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The Impact of Multidimensionality on Extraction of Latent Classes in Mixture Rasch Models
Author(s) -
Jang Yoonsun,
Kim SeockHo,
Cohen Allan S.
Publication year - 2018
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/jedm.12185
Subject(s) - rasch model , polytomous rasch model , latent class model , mixture model , statistics , local independence , class (philosophy) , latent variable , psychology , latent variable model , mathematics , item response theory , computer science , psychometrics , artificial intelligence
This study investigates the effect of multidimensionality on extraction of latent classes in mixture Rasch models. In this study, two‐dimensional data were generated under varying conditions. The two‐dimensional data sets were analyzed with one‐ to five‐class mixture Rasch models. Results of the simulation study indicate the mixture Rasch model tended to extract more latent classes than the number of dimensions simulated, particularly when the multidimensional structure of the data was more complex. In addition, the number of extracted latent classes decreased as the dimensions were more highly correlated regardless of multidimensional structure. An analysis of the empirical multidimensional data also shows that the number of latent classes extracted by the mixture Rasch model is larger than the number of dimensions measured by the test.