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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
Kourea, Lefki; Lo, Ya-yu – International Journal of Research & Method in Education, 2016
Improving academic, behavioural, and social outcomes of students through empirical research has been a firm commitment among researchers, policy-makers, and other professionals in education across Europe and the United States (U.S.). To assist in building scientific evidences, executive bodies such as the European Commission and the Institute for…
Descriptors: Evidence Based Practice, Validity, Randomized Controlled Trials, Research Methodology
Bai, Li; Hudson, Peter; Millwater, Jan; Tones, Megan – International Journal of Research & Method in Education, 2013
A 30-item survey was devised to determine Chinese TEFL (Teaching English as a Foreign Language) academics' potential for conducting research. A five-part Likert scale was used to gather data from 182 academics on four factors: (1) perceptions on teaching-research nexus, (2) personal perspectives for conducting research, (3) predispositions for…
Descriptors: Productivity, Factor Analysis, Statistical Analysis, English (Second Language)