777421

Laura Winther Balling (course coordinator, associate professor of experimental psycholinguistics, CBS) and

Søren Feodor Nielsen (professor of statistics, CBS).

The main prerequisite is an interest in doing quantitative research. The course does not require any prior knowledge about mathematics or statistics. The starting point is questions and datasets from communication studies and linguistics, but a background in these fields is not assumed. We assume that the participants have read and worked seriously with the assigned readings and are prepared for a steep learning curve. Participants who have data from their own projects may bring them for discussion and analysis during the course but this is not a requirement.

The purpose of the course is to introduce the participants to methods and tools that will enable them to conduct sound quantitative investigations and appropriate statistical analyses in the field of professional intercultural communication, in their PhD dissertations and further careers inside and outside of academia. The course is part of the PhD programme in professional intercultural communication but is of interest to all students from communication studies, linguistics and the humanities more generally who are doing or plan to do quantitative research. We focus on the practical applications of statistics and include a substantial amount of hands-on examples and exercises.

The course will cover the following topics:

• Fundamentals of quantitative research design
• Quantitative methods for studying language and communication behaviour: experiments, corpora and questionnaires
• Data manipulation in spread sheets and the statistics environment R (www.r-project.org)
• Graphical data exploration
• Basic statistical tests
• Regression analyses with a focus on mixed-effect models

The course will consist of a mixture of lectures, discussions and exercises. Throughout the course, there will be a strong emphasis on practical applications and real-life data. The course will be taught in English.

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The participants will achieve

• An understanding of quantitative research design.
• An understanding of why statistics is important and what it can do for their research.
• A basic familiarity with R and relative ease of working with large sets of quantitative data.
• Knowledge about a range of quantitative methods that may be used to explore professional intercultural communication.
• An ability to explore quantitative data using appropriate graphics and descriptive statistics.
• An ability to choose and apply appropriate statistical tests from a range of tests common to the field(s).
• A basic understanding of the possibilities offered by regression designs and mixed-effects models.
• An ability to understand and evaluate the use of quantitative data and statistics by researchers and practitioners in their field.
• The ability to continue learning statistics.

Participants will have the chance to hand in a final report (maximally 10 pages) in which they use the methods from the course to address a research problem within their own research area. The final report must describe the problem the participant wants to examine, the type of conclusions the participant would like to draw, and the data collection and statistical analysis that is needed in order to support the conclusions. The deadline for submitting the report will be around October 1, more information will be provided at the beginning of the course. Approval of the report will give the participant an additional 1 ECTS.

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Computer preparation

Participants should bring a laptop with the following programmes and packages installed:

• the R language for statistical computing, from www.r-project.org
• the R Studio environment from www.rstudio.com which is the interface we recommend for working in R. There are short videos on the website to familiarise you with it; otherwise, we’ll work with it during the course.
• The R-packages: rms, languageR, Epi, ltm, psy, lmerTest, car (and packages that are automatically installed with these, if you check “install dependencies” in RStudio)

Before the course, participants should familiarise themselves with the R language by typing all the code in the first two chapters in Baayen’s book in their own version of R/RStudio, and by working through the R code school that can be found here http://tryr.codeschool.com/levels/1/challenges/1.

Other preparation

Participants should submit a 1-2 page summary of their PhD (or other research project) tolwb.ibc@cbs.dk by July 1. The summary should be written so that it is understandable to readers who are unfamiliar with the research field.

Anni Olesen
Email: ao.research@cbs.dk
Tel: +45 3815 3939