Performance of Velicer’s Minimum Average Partial Factor Retention Method With Categorical Variables
By L. E. Garrido, F. J. Abad, Vicente Ponsoda. 2011
114 citations recorded by the source corpus.
Subfields: Food Science, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/5ccc75b52e642b761117eb4ce787bccdb15cfd61
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Citations in the corpus
- Breaking it Down: Engineering Student STEM Confidence at the Intersection of Race/Ethnicity and Gender (2014, methodology): “…order to create these four aggregate predictor variables, along with the aggregate outcome variable, a principal components analysis was conducted for each respective survey subsection based on the polychoric correlation matrix, which is appropriate for use with ordinal data (Garrido et al. 2011).”
- Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research (2016, background): “Thus, in such situations, parallel analysis, MAP and comparing different number of factors via BIC perform bad, in average, proving that estimating the number of factors is still a non-trivial task, in spite of the past decades’ developments.”