A Selective Review on Novel Dimensionality Assessment Methods for Measurement Model Users

Abstract

Determining the “correct” dimensionality is crucial in data analysis using measurement models such as factor analysis and item response theory. This is significantly important because parameter estimates could be substantially biased when dimensionality is misspecified, especially in case of underfactoring. Various methods for assessing dimensionality have been proposed; however, no golden standard has been established. Moreover, new methods continue to emerge in the literature, drawing on novel theories and techniques from research areas beyond psychological and educational measurement. This methodological proliferation poses a challenge for applied researchers to catch up on the state-of-the-art methods. The present study therefore aims to bridge this gap by offering a selective review on the novel dimensionality assessment methods, with a particular focus on (a) parallel analysis and its variants, (b) model selection approach, (c) network psychometrics approach, and (d) machine learning approach. We discuss the advantages and disadvantages of these approaches and suggest potential directions for future research.