🤖 AI Summary
This study addresses construct type misspecification in structural equation modeling (SEM): specifically, the consequences of mismatching the assumed construct type—latent variable, causal-formative construct, or composite indicator—with its true underlying nature. Using large-scale Monte Carlo simulations, we systematically disentangle the independent effects of construct misspecification from those of estimation methods (e.g., maximum likelihood, PLS), examining bias patterns across six realistic–hypothetical construct-type pairings. Results demonstrate that construct misspecification alone induces substantial and systematic bias in path coefficient estimates. Moreover, conventional fit indices—including χ², CFI, and RMSEA—fail to reliably distinguish the correct construct type. The findings underscore that theoretically grounded construct definition must take precedence over statistical fit criteria. This work provides critical methodological guidance for SEM practice, cautioning against reliance on global fit metrics to justify construct typology and highlighting the necessity of substantive theory in model specification.
📝 Abstract
Empirical research in many social disciplines involves constructs that are not directly observable, such as behaviors. To model them, constructs must be operationalized using their relations with indicators. Structural equation modeling (SEM) is the primary approach for this purpose. In SEM, three types of constructs are distinguished: latent variables, causal-formative constructs, and composites. To estimate the parameters of the different models, various estimators have been developed. Many Monte Carlo studies have examined the estimation performances of different estimators for the construct types. One aspect evaluated is the consequences of construct misspecification - when the true construct type differs from the modeling choice - on parameter estimates and model fit. For example, parameter bias in models that misspecify latent variables as composites is often attributed to the chosen estimator, although model parameters depend on different estimators, making it impossible to examine the factors individually. This article aims to disentangle the issues of construct misspecification and parameter estimation by a comprehensive Monte Carlo study of all combinations between true and assumed construct types. To focus on misspecification, we used the same estimator for all models, namely the maximum likelihood (ML) estimator. To generalize beyond ML, we replicated the simulation using another estimator. We aim to examine the role of construct misspecification, not estimator choice, on the estimation performance and show that misspecification leads indeed to biased path coefficient estimates. Further, we evaluate whether fit measures can distinguish models with correct from those with misspecified constructs. We find that none of the criteria considered is suited for this. These findings stress the importance of thoughtful construct specification and the need for further research.