Students

MKTG3014 – Quantitative Insights in Marketing

2022 – Session 2, In person-scheduled-weekday, North Ryde

General Information

Download as PDF
Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor/Lecturer
Dr. Jun Yao
jun.yao@mq.edu.au
Contact via Email
4 Eastern Road, Room 207
Thursday 3:00pm - 4:00pm
Credit points Credit points
10
Prerequisites Prerequisites
40cp at 2000 level or above including MKTG2002 or MKTG202
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

Quantitative Insights plays a key role to business success, enabling marketers to effectively and insightfully understand markets and consumer behaviour. By employing sophisticated quantitative data collection and analysis methods, marketers are able to identify and evaluate market opportunities, analyse and select target markets, plan and implement marketing mix strategies, as well as assess marketing performance. This unit develops students’ knowledge of advanced data procedures in the context of academic and applied research in marketing. This unit focuses on developing students’ skills in using multivariate statistical techniques to analyse survey data and using quantitative models to analyse consumer discrete choice behaviour. In this unit, students gain knowledge to design and implement advanced quantitative research to address specific marketing questions, and to inform decision makers with the interpretation of results.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Explain and evaluate a range of quantitative techniques that are appropriate for examining marketing issues
  • ULO2: Design and implement research instruments to collect data to address marketing issues
  • ULO3: Analyse quantitative data, interpret and effectively communicate the results.

General Assessment Information

Late Assessment Submission Penalty (written assessments) 

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11.55pm. A 1-hour grace period is provided to students who experience a technical concern.  

For any late submissions of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.

Assessment Tasks

Name Weighting Hurdle Due
Practice-based activities 20% No Week 2 - Week 12
Quantitative Analysis (Multivariate Analysis Report) 40% No Week 8
Quantitative Analysis (Categorical Data Analysis Report) 40% No Week 13

Practice-based activities

Assessment Type 1: Participatory task
Indicative Time on Task 2: 11 hours
Due: Week 2 - Week 12
Weighting: 20%

 

This is an individual task comprising of weekly tutorial activities. These in-class activities will be focusing on quantitative data analysis and interpretation of results. Each activity is worth 2%. Students must have a minimum of 10 submissions.

 
On successful completion you will be able to:
  • Explain and evaluate a range of quantitative techniques that are appropriate for examining marketing issues
  • Analyse quantitative data, interpret and effectively communicate the results.

Quantitative Analysis (Multivariate Analysis Report)

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 12 hours
Due: Week 8
Weighting: 40%

 

This is an individual assessment that requires students to complete an appropriate set of multivariate analyses. Length: Max 2500 words (excluding Cover-page, Headings, Tables, Graphs, or Appendices).

 
On successful completion you will be able to:
  • Design and implement research instruments to collect data to address marketing issues
  • Analyse quantitative data, interpret and effectively communicate the results.

Quantitative Analysis (Categorical Data Analysis Report)

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 12 hours
Due: Week 13
Weighting: 40%

 

This is an individual assessment that requires students to complete an appropriate set of analyses on the choice data. Length: Max 2500 words (excluding Cover-page, Headings, Tables, Graphs, or Appendices).

 
On successful completion you will be able to:
  • Design and implement research instruments to collect data to address marketing issues
  • Analyse quantitative data, interpret and effectively communicate the results.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

Classes

  • 3 hours teaching per week consisting of: 1 × 1-hour lecture (online) and 1 × 2-hour tutorial (lab session). Tutorials commence in Week 1.
  • The timetable for classes can be found on the university website at: http://timetable.mq.edu.au

Required and Recommended Texts and/or Materials 

Prescribed text:

  • Joseph F Hair, Barry J. Babin, Rolph E. Anderson and William C. Black (2018) Multivariate Data Analysis, 8th Edition. Cengage. ISBN: 9781473756540

Recommended text:

  • Andrew F. Hayes (2018) Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, 2nd Edition. Guilford Publications. ISBN:  9781462534654
  • J. Scott Long and Jeremy Freese (2014) Regression Models for Categorical Dependent Variables Using Stata, 3rd Edition. Stata Press. ISBN: 9781597181112
  • Philip Hans Franses and Richard Paap (2010) Quantitative Models in Marketing Research. Cambridge University Press. ISBN: 9780511753794
  • James H. Myers and Gary M. Mullet (2003) Managerial Applications of Multivariate Analysis in Marketing. South-Western Educational Pub. ISBN: 9780877573012

Additional reading:

  • Zhao, X., Lynch Jr, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197-206.
  • Bergkvist, L., & Rossiter, J. R. (2007). The predictive validity of multiple-item versus single-item measures of the same constructs. Journal of Marketing Research, 44(2), 175-184.
  • Malhotra, N. K. (1984). The use of linear logit models in marketing research. Journal of Marketing Research, 21(1), 20-31.
  • Paas, L. J., & Sijtsma, K. (2008). Nonparametric item response theory for investigating dimensionality of marketing scales: A SERVQUAL application. Marketing Letters, 19(2), 157-170.
  • Rossiter, J. R. (2002). The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19(4), 305-335.

Technology Used and Required

  • Students will need to have access to a personal computer, with access to the Internet and word processor software.
  • In laboratories, we will use MS-Word, MS-Excel, SPSS and Sawtooth software.

Unit Web Page

  • The web page for this unit can be found at: iLearn http://ilearn.mq.edu.au
  • All announcements and resources will be available on the web site. Resource materials include lecture slides, practice questions, case studies and practice exam questions for both the within-semester and final exams. There is also a forum for student interaction and contact with faculty. You should consult the course Website several times per week for messages and updates.

Teaching and Learning Strategy

This unit is aimed at students who have developed higher levels of strategic insight and who desire improved skills in data manipulation, analysis and presentation. This is a predominantly applied course, designed to provide students with technical and analytical skills. Lecture attendance is critical, as it is only by attending lectures that students will understand the concepts used in tutorials. Tutorials are held in PC Labs and provide an opportunity to practice analytics hands-on using MS-Excel and Tableau software. The limited time in class is not sufficient to learn all that we will need to develop some competence in the software and methods discussed and examined. Students will need to practice and research outside of the classroom.

Satisfactory Completion of Unit

It is normally expected that students attempt all assessment tasks for this unit. Students are required to accumulate at least 50% of the total marks possible in order to satisfactorily pass this unit.

Unit Schedule

Please refer to iLearn.

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

The Writing Centre

The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via AskMQ, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Changes from Previous Offering

NA

Unit information based on version 2022.04 of the Handbook