Essential Quantitative Skills for Social Sciences

Author

Bogdan G. Popescu

Course Information

Instructor: Bogdan G. Popescu
Hours: TBA
Total Hours of Contact: 2:30 per week
Room: TBA

Credits: 3
Prerequisites: None
Office Hours: TBA

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.

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.

Learning Outcomes

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

  • Use statistical core terminology accurately and interpret results from descriptive statistics, hypothesis tests, and regression analyses.
  • Organize, clean, and merge datasets using R, and summarize data with numerical and graphical methods.
  • Conduct hypothesis tests (z-tests, t-tests) and evaluate statistical significance, p-values, and confidence intervals.
  • Estimate and interpret bivariate and multivariate regression models, including coefficients, standard errors, and goodness-of-fit measures.
  • Distinguish correlation from causation using causal diagrams (DAGs) and identify confounders, colliders, and mediators.
  • Apply causal inference methods including randomized controlled trials (RCTs), matching, and differences-in-differences.
  • Create effective data visualizations (scatterplots, histograms, choropleth maps, coefficient plots) using ggplot2 and produce reproducible reports in Quarto.

Assessment methods

  • Five problem sets: 35% of the final grade (7% each)
  • Colleague evaluation: 5% of the final grade
  • 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).

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 individually. Note: no group submission is permitted. Each one of you has to submit the second attempt of the assignment individually. 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 at the end of the semester. This evaluation is separate from the individual and group submissions. It measures your ability to assist your fellow students. This component will count towards your final grade at the end (5%).

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%.

Attendance Requirements

Students are required to attend classes following the University’s policies. Students with more than two unexcused absences are assumed to have withdrawn from the course. Students with a justified reason not to attend class have to send me an email explaining why they cannot attend ahead of class and need to submit a form to the Dean’s Office.

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.

Grade Description of Academic Work

  • A (95-100)
  • B (85-94)
  • C (75-84)
  • D (65-74)
  • F (65 and below)

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.

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) https://bookdown.org/paul/applied-causal-analysis/.

Keyes, David, R for the Rest of Us: 2025. A Statistics-Free Introduction https://book.rfortherestofus.com. No Stach Press.

Lovelace, Robin, Nowosad, Jakub, and Jannes Muenchow. 2021. Geocomputation with R. https://bookdown.org/robinlovelace/geocompr/.

Mieno, Tara. 2023. R as GIS for Economists. https://tmieno2.github.io/R-as-GIS-for-Economists/.