Outline

Overview

This unit provides students with an introduction to advanced quantitative analysis techniques that they may be required to know, discuss or conduct, both during their PhD and in their future working lives. The unit is divided into four segments. The first segment reviews basic quantitative methods covered in BUSS7902 before considering issues around estimation and forecasting. Focus then switches to approaches for dealing with repeated measures including panel estimation methods and time series analysis, before consideration of ANOVA techniques and analogous non-parametric methods. Consideration is then given to the most widely used multivariate methods including factor analysis, multiple discriminant analysis, cluster analysis and structural equation modelling. The final segment covers categorical and discrete choice data analysis covering both the theory and practice of designing choice experiments and conducting sophisticated logit modelling applications. The unit covers both the theory and application of the various techniques with hands-on lab-based sessions and assignments crucial to the quality of the learning experience.

Unit details and rules

Academic unit	Business School
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	ECOF7904
Assumed knowledge ?	BUSS7902
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Yi Li, yi.li2@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written work	Assignment 3: fsQCA Application and Reflection Written Report	30%	STUVAC Due date: 16 Nov 2025 at 23:59 Closing date: 23 Nov 2025	1000 - 1200 words	AI allowed
Outcomes assessed:
Written work	Assignment 1: Textual Analysis Project Written Report	30%	Week 07 Due date: 21 Sep 2025 at 23:59 Closing date: 28 Sep 2025	500 - 700 words	AI allowed
Outcomes assessed:
Written work	Assignment 2: Regression Analysis Report Written Report	40%	Week 13 Due date: 09 Nov 2025 at 23:59 Closing date: 16 Nov 2025	1000 - 1200 words	AI allowed
Outcomes assessed:

Assessment summary

Assessment Overview

Assignment 1: Textual Analysis Project

Module: Module 2 – Textual Data Analysis
Weight: 30%
Due: Week 7
Brief:
Students will complete a practical textual analysis project using Python. The assignment involves building a corpus, performing text preprocessing (e.g., tokenization,
stop-word removal, and tf-idf), and conducting either sentiment analysis or topic modeling. Students are required to submit a Jupyter Notebook with well-documented
code and explanatory markdown (exported to HTML or PDF), alongside a short summary (500–700 words) explaining their analytical process and key findings.

Assignment 2: Regression Analysis Report

Module: Module 3 – Regression Analysis with Stata
Weight: 40%
Due: Week 13
Brief:
This assignment involves conducting and interpreting multiple regression analyses using Stata. Students will choose or be provided with a structured panel dataset, and
test relevant hypotheses using multivariate regression models through Stata software. A professional-style report (1,000–1,200 words) must include code output and
interpretation of results.

Assignment 3: fsQCA Application and Reflection

Module: Module 4 – Set-Theoretic Methods
Weight: 30%
Due: Stuvac
Brief:
In this assignment, students will apply fsQCA to a real or simulated dataset. Key steps include data calibration, building truth tables, and identifying sufficient and
necessary conditions for an outcome. A written report (1,000–1,200 words) must explain the logic of set-theoretic methods, report the results using standard notations,
and reflect on the implications of findings.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The School Late Penalty policy applies

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Module 1 – Introduction to Advanced Quantitative Methods in Business Research	Block teaching (3 hr)
Week 02	Module 2 – Managing and Accessing Textual Data for Business Research	Block teaching (3 hr)
Week 03	Module 2 – Constructing Textual Databases: Web Crawling and Preprocessing	Block teaching (3 hr)
Week 04	Module 2 – From Raw Text to Structured Features: Parsing and Tokenization	Block teaching (3 hr)
Week 05	Module 2 – Extracting Meaning: Sentiment Analysis and Topic Modeling	Block teaching (3 hr)
Week 06	Module 3 – Introduction to Stata and Linear Regression Basics	Block teaching (3 hr)
Week 07	Module 3 – Applied OLS Regression Using Stata	Block teaching (3 hr)
Week 08	Module 3 – Moderation and Interaction Effects in Regression Models	Block teaching (3 hr)
Week 09	Module 3 – Mediation Analysis in Regression Frameworks	Block teaching (3 hr)
Week 10	Module 3 – Panel Data Regression: Fixed and Random Effects Models	Block teaching (3 hr)
Week 11	Module 3 – Meta-Analysis with Stata: Concepts and Applications	Block teaching (3 hr)
Week 12	Module 4 – Fundamentals of fsQCA	Block teaching (3 hr)
Week 13	Module 4 – fsQCA Applications in Business Research	Block teaching (3 hr)

Attendance and class requirements

Students should ensure they attend and participate in all classes.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

R. Davidson and J. MacKinnon - Econometric Theory and Methods. (2004, Oxford University Press)
Bruce Hansen's Econometric Webpage: https://www.ssc.wisc.edu/~bhansen/econometrics/
Probability and Statistics: https://www.ssc.wisc.edu/~bhansen/probability/Probability.pdf
An Introduction to Statistical Learning with Application in R: https://web.stanford.edu/~hastie/ISLRv2_website.pdf
The Elements of Statistical Learning: https://web.stanford.edu/~hastie/Papers/ESLII.pdf
Analysis of Financial Time Series: https://www.wiley.com/en-us/Analysis+of+Financial+Time+Series%2C+3rd+Edition-p-9781118017098

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. understand some fundamental concepts and methods in statistics
LO2. employ advanced statistical models and appropriate statistical methods to analyse the data using statistical packages
LO3. interpret computer output and summarize research findings
LO4. relate statistical knowledge and outcomes to your research project.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Primary change has been included because of unit restructuring.

Additional information

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

BUSS7904: Advanced Quantitative Methods

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links