Using latent class modeling to detect bimodality in spacing effect data

Peter P.J.L. Verkoeijen*, Samantha Bouwmeester

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)

Abstract

A recently proposed theory of the spacing effect [Raaijmakers, J. G. W. (2003). Spacing and repetition effects in human memory: application of the SAM model. Cognitive Science, 27, 431-452.] suggests that the spacing effect is conditional on study-phase retrieval leading to two groups of students showing different magnitudes of the spacing effect. This bimodality was also observed in histograms of spacing-effect data. In this study, we used latent class regression analysis to investigate whether these groups can be detected in existing datasets (Experiment 1). Specific hypotheses about the magnitude of the spacing effect in the latent classes were assessed in Experiment 2. Latent class regression analysis in both experiments showed that the fit of the two-class model was considerably better than the (1-class) ANOVA model. Moreover, the results of Experiment 2 showed, in line with our predictions, that when the presentation rate changed from 1 s to 4 s the increase in spacing effect was larger for the low-performing class than for the high-performing class.

Original languageEnglish
Pages (from-to)545-555
Number of pages11
JournalJournal of Memory and Language
Volume59
Issue number4
DOIs
Publication statusPublished - Nov 2008

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