• Vanhove Jan

Language of teaching: In even years (2018, 2020 etc.), this course is taught in English; in uneven years (2019, 2021 etc), it is taught in German. The written exam questions will be in the language of teaching, but students are welcome to answer in English, French, German, or a mix. They can also hand in assignments in any of these three languages.

Unterrichtssprache: In geradzahligen Jahren (2018, 2020, usw.) wird dieser Kurs auf Englisch unterrichtet; in ungeradzahligen Jahren auf Deutsch. Die Fragen an der schriftlichen Prüfung werden in der jeweiligen Unterrichtssprache gestellt; antworten dürfen die Studierenden jedoch auf Englisch, Französisch oder Deutsch. Ähnliches gilt für sonstige Aufgaben.

The overarching goal of this course (3 ECTS) is to impart the basic principles of quantitative research. While the focus is mainly on language and multilingualism research, the principles illustrated apply more broadly, too.

We will discuss the most commonly used research designs (e.g., pretest/posttest experiments and correlational studies) and elucidate terms and concepts frequently encountered in quantitative research.

Furthermore, we will devote special attention to the construction of questionnaires, as these are used - in some form or other - in most studies.

While research design and statistical analysis are inextricably intertwined, this course is not a statistics class. That said, students will learn how to visualise quantitative data using free, state-of-the-art software (viz., R; see http://r-project.org), which should smooth the learning curve for those interested in learning more about data analysis.

By the end of this course, students should:

- be familiar with the basic terms and concepts of quantitative research;

- know about the defining characteristics of the most commonly encountered research designs (experiments, quasi-experiments, observational studies) and be able to gauge the strengths and weaknesses of these designs;

- be familiar with common elicitation techniques and useful resources;

- be able to draw straightforward but effective graphs on the basis of real data.