Overview

The content of the course will comprise of the following elements: Statistics & Experimental Design, Statistical Power & Effect Sizes, Advanced Regression, ANOVA and ANCOVA, Repeated-measures and Mixed ANOVA, Non-Parametric tests, Categorical data & Logistic Regression, and Predictive Analysis.

Assessment Strategy

-threshold -50 + Adequate answer to the question, largely based on lecture material. No real development of arguments. -good -60+ Reasonably comprehensive coverage. Well organised and structured. Good understanding of the material. -excellent -70+ Comprehensive and accurate coverage of the area clarity of argument and expression. Depth of insight into theoretical issues.

Learning Outcomes

• Be able to deeply understand one of the Research Methods topics of the module as to write a literature review that could be understood by an Undergraduate Psychology student

• Demonstrate an understanding of the questions asked by a number of statistical methods including (but not limited to) logistic regression, factor analysis, and multivariate ANOVA, and when such analyses can be used.

• Demonstrate an understanding of when the t-test, ANOVA and regression analyses can be used and their non parametric equivalent tests.

• Demonstrate knowledge of a broad range of methodological techniques including their theoretical background and applications.

• Interpret the output from SPSS or R analyses

• Use the statistical package (SPSS or R) to run t-tests, ANOVA and regression analyses and their non parametric equivalent tests, along with more advanced statistical methods