<P>Like its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment.</P><P>After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single-level and multilevel data.</P><P>The third edition of the book includes several new topics that were not present in the second edition. Specifically, a new chapter has been included, focussing on fitting multilevel latent variable modeling in the R environment. With R, it is possible to fit a variety of latent variable models in the multilevel context, including factor analysis, structural models, item response theory, and latent class mode
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<P>Like its bestselling predecessor, <I><STRONG>Multilevel Modeling Using R, Second Edition</I></STRONG> provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment.</P><P>After...
kr 769.00
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<P>This book demonstrates how to conduct latent variable modeling (LVM) in R by highlighting the features of each model, their specialized uses, examples, sample code and output, and an interpretation of the results. Each chapter features a detailed...
kr 699.00
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<p><b><i>Regression Analysis in R: A Comprehensive View for the Social Sciences </i></b>covers the basic applications of multiple linear regression all the way through to more complex regression applications and extensions....
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<p>Researchers in the social sciences are faced with complex data sets in which they have relatively small samples and many variables (high dimensional data). Unlike the various technical guides currently on the market, <b><i>Applied...
kr 979.00
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