Increasing normal approximation in psychometric health care data analyses using a compositional data approach
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2024-01-03
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1536
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Psychometric health care focuses on the development and improvement of psychotherapeutic measures. Adequate psychological profiling and advanced statistical evaluation are fundamental to assessing the efficacy and measure-associated benefits. Consider psychological constructs operationalized as means or sums of item response values of bipolar Likert scales. Using estimates of the effect size and statistical tests the relevance of a psychotherapeutic measure can be assessed, e.g., via the computation of (partial) correlations of different constructs. Many statistical procedures depend on approximate normal distribution, e.g., t-tests, linear regression and partial least squares path modeling. Increasing the degree of approximate normality of means and sums of item responses enhances the quality of statistical evaluations. Via simulation we provide evidence that applying the isometric log-ratio (ilr) transformation to bipolar Likert scales data prior to the computation of item response means or sums increases the degree of approximate normality. That is, a shift towards normality is observed enhancing the quality of subsequent statistical analyses. As a result, the quality of statistical evaluations enhances. The reliability of psychological diagnostics increases and the development of psychometric scales can be improved enhancing patient welfare. Moreover, reliability and significance affect grant funding in health economics.
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Service Analytics, bipolar scale, isometric log-ratio, normal distribtion, statistical power
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10 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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