Quantitative research methods

The course aims to develop students' understanding of quantitative methods in the social sciences. The course focuses primarily on developing students' skills in analyzing quantitative data; therefore, a significant part of the course is devoted to an introduction to statistics. The course is also complemented by a workshop devoted to basic applications of Python to statistical analysis, NLP, and image analysis.

Students will become familiar with quantitative conceptualizations of social and political life, their advantages and limitations. They will

Learn why it is worthwhile to learn the basics of statistics - why statistics is indispensable for describing social and political processes.

Students will learn the basics of descriptive statistics: univariate analysis (central tendency, dispersion, shape of distribution) and bivariate analysis (correlation).

analysis (correlation). They will also become familiar with the most elementary forms of data visualization, including histograms, box plots, and scatter plots,

plots, and scatter plots. During the workshops, students will learn how to use Python for statistical analysis. They will work with

IPython. They will learn how to install Python, work with iPython notebooks, install Python libraries, and write and run code. Data analysis will be

using NumPy, pandas, Seaborn, spaCy, and scikit-image.

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.

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).

20% of final grade will be based on student activities (small projects, homework) in a discussion section. 40% of the final grade will be based on a coding project. 40% of the final grade will be based on the final test score. The test must be passed by the student. Students will be allowed two absences from the discussion section and two absences from the workshop.