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Traditionally, multinomial processing tree (MPT) models have been validated by means of experimental manipulations, thereby testing selective effects of discrete independent variables on specific model parameters. More recently, hierarchical MPT models which account for parameter heterogeneity between participants have been introduced. These models offer a new possibility of parameter validation by analyzing selective covariations of interindividual differences in MPT model parameters with continuous covariates. Thereby, parameter validity in terms of functional dissociations, including convergent validity and discriminant validity in a nomological network, can be tested. In this project, we apply this novel approach to a multidimensional source-monitoring model in the domain of stereotype formation based on pseudocontingency inference. Using hierarchical Bayesian MPT models, we test the validity of source-guessing parameters as indicators of specific source evaluations on the individual level. Interindividual differences in direct measures of source evaluations predicted interindividual differences in specific source-guessing parameters, thus validating their substantive interpretation. In an exploratory analysis, we examined relations of memory parameters and guessing parameters with cognitive performance measures from a standardized cognitive assessment battery. Experimental data and R scripts for all analyses reported in Bott, Heck, and Meiser (2020) can be downloaded from this repository.
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