Data for an example about predicting exercise based on TV watching.

Empirical Research Methods[Enter Course]


In this course you will learn how to conduct research using empirical methods, which rely on observation and experimentation. This course is appropriate for those interested in using empirical research methods in their field, particularly students in the social and behavioral sciences. Topics include the formulation of the question to be investigated and the of resulting hypotheses, the collection of data and the analysis of the data collected, and the interpretation and study of analysis results.

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.

Additional Course Details

Topics Covered:
Regression, Prediction and Causation, Inference, Making a Prima Facie Case, Alternatives to Simple Causation.
Additional Software or Materials Required:
You will need to have Flash, Java, and MathML installed. These programs are free. More detailed information is provided in the course under “Test and Configure Your System.”
Course Last Updated Date:
Fall 2010
Changes in This Update Include:
Revision of Units 1 and 2 and addition of Units 3, 4 and 5.
Maintenance Fee (per student):
Free for both independent learners and academic students.

In-Depth Description

This course helps students connect the mathematical foundations of regression with its practical application. Students learn how to move from an interesting question about the world to a regression model that, when estimated, meaningfully addresses the question asked. We emphasize causal analysis as the main research goal and multivariate linear regression as the main statistical tool, by presenting 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.
  5. Critically interpreting the results of these analyses.

A student 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.

We present the course topics 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 follows the format of:

  1. Locate the current topic in the big-picture—the stage of ERM process.
  2. Situate task/concepts to be taught in an interesting case study.
  3. Present tasks/concepts abstractly.
  4. Present interactive exercises and tutors to support learning of tasks/concepts.
  5. Extended problem solving episode in an interesting case study using Causality Lab and StatTutor.
  6. We use many case studies and data sets to illustrate various themes that arise in the application of regression methods to interesting problems.