Latent variable models for partially ordered responses and trajectory analysis of anger-related feelings.
A general framework is presented for the analysis of partially ordered set (poset) data. The work is motivated by the need to analyse poset data such as multi-componential responses in psychological measurement and partially accomplished cognitive tasks in educational measurement. It is shown how the generalized loglinear model can be used to represent poset data that form a lattice and how latent-variable models can be constructed by further specifying the canonical parameters of the loglinear representation. The approach generalizes a class of latent-variable models for completely ordered data. We apply the methods to analyse data on the frequency and intensity of anger-related feelings. Furthermore, we propose a trajectory analysis to gain insight into the response function of partially ordered emotional states.
Meulders
Michel
M
Department of Psychology, University of Leuven, Belgium. michel.meulders@psy.kuleuven.ac.be
Ip
Edward H
EH
De Boeck
Paul
P
D000786:Anger; D003071:Cognition; D003196:Computer Graphics; D003625:Data Collection / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D004521:Educational Measurement / Q000706:statistics & numerical data; D004644:Emotions; D006801:Humans; D007508:Irritable Mood; D011594:Psychometrics / Q000706:statistics & numerical data*; D011889:Rage; D015203:Reproducibility of Results