Multivariate Data Analysis

Home/ Multivariate Data Analysis
Course TypeCourse CodeNo. Of Credits
Foundation ElectiveSBP3PM2022

Semester and Year Offered: 2nd Semester

Course Coordinator and Team:Nidhi Kaicker

Email of course

Pre-requisites: None

Aim: The objective of the course is to equip the participants with theoretical, computational, and interpretive issues of multivariate techniques.

Course Outcomes:

At the end of the course, the participants will be able to:

  1. Use and theoretically describe methods for exploratory and predictive multivariate data analysis
  2. Understand the specificity of each type of multivariate method and determine which one is appropriate for a given research problem
  3. Implement the analysis using atleast one statistical software such as SPSS, R, Matlab
  4. Interpret results and identify managerial / policy implications from the same.

Brief description of modules/ Main modules:

Unit 1: Introduction to Multivariate Analysis: Working with multivariate data; Multivariate descriptive statistics; Partial correlations; Multivariate analysis of variance (MANOVA).

Unit 2: Data Reduction Techniques: Exploratory factor analysis; Principal component analysis; Cluster analysis;Multidimensional scaling; Correspondence analysis – implementation of analysis using SPSS

Unit 3: Dependence Methods: Multiple regression; Logistic regression; Discriminant analysis; Predictive Analytics

Unit 4: Structural Equation Modelling: SEM estimation and assessment; Theoretical introduction to structural equationmodelling; Structural equation modelling using AMOS; Interpretation and reporting SEM output; Confirmatory factor analysis; Testing structural equation models

Assessment Details with weights:

Nature of Assessment


Term Paper / Project


End Term Assessment / Viva



Reading List:

  1. Anderson, T. W. (2003). An introduction to multivariate statistical analysis. 3rd Edition. John Wiley & Sons.
  2. Bhaduri, S. N., &Selarka, E. (2016). Impact of corporate governance on corporate social responsibility: an empirical exploration using structural equation technique. In Corporate Governance and Corporate Social Responsibility of Indian Companies, 115-125. Springer Singapore.
  3. Byrne, B. M. (2016). Structural equation modeling with AMOS: basic concepts, applications, and programming. 3rd Edition. Routledge.
  4. Claudio-González, M. G., Martín-Baranera, M., &Villarroya, A. (2016). A cluster analysis of the business models of Spanish journals. Learned Publishing, 29(4), 239-248.
  5. Hair, J. F., Black, W. C., Babin, B. J., and Anderson, R. E. (2010). Multivariate
  6. data analysis. 7th Edition. Prentice Hall: Upper Saddle River.
  7. Hair, J.F., Wolfinbarger, M. W., Money, A. H., Samouel, P. & Page, M.J. (2011). Essentials of business research methods. 2nd Edition. Routledge.
  8. Jabeur, S. B. (2017). Bankruptcy prediction using partial least squares logistic regression. Journal of Retailing and Consumer Services, 36, 197-202.
  9. Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis. 6th Edition, Prentice Hall: Upper Saddle River.
  10. Matsuno, K., Zhu, Z., & Rice, M. P. (2017). The effects of marketing - R&D integration and R&D strength on business growth and customer equity: a corporate entrepreneurship study. The Customer is NOT Always Right? Marketing Orientations in a Dynamic Business World. 156-159. Springer, Cham.
  11. Mertler, C. A. & Reinhart, R.V. (2016). Advanced and multivariate statistical methods: practical application and interpretation. 6th Edition. Routledge.
  12. Nargundkar R. (2008). Marketing research: text and cases. 3rd Edition, Tata McGraw Hill.
  13. Sarkar, D. (2008). Lattice: multivariate data visualization with R. Springer Science & Business Media.
  14. Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2012). Business research methods. 9th Edition,Cengage Learning.


  • A set of readings and Exercises are provided to the participants in form of a course manual as the commencement of the session.