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2022 | OriginalPaper | Buchkapitel

Panel Data Analysis: A Non-technical Introduction for Marketing Researchers

verfasst von : Arnd Vomberg, Simone Wies

Erschienen in: Handbook of Market Research

Verlag: Springer International Publishing

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Abstract

The analysis of panel data is now part of the standard repertoire of marketers and marketing researchers. Compared to the analysis of cross-sectional data, panel data allow marketers to alleviate endogeneity concerns when linking an independent variable (e.g., price) to an outcome variable (e.g., sales volume). The more accurate estimates that result from panel data analysis help improve marketers’ decision-making in focal areas such as price setting and marketing budget allocation. Besides, panel data allow marketers to track customer behavior changes and distinguish real loyalty effects (i.e., same customer repeatedly buys a brand) from spurious effects (i.e., the same number of, but each time different set of, customers buys a brand). This chapter provides a nontechnical introduction to panel data analysis. Marketers will learn how to manage and analyze panel datasets in Stata. They will learn about the focal panel data estimators (pooled OLS, fixed effects, and random effects estimator), their underlying assumptions, advantages, and pitfalls. Besides, we introduce the between effects estimator, the combined approach, the Hausman-Taylor approach, and the first differences estimator as further techniques to analyze panel data. Finally, readers will receive an introduction to advanced topics such as dynamic panel models, panel data multilevel modeling, and using panel data to address measurement errors.

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Vorheriges Kapitel Multilevel Modeling

Nächstes Kapitel Applied Time-Series Analysis in Marketing

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Titel: Panel Data Analysis: A Non-technical Introduction for Marketing Researchers
verfasst von: Arnd Vomberg
Simone Wies
Verlag: Springer International Publishing
Buch: Handbook of Market Research
Print ISBN: 978-3-319-57411-0

Electronic ISBN: 978-3-319-57413-4

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-319-57413-4_19