Key Advances in the History of Structural Equation Modeling

Structural equation modeling (SEM) has advanced considerably in the social sciences. The direction of advances has varied by the substantive problems faced by individual disciplines. For example, path analysis developed to model inheritance in population genetics, and later to model status attainment in sociology. Factor analysis developed in psychology to explore the structure of intelligence, and simultaneous equation models developed in economics to examine supply and demand.
These largely discipline-specific advances came together in the early 1970s to create a multidisciplinary approach to SEM. Later, during the 1980s, responding to criticisms of SEM for failing to meet assumptions implied by maximum likelihood estimation and testing, SEM proponents responded with estimators for data that departed from multivariate normality, and for modeling categorical, ordinal, and
limited dependent variables. More recently, advances in SEM have incorporated additional statistical models (growth models, latent class growth models, generalized linear models, and multi-level models), drawn upon artificial intelligence research to attempt to "discover" causal structures, and finally, returned to the question of causality with formal methods for specifying assumptions necessary for inferring causality with non-experimental data.
In this chapter, I trace the key advances in the history of structural equation modeling. I focus on the early history, and try to convey the excitement of major developments in each discipline, culminating with cross-disciplinary integration in the 1970s. I then discuss advances 2 in estimating models from data that depart from the usual assumptions of linearity, normality,and continuous distributions. I conclude with brief treatments of more recent advances to provide introductions to advanced chapters in this volume.