Developing Multidimensional Likert Scales Using Item Factor Analysis
By Rodrigo A. Asún, Karina Rdz-Navarro, Jesús M. Alvarado. 2016
172 citations recorded by the source corpus.
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.
Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/06d75e4ed3bf49db9a19f41d3c8e2bf0ef9c0b00
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In Computer Networks and Communications
In Management Science and Operations Research
In Statistics and Probability
Citations in the corpus
- Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods (2020, background): “There is a diversity of methodological literature addressing this issue (Dolan, 1994; DiStefano, 2002; Lei, 2009; Flora et al., 2012; Rhemtulla et al., 2012; Sass et al., 2014; Barendse et al., 2015; Asún et al., 2016; Li, 2016; Jia andWu, 2019).”