ERIC Number: EJ1086304
Record Type: Journal
Publication Date: 2016
Abstractor: As Provided
Reference Count: 38
The Random Forests Statistical Technique: An Examination of Its Value for the Study of Reading
Matsuki, Kazunaga; Kuperman, Victor; Van Dyke, Julie A.
Scientific Studies of Reading, v20 n1 p20-33 2016
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 article, we discuss the method of Random Forests and demonstrate its suitability for addressing the statistical concerns raised by such data sets. The method is contrasted with other methods of estimating relative variable importance, especially Dominance Analysis and Multimodel Inference. All methods were applied to a data set 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.
Descriptors: Reading Ability, Statistical Analysis, Research Methodology, Inferences, Eye Movements, Reading Processes, Reading Comprehension, Decision Making, Undergraduate Students, Cognitive Ability, Verbal Ability, Oral Reading, Reading Tests, Predictor Variables, Decoding (Reading), Reading Rate, Vocabulary, Time, Goodness of Fit, Models, Regression (Statistics), Intelligence Tests, Scores
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Sponsor: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (NIH)
Authoring Institution: N/A
Identifiers - Assessments and Surveys: Gray Oral Reading Test
IES Grant or Contract Numbers: HD001994|HD073288