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Advanced Methods of Market and Management Research - (B-E-F-M)

General managers, management consultants, investment advisors, brand managers as well as sales managers need an excellent knowledge of market and management research methods for at least three reasons:
Course code
QUANT501
Course type
MSc Course
Weekly Hours
2,5
ECTS
5.0
Term
FS 2024
Language
Englisch
Lecturers
Prof. Dr. Walter Herzog
Please note that exchange students obtain a higher number of credits in the BSc-program at WHU than listed here. For further information please contact directly the International Relations Office.

This course covers two fundamental aspects of research methodology: Measurement and causality. The first part of the lecture (chapters 2-5) provides an extensive introduction to the measurement of organizational concepts (e.g., salesperson motivation) and consumer psychological variables (e.g., customer satisfaction). The second part of the lecture (chapters 6 and 7) focuses on causal inference, that is, we discuss how to estimate causal relationships among important business variables and test research hypotheses. In the final part of the lecture (chapter 8), a free and easy to use software tool is introduced which enables participants to implement all discussed methods and models for their own work (e.g., in the course of their MSc thesis).


1. Introduction

  • Relevance versus rigor: A misconception
  • The relevance of rigorous measurement
  • The relevance of rigor in causal inference
  • Measurement and causality: An overview


2. Foundations of psychometric measurement

  • Observed variables
  • Latent variables
  • Classical latent variable theory
  • Operationalism
  • Properties of measurement models: Dimensionality, reliability, and validity


3. Dimensionality

  • Local independence
  • Partial correlations
  • The one-factor model
  • Observed and implied covariance matrix
  • Model identification
  • Maximum likelihood estimation
  • Model fit
  • Confirmatory factor analysis


4. Reliability

  • Cronbach's alpha coefficient
  • Composite reliability
  • Indicator reliability
  • Average variance extracted


5. Validity

  • Discriminant validity
  • Criterion validity
  • Content validity


6. Structural equation modeling

  • Introduction of a structural or "causal" model component
  • Observed and implied covariance matrix
  • Model identification and estimation
  • Model fit
  • Interpretation of structural parameters
  • Limitations and extensions


7.Causality, experiments, and instrumental variables

  • Classical conditions of causality
  • Limitations of observational studies
  • Advantages of experiments
  • Experimental design and analysis of experimental data
  • Measurement models and causal models: An integrative perspective
  • Instrumental variable analysis


8. Software

  • Introduction of a powerful statistical software package ("R") that enables participants to estimate all models discussed in chapters 1-7
  • "R" is available for free and can be used by participants for their own analyses (e.g., for their MSc theses)
  • "R" is the standard statistical software package in many research areas and it is used by firms for many purposes (market research projects, finance applications, etc.)
Date Time
Tuesday, 09.01.2024 15:30 - 18:45
Thursday, 11.01.2024 15:30 - 18:45
Monday, 15.01.2024 08:00 - 11:15
Monday, 22.01.2024 11:30 - 15:15
Wednesday, 24.01.2024 08:00 - 11:15
Tuesday, 30.01.2024 15:30 - 18:45
Tuesday, 13.02.2024 11:30 - 15:15
Friday, 16.02.2024 09:45 - 11:15
Monday, 26.02.2024 10:00 - 11:30
This course provides excellent preparation for students interested in writing a quantitative MSc thesis. Specifically, students will learn how to create reliable measures of variables, test causal relationships among variables, and defend their methodological approach against critical comments. Moreover, students will acquire the skills to implement all introduced methods using the free statistical software package R.

Importantly, knowledge of the discussed analytical methods is also a valuable asset in the corporate world:

  • First, understanding analytical methods equips you to address crucial questions facing any business: How do our customers and employees perceive us? How do our activities influence customer and employee behavior? Thus, familiarity with analytical methods empowers you to make informed decisions.
  • Second, proficiency in analytical methods enables you to back up your discussion position with empirical evidence and quantitative facts. In internal debates, individuals who can provide empirical evidence and quantitative facts often present the most compelling arguments. Therefore, expertise in analytical methods enhances your internal authority and equips you to better champion your ideas.
  • Third, mastery of analytical methods allows you to identify methodological flaws and challenge false claims made by others, such as management consultants or market researchers. As Benjamin Disraeli noted, 'there are three kinds of lies: lies, damned lies, and statistics.' Therefore, expertise in analytical methods enables you to defend against assertions and manipulative tactics.
Basic readings: The content of this course is mainly based on original research articles. The "essence" of these articles is summarized in the lecture slides. Selected articles are provided (for examples see "optional readings"), but the main resources for learning are lecture slides and exercises.Optional readings:- Bagozzi, R. P., & Yi, Y. (1989). On the use of structural equation models in experimental designs. Journal of Marketing Research, 26, 271-284.- Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.- Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53, 605-634.- Gerbing, D. W., & Anderson, J. C. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research, 25, 186-192.- Jöreskog, K. G. (1971). Statistical analysis of sets of congeneric tests. Psychometrika, 36, 109-133.- Netemeyer, R. G., Bearden, W. O., & Sharma, S. (2003). Scaling procedures: Issues and applications. Thousand Oaks, CA: Sage.- Rubin, D. B. (2007). Statistical inference for causal effects. In C. R. Rao and S. Sinharay (Eds.), Handbook of Statistics: Psychometrics (pp.769-800). Amsterdam: Elsevier.
Students attend lectures, which are mainly based on original research articles. Students apply the knowledge they gained during those lectures by solving corresponding practice sets after each lecture. A mock exam is provided to prepare students for the written examination at the end of the course.
Exam (100 %)
Basic knowledge in statistics (e.g., regression analysis)
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