MSA: The forgotten index for identifying inappropriate items before computing exploratory item factor analysis

Urbano Lorenzo-Seva,Pere J Ferrando

doi:10.5964/meth.7185

Abstract

Kaiser’s single-variable measure of sampling adequacy (MSA) is a very useful index for debugging inappropriate items before a factor analysis (FA) solution is fitted to an item-pool dataset for item selection purposes. For reasons discussed in the article, however, MSA is hardly used nowadays in this context. In our view, this is unfortunate. In the present proposal, we first discuss the foundation and rationale of MSA from a ‘modern’ FA view, as well as its usefulness in the item selection process. Second, we embed the index within a robust approach and propose improvements in the preliminary item selection process. Third, we implement the proposal in different statistical programs. Finally, we illustrate its use and advantages with an empirical example in personality measurement.

Highlights

Kaiser’s single-variable measure of sampling adequacy (MSA) is a very useful index for debugging inappropriate items before a factor analysis (FA) solution is fitted to an item-pool dataset for item selection purposes
We illustrate how robust MSA can be used to decide whether some items need to be removed from the item pool before an exploratory item factor analysis (IFA) is performed for purposes of item analysis
When the 95% confidence intervals obtained with bootstrap sampling were considered, 19 items were proposed for removal

Summary

Introduction

Kaiser’s single-variable measure of sampling adequacy (MSA) is a very useful index for debugging inappropriate items before a factor analysis (FA) solution is fitted to an item-pool dataset for item selection purposes. We first discuss the foundation and rationale of MSA from a ‘modern’ FA view, as well as its usefulness in the item selection process. We embed the index within a robust approach and propose improvements in the preliminary item selection process.

Results

Conclusion