Psychometric properties and comparison of different techniques for factor analysis on the Big Five Inventory from a Flemish sample

In this paper we examine the Dutch language version of the Big Five Inventory, a short questionnaire used to measure the Big Five personality factors, on a Flemish sample coming from the Divorce in Flanders study. Our aim is twofold. First, we show that based on the Flemish sample the Dutch BFI has good psychometric properties and a clear factor structure comparable to a previous Dutch sample and in the international Big Five research literature. Second, we compare the usual method of analysis, namely factor analysis with principal component extraction with varimax rotation, to several methods... Mehr ...

Verfasser: Lovik, Aniko
Nassiri, Vahid
Verbeke, Geert
Molenberghs, Geert
Sodermans, An Katrien
Dokumenttyp: Artikel
Erscheinungsdatum: 2017
Verlag/Hrsg.: PERGAMON-ELSEVIER SCIENCE LTD
Schlagwörter: Big Five Inventory / Divorce in Flanders / Factor analysis / Factor rotation / Ipsatisation / Listwise deletion / Missing data / Multiple imputation / Ordinal variables / Polychoric correlation
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-29058345
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : http://hdl.handle.net/1942/24449

In this paper we examine the Dutch language version of the Big Five Inventory, a short questionnaire used to measure the Big Five personality factors, on a Flemish sample coming from the Divorce in Flanders study. Our aim is twofold. First, we show that based on the Flemish sample the Dutch BFI has good psychometric properties and a clear factor structure comparable to a previous Dutch sample and in the international Big Five research literature. Second, we compare the usual method of analysis, namely factor analysis with principal component extraction with varimax rotation, to several methods that each address a common problem in factor analysis. We compare the original analysis to factor analysis with a non-orthogonal rotation (addressing the problem of correlated factors), after ipsatisation (considering individual response styles), using polychoric correlations (taking into account the type of the responses), and using multiple imputation to handle missingness (to account for potential bias due to listwise deletion). The five factor analyses do not differ substantially. However, the analysis using polychoric correlations has the highest factor loadings and explains more of the variance than any other analysis; the analysis using ipsatised scores provides the worst results in supporting the Big Five structure. (C) 2017 Elsevier Ltd. All rights reserved.