MKF3500 - Survey data analysis - 2017

Introduction to multivariate statistical techniques for the analysis of survey data and models to analyse the discrete choice behaviour of individuals. Topics include multivariate analysis of variance, principal components analysis, factor analysis, correspondence analysis and models of discrete choice behaviour. Statistical software and case studies will be utilised during this unit and students will apply the techniques to a variety of practical problems.

The learning goals associated with this unit are to:

1. demonstrate an understanding of the role that multivariate statistical techniques such as factor analysis, structural equation modelling, logistic regression, categorical data analysis, cluster analysis, multidimensional scaling and correspondence analysis, play in uncovering relationships and patterns in survey data
2. appraise the strengths and limitations of these techniques
3. apply tools in SPSS to generate solutions for the appropriate statistical techniques
4. demonstrate skills in using the appropriate statistical techniques from a user and provider perspective
5. demonstrate skills in communicating the results of the analysis so that decision making can be implemented.

Within semester assessment: 40% + Examination: 60%

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning, peer directed learning and online engagement.

See also Unit timetable information

Dr Anastasios Panagiotelis

ETX2111, ETX2121 or MKF2121

ETC3500, ETF3500, MKC3500