Students

MQBS8950 – Advanced Business Research Methods II

2025 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Mostafa Hasan
mostafa.hasan@mq.edu.au
Jianlei Han
jianlei.han@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
Admission to Graduate Diploma of Research in Business
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

This unit is designed to equip students with an in-depth understanding and practical application of advanced research methods in business. Building upon foundational knowledge about the research process and basic research methods, this unit delves into more advanced methods, including advanced multivariate analysis and machine learning, that are essential for conducting rigorous and impactful research to solve contemporary business problems.

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: Apply a range of multivariate methods to analyse data in business contexts and communicate the results.
  • ULO2: Employ quantitative research methods in a variety of business contexts and communicate the results.
  • ULO3: Critically analyse the relevance and limitations of advanced quantitative research methods used in business research.

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 apply for Special Consideration.

Assessment Tasks

Name Weighting Hurdle Due
Professional Practice: Advanced Data Analysis for Business 25% No Week 4
Skills Development: Advanced Quantitative Analysis 25% No Week 8
Professional Practice: Applied Quantitative Analysis 50% No Week 13

Professional Practice: Advanced Data Analysis for Business

Assessment Type 1: Project
Indicative Time on Task 2: 28 hours
Due: Week 4
Weighting: 25%

 

The purpose of this assessment is for you to apply advanced data analysis techniques to real-world business problems.

You will engage with complex datasets to detect patterns, test hypotheses, and derive evidence-based insights, using statistical software and critically reflecting on your methodology.

Skills in focus:

  • Digital skills
  • Critical thinking and problem solving
  • Discipline knowledge

Deliverable: Quantitative analysis report (generated using statistical software, e.g., Stata) (further details provided on iLearn)

Individual assessment

 
On successful completion you will be able to:
  • Apply a range of multivariate methods to analyse data in business contexts and communicate the results.
  • Employ quantitative research methods in a variety of business contexts and communicate the results.

Skills Development: Advanced Quantitative Analysis

Assessment Type 1: Project
Indicative Time on Task 2: 28 hours
Due: Week 8
Weighting: 25%

 

The purpose of this assessment is for you to independently design and execute a analysis using advanced quantitative methods.

You will define a business problem, justify your methodology, analyse data using statistical software, and communicate your findings in a structured report with critical reflection on your approach.

Skills in focus:

  • Work readiness
  • Digital skills
  • Critical thinking and problem solving

Deliverable: Structured quantitative analysis report (details provided on iLearn).

Individual assessment

 
On successful completion you will be able to:
  • Apply a range of multivariate methods to analyse data in business contexts and communicate the results.
  • Critically analyse the relevance and limitations of advanced quantitative research methods used in business research.

Professional Practice: Applied Quantitative Analysis

Assessment Type 1: Report
Indicative Time on Task 2: 42 hours
Due: Week 13
Weighting: 50%

 

The purpose of this assessment is for you to demonstrate your understanding of core advanced business research methods, including multivariate techniques and introductory machine learning concepts.

You will complete a research report that applies these methods to business problems, interpreting results and critically evaluating the appropriateness of each technique.

Skills in focus:

  • Work readiness
  • Critical thinking and problem solving
  • Digital skills

Deliverable: Written Report(details provided on iLearn)

Individual assessment

 
On successful completion you will be able to:
  • Apply a range of multivariate methods to analyse data in business contexts and communicate the results.
  • Employ quantitative research methods in a variety of business contexts and communicate the results.
  • Critically analyse the relevance and limitations of advanced quantitative research methods used in business research.

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

Seminars:

There are 3 hours of face-t-face teaching per week The timetable for classes can be found on the University web site at: http://www.timetables.mq.edu.au/

Required Text:

Daniels, L., & Minot, N. (2025). An introduction to statistics and data analysis using Stata®: From research design to final report. 2e, Sage Publications.

Statistical package:

Stata

Consultation Times: The consultation timetable will be made available on iLearn at the beginning of the session. Consultation will start from Week 2. You are encouraged to seek help at a time that is convenient to you from a staff member teaching on this unit during their regular consultation hours.

Students experiencing significant difficulties with any topic in the unit must seek assistance immediately.

Unit Web Page: Course material is available on the learning management system (iLearn). The web page for this unit can be found at: https://ilearn.mq.edu.au/ from where you need to login to iLearn.

Unit Schedule

Week

Thursday

Lecture Topics

Readings

(from Daniels and

Minot, 2025)

Assessment Due

1

31-Jul
  • A brief overview of the research process
  • Sampling techniques

Chapter 1

Chapter 2

 

2

7-Aug
  • An introduction to Stata
  • Preparing and transforming your data
  • Data source: an introduction to WRDS

Chapter 4

Chapter 5

 

In-class practice

Compustat

Corporate culture data

3

14-Aug
  • Descriptive statistics
  • Correlation and mean/median difference tests
  • Chi square test

Chapter 6

Chapter 7 - 11

In-class practice

 

Data file: corporate culture and carbon emission

4

21-Aug

Linear regression analysis: Coding, presentation, tabulation, interpretation

Chapter 12

 

In-class practice

Data file: corporate culture and carbon emission

Project submission

5

28-Aug

Regression diagnostics

Chapter 13

 

6

4-Sep

Regression analysis with binary dependent variable

Chapter 14

In-class practice

7

11-Sep

Introduction to advanced topics in regression analysis

 

 

8

18-Sep

Writing a Research Paper (I)

Chapter 15

In-class practice

Project submission

 

Mid-Session Break

 

 

9

9-Oct

Writing a Research Paper (II)

Chapter 16

 

10

16-Oct

Machine Learning Methods (I)

 

 

11

23-Oct

Machine Learning Methods (II)

 

 

12

30-Oct

Introduction to Text Mining

 

 

13

6-Nov

Revision

 

Report submission

Note: This unit schedule is subject to change at the discretion of Unit Convenor.

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/

Academic Success

Academic Success 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 2025.04 of the Handbook