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Organisatorische Informationen Kursbeschreibung
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eventdetails open Kursbeschreibung

Sprache:
Englisch
ECTS-Credits:
5.0
Art der Prüfung (Deutsch):
Exam (100 %)
Art der Prüfung (Englisch):
Exam (100 %)
Kursinhalt (Deutsch):
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).The second part of the lecture (chapters 6 and 7) focuses on causal inference, that is, we discuss how to estimate 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
  • 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
  • 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.)
Kursinhalt (Englisch):
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).The second part of the lecture (chapters 6 and 7) focuses on causal inference, that is, we discuss how to estimate 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
  • 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
  • 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.)
Voraussetzungen für die Teilnahme (Deutsch):
Basic knowledge in statistics (e.g., regression analysis)
Voraussetzungen für die Teilnahme (Englisch):
Basic knowledge in statistics (e.g., regression analysis)
Literatur (Deutsch):
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.

Literatur (Englisch):
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.

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GesamtworkloadZusammensetzung open Gesamtworkload und seine Zusammensetzung:

Gesamtworkload (in h):
150
Selbststudium (in h):
126
Kontaktzeit (in h):
22,5
Prüfung (in h):
1,5
.