A Monte Carlo comparison of model modification strategies in covariance structure modeling
Description
A covariance structure model consists of a measurement model, which is a collection of variables and their associated constructs or factors, and a structural model, which is a set of predictions about the causal relationships among the constructs. Data are collected on the variables and a covariance or correlation matrix is computed. The covariance structure modeling (CSM) procedure estimates the parameter values in the model that has been specified and assesses how well the model fits the data. Models failing to provide a good fit to the data are often modified. This process is known as a specification search The current Monte Carlo study investigated the effectiveness of the choice of one of five initial structural models, the method used to address the measurement model, and the search statistics used (Lagrange multiplier and expected parameter change) on the success rate of specification searches. 700 specification searches were conducted on 40 sample matrices (N = 300) generated from 4 population models. The effectiveness of the search statistics differed by the method used to address the measurement model with the expected parameter change having a higher success rate when the measurement model was assessed independently of the structural model and the Lagrange multiplier having a higher success rate when the measurement and structural models were assessed simultaneously. Successful model recovery did not differ significantly by the initial structural model used but the full initial model always performed equal to or superior to the other initial models. Successful model recovery differed markedly by population model with success rates ranging from 16% to 73%. The research concludes with a call for future research and a list of suggested research topics