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Tutz, Gerhard; Berger, Moritz – Journal of Educational and Behavioral Statistics, 2016
Heterogeneity in response styles can affect the conclusions drawn from rating scale data. In particular, biased estimates can be expected if one ignores a tendency to middle categories or to extreme categories. An adjacent categories model is proposed that simultaneously models the content-related effects and the heterogeneity in response styles.…
Descriptors: Response Style (Tests), Rating Scales, Data Interpretation, Statistical Bias
Castellano, Katherine E.; Ho, Andrew D. – Council of Chief State School Officers, 2013
This "Practitioner's Guide to Growth Models," commissioned by the Technical Issues in Large-Scale Assessment (TILSA) and Accountability Systems & Reporting (ASR), collaboratives of the "Council of Chief State School Officers," describes different ways to calculate student academic growth and to make judgments about the…
Descriptors: Guides, Models, Academic Achievement, Achievement Gains
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Ding, Lin; Beichner, Robert – Physical Review Special Topics - Physics Education Research, 2009
This paper introduces five commonly used approaches to analyzing multiple-choice test data. They are classical test theory, factor analysis, cluster analysis, item response theory, and model analysis. Brief descriptions of the goals and algorithms of these approaches are provided, together with examples illustrating their applications in physics…
Descriptors: Multiple Choice Tests, Factor Analysis, Data Interpretation, Item Response Theory
Jackson, David F. – 1991
The ability of the microcomputer to compile voluminous and detailed measures of students' learning activities automatically has seemingly outpaced the ability of commonly-used data analysis methods to make effective utilization of this wealth of potentially useful information. This study explores the use of computer-based quantitative methods,…
Descriptors: Cluster Analysis, Computer Assisted Instruction, Computer Graphics, Data Analysis