Book Review: An Introduction to Multilevel Modeling Techniques

The need for statistical methods that address hierarchical data structures is predominant in education and the behavioral and social sciences. Added to this is often a necessity to account for the fallible nature of observed variables taken as measures of theoretical constructs. In addition to multilevel data structures and data measured with error, variables may also be measured according to interval or categorical scales. Heck and Thomas (2009) provide an introduction to these topics taking a multilevel structural equation modeling approach. In its second edition with 8 years between editions, this book expands previous material to include multilevel models for categorical data (including dichotomous and ordered categories), as well as mixture models to handle population heterogeneity. The book consistently emphasizes common motivations for applying these models and provides relevant background to the research design and analysis of data that may rely on these methods. Although the authors focus on the use of HLM 6 and Mplus version 5, researchers who are familiar with other programs and who are well versed in the methods described should be able to apply many of these methods using other software programs. Both data and input syntax for the examples presented in the book are said to be included on the publisher’s website. Chapter 1 provides a conceptual introduction to multilevel models in the context of organizational data structures. The discussion provides a logical basis for the book, transitioning readers from single-level univariate and multivariate analysis to singleand multilevel structural equation models with latent variables. Chapter 2 reviews multilevel models for organizational data structures, describing potential sources of variation at the withinand the between-group levels and the impact of ignoring these distinct sources of variation on statistical inference. The authors effectively draw on examples based on single-level data to highlight features of a multilevel model. This then leads to the essential topics that lie at the core of multilevel models, namely, the intraclass correlation coefficient, withinand between-group effects, and cross-level effects. The issue of measurement error follows, in which the authors provide a brief, nontechnical overview of confirmatory factor analysis (CFA) and structural equation modeling (SEM) for singleand multilevel data, thus building a bridge from basic multilevel concepts to more advanced statistical topics. The chapter concludes with a description of models for repeated measures. The authors first consider the study of change in behaviors studied over time. They rely on analyses based on repeated-measures ANOVA to draw on common statistical knowledge and to highlight important features unique to multilevel models (e.g., study of individual-level change). Next, the authors consider repeated cross-sectional data, in which distinct groups of individuals are studied at different points in time. Although data of this sort cannot provide information about change at the individual level, such data are often observed according to multiple levels (e.g., students nested within schools).