New to this edition:
- An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals;
- Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches;
- Expanded coverage illustrating different model-building sequences and how to use results to identify possible model improvements;
- An expanded set of applied examples used throughout the text;
- Use of four different software packages (i.e., Mplus, R, SPSS, Stata), with selected examples of model-building input files included in the chapter appendices and a more complete set of files available online.
This is an ideal text for graduate courses on multilevel, longitudinal, latent variable modelling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology. Recommended prerequisites are introductory univariate and multivariate statistics.
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