Students

MKTG3014 – Quantitative Insights in Marketing

2024 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor/ Senior Lecturer
Pardis Mohajerani
pardis.mohajerani@mq.edu.au
Contact via Email
Room 238, 4 Eastern Road
Mondays 3:30pm-4:30pm, online or F2F by appointment
Credit points Credit points
10
Prerequisites Prerequisites
130cp at 1000 level or above including MKTG2002
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 30% No Week 2 - Week 13
Quantitative Analysis Report One: Interdependence Techniques 35% No Week 8
Quantitative Analysis Report Two: Dependence Techniques 35% No Week 13

Practice-based activities

Assessment Type 1: Participatory task
Indicative Time on Task 2: 11 hours
Due: Week 2 - Week 13
Weighting: 30%

 

This is an individual task comprising weekly in-class activities that focus on quantitative data analysis, interpretation of results and provision of applied insights to real-world marketing problems/scenarios. Students will submit 10 activities and each activity is worth 3%.

 
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 Report One: Interdependence Techniques

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 12 hours
Due: Week 8
Weighting: 35%

 

This is an individual assessment that requires students to work on managerial and research problem and complete an appropriate set of interdependence quantitative data analyses to provide managerial insights. Word count: 2000 words

 
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 Report Two: Dependence Techniques

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 12 hours
Due: Week 13
Weighting: 35%

 

This is an individual assessment that requires students to complete an appropriate set of dependence quantitative data analyses and provide managerial insights and recommendations to solve real-world managerial problem. Word count: 2000 words

 
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

Delivery Format

  • 3 hours teaching per week consisting of: 1-hour pre-recorded lecture which will be uploaded on ilearn and 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

 

Teaching and Learning Strategy

This unit is aimed at students who have developed higher levels of strategic insight and who desire to improve their skills in data manipulation, analysis and presentation. This is a predominantly applied unit, designed to provide students with technical and analytical skills. Watching pre-recorded lectures prior to weekly tutorials is critical, as it is only by reviewing 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  SPSS software.  Students will need to practice and research outside of the classroom.

 

Classes

  • Students must familiarise themselves with the unit’s iLearn site.
  • Students should access the unit’s iLearn site regularly (minimum twice a week) and check for announcements, updates and posts.
  • Details of teaching times and locations are available at: https://timetables.mq.edu.au/
  • Students are expected to participate in classes, be prepared to discuss and particpate in class activities in each week.
  • Students are expected to arrive on time and not to leave until the class ends.

 

Required and Recommended Texts and/or Materials 

Prescribed text:

  • Joseph F Hair, Barry J. Babin, Rolph E. Anderson and William C. Black (2019) Multivariate Data Analysis, 8th Edition. Cengage. ISBN: 9781473756540
  • Note: MQ library provides free access to the 7th edition of the above book (e-book). More detailed information is provide on ilearn under 'Unit Infromation and Resources'. 

 

Recommended texts:

  • 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

  • The unit will make use of iLearn and email for communication with the teaching staff and between students.
  • Students will need access to a reliable Internet service to complete this unit.
  • Use of a PC, laptop and smart devices is required to complete tasks on iLearn, to access iLearn for course materials and to read the textbook.
  • In laboratories, we will use MS-Word, MS-Excel, SPSS software. 

 

Satisfactory Completion of 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 for more detailed information about Unit Schedule.

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 connect.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 the Service Connect Portal, 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.

Unit information based on version 2024.04 of the Handbook