Variance estimation in factor analysis: An application of the bootstrap

Sangit Chätterjee

56 citations0/0 files storedBritish Journal of Mathematical and Statistical Psychology

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

Abstract

Sampling variability of the estimates of factor loadings is neglected in modern factor analysis. Such investigations are generally normal theory based and asymptotic in nature. The bootstrap, a computer‐based methodology, is described and then applied to demonstrate how the sampling variability of the estimates of factor loadings can be estimated for a given set of data. The issue of the number of factors to be retained in a factor model is also addressed. The bootstrap is shown to be an effective data‐analytic tool for computing various statistics of interest which are otherwise intractable.

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1990Direct and Indirect Effects: Classical and Bootstrap Estimates of Variability 1987"How Big Is Big Enough?": Sample Size and Goodness of Fit in Structural Equation Models with Latent Variables 2001Performance of Bootstrapping Approaches to Model Test Statistics and Parameter Standard Error Estimation in Structural Equation Modeling 1998Bootstrapping correlation coefficients using univariate and bivariate sampling.2005Centre for Efficiency and Productivity Analysis 1993SIMSTAT: Bootstrap computer simulation and statistical program for IBM personal computers 2010Bootstrap Confidence Intervals for Ordinary Least Squares Factor Loadings and Correlations in Exploratory Factor Analysis 2007A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis

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