Course Syllabus

O’Neill School of Public and Environmental Affairs
SPEA-V 370 Research Methods and Statistical Modeling
Sections: 9533 & 14212

A downloadable link to the pdf or docx copy of the complete course Syllabus is available  on the Home  or Modules pages located in the left-sidebar navigation.

Course Description

This course will introduce the student to the basic methods, issues, analytical techniques, and ethical considerations of evaluation research.

Course Content

The course consists of three modules linked with each other: (i) Statistical (econometric) modeling, and (ii) Research Methods and (iii) Elements of Machine Learning. The first module of the course covers basics of the regression analysis and use of statistical software. Students will be introduced to intermediate-level perspective on statistical concepts and techniques for analyzing and modeling modern data with emphasis on practical and applied modeling aspects. The module content includes estimating the parameters of econometric models, testing hypotheses, making inference and forecasting. Multivariate regression analysis is one of the primary tools for statistical modeling for purposes of policy analysis, program evaluation, simulation of systems, and general forecasting. Students will be actively involved with computer exercises using the Stata (and R or Gretl)  econometrics program available in every STC Computer Lab and for installation to personal computers PC and Macs. Throughout the course you will use Stata to implement a series of econometrics exercises designed to provide experience with various tests and estimation procedures. The second module of the course is designed to develop each student's ability to design an empirical study within the ethical constraints of human research and to understand the results of empirical research presented in professional journals. The third module will introduce students to concepts and applications of modern topics of data analysis including Machine Learning and use and misuse of GenAI. Practical exercises will be done via user-friendly employed in the first module. Modeling examples will be drawn from the fields of business, law, public policy, education and others with a special emphasis on social issues and policy analysis research (e.g. environmental concerns, global climate change, legal issues, COVID-19 etc.).

Course Objectives and Learning Outcomes

The purpose of this course is to introduce students to basic statistical concepts and techniques. At the end of this course, you should be able to:

  • read social science and policy analytical materials; draw information from different sources;
  • know how to design a valid policy/social study/experiment;
  • recognize and disclosure some of the misuses of statistical analysis;
  • develop basic econometric models, carry out an adequate statistical analysis for the basic types of data, and interpret and present results;
  • use Stata (and R/Gretl software) for policy analysis/empirical studies (Prior knowledge of Stata or R is not required);
  • understand concepts and apply selected Machine Learning techniques in data analysis;
  • understand use and misuse of GenAI as research assistant.

Detailed learning outcomes for each week of the class are included on the Canvas Module pages or Handouts.

Course Meeting information

Class  9533 meets on Mondays and Wednesdays (MW) at 2:20-3:35 pm in Room  A201 (SPEA building)

Class 14212 meets on Tuesdays and Thursdays (TR) at 9:35-10:50am in Room  A221 (SPEA building)

Textbook

Required textbook: Damodar Gujarati, Essentials of Econometrics, 5/e, Sage, 2021 is available via IU eTexts (Unizin Engage) located in the left-sidebar navigation (no purchase required). 

Additional readings will be posted via Canvas.

Course load

Expected out-of-class time load is  7-10 hours/week.