Psych ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 562-578
Author(s):  
Laura Kolbe ◽  
Frans Oort ◽  
Suzanne Jak

The association between two ordinal variables can be expressed with a polychoric correlation coefficient. This coefficient is conventionally based on the assumption that responses to ordinal variables are generated by two underlying continuous latent variables with a bivariate normal distribution. When the underlying bivariate normality assumption is violated, the estimated polychoric correlation coefficient may be biased. In such a case, we may consider other distributions. In this paper, we aimed to provide an illustration of fitting various bivariate distributions to empirical ordinal data and examining how estimates of the polychoric correlation may vary under different distributional assumptions. Results suggested that the bivariate normal and skew-normal distributions rarely hold in the empirical datasets. In contrast, mixtures of bivariate normal distributions were often not rejected.


1980 ◽  
Vol 17 (3) ◽  
pp. 391-394 ◽  
Author(s):  
Ulf Olsson

The product moment correlation coefficient is often used even for ordinal data with only a few scale steps. This procedure may lead to biased results, where the bias depends on the number of scale steps and on the skewnesses of the observed variables. The polychoric correlation coefficient, which is a generalization of the tetrachoric correlation to the general case, is discussed as a possible measure of correlation for this kind of data.


1991 ◽  
Vol 28 (4) ◽  
pp. 491-497 ◽  
Author(s):  
Edward E. Rigdon ◽  
Carl E. Ferguson

In a simulation study, no combination of the polychoric correlation coefficient with any LISREL 7 fitting function produced unbiased estimated standard errors or a correctly distributed chi square statistic. However, there were major differences in the performance of the five fitting functions in the analysis of ordinal data.


2022 ◽  
Vol 12 ◽  
Author(s):  
Jari Metsämuuronen

Underestimation of reliability is discussed from the viewpoint of deflation in estimates of reliability caused by artificial systematic technical or mechanical error in the estimates of correlation (MEC). Most traditional estimators of reliability embed product–moment correlation coefficient (PMC) in the form of item–score correlation (Rit) or principal component or factor loading (λi). PMC is known to be severely affected by several sources of deflation such as the difficulty level of the item and discrepancy of the scales of the variables of interest and, hence, the estimates by Rit and λi are always deflated in the settings related to estimating reliability. As a short-cut to deflation-corrected estimators of reliability, this article suggests a procedure where Rit and λi in the estimators of reliability are replaced by alternative estimators of correlation that are less deflated. These estimators are called deflation-corrected estimators of correlation (DCER). Several families of DCERs are proposed and their behavior is studied by using polychoric correlation coefficient, Goodman–Kruskal gamma, and Somers delta as examples of MEC-corrected coefficients of correlation.


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