README.md

(thanks for the promo, @JoshQuicksall!)

This is the Fall 2022 offering of this course. For older versions, see here: Spring 2021 • 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 conducting 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 primarily 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 3:05-4:25pm, Eastern Time
• Assignments, private announcements: Canvas
• Slides: this website
• Reading materials: Shared Google Drive folder accessible with CMU login
• Instructor: Bogdan Vasilescu
• Teaching assistant: Hongbo Fang

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 Fall 2022. Each link points to a dedicated page with materials and more details. All videos are published on this YouTube channel.

Note: The schedule is subject to change and will be updated as the semester progresses.

Date	Topic	Notes
Tue, Aug 30	Introduction	slides • video
Thu, Sep 1	Formulating research questions	slides • video
Tue, Sep 6	The role of theory	slides • video
Thu, Sep 8	Literature review	slides • video
Tue, Sep 13	Conducting interviews	slides • video
Thu, Sep 15	Exemplar interview papers	slides • video
Tue, Sep 20	Qualitative data analysis	slides • video
Thu, Sep 22	Class cancelled for the S3D launch event
Tue, Sep 27	Survey design	slides • video
Thu, Sep 29	In-class activity: qualitative coding & thematic analysis	no slides • no video
Tue, Oct 4	Project proposal presentations	no slides • no video
Thu, Oct 6	Mixed-methods designs	slides • video
Tue, Oct 11	Experimental design (part I)	slides • video
Thu, Oct 13	Experimental design (part II)	slides • video
Tue, Oct 18	Fall break, no class
Thu, Oct 20	Fall break, no class
Tue, Oct 25	Experimental design (part III)	slides • video
Thu, Oct 27	Intro to regression modeling	slides • video
Tue, Nov 1	Diagnostics, factors, std coefficients	slides • video
Thu, Nov 3	Simpson’s paradox, exemplar papers, in-class activity	slides • video
Tue, Nov 8	Interrupted time series design	slides • video
Thu, Nov 10	Mixed-effects
Tue, Nov 15	Social network analysis (part I)
Thu, Nov 17	Social network analysis (part II)
Tue, Nov 22	Diff-in-diff + CausalImpact
Thu, Nov 24	Thanksgiving, no class
Tue, Nov 29	Research vs researcher
Thu, Dec 1	Agree to disagree
Tue, Dec 6	Final presentations (part I)
Thu, Dec 8	Final presentations (part II)