Structural equation modeling: strengths, limitations, and misconceptions.
Because structural equation modeling (SEM) has become a very popular data-analytic technique, it is important for clinical scientists to have a balanced perception of its strengths and limitations. We review several strengths of SEM, with a particular focus on recent innovations (e.g., latent growth modeling, multilevel SEM models, and approaches for dealing with missing data and with violations of normality assumptions) that underscore how SEM has become a broad data-analytic framework with flexible and unique capabilities. We also consider several limitations of SEM and some misconceptions that it tends to elicit. Major themes emphasized are the problem of omitted variables, the importance of lower-order model components, potential limitations of models judged to be well fitting, the inaccuracy of some commonly used rules of thumb, and the importance of study design. Throughout, we offer recommendations for the conduct of SEM analyses and the reporting of results.
Tomarken
Andrew J
AJ
Department of Psychology, Vanderbilt University, Nashville, Tennessee 37203, USA. andrew.j.tomarke@vanderbilt.edu
Waller
Niels G
NG
D035841:Behavioral Research / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical