Applied Research Methods: Statistical Analysis of Comparisons & Group Differences

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Determine the appropriate data analytic technique to use with different research designs
• Design effective research studies using between groups and repeated measures designs
• Understand how repeated measures are incorporated in ANOVA
• Identify ANOVA designs from description of experiments
• Analyse quantitative data in SPSS appropriately using: Simple, Factorial, Repeated measures and Mixed Model ANOVAs, ANCOVA, or their non-parametric alternative
• Use standard software packages for data analysis (e.g. SPSS)
• Interpret statistical interactions
• Critique published reports that employ analysis of variance in psychology

• T-tests, One way ANOVA and non-parametric equivalents
• Factorial ANOVA
• Repeated measures ANOVA and mixed model ANOVA
• ANCOVA
• MANOVA

Each three-hour weekly session is a combination of lecture and hands-on practical application.

Formal lecture is kept to a minimum, with an emphasis on developing a workshop atmosphere through the creative use of a variety of practical exercises. The practical exercises include guided small group exercises (e.g. designing experiments to test certain hypotheses, article critiques (e.g., to discuss suitability of data analysis), statistical analysis and interpretation of results based on existing datasets (e.g., various forms of ANOVA). Staff provide verbal formative feedback during these activities to help you gauge and develop your knowledge and understanding.

A variety of e-learning resources relevant to the course are available on Blackboard. These include recordings of lectures, copies of in-class assignments and exercises, datasets for analysis, online tutorials, a research methods and statistics glossary, and links to online statistics and research methods textbooks.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.