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Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables

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Given that the use of Likert scales is increasingly common in the field of social research it is necessary to determine which methodology is the most suitable for analysing the data obtained; although, given the categorization of these scales, the results should be treated as ordinal data it is often the case that they are analysed using techniques designed for cardinal measures. One of the most widely used techniques for studying the construct validity of data is factor analysis, whether exploratory or confirmatory, and this method uses correlation matrices (generally Pearson) to obtain factor solutions. In this context, and by means of simulation studies, we aim to illustrate the advantages of using polychoric rather than Pearson correlations, taking into account that the latter require quantitative variables measured in intervals, and that the relationship between these variables has to be monotonic. The results show that the solutions obtained using polychoric correlations provide a more accurate reproduction of the measurement model used to generate the data.

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Correspondence to Francisco Pablo Holgado–Tello.

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The present study forms part of the results obtained in research project SEJ2004–05360/EDUC funded by Spain’s Ministerio de Educación y Ciencia.

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Holgado–Tello, F.P., Chacón–Moscoso, S., Barbero–García, I. et al. Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables. Qual Quant 44, 153–166 (2010).

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