Estimating ordinal factor analysis and item response theory models: A comparison of full- and limited-information techniques.

Factor analysis and item response theory models are conceptually equivalent and feature interchangeable parameters; however, they differ in their estimation techniques. Item response theory typically employs full-information techniques, such as marginal maximum likelihood (MML), whereas ordinal factor analysis relies on limited-information techniques, like weighted least squares mean and variance adjusted and unweighted least squares mean and variance adjusted. Previous studies comparing these techniques have produced conflicting results without clear explanations. Moreover, there is limited guidance on the optimal use of limited-information techniques and the effects of nonnormal distributions, leaving critical gaps in understanding. This study addresses these gaps through a comprehensive Monte Carlo simulation that incorporates diverse nonnormal distributions and reevaluates approaches to handling nonconvergent solutions. The results show that the case-wise exclusion approach, commonly used in prior research, unfairly penalizes high-convergence techniques, such as MML. In contrast, the dataset-wise exclusion approach, which removes all datasets with nonconvergent solutions, enables fairer comparisons and highlights MML's superior performance under most conditions. Additionally, while skewed leptokurtic distributions confirm the expected effects of normality violations, other nonnormal distributions yield unexpected results, cautioning against generalizing findings from one type of nonnormality to all. (PsycInfo Database Record (c) 2025 APA, all rights reserved).