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, evaluation, and modification. Chapters cover: Confirmatory Factor Analysis (CFA); Structural Equation Models (SEM); SEM for Longitudinal Data; Multi;
Vergelijkbare producten zoals Structural Equation Modeling
answer. In this book, McArdle and Nesselroade identify five basic purposes of longitudinal structural equation modeling. For each purpose;
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An in-depth guide to executing longitudinal confirmatory factor analysis (CFA) and structural equation modeling (SEM) in Mplus, this book;
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with quantitative data. It covers: * 2-level Multilevel Models * Structural Equation Modeling (SEM;
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capabilities in the context of confirmatory factor analysis, path analysis, structural equation modeling, longitudinal models, and multiple-group;
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Featuring actual datasets as illustrative examples, this book reveals numerous ways to apply structural equation modeling (SEM) to any;
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analysis, dynamic factor analysis, partial least squares and structural equation modeling, multilevel data analysis, modeling of longitudinal data;
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analysis, dynamic factor analysis, partial least squares and structural equation modeling, multilevel data analysis, modeling of longitudinal data;
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includes the theory and logic of structural equation models, assessing the fit' of structural equation models to the data, and their implementation;
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includes the theory and logic of structural equation models, assessing the ''fit'' of structural equation models to the data, and their SEMs;
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-analysis, Bayesian data analysis, and cluster analysis. Part Five: Structural Equation Models: addresses topics in general structural;
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advanced structural equation models, and survival analysis. An ideal supplement for graduate level courses on data analysis and/or longitudinal;
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advanced structural equation models, and survival analysis. An ideal supplement for graduate level courses on data analysis and/or longitudinal;
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This comprehensive resource reviews structural equation modeling (SEM) strategies for longitudinal data to help readers see which modeling;
Vergelijkbare producten zoals Longitudinal Structural Equation Modeling
This comprehensive resource reviews structural equation modeling (SEM) strategies for longitudinal data to help readers see which modeling;
Vergelijkbare producten zoals Longitudinal Structural Equation Modeling
This popular text provides an accessible guide to the application, interpretation, and pitfalls of structural equation modeling (SEM;
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explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling;
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Presents a novel approach to conducting meta-analysis using structural equation modeling. Structural equation modeling (SEM) and meta;
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using the data-sets as an appendix. This new edition of Analysis for Longitudinal Data provides a thorough and expanded revision of this;
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explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling;
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Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical;
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; explores methods for analyzing longitudinal data for individuals nested within groups, such as families, classrooms, and treatment groups;
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; explores methods for analyzing longitudinal data for individuals nested within groups, such as families, classrooms, and treatment groups;
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Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces latent variable models by utilizing;
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Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces latent variable models by utilizing;
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within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to;
Vergelijkbare producten zoals Data Analysis Using Hierarchical Generalized Linear Models with R
within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to;
Vergelijkbare producten zoals Data Analysis Using Hierarchical Generalized Linear Models with R
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