A robust alternative estimator for small to moderate sample SEM: Bias-corrected factor score path analysis. (July 2019) - Kelcey, Ben
Abstract: Structural equation modeling with full information maximum likelihood estimation is the predominant method to empirically assess complex theories involving multiple latent variables in addiction research. Although full information estimators have many desirable properties including consistency, a major limitation in structural equation models is that they often sustain significant bias when implemented in small to moderate size studies (e.g., fewer than 100 or 200). Recent literature has developed a limited information estimator designed to address this limitation—conceptually implemented through a bias-corrected factor score path analysis approach—that has been shown to produce unbiased and efficient estimates in small to moderate sample settings. Despite its theoretical and empirical merits, literature has suggested that the method is underused because of three primary reasons—the methods are unfamiliar to applied researchers, there is a lack of practical and accessible guidance and software available for applied researchers, and comparisons against full information methods that are grounded in discipline-specific examples are lacking. In this study, I delineate this method through a step-by-step analysis of a sequential mediation case study involving internet addiction. I provide example R code using the lavaan package and data based on a hypothetical study of addiction. I examine the differences between the full and limited information estimators within the example data and subsequently probe the extent to which these differences are indicative of a consistent divergence between the estimators using a simulation study. The results suggest that the limited information estimator outperforms the conventional full information maximum likelihood estimator in small to moderate sample sizes in terms of bias, efficiency, and power. Highlights: Conventional SEM estimators perform poorly in small to moderate samples. Bias corrected estimators offer a simple and robust alternative. We outline the method with an applied example and R code. Results demonstrate the proposed estimator is largely unbiased and more efficient.