Singapore University of Social Sciences

Multivariate Analysis

Multivariate Analysis (MKT355)

Applications Open: 01 October 2020

Applications Close: 16 December 2020

Next Available Intake: January 2021

Course Types: Modular Undergraduate Course

Language: English

Duration: 6 months

Fees: To be confirmed

Area of Interest: Business Administration

Schemes: Lifelong Learning Credit (L2C)

Funding: To be confirmed

School/Department: School of Business


Multivariate Analysis provides a firm understanding of the statistical and managerial principles underlying multivariate analysis. Topics include : how to prepare your data and the use of univariate tests, analysis of variance (ANOVA & MANOVA), Factor Analysis, Discriminant Analysis, Cluster Analysis, Regression Analysis, Logistic Regression and Multidimensional techniques.Note: The course does not require students to bring a laptop. Students can choose to bring a laptop or share laptops with classmates during class, if needed.

Level: 3
Credit Units: 5
Presentation Pattern: Every January
E-Learning: BLENDED - Learning is done MAINLY online using interactive study materials in Canvas. Students receive guidance and support from online instructors via discussion forums and emails. This is supplemented with SOME face-to-face sessions. If the course has an exam component, This will be administered on-campus.


  • Fieldwork, Data Preparation & Cross-tabulations
  • Nonparametric Hypothesis Test
  • Analysis of Variance & Covariance
  • Regression Analysis
  • Discriminant & Logit Analysis
  • Factor Analysis
  • Multidimensional Scaling
  • Cluster Analysis
  • Conjoint Analysis

Learning Outcome

  • Discuss the statistical and managerial principles underlying multivariate analysis.
  • Evaluate, undertake and interpret results of empirical studies using multivariate statistical techniques.
  • Prepare data into proper format for statistical analysis.
  • Analyze using non-parametric tests.
  • Apply ANOVA to account for overall effects, main effects and interaction effects in marketing applications.
  • Compute multiple regression analysis and explain the meanings of the coefficients in marketing applications.
  • Compute discriminant, factor and cluster analyses on marketing applications.
  • Analyze the use of logit modelling and multidimensional scaling in marketing applications.
  • Apply statistical principles and practices to hypothetical situations.
  • Organise information and apply them to particular marketing scenarios.
  • Develop course competence through discussions.
  • Demonstrate the essential knowledge and interpersonal skills to work effectively in a team.
  • Show proficiency with the use of SPSS software.
  • Prepare oral presentations in areas related to multivariate analysis.
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