This module is not currently running in 2024 to 2025.
This module provides a postgraduate-level orientation to both basic and advanced contemporary statistical and methodological issues in psychology. The methodological issues considered include qualitative research methodologies; experimental, quasi-experimental, and correlational research designs in the laboratory and field; and the fundamental issues in psychological measurement including reliability and validity. The statistical techniques taught include univariate and multivariate descriptive and inferential statistics; basic and advanced topics in ANOVA and ANCOVA; linear and logistic multiple regression; some scaling methods; classical test theory, factor analysis; fundamentals of structural equation modelling (path analysis, confirmatory factor analysis, multiple-group analysis), and some item response theory.
Total contact hours: 110
Private study hours: 290
Total study hours: 400
Compulsory to:
•MSc Cognitive Psychology/Neuropsychology
•MSc Developmental Psychology
•MSc Forensic Psychology
•MSc Political Psychology
•MSc Social Psychology
Main assessment methods
Two Online Tests, each formed of a separate 45 minute theory paper and a separate 2 hour computing paper.
Reassessment methods
Like for Like.
The most up to date reading list for each module can be found on the university's reading list pages.
See the library reading list for this module (Canterbury)
The intended subject specific learning outcomes. On successfully completing the module students will be able to:
8.1. Demonstrate a systemic understanding of the complex concepts and logic of statistical reasoning, using appropriate descriptive and inferential methods;
8.2. Comprehensively understand the fundamentals of scaling and methods used for measuring psychological variables;
8.3. Demonstrate a systemic understanding of the concepts of statistical model and model testing;
8.4. Use software to manage data, conduct descriptive analyses and test hypotheses; use software to specify and test structural equation models;
8.5. Interpret results of statistical analyses and outputs of statistical software; make inferences from the results in applied settings;
8.6. Systematically evaluate the appropriateness of statistical analysis methods to research design and data;
8.7. Effectively communicate results of statistical analyses orally and in writing.
8.8. Demonstrate a systemic understanding of how to apply qualitative, correlational and experimental research methods
The intended generic learning outcomes. On successfully completing the module students will be able to:
9.1 Demonstrate an understanding of complex theoretical positions and controversies related to methodology;
9.2 Demonstrate an appreciation of the diverse applications of statistics and its relevance to students' fields of study and social sciences more broadly.
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