M.A. programme in English Philology: Second year of study: Written preparation of scientific research — winter term 2025–2026

What is this list?

This is a list of courses in written preparation of scientific research (Polish: opracowanie pisemne badań naukowych) we intend to launch in the winter term (October–February) in our full-time M.A. programme in English philology (Filologia angielska) whose second year of study is the academic year 2025–2026. This list is intended for:

1. Students at the Faculty of English who are about to enter the second year of their study: this is your reference point before your enrolment into the courses;
2. Candidates for the programme: this list gives you a snapshot of what courses were on offer for the study cycle that started in 2024.

How to navigate the list?

The list is sorted first by the discipline (linguistics precedes literary studies) and then by the name of the teacher. The format of the entries is as follows: the generic title of the course (not necessarily reflecting its focus), the name of the teacher, and the description of the course.

Note: as per the syllabus of the study programme, courses in linguistics listed here are classified as Written preparation of linguistic scientific research—courses in literary studies listed here are classified as Written preparation of literary scientific research.

Linguistics

Data analysis: Quantitative methods for modeling categorical data

prof. UAM dr hab. Karolina Krawczak-Glynn

This course offers a practical introduction to quantitative methods in linguistic research, with a particular focus on observational data (e.g., corpus data). It combines theoretical foundations with hands-on training in modeling categorical data. The course begins with an overview of the advantages of quantitative approaches, principles of research design, and the operationalization of research questions into testable hypotheses. Core statistical concepts, including data types, variables, and types of statistical error, are also introduced.

The practical component centers on R [external link]. To start with, students will learn how to install packages, import and explore datasets, modify data structures, and create visualizations of frequencies and proportions. In addition, students will be introduced to simple bivariate tests of independence as a foundation for more advanced analyses. Building on these skills, the course turns to collocational and collostructional analyses, emphasizing both their methodological strengths and limitations in measuring lexical and grammatical associations.

Following this, the course introduces multivariate approaches to data modeling, including correspondence analysis, cluster analysis, log-linear models, logistic regression, and decision tree models. By the end of the course, students will be equipped to analyze categorical linguistic data quantitatively, interpret results critically, and formulate well-founded generalizations about language use.

Evaluation will be based on students’ active involvement in the hands-on sessions and a short written report of a quantitative study using either data provided by the teacher or, with prior approval, a dataset chosen by the student.

Written preparation of linguistic scientific research

mgr Ewa Tomczak-Łukaszewska

This course provides a foundation in statistical methods tailored for linguistic analysis and experimental data. It moves from core concepts in statistics to practical application, focusing on the quantitative data typical in linguistic research, particularly on reaction times, eye movement data, acceptability judgments, and survey responses. You will learn how to move from your raw experimental results to statistically sound conclusions. You will learn to formulate research hypotheses, select appropriate statistical methods for data exploration and analysis, create data visualisations, accurately interpret your results and communicate your findings with clarity and academic integrity. A considerable practical component involves hands-on analysis using specialised statistical software. Through hands-on practice, you will develop the skills to manage datasets, perform key analyses, and apply both exploratory and inferential methods critical for experimental work in linguistics. Beyond the numbers, this course emphasises ethical research practices, collaborative work, and the clear communication of your findings. Bring your laptop and readiness to explore – no prior statistical knowledge is required, just a curiosity about the scientific study of language.

Literary studies

Written preparation of literary scientific research

dr Urszula Kizelbach
dr Marcin Markowicz
(the two teachers run their separate classes individually)

Designed as a continuation of the academic writing class, this course aims to support students in the process of developing and refining the written components of their MA papers. This means it will be centered primarily on students’ own research and include activities such as preparing outlines of selected chapters, drafting and revising chapters, collecting, evaluating, and working with sources, building arguments, and publicly presenting findings. The course will be structured around in-class sessions for the whole group, individual consultations and written feedback to help students make continuous progress on their papers. Students will deliver in-class presentations and engage in peer review and group discussion sessions to strengthen the content and clarity of their work. Assessment will be based on the timely submission and quality of written assignments, active participation in class and consultations, as well as constructive engagement with peers’ work.