Multivariate Data Analysis for Business and  Management Research

2019-2020

Course Objective

Upon successful completion of the course, students will:
• Be able to evaluate the quality of quantitative data; including
dealing with missing data and outliers
• Have insight into the strengths and limitations of various
multivariate analysis techniques, such as multiple regression, PCA, FA,
structural equation modeling and multilevel modeling
• Be able to perform multivariate analysis using the R-package
• Be able to interpret output of multivariate analyses
• Have started developing their skills of communicating about research
methods in writing

Course Content

This course will emphasize understanding, implementation, and
interpretation of multivariate statistical methods. The course will
involve both lecture and lab work. First, we discuss how to analyze and
deal with missing data. Second, the course will focus on multivariate
techniques such as analysis of (co)variance and regression analysis
including moderation and mediation. Third, you will learn some more
advanced techniques using latent variables and apply confirmatory factor
analysis to multi-item scales. You will be introduced to structural
equation modeling (SEM). You will learn to analyze SEM models and assess
their fit. Lastly, multi-level analysis is learned.

This course prepares the student for analyzing datasets using the freely
available programming language R. R is a platform for which many
scholars write packages. The basis enables you to manipulate data, clean
data, and test hypotheses. The packages enable you to use advanced
methods such as structural equation modeling. You will learn how to read
various datasets into R, how to create and change variables, and how to
conduct manipulations such as recoding data.

Teaching Methods

Combined lecture - tutorial sessions

Method of Assessment

30% Midterm Assignment
70% Final assignment

Entry Requirements

Methods and statistics courses at the BSc level in Business
Administration or Economics

Literature

Tabachnick, B. G., & Fidell, L. S. (2014). Using multivariate
statistics. Pearson Education.
Chapman, C., & Feit, E. M. (2015). R for marketing research and
analytics. New York, NY: Springer.
Selected articles

Target Audience

Due to the entry requirements of the programme, the courses of the
Research Master Business in Society are only available for students
registered for this master’s programme and, upon approval of the
programme director, to other Research Master programmes or PhD students.

Recommended background knowledge

Basic programming skills in R

General Information

Course Code E_BIS_MDABMR
Credits 6 EC
Period P4
Course Level 400
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator prof. dr. H. van Herk
Examiner prof. dr. H. van Herk
Teaching Staff prof. dr. H. van Herk

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture
Target audiences

This course is also available as: