An Introduction to Multilevel Modeling Techniques: MLM and SEM Approaches

Quantitative Methodology

by Ronald H. Heck, Scott L. Thomas

Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives.

New to this



An expanded focus on the nature of different types of multilevel data structures (e.g., cross-section, 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.

Genres: Summer

404 pages, Kindle Edition

First published November 1, 1999

Reviews

[Dana · Rating 5 out of 5]

This is the second edition, and it is much-improved from the first. The step-by-step instructions in SPSS are very helpful and just enough conceptual material is covered for the analyses to make sense to a multilevel novice. I highly recommend this for graduate level multivariate stats courses.

[Natalie]

I am a little bit disappointed with the book because it does not present the results as generated by the Mplus output, being difficult to follow the author's interpretation of the results.