Indicators per Factor in Confirmatory Factor Analysis: More Is Not Always Better

• In Event: Marriott Roundtable Session 9

Abstract

Although some studies in confirmatory factor analysis have suggested that more indicators per factor is generally better, studies have also documented that sample size requirements increase as model complexity increases. The present study used a Monte Carlo simulation to investigate the effect of indicators per factor on sample size requirements. Results demonstrated that a moderate number of indicators per factor was associated with the minimum required sample size while avoiding six important consequences for the analysis, such as bias in the model chi-square statistic. The results suggested that there is an upper limit for the optimal number of indicators per factor.

Author

• Jennifer Koran, Southern Illinois University Carbondale