Classification Based on Hierarchical Linear Models: The Need for Incorporation of Social Contexts in Classification Analysis

Brandon K. Vaughn*, Dr. Qiu Wang**
* Assistant Professor, The University of Texas at Austin.
** Doctoral Candidate in the Program of Measurement and Quantitative Methods, Michigan State University.
Periodicity:May - July'2009
DOI : https://doi.org/10.26634/jpsy.3.1.183

Abstract

Many areas in educational and psychological research involve the use of classification statistical analysis. For example, school districts might be interested in attaining variables that provide optimal prediction of school dropouts. In psychology, a researcher might be interested in the classification of a subject into a particular psychological construct. The purpose of this study is to investigate alternative procedures to classification other than the use of discriminant and logistic regression analysis. A classification rule utilizing hierarchical linear modeling (HLM) will be derived and examined, with a following example which will show the benefit for using such an approach by comparing the hit rates to those of a logistic regression analysis.

Keywords

Classification, Hierarchical Linear Models, Multilevel Models.

How to Cite this Article?

Brandon K. Vaughn and Dr. Qiu Wang (2009). Classification Based on Hierarchical Linear Models: The Need for Incorporation of Social Contexts in Classification Analysis. i-manager’s Journal on Educational Psychology, 3(1), 34-42. https://doi.org/10.26634/jpsy.3.1.183

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