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Cited by 474

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Are Studies of Dark Side Variables Confounded by Socially Desirable Responding? The Case of Materialism

Mick, David Glen·1996·Journal of Consumer Research

Abstract

Socially desirable responding (SOR) is the tendency of individuals to make them-selves look good according to current cultural norms when answering research-ers ' questions. For over 50 years in the social sciences, SOR has been a complex and controversial issue, typically viewed as a contaminating response bias. Mean-while, most consumer researchers have neglected SOR, including those studying sensitive "dark side " topics where SOR could have an especially detrimental impact on research conclusions. This article reviews conceptual, measurement, and statistical analysis issues related to SOA. Two surveys are then reported that explore the effect of SOR on testing propositions about the nomological network surrounding the materialism value. Implications and recommendations are dis-cussed for investigating SOR in consumer research, including opportunities for future contributions. I n recent years the agenda of consumer research topics has widened considerably, becoming less managerial and more societal in nature (Belk 1995). One of the most important new areas concerns the negative side, or "dark

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Metadata

Authors
Mick, D. G.
Venue
Journal of Consumer Research
Published
1996-09
Volume
23
Issue
2
Pages
106
ISSN
  • 0093-5301 (ISSN-L)
  • 1537-5277
OpenAlex
W2131846293
Cited by
474
References
6
FWCI
9.83
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Are Studies of Dark Side Variables Confounded by Socially Desirable Responding? The Case of Materialism. https://app.cochat.ai/papers/10.1086/209470
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Mick, D. G. (1996). Are Studies of Dark Side Variables Confounded by Socially Desirable Responding? The Case of Materialism. Journal of Consumer Research. https://doi.org/10.1086/209470

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.