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Marketing survey research best practices: evidence and recommendations from a review of JAMS articles

• Methodological Paper
• Published: 10 April 2017
• Volume 46 , pages 92–108, ( 2018 )

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• John Hulland 1 ,
• Hans Baumgartner 2 &
• Keith Marion Smith 3  

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Survey research methodology is widely used in marketing, and it is important for both the field and individual researchers to follow stringent guidelines to ensure that meaningful insights are attained. To assess the extent to which marketing researchers are utilizing best practices in designing, administering, and analyzing surveys, we review the prevalence of published empirical survey work during the 2006–2015 period in three top marketing journals— Journal of the Academy of Marketing Science ( JAMS ), Journal of Marketing ( JM ), and Journal of Marketing Research ( JMR )—and then conduct an in-depth analysis of 202 survey-based studies published in JAMS . We focus on key issues in two broad areas of survey research (issues related to the choice of the object of measurement and selection of raters, and issues related to the measurement of the constructs of interest), and we describe conceptual considerations related to each specific issue, review how marketing researchers have attended to these issues in their published work, and identify appropriate best practices.

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In conducting their topical review of publications in JMR , Huber et al. ( 2014 ) show evidence that the incidence of survey work has declined, particularly as new editors more skeptical of the survey method have emerged. They conclude (p. 88)—in looking at the results of their correspondence analysis—that survey research is more of a peripheral than a core topic in marketing. This perspective seems to be more prevalent in JMR than in JM and JAMS , as we note above.

A copy of the coding scheme used is available from the first author.

Several studies used more than one mode.

Traditionally, commercial researchers used phone as their primary collection mode. Today, 60% of commercial studies are conducted online (CASRO 2015 ), growing at a rate of roughly 8% per year.

Although the two categories are not necessarily mutually exclusive, the overlap was small ( n  = 4).

This is close to the number of studies in which an explicit sampling frame was employed, which makes sense (i.e., one would not expect a check for non-response bias when a convenience sample is used).

It is interesting to note that Cote and Buckley examined the extent of CMV present in papers published across a variety of disciplines, and found that CMV was lowest for marketing (16%) and highest for the field of education (> 30%). This does not mean, however, that marketers do a consistently good job of accounting for CMV.

In practice, these items need to be conceptually related yet empirically distinct from one another. Using minor variations of the same basic item just to have multiple items does not result in the advantages described here.

In general, the use of PLS (which is usually employed when the measurement model is formative or mixed) was uncommon in our review, so it appears that most studies focused on using reflective measures.

Most of the studies discussing discriminant validity used the approach proposed by Fornell and Larcker ( 1981 ). A recent paper by Voorhees et al. ( 2016 ) suggests use of two approaches to determining discriminant validity: (1) the Fornell and Larcker test and (2) a new approach proposed by Henseler et al. ( 2015 ).

This solution is not a universal panacea. For example, Kammeyer-Mueller et al. ( 2010 ) show using simulated data that under some conditions using distinct data sources can distort estimation. Their point, however, is that the researcher must think carefully about this issue and resist using easy one-size-fits-all solutions.

Podsakoff et al. ( 2003 ) also mention two other techniques—the correlated uniqueness model and the direct product model—but do not recommend their use. Only very limited use of either technique has been made in marketing, so we do not discuss them further in this paper.

These techniques are described more extensively in Podsakoff et al. ( 2003 ), and contrasted to one another. Figure 1 (p. 898) and Table 4 (p. 891) in their paper are particularly helpful in understanding the differences across approaches.

It is unclear why the procedure is called the Harman test, because Harman never proposed the test and it is unlikely that he would be pleased to have his name associated with it. Greene and Organ ( 1973 ) are sometimes cited as an early application of the Harman test (they specifically mention “Harman’s test of the single-factor model,” p. 99), but they in turn refer to an article by Brewer et al. ( 1970 ), in which Harman’s one-factor test is mentioned. Brewer et al. ( 1970 ) argued that before testing the partial correlation between two variables controlling for a third variable, researchers should test whether a single-factor model can account for the correlations between the three variables, and they mentioned that one can use “a simple algebraic solution for extraction of a single factor (Harman 1960 : 122).” If measurement error is present, three measures of the same underlying factor will not be perfectly correlated, and if a single-factor model is consistent with the data, there is no need to consider a multi-factor model (which is implied by the use of partial correlations). It is clear that the article by Brewer et al. does not say anything about systematic method variance, and although Greene and Organ talk about an “artifact due to measurement error” (p. 99), they do not specifically mention systematic measurement error. Schriesheim ( 1979 ), another early application of Harman’s test, describes a factor analysis of 14 variables, citing Harman as a general factor-analytic reference, and concludes, “no general factor was apparent, suggesting a lack of substantial method variance to confound the interpretation of results” (p. 350). It appears that Schriesheim was the first to conflate Harman and testing for common method variance, although Harman was only cited as background for deciding how many factors to extract. Several years later, Podsakoff and Organ ( 1986 ) described Harman’s one-factor test as a post-hoc method to check for the presence of common method variance (pp. 536–537), although they also mention “some problems inherent in its use” (p. 536). In sum, it appears that starting with Schriesheim, the one-factor test was interpreted as a check for the presence of common method variance, although labeling the test Harman’s one-factor test seems entirely unjustified.

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Acknowledgements

The constructive comments of the Editor-in-Chief, Area Editor, and three reviewers are gratefully acknowledged.

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John Hulland

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Putting the Harman test to rest

A moment’s reflection will convince most researchers that the following two assumptions about method variance are entirely unrealistic: (1) most of the variation in ratings made in response to items meant to measure substantive constructs is due to method variance, and (2) a single source of method variance is responsible for all of the non-substantive variation in ratings. No empirical evidence exists to support these assumptions. Yet when it comes to testing for the presence of unwanted method variance in data, many researchers suspend disbelief and subscribe to these implausible assumptions. The reason, presumably, is that doing so conveniently satisfies two desiderata. First, testing for method variance has become a sine qua non in certain areas of research (e.g., managerial studies), so it is essential that the research contain some evidence that method variance was evaluated. Second, basing a test of method variance on procedures that are strongly biased against detecting method variance essentially guarantees that no evidence of method variance will ever be found in the data.

Although various procedures have been proposed to examine method variance, the most popular is the so-called Harman one-factor test, which makes both of the foregoing assumptions. Footnote 14 While the logic underlying the Harman test is convoluted, it seems to go as follows: If a single factor can account for the correlation among a set of measures, then this is prima facie evidence of common method variance. In contrast, if multiple factors are necessary to account for the correlations, then the data are free of common method variance. Why one factor indicates common method variance and not substantive variance (e.g., several substantive factors that lack discriminant validity), and why several factors indicate multiple substantive factors and not multiple sources of method variance remains unexplained. Although it is true that “if a substantial amount of common method variance is present, either (a) a single factor will emerge from the factor analysis, or (b) one ‘general’ factor will account for the majority of the covariance in the independent and criterion variables” (Podsakoff and Organ 1986 , p. 536), it is a logical fallacy (i.e., affirming the consequent) to argue that the existence of a single common factor (necessarily) implicates common method variance.

Apart from the inherent flaws of the test, several authors have pointed out various other difficulties associated with the Harman test (e.g., see Podsakoff et al. 2003 ). For example, it is not clear how much of the total variance a general factor has to account for before one can conclude that method variance is a problem. Furthermore, the likelihood that a general factor will account for a large portion of the variance decreases as the number of variables analyzed increases. Finally, the test only diagnoses potential problems with method variance but does not correct for them (e.g., Podsakoff and Organ 1986 ; Podsakoff et al. 2003 ). More sophisticated versions of the test have been proposed, which correct some of these shortcoming (e.g., if a confirmatory factor analysis is used, explicit tests of the tenability of a one-factor model are available), but the faulty logic of the test cannot be remedied.

In fact, the most misleading application of the Harman test occurs when the variance accounted for by a general factor is partialled from the observed variables. Since it is likely that the general factor contains not only method variance but also substantive variance, this means that partialling will not only remove common method variance but also substantive variance. Although researchers will most often argue that common method variance is not a problem since partialling a general factor does not materially affect the results, this conclusion is also misleading, because the test is usually conducted in such a way that the desired result is favored. For example, in most cases all loadings on the method factor are restricted to be equal, which makes the questionable assumption that the presumed method factor influences all observed variables equally, even though this assumption is not imposed for the trait loadings.

In summary, the Harman test is entirely non-diagnostic about the presence of common method variance in data. Researchers should stop going through the motions of conducting a Harman test and pretending that they are performing a meaningful investigation of systematic errors of measurement.

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Hulland, J., Baumgartner, H. & Smith, K.M. Marketing survey research best practices: evidence and recommendations from a review of JAMS articles. J. of the Acad. Mark. Sci. 46 , 92–108 (2018). https://doi.org/10.1007/s11747-017-0532-y

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Received : 19 August 2016

Accepted : 29 March 2017

Published : 10 April 2017

Issue Date : January 2018

DOI : https://doi.org/10.1007/s11747-017-0532-y

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