Categorical Data Analysis with Structural Equation Models: Applications in Mplus and lavaan [Hardcover]

Multivariate categorical outcomes, such as Likert scale responses and disease diagnoses, require specialized structural equation modeling (SEM) software to be analyzed properly.Providing needed skills for applied researchers and graduate students, this book leads readers from regression analysis with categorical outcomes to complex SEMs with latent variables for categorical indicators. The initial section sets the stage by demonstrating regression analyses for binary, ordered, or count outcomes using R. Chapters then reanalyze the same data using Mplus and R lavaan to show how univariate models for categorical outcomes can be estimated and interpreted with SEM programs. Subsequently, the book turns to multivariate models, discussing path models, confirmatory factor models, and latent variable path models with categorical outcomes. Concluding chapters cover advanced SEM with categorical outcomes, including growth models, latent class models, and survival models. Worked-through examples are featured throughout. The companion website provides R (including lavaan), Mplus, and SAS code, as applicable, for the examples.“Grimm once again shows his knack for taking complex statistical models and ideas and expressing them in understandable terms. Categorical data come in many forms: binary, ordinal, and count variables, among others. Grimm explains modeling options for each type of analytic model, from regression models to more advanced models. Example scripts for Mplus and lavaan provide readers with clear roadmaps for conducting analyses and understanding results. This book is a ‘must read’ for anyone interested in learning about categorical data analysis in the social sciences using state-of-the-art methods.”--Keith F. Widaman, PhD, Distinguished Professor Emeritus of Education and Distinguished Professor of the Graduate Division, University of California, Riverside

"This book fills an important gap in texts on SEM. Grimm prolcM