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Random Forest as an Imputation Method for Education and Psychology Research: Its Impact on Item Fit and Difficulty of the Rasch Model
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Golino, Hudson F.; Gomes, Cristiano M. A. – International Journal of Research & Method in Education, 2016
This paper presents a non-parametric imputation technique, named random forest, from the machine learning field. The random forest procedure has two main tuning parameters: the number of trees grown in the prediction and the number of predictors used. Fifty experimental conditions were created in the imputation procedure, with different…
Descriptors: Item Response Theory, Regression (Statistics), Difficulty Level, Goodness of Fit