Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
Students will be exposed to a key element of the research process - data analysis. They will gain hands-on skills to analyze quantitative data and offer solutions to the research questions posed. This course provides students with an introduction to the fundamentals of multivariate data analysis. Students will acquire skills in the analysis of multivariate data from experimental and survey designs commonly used in business research. The course mainly deals with the techniques of analysis of variance, multiple regression analysis and factor analysis, and with their specific application to research in the applied business disciplines (eg. accounting, business information systems, international business, management, marketing, etc.). Emphasis is placed on the concepts and statistical theory of multivariate data analysis, along with a refresher on the basics of univariate analysis: descriptive and inferential statistical procedures. This practical and applied course is lab-based with a mix of short seminar style presentations and instruction in the use of statistical packages for specifying and estimating models involving multivariate data analysis.
This courseﾠis aimed at closing the widening statistics gap confronting research students. Business journals are becoming increasingly sophisticated. For example, at least one in five papers in the very top marketing/management journals uses quantitative modelling techniques. To read and critically evaluate the research output in leading journalsﾠstudents must develop knowledge of quantitative methods.ﾠ
RBUS6902 would be useful to students who wish to develop skills in the analysis of multivariate data from experimental and survey designs. Such data analysis skills are in high demand both in the industry and academia. This course will provide an introduction to multivariate data analysis with a specific emphasis on (a) the fundamentals of multiple regression (i.e., foundational concepts and statistical theory) and, (b) factor analysis. RBUS6902 will offer students a mix of classroom- and lab-based learning experiences. The lab sessions will have the purpose of providing students with an introduction to the application of multivariate data analysis.
However, the course will begin withﾠfundamental concepts of univariate and bivariate analysis to develop a proper foundation for understanding multivariate data analysis. The course will also introduce students to the statistical software SPSS.
Course requirements
Assumed background
Completion of the pre-requisite courses is assumed.ﾠThis course is designed for students intending to undertake an honours year in business management. Participants are expected to have a basic understanding of statistical analysis taught at the undergraduate level.
Restrictions
Restricted to students in the BAdvBus(Hons), BBusMan(Hons), GCBA, GDipBRM, MBA, MPhil and PhD programs. To enrol: BAdvBus(Hons) students must email bel@uq.edu.au; MBA students must email mba@business.uq.edu.au; all other students must email info@business.uq.edu.au
Quota: Min. 10 enrolments.
Course contact
Course staff
Lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please note: Teaching staff do not have access to the timetabling system to help with class allocation. Therefore, should you need help with your timetable and/or allocation of classes, please ensure you email business.mytimetable@uq.edu.au from your UQ student email account with the following details:
- Full name
- Student ID
- Course Code
Aims and outcomes
The course aims to prepare research students to develop knowledge of the research process as well as for reading and writing for leading journals by developing and deepening their understanding of multivariate data analysis. The course will help research students to better prepare for future academic careers as independent researchers. We begin with understanding the research process, including defining research questions, and contemplating a method to answer those questions by collecting, managing, and analysing relevant data. Learning data analysis will begin with a revision of fundamentals of statistics and basic univariate and bivariate analysis. We then discuss formulating, estimating, and interpreting multiple regression equations. This will be followed up by factor analysis. Both these techniques are vital in their own rights as well as for understanding more advanced techniques such as Structural Equation Modelling.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Develop knowledge of the fundamental theory (concepts, statistical theory) and application of statistical data analysis.
LO2.
Apply data analysis to your area of business research (e.g., management, marketing, strategy).
LO3.
Conduct data analysis using linear regression and factor analysis.
LO4.
Critically evaluate published research that uses multivariate data analysis techniques covered in the course.
LO5.
Report the results of data analysis per discipline standards.
LO6.
Critically discuss the validity of a research study, that uses any of the different types of multivariate data analysis techniques covered in this course, with an academic researcher.
Assessment
Assessment summary
Assessment details
Report
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
17/04/2025 2:00 pm
- Learning outcomes
- L01, L02, L05, L06
Task description
The report focuses on the completion of a computer exercise (Application of a Data Analysis technique) using software (e.g., SPSS). The data and topic on which students are to run the exercise will be provided in the class.
You will also be required to reflect on the principles/material covered and your learning experience in this course (first 6 weeks).
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
The assessment will be submitted via Blackboard.
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Report
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 60%
- Due date
13/06/2025 2:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
The report focuses on the completion of computer exercises using software (e.g., SPSS).
The data and topic on which students are to run the model for the exercise will be provided in the class.
You will also be required to reflect on the principles/material covered and your learning experience in this course.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
The assessment will be submitted via Blackboard.
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 29 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment
Supplementary assessment is available for this course.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
Seminar |
Introduction Course overview, core research design concepts, introduction to multivariate data analysis Learning outcomes: L01, L02, L04, L05 |
Week 2 |
Seminar |
Measurement, Scaling & Descriptive Statistics Learning outcomes: L01, L02, L05, L06 |
Week 3 |
Seminar |
Sampling & Correlation Analysis Lab 1: Intro to SPSS & Basic Data Management Lab 2: Descriptive statistics Learning outcomes: L01, L02, L05, L06 |
Week 4 |
Seminar |
Hypothesis testing & Crosstabulations Lab 3: Correlations Assignment 1 briefing Learning outcomes: L01, L02, L05, L06 |
Week 5 |
Seminar |
Research design & t-test Lab 4: Cross tabs Learning outcomes: L01, L02, L05 |
Week 6 |
Seminar |
Primary data collection: Experiments & ANOVA Lab 5: t-test Learning outcomes: L01, L02, L03, L05 |
Week 7 |
Seminar |
Primary data collection: Survey methods Lab 6: Analysis of variance Learning outcomes: L01, L02, L03, L04, L05, L06 |
Week 8 |
No student involvement (Breaks, information) |
Easter - Good Friday no class Good Friday Public Holiday - Friday 18 April 2025 |
Mid-sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 9 |
Seminar |
Measurement instrument design & Multiple regression analysis Lab 7: Multiple regression analysis Learning outcomes: L01, L02, L03, L04, L05, L06 |
Week 10 |
Seminar |
Mediation Analysis Assignment 2 briefing Learning outcomes: L01, L02, L03, L04, L05, L06 |
Week 11 |
Seminar |
Moderation Analysis Learning outcomes: L01, L02, L03, L04, L05, L06 |
Week 12 |
Seminar |
Factor Analysis Lab 8: Factor analysis Learning outcomes: L01, L02, L03, L04, L05, L06 |
Week 13 |
Seminar |
Course Review Learning outcomes: L01, L02, L03, L04, L05, L06 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.