Excellence in

Advanced Methods of Market and Management Research
Contacts hour per week
Herzog, Walter
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This course deals with the two fundamental cornerstones 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). This background is necessary because causal inference (chapters 6 and 7) requires valid measurement instruments.

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
  • Exploratory factor analysis
  • Confirmatory factor analysis

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

5. Validity
  • Discriminant validity
  • Criterion validity
  • Content validity
  • The process of scale validation

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. Experiments and Rubin's Causal Model
  • Classical conditions of causality
  • Limitations of observational studies
  • Advantages of experiments
  • Rubin's Causal Model, individual causal effects, average causal effects
  • Experimental design and analysis of experimental data
  • Measurement models and causal models: An integrative perspective
  • Statistics I + II (BSc) or similar courses are mandatory
  • Market Research (BSc) or similar course is helpful
Course description
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:
  • First, knowledge of analytical methods enables you to soundly answer crucial questions facing any business: How do our customers and employees perceive us? How do our activities influence customer and employee behavior? Thus, knowledge of analytical methods enables you to make better decisions.
  • Second, knowledge of analytical methods enables you to back up your discussion position with empirical evidence and quantitative facts. In internal debates, those who are able to provide empirical evidence and quantitative facts typically have the most powerful arguments. Thus, knowledge of analytical methods increases your internal authority and enables you to better champion your ideas.
  • Third, knowledge of analytical methods enables you to detect methodological flaws and to challenge false claims made by others (for instance, management consultants, market researchers, internal opposition). As Benjamin Disraeli has noted, "there are three kinds of lies: lies, damned lies, and statistics". Thus, knowledge of analytical methods enables you to defend against assertions and manipulative tactics.
  • Finally, the course is an excellent preparation for students who are considering a PhD thesis after their MSc studies.
Teaching methods
Lectures, discussions, exercises
Psychometric theory, statistical theory, partial correlations, exploratory factor analysis, confirmatory factor analysis, covariance structure analysis, structural equation modeling, maximum likelihood estimation, experimental design, Rubin's Causal Model
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.
Further literature
  • Bagozzi, R. P., & Phillips, L. W. (1982). Representing and testing organizational theories: A holistic construal. Administrative Science Quarterly, 27, 459-489.
  • 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.
Method of examination
Exam (100 %)