Statistical Methods in Neuropsychology

Recommended Prerequisites

Some basic knowledge of statistics is recommended, such as measures of centrality and dispersion, hypothesis testing (e.g. t-tests, ANOVA and chi-square) and the concepts of correlation and regression.

Teaching Methods

This course will use a multitude of strategies and activities, using oral presentations, in-class discussions, group work, problem solving and data analysis using open source statistical software such as R and RStudio, and critical analysis of the different applications of the methodologies covered.

Learning Outcomes

This course introduces advanced statistical methods when working with empirical measurements of abstract constructs/instruments and multivariate analysis. Its main objective is to provide students with the understanding of the different statistical methods that will allow them to choose the appropriated analysis strategies in the multiple scenarios that can arise in neuropsychology.

Work Placement(s)

No

Syllabus

This course main topics are:

- Bayesian statistics,

- Non- and semi-parametric regression models,

- Conditional process models: Mediation Analysis, Moderated Mediation Analysis, Moderation Analysis, and Mediated Moderation Analysis,

- Reliability and validity of measurement,

- Exploratory and confirmatory factor analysis,

- Structural equation modeling,

- Growth curve modeling.

 

The primary software used in the course will be R (https://www.r-project.org/), and in particular RStudio (https://rstudio.com/), both open source software.

Head Lecturer(s)

Bruno Cecílio de Sousa

Assessment Methods

Assessment
Laboratory work or Field work: 50.0%
Frequency: 50.0%

Bibliography

Adams,K.,& Waldron-Perrine,B.(2014).Psychometrics, test design, and essential statistics. In K. Stucky et al (Eds.),Clinical neuropsychology study guide & Board Review (pp.79-114).NY: OUP.

Brown, T. (2006). Confirmatory factor analysis for applied research. NY: The Guilford Press.

Duncan, T. E., Duncan, S. C.  & Strycker, L. A.  (2004). An introduction to latent variable growth curve modeling: concepts, issues, and application (2^nd Edition). New York: Psychology Press.

Hayes, A. F. (2017). Introduction to Mediation, Moderation and Conditional Process Analysis (2^nd ed.). New York: Guilford.

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: The Guilford Press.

Snijders, T. A. B. & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). London: SAGE.

Tabachnick, B. & Fidell, L. (2007). Using multivariate statistics (5th ed.). Boston: Allyn & Bacon.