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日期：2024-01-14 07:21

SIPA U6500 Quantitative Analysis for International and Public

Course Overview and Objectives

This course introduces students to the fundamentals of statistical analysis. We will examine the principles and basic

methods for analyzing quantitative data, with a focus on applications to problems in public policy, management, and

the social sciences. We will begin with simple statistical techniques for describing and summarizing data and build

toward the use of more sophisticated techniques for drawing inferences from data and making predictions about the

social world.

The course will assume that students have little mathematical background beyond high school algebra. The for-

mal mathematical foundation of statistics is downplayed; students who expect to make extensive and customized use

of advanced statistical methods may be better served by a different course. This course also offers less practice in

writing research papers using quantitative analysis than some courses (e.g., Political Science 4910). Most SIPA stu-

dents, however, should benefit from our emphasis on generating and interpreting statistical results in many different

practical contexts.

Students will be trained on STATA, which is supported in the SIPA computer lab. This powerful statistical

package is frequently used to manage and analyze quantitative data in many organizational/institutional contexts. A

practical mastery of a major statistical package will be an important proficiency for many of you down the road. You

can obtain more information about your lab sticker at the SIPA lab, which is located on the 5th floor of IAB.

Requirements and Recommendations

Students are required to attend class. Lectures will sometimes cover matters related directly to the homework

assignments that are not covered fully in the assigned readings. Students are also required to actively participate

in the learning process - paying attention, taking notes, asking question, solving in-class exercises, etc. The use of

laptops during the class is strongly discouraged.

Students are required to review and obtain any relevant material (e.g., weekly handouts) in advance of each

class by going to Courseworks at https://courseworks.columbia.edu. This site will include all course

materials including: the syllabus, weekly class handouts, class summaries, homework assignments, answer keys for

assignments, policy papers discussed in class, midterm and final exam review sheets, and information on data as

well as downloadable datasets.

Students are required to come to class having already completed the assigned readings for that class. The purpose

of this requirement is to ensure that lectures focus on learning how to bring statistical concepts and methods to life

in an applied context. Class will be conducted in a manner that assumes this advance preparation has been done.

Students are recommended to download, print, and bring to class the weekly class handout. The weekly class

handout is integrated with the lecture and is meant to serve two purposes. First, it allows students to take notes

during the class and organize these notes within the flow of the lecture. Second, it provides a preview of the topics to

be covered in class. At the minimum, students must absolutely read the handout slides labeled “Read Before Class”

and attempt or think about the “In-Class Exercises” before attending the lecture.

Students are required to attend one weekly lab session in addition to the regular lecture. These labs will be

important supplements to each lecture, where concepts and methods will be reviewed and students will receive

direction and support as they learn STATA. In certain weeks, some concepts we did not have time to cover in class

will be taught in the labs.

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Grading

The three components to the final course grade will include weekly homework (problem sets and quizzes) (30%),

a midterm exam, and a final exam. The exam with a larger score will get a 40% weight and the other exam a

30% weight. In “borderline” cases, the quality of your class attendance and participation will be considered in

determining your final grade.

Problem Sets

The role of the homework is both to solidify concepts covered in the previous lectures, by providing students with

opportunities to practice their applications, as well as to prepare students for the concepts to be covered in future

lectures. As such, the problem sets will cover both the topics covered in the previous lectures and the readings for

the upcoming lecture.

Problem sets will be assigned at least a week in advance of their due dates. Late problem sets will not be

accepted for credit. You are encouraged to be actively engaged in the completion of every problem set since hands-

on work (computer-based or otherwise) is essential to fully understanding the material presented in this course.

Problem sets may be done individually or in groups of up to three students. Groups may be formed or dissolved as

students see fit throughout the semester.

Problem sets will be turned in as hard copy at the beginning of a lecture on Monday. Only one hard copy of the

problem set must be turned in by students in a group.

Quizzes

Throughout the semester there will be opportunities to earn extra credit points through optional quizzes. The quizzes

are due on Mondays at 10am. The points earned on quizzes will be counted toward the score on problem sets.

Exams

The Midterm Exam will take place on Friday, March 3rd, at a time to be determined later. The Final Exam will take

place on Monday, May 8th, at a time to be determined later.

Students must take both the midterm exam and the final exam. Failure to do so may result in failing the course.

We will do our best to provide reasonable accommodations to conflicts with the exam, but that is not guaranteed in

all cases.

STATA Use

SIPAIT is pleased to announce that it has signed a one year Stata BE 17 site license for use by SIPA students only.

You can find more information here: https://www.sipa.columbia.edu/information-technology/

software-download/stata-students .

SIPA Computer Lab Policy 2022 - 2023

The SIPA computer lab accommodates a maximum of 44 students per session. All students taking classes or at-

tending recitations in the computer lab must adhere to this limit. Additional students will not be allowed to share

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computer stations, sit on the floor, or sit in the back of the room. Instructors, TAs, and computer lab staff will enforce

this policy.

All SIPA students must have a valid SIPA Lab ID to access the SIPA lab resources. Validating the Columbia

University ID can be done in room 510 IAB each semester. All registered SIPA students are billed automatically a

fee each semester during the academic year based on their program.

Non-SIPA students are issued a guest ID for access to attend a class in the SIPA instructional lab. Guest IDs are

issued after information is received from the Office of Student Affairs in the second week of classes.

Non-SIPA students who wish to use the SIPA computer lab outside of regular class/recitation time must

pay $180 per semester (payable by check or cash in 510 IAB). Non-SIPA students who choose not to pay this fee

should consult their course instructor and the IT office at their own school about any special software required for

the course. SIPA IT is not equipped to provide technical support to non-SIPA students who have not paid the $180

per semester fee.

For more information: https://www.sipa.columbia.edu/information-technology/it-policies-procedures/

computing-guidelines-sipa

Academic Integrity Statement

The School of International & Public Affairs does not tolerate cheating and/or plagiarism in any form. Those students

who violate the Code of Academic & Professional Conduct will be subject to the Dean’s Disciplinary Procedures.

Please familiarize yourself with the proper methods of citation and attribution. The School provides some useful

resources online; we strongly encourage you to familiarize yourself with these various styles before conducting your

research.

You are requested to view the Code of Academic & Professional Conduct here: http://new.sipa.columbia.

edu/code-of-academic-and-professional-conduct

Violations of the Code of Academic & Professional Conduct will be reported to the Associate Dean for Student

Affairs.

Readings

The required and recommended textbooks may be purchased at Book Culture (536 West 112th Street).

Required Texts:

D. Moore, G. McCabe, and B. Craig “Introduction to the Practice of Statistics” 9th edition (2017), W. H. Freeman

and Company

C. Lewis-Beck and M. Lewis-Beck, “Applied Regression” 2nd edition (2015) SAGE

Recommended Texts:

Lawrence C. Hamilton “Statistics with STATA: Version 12”

X. Wang “Performance Analysis for Public and Nonprofit Organizations”

E. Berman and X. Wang “Essential Statistics for Public Managers and Policy Analysts”

Supplemental Texts:

T. Wonnacott and R. Wonnacott “Introductory Statistics” 5th edition (1990)

C. Achen “Interpreting and Using Regression” (1982)

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Course Outline

Session 1: Orientation and Research Design

Monday, January 23rd

Orientation

– Introduction of course, teaching style, expectations

– Discussion of the syllabus

– Roadmap of the material

Research design

– Causality and Observational Studies

– Two-group randomized comparative experiment

– Other experiment designs (matched pairs, blocked design)

Readings:

Syllabus and Syllabus FAQ

Why Study Quantitative Analysis?

M&M Chapter 2.7 - The Question of Causation

M&M Chapter 3.1 - Sources of Data

M&M Chapter 3.2 - Design of Experiments

Session 2: Sampling and Exploratory Data Analysis

Monday, January 30th

Sampling

– Representative samples

– Simple random sample

– Introduction to statistical inference

Classification of variables

Graphical and numerical summaries of one variable

– Bar Charts, Pie Charts, Histograms

– Measures of central tendency (mean, median, mode)

– Measures of dispersion (Range, Quartiles, Boxplots, Variance, Standard Deviation)

Association between two quantitative variables

– Scatterplot and correlation coefficient

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Readings:

M&M Chapter 1.1 - Data

M&M Chapter 1.2 - Displaying Distributions with Graphs M&M Chapter 1.3 - Displaying Distributions with Numbers

M&M Chapter 2.1 - Relationships

M&M Chapter 2.2 - Scatterplots

M&M Chapter 2.3 - Correlations

M&M Chapter 3.3 - Sampling Design

Focus before class: M&M pages 9-11, 14-20, 28-38, 86, 88-89, 101, 189, 191

Session 3: Density curves, Normal density, and Introduction to Probability

Monday, February 6th

Density curves

– Population parameters: mean, standard deviation, median, skewness

Normal density curves

– Properties of normal density (shape, rule of 68-95-99.7)

– Standard normal and Z-tables

– Other Normal distributions

Introduction to probability

Readings:

M&M Chapter 1.4 - Density Curves and Normal Distributions

M&M Chapter 4.1 - Randomness

Focus before class: M&M pages 54-56, 59-63, 216-218

Session 4: Probability and Random Variables

Monday, February 13th

Probability

– Probability models

– Rules for probability

– Conditional probability

Random variables

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– Mean and variance of random variables

– Sums and differences of random variables

Readings:

M&M Chapter 4.1 - Randomness

M&M Chapter 4.2 - Probability Models

M&M Chapter 4.3 - Random Variables

M&M Chapter 4.4 - Means and Variances of Random Variables

Focus before class: M&M pages 221-225, 228-229, 232, 236, 241, 246-248, 254, 256-258

Session 5: Sampling Distributions and Statistical Inference

Monday, February 20th

Introduction to sampling distributions

– Statistics

– Sample mean as random variable

– The sampling distribution of the sample mean

Statistical Inference

– Confidence intervals

Readings:

M&M Chapter 5.1 - Toward Statistical Inference M&M Chapter 5.2 - The Sampling Distribution of a Sample Mean

M&M Chapter 6.1 - Estimating with Confidence

Focus before class: M&M pages 297-300, 307, 346-347, 349

Session 6: Hypothesis Testing

Monday, February 27th

Hypothesis Testing

– One-tailed test of significance

– Two-tailed test of significance

Readings: M&M Chapter 6.2 - Tests of Significance

Focus before class: M&M pages 363-366, 371-372, 375, 379

Session 7: The t-distribution and Comparing two population means

Monday, March 6th

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? Difference in differences as a tool to answer policy questions using observational data

? Statistical inference when the standard deviation is not known

– The t-distribution

– Confidence intervals and hypothesis testing using the t-distribution

? Comparing the means of two populations

Readings:

? M&M Chapter 7.1 - Inference for the Mean of a Population

? M&M Chapter 7.2 - Comparing Two Means

Focus before class: M&M pages 408-413, 433-437, 440

Session 8: Ordinary Least Squares Regressions

Monday, March 20th

? Comparing the means of two populations with the same standard deviation

? Ordinary Least Squares Regression

– Formal statistical model

– OLS regression properties

? Comparing the means of two populations

Readings:

? M&M Chapter 2.4 - Least Square Regressions

? M&M Chapter 10.1 - Simple Linear Regression

Focus before class: M&M pages 107-112, 115, 556-560, 567

Session 9: Statistical Inference in Regressions

Monday, March 21st

? Properties of regression coefficients

? Statistical inference in regressions

? Assumptions of OLS models

– Residual plots

– Normal quantile plots

Readings:

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? M&M Chapter 1.4 - Density Curves and Normal Distribution

? M&M Chapter 11.1 - Inference for Multiple Regressions

Focus before class: M&M pages 66-69, 567-569, 608-613

Session 10: Multivariate Regressions

Monday, April 3rd

? Multivariate regression

? Interaction terms

? Difference-in-differences

Readings: Handout

Focus before class: Handout

Session 11: Analysis of Variation

Monday, April 10th

Dummy variables

Analysis of Variation

– Goodness of fit

– R squared and adjusted R squared

– F-test

Readings:

M&M Chapter 11.1 - Inference for Multiple Regressions

M&M Chapter 12.1 - Inference for One-Way Analysis of Variance

Focus before class: M&M pages 613-616, 651-653, 656, 660-662

Session 12: Predictions in regression

Monday, April 17th

Prediction in regression

– Predicted values

– Confidence intervals for the mean predicted values

– Forecast intervals for predicted values

Categorical response variables

– Binomial distribution

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Readings: M&M Chapter 10.1 - Simple Linear Regression

Focus before class: M&M pages 570-573

Session 13: Sampling distribution and Inference for one proportion

Monday, April 24th

Sampling distribution for proportions and counts

Inference for a population proportion

Readings:

M&M Chapters 5.3 - Sampling Distributions for Counts and Proportions

M&M Chapters 8.1 - Inference for a Single Proportion

Focus before class: M&M pages 312-314, 317-322, 332-333, 486, 491, 500

Session 14: Comparison of Two Population Proportions

Monday, May 1st

Inference for the difference between two population proportion

Linear probability model regressions

Readings: M&M Chapter 8.2 - Comparing Two Proportions

Focus before class: M&M pages 506-507, 511-513