MKF5500 - Survey data analysis - 2017

This unit adopts a very practical approach to introducing multivariate statistical techniques that are currently popular in the analysis of business and marketing survey data. The main topics covered are: a review of statistical tools, factor analysis, structural equation modelling, cluster analysis, discriminant analysis, correspondence analysis and multivariate analysis of variance. SPSS software will be used for problem solving. The emphasis will be on understanding, interpreting and reporting results of the analysis and on the proper use of techniques. Case studies and exercises will be drawn from diverse areas of the business world.

The learning goals associated with this unit are to:

1. introduce exploratory factor analysis and structural equation modelling
2. show how multiple regression and multivariate analysis of covariance can be used to analyse survey data
3. provide exposure to logistic regression in discrete-choice modelling studies
4. demonstrate the role of cluster analysis and multidimensional scaling and correspondence analysis in understanding multivariate data
5. provide an opportunity to become competent in the use of a statistical package (SPSS).

Within semester assessment: 50% + Examination: 50%

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

Professor Felix Mavondo

ETX2121, ETX2111 or MBA9007

ETC3500, ETF3500, MKC3500, MKF3500