This module provides a postgraduate-level orientation to advanced statistical issues in predictive models with a one-variable outcome. Students will learn techniques typically used for research in psychology and other disciplines that use sampling statistics, which ultimately depend on a common basis of linear modelling that follows a complex evolution into multiple specific applications. These may include: moderation with interactions involving ordinal variables; mediation models using regression; connections between linear models and traditional ANOVA approaches; factorial ANOVA, ANCOVA, repeated and mixed ANOVA; and multilevel modelling. The teaching assumes recent experience with basic statistical concepts, software, and tests which will be provided by a prerequisite module.
Total contact hours: 30
Total private study hours: 70
Total module study hours: 100
Compulsory to:
MSc Cognitive Psychology/Neuropsychology
MSc Developmental Psychology
MSc Forensic Psychology
MSc Political Psychology
MSc Social Psychology
Also compulsory on Psychology Postgraduate Research Courses.
Also available as an elective module.
20% Online Test (10 x 15 minutes in workshop)
80% Exam (1 x 120 minutes)*
*This element is pass compulsory and must be passed to achieve the learning outcomes of the module
Reassessment methods:
100% exam
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages.
1. Demonstrate conceptual and practical understanding of the rationale and technique of complex statistical approaches using linear models: for example, logistic regression, mediation, moderation, general linear models for ANOVA and repeated measures ANOVA, and multilevel analysis.
2. Use appropriate statistical software to conduct complex regression and ANOVA analyses including the specification of advanced-level models, working autonomously;
3. Interpret and critically evaluate results of complex regression and ANOVA analyses and outputs of statistical software, and make inferences from the results in applied settings;
4. Understand, generate, and critically evaluate results of complex regression and ANOVA procedures as they would be reported across various applied and basic psychological literatures.
The intended generic learning outcomes.
1 Appreciate positions and controversies related to advanced inferential statistical analysis;
2 Demonstrate an appreciation of the diverse applications of the taught applications of advanced linear modelling statistics and their relevance to the student's field of study and social sciences more broadly;
3 Acquire or improve competence in the use of statistical software to manage and code data, and to conduct inferential and descriptive analyses for a range of applications.
4 Act autonomously in problem-solving and be able to communicate observations to specialist and non-specialist audiences
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