gulcalikli / empirical-methods

Homepage for 17-803 "Empirical Methods" at Carnegie Mellon University

(thanks for the promo, @JoshQuicksall!)

This is the Spring 2021 offering of this course. For older versions, see here: Fall 2018.

Overview

Empirical methods play a key role in the design and evaluation of tools and technologies, and in testing the social and technical theories they embody. No matter what your research area is, chances are you will be conducing some empirical studies as part of your work. Are you looking to evaluate a new algorithm? New tool? Analyze (big) data? Understand what challenges practitioners face in some domain?

This course is a survey of empirical methods designed for computer science PhD students, that teaches you how to go about each of these activities in a principled and rigorous way. You will learn about and get hands-on experience with a core of qualitative and quantitative empirical research methods, including interviews, qualitative coding, survey design, and many of the most useful statistical analyses of (large-scale) data, such as various forms of regression, time series analysis, and causal inference. And you will learn how to design valid studies applying and combining these methods.

There will be extensive reading with occasional student presentations about the reading in class, homework assignments, and a semester-long research project for which students must prepare in-class kickoff and final presentations as well as a final report.

After completing this course, you will:

• become a more sophisticated consumer of empirical research, both in your field and outside
• develop the methodological skills that can help you design and carry out empirical components in your own research program
• be able to analyze empirical data, draw conclusions, and present results
• be able to read, summarize, present, but most importantly critique academic empirical research papers on a deep technical level

As a side effect, this course helps you develop a healthy dose of skepticism towards scientific results in general. Does the study design really allow the authors to make certain claims? Does the analysis technique? Is the evidence provided as strong as it could be? Are there fundamental flaws and threats to validity?

Coordinates

• Lectures: Tuesdays & Thursdays 2:20-3:40pm, Eastern Time, on Zoom
• Assignments, private announcements: Canvas
• Slides: this website
• Instructor: Bogdan Vasilescu

Course Syllabus and Policies

The syllabus covers course overview and objectives, evaluation, time management, late work policy, and collaboration policy.

Learning Goals

The learning goals describe what I want students to know or be able to do by the end of the semester. I evaluate whether learning goals have been achieved through assignments, written project reports, and in-class presentations.

Schedule

Below is a preliminary schedule for Spring 2021. Each link points to a dedicated page with materials and more details. Note: The schedule is subject to change and will be updated as the semester progresses.

Date	Topic	Notes
Tue, Feb 2	Introduction
Thu, Feb 4	Formulating research questions
Tue, Feb 9	The role of theory
Thu, Feb 11	Literature review
Tue, Feb 16	Conducting interviews
Thu, Feb 18	Interview model papers
Tue, Feb 23	Break Day; No Classes
Thu, Feb 25	Qualitative data analysis (coding, reliability, trustworthiness)
Tue, Mar 2	Survey design (part I)
Thu, Mar 4	Survey design (part II)
Tue, Mar 9	Project proposal presentations
Thu, Mar 11	Numbers and nonsense	slides • youtube
Tue, Mar 16	Experimental design (part II)
Thu, Mar 18	Experimental design (part III)
Tue, Mar 23	Regression modeling (part I)
Thu, Mar 25	Regression discontinuity design
Tue, Mar 30	Causal inference part 1 (first differences, diff in diff)
Thu, Apr 1	Causal inference part 2 (causal impact)
Tue, Apr 6	Mixed-methods designs
Thu, Apr 8	Big data vs thick data
Tue, Apr 13	Researchers are human, too
Thu, Apr 15	Spring Carnival; No Classes
Tue, Apr 20	Agree to disagree
Thu, Apr 22	Stepping up your typesetting and graphics game
Tue, Apr 27	Stepping up your presentation game
Thu, Apr 29	Retrospective
Tue, May 4	Final presentations (part I)
Thu, May 6	Final presentations (part II)