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Journal of the Academy of Marketing Science
Published in: Journal of the Academy of Marketing Science 5/2023

14-04-2023 | Editorial

Using a strobelight analysis to examine relationship inconsistency

Authors: Alec Pappas, Charles F. Hofacker, Willy Bolander

Published in: Journal of the Academy of Marketing Science | Issue 5/2023

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Excerpt

The relationship between variables represents the starting point of nearly all social science research. Our analysis was no different. We wanted to know how performance anxiety (i.e., negative views about one’s ability to perform) related to salesperson performance in our data (both surveys and objective performance from an insurance company). Ordinary least squares (OLS) regression results suggested a statistically significant beta estimate of -327.19, interpreted as: for every unit increase in performance anxiety, sales will decrease by roughly $327. Yet this effect, what we deem a conditional mean estimate (CME), was inconsistent with most individuals in our data (when viewing the relationship through a more flexible lens detailed herein, we found it was consistent with only 32% of our sample). Our estimate was statistically significant; but our ability to provide precise insight to managers was hindered. …
previous article Drivers and outcomes of smallholder market participation in Sub-Saharan Africa

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Footnotes
1
While we focus on OLS regression in our examples, the label “CME” applies to structural equation models, multi-level models, and other analysis methods that estimate effects at the conditional mean of y | x.
 
2
We note here that mean-centering y changes the value of the intercept (to zero) but does not otherwise change CME results or the results we will discuss in this work. Likewise, mean-centering X values can change the interpretation of individual coefficients but does not materially affect our argument.
 
3
One limitation of quantile regression is its inability to work well with outcome variables that have a small number of observed quantile values (i.e., discrete style measures like Likert variables). Using a more continuous DV (e.g., revenue, purchase price) provides a better chance to observe heterogeneous variance across its range.
 
4
While the focus of our strobelight analysis is on 5% intervals to illuminate nuance within the relationship across the entire range of the DV, the Web application (accessed at https://​strobelight.​shinyapps.​io/​strobelight/​) offers a slider tool to adjust quantile intervals to provide more flexibility for one’s preferred analysis (e.g., 15%, 20%, etc.).
 
5
All variable scales and constructs are from published works. A full citation list is available upon request from the authors.
 
Literature
Boichuk, J. P., Bommaraju, R., Ahearne, M., Kraus, F., & Steenburgh, T. J. (2019). Managing laggards: The importance of a deep sales bench. Journal of Marketing Research, 56(4), 652–665.CrossRef
Koenker, R., & Bassett, G. Jr. (1978). Regression quantiles. Econometrica: Journal of the Econometric Society, 33–50.
Li, M. (2015). Moving beyond the linear regression model: Advantages of the quantile regression model. Journal of Management, 41(1), 71–98.CrossRef
Petscher, Y., & Logan, J. A. (2014). Quantile regression in the study of developmental sciences. Child Development, 85(3), 861–881.CrossRef
Metadata
Title
Using a strobelight analysis to examine relationship inconsistency
Authors
Alec Pappas
Charles F. Hofacker
Willy Bolander
Publication date
14-04-2023
Publisher
Springer US
Published in
Journal of the Academy of Marketing Science / Issue 5/2023
Print ISSN: 0092-0703
Electronic ISSN: 1552-7824
DOI
https://doi.org/10.1007/s11747-023-00940-8

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