Advanced Data Analysis in Clinical and Health Psychology

Recommended Prerequisites

This curricular unit implies basic knowledge in descriptive and inferential statistics, as well as basic notions of research methods in social and human sciences, particularly in the area of Psychology.

Teaching Methods

This course unit is guided by the principle of "learning by doing", and is achieved by interactive moments in classes: one theoretical-practical (lecture method) that includes the presentation of data analysis methods and discussion of examples of practical applications (e.g. analysis of articles); and one essentially applied, including the training of practical skills and problem solving, and the application by students of different methods using statistical software (e.g. SPSS, PROCESS), critical analysis of applications of different techniques and presentation of results in thesis/article. 

Learning Outcomes

The aim of this course unit is to develop and consolidate the knowledge and skills that promote a more advanced practice in data analysis through the use of established statistical software. This course unit also aims to provide methodological and technical knowledge related to the concept of measure (definition and operationalization) in Clinical and Health Psychology (CHP), including different procedures in the application and validation of assessment instruments. It is intended that students develop practical skills of analysis of quantitative and qualitative data, and strengthen their statistical reasoning and the ability to decide and select the most appropriate techniques for different and complex research problems/plans (including the use of statistical software), develop skills for interpreting the results obtained, and use clear and correct statistical language, including its writing based on commonly accepted publishing standards (e.g., APA).

Work Placement(s)

No

Syllabus

1 Basic notions of data analysis/statistics and use of statistical software

 

2 Descriptive statistics and graphic representation

 

3 Inferential statistics

3.1 Parametric and non-parametric univariate and multivariate methods

3.2 Multivariate analysis of variance, covariance and repeated measures

3.3 Models of simple, multiple and logistic regression

3.4 Report findings in articles

 

4. Psychometrics

4.1 Measurement theory/Psychometrics and statistics applied to psychometrics

4.2 Adaptation/validation of psychological assessment questionnaires: Precision and validity

4.3 Exploratory factor analysis and confirmatory factor analysis

4.4 Report results in articles

 

5 Modulation: Mediation and moderation

5.1 Mediation: Partial and total; Total, direct and indirect effects

5.2 Moderation: Main and interaction effects; categorical and continuous moderators

5.3 Applications with software (SPSS e PROCESS)

5.4 Report findings in articles

 

6. Methods of analysis of qualitative data

Head Lecturer(s)

Helena Teresa da Cruz Moreira

Assessment Methods

Assessment
Individual test, in the classroom: 100.0%

Bibliography

Byrne, B. (2010). Structural equation modeling with Amos: Basic concepts, applications and programming (2nd ed.). New Jersey, NJ: Lawrence Erlbaum Associates.

 

Cohen, R.J., & Swerdlik, M.E. (2018). Psychological testing and assessment: An introduction to tests and measurement (9th ed.). New York, NY: McGraw-Hill.

 

Field, A. (2017). Discovering statistics using SPSS (5th ed.). London: Sage Publications.

 

Hayes, A.F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford Press.

 

Jose, P.E. (2013). Doing statistical mediation and moderation. New York, NY: The Guilford Press.

 

Miles, M.B., Huberman, A.M., & Saldana, J. (2019). Qualitative data analysis: A methods sourcebook (4th ed.). London: Sage.

 

Tabachnick, B., & Fidell, L. (2014). Using multivariate statistics (6th ed.). Harlow: Pearson Education.