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Developing Multidimensional Likert Scales Using Item Factor Analysis

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Listed:
  • Rodrigo A. Asún
  • Karina Rdz-Navarro
  • Jesús M. Alvarado

Abstract

This study compares the performance of two approaches in analysing four-point Likert rating scales with a factorial model: the classical factor analysis (FA) and the item factor analysis (IFA). For FA, maximum likelihood and weighted least squares estimations using Pearson correlation matrices among items are compared. For IFA, diagonally weighted least squares and unweighted least squares estimations using items polychoric correlation matrices are compared. Two hundred and ten conditions were simulated in a Monte Carlo study considering: one to three factor structures (either, independent and correlated in two levels), medium or low quality of items, three different levels of item asymmetry and five sample sizes. Results showed that IFA procedures achieve equivalent and accurate parameter estimates; in contrast, FA procedures yielded biased parameter estimates. Therefore, we do not recommend classical FA under the conditions considered. Minimum requirements for achieving accurate results using IFA procedures are discussed.

Suggested Citation

  • Rodrigo A. Asún & Karina Rdz-Navarro & Jesús M. Alvarado, 2016. "Developing Multidimensional Likert Scales Using Item Factor Analysis," Sociological Methods & Research, , vol. 45(1), pages 109-133, February.
    • Handle: RePEc:sae:somere:v:45:y:2016:i:1:p:109-133
    DOI: 10.1177/0049124114566716
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    1. Anders Christoffersson, 1977. "Two-step weighted least squares factor analysis of dichotomized variables," Psychometrika, Springer;The Psychometric Society, vol. 42(3), pages 433-438, September.
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