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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The work of M.E.J.R. was funded by a VIDI grant of the NWO. W.H.B.’s work on this article was supported in part by a National Science Foundation grant to X. Hu and W.H.B. (Co-PIs, SES-0616657) and by a grant from the U.S. Air Force Office of Scientific Research (FA9550-09-05).
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Schmittmann, V.D., Dolan, C.V., Raijmakers, M.E.J. et al. Parameter identification in multinomial processing tree models. Behavior Research Methods 42, 836–846 (2010). https://doi.org/10.3758/BRM.42.3.836
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DOI: https://doi.org/10.3758/BRM.42.3.836