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.
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
Classical latent variable theory
Properties of measurement models: Dimensionality, reliability, and validity
The one-factor model
Observed and implied covariance matrix
Maximum likelihood estimation
Exploratory factor analysis
Confirmatory factor analysis
Cronbach's alpha coefficient
Average variance extracted
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
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
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.
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.
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.