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Dependent Data in Social Sciences Research
Englisch
488 b
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This book shares the latest developments and applications of statistical models for the analysis of data for which assumptions of independence are violated. Applications will be useful for researchers in the social sciences, econometrics and medicine. The overarching theme is data that are considered 'dependent'. When data are dependent, any application of standard statistical methods for independent data can result in biased estimation of parameters and, therefore, incorrect conclusions. This problem is not new and is reflected in the development of special methods for the analysis of dependent data in diverse areas of statistical analysis (for instance, methods for the analysis of longitudinal data), corrections for dependency or corrections for degrees of freedom. The five sections covered in this book are: growth curve modeling, directional dependence in regression models, dyadic data modeling, item response modeling, and other methods for the analysis of dependent data. This is a comprehensive approach applicable to data and studies in the social sciences and beyond.§
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