TY - JOUR
T1 - Estimating linear effects in ANOVA designs
T2 - The easy way
AU - Pinhas, Michal
AU - Tzelgov, Joseph
AU - Ganor-Stern, Dana
N1 - Funding Information:
We wish to thank Michael Bendersky for constructive remarks, Marc Brysbaert and an anonymous reviewer for helpful comments on an earlier draft of this manuscript and Desiree Meloul for editing. This research was supported by grant no. 1664/08 for the Center for the Study of the Neurocognitive Basis of Numerical Cognition from the Israeli Science Foundation.
PY - 2012/9/1
Y1 - 2012/9/1
N2 - 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.
AB - 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.
KW - Distance effect
KW - Linear effect
KW - Numerical cognition
KW - Repeated measures ANOVA
KW - SNARC effect
UR - http://www.scopus.com/inward/record.url?scp=84865623355&partnerID=8YFLogxK
U2 - 10.3758/s13428-011-0172-y
DO - 10.3758/s13428-011-0172-y
M3 - Article
AN - SCOPUS:84865623355
SN - 1554-351X
VL - 44
SP - 788
EP - 794
JO - Behavior Research Methods
JF - Behavior Research Methods
IS - 3
ER -