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Testing Thurstonian Case V ranking models using posterior predictive checks
Author(s) -
Tsai RungChing,
Yao Grace
Publication year - 2000
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711000159330
Subject(s) - ranking (information retrieval) , goodness of fit , statistics , monte carlo method , paired comparison , computer science , econometrics , statistical hypothesis testing , data mining , artificial intelligence , mathematics , machine learning
This paper presents the results of a Monte Carlo study which investigates the validity of the method of posterior predictive checks (PPC) for testing the fit of a Thurstonian Case V ranking model. The PPC method is employed as an alternative to standard goodness‐of‐fit tests which are of limited use even when the number of items to be ranked is small. Several test quantities are formed to assess the fit of the Case V ranking model to data for various sample sizes and for two types of violations of the Case V assumptions: heterogeneous stimulus variances and rankers from different populations. The study concludes that the PPC method is useful in detecting local and global misfits of a Thurstonian Case V model, even when the ranking data are sparse.