Dr. Shear's primary research interests address topics in psychometrics and applied statistics in educational research and assessment. His areas of expertise include validity theory, differential item functioning, diagnostic classification models, and categorical data analysis techniques. His work aims to inform our perspectives about test score meaning and use for research and accountability purposes, and to help answer questions such as, “what do test scores measure, and how do we know?” His work addresses these questions by developing and applying statistical models that can generate more useful test scores and make better use of existing large-scale assessment data. An example of the former is applying diagnostic classification models to improve the use of tests for formative purposes, while an example of the latter is the development of new ways to use ordered probit models in the analysis of large-scale state accountability testing data.
EDUC 7396 - Categorical Data Analysis
Primary Instructor
-
Spring 2019 / Spring 2020
Introduces contemporary advanced multivariate techniques and their application in social science research. Methods include multivariate regression and analysis of variance, structural equation models, and factor analysis. Prior experience with Anova and multiple regression is assumed.
EDUC 8230 - Quantitative Methods I
Primary Instructor
-
Fall 2018 / Fall 2019
Explores the use of statistics to formalize research design in educational research. Introduces descriptive statistics, linear regression, probability, and the basics of statistical inference. Includes instruction in the use of statistical software, (e.g., SPSS.).
EDUC 8720 - Advanced Topics in Measurement
Primary Instructor
-
Spring 2018
Focuses on psychometric models for measurement and their applications in educational and psychological research. Emphasizes understanding and evaluating the utility of models from item response theory (IRT). Applies and compares measurement models in the context of simulated or empirical data sets. Recommended prerequisite: EDUC 8710.
