(in Polish) Analiza danych w badaniach językoznawczych 4100-IMADWBJ
The aim of the course is to familiarize students with basic data analysis methods used in applied linguistics, with particular emphasis on educational and glottodidactic contexts. The course focuses primarily on quantitative data analysis while also providing an introduction to qualitative analyses, enabling students to develop skills in interpreting results and formulating conclusions based on empirical data.
The course aim is to prepare students for independently conducting basic data analyses in linguistic research, with particular attention to data from studies on language education and second language acquisition. Students learn to consciously select analytical methods and techniques appropriate to the type of data and research questions, critically evaluate the quality of results and research best practices, including presenting data in accordance with principles of scientific rigour.
The classes are workshop-based, allowing practical application of the data analysis methods learned in relation to real empirical data.
The course includes:
- quantitative data analysis using specialized software, including data preparation, calculation of descriptive statistics, testing relationships and hypotheses, visualizing results, and interpreting statistical outcomes in the context of linguistic research;
- an introduction to qualitative analysis, covering basic data coding techniques, identification of categories and patterns, and linking qualitative results to the context of educational research;
- selection of analytical methods, evaluation of their adequacy, and interpretation and presentation of data in accordance with principles of scientific rigour and in reference to relevant literature.
The course prepares students for basic empirical data analysis necessary for research work conducted as part of preparing theses in master’s seminars.
Course coordinators
Type of course
Mode
Learning outcomes
Knowledge – the student knows and understands:
- the main methods and techniques of data analysis used in linguistic research, the principles of selecting analytical tools for a given research problem, and examples of applications of data analysis methods in empirical studies concerning foreign language education and language acquisition (K_W05).
Skills – the student is able to:
- apply selected techniques and tools of data analysis in empirical studies concerning foreign language education and language acquisition, critically interpret the results of analyses, and relate them to the existing research literature in applied linguistics (K_U01),
- use specialized software and IT tools to process and visualize research results (K_U04).
Social competences – the student is ready to:
- critically evaluate materials as well as their own knowledge and skills in the field of data analysis in empirical studies concerning foreign language education and language acquisition (K_K01),
- seek sources of knowledge and expert support in the field of data analysis in empirical studies concerning foreign language education and language acquisition (K_K02).
Assessment criteria
COURSE COMPLETION REQUIREMENTS
A prerequisite for obtaining credit is:
1. attendance at on-site classes (one absence at on-site classes is allowed),
2. completion of all tasks scheduled for both on-campus classes and the e-learning platform.
Methods of assessment:
The course is passed on the basis of:
1. a grade for the practical data analysis task administered at the end of the semester (verified learning outcomes: K_W05, K_U01, K_U04, K_K01, K_K02) – weight in the final course grade: 60%.
2. assessment of the student’s activity during on-site classes (verified learning outcomes: K_W05, K_U01, K_U04, K_K01, K_K02) – weight in the final course grade: 40%.
Assessment criteria:
1. Grade for the practical data analysis task administered at the end of the semester – 60 points
– accuracy and completeness of the analysis, accuracy and clarity of the presentation of results, and accuracy of the interpretation of results.
Point scale (0–60 pts):
54–60 pts → very good (5.0)
51–53 pts → good plus (4.5)
45–50 pts → good (4.0)
42–44 pts → satisfactory plus (3.5)
36–41 pts → satisfactory (3.0)
0–35 pts → unsatisfactory (2.0)
2. Grade for student activity during in-class sessions – 40 points.
– subject matter preparation for classes – degree of familiarity with the literature and course materials and their use during classes (15 pts),
– participation in discussions and practical exercises in class – frequency, quality of comments, independence, ability to justify one’s own observations based on the data analysis, the use of PS IMAGO PRO sotfware (15 pts).
Point scale (0–40 pts):
36–40 pts → very good (5.0) – very high activity and engagement in class, the student fully achieves the learning outcomes.
34–35 pts → good plus (4.5) – very high activity and engagement in class, the student achieves the learning outcomes to a high degree.
30–33 pts → good (4.0) – high activity and engagement in class, the student achieves the learning outcomes to a high degree.
28–29 pts → satisfactory plus (3.5) – satisfactory activity and engagement in class, the student achieves most of the learning outcomes.
24–27 pts → satisfactory (3.0) – minimal activity and engagement in class, the student achieves some of the learning outcomes but to a limited extent.
0–23 pts → unsatisfactory (2.0) – very low activity and engagement in class, the student does not achieve most of the learning outcomes.
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The student must obtain an overall result (based on grades from the test, class activity and tasks related to using the e-learning platform) of at least 60% in order to receive a positive grade and pass the course.
90–100% → very good (5.0)
85–89% → good plus (4.5)
75–84% → good (4.0)
70–74% → satisfactory plus (3.5)
60–69% → satisfactory (3.0)
0–59% → unsatisfactory (2.0)
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The use of AI tools for completing tasks during the course is permitted only to the extent that it does not conflict with the achievement of the intended learning outcomes and only after obtaining written consent from the instructor, who verifies the proposed purpose, scope, and method of their use for compliance with these outcomes. The student is obliged to present the full scope of AI tools used in the preparation and execution of tasks within the course, including the objectives and methods of use, with clear indication of elements produced with AI support. Failure to meet the above requirements is considered a violation of the principles of independent work and results in the work being deemed non-independent and assessed as unsatisfactory.
Practical placement
Not applicable.
Bibliography
Compulsory literature (selected chapters and excerpts from the items below):
Józefacka, N., Kołek, M.F., & Arciszewska-Leszczuk, A. (2023). Metodologia i statystyka: Przewodnik naukowego turysty. PWN.
Field, A. (2024). Discovering statistics using IBM SPSS Statistics. 6th edition. Sage.
Brown, J. D., & Rodgers, T. S. (2014). Doing second language research. OUP.
Cohen, L., Manion, L., & Morrison, K. (2017). Research methods in education. Routledge.
Larson-Hall, J. (2016). A guide to doing statistics in second language research using SPSS and R. Routledge.
Braun, V., & Clarke, V. (2013). Successful qualitative research : A practical guide for beginners. SAGE.
Braun, V., & Clarke, V. (2022). Thematic Analysis : A Practical Guide. SAGE.
O’Reilly, K. (2025). Qualitative research methods for everyone : An essential toolkit. Bristol University Press.
Tracy, S. J. (2025). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact. Wiley Blackwell.
Students are also required to read/familiarize themselves with the didactic materials posted on the e-learning platform or provided for classes by the lecturer. These are articles, chapters, studies, and electronic materials (e.g. presentations, videos).
Notes
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Term 2025Z:
During the course, the PS IMAGO PRO (SPSS) software is used. |
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: