Statistics for linguists – basics

obligatory courses

Remote learning

The aim of this course is to familiarize students with the basics of statistical thinking and analysis. Basic notions, including types of variables, types of research designs and statistical tests, the difference between inferential and descriptive statistics, will be introduced. Practical skills will be learned using IBM SPSS software. After completing the course, learners will be able to understand the importance of statistics to obtain reliable empirical results and will be able to (1) assess the data, (2) run the most popular tests to compare groups, (3) run correlations and simple regressions.

1. Understand the importance of a well-structured research design for data analysis.

2. Understand how to visualise and run diagnostic data analysis to ensure the use of an appropriate statistical test.

3. Learn how to deal with problems with the data.

4. Understand how to clearly report results from statistical analysis.

5. Become familiar with different types of tests and their suitability to different analyses.

6. Become better able to search for and identify reliable sources of information on different statistical tests.

Module I: Basics of Statistics using IBM SPSS

Content:

1. The importance of statistics

2. Variables and organizing data

3. The SPSS interface.

4. Test assumptions and running data diagnostics

Module II: Comparing means and medians: between and within-subject designs

Content:

1. Comparing two means (t tests and nonparametric alternatives)

2. Comparing two or more means (ANOVAs and nonparametric alternatives)

3. Hands-on data analysis (assessment)

Module III: Correlations and linear regression analyses

Content:

1. Parametric and nonparametric correlations

2. Simple linear regressions

3. Diagnosing residuals and spotting influential outliers

4. Hands-on data analysis (assessment)

Knowledge: the graduate knows and understands:

-in-depth, the main directions of development and contemporary research trends in linguistics (theoretical, computational, cognitive, corpus, social/text linguistics) and knows where the most important linguistic research centers in Europe and the world are located

-in-depth, research methods used in: psycholinguistics, neurolinguistics, corpus linguistics, digital linguistics and text linguistics, as well as statistical models

-in-depth, the relationship of linguistics (theoretical, computational, cognitive, corpus linguistics) with other fields of (neuro)science

Skills: the graduate is able to:

-use in-depth theoretical and practical knowledge to carry out research work and solve complex problems in linguistics (theoretical, computational, cognitive, corpus, social/text linguistics) using appropriate methodology

-to an in-depth degree, search, analyze, evaluate and select information in the native language and foreign languages

-use advanced research tools of linguistics (theoretical, computational, cognitive, corpus-based) and select research methods appropriately to the problems undertaken

-use in-depth knowledge of linguistics (theoretical, computational, cognitive, corpus-based, social/text linguistics) through the selection and appropriate application of modern information and communication technology (including statistical, corpus, eye-tracking and EEG analysis software) when working with research data

-use in-depth knowledge of research methodologies (acoustic analysis, eye-tracking, EEG, corpus analysis, data analysis: R, Python) used in modern linguistics to plan and carry out a methodologically correct scientific study, as well as to analyze its results and identify implications

-use a foreign language to an advanced degree (C2 level) in specialized terminology in the field of linguistics (theoretical, computational, cognitive, corpus, social/text linguistics)

-lead the work of a team in a linguistics project using advanced research methods and new technologies

-independently acquire knowledge in the field of linguistics (theoretical, computational, cognitive, corpus, social/text linguistics) and evaluate the usefulness of the learned methods, practices and procedures in their own professional activity

Social competences: the graduate is ready to:

-recognize the importance of the latest linguistic knowledge and critically evaluate research in linguistics (theoretical, computational, cognitive, corpus, social/text linguistics), especially those published in scientific journals and monographs and popular science sources

-recognize the importance of linguistic knowledge in solving cognitive and practical problems and to consult with experts

-initiate and carry out linguistic research, in particular to improve accessibility in the dimensions of language, information and communication

After completing the course, a participant will:

 learn how to manage the large datasets for future analyses;

 Learn how to analyse data, check for assumptions in the data, and learn different ways to deal with the assumption in the data.

 understand the data format from common correlational and experimental research designs;

 better understand third party research findings;

 Conduct the most common statistical analysis (T-test, ANOVAs, correlations, linear regressions)