PL208 Statistical Analysis for Political Science

1 Details

Instructor: Bogdan G. Popescu
Hours: MW 08:30-09:15AM
Total Hours of Contact: 2:30 per week
Room: G.1.1-Guarini Campus, First Floor, Room 1

Credits: 3
Prerequisites: None
Office Hours: TBA

2 Course Description

This is an applied course on statistical methods commonly used in social science research (including political science and sociology) and provides the necessary foundation to conduct your analysis. Students will learn how to use a widely-used statistical package in data analytics - R and prepare HTML Quarto documents. More generally, students will learn how to read statistics, use statistical tests, and draw conclusions based on them. Students will also learn to carry out statistical tests using statistical packages and interpret results based on their analyses. About 50% of the class will be dedicated to coding.

3 Summary of Course Content

The course will convene twice a week. There will be lectures covering statistical concepts applied to political science and practical lab sessions where students will use statistical software to conduct the tests. There will be assignments that have to be completed every week. The grades will be 30% midterm, 30% final exam, and 35% weekly assignments, and 5% how much you help your colleagues during the assignments.

4 Learning Outcomes

Upon successful completion of this course the students will be able to:

  • Use statistical core terminology accurately.
  • Organize data using both numerical and graphical methods.
  • Use measures of central tendency and variability to summarize a data set.
  • Carry out tests of hypothesis about population parameters.

5 Assessment methods

  • Five problem sets: 35% of the final grade (7% each)
  • Mid term exam: 30% of the final grade
  • Final exam: 30% of the final grade

You will be graded on five problem sets during the semester (35% of your grade), the extent to which you help your colleagues for the problem sets (5%) and two exams (each 30% of your grade).

5.1 Problem Sets

1.Initial Individual Submission: This component contributes 50% of the overall grade for the problem set. When you first complete the problem set independently and submit it to the instructor, your grade for this component will be calculated based on the quality of your independent work. This grade will be weighted at 50% of the total assignment grade.

2.Final Submission After Group Consultation: This component also contributes 50% of the overall grade for the problem sets. After discussing the problem set with your group members and documenting the correct answers, you will submit this revised version. Your grade for this component will be based on the quality of your final submission after group consultation. This grade will also be weighted at 50% of the total assignment grade.

3.Colleague Evaluation: Your colleagues will assess how much you contributed to helping them with the problem sets. This evaluation is separate from the individual and group submissions. It measures your ability to assist your fellow students. While the specific weight assigned to this evaluation is not mentioned in the text, it is likely that this evaluation will influence your final grade for the problem sets. The text mentions that “part of the grade for the problem set will also be how much you help your colleagues.” This component will count towards your final grade at the end (5%).

5.2 Mid term and Final exam

There will also be an in-class midterm and a final exam consisting of specific questions and problems. Each is worth 30%.

6 Attendance Requirements

Students are required to attend classes following the University’s policies. Students who have more than four unexcused absences (two weeks) will get an F grade on the course. Thus, students must attend classes.

6.1 Examination policy

A major exam (midterm or final) cannot be made up without the permission of the Dean’s Office. The Dean’s Office will grant such permission only when the absence was caused by a serious impediment, such as a documented illness, hospitalization or death in the immediate family (in which you must attend the funeral) or other situations of similar gravity. Absences due to other meaningful conflicts, such as job interviews, family celebrations, travel difficulties, student misunderstandings or personal convenience, will not be excused. Students who will be absent from a major exam must notify the Dean’s Office prior to that exam. Absences from class due to the observance of a religious holiday will normally be excused. Individual students who will have to miss class to observe a religious holiday should notify the instructor by the end of the Add/Drop period to make prior arrangements for making up any work that will be missed.

7 Academic Honesty

As stated in the university catalog, any student who commits an act of academic dishonesty will receive a failing grade on the work in which the dishonesty occurred. In addition, acts of academic dishonesty, irrespective of the weight of the assignment, may result in the student receiving a failing grade in the course. Instances of academic dishonesty will be reported to the Dean of Academic Affairs. A student who is reported twice for academic dishonesty is subject to summary dismissal from the University. In such a case, the Academic Council will then make a recommendation to the President, who will make the final decision

8 Students with Learning or Other Disabilities

John Cabot University does not discriminate on the basis of disability or handicap. Students with approved accommodations must inform their professors at the beginning of the term. Please see the website for the complete policy

Optional Books

Bauer, Paul C. and Dennis Cohen. 2023. Applied Causal Analysis (with R)

Lovelace, Robin, Nowosad, Jakub, and Jannes Muenchow. 2021. Geocomputation with R.

Mieno, Tara. 2023. R as GIS for Economists.

Week 1

Class 1: Overview of Statistics
01/15/2024 - Mon - Lecture

  • What are variables?
  • Measuring variables
  • Population versus sample

Class 2: Overview of Statistics
01/17/2024 - Wed - Lab

  • Intro to R and R Studio
  • Data management in R


  • Warne (2018) - C1: Statistics and Models
  • Fogarty (2019) - C1: Introduction and C2: Introduction to R and RStudio

Week 2

Class 1: Levels of Data
01/22/2024 - Mon - Lecture

  • Conceptual operationalization and validity
  • Types of data: nominal, ordinal, interval, ratio

Class 2: Levels of Data
01/24/2024 - Wed - Lab | Lab Answers

  • Reading datasets in R
  • Working with lists, numbers, strings, dataframes
  • Subsetting


  • Warne (2018) - C3: Visual Models
  • Warne (2018) - C4: Models of Central Tendency and Variability

Assignment 1 | Assignment 1 Template

Week 3

Class 1: Descriptive Statistics
01/29/2024 - Mon - Lecture

  • Frequency tables and histograms
  • Measures of central tendency: mean, mode, median, standard deviation
  • Sigma Notation

Class 2: Descriptive Statistics
01/31/2024 - Wed - Lab

  • Measures of central tendency: mean
  • Data cleaning and data merges
  • Data visualization: scatterplots


  • Fogarty (2019) - C6: Developing hypotheses
  • Fogarty (2019) - C7: Univariate and descriptive statistics

Week 4

Class 1: Probability - I
02/05/2024 - Mon - Lecture

  • Probability distributions
  • Joint probabilities
  • Theoretical vs. empirical probability

Class 2: Probability - I
02/07/2024 - Wed - Lab

  • Data cleaning and data merges
  • Data visualization: barplots and lineplot


  • Warne (2018) - C6: Probability and Central Limit Theorem

Class 3: Probability - II
02/09/2024 - Fri - Lecture

  • Probability Mass Functions (PMFs)
  • Probability Distribution Functions (PDFs)
  • Cumulative Distribution Functions (CDFs)

Week 5

Class 1: Probability - II
02/12/2024 - Mon - Lab

  • Probability distributions: standard normal distributions, generating random samples, cumulative distribution functions, probability density functions.


  • Warne (2018) - C6: Probability and Central Limit Theorem

Class 2: Statistical Significance testing and Z-Tests
02/14/2024 - Wed - Lecture

  • Null hypotheses
  • One vs.two-tailed tests
  • Type 1 and Type 2 errors

Assignment 2

Week 6

Class 1: Hypothesis Testing
02/19/2024 - Mon - Lab

  • Hypothesis testing


  • Warne (2018) - C8: One-Sample t-Tests
  • Warne (2018) - C9: Paired-Samples t-Tests
  • Warne (2018) - C10: Unpaired Two Sample t-Tests
  • Fogarty (2019) - C9: Hypothesis Testing

Class 2: Correlation
02/21/2024 - Wed - Lecture

  • Pearson correlation coefficient
  • Visualizing correlations
  • Correlation vs. causation

Spring Break
02/26/2024 - 03/03/2024

Week 7

Class 1: Midterm Revision
03/04/2024 - Mon - Lecture

Class 2: MIDTERM
03/06/2024 - Wed

Week 8

Class 1: Hypothesis Testing

03/11/2024 - Mon - Lab

  • Hypothesis testing
  • Visualizing differences: boxplots
  • Choropleth Maps

Class 2: Bivariate Regression
03/13/2024 - Wed - Lecture

  • Regression line
  • Coefficients
  • Standard errors
  • Goodness of fit


  • Fogarty (2019) - C10: Bivariate Analysis
  • Fogarty (2019) - C11: Linear Regression and Model building

Assignment 3

Week 9

Class 1: Bivariate Regression

03/18/2024 - Mon - Lab

  • Running bivariate regressions in R
  • Interpreting results
  • Making tables
  • Coefficient plots
  • Predicted values and Residuals

Class 2: Bivariate and Multivariate Regressions

03/20/2024 - Wed - Lecture

  • Assumptions of OLS
  • Regression with a Binary Independent Variable
  • Counterfactuals
  • Multivariate regressions
  • Interaction Effects
  • Gauss-Markov Assumptions


  • Warne (2018) - C13: Regression
  • Warne (2018) - C15: Applying Statistics to Research
  • Ismay, Chester, and Albert Y. Kim. 2019. Statistical Inference via Data Science: A ModernDive into R and the Tidyverse. Chapman and Hall / CRC. Chapter 6
  • Huntington-Klein, Nick. 2021. The Effect: An Introduction to Research Design and Causality. Boca Raton, Florida: Chapman and Hall / CRC. Chapter 13

Week 10

Class 1: Theories of Change
03/25/2024 - Mon - Lecture

  • Causal Diagrams - DAGs
  • Confounders, Colliders, Mediators
  • The Fundamental Problem of Causal Inference
  • Selection Bias
  • Randomization and RCTs

Class 2: RCT Data
03/27/2024 - Wed - Lab

  • Analysis of RCT Data
  • Mapping Rasters and Shapefiles
  • Interactive Maps with leaflet
  • Summary Statistics
  • Covariate Balance
  • Plotting Average Treatment Effects


  • Huntington-Klein, Nick. 2021. The Effect: An Introduction to Research Design and Causality. Boca Raton, Florida: Chapman and Hall / CRC. Chapter 5

Assignment 4

Week 11

Class 1: NO CLASS
04/01/2024 - Mon

Class 2: Threats to Validity
04/03/2024 - Wed - Lecture

  • Construct Validity
  • Statistical Conclusion Validity
  • Internal and External Validity
  • Matching

Week 12

Class 1: DAGs
04/08/2024 - Mon - Lab

  • DAGs (Directed acyclic graphs)
  • Nearest Neighbor Matching based on Mahalanobis Distance
  • Matching based on Inverse Probability Weights

Class 2: Differences in Differences
04/10/2024 - Mon - Lecture

  • Quasi-Natural Experiments
  • Indicators and Interactions
  • Differences in Differences
  • Assumptions


  • DAGS: Huntington-Klein, Nick. 2021. The Effect: An Introduction to Research Design and Causality. Boca Raton, Florida: Chapman and Hall / CRC. Chapter 6, Chapter 7, Chapter 8

  • Matching: Huntington-Klein, Nick. 2021. The Effect: An Introduction to Research Design and Causality. Boca Raton, Florida: Chapman and Hall / CRC. Chapter 14

Week 13

Class 1: Differences in Differences
04/15/2024 - Mon - Lab

  • Analysis of Differences-in-Differences
  • Event-Study Analyses

Class 2: Data Visualization 1
04/17/2024 - Wed - Lab

  • Color contrasts
  • Principles of ggplot
  • Aesthetics and geoms
  • Labels and Facets


Huntington-Klein, Nick. 2021. The Effect: An Introduction to Research Design and Causality. Boca Raton, Florida: Chapman and Hall / CRC. Chapter 18

Assignment 5

Week 14

Class 2: Data Visualization 2
04/24/2024 - Mon - Lab

  • Barplots
  • Uncertainty
  • Boxplots and Violin Plots
  • Annotations
  • Temporal Plots

Class 2: Revision for Final Exam
04/26/2024 - Wed - Lecture