Conducted in term: 2025Z

Erasmus code: 05.1

ISCED code: 0542

ECTS credits: 10

Language: English

Organized by: Institute of English Studies

Basics of Statistics with IBM SPSS 3301-JS2927-2ST

When conducting studies, researchers often collect numerical data to answer certain questions and/or to test certain hypotheses. The data collected always tells a story, but this story becomes far more interesting when it is possible to generalize your findings. That is, when it is possible to claim that the results obtained in your study can be generalized to the broader population. What is more interesting? To be able to say that 1) teaching method A works better than teaching method B in the group of 50 students that you investigated, or 2) that method A works better than method B in general, among Polish students? The second option is a much more useful finding. However, to be able to claim this, one needs statistics. And fortunately, statistical software now does all the calculation for researchers.

This course aims to introduce participants to the basics of inferential statistics using IBM SPSS, a popular statistical software. The course is mostly practical, focused on checking data, organizing data, and using the software, running statistical analyses. However, some theoretical discussions are needed, especially at the beginning of the course. Please note that no background in statistics is needed, and only a basic understanding of math is expected.

The topics covered in the course will be the following:

Part 1:

1. The importance of statistics.

2. Variables and organizing data.

3. The SPSS interface.

4. Test assumptions and running data diagnostics.

5. Comparing two means (t tests and nonparametric alternatives): running tests and reporting results.

6. Comparing two or more means (ANOVAs and nonparametric alternatives): running tests and reporting results.

7. Data analysis with 2 (or more) categorical independent variables: How to understand interactions in ANOVAs.

8. Data Visualization 1: Using box plots and line graphs to visualize group comparison.

Part 2:

9. Introduction to the concept of linearity and residuals.

10. Introduction to simple linear regression with a continuous dependent variable (additional tests: ensuring linearity and detecting outliers).

11. Regression analyses with categorical predictors: Dummy variables

12. Introduction to multiple linear regression with a continuous dependent variable (additional test: checking for collinearity).

13. Main effects vs. simple effects: Interactions in regression analyses.

14. Data Visualization 2: Scatterplots.

15. Reporting results of regression analyses.

16. Multiple linear regression with binomial dependent variables.

17. How to visualize and report probabilities and odd ratios.

Type of course

elective courses

Mode

Blended learning
Remote learning
Classroom

Prerequisites (description)

- Having completed the introduction to research methodology in the first-cycle studies (or equivalent). - Basic computer skills. - Access to a computer. Students must have access to a computer in class. The computer will be used to run the software IBM SPSS. The licensed software will be provided to students. - Proficiency in English at level B2 (upper intermediate) or higher.

Course coordinators

Breno Barreto Silva

Learning outcomes

Knowledge

The graduate has in-depth familiarity with:

- K_W01 advanced terminology, theory and research methods corresponding to the state of the art in the discipline of linguistics, in accordance with their chosen specialization (and educational path)

- K_W04 concepts and principles concerning the protection of intellectual property and copyright

Abilities

The graduate is able to:

- K_U01 apply the advanced terminology, theories and methods of linguistic research to solve complex and original research problems in accordance with his/her chosen specialization (and educational path)

- K_U04 apply the concepts and principles of intellectual property protection and copyright law

Social competences

The graduate is ready to:

- K_K01 critically appraise their knowledge and content obtained from various sources

- K_K02 recognize the importance of knowledge in solving cognitive and practical problems; consult experts when required

Assessment criteria

The final grade is based on:

- In-class participation (50% of the mark). This includes the following:
• Performance in short tasks done in class
• Participation during class discussions

- The final practical assessment (50% of the mark). This may include one or both of the following:
• A practical test consisting of 3 or more larger tasks.
• A theoretical test.

Attendance: 4 absences are allowed.

If the participant receives an unsatisfactory grade, a second practical assessment (i.e., a second set of short tasks) will be provided.

Bibliography

The course is mostly practical, and any needed theoretical material will be provided. The list below refers to books that may be useful should participants intend to explore the topics further.
Field, A. (2017). Discovering statistics using IBM SPSS statistics (5th Ed.). Los Angeles: Sage.
Howell, D. C. (2013). Statistical methods for Psychology (8th Edition). Belmont: Wadsworth.
Salkind, N. J., & Frey, B. B. (2019). Statistics for people who (think they) hate statistics (7th Ed.). Los Angeles: Sage.

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 3301-JS2927-2ST in USOSweb