The Random Forests statistical technique: An examination of its value for the study of reading

Scientific Studies of Reading : the Official Journal of the Society for the Scientific Study of Reading
Kazunaga MatsukiJulie A Van Dyke

Abstract

Studies investigating individual differences in reading ability often involve data sets containing a large number of collinear predictors and a small number of observations. In this paper, we discuss the method of Random Forests and demonstrate its suitability for addressing the statistical concerns raised by such datasets. The method is contrasted with other methods of estimating relative variable importance, especially Dominance Analysis and Multimodel Inference. All methods were applied to a dataset that gauged eye-movements during reading and offline comprehension in the context of multiple ability measures with high collinearity due to their shared verbal core. We demonstrate that the Random Forests method surpasses other methods in its ability to handle model overfitting, and accounts for a comparable or larger amount of variance in reading measures relative to other methods.

References

Mar 1, 1968·Psychological Bulletin·R B Darlington
Apr 17, 2008·Behavior Research Methods·Danie Jl AachesonMaryellen C MacDonald
Jun 29, 2011·Journal of Memory and Language·Victor Kuperman, Julie A Van Dyke
Sep 24, 2013·Developmental Review : DR·Erik D ReichleKeith Rayner

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Citations

Mar 12, 2016·Scientific Studies of Reading : the Official Journal of the Society for the Scientific Study of Reading·Jason D Zevin, Brett Miller
Mar 8, 2020·Pain Practice : the Official Journal of World Institute of Pain·Eric-Jan van GorpPierre-Philippe Luyet
Nov 17, 2019·Memory & Cognition·Peter C GordonMatthew W Lowder
Dec 18, 2020·Frontiers in Cellular and Infection Microbiology·Kaicheng WenYue Zhu
Feb 5, 2019·Journal of Memory and Language·Ariel N JamesDuane G Watson
Mar 12, 2021·PloS One·Aki-Juhani Kyröläinen, Victor Kuperman
Jul 6, 2021·Clinical Kidney Journal·Victoria Garcia-MontemayorMariano Rodriguez
Nov 17, 2021·Journal of Environmental Quality·William R OsterholzKevin W King
Dec 24, 2021·Journal of Strength and Conditioning Research·Justin J MerriganJoshua A Hagen

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Software Mentioned

Random
Random Forests

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