Course Description

In this course, we learn about different designs for collecting data and their implications for statistical inference. We cover two main topics: how to design surveys of populations in ways that give reliable estimates, and how to design studies in ways that allow for valid causal claims. With regard to surveys, we investigate the mathematical underpinnings of randomization as a tool for data collection. We focus on the benefits and pitfalls of deviating from purely randomized samples, including stratification, clustering, and convenience sampling. We learn how to design and analyze complicated surveys typically employed by government agencies. We also discuss special designs for hard to reach populations and issues of fairness and generalizability when using big data to train algorithms for predictive analytics. With regard to causal studies, we again discuss the central role of randomization as a tool for ensuring fair comparisons of treatments. We focus on the benefits and pitfalls of variations on randomized designs, including blocking and factorial designs. We discuss design for observational studies, focusing on methods like propensity score matching. Throughout we discuss a variety of genuine designs spanning applications in public policy, health, and the social and natural sciences.

Course Objectives

• To learn the connection between study design and validity of statistical inferences, including ways to analyze data from commonly used survey designs and causal study designs.
• To understand the trade offs (e.g., cost versus accuracy) in selecting different basic study designs.
• To learn to be critical of how data were collected when assessing claims from analytical methods.
• To gain skills useful for understanding and developing statistical methodology by deriving results from first principles and developing computer simulations.

Logistics

• Lecture Times and Location:  Mon. and Wed., 3:05 PM - 4:20 PM,  in 103 Gross Hall.
• Instructor:  Professor Jerry Reiter,   212 Old Chemistry Building,   668-5227,   jreiter@duke.edu
• Instructor's Office Hours:    Monday, 4:30 - 5:30 pm in Gross Hall 103, and Tuesday, 12:30 - 2:00 pm in 212 Old Chemistry Building.
  
• Teaching Assistants:   Maggie Nguyen, office hours: Sunday, 4:30 - 6:30 pm, 211a Old Chemistry. Sonya Zhang, office hours: Tuesday, 5:10 - 7:00 pm , 211a Old Chemistry.
  

Prerequisites

There are no required texts for this course. Instead, we will read book chapters and articles posted on the course website on Sakai. Two useful texts for reference include:

Lohr, S. L.  (2010),  Sampling: Design and Analysis, 2nd edition  Cengage Learning.  ISBN 0495105279.

Imbens, G. W. and Rubin, D. B. (2015), Causal Inference for Statistics, Social, and Behavioral Sciences: An Introduction, Cambridge University Press. ISBN 0521885884

Computing

We will use the statistical software package R for analyzing data.  It can be downloaded for free at http://www.r-project.org/.  Alternatively, R is available on the public computers on campus.

Calculator

Students don't need a calculator for this course.

Schedule of Topics

We will cover the topics in the table below.  We may spend different amounts of time on each topic than shown, depending on the interests of the participants in the course.

Graded work

Graded work for the course will consist of two term exams, home work assignments, and two projects.  Students' final grades will be determined as follows:

Assignments	30%
Midterm Exam 1	30%
Midterm Exam 2	30%
Projects	5% each

There are no make-ups for graded work except for medical or familial emergencies or for reasons approved by the instructor before the due date.  See the instructor in advance of relevant due dates to discuss possible alternatives.

Descriptions of graded work

Assignments:

Assignments are posted on the Statistics 322/522 course web site on Sakai.  Students turn in these assignments at the beginning of class on the due date.  Students are permitted to work with others on the assignments, but each person must write up and turn in their own answers.  The assignments are designed to build students' knowledge of the computational and the mathematical aspects of study design, and to analyze survey or causal inference data.

Exams:

The first midterm exam will cover mathematical and conceptual aspects of survey sampling. The second midterm exam will cover mathematical and conceptual aspects of causal inference.   

Projects:

One project will cover surveys and the other project will cover causal inferences. Students work in pairs on the projects. The projects involve designing or analyzing surveys/causal studies, applying the methods learned in the course.   

Students are expected to abide by Duke's Community Standard for all work for this course.  Violations of the Standard will result in a zero grade for the relevant assignment and will be reported to the Dean of Students for adjudication. Additionally, there may be penalties to the final grade for the course.   Ignorance of what constitutes academic dishonesty is not a justifiable excuse for violations.

For the exams, students are required to work alone.  For the assignments, students may work with others but each student must submit his or her own answers. For assignments involving computer programming, students can get advice from each other but are required to write their own code. For the projects, students are required to work in teams of two individuals. Teams are permitted to talk with others in the course, but each team must write up their own project report.