Statistics for Public Policy and Administration

SPP 608 • 3 Credits • Spring 2020 • Bartlett 61

Tuesday/Thursday, 8:30-9:45 • Tuesday Lab 10-11:15

Michael Ash, Professor of Economics & Public Policy

School of Public Policy • University of Massachusetts Amherst

Instructor and course information

Michael Ash 306 Crotty Hall (412 North Pleasant Street)

Monday/Wednesday 9:30–11 AM

Email mash@umass.edu Telephone 413-545-6329

This syllabus: http://courses.umass.edu/pubp608 

Hard copy: http://courses.umass.edu/pubp608/syllabus.pdf 

Readings & Software

REQUIRED. Garner, Roberta. 2010. The Joy of Stats, 2nd Edition. University of Toronto Press. https://utorontopress.com/us/the-joy-of-stats-3 (around $40 new, around $16 used, ISBN 9781442601888).

REQUIRED. Eileen Tipoe and Ralf Becker. Doing Economics: Empirical Projects. 2019. The CORE Project. https://www.core-econ.org/doing-economics/ (free and open source; please register for access now).

REQUIRED. Access to Google Sheets spreadsheet software.

RECOMMENDED. Freedman, David, Robert Pisani, and Roger Purves. Statistics. Norton. Any edition will do. (used 3rd edition should be $10 or less, used 4th edition somewhat more)

OPTIONAL additional math refresher if needed. Bennett, J.O., Briggs, W.L. and Badalamenti, A., 2008. Using and understanding mathematics: A quantitative reasoning approach. Reading, MA: Pearson Addison Wesley.  (Buy used for less than $10 -- any edition ok.)

Objectives

Statistics and related quantitative empirical methods constitute a key device by which public decision-makers and policy advocates understand reality, communicate their understanding, and campaign to change it.

This course will introduce statistical methods and modelling for policy analysis and public administration. Students will learn how to apply statistical methods to interesting questions in public affairs. They will also develop the capacity to identify and critique good and bad statistical practice.

Content and Methods

The effective use of statistical methods in policy and administration demands persuasive writing to introduce quantitative analysis and to present quantitative results. Students will learn how to operationalize variables, carry out analysis, and write up results in readable prose that will convince educated lay readers, decision-makers, and social scientists.

We will apply the standard methods of descriptive and inferential statistics. Students will learn to describe and test the distribution of variables with both quantitative and graphical methods. They will also learn to model and test association between variables. The course emphasizes research design as the basis for plausible claims of causal relationships between variables. By the end of the course, students should understand the conditions that make a plausible case for an association to be considered causal. Comfort with quantitative methods also implies the capacity to critique their misuse. Students will learn to criticize both poorly elaborated models and overstated claims of causality based on statistical association.

The course includes an extensive lab component. Students will gain comfort with statistical methods in both theory and practice. The course will also introduce students to the use of graphics as an effective tool for communicating quantitative information. The overarching course goals are (1) to have students understand the statistical concepts and theories and (2) to prepare students to apply statistical analysis to policy questions in future SPP courses and in real world applications. More specifically, the policy-oriented practical skills that you’ll learn in this course include:

The main software for the course will be Google Sheets spreadsheet software. Datasets will be distributed via Google Sheets, and in-class, lab, and homework assignments will depend on access to Google Sheets. The aim is have all students develop comfort with this widely available tool. The skills are directly transferable to other spreadsheet software, including  OpenOffice Calc, which is free and open source, and Microsoft Excel. If time permits and there is student interest, the course may introduce the R statistical language and environment, which is free and open source.

Resources, Assignments, and Grading

Teaching Assistant

Matt Brown, matbbrown@umass.edu, a second-year student in the MPPA program who

excelled in this course, will be the teaching assistant. The TA holds weekly office hours

(TBA) to discuss course material and assignments and conducts a scheduled weekly math

review session that complements course material (TBA). The times will be determined at the

first class meeting. You may also make an appointment to meet with the TA at a mutually

agreeable time.

Prerequisites

For students enrolled in the MPP/A program, there is no prerequisite for this course. To review math skills, please read carefully the Math Refresher in The Joy of Stats (Garner, p. 289, et seq.). You may also want to browse chapters 2,3,4, and 5 of Jeffrey O Bennett and William L Briggs. If the material looks familiar, then you’ll have no problem with the mathematics in this course. For example, you should know in advance how to graph y against x and how to determine the slope of the line for an equation like 5x + 6y = 240; how to use unit analysis, e.g., 8 hours × $15/hour = $120; and how to compute percent changes, e.g., 1,600 gallons decreasing to 1,400 gallons is a −12.5% change. Other skills are included in The Joy of Stats Math Refresher and will be covered in class or by the teaching assistant.

Assignments, Assessments, and Evaluation

Participation

Preparation, attendance, and participation are required. Do the reading and prepare to think and engage. Please bring a stack of 3”×5” index cards to every class session. If you miss a class for any reason, it is your responsibility to seek out notes, assignments, handouts, etc., from your classmates or from me. If you miss a class because of illness or another good reason, I will be happy to sit down with you and go over what you missed.

Assignments

There will be several written assignments (problem sets) of the course of the semester. These will mostly be based on Doing Economics: Empirical Projects (Eileen Tipoe and Ralf Becker). Answers should be written up “the right amount,” with enough diagram, computation, and text to answer the question, but they need be neither typed nor presented as a polished memo. Answers must be submitted on paper.

To get full credit for the assignment, you must answer all the questions and turn the assignment in on time (start of class on the due date). If you turn an assignment late, you will partial partial credit for it if it is turned in within one week of the due date. I encourage you to work together with classmates and the TA on the questions. You are responsible for understanding the material in all of the questions by the end of the semester. We can also dedicate class time to discussing the answers.

Midterm and Final Exam

The exams are cumulative and will cover all course material through the exam date.

Grading

You will earn a grade based on class participation, homework, and exams. A

breakdown of the grade follows:

Course Component

Points

Participation

30

Assignments

30

Midterm Exam

20

Final Exam

20

Total

100

Grades will be assigned according to the following schedule:

Cut-off

93

90

87

83

80

77

73

70

60

Grade

A

A‒

B+

B

B‒

C+

C

C‒

D

F

Please note that your grade depends on a fixed standard of comprehension and expression and not on comparisons to other students. Therefore, you should feel comfortable discussing and sharing your notes, ideas, and writing with your fellow students.

Academic Honesty

The integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research. Academic honesty is required of all students at the University of Massachusetts Amherst. Academic dishonesty is prohibited in all programs of the University (see https://www.umass.edu/honesty/). Academic dishonesty includes but is not limited to: cheating, fabrication, plagiarism, and facilitating dishonesty. Appropriate sanctions may be imposed on any student who has committed an act of academic dishonesty. Instructors will take reasonable steps to address academic misconduct. Any person who has reason to believe that a student has committed academic dishonesty should bring such information to the attention of the appropriate course instructor as soon as possible. Students are expected to be familiar with this policy and the commonly accepted standards of academic integrity. Ignorance of such standards is not normally sufficient evidence of lack of intent. The University’s policy on academic honesty will be rigorously enforced, and acts of cheating, plagiarism, or other forms of academic dishonesty may result in a failing grade for the entire course.

Welcoming and respectful classroom: climate and electronics.

The instructors of SPP 608 are committed to providing a welcoming, inclusive, respectful, and safe classroom for all participants. Our classroom promotes respect and sensitivity concerning differences of class, religion, politics, parity, sexual orientation, gender, gender variation, or nationality and people affected by racism. Please make a commitment to respect, courtesy, and sensitivity and to work through any problems if they arise. Feel free to let your classmates, teaching assistant, and me know your preferred mode of address (name, pronoun, etc.).

Statistics is an intense, political subject that necessarily involves categorization and summaries. The course will emphasize scholarly disagreement and the significant consequences of alternative choices. All views will be respected, even in disagreement. The expectation and the rule are that we will engage in respectful, civil dialog throughout. If you have any questions or concerns, I urge you to contact me.

The use of electronics must be strictly limited to participation in classroom activities. Other uses are distracting and disrespectful. Please suppress all notifications.

Accommodation Policy

The University of Massachusetts Amherst is committed to providing an equal educational opportunity for all students. If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), Learning Disabilities Support Services (LDSS), or Psychological Disabilities Services (PDS), you may be eligible for reasonable academic accommodations to help you succeed in this course. If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements. If you have a disability or impairment that might affect participation, I can support you better if you let me know.


Topics

The Joy of Stats

Chapter One: Basic Concepts

Part I: Variables

Part II: Thinking about Procedures

Chapter Two: Describing Distributions

Part I: Frequency Distributions

Household income sample

Tipoe and Becker, “11 Measuring willingness to pay for climate change mitigation”

Part II: Summary Measures

Part III: Graphing and Visual Displays of Distributions

Freedman et al. “The Histogram” (Chapter 3)

Part IV: Standardized Scores or Z-scores

Freedman et al. “The Normal Approximation for Data” (Chapter 5)

Chapter Three: Statistical Inference

Part I: Thinking about Statistical Inference

Freedman, et al. “Measurement Error” (Chapter 6)

Tipoe and Becker, “6 Measuring management practices”

Part II: Doing Statistical Inference

Freedman, et al. “The Law of Averages” (Chapter 16) and “The Expected Value and Standard Error (Chapter 17)

Tipoe and Becker, “3 Measuring the effect of a sugar tax”

Chapter Four: Relationships Among Variables

Part I: An Overview - Thinking About Variable Relationships

Part II: Data Analysis Techniques

Section One: Regression Analysis

Freedman, et al. “Correlation” (Chapter 8), “Regression” (Chapter 10)

Tipoe and Becker, “9 Credit-excluded households in a developing country”

Section Two: Crosstabs

Section Three: ANOVA

Section Four: Logistic Regression

Selecting Data Analysis Techniques

Causality & Research Design: Counterfactuals; Observation; Experiments; Confounders; Mediators

Dowd and Town, 2002, “Does X Really Cause Y” http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.169.9944  

Surveys and Data

Freedman et al. “Sample Surveys” (Chapter 19)  and “Measuring Employment and Unemployment” (Chapter 22)

Measuring Inequality

Income Inequality: Are the rich getting richer and the poor getting poorer? US Census Bureau, https://www.census.gov/topics/income-poverty/income-inequality.html 

Tipoe and Becker, “5 Measuring inequality: Lorenz curves and Gini coefficients”

Measuring Prices and Inflation

Introduction to U.S. Economy: Inflation. 2019. Congressional Research Service, 2019, https://crsreports.congress.gov/product/pdf/IF/IF10477 

Overview of BLS Statistics on Inflation and Prices, Bureau of Labor Statistics, https://www.bls.gov/bls/inflation.htm 

Measuring Poverty.

Gordon M. Fisher, 1997, The Development and History of the U.S. Poverty Thresholds — A Brief Overview. Department of Health and Human Services https://aspe.hhs.gov/history-poverty-thresholds.

Probability

Freedman et al. “What are the Chances” (Chapter 13) and “More about Chance” (Chapter 14)

Bayes’s Theorem