Quantitative Research Methods in Business Administration

2018-2019

Course Objective

Upon successful completion of the course, students will
• have furthered their academic knowledge of quantitative survey
research methods in business in general
• have started to develop their knowledge of particular quantitative
data analyses methods
• be able to link concrete technologies and methodologies to research
designs in quantitative methods
• be able to critically evaluate the quality of the research design of a
study
• be able to evaluate the quality of quantitative data sources and
methods for analysis
• have practiced with the most common techniques in survey research, as
shown below
• have started developing their skills of communicating about research
methods orally and in writing

Course Content

This course introduces several important topics in conducting
quantitative survey research.

The course is preceded by an introduction to R in which the following
topics are covered: read data, create and change variables, and conduct
elementary manipulations.

• The course starts with the essentials of data analysis. Cleaning data
is discussed, and strategies are provided on how you can deal with
missing values. Topics such as missing at random, missing completely at
random (MCAR) are introduced. Lastly, we discuss strategies how to deal
with missing data: list wise deletion, pairwise deletion and imputation.
• The second week we present the general linear model. This serves as a
framework for the simple and multiple regression analyses that have been
introduced in earlier (Bachelor or Master) courses.
• The third week ANOVA and ANCOVA will be introduced.
• The fourth week, latent variables are introduced using factor
analysis. Students will learn principal components analysis (PCA) and
principal axis factoring (PAF) and practice with interpretation in
class.
• In the fifth week, students will build upon what they learned in the
preceding weeks and start using techniques such as confirmatory factor
analysis (CFA) using the lavaan package in R.
• In the sixth week, multilevel modelling will be introduced. The
statistical challenges when dealing with nested data (e.g., employees
nested in teams or consumers within countries) will be discussed.

All the topics are illustrated in R and students will practice using all
techniques themselves.

Teaching Methods

Weekly: Two 2-hour lecture/tutorial per week (including computer lab).
The teacher will present new concepts and explain how these analyses
should be interpreted.
During the lecture exercises will be given so that students can
immediately apply the methods and practice programming.

Method of Assessment

- Two take-home assignments in which both an R-program is written to
analyze survey-data and the results are interpreted (40%)
- A 2 hours 45 minutes individual written examination on interpreting
research using the main techniques taught in class (60%)

Conditions to pass the course:
- The score for the individual examination and final grade must be 5.5
or higher.
- Attendance is mandatory. To pass the course, students cannot miss
more than one class.

Resit:
- Students can only retake the individual exam.
- Results obtained for the assignments will remain valid for the resit.

Entry Requirements

Knowledge of statistics and business research methods at the level of a
Bachelor in Business or Economics

Literature

Tabachnick, Barbara G. and Linda S. Fidell (2013). “Using Multivariate
Statistics”, Pearson New International Edition. (for sale at bol.com;
AMAZON as printed book and as an e-book on Kindle)

Chapman, Chris, and Elea McDonnell Feit (2015). “R for Marketing
Research and Analytics”, Springer (available as e-book via UBVU)

Baron, Reuben M.; Kenny, David A. (1986) “The moderator–mediator
variable distinction in social psychological research: Conceptual,
strategic, and statistical considerations.” Journal of Personality and
Social Psychology, Vol 51(6), 1173-1182.

Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and
Kenny: Myths and truths about mediation analysis. Journal of consumer
research, 37(2), 197-206

Yves Rosseel (2012). lavaan: An R Package for Structural Equation
Modeling. Journal of Statistical Software, 48(2), 1-36
Yves Rosseel (2017). The lavaan tutorial. (online will be put on Canvas)

NOTE: It is preferable that students take a laptop with R installed to
class.

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, MSc Honours
or PhD students.

General Information

Course Code E_BIS_QNRMBA
Credits 6 EC
Period P5
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: