The Reliability Paradox in Assessing Structural Relations Within Covariance Structure Models

Abstract

A two-step process is commonly used to evaluate data–model fit of latent variable path models, the first step addressing the measurement portion of the model and the second addressing the structural portion of the model. Unfortunately, even if the fit of the measurement portion of the model is perfect, the ability to assess the fit within the structural portion is affected by the quality of the factor–variable relations within the measurement model. The result is that models with poorer quality measurement appear to have better data–model fit, whereas models with better quality measurement appear to have worse data–model fit. The current article illustrates this phenomenon across different classes of fit indices, discusses related structural assessment problems resulting from issues of measurement quality, and endorses a supplemental modeling step evaluating the structural portion of the model in isolation from the measurement model.