The structure of self-reported emotional experiences: A mixed-effects Poisson factor model

Ulf Böckenholt*, Wagner A. Kamakura, Michel Wedel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)215-229
Number of pages15
JournalBritish Journal of Mathematical and Statistical Psychology
Issue number2
StatePublished - Nov 2003

ASJC Scopus subject areas

  • General Psychology
  • Arts and Humanities (miscellaneous)
  • Statistics and Probability


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