Resumen

LORENZO-SEVA, Urbano  y  VAN GINKEL, Joost R.. Multiple Imputation of missing values in exploratory factor analysis of multidimensional scales: estimating latent trait scores. Anal. Psicol. [online]. 2016, vol.32, n.2, pp.596-608. ISSN 1695-2294.  https://dx.doi.org/10.6018/analesps.32.2.215161.

ABSTRACT Researchers frequently have to analyze scales in which some participants have failed to respond to some items. In this paper we focus on the exploratory factor analysis of multidimensional scales (i.e., scales that consist of a number of subscales) where each subscale is made up of a number of Likert-type items, and the aim of the analysis is to estimate participants' scores on the corresponding latent traits. We propose a new approach to deal with missing responses in such a situation that is based on (1) multiple imputation of non-responses and (2) simultaneous rotation of the imputed datasets. We applied the approach in a real dataset where missing responses were artificially introduced following a real pattern of non-responses, and a simulation study based on artificial datasets. The results show that our approach (specifically, Hot-Deck multiple imputation followed of Consensus Promin rotation) was able to successfully compute factor score estimates even for participants that have missing data.

Palabras clave : Missing data; Hot-Deck imputation; Predictive mean matching imputation; Multiple imputation; Consensus Rotation; Factor scores; Exploratory factor analysis.

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