Class Data Analysis Methods in Neuropsychology

This course aims to provide students with the conceptual knowledge and practical skills required to implement the various data analysis methods applicable within the scope of Clinical Neuropsychology. Following up the syllabus covered in Research Methods in Neuropsychology, the classes will rely on examples of study designs and methods discussed throughout this previous course (e.g., EEG, fNIR). Furthermore, it is intended that students not only acquire a global understanding of existing analysis methods, but also the critical reasoning necessary to identify which procedures are most adequate for their research project. Thus, this course is a critical component in the development of the required skills for the personalized conception of the data analysis strategy to be implemented in  “Dissertation Project in Clinical Neuropsychology”, which will then be employed for the Dissertation in the 2nd year of Master’s.

Not applicable

S1: Introduction to statistics in research and its role in the extraction of empirical evidence: - Hypothesis formulation and testing; - Sampling and significance. S2: Association Tests (Correlation and Linear Regression) and Difference Tests (t-test, ANOVA and ANCOVA).   S3: Multivariate Models of Dependence with one or more Outcome Variables (Multiple Regression, MANOVA, MANCOVA). S4: Multivariate Interdependence Models and Dimensionality Reduction (Factorial Analysis, Cluster Analysis). S5: Relevance and operationalization of Open Science best practices in data analysis and interpretation: planning, reporting and resource sharing.

In theoretical-practical classes, the method of oral exposition with audiovisual resources (TM1) aims to provide the theoretical-conceptual bases for various data analysis methodologies, present he main tools for their implementation and their applicability in neuropsychology research.   The practical-laboratory classes aim to carry out various data analysis procedures (checking assumptions, interpreting outputs, and writing results) on real open access bases with variables of interest to neuropsychology (applied training, TM2). This exercise will be carried out using JASP (open-source tool), allowing the acquisition of skills that can be transferred to the experimental and clinical research planned for attaining the master’s degree. There will be moments of sharing solutions to the proposed exercises with feedback from the professors and peers (ME3, ME4) to monitor the development of the practical work to be completed by the end of the course.

Dienes, Z. (2008). Understanding psychology as a science: An introduction to scientific and statistical inference. New York: Palgrave Macmillan. Tabachnick, B. G., & Fidell, L. S. (2012). Using multivariate statistics. Prentice Hall. Navarro, D.J., Foxcroft, D.R., & Faulkenberry, T.J. (2019). Learning Statistics with JASP: A Tutorial for Psychology Students and Other Beginners. Grahe, J. (2021). A journey into open science and research transparency in psychology. Routledge.