Course Syllabus

O’Neill School of Public and Environmental Affairs

SPEA V379 — Performance Measurement & Program Evaluation

2026_fall_syllabus_Wu.pdf

Instructor/Contact Information

Instructor: Jiaen Wu
Contact Information: jiaewu@iu.edu
Office Location:
Class Meeting: Monday and Wednesday 11:10 AM – 12:25 PM, Geological Sciences 1043
Office Hours: Monday, SPEA  412, 3-4:00 PM. Zoom Link: 

https://iu.zoom.us/j/83065500900

And by appointment. (If you would like to have a ZOOM meeting, please email me ahead of time.)

Teaching Assistant/Contact Information

Teaching Assistant: Elise Benware
Teaching Assistant Email Address: ebenware@iu.edu
Teaching Assistant Office Hours: I host a regular weekly office hour 1-2pm on Thursdays in-person at SPEA A208 or on Zoom. I am also happy to schedule meetings by appointment on Fridays.

Zoom link: https://iu.zoom.us/j/88698572596

Email Instruction: Please put the course number (V379) at the beginning of the subject line of all course-related emails. When sending an email to ask questions about course materials, get clarification about assignment instructions, etc., please include both me and the teaching assistant as recipients to ensure a timely response. If your question requires a lengthy/complicated response to address via email, we may instead ask you to come to office hours or arrange an appointment to discuss it. Please don’t hesitate to contact me with any questions or other feedback about the course.

Course Description

This course introduces performance measurement and program evaluation in public affairs. Students will use R and AI-assisted coding to analyze data, study randomized and quasi-experimental research designs, and evaluate when statistical relationships support causal conclusions.

Learning Outcomes

By the end of the course, you should be able to:

• Formulate clear evaluation questions and identify appropriate measures of program outcomes and performance.
• Clean, organize, visualize, and analyze data using R.
• Explain the logic and key assumptions of randomized experiments, regression, difference-in-differences, and regression discontinuity designs.
• Distinguish statistical association from causal evidence and identify threats such as omitted-variable bias, selection bias, reverse causality, and measurement error.
• Use generative AI responsibly to assist with coding while independently verifying code, output, and interpretation.
• Communicate data analysis, causal limitations, and policy implications clearly in written and oral formats.

O’Neill School expectations of civility and professional conduct

The O’Neill School takes matters of honesty and integrity seriously because O’Neill is the training ground for future leaders in government, civic organizations, health organizations, and other institutions charged with providing resources for the public, and for members of society who are vulnerable and who are lacking in power and status. Precisely because O’Neill graduates tend to rise to positions of power and responsibility, it is critical that the lessons of honesty and integrity are learned early.

O’Neill requires that all members of its community – students, faculty, and staff – treat others with an attitude of mutual respect both in the classroom and during all academic and nonacademic activities outside the classroom. A student is expected to show respect through behavior that promotes conditions in which all students can learn without interruption or distraction. These behaviors foster an appropriate atmosphere inside and outside the classroom:

• Students are expected to attend class regularly and to be prepared for class.
• Students must be punctual in their arrival to class and be present and attentive for the duration of the class. Eating, sleeping, doing work for another class, wandering in and out of the classroom, and packing up or leaving class early are not civil or professional behaviors.
• Students must abide by the course policy regarding use of electronic devices in the classroom.
• Students must responsibly participate in class activities and during team meetings.
• Students must address faculty members, other students, and others appropriately and with respect, whether in person, in writing, or in electronic communications.
• Students must show tolerance and respect for others.
• Students must not destroy or deface classroom property nor leave litter in the classroom.

These expectations are excerpted from the O’Neill School Honor Code which can be found under Student Ethics and Policies: https://oneill.indiana.edu/undergraduate/course-advising/advising/resources.html

Course Texts

Angrist, Joshua D., and Jörn-Steffen Pischke. Mastering 'Metrics: The path from cause to effect. Princeton University Press, 2014. (AP)

Gertler, Paul J., Sebastian Martinez, Patrick Premand, Laura B. Rawlings, and Christel M. J. Vermeersch. 2016. Impact Evaluation in Practice, Second edition. Washington, DC: Inter-American Development Bank and World Bank. (WB)
Link: https://www.worldbank.org/en/programs/sief-trust-fund/publication/impact-evaluation-in-practice (online available for free)

Additional required readings will be posted on Canvas.

I strongly recommend that you use the following for help with R:

Healy, Kieran. Data Visualization: A practical introduction. Princeton University Press, 2019.
Link: https://socviz.co (online available for free)

Computing & Course Software

We will often spend class time analyzing data. That means you should bring a laptop computer with you when required.

This course uses R and RStudio Desktop, both of which are free. R is the statistical software, while RStudio is the interface we will use to write and run R code. Please install R first and then install RStudio Desktop. You can follow this instruction: https://rstudio-education.github.io/hopr/starting.html

Install R

• Open a browser and go to: https://cloud.r-project.org/
• Click the download link for whatever operating system you use on your computer. (There is separate link for the macOS if you using an Apple computer, for the Windows OS if you have a Microsoft Windows based computer, and for Linux if you are running Linux).
• Once the file downloads, double click to open it and follow the installation instructions.

Install RStudio

• Open a browser and go to https://posit.co/downloads
• Click the download button for the RStudio desktop. (Choose the version recommended for your computer based on the OS.)
• Once the file downloads, double click it and follow the instructions.

Course Grades and Requirements

Data Analysis Assignments (30% of Course Grade)
There will be four data analysis assignments. Students will use R to clean, visualize, and analyze data and to interpret evidence from randomized and quasi-experimental designs. Class time will be provided for guided practice, but assignments must be submitted individually. Students may discuss their work with classmates, but each student must write and submit their own responses and acknowledge any collaborators.

Midterm Exam (25% of Course Grade)
There will be one midterm exam covering material from the beginning of the course through randomized experiments. The exam will consist primarily of multiple-choice questions and short interpretation questions.

Final Project Portfolio (35% of Course Grade)
Working normally in teams of three, students will develop an evaluation question, analyze data in R, present their findings, and explain why observed relationships may not support causal conclusions. The portfolio consists of a Research Proposal (30% of the project grade) and a Final Research Project (70% of the project grade). Rubrics and detailed instructions will be posted on Canvas.

Participation and In-class Activities (10% of Course Grade)
Over the course of the semester, there will be a number of short quizzes or discussion questions in Canvas or in class about the reading assignments or class materials. These will be due before class or during class. There is no makeup for these assignments. The two lowest grades will be dropped.

Course Policies

Attendance
I do not take attendance. However, students must be aware that I do not record lectures and will not make notes available outside of class. There are no makeups for the short assignments, which can be given in class.

Make-up Exams
Make-up exams will not be given.

Late Assignments
Extensions can be granted in extreme circumstances if requested at least 2 days prior to the due date. Late assignments will be accepted with a 10% deduction for each day that an assignment is late.

Technology Use
Because we will be using R for our data assignments and will be working on assignments together in class, please bring your laptops to class. You are allowed to use laptops for class-related matters, such as taking notes. However, use of cell phones is not allowed except during designated times or breaks. You may leave your phone turned on in your pocket or backpack so that you can still receive emergency alerts. If you really need to use your phone during class, please step outside first. The basic reasoning for this policy is that having phones out (which inevitably leads to some people checking email, using social media, etc. even if they have the best intentions of staying focused on class) creates a distraction not only for those using them but also those around them.

Late Withdrawal
NOTE: The auto-W deadline is NO LONGER THE LAST DAY OF CLASS (as it was during and post-Covid). After the auto-W deadline, withdrawal will be significantly limited and you will need permission to withdraw and must meet requirements established by O’Neill.

Academic Integrity
You are expected to adhere to the Indiana University guidelines regarding academic standards and behavior. I expect that all work you turn in will be your own. If you synthesize or rely on other people’s ideas or words to make your argument, you must cite them. Please take a moment to review your responsibilities here: https://studentcode.iu.edu/.

Use of AI (such as ChatGPT)
Generative AI may be used to explain error messages and assist with writing, cleaning, visualizing, and troubleshooting R code. Students remain responsible for running, understanding, and verifying all code and output.

AI may not replace your own research question, hypothesis, causal interpretation, discussion of limitations, or conclusions. When AI is used, include a brief disclosure naming the tool, its purpose, and how the output was verified. Do not upload protected course materials or restricted data to public AI systems.

WARNING: Submitting any questions, assignments, notes, slides, or other instructor-produced course materials into Generative AI without permission is a violation of the faculty member’s exclusive rights and will be reported to the Dean of Students as academic misconduct.

*Plagiarism: Plagiarism is defined as presenting someone else’s work, including the work of other students, as one’s own. Any ideas or materials taken from another source for either written or oral use must be fully acknowledged, unless the information is common knowledge. https://studentcode.iu.edu/responsibilities/academic-misconduct.html. (emphasis added)

Course Materials—Instructor’s Exclusive Rights.
The faculty member teaching this course holds the exclusive right to distribute, modify, post, and reproduce course materials, including all written materials, study guides, lectures, assignments, exercises, and exams. While you are permitted to take notes on the online materials and lectures posted for this course for your personal use, you are not permitted to re-post in another forum, upload to AI, distribute, reproduce content from this course, or sell the instructor’s course materials (including notes and study guides) without the express written permission of the faculty member.

Violations of this policy will be reported to the Dean of Students as academic misconduct (violation of course rules). Sanctions for academic misconduct may include a failing grade on the assignment for which the notes/study guides are being sold, a reduction in your final course grade, or a failing grade in the course, among other possibilities.

Additionally, you should know that selling a faculty member’s course using IU email, or via Canvas may also constitute a violation of IU information technology and IU intellectual property policies; additional consequences may result.

Key Assignment and Project Dates

• Data Assignment 1 due September 27
• Data Assignment 2 due October 11
• Research Proposal due October 25
• Data Assignment 3 due November 15
• Data Assignment 4 due November 29
• Final Project presentations December 2, 7, and 9; written project due December 18

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“The above schedule is subject to limited change in the event of extenuating circumstances.”
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Schedule of Class Topics

(*Tentative and subject to change with advance notice from the instructor)

Week	Class	Date	Topic	Reading
Week1	1	August 24	Course Introduction	None
	2	August 26	What Is Program Evaluation?	WB, Ch. 1
Week2	3	August 31	From Policy Questions to Evidence	WB, Ch. 2
	4	September 2	R/RStudio Installation Bring Laptops	No reading
Week3		September 7	No class, Labor Day	No readings
	5	September 9	Data Literacy: Variables, Coding, Missing Data, and Outcomes	Readings will be posted on Canvas
Week4	6	September 14	Introduction to R I: Importing and Exploring Data Bring Laptops	Recommended: Healy, Ch. 1-2
	7	September 16	Introduction to R II: Cleaning and Visualization Bring Laptops	Recommended: Healy, Ch. 1-2
Week5	8	September 21	Income and Poverty Data Lab	Institute for Research on Poverty at the University of Wisconsin. Poverty FAQs. How Is Poverty Measured? link Who Is Poor? link What are Poverty Thresholds and Poverty Guidelines? link What is the Consumer Price Index? link
	9	September 23	Statistics Review	Optional reading will be posted on Canvas
Week6	10	September 28	Potential Outcomes and Causal Inference Deadline for Assignment 1 (September 27)	AP, Introduction WB, Ch. 3
	11	September 30	Randomized Controlled Trials	WB, Ch. 4 AP, Ch. 1
Week7	12	October 5	Applied Randomized Experiments in Public Affairs I	Readings will be posted on Canvas
	13	October 7	Applied Randomized Experiments in Public Affairs II	Readings will be posted on Canvas
Week8	14	October 12	Midterm Review Deadline for Assignment 2 (October 11)	No readings
	15	October 14	Midterm Exam	No readings
Week9	16	October 19	Final Project Workshop I: Research Question and Data	Project instructions will be posted on Canvas
	17	October 21	Regression I	AP, Ch. 2
Week10	18	October 26	Regression II and AI-Assisted Coding Lab Bring Laptops Deadline for Research Proposal (October 25)	Readings will be posted on Canvas
	19	October 28	Why Regression Is Not Necessarily Causal: Bias and Matching Intuition	AP, Ch. 2 Additional reading will be posted on Canvas
Week11	20	November 2	Difference-in-Differences I	WB, Ch. 7 AP, Ch. 5
	21	November 4	Difference-in-Differences II and R Lab Bring Laptops	Readings will be posted on Canvas
Week12	22	November 9	Regression Discontinuity Design I	WB, Ch. 6 AP, Ch. 4
	23	November 11	Regression Discontinuity Design II	WB, Ch. 6 AP, Ch. 4
Week13	24	November 16	Applied Regression Discontinuity Lab Bring Laptops Deadline for Assignment 3 (November 15)	Readings will be posted on Canvas
	25	November 18	Final Project Workshop II: Preliminary Analysis and Causal Interpretation Audit	Project instructions will be posted on Canvas
Week14		November 23	No class, Thanksgiving Recess	No readings
		November 25	No class, Thanksgiving Recess	No readings
Week15	26	November 30	Final Project Workshop III: Results, Presentation, AI Verification, and Research Ethics Deadline for Assignment 4 (November 29)	Project instructions will be posted on Canvas
	27	December 2	Final Project Presentations I	No readings
Week16	28	December 7	Final Project Presentations II	No readings
	29	December 9	Final Project Presentations III	No readings
Week17		December 14-18	Final Exam Week	There is no final exam in this course.

Diversity statement

In my classroom, I value diversity as an essential component of education and training. I am committed to fostering a learning environment that is inclusive, respectful, and equitable for all students, regardless of their backgrounds or experiences.

As a class, we recognize that diversity encompasses many dimensions, including but not limited to race, ethnicity, gender, sexuality, age, religion, socio-economic status, ability, and cultural background. We believe that these differences can enrich our learning experience and prepare us to be effective leaders in diverse and multicultural communities.

The SPEA V-379 course curriculum is designed to reflect this commitment to diversity, with readings, discussions, and assignments that explore a range of perspectives and experiences. I encourage my students to critically engage with these diverse viewpoints, to challenge their own assumptions and biases, and to actively seek out and learn from voices that differ from their own.

As part of our commitment to diversity, I also recognize the importance of creating a safe and respectful learning environment that promotes open communication and dialogue. I encourage all students to share their thoughts and experiences, and as a class, we pledge to listen and respect each other's views, even if they differ from our own.

Additional information

Counseling and Psychological Services
For information about services offered to students by CAPS: http://healthcenter.indiana.edu/counseling/index.shtml

TimelyCare
Indiana students have free, 24/7 access to virtual mental health care services with TimelyCare. Students do not need insurance to access TimelyCare services. https://www.iu.edu/mental-health/find-resources/timely-care.html#0

Religious Accommodation Requests
The "Request for Accommodation for Religious Observances" form must be submitted by the student to the professor no later than two weeks prior to the anticipated absence. A separate form must be submitted for each absence and for each course. Faculty do not have to consider accommodations for the purpose of allowing students to travel away from Bloomington for religious observance. Information about the policy is found here: https://vpfaa.indiana.edu/resources/religious-observances-information.html.

Accessible Educational Services (formerly Disability Services for Students)
Securing accommodations for a student with disabilities is a responsibility shared by the student, the instructor, and the AES Office. For information about support services or accommodations available to students with disabilities, and for the procedures to be followed by students and instructors: https://studentlife.indiana.edu/student-support/iub-aes/index.html

Sexual Harassment
As your instructor, one of my responsibilities is to help create a safe learning environment on our campus. Title IX and our own Sexual Misconduct policy prohibit sexual misconduct. If you have experienced sexual misconduct, or know someone who has, the University can help.

If you are seeking help and would like to talk to someone confidentially, you can make an appointment with:

• The Sexual Assault Crisis Service (SACS) at 812-855-8900
• Counseling and Psychological Services (CAPS) at 812-855-5711
• Confidential Victim Advocates (CVA) at 812-856-2469
• IU Health Center at 812-855-4011

For more information about available resources: http://stopsexualviolence.iu.edu/help/index.html. It is also important to know that federal regulations and University policy require me to promptly convey any information about potential sexual misconduct known to me to our campus’ Deputy Title IX Coordinator or IU’s Title IX Coordinator. In that event, they will work with a small number of others on campus to ensure that appropriate measures are taken and resources are made available to the student who may have been harmed. Protecting a student’s privacy is of utmost concern, and all involved will only share information with those that need to know to ensure the University can respond and assist. I encourage you to visit http://stopsexualviolence.iu.edu/help/index.html to learn more.

IU Culture Centers: https://studentlife.indiana.edu/communities/culture-centers/index.html