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Data Analysis: A Model Comparison Approach to Regression, ANOVA,
and Beyond is an integrated treatment of data analysis for the
social and behavioral sciences. It covers all of the statistical
models normally used in such analyses, such as multiple regression
and analysis of variance, but it does so in an integrated manner
that relies on the comparison of models of data estimated under the
rubric of the general linear model. Data Analysis also describes
how the model comparison approach and uniform framework can be
applied to models that include product predictors (i.e.,
interactions and nonlinear effects) and to observations that are
nonindependent. Indeed, the analysis of nonindependent observations
is treated in some detail, including models of nonindependent data
with continuously varying predictors as well as standard repeated
measures analysis of variance. This approach also provides an
integrated introduction to multilevel or hierarchical linear models
and logistic regression. Finally, Data Analysis provides guidance
for the treatment of outliers and other problematic aspects of data
analysis. It is intended for advanced undergraduate and graduate
level courses in data analysis and offers an integrated approach
that is very accessible and easy to teach. Highlights of the third
edition include: a new chapter on logistic regression; expanded
treatment of mixed models for data with multiple random factors;
updated examples; an enhanced website with PowerPoint presentations
and other tools that demonstrate the concepts in the book;
exercises for each chapter that highlight research findings from
the literature; data sets, R code, and SAS output for all analyses;
additional examples and problem sets; and test questions.
Data Analysis: A Model Comparison Approach to Regression, ANOVA,
and Beyond is an integrated treatment of data analysis for the
social and behavioral sciences. It covers all of the statistical
models normally used in such analyses, such as multiple regression
and analysis of variance, but it does so in an integrated manner
that relies on the comparison of models of data estimated under the
rubric of the general linear model. Data Analysis also describes
how the model comparison approach and uniform framework can be
applied to models that include product predictors (i.e.,
interactions and nonlinear effects) and to observations that are
nonindependent. Indeed, the analysis of nonindependent observations
is treated in some detail, including models of nonindependent data
with continuously varying predictors as well as standard repeated
measures analysis of variance. This approach also provides an
integrated introduction to multilevel or hierarchical linear models
and logistic regression. Finally, Data Analysis provides guidance
for the treatment of outliers and other problematic aspects of data
analysis. It is intended for advanced undergraduate and graduate
level courses in data analysis and offers an integrated approach
that is very accessible and easy to teach. Highlights of the third
edition include: a new chapter on logistic regression; expanded
treatment of mixed models for data with multiple random factors;
updated examples; an enhanced website with PowerPoint presentations
and other tools that demonstrate the concepts in the book;
exercises for each chapter that highlight research findings from
the literature; data sets, R code, and SAS output for all analyses;
additional examples and problem sets; and test questions.
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