5. BCN Statistics Course 2024

Optional BCN course

Preferably in the second year. Important: other statistics courses more tailored to your own background may be used to get exemption for this course.

Theme

Statistics in a bird’s eye view

Content

This course provides PhD students with an overview of the most important statistical concepts and methods and trains them to perform most of the basic (and some slightly more advanced) statistical analyses. We will be using the software R (and RStudio), which is freely available and provides excellent facilities for statistical analyses. The topics covered include the following: statistical concepts, descriptive statistics, correlations, t-tests, ANOVA, non-parametric tests, regression analysis.

Target Group

PhD students within BCN.

Application

Click here to register.

Credits

2 EC. Credits are assigned to students who attended at least 4 sessions (until the end of each session, or until the lab exercises have been made and checked).

Objectives

This course provides students with an overview of the most relevant aspects in using statistics through a practical and problem-oriented approach. The focus of the course will be on determining which test to use, how to use it, and how to interpret the results. The overall aim of the statistics lectures and computer seminars is for participants to feel comfortable with using the basic statistical procedures and to give them basic theoretical and practical knowledge necessary for (the supervision of) future research projects.
Given that the teacher of the course is a psycholinguist, the examples used in this course will focus on linguistic material. I should, however, be fairly straightforward to apply these statistics to data from your own field.
After completing this course, students will be able to understand, explain, and critically evaluate the soundness of the most commonly used methods and statistical results reported on in scientific articles. Moreover, they will be able to process and analyse their own data in R.

Form

The course consists of online pre-recorded videos in combination with five afternoon sessions of about 4 hours each. The pre-recorded videos cover a variety of topics and are designed as an introductory course in statistics. These videos can be viewed online at any time, but should (ideally) be viewed before the specific afternoon session. During the five scheduled afternoon sessions, questions about the topic and the content of the videos for that session will be discussed. The main part of the session, however, will be spent working on the associated lab session of that day.

The schedule of the course is as follows:
Session 1: Introduction, Variables & Descriptive Statistics
Session 2: Statistical Logic & Inferential Statistics
Session 3: Assessing Relationships versus Comparing Groups
Session 4: Simple and Multiple Linear Regression
Session 5: More Advanced Group Comparisons and Regression Models

Period

On 5 afternoons, probably April 2025.

Final attainment level

After the course, you are able to select and perform an appropriate statistical method for the most frequently occurring problems in the cognitive and behavioural sciences.

Required Entrance Knowledge

This introductory course in statistics and R/RStudio was designed for students who:
are not familiar with statistics and/or using R/RStudio and who do need the knowledge and skills to be able to analyse their own research results;
are familiar with statistics and/or using R/RStudio, but who would like to update their existing knowledge and skills;
are familiar with statistics and using SPSS (or similar software) and would like to switch to using R.

Session-to-session program

Session 1: Introduction, Variables & Descriptive Statistics

The first lecture will start out with a general introduction to statistics during which we will discuss basic terminology that participants need to understand in order to conduct the most commonly used statistical analyses. Furthermore, we will discuss the most important conventions that characterise empirical research and will look into descriptive statistics.
The first computer seminar will consist of an introduction to R and RStudio, and you will learn to work with a particular package (R Markdown) that allows you to turn your analyses into an easy-to-read document. You will also perform some first analyses involving descriptive statistics.

Session 2: Statistical Logic & Inferential Statistics

Whereas descriptive statistics can be used to get a first impression of the data, inferential statistics should be used to assess whether there is an effect of or a relationship between variables. The second meeting will focus on the logic behind inferential or inductive statistics and we will discuss the issue of generalisation from samples to populations.
During the second computer seminar, you will be given a first introduction to performing one of the most common statistical tests (the t-test). 

Session 3: Assessing Relationships versus Comparing Groups

The third session lecture will elaborate on specific types of statistical tests within, and to a certain degree slightly beyond, the two broad categories that are often distinguished within statistics: assessing relationships versus comparing group means.
The associated computer seminar will allow participants to practise doing simple means analysis as well as correlation analysis.

Session 4: Simple and Multiple Linear Regression

Regression is a technique used to predict an outcome on the basis of one or more predictor variables. It is an important statistical technique, especially since experimental researchers in the field are increasingly using more flexible and advanced regression techniques to analyse their data (e.g., mixed-effects regression). In order to be able to understand these techniques, it is crucial to have a solid understanding of the basics of regression analyses, which will be introduced and discussed during today’s lecture.
Participants will get some hands-on experience performing a regression analysis during the computer seminar of session 4.

Session 5: More Advanced Group Comparisons and Regression Models

When you start to get the hang of research, you will probably start to add more groups or more variables to your research designs and we will discuss the consequences of these choices for the analyses and the interpretations during our last meeting on Friday. You will learn about the more complicated, but commonly used, statistics for group comparisons (e.g., factorial ANOVAs) and we will also discuss the more advanced statistics that are increasingly being used (e.g. mixed-effects regression analyses).
During the last computer seminar using R and RStudio, participants will perform a factorial ANOVA. Additionally, they will have the option to practise conducting some of the other tests discussed during the course.

Exemption

Students who have already participated in a similar statistics course may ask their supervisor to send a brief e-mail to the PhD coordinator explaining why he or she should receive exemption for this course.