COURSE OUTLINE
|
Course Title |
Introduction to Quantitative Research Methods |
|
Course Code |
ΜΗΤ_880 |
|
Prerequisite |
None |
|
Level |
Doctoral |
|
Year / Semester |
Fall semester |
|
ECTS |
10 |
|
Academic Year |
2026-2027 |
Course Coordinator
Coordinator Name: Dr Katerina Pericleous
Email: katerina.pericleous@cut.ac.cy
Office: Room 310, 3rd Floor, School of Tourism Management, Hospitality and Entrepreneurship Building, Paphos
Office Hours: Monday 12:00–13:00 & Thursday 12:00–13:00
COURSE DESCRIPTION
The course is intended as an introduction to a diverse range of quantitative analysis methods and research techniques commonly used in the social sciences. Since the central objective of the course is to enable students to become informed and critical readers of research literature, emphasis is placed on the purposes and limitations of selected statistical procedures. This requires an understanding of the fundamental principles underpinning data collection and quantitative data analysis, with particular emphasis on univariate and bivariate statistical techniques, as well as more advanced multivariate methods.
The course provides a comprehensive introduction to quantitative research methods, progressing from fundamental concepts to advanced applications. It begins with an overview of research methodology, research ethics, the formulation of research questions, literature review, and questionnaire design, followed by sampling techniques and the application of descriptive and inferential statistics, including measures of central tendency and dispersion, hypothesis testing, t-tests, ANOVA, chi-square tests, and correlation analysis. Advanced topics include Principal Component Analysis (PCA), Factor Analysis, Cluster Analysis, Discriminant Analysis, Linear and Non-Linear Regression Models, and theory testing through mediation and moderation analyses. Particular emphasis is also placed on Structural Equation Modeling (SEM) using AMOS, Big Data and analytics methodologies, mixed-methods research, and the application of Artificial Intelligence (AI) tools in research. The course concludes with student project presentations and a comprehensive review of the material covered.
The course also includes a laboratory component designed to familiarize students with statistical software packages commonly used for data analysis.
COURSE AIMS
The aim of this course is to develop students’ understanding of quantitative research methods, statistical analysis, and data interpretation within the social sciences, business and management. Through this course, students will have the opportunity to:
- develop knowledge of basic statistical procedures and their applications in social science research.
- strengthen their ability to select and apply appropriate statistical methods to different research questions and problems.
- develop a responsible and ethical approach to the use of statistical techniques, avoiding misuse and misinterpretation of data.
- gain an understanding of the characteristics, strengths, and limitations of quantitative methods in business and management research.
- enhance their skills in identifying, interpreting, and utilizing quantitative data from scientific and professional publications.
- develop a critical perspective regarding the validity, reliability, and quality of quantitative research findings.
- gain an understanding of the fundamental concepts of validity and reliability in quantitative analysis.
- strengthen their ability to evaluate the appropriateness and rigor of statistical analyses reported in published studies.
- develop responsibility and transparency in the interpretation, evaluation, and presentation of research findings.
- become familiar with the basic functions and applications of statistical software packages such as SPSS, AMOS, and R.
- acquire practical skills in entering, managing, analyzing, and interpreting data using statistical software.
- develop readiness to adopt and utilize emerging analytical tools and technologies in quantitative research and data analysis.
LEARNING OUTCOMES
Successful completion of the course will enable students to achieve the following outcomes:
|
Knowledge and Understanding |
Demonstrate critical understanding of quantitative research methodologies, statistical procedures, and data analysis techniques used in management and the social sciences. |
|
Intellectual/Cognitive Skills |
Select, evaluate, and apply appropriate statistical techniques; critically assess quantitative analyses and methodological choices in published research. |
|
Practical Skills |
Analyse quantitative data using statistical software and apply advanced analytical techniques |
|
Key Transferable Skills |
Develop analytical thinking, evidence-based decision making, quantitative reasoning, problem-solving abilities, and research literacy relevant to doctoral studies and professional research environments. |
TEACHING METTHODS
Teaching methods include:
- Interactive seminar sessions
- Group discussions & activities
- Use of visual materials
- Interactive exercises
PROGRAMME AND CONTENT
|
Session |
Title |
|
1 |
Introduction to Quantitative Research Design and Methodology |
|
2 |
Sampling Strategies and Data Collection Methods |
|
3 |
Descriptive and Inferential Statistical Analysis |
|
4 |
Factor Analysis and Principal Components Analysis |
|
5 |
Cluster and Discriminant Analysis |
|
6 |
Regression Analysis and Predictive Modeling |
|
7 |
Moderation, Mediation, and Theory Testing |
|
8 |
Structural Equation Modeling (SEM) |
|
9 |
Big Data Analytics and Business Applications |
|
10 |
Mixed Methods Research Design |
|
11 |
Artificial Intelligence Tools in Research and Data Analysis |
|
12 |
Research Project Presentations |
|
13 |
Course Review and Revision |
ASSESSMENT
|
Assessment Method |
Date |
Weighting |
|
Mid-term assessment |
- |
50% |
|
Final Exam |
- |
50% |
The grading system is numerical, ranging from 0 to 10 in increments of 0.5. The minimum passing grade is 5.
Mid-term assessment
Students are required to complete an individual quantitative research project of approximately 4,000 words. The project involves conducting a critical review of relevant quantitative research literature, designing and piloting a questionnaire, collecting and analyzing data from a sample of at least 100 participants, and presenting the findings using appropriate statistical techniques.
Students are expected to demonstrate their ability to formulate research questions, evaluate the validity and reliability of research instruments, interpret quantitative data, and communicate research findings effectively.
The completed project will provide evidence of students’ development of quantitative research skills, statistical literacy, methodological understanding, and critical analytical abilities throughout the course.
INDICATIVE BIBLIOGRAPHY
- Christou, P. A., & Pericleous, K. (2026). The research handbook: Short and smart. CABI.
- Περικλέους, Κ., & Χρίστου, Π. (2024). Το εγχειρίδιο έρευνας. Εκδόσεις Μπαρμπουνάκης.
- Giri, A., & Biswas, D. (2018). Research methodology for social sciences. Sage.
- Siegel, A. F. (2012). Practical business statistics. Academic Press.
- Fabrigar, L. R., & Wegener, D. T. (2012). Exploratory factor analysis. Oxford University Press.
- Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis (5th ed.). John Wiley & Sons.
- Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). The Guilford Press.
- Knapp, H. (2016). Introductory statistics using SPSS (2nd ed.). Sage.
- Morabito, V. (2015). Big data and analytics: Strategic and organizational impacts. Springer.
- Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Lawrence Erlbaum Associates.
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Pearson.
- Wooldridge, J. M. (2016). Introductory econometrics: A modern approach (6th ed.). Nelson Education.
- Moodle and supplementary teaching materials.
