Abstract
Model misspecification is typical in applied structural equation modeling (SEM). Traditional specification search methods, such as modification indices, search for misspecifications within the model’s variables but overlook influential variables not initially included and fail to detect interactions. This study evaluates SEM forests as a complementary method to conduct SEM specification search related to omitted influential covariates. The omitted influential paths include unique, mixed, and interaction paths. SEM forests’ performance is evaluated under different factor loading magnitudes, covariate path magnitudes, and sample sizes. Results show SEM forests accurately identify omitted influential covariates without falsely identifying non-influential covariates in large samples (1,000) with strong covariate-latent variable paths ( β = .5).
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