Aims: To cover in greater depth and detail some of the topics covered in undergraduate psychology research and statistics courses.
To introduce to more advanced data analysis procedures, particularly those that introduce statistical control into the analysis of data or permit the advanced statistical modelling of data. To introduce concepts and tools (such as matrix algebra, path analysis and structural equation modelling) that can help to provide a unified approach to the understanding or application of advanced statistical techniques.
OBJECTIVES:
By the end of the course, students should be confident in using a number of advanced statistical techniques to analyse data, including:
ANOVA
ANCOVA
Multiple linear regression
Logistic regression
Factor analysis (principal components analysis)
Path analysis
Power analysis
Students are expected to understand when these techniques can be used and have an appreciation of how to address common problems encountered when using these procedures. Students should know how interpret and report these techniques appropriately, and be aware of common errors in the interpretation of data.
In addition students will know how to interpret the results of structural equation models and to know when these approaches are appropriate.
Students will have considerable experience of using SPSS to manage and analyse data, and should have sufficient statistical computing skills to independently analyse a postgraduate research project.
To introduce to more advanced data analysis procedures, particularly those that introduce statistical control into the analysis of data or permit the advanced statistical modelling of data. To introduce concepts and tools (such as matrix algebra, path analysis and structural equation modelling) that can help to provide a unified approach to the understanding or application of advanced statistical techniques.
OBJECTIVES:
By the end of the course, students should be confident in using a number of advanced statistical techniques to analyse data, including:
ANOVA
ANCOVA
Multiple linear regression
Logistic regression
Factor analysis (principal components analysis)
Path analysis
Power analysis
Students are expected to understand when these techniques can be used and have an appreciation of how to address common problems encountered when using these procedures. Students should know how interpret and report these techniques appropriately, and be aware of common errors in the interpretation of data.
In addition students will know how to interpret the results of structural equation models and to know when these approaches are appropriate.
Students will have considerable experience of using SPSS to manage and analyse data, and should have sufficient statistical computing skills to independently analyse a postgraduate research project.
- Module Supervisor: Nicolas Geeraert
- Module Supervisor: John Hanley
- Module Supervisor: Steffan Kennett