Preface
In the past two decades structural equation modeling (SEM) has quickly pervaded various fields, such as psychiatry, psychology, sociology, economics, education, demography, political sciences, as well as biology and health studies. Compared with traditional statistical methods such as multiple regression, ANOVA, path analysis, and multilevel models, the advantages of SEM include, but are not limited to, the ability to take into account measurement errors; model multiple dependent variables simultaneously; test overall model fit; estimate direct, indirect and total effects; test complex and specific hypothesis; handle difficult data (time series with auto-correlated error, non-normal, censored, and categorical outcomes); test model parameter invariance across multiple populations/groups, and conduct mixture modeling to deal with population heterogeneity. However, SEM is still an under-utilized technique in social science studies and health studies. The intent of this book is to provide a resource for learning SEM, and a reference guide for some advanced SEM models.
The book emphasizes basic concepts, methods and applications of structural equation modeling. It covers the fundamentals of SEM, as well as some recently developed advanced SEM models. Written in non-mathematical terms, a variety of SEM models for studying both cross-section and longitudinal data are discussed. Examples of various SEM models are demonstrated using real-world research data. The internationally ...
