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Latent variable models for partially ordered responses and trajectory analysis of anger‐related feelings
Author(s) -
Meulders Michel,
Ip Edward H.,
Boeck Paul
Publication year - 2005
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711005x38555
Subject(s) - latent variable , partially ordered set , anger , latent class model , mathematics , latent variable model , set (abstract data type) , representation (politics) , variable (mathematics) , trajectory , function (biology) , psychology , computer science , statistics , social psychology , discrete mathematics , mathematical analysis , physics , astronomy , evolutionary biology , politics , political science , law , biology , programming language
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.