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Prevett, Pauline S.; Black, Laura; Hernandez-Martinez, Paul; Pampaka, Maria; Williams, Julian – International Journal of Research & Method in Education, 2021
A novel approach to integrating Cluster Analysis (CA) within qualitative inquiry is presented, grounded in a large, unstructured dataset from open and rather unstructured interviews. This dataset was previously subjected to typical (theory sensitive) thematic analyses. Transformed into quantitative binary matrix structures, the CA offers…
Descriptors: Multivariate Analysis, Qualitative Research, Semi Structured Interviews, Robustness (Statistics)
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Pampaka, Maria; Hutcheson, Graeme; Williams, Julian – International Journal of Research & Method in Education, 2016
Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their…
Descriptors: Data Analysis, Statistical Inference, Error of Measurement, Computation
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Ong, Yoke Mooi; Williams, Julian; Lamprianou, Iasonas – International Journal of Research & Method in Education, 2013
Researchers interested in exploring substantive group differences are increasingly attending to bundles of items (or testlets): the aim is to understand how gender differences, for instance, are explained by differential performances on different types or bundles of items, hence differential bundle functioning (DBF). Some previous work has…
Descriptors: Mathematics Tests, Gender Differences, Mathematics Instruction, Mathematical Models