Categorization and measurement quality. The choice between Pearson and Polychoric correlations

Abstract

This chapter first reviews the major consequences of ordinal measurement in Confirmatory Factor Analysis (CFA) models and introduces the reader to the Polychoric correlation coefficient, a measure of association designed to avoid these consequences. Next, it presents the results of a Monte Carlo experiment in which the performance of the estimates of a CFA model based on Pearson and Polychoric correlations is compared under different distributional settings. The chapter concludes that, in general, it does not matter too much which measure of association one uses, as long as one is aware that factor loadings should be interpreted differently, depending on whether Pearson or Polychoric correlations are analysed.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 28 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.