Estimating linear effects in ANOVA designs: The easy way

Michal Pinhas, Joseph Tzelgov, Dana Ganor-Stern

Research output: Contribution to journalArticlepeer-review


Research in cognitive science has documented numerous phenomena that are approximated by linear relationships. In the domain of numerical cognition, the use of linear regression for estimating linear effects (e. g., distance and SNARC effects) became common following Fias, Brysbaert, Geypens, and d'Ydewalle's (1996) study on the SNARC effect. While their work has become the model for analyzing linear effects in the field, it requires statistical analysis of individual participants and does not provide measures of the proportions of variability accounted for (cf. Lorch & Myers, 1990). In the present methodological note, using both the distance and SNARC effects as examples, we demonstrate how linear effects can be estimated in a simple way within the framework of repeated measures analysis of variance. This method allows for estimating effect sizes in terms of both slope and proportions of variability accounted for. Finally, we show that our method can easily be extended to estimate linear interaction effects, not just linear effects calculated as main effects.

Original languageEnglish
Pages (from-to)788-794
Number of pages7
JournalBehavior Research Methods
Issue number3
StatePublished - 1 Sep 2012


  • Distance effect
  • Linear effect
  • Numerical cognition
  • Repeated measures ANOVA
  • SNARC effect

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Psychology (miscellaneous)
  • Psychology(all)


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