Derek Briggs's research agenda focuses upon building sound methodological approaches for the measurement and evaluation of growth in student achievement. More generally, he is interested in conceptual issues that inform our understanding of measurement in the context of education. Examples of his research interests related to educational assessment include (1) the use (and misuse) of developmental score scales to model growth in student learning, (2) creating psychometric models that facilitate diagnostic inferences about hypothesized student learning progressions. Examples of research interests in applied statistics include the critical analysis of the statistical models used to make causal inferences about the effects of teachers, schools and other educational interventions on the growth of student achievement.
EDUC 7396 - Categorical Data Analysis
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
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 8710 - Measurement in Survey Research
Introduces students to classical test theory and item response theory. Emphasizes the process of developing, analyzing and validating a survey instrument. Focuses on developing a survey instrument with items that derive from a clearly delineated theory for the construct to be measured. Analyzes item responses and put together a validity argument to support the proposed uses of the survey.
EDUC 8720 - Advanced Topics in Measurement
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.