Quantitative research methods 2100-SPP-L-D2QRM2
During the class the below topics will be discussed:
Course Structure:
The course consists of 12 sessions combining theoretical seminars with practical coding workshops.
Session 1 (2025-02-20): Organizational Issues and Introduction
Seminar: Introduction to quantitative methods in social sciences, course objectives, and assessment criteria
Workshop: Introduction to Python programming environment and tools
Session 2 (2025-02-27): Distribution
Seminar: Understanding statistical distributions, frequency distributions, and probability distributions in social science research
Workshop: Installing Python, introduction to IPython notebooks, basic Python syntax, and data structures
Session 3 (2025-03-07): Standard Deviation
Seminar: Measures of central tendency and dispersion, understanding variance and standard deviation for data analysis
Workshop: Working with NumPy and pandas for basic statistical calculations
Session 4 (2025-03-13): Correlation
Seminar: Bivariate analysis, correlation coefficients, scatter plots, and interpretation of relationships between variables
Workshop: Calculating correlations using Python libraries and understanding their significance
Session 5 (2025-03-20): Test and Data Visualization
Seminar: First test covering material from sessions 1-4; Introduction to data visualization principles and techniques
Workshop: Creating visualizations using Seaborn: histograms, box plots, and scatter charts
Session 6 (2025-03-27): Hypothesis
Seminar: Hypothesis testing, null and alternative hypotheses, significance levels, p-values, and Type I and Type II errors
Workshop: Implementing hypothesis tests in Python and interpreting results
Session 7 (2025-04-10): Sampling
Seminar: Sampling methods, sampling distributions, central limit theorem, and confidence intervals
Workshop: Working with samples and statistical inference in Python
Session 8 (2025-04-17): Second test covering material from sessions 5-7; T-test and ANOVA
Seminar: Student's t-test (one-sample, two-sample, paired), Analysis of Variance (ANOVA), and their applications
Workshop: Performing t-tests and ANOVA using Python statistical libraries
Session 9 (2025-04-24): Statistical Tests
Seminar: Overview of various statistical tests, choosing appropriate tests for different research questions
Workshop: Coding test - practical assessment of Python programming skills for data analysis
Session 10 (2025-05-15): Linear Regression
Seminar: Simple and multiple linear regression, interpretation of coefficients, R-squared, model diagnostics
Workshop: Implementing linear regression models in Python and evaluating model performance
Session 11 (2025-05-22): The FINAL TEST
Seminar: Comprehensive final examination covering all course material
Workshop: Project presentation - students demonstrate their integrated knowledge of statistical methods and computational tools
Session 12 (2025-05-29): Recap
Seminar: Review of key concepts, discussion of common challenges in quantitative analysis, Q&A session
Workshop: Recap of Python tools and techniques, best practices for data analysis
AI usage -- Level 4: AI Task Completion, Human Evaluation
AI is used to complete certain elements of the task, with students providing discussion or commentary on the AI-generated content. This level requires critical engagement with Al generated content and evaluating its output.
You will use Al to complete specified tasks in your assessment. Any Al created content must be cited.
Prerequisites (description)
Course coordinators
Learning outcomes
Upon completion, students will
-> Be able to collect, analyze and interpret quantitative data used in the process of designing and analyzing political processes (K_W02)
-> Design complex social research projects based on data and methods characteristic of quantitative analysis (K_U01).
-> Be able to use statistical methods to analyze political processes and their economic, social and cultural determinants (K_U03)
-> Prepare an oral presentation (individually and in a group) demonstrating their ability to apply quantitative data analysis (K_U06, K_K02).
-> Critically evaluate quantitative data sets available on the Internet (K_K03).
Assessment criteria
20% of the final grade will be based on the scores achieved in two minor tests. 40% of the final grade will be based on the coding test at the end of the workshop. The remaining 40% of the final grade will be based on the final test at the end of the seminar. All students are required to achieve a passing grade in all tests and the project. Students are permitted two absences from both the discussion section and the workshop.
Practical placement
not applicable
Bibliography
Pyrczak F. & Oh D. M. (2018). Making sense of statistics : a conceptual overview (Seventh). Routledge Taylor & Francis Group.
And selected chapters from:
-> MacAonghuis I. (2022). Statistical inference and probability (1st ed.). SAGE Publications.
-> Martin P. (2022). Linear regression : an introduction to statistical models (1st ed.). SAGE Publications.
-> Martin P. & Martin P. (2022). Regression models for categorical and count data ed. 1. SAGE Publications.
-> McCoach D. B. & Cintron D. (2022). Introduction to modern modelling methods (1st ed.). SAGE Publications.
-> McBee M. (2022). Statistical approaches to causal analysis. SAGE Publications.
-> Castellani B. C. & Rajaram R. (2022). Big data mining and complexity. SAGE Publications.
-> McKinney W. (2022). Python for data analysis : data wrangling with pandas numpy and jupyter (Third). O'Reilly Media. and additional online materials.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: