COURSE OUTLINE
|
Course Title |
Advanced Quantitative Research Methods |
|
Course Code |
MGT887 |
|
Prerequisite |
MGT880 |
|
Level |
Doctoral |
|
Year / Semester |
Spring semester |
|
ECTS |
10 |
|
Academic Year |
2026-2027 |
Module Lead
Module Leader Name: TBC
Email: TBC
Office: Room 317, 3rd Floor, School of Tourism Management, Hospitality and Entrepreneurship Building, Paphos
Office Hours: TBC
COURSE DESCRIPTION
The course introduces a variety of standard statistical methods used to analyze multivariate data, emphasizing the implementation and interpretations of these methods. Topics covered include computation of summary statistics, graphical techniques, the multivariate normal distribution, principal component analysis, factor analysis, classification/discrimination, cluster analysis, longitudinal analysis, non-linear model analysis.
COURSE AIMS
By the end of this course, students should successfully be able to:
- Comprehend the statistical rationale behind methods for multivariate data analysis
- Understand the linear algebraic formulations of multivariate techniques and their statistical properties
- Build statistical models for multivariate data
- Use appropriate multivariate methods for data analysis and interpret the results in the context of the data problem.
LEARNING OUTCOMES
Successful completion of the course will enable students to achieve the following outcomes:
|
Knowledge and Understanding |
Demonstrate advanced understanding of the theoretical foundations, statistical assumptions, and mathematical principles underlying multivariate data analysis techniques, including dimensionality reduction, classification, clustering, longitudinal analysis, time series analysis, and non-linear modelling. |
|
Intellectual/Cognitive Skills |
Critically evaluate research designs and multivariate analytical approaches; select appropriate statistical methods for complex research questions; interpret statistical findings and assess their theoretical and practical implications. |
|
Practical Skills |
Apply multivariate statistical techniques to real-world datasets using appropriate software; construct, estimate, validate, and interpret multivariate statistical models; conduct data screening, preliminary analyses, and advanced statistical procedures. |
|
Key Transferable Skills |
Develop analytical reasoning, quantitative problem-solving, data literacy, research evaluation, and evidence-based decision-making skills; communicate complex statistical findings effectively to academic and professional audiences. |
TEACHING METHODS
The course will utilise a mixture of online lectures and practical activities (in out of the class, formative or summative). Students will be asked to engage in participatory and reciprocal learning, drawing on their own experiences. All the material will be uploaded on Moodle. Students are expected to study the new material but also practice. Students have the responsibility to check Moodle regularly to ensure they have all relevant materials prepared for each week of the semester.
Students will be required to use a statistical software for the data analysis. Instructions for data analysis using the software will be given in class. Most homework assignments will require some computing (in R).
PROGRAMME AND CONTENT
|
Week |
Content |
|
1 |
Research philosophy, approaches to theory development, research design, research process. |
|
2 |
Primary and Secondary Data, Experiments and Observational Studies |
|
3 |
Data screening and preliminary analyses: Introduction to Univariate and Bivariate Analysis |
|
4 |
Introduction to Multivariate Analysis: Multivariate analysis of group differences. Repeated measure analysis of Variance/Analysis of Covariance |
|
5 |
Principal Component Analysis/Exploratory Factor Analysis/Confirmatory Factor Analysis |
|
6 |
Cluster Analysis |
|
7 |
Structural Equation Modeling (SEM) |
|
8 |
Longitudinal data analysis |
|
9 |
Multivariate Time Series analysis |
|
10 |
Non-Linear Models |
|
11 |
Non-Linear Models |
|
12 |
Meta Analysis |
|
13 |
Revision |
ASSESSMENT
|
Assessment Method |
Date |
Weighting |
|
Portfolio Activity |
- |
40% |
|
Midterm |
- |
25% |
|
Final Exam |
- |
35% |
The grading system is numerical, ranging from 0 to 10 in increments of 0.5. The minimum passing grade is 5.
Late assignments
No late homework assignments will be accepted with few exceptions. If you have documented reasons for missing work or needing extra time, please contact me as soon as possible. Where appropriate, due dates could be extended.
Academic Misconduct
Although students are encouraged to work together on assignments, each student is expected to write and submit individual solutions to homework problems. Academic misconduct will not be tolerated and will be dealt with procedurally in accordance with university policy.
INDICATIVE BIBLIOGRAPHY
Readings and resources for each seminar session will be made available through Moodle and supplementary teaching materials. Students will be expected to engage extensively with academic journal articles and methodological resources prior to each seminar session.
Required Reading
- Multivariate Data Analysis, by Joseph F. Hair Jr., William C. Black, Barry J. Babin, Rolph E. Anderson
https://www.amazon.com/Multivariate-Analysis-William-author-Anderson/dp/1473756545
- An Introduction to Applied Multivariate Analysis, by Tenko Raykov and George A. Marcoulides
https://onlinelibrary.wiley.com/doi/10.1111/j.1751-5823.2009.00074_18.x
- Applied Multivariate Data Analysis, by Brian S. Everitt, Graham Dunn
Applied Multivariate Data Analysis | Wiley Online Books
- An Introduction to Applied Multivariate Analysis with R, by Brian S. Everitt, Graham Dunn
An Introduction to Applied Multivariate Analysis with R | SpringerLink
