units

MKX4080

Faculty of Business and Economics

Undergraduate - Unit

This unit entry is for students who completed this unit in 2014 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

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6 points, SCA Band 3, 0.125 EFTSL

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered, or view unit timetables.

LevelUndergraduate
FacultyFaculty of Business and Economics
Organisational UnitDepartment of Marketing
OfferedCaulfield First semester 2014 (On-campus split block of classes)
Coordinator(s)Associate Professor Yelena Tsarenko

Synopsis

This unit introduces both multivariate statistical techniques for the analysis of survey data and models to analyse the discrete choice behaviour of individuals. These techniques enable the researcher to effectively analyse survey data and aid in the understanding of markets and of consumer behaviour. In this unit, we place emphasis on understanding the underlying assumptions required to conduct such analyses, appropriate interpretation and reporting of results and the use of these techniques in informing decision makers and academics.

Outcomes

The aim of this unit is to provide students with knowledge regarding multivariate data procedures in conduct of academic research in marketing. This unit is designed to fill the gaps in data analysis using sophisticated approaches and data collection methods. The aim is to provide knowledge of available data analysis techniques to address specific research questions, their strengths and weaknesses (limitations). The relevance to research design and sampling issues.

Upon successful completion of this unit students will:

  1. have an understanding of a range of data analysis procedures that may be appropriate when examining marketing issues.
  2. be able to identify, specify and run the various software programs.
  3. be able to interpret the output of these data analysis procedures.
  4. demonstrate an understanding of designing quantitative research and implications for sampling and sampling units.
  5. design research instruments to achieve validity and reliability and generalisability.

Assessment

Within semester assessment: 100%

Chief examiner(s)

Workload requirements

Minimum total expected workload equals 144 hours per semester