This course provides a systematic approach to theory development and/or refinement and constructing innovative analytic models for non-experimental and experimental research designs. An integrated approach to theory, measurement, model development and analysis is the focus of the course as compared to an over-emphasis on traditional null hypothesis statistical testing (NHST). Statistical and mathematical modeling (which subsumes NHST) has developed into a powerful epistemological system, within which NHST plays an important though not expansive role. In the workshop we transition from qualitative world of language into concepts then mathematical models. Mathematical models force theoretical precision, their assumptions can be more easily studied, they promote data analysis, and they have more practical applications. For example, in psychology and other behavioral sciences, methodologists, and applied researchers describe the world in part through mathematical models.
In this workshop, we focus on research problems/questions of individual faculty to develop and/or refine a theory. Next, analytic (statistical & mathematical) models are then constructed that align with the goal(s) of one’s research. This process begins by introducing and applying concepts, constructs, theory, and measurement using faculty-specific examples.
Prerequisites: There are no prerequisites for this course; however, ideally one should have had two graduate-level courses in quantitative methods (exposure to correlation and regression analysis is highly desirable) earlier in their career. The workshop is applicable to research problems/questions in education, liberal and applied arts, psychology, geography, sociology, mathematics and the biological and physical sciences.
This workshop is co-sponsored by the Office of Academic Development and Assessment (ADA); the Testing, Research-Support, and Evaluation Center (TREC); and the Institute for Interdisciplinary Research Design and Analysis (IIRDA).
