A nonnormal look at polychoric correlations: Modeling the change in correlations before and after discretization
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Date
2016-03-08
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Springer
Abstract
Two algorithms for establishing a connection between correlations before and after ordinalization under a wide spectrum of nonnormal underlying bivariate distributions are developed by extending the iteratively found normal-based results via the power polynomials. These algorithms are designed to compute the polychoric correlation when the ordinal correlation is specified, and vice versa, along with the distributional properties of latent, continuous variables that are subsequently ordinalized through thresholds dictated by the marginal proportions. The method has broad applicability in the simulation and random number generation world where modeling the relationships between these correlation types is of interest.
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Keywords
Mathematics, Random number generation, Simulation, Nonnormality, Threshold concept, Pattern-mixture models, Ignorable drop-out, Ordinal data, Multiple imputation, Power polynomials, Distributions, Performance, Coefficient, Generation
Citation
Demirtaş, H. vd. (2016). "A nonnormal look at polychoric correlations: Modeling the change in correlations before and after discretization". Computational Statistics, 31(4), 1385-1401.