychological Intervention, Freie Universität Berlin, Schwendener Str. 27, Berlin 14195, Germany. Email: andre.kerber@ 123456fu-berlin.de
                Author information
                https://orcid.org/0000-0002-8588-7784
                https://orcid.org/0000-0002-9177-6300
                https://orcid.org/0000-0001-6975-2356
                Article
                10.1177_1073191120971848
                10.1177/1073191120971848
                8866743
                33371717
                945e10b5-5b1b-4d99-8704-a4fd1238a8d2
                © The Author(s) 2020

                This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

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                pid-5,dsm-5,icd-11,screening tool,maladaptive personality traits,ant colony optimization,personality disorder

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