Parameter identification in multinomial processing tree models |
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Authors: | Verena D Schmittmann Conor V Dolan Maartje E J Raijmakers and William H Batchelder |
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Institution: | (1) University of California, Irvine, California;(2) Department of Psychology, Ohio State University, 1835 Neil Avenue, 43210 Columbus, OH |
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Abstract: | Multinomial processing tree models form a popular class of statistical models for categorical data that have applications
in various areas of psychological research. As in all statistical models, establishing which parameters are identified is
necessary for model inference and selection on the basis of the likelihood function, and for the interpretation of the results.
The required calculations to establish global identification can become intractable in complex models. We show how to establish
local identification in multinomial processing tree models, based on formal methods independently proposed by Catchpole and
Morgan (1997) and by Bekker, Merckens, and Wansbeek (1994). This approach is illustrated with multinomial processing tree
models for the source-monitoring paradigm in memory research. |
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