TY - JOUR

T1 - The structure of self-reported emotional experiences

T2 - A mixed-effects Poisson factor model

AU - Böckenholt, Ulf

AU - Kamakura, Wagner A.

AU - Wedel, Michel

PY - 2003/11

Y1 - 2003/11

N2 - Multivariate count data are commonly analysed by using Poisson distributions with varying intensity parameters, resulting in a random-effects model. In the analysis of a data set on the frequency of different emotion experiences we find that a Poisson model with a single random effect does not yield an adequate fit. An alternative model that requires as many random effects as emotion categories requires high-dimensional integration and the estimation of a large number of parameters. As a solution to these computational problems, we propose a factor-analytic Poisson model and show that a two-dimensional factor model fits the reported data very well. Moreover, it yields a substantively satisfactory solution: one factor describing the degree of pleasantness and unpleasantness of emotions and the other factor describing the activation levels of the emotions. We discuss the incorporation of covariates to facilitate rigorous tests of the random-effects structure. Marginal maximum likelihood methods lead to straight-forward estimation of the model, for which goodness-of-fit tests are also presented.

AB - Multivariate count data are commonly analysed by using Poisson distributions with varying intensity parameters, resulting in a random-effects model. In the analysis of a data set on the frequency of different emotion experiences we find that a Poisson model with a single random effect does not yield an adequate fit. An alternative model that requires as many random effects as emotion categories requires high-dimensional integration and the estimation of a large number of parameters. As a solution to these computational problems, we propose a factor-analytic Poisson model and show that a two-dimensional factor model fits the reported data very well. Moreover, it yields a substantively satisfactory solution: one factor describing the degree of pleasantness and unpleasantness of emotions and the other factor describing the activation levels of the emotions. We discuss the incorporation of covariates to facilitate rigorous tests of the random-effects structure. Marginal maximum likelihood methods lead to straight-forward estimation of the model, for which goodness-of-fit tests are also presented.

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U2 - 10.1348/000711003770480011

DO - 10.1348/000711003770480011

M3 - Article

C2 - 14633333

AN - SCOPUS:0242719943

VL - 56

SP - 215

EP - 229

JO - British Journal of Mathematical and Statistical Psychology

JF - British Journal of Mathematical and Statistical Psychology

SN - 0007-1102

IS - 2

ER -