TY - JOUR
T1 - Structural equation modeling
T2 - Strengths, limitations, and misconceptions
AU - Tomarken, Andrew J.
AU - Waller, Niels G.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
KW - Causal models
KW - Confirmatory factor analysis
KW - Covariance structure analysis
KW - Latent variables
KW - Path analysis
KW - Statistical modeling
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U2 - 10.1146/annurev.clinpsy.1.102803.144239
DO - 10.1146/annurev.clinpsy.1.102803.144239
M3 - Review article
C2 - 17716081
AN - SCOPUS:33645009302
SN - 1548-5943
VL - 1
SP - 31
EP - 65
JO - Annual Review of Clinical Psychology
JF - Annual Review of Clinical Psychology
ER -