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Unit outline_

BUSS7902: Quantitative Business Research Methods

Semester 2, 2024 [Normal day] - Camperdown/Darlington, Sydney

This unit introduces Business School HDR students to quantitative techniques for research. It provides students with a review or introduction to the types of quantitative analyses that they may be required to know, discuss or conduct, both during their PhD and in their future working lives. It aims to provide a basic training with a focus on statistical and business analysis methods.

Unit details and rules

Academic unit Business School
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

Basic knowledge of statistical concepts

Available to study abroad and exchange students

No

Teaching staff

Coordinator Claire Liu, claire.liu@sydney.edu.au
Lecturer(s) Claire Liu, claire.liu@sydney.edu.au
The census date for this unit availability is 2 September 2024
Type Description Weight Due Length
Assignment Individual Assignment 1
Data analysis and problem solving
35% Week 07
Due date: 10 Sep 2024 at 23:59

Closing date: 10 Sep 2024
Maximum 12 pages (incl. computer output)
Outcomes assessed: LO1 LO2 LO3
Assignment Individual Assignment 2
Data analysis and problem solving
35% Week 11
Due date: 18 Oct 2024 at 23:59

Closing date: 18 Oct 2024
Maximum 12 pages (incl. computer output)
Outcomes assessed: LO1 LO2 LO3
Tutorial quiz Weekly Small Quiz
Computer-based/paper-based quiz
30% Weekly 30 minutes
Outcomes assessed: LO1 LO3 LO2

Assessment summary

  • Tutorial Quizzes: Throughout Week 2 to Week 12, students will participate in weekly in-class quizzes during tutorial sessions. These quizzes are desiged to assess understanding of fundamental statistical concepts and proficiency in problem-solving with Excel. The final mark for this assessment task will be determined by the highest scores achieved in 8 out of the 11 weekly quizzes. There will be no provision for replacement quizzes.
  • Individual Assignments: These practical assignments require students to apply correct statistical tools to familiar situations, exercise critical judgment, use a problem-solving approach, analyse data using Excel, and demonstrate their understanding of the concepts. These assignments have to be submitted electronically in PDF format via Turnitin.

Detailed information for each assessment task can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (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.

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 Assessment Procedures 2011 provide that any written work submitted after the due date will be penalised by 5% of the maximum awardable mark for each calendar day after the due date. If the assessment is submitted more than ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 2023 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 2023. 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

WK Topic Learning activity Learning outcomes
Week 02 Introduction to Probability & Statistics (first class on 7 August) Lecture and tutorial (3 hr) LO1 LO2
Week 03 Variables and Probability Distributions (14 August) Lecture and tutorial (3 hr) LO1 LO2
Week 04 Sampling Distribution (21 August) Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 05 Statistical Inference I & II (28 August) Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 06 Statistical Inference III & IV (4 September) Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 07 Regression Analysis I (11 September) Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 08 Regression Analysis II (18 September) Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 09 Regression Analysis III (25 September) Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 10 Regression Analysis IV (9 October) Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 11 Review and consultation Seminar (3 hr) LO1 LO2 LO3

Attendance and class requirements

Lecture recordings: All lectures and seminars are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. 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

Useful references:

  • Selvanathan E.A., Selvanathan, S and Keller, G. (2017). Business Statistics, Australia and New Zealand, 7th or 8th Edition, Cengage Learning. 
  • James Stock and Mark Watson (2019). Introduction to Econometrics, 4th Edition, Pearson.

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.

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

  • LO1. Apply research principles and methods for gathering and analysing data/information relevant to your major field of study
  • LO2. Use the descriptive statistics tools to create and analyse results using your data for the research project, and apply the learnt statistical concepts to the research project
  • LO3. Evaluate and interpret the results of using quantitative methods in your project, and communicate statistical knowledge and outcomes relevant to the 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Replaced the small test (10%) by weekly tutorial quizzes (30%) Reduced Assignment 1 weight from 40% to 35% Reduced Assignment 2 weight from 50% to 35% Update the timing of delivery of lectures and assessment due dates.

Work, health and safety

Attendance is compulsory for the weekly in-class tutorial quizzes. 

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.