Application of the Bootstrap Methods in Factor Analysis

Masanori Ichikawa · Sadanori Konishi

80 citations0/0 files storedPsychometrika

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Psychometric Methodologies and Testing Statistical Methods and Applications Statistical Methods and Bayesian Inference

Abstract

A Monte Carlo experiment is conducted to investigate the performance of the bootstrap methods in normal theory maximum likelihood factor analysis both when the distributional assumption is satisfied and unsatisfied. The parameters and their functions of interest include unrotated loadings, analytically rotated loadings, and unique variances. The results reveal that (a) bootstrap bias estimation performs sometimes poorly for factor loadings and nonstandardized unique variances; (b) bootstrap variance estimation performs well even when the distributional assumption is violated; (c) bootstrap confidence intervals based on the Studentized statistics are recommended; (d) if structural hypothesis about the population covariance matrix is taken into account then the bootstrap distribution of the normal theory likelihood ratio test statistic is close to the corresponding sampling distribution with slightly heavier right tail.

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References

1958The Varimax Criterion for Analytic Rotation in Factor Analysis 1992Bootstrapping Goodness-of-Fit Measures in Structural Equation Models 1984Asymptotically distribution‐free methods for the analysis of covariance structures 1990Direct and Indirect Effects: Classical and Bootstrap Estimates of Variability 1956Statistical Inference in Factor Analysis 1973Factor Analysis as a Statistical Method.1972Factor Analysis as A Statistical Method.1989Sociological Methodology, 1987.

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