Reliability Estimation in Multidimensional Scales: Comparing the Bias of Six Estimators in Measures With a Bifactor Structure

• Main Authors: Italo Trizano-Hermosilla, José L. Gálvez-Nieto, Jesús M. Alvarado, José L. Saiz, Sonia Salvo-Garrido
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-06-01
• Series: Frontiers in Psychology
• Subjects: reliability; multidimensional; bifactor; Monte – Carlo simulation; measurement
• Online Access: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.508287/full

In the context of multidimensional structures, with the presence of a common factor and multiple specific or group factors, estimates of reliability require specific estimators. The use of classical procedures such as the alpha coefficient or omega total that ignore structural complexity are not appropriate, since they can lead to strongly biased estimates. Through a simulation study, the bias of six estimators of reliability in multidimensional measures was evaluated and compared. The study is complemented by an empirical illustration that exemplifies the procedure. Results showed that the estimators with the lowest bias in the estimation of the total reliability parameter are omega total, the two versions of greatest lower bound (GLB) and the alpha coefficient, which in turn are also those that produce the highest overestimation of the reliability of the general factor. Nevertheless, the most appropriate estimators, in that they produce less biased estimates of the reliability parameter of the general factor, are omega limit and omega hierarchical.