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JOHN CABOT UNIVERSITY

COURSE CODE: "PL 208-2"
COURSE NAME: "Statistical Analysis for Political Science"
SEMESTER & YEAR: Spring 2025
SYLLABUS

INSTRUCTOR: Davide Orsitto
EMAIL: [email protected]
 HOURS: TTH 8:30 AM 9:45 AM
TOTAL NO. OF CONTACT HOURS: 45
CREDITS: 3
PREREQUISITES:
OFFICE HOURS:
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 own analysis and to help you interpret the numbers presented in the media. Students will learn how to read statistics in a research context, what data to use for different research topics, to adopt research designs that are relevant for the research question, use statistical tests and draw conclusions based on statistical tests. Students will also learn how to carry out statistical tests using statistical packages, and to interpret results based on their own analyses.
SUMMARY OF COURSE CONTENT:

This course offers a hands-on approach to quantitative and statistical methods commonly employed in political and social science research. It is designed to provide you with the methodological foundations necessary for understanding and conducting quantitative research in political and social sciences and giving you the tools to critically read and structure a quantitative research paper in its various components.

In the introductory part of the course, we will cover the essentials of epistemology, the key steps to set up a quantitative research paper and the types of research design. You will explore how political and social scientists use the scientific method to address their questions, while diving into the nuances of the demarcation problem— the distinction between the so-called "hard" and "soft" sciences in conducting research.

The main part of the course delves into the essential elements of statistics and its practical application in data analysis, the key components of a solid research design. We will explore concepts of descriptive and inferential statistics, learning ways to summarize and organize data with measures of central tendency and dispersion and then being able to infer about populations basing on a sample by means of hypothesis testing, analysis of variance techniques and regression analysis.  In this module, we will explore strategies for analysing large datasets and interpreting the statistical outputs commonly encountered in political and social science literature. With a solid theoretical foundation and real-world examples, you will learn how to select appropriate datasets and apply relevant statistical methods to tackle specific research questions.  By the end of the course, you will be able to design research, read and interpret statistics, choose the right elementary methods to address your research questions, and draw meaningful conclusions from your statistical tests.

The course meets twice a week, blending frontal lectures that apply statistical methods to political science research designs with problem-solving labs. Students will also complete a group assignment, which involves preparing a written report and presenting it in class. Grades will be allocated as follows: 15% for class participation, 15% for the group assignment, 35% for the midterm exam, and 35% for the final exam. 

LEARNING OUTCOMES:

Upon successful completion of the course, students will understand and be able to use broad concepts such as:

 

  • The essentials of the scientific method and the research design
  • How to structure a research paper
  • Choose the right research method according to the data
  • Choose the right statistical technique to be used with the research method
  • Understand when to apply which statistical procedure
  • Evaluate, infer, and understand a product, situation, services, or a treatment option through statistics
  • Interpret statistical literature, research articles, and the claims made based on statistics
  • Carry out a hypothesis test and make statistical inferences
  • Develop a research proposal that can be used for future academic work

 

Technically and operationally, students will be able to:

 

·       Distinguish between a population and a sample.

·       Distinguish between categorical, discrete numerical, and continuous numerical data.

·       Construct a frequency distribution and a bar chart, and describe the distribution of a categorical variable.

·       Confidently use central tendency and dispersion indexes.

·       Distinguish between an observational study and an experiment.

·       Learn the basic properties of probabilities and calculate the probability of events, as well as conditional probabilities.

·       Estimate probabilities empirically and using simulation.

·       Explore probability distributions and their applications in data analysis.

·       Distinguish between simple random sampling, stratified random sampling, and cluster sampling.

·       Use properties of the sampling distribution to reason about the value of a population mean or a population proportion.

·       Understand the relationships between sample size, margin of error, and the width of a confidence interval.

·       The difference between a statistical relationship and a causal relationship (the difference between correlation and causation).

·       Understand and perform inferential statistics such as one and two-sample z-tests, t-tests, F-tests, ANOVA.

·       Carry out a test for a difference in population means using independent samples or paired samples and interpret the results.

·       Understand the differences between goodness-of-fit tests, tests for homogeneity, and tests of independence.

·       Carry out a hypothesis test to determine whether there is evidence that two or more population or treatment means are not all equal and interpret the results in context.

·       That the simple linear regression model provides a basis for making inferences about linear relationships.

·       Using a multiple regression model to establish a relationship between a dependent variable and two or more independent variables.

·       Learning the concept of interaction in a multiple regression setting, estimate the parameters in a multiple regression model, and assess the usefulness of the model.
TEXTBOOK:
NONE
REQUIRED RESERVED READING:
Book TitleAuthorPublisherISBN numberLibrary Call NumberComments
Peck, R., & Olsen, C. (2020). Introduction to statistics and data analysis (6th ed.). Cengage LearningPeck, R. Olsen, C.Cengage learning9781337793612 https://www.amazon.com/Introduction-Statistics-Data-Analysis-Roxy/dp/1337793612

RECOMMENDED RESERVED READING:
NONE
GRADING POLICY
-ASSESSMENT METHODS:
AssignmentGuidelinesWeight
test  
Class ParticipationIn most classes, you'll find a mix of mandatory and recommended readings, accompanied by a discussion of these materials. Students will be assessed based on their engagement, contributions to the discussion, and grasp of the topics covered in the lecture.15%
Group AssignmentThe group assignment involves preparing a written report that critically examines a scientific paper assigned to the group and presenting the analysis in class. This report should analyse the paper in relation to the material covered in class, providing commentary on its structure—highlighting any omissions, deviations from the instructed framework, and identifying the type of research design employed in the study. The work is worth 15% of the final grade.15%
Midterm Exam 35%
Final Exam 35

-ASSESSMENT CRITERIA:
AWork of this quality directly addresses the question or problem raised and provides a coherent argument displaying an extensive knowledge of relevant information or content. This type of work demonstrates the ability to critically evaluate concepts and theory and has an element of novelty and originality. There is clear evidence of a significant amount of reading beyond that required for the course.
BThis is highly competent level of performance and directly addresses the question or problem raised.There is a demonstration of some ability to critically evaluatetheory and concepts and relate them to practice. Discussions reflect the student’s own arguments and are not simply a repetition of standard lecture andreference material. The work does not suffer from any major errors or omissions and provides evidence of reading beyond the required assignments.
CThis is an acceptable level of performance and provides answers that are clear but limited, reflecting the information offered in the lectures and reference readings.
DThis level of performances demonstrates that the student lacks a coherent grasp of the material.Important information is omitted and irrelevant points included.In effect, the student has barely done enough to persuade the instructor that s/he should not fail.
FThis work fails to show any knowledge or understanding of the issues raised in the question. Most of the material in the answer is irrelevant.
-ATTENDANCE REQUIREMENTS:
Students are expected to attend classes in accordance with the University’s policies. More than two unexcused absences will result in the assumption that the student has withdrawn from the course. If a student has a valid reason for missing class, they must email me in advance to explain their absence and submit the appropriate form to the Dean’s Office.
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.
SCHEDULE

Week 1: Scientific Method and Research Design for quantitative Political and Social Sciences

Week 2:  Data Analysis for writing Empirical research: The Essentials

Week 3: Measuring Variables

Week 4: Making Sense of Data: Descriptive Statistics

Week 5: Probability Theory

Week 6: Random Variables, Probability Distributions and Sampling

Week 7: Statistical Inference: Hypothesis testing, Validity and Bias

Week 8 : Midterm

Week 9: Correlation

Week 10: Parametric tests: Categorical Data and ANOVA

Week 11: Parametric Tests: Linear Regression

Week 12:  Multiple Regression

Week 13: Theories of Change and Practical Causal Inference

Week 14: Wrap-up: Writing a Quantitative Research Paper