XSL Content

Quantitative methods

General details of the subject

Face-to-face degree course

Description and contextualization of the subject

Introduction to Matlab, Introduction to R; Formulating hypotheses and making predictions; Probability theory; Summarizing and displaying data, graphical methods, Experimental design:; Programming for stimulus presentation (DMDX, Presentation, cogent)

Teaching staff

NameInstitutionCategoryDoctorTeaching profileAreaE-mail
CABALLERO GAUDES, CESARBCBL- Basque Center on Cognition, Brain and
CARRION CASTILLO AMAIABCBL- Basque Center on Cognition, Brain and
GURTUBAY ANTOLIN, ANEBCBL- Basque Center on Cognition, Brain and


CE1. Working with descriptive data: summarising, classifying, graphic representation and oral presentation.15.0 %
CE2. Using statistical data analysis programmes (Excel, Statistica, R, Matlab etc.).15.0 %
CE3. Using data acquisition programmes15.0 %
CE1. Advanced knowledge of experimental design and data anaysis.15.0 %
CE2. Techniques for controlling and manipulating variables.10.0 %
CE3. Evaluating and interpreting new developments in data anlaysis.10.0 %
CE4. Applying theory to specific experimental design.10.0 %
CE5. Formulating hypotheses and designing experiments10.0 %

Study types

TypeFace-to-face hoursNon face-to-face hoursTotal hours
Applied classroom-based groups202040
Applied computer-based groups205070

Assessment systems

NameMinimum weightingMaximum weighting
Written examination (problems)50.0 % 0.0 %
Written examination (theory)50.0 % 0.0 %


The Quantitative Methods course is divided in three sections.

In the first section, you will get acquainted with the types of variables and data distributions that we use and observe in research related to the cognitive neuroscience of language. Core concepts of central tendency and measures of dispersion will be discussed and you will learn how to determine or calculate those. We will cover basic null-hypotheses testing (with, for example, t-tests and the chi-square test) and how to draw the appropriate conclusions from those tests. The relationship between variables is also covered, and you will learn to interpret output tables generated by statistical software.

The second section focuses on analysis of variance (ANOVA), the mostly commonly used statistical technique in much of the field. We will cover single-factor and two-factor designs, and the necessary follow-up tests for each of these. A single-factor ANOVA is essentially an expanded version of a t-test, with the expansion allowing you to run experiments that compare more than two groups. The two-factor ANOVA expands on the single-factor case, allowing you to look at the effects of two independent variables simultaneously. The follow-up tests allow you to pinpoint exactly where any observed effects in an ANOVA are coming from. We will see how each of these statistical tests is built to do what it is designed to do, and we will cover the assumptions that one makes when using these tests.

The third section of the course focuses on regression, another popular analytical method from the same family of tests as those covered in the first and second sections of the course, which is particularly well-suited for analyzing independent and dependent measures that fall across a continuum. Topics will include an overview of regression and its relation to other statistical tests, estimating regression coefficients, identifying and addressing violations of regression assumptions, and hypothesis testing.

Collectively, these three sections will provide a solid foundation for completing standard statistical analyses and prepare you for future learning about related analytical techniques.


Compulsory materials

Part 1: Learning statistics with R: A tutorial for psychology students and other beginners. (Danielle Navarro):

Part 2: Design and Analysis- a researcher’s handbook (G.Keppel and T.D. Wickens). BCBL would provide the textbook to each student at the beginning of the course.

Part 3: Discovering Statistics Using IBM SPSS Statistics (Andy Field)

Basic bibliography

Matlab for Neuroscientists by Wallisch et al. (selected chapters)

Linear Models with R by Julian Faraway (selected chapters)

XSL Content

Suggestions and requests