Data quality

Any analysis can only be as good as the data on which it is based. Following this principle, the research field of data quality deals with fundamental questions about the measurement of phenomena that are not directly observable. These include consumer attitudes towards advertising and brands, purchase intentions or customer satisfaction. Current research projects aim to transfer metrological approaches from the natural sciences to the behavioral sciences. This should promote a higher replicability of behavioral science studies. Further research is addressing the question of how data quality can be ensured in machine learning methods.

Recent publications
Sarstedt, M., Adler, S. J., Rau, L, Schmitt, B. (2024). Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines. Psychology & Marketing, https://doi.org/10.1002/mar.21982
Rigdon, E. E., & Sarstedt, M. (2022). Accounting for uncertainty in the measurement of unobservable marketing phenomena. In: H. Baumgartner and B. Weijters (Eds.), Review of Marketing Research, Volume 19 (pp. 53-73). Bingley, UK: Emerald.
Hair, J. F., & Sarstedt, M. (2021). Data, measurement, and causal inferences in machine learning: Opportunities and challenges for marketing. Journal of Marketing Theory & Practice, 29(1), 65-77.
Rigdon, E. E., Sarstedt, M., & Becker, J.-M. (2020). Quantify uncertainty in behavioral research. Nature Human Behaviour, 4, 329-331.
Rigdon, E. E., Becker, J.-M., & Sarstedt, M. (2019). Factor indeterminacy as metrological uncertainty: Implications for advancing psychological measurement. Multivariate Behavioral Research, 54(3), 429-443.
Rigdon, E. E., Becker, J.-M., & Sarstedt, M. (2019). Parceling cannot reduce factor indeterminacy in factor analysis: A research note. Psychometrika, 84(3), 772–780.

Data quality

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