Open Learning Initiative

Empirical Research Methods course bridges the gap between the mathematical foundations of regression and its practical application. We are making this course available while it is under development.

The Empirical Research Methods course is currently under development and we are making the course available while it is under development.

Regression analysis is an enormously popular and powerful tool, used ubiquitously in the social and behavioral sciences. Most courses on the subject immediately dive into the mathematical aspects of the subject and illustrate the technique on problems that are already highly structured. As a result, most students come away with little idea of the wide range of problems to which regression analysis can be applied and how to represent those problems in a way that cleverly utilizes readily available data. Few understand, at a conceptual level, the limitations of regression analysis.

The OLI Empirical Research Methods course bridges the gap between the mathematical foundations of regression and its practical application. We teach students how to move from an interesting question about the world to a regression model that, when estimated, meaningfully addresses the question asked. It emphasizes causal analysis as the main research goal and multivariate linear regression as the main statistical tool. We teach a process that involves:

1. Formulating a research problem,
2. Developing and formalizing hypotheses,
3. Collecting data relevant to these hypotheses,
4. Analyzing the data using an appropriate regression model, and
5. Critically interpreting the results of these analyses

A learner who successfully completes our course will be able to do much more than mechanically estimate a regression model with standard statistical software like SPSS or Minitab, or check whether coefficient estimates are “significant” at the .05 or .01 level. They will be able to bring to bear their own scientific imagination in order to use regression as a tool to investigate problems about the real world. They will be able, perhaps not with professional sophistication, but with competence, to do real empirical research.

Pre-requisites

We assume that learners entering Empirical Research Methods (ERM) have taken at least a semester or year-long course in statistics, and through this or some other experience have been exposed to the following concepts:

• Random Variables
• Population and Samples
• Data Tables (rows=sample units and columns=variables)
• Summary Statistics: Mean, Median, Variance, Covariance, Correlation
• Graphs: Boxplots, Barcharts, Histograms, Scatterplots
• Inference: standard errors, confidence intervals, hypothesis tests, etc.
• Models: Bivariate Regression, perhaps ANOVA

If learners have not had such exposure, they can follow the appropriate links into the OLI Introductory Statistics course to review the required concepts.

Learning Objectives

Our primary goal is to teach learners to bring empirical data to bear on interesting questions by using regression analysis in a way that is scientifically credible. We begin by considering problems in which hypotheses have been formulated, the unit of analysis defined, and data located to construct variables to test the hypotheses. The next tasks are to determine how to construct variables consistent with the hypothesized relationships and that can be implemented with the data. We provide various examples that illustrate how to do this. Next we teach learners to relate these variables with a regression model, and then interpret the results of the regression estimation in a scientifically informed manner, both with respect to the inferences that can be made and the inferences that cannot.

Sequence of Instruction

UNIT 1: Introduction and Course Overview
	Module 1: Course Introduction
	Module 2: A Brief Tour of the Course
UNIT 2: Regression, Prediction and Causation
	Module 3: Motivation and Overview
	Module 4: Prediction
	Module 5: Causation
UNIT 3: Inference and Bivariate Regression
	Module 6: Introduction
	Module 7: Case III
UNIT 4: The Art of Making a Prima Facie Case
	Module 8: Introduction
	Module 9: The Components of a Prima Facie Case
UNIT 5: Challenges to the Prima Facie Case: Alternatives to Simple Causality
	Module 10: Introduction
	Module 11: Modeling Direct Causation
	Module 12: Indirect Causation
	Module 13: Common Causes (Confounding)
	Module 14: Reverse Causality
	Module 15: Advanced Topic: Feedback
	Module 16: General Lessons

Teaching approach

We teach the topics above using the approach that has proven to be successful in our OLI statistics and causal reasoning courses. Many of the activities also use an extended version of StatTutor, the computer based statistics tutor that supports the OLI introductory statistics course and the Causality Lab, the virtual social science experiment lab environment that supports the OLI Causal and Statistical reasoning course. This similarity in structure also allows instructors to easily combine and sequence modules from the statistics course, the causal reasoning course and the empirical research methods course to tailor a course in this domain to fit the needs of their students.

Each of the modules above follows the format of:

• Locate the current topic in the big-picture – the stage of ERM process
• Situate task/concepts to be taught in an interesting case study
• Present tasks/concepts abstractly
• Present interactive exercises and tutors to support learning of tasks/concepts
• Extended problem solving episode in an interesting case study using Causality Lab and StatTutor

We use many case studies and data sets to illustrate various themes that arise in the application of regression methods to interesting problems.