Data analysis in linguistic research 4100-IMADWBJ

Course learning outcomes (P) aligned with programme learning outcomes (K):
Knowledge – the student knows and understands:
P_W01. The student knows and explains the assumptions underlying and potential applications of quantitative and qualitative data analysis methods in research on language education and language acquisition. (K_W05)
P_W02. The student understands and justifies the criteria for selecting methods, techniques and digital tools appropriate to the type of data and the purpose of linguistic research. (K_W05)

Skills – the student is able to:
P_U01. The student is able to select and justify an analytical method and technique appropriate to the research problem and the type of linguistic data collected. (K_U01)
P_U02. The student is able to analyse empirical linguistic data using appropriate analytical techniques and specialised digital tools. (K_U01, K_U04)
P_U03. The student is able to interpret, critically evaluate and present data analysis results, taking into account the limitations of the method applied and the principles of research integrity. (K_U01, K_U04)

Social competences – the student is ready to:
P_K01. The student is ready to critically evaluate their own knowledge, the analytical assumptions adopted and the credibility of interpretations of linguistic research findings. (K_K01)
P_K02. The student is ready to recognise the importance of methodological knowledge in solving research problems and to seek expert advice when encountering difficulties in selecting a method or tool or in interpreting results. (K_K02)

1. Conditions for passing the course
The conditions for being admitted to the final course assessment are:
– attendance at classes (1 absence is permitted; unexcused absences exceeding this limit result in an NK grade and the requirement to retake the course),
– timely completion of all compulsory written and/or oral assignments carried out during classes and on the e-learning platform.
The final grade is determined on the basis of the assessment methods and criteria described in sections 2, 3 and 4, in accordance with the assigned weightings.

The use of AI tools and technologies supporting language processing is permitted only insofar as it does not interfere with the achievement of the intended learning outcomes, with the instructor’s consent, after agreeing on the scope of their use and subject to the submission of a written declaration. The use of AI tools without the instructor’s consent is treated as a breach of the principles of independent work and results in a failing grade.

2. Assessment methods
a) a data analysis skills test using specialised software, including the presentation and interpretation of results, consisting of practical tasks,
learning outcomes assessed: K_W05, K_U01, K_U04, K_K01, K_K02),
weighting in the final course grade: 75%,
b) a knowledge test covering data analysis and the presentation and interpretation of results – knowledge, identification and application of basic concepts, classifications, objectives and techniques,
- learning outcomes assessed: K_W05, K_K01, K_K02)
- weighting in the final course grade: 25%.

3. Assessment criteria
a) Assessment criteria for the skills test:
- accuracy and completeness of the analysis,
- accuracy and clarity of the presentation of results, and
- accuracy and completeness of the interpretation of results.
b) Assessment criteria for the knowledge test:
- accuracy and completeness of the answers.
The assessment criteria for each test are applied jointly. All criteria must be met to at least a satisfactory degree. Each test is graded in accordance with the applicable scoring system and grading scale.

4. Grading scale
The threshold for obtaining a passing grade and passing the course is a minimum score of 60% on each test – a) and b).
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% → fail (2,0)

Not applicable.

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).