During the last two decades, structural equation modelling (SEM) has emerged as a powerful multivariate data analysis tool in social science research settings, especially in the fields of sociology, psychology, and education. Social science researchers and students benefit greatly from acquiring knowledge and skills in SEM, since the methods can provide a bridge between the theoretical and empirical aspects of behavioural research. Ramlall explains in a rigorous, concise, and practical manner all the vital components embedded in structural equation modelling (SEM). Focusing on R and Stata to implement and perform various structural equation models, Ramlall examines the types, benefits, and drawbacks of SEM, delving into model specifications and identifications, fit evaluations, and path diagrams.

"Known as causal models with a conspicuous presence in the field of consumer psychology, structural equation model (SEM) allows complex modelling of correlated multivariate data in order to sieve out their interrelationships among observed and latent variables. This graduate textbook introduces the four main types of structural equation models, the path diagram for model specification, model identification, model estimation, model fit evaluation, and practical issues to consider when implementing models. Example code demonstrates applied structural equation modelling using R and STATA." - (protoview.com)