Robust factor analysis in the presence of normality violations, missing data, and outliers: Empirical questions and possible solutions

Abstract

Although a mainstay of psychometric methods, several reviews suggest factor analysis is often applied without testing whether data support it, and that decision-making process or guiding principles providing evidential support for FA techniques are seldom reported. Researchers often defer such decision-making to the default settings on widely-used software packages, and unaware of their limitations, might unwittingly misuse FA. This paper discusses robust analytical alternatives for answering nine important questions in exploratory factor analysis (EFA), and provides R commands for running complex analysis in the hope of encouraging and empowering substantive researchers on a journey of discovery towards more knowledgeable and judicious use of robust alternatives in FA. It aims to take solutions to problems like skewness, missing values, determining the number of factors to extract, and calculation of standard errors of loadings, and make them accessible to the general substantive researcher.