Multilevel Modeling

Till Haumann (), Roland Kassemeier () and Jan Wieseke ()
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Till Haumann: South Westphalia University of Applied Sciences
Roland Kassemeier: University of Warwick
Jan Wieseke: University of Bochum

A chapter in Handbook of Market Research, 2022, pp 369-409 from Springer

Abstract: Abstract Many phenomena in marketing involve multiple levels of theory and analysis. Adopting a multilevel lens to marketing phenomena can often yield richer and more rigorous results. However, the consideration of multiple levels of theory and analysis often leads to the challenge to cope with nested data structures in which a lower level unit of analysis is nested within a higher level unit of analysis. Explicitly acknowledging such nested data structures is important as its analysis with single level analysis techniques may result in biased results and thus incorrect conclusions because nested data structures often violate assumptions of conventional single level analysis techniques. A methodological approach which explicitly accounts for multiple levels of analysis and thus the nested structure of data is referred to as multilevel modeling. This chapter attempts to help researchers and practitioners interested in investigating multilevel phenomena by providing an introduction to multilevel modeling. It therefore describes the theoretic fundamentals of multilevel modeling by outlining the conceptual and statistical relevance of multilevel modeling. Furthermore, it provides guidance how to build a multilevel regression model using a step-by-step approach. The chapter also discusses how to assess the fit of multilevel models, how to center variables at different levels of analysis, and how to determine the sample sizes to adequately estimate multilevel models. Moreover, it offers insights how the logic of multilevel regression analysis could be expanded to multilevel structural equation modeling, discusses different statistical software packages that can be employed to estimate multilevel models, and provides a detailed example of building and estimating a multilevel model.

Keywords: Random coefficient modeling; Hierarchical linear modeling; Nested data structures; Hierarchical data; Between variance; Within variance; Random intercept; Random slope; Cross-level interaction; Intraclass correlation coefficient; Group mean centering; Grand mean centering (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-57413-4_18

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DOI: 10.1007/978-3-319-57413-4_18

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