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Factor Analyses for Non-normal Variables by Fitting Characteristic Functions

  • Author(s): van Montfort, Kees
  • de Leeuw, Yan
  • et al.
Abstract

This paper deals with a factor analysis model. Usually one assumes that the model has normally distributed variables. If these variables are not normally distributed, the well-known estimators using only variances and covariances of the observed variables may lead to biased estimates of the factor loadings. This paper uses characteristic functions to derive an approximately consistent BGLS-estimator of the factor loadings in a model with both normal and non-normal variables.

An example with generated data and an empirical example will evaluate the estimator. Furthermore, the estimator will be compared to well-known estimators using only second order moments, consistent estimators using third order moments and estimators using transform ations to get normal variables.

It turns out that our method gives good estimates of the unknown model parameters for sample sizes larger than 50. Our estimation method also avoids the disadvantage of the above mentioned estimation methods.

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