Factor Analysis and MANOVA
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|Date:||Monday 20 March 2017|
|Venue:||EN409 Hawthorn Campus|
Course: STAA0005B - Factor Analysis and MANOVA
Blackboard Site: STAA0005
Dates: Six 3 hour sessions: Mon 20/3-1/5 (5.30 pm – 8.30 pm) (18 Hours)
Assumed Knowledge: Simple Linear Regression and ANOVA (eg as in STAA0002)
Software used: SPSS
Maximum 20 students
Factor Analysis covers exploratory factor analysis (EFA). The various methods for extracting and rotating factors are discussed as are the interpretation of factors and the creation of factor scores and summated.
scales. EFA is a descriptive technique. That is, it is designed to help us understand and explain patterns in the data, without making any formal predictions about what results will look like. However, it is not our data’s job to tell us what its underlying structure is and a sound factor analytic study will begin with a great deal of prior thinking about the nature of the concept that we want to understand, appropriate indicators of that concept, appropriate population, and how results of factor analysis will be used. So even before we begin data collection, let alone data analysis, we will have an expectation about what the results might look like. The job of the data is then to show us how well our expectations are reflected in the ‘real world’. The results of exploratory factory analysis can then be used inform future hypotheses. These hypotheses are subsequently tested using confirmatory factor analysis (CFA), which is conducted within the structural equation modelling framework (not covered in this subject).
MANOVA examines between subjects, within subjects and mixed multivariate analysis of variance. Particular attention is paid to assumption checking, the testing of specific contrasts and report writing. Make sure that you have access to SPSS and please revise the relevant material for the ANOVA and Simple Linear Regression short course beforehand.
Make sure that you have access to SPSS and please revise the relevant material for the ANOVA and Simple Linear Regression short course beforehand.
Online quizzes will be available for self-assessment.
Contact Information: Charlotte Coles
Email: firstname.lastname@example.org Tel: 9214 8434