title: A hierarchical process-dissociation model.
abstract: In fitting the process-dissociation model (L. L. Jacoby, 1991) to observed data, researchers aggregate outcomes across participant, items, or both. T. Curran and D. L. Hintzman (1995) demonstrated how biases from aggregation may lead to artifactual support for the model. The authors develop a hierarchical process-dissociation model that does not require aggregation for analysis. Most importantly, the Curran and Hintzman critique does not hold for this model. Model analysis provides for support of process dissociation--selective influence holds, and there is a dissociation in correlation patterns among participants and items. Items that are better recollected also elicit higher automatic activation. There is no correlation, however, across participants; that is, participants with higher recollection have no increased tendency toward automatic activation. The critique of aggregation is not limited to process dissociation. Aggregation distorts analysis in many nonlinear models, including signal detection, multinomial processing tree models, and strength models. Hierarchical modeling serves as a general solution for accurately fitting these psychological-processing models to data.
authors.affiliation: Department of Psychological Sciences, University of Missouri-Columbia, Columbia, MO 65211, USA. rouderj@missouri.edu
authors.forename: Jeffrey N
authors.initials: JN
authors.lastname: Rouder
authors.forename: Jun
authors.initials: J
authors.lastname: Lu
authors.forename: Richard D
authors.initials: RD
authors.lastname: Morey
authors.forename: Dongchu
authors.initials: D
authors.lastname: Sun
authors.forename: Paul L
authors.initials: PL
authors.lastname: Speckman
mesh_terms: D001288:Attention; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D006801:Humans; D011939:Mental Recall; D015233:Models, Statistical; D011214:Practice, Psychological; D011930:Reaction Time; D012153:Retention, Psychology; D017603:Signal Detection, Psychological; D014706:Verbal Learning