|Course Type||Course Code||No. Of Credits|
Semester and Year Offered: 2nd Semester
Course Coordinator and Team:Nidhi Kaicker
Email of course coordinator:email@example.com
Aim: The objective of the course is to equip the participants with theoretical, computational, and interpretive issues of multivariate techniques.
At the end of the course, the participants will be able to:
- Use and theoretically describe methods for exploratory and predictive multivariate data analysis
- Understand the specificity of each type of multivariate method and determine which one is appropriate for a given research problem
- Implement the analysis using atleast one statistical software such as SPSS, R, Matlab
- 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
- Anderson, T. W. (2003). An introduction to multivariate statistical analysis. 3rd Edition. John Wiley & Sons.
- 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.
- Byrne, B. M. (2016). Structural equation modeling with AMOS: basic concepts, applications, and programming. 3rd Edition. Routledge.
- 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.
- Hair, J. F., Black, W. C., Babin, B. J., and Anderson, R. E. (2010). Multivariate
- data analysis. 7th Edition. Prentice Hall: Upper Saddle River.
- Hair, J.F., Wolfinbarger, M. W., Money, A. H., Samouel, P. & Page, M.J. (2011). Essentials of business research methods. 2nd Edition. Routledge.
- Jabeur, S. B. (2017). Bankruptcy prediction using partial least squares logistic regression. Journal of Retailing and Consumer Services, 36, 197-202.
- Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis. 6th Edition, Prentice Hall: Upper Saddle River.
- 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.
- Mertler, C. A. & Reinhart, R.V. (2016). Advanced and multivariate statistical methods: practical application and interpretation. 6th Edition. Routledge.
- Nargundkar R. (2008). Marketing research: text and cases. 3rd Edition, Tata McGraw Hill.
- Sarkar, D. (2008). Lattice: multivariate data visualization with R. Springer Science & Business Media.
- 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.