Selected Topics in Psychological Statistics

Subject description

1. Research design and data analysis:

research designs in psychology and psychometrics, and their epistemological aspects;
computer simulation;
specific aspects of reporting the research outcomes in the area of psychological statistics.

2. Selected topics in psychometrics:

conceptual problems of psychological measurement (the validity problem, measurement scales, the nature of latent variables);
method variance in psychological measurement
comparative evaluation of psychometric paradigms.

3. Advanced linear modeling

the general linear model and its properties;
optimization methods in multivariate analysis;
resampling and robust methods;
(multivariate) analysis of (co)variance, repeated measures analysis;
linear mixed models;
latent variables and advanced topics in factor analysis;
structural equation modeling: path analysis, confirmatory factor analysis, general structural model; interaction, moderation and mediaton; modelling of growth and change;
latent class models.

4. Trends and challenges in psychological statistics:

Reproducibility of psychological research and credibility of results and conclusions in social sciences:
Bayesian methods in psychology and psychometrics.

The subject is taught in programs

Objectives and competences

Students upgrade their knowledge of the statistical and research methods, commonly used in psychology. They are familiar with all important types of research design and understand both their technical application and epistemological implications. They understand the principles of psychometric paradigms, methods for the analysis of group differences and psychometric modelling methods. They can perform an appropriate analysis even in nonstandard situations.

Students are able to use specialized software for complex analyses and acquires basic programming skills, which enables him/her an adjustment of analyses. They possess the skills of scientific communation and can explain the methods and results to specialists in other disciplines as well to laypeople.

Each student additionally deepens his/her knowledge of a selected area of methodology with regard to the topic of the dissertation.

Teaching and learning methods

Lectures, seminar presentations, seminar workshops.

Expected study results

Knowledge and understanding:

Students are familiar with all important types of research design and understand both their technical application and epistemological implications. They understand the principles of psychometric paradigms, methods for the analysis of group differences and psychometric modelling methods.

Basic sources and literature

Borsboom, D. (2005). Measuring the mind: Conceptual issues in contemporary psychometrics. Cambridge University Press.

Jason, L., & Glenwick, D. (Eds.). (2016). Handbook of methodological approaches to community-based research: Qualitative, quantitative, and mixed methods. Oxford university press.

Kline, R. B. (2003). Principles and Practice of Structural Equation Modeling (2nd Ed.). The Guilford Press.

Levy, R. in Mislevy, R. (2016). Bayesian psychometric modeling. CRC Press.

Sijtsma, K. in van der Ark, A. (2021). Measurement Models for Psychological Attributes. CRC Press.

Skrondal, A. & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Chapman & Hall/CRC.

West, B. T., Welch, K. B., & Galecki, A. T. (2006). Linear mixed models: a practical guide using statistical software. Chapman and Hall/CRC. ali Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (Vol. 1). Sage.

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