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

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Abstract

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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Notes

  1. 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.

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

  3. Several studies used more than one mode.

  4. 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.

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

  6. 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).

  7. 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.

  8. 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.

  9. 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.

  10. 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).

  11. 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.

  12. 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.

  13. 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.

  14. 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.

References

  • Ahearne, M., Haumann, T., Kraus, F., & Wieseke, J. (2013). It’s a matter of congruence: How interpersonal identification between sales managers and salespersons shapes sales success. Journal of the Academy of Marketing Science, 41(6), 625–648.

    Article  Google Scholar 

  • Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402.

  • Arnold, T. J., Fang, E. E., & Palmatier, R. W. (2011). The effects of Customer acquisition and retention orientations on a Firm’s radical and incremental innovation performance. Journal of the Academy of Marketing Science, 39(2), 234–251.

    Article  Google Scholar 

  • Bagozzi, R. P., & Yi, Y. (1990). Assessing method variance in Multitrait-Multimethod matrices: The case of self-reported affect and perceptions at work. Journal of Applied Psychology, 75(5), 547–560.

    Article  Google Scholar 

  • Baker, R., Blumberg, S. J., Brick, J. M., Couper, M. P., Courtright, M., Dennis, J. M., & Kennedy, C. (2010). Research synthesis AAPOR report on online panels. Public Opinion Quarterly, 74(4), 711–781.

    Article  Google Scholar 

  • Baker, T. L., Rapp, A., Meyer, T., & Mullins, R. (2014). The role of Brand Communications on front line service employee beliefs, behaviors, and performance. Journal of the Academy of Marketing Science, 42(6), 642–657.

    Article  Google Scholar 

  • Baumgartner, H., & Steenkamp, J. B. E. (2001). Response styles in marketing research: A cross-National Investigation. Journal of Marketing Research, 38(2), 143–156.

    Article  Google Scholar 

  • Baumgartner, H., & Weijters, B. (2017). Measurement models for marketing constructs. In B. Wierenga & R. van der Lans (Eds.), Springer Handbook of marketing decision models. New York: Springer.

    Google Scholar 

  • Bell, S. J., Mengüç, B., & Widing II, R. E. (2010). Salesperson learning, Organizational learning, and retail store performance. Journal of the Academy of Marketing Science, 38(2), 187–201.

    Article  Google Scholar 

  • Bergkvist, L., & Rossiter, J. R. (2007). The predictive validity of multiple-item versus single-item measures of the same constructs. Journal of Marketing Research, 44(2), 175–184.

    Article  Google Scholar 

  • Berinsky, A. J. (2008). Survey non-response. In W. Donsbach & M. W. Traugott (Eds.), The SAGE Handbook of Public Opinion research (pp. 309–321). Thousand Oaks: SAGE Publications.

    Chapter  Google Scholar 

  • Brewer, M. B., Campbell, D. T., & Crano, W. D. (1970). Testing a single-factor model as an alternative to the misuse of partial correlations in hypothesis-testing research. Sociometry, 33(1), 1–11.

  • Carmines, E. G., and Zeller, R.A. (1979). Reliability and validity assessment. Sage University Paper Series on Quantitative Applications in the Social Sciences, no. 07-017. Beverly Hills: Sage.

  • CASRO. (2015). Annual CASRO benchmarking financial survey.

    Google Scholar 

  • Cote, J. A., & Buckley, M. R. (1987). Estimating trait, method, and error variance: Generalizing across 70 construct validation studies. Journal of Marketing Research, 24(3), 315–318.

  • Curtin, R., Presser, S., & Singer, E. (2005). Changes in telephone survey nonresponse over the past quarter century. Public Opinion Quarterly, 69(1), 87–98.

    Article  Google Scholar 

  • De Jong, A., De Ruyter, K., & Wetzels, M. (2006). Linking employee confidence to performance: A study of self-managing service teams. Journal of the Academy of Marketing Science, 34(4), 576–587.

    Article  Google Scholar 

  • Diamantopoulos, A., Riefler, P., & Roth, K. P. (2008). Advancing formative measurement models. Journal of Business Research, 61(12), 1203–1218.

    Article  Google Scholar 

  • Doty, D. H., & Glick, W. H. (1998). Common methods bias: Does common methods variance really bias results? Organizational Research Methods, 1(4), 374–406.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(3), 39–50.

  • Goodman, J. K., Cryder, C. E., & Cheema, A. (2013). Data collection in a flat world: The strengths and weaknesses of Mechanical Turk samples. Journal of Behavioral Decision Making, 26(3), 213–224.

    Article  Google Scholar 

  • Graesser, A. C., Wiemer-Hastings, K., Kreuz, R., Wiemer-Hastings, P., & Marquis, K. (2000). QUAID: A questionnaire evaluation aid for survey methodologists. Behavior Research Methods, Instruments, & Computers, 32(2), 254–262.

    Article  Google Scholar 

  • Graesser, A. C., Cai, Z., Louwerse, M. M., & Daniel, F. (2006). Question understanding aid (QUAID) a web facility that tests question comprehensibility. Public Opinion Quarterly, 70(1), 3–22.

    Article  Google Scholar 

  • Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576.

    Article  Google Scholar 

  • Greene, C. N., & Organ, D. W. (1973). An evaluation of causal models linking the received role with job satisfaction. Administrative Science Quarterly, 95-103.

  • Grégoire, Y., & Fisher, R. J. (2008). Customer betrayal and retaliation: When your best customers become your worst enemies. Journal of the Academy of Marketing Science, 36(2), 247–261.

    Article  Google Scholar 

  • Groves, R. M. (2006). Nonresponse rates and nonresponse bias in household surveys. Public Opinion Quarterly, 70(5), 646–675.

    Article  Google Scholar 

  • Groves, R. M., & Couper, M. P. (2012). Nonresponse in household interview surveys. New York: Wiley.

    Google Scholar 

  • Groves, R. M., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2004). Survey methodology (Second ed.). New York: McGraw-Hill.

    Google Scholar 

  • Harman, H. H. (1960). Modern factor analysis. Chicago: University of Chicago Press.

    Google Scholar 

  • Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–161.

    Article  Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.

    Article  Google Scholar 

  • Hillygus, D. S., Jackson, N., & Young, M. (2014). Professional respondents in non-probability online panels. In M. Callegaro, R. Baker, J. Bethlehem, A. S. Goritz, J. A. Krosnick, & P. J. Lavrakas (Eds.), Online panel research: A data quality perspective (pp. 219–237). Chichester: John Wiley & Sons.

  • Hinkin, T. R. (1995). A review of scale development practices in the study of organizations. Journal of Management, 21(5), 967–988.

    Article  Google Scholar 

  • Huber, J., Kamakura, W., & Mela, C. F. (2014). A topical history of JMR. Journal of Marketing Research, 51(1), 84–91.

    Article  Google Scholar 

  • Hughes, D. E., Le Bon, J., & Rapp, A. (2013). Gaining and leveraging Customer-based competitive intelligence: The pivotal role of social capital and salesperson adaptive selling skills. Journal of the Academy of Marketing Science, 41(1), 91–110.

    Article  Google Scholar 

  • Hulland, J. (1999). Use of partial least squares (PLS) in Strategic Management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204.

  • Jap, S. D., & Anderson, E. (2004). Challenges and advances in marketing strategy field research. In C. Moorman & D. R. Lehman (Eds.), Assessing marketing strategy performance (pp. 269–292). Cambridge: Marketing Science Institute.

    Google Scholar 

  • Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199–218.

    Article  Google Scholar 

  • Kamakura, W. A. (2001). From the Editor. Journal of Marketing Research, 38, 1–2.

    Article  Google Scholar 

  • Kammeyer-Mueller, J., Steel, P. D., & Rubenstein, A. (2010). The other side of method bias: The perils of distinct source research designs. Multivariate Behavioral Research, 45(2), 294–321.

    Article  Google Scholar 

  • Kemery, E. R., & Dunlap, W. P. (1986). Partialling factor scores does not control method variance: A reply to Podsakoff and Todor. Journal of Management, 12(4), 525–530.

    Article  Google Scholar 

  • Lance, C. E., Dawson, B., Birkelbach, D., & Hoffman, B. J. (2010). Method effects, measurement error, and substantive conclusions. Organizational Research Methods, 13(3), 435–455.

    Article  Google Scholar 

  • Lenzner, T. (2012). Effects of survey question comprehensibility on response quality. Field Methods, 24(4), 409–428.

    Article  Google Scholar 

  • Lenzner, T., Kaczmirek, L., & Lenzner, A. (2010). Cognitive burden of survey questions and response times: A psycholinguistic experiment. Applied Cognitive Psychology, 24(7), 1003–1020.

    Article  Google Scholar 

  • Lenzner, T., Kaczmirek, L., & Galesic, M. (2011). Seeing through the eyes of the respondent: An eye-tracking study on survey question comprehension. International Journal of Public Opinion Research, 23(3), 361–373.

    Article  Google Scholar 

  • Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114–121.

    Article  Google Scholar 

  • Lohr, S. (1999). Sampling: Design and analysis. Pacific Grove: Duxbury Press.

    Google Scholar 

  • MacKenzie, S. B., Podsakoff, P. M., & Jarvis, C. B. (2005). The problem of measurement model misspecification in Behavioral and Organizational research and some recommended solutions. Journal of Applied Psychology, 90(4), 710.

    Article  Google Scholar 

  • MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and Behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293–334.

    Article  Google Scholar 

  • Meade, A. W., & Craig, S. B. (2012). Identifying careless responses in survey data. Psychological Methods, 17(3), 437–455.

    Article  Google Scholar 

  • Nunnally, J. (1978). Psychometric methods (Second ed.). New York: McGraw Hill.

    Google Scholar 

  • Oppenheimer, D. M., Meyvis, T., & Davidenko, N. (2009). Instructional manipulation checks: Detecting satisficing to increase statistical power. Journal of Experimental Social Psychology, 45(4), 867–872.

    Article  Google Scholar 

  • Ostroff, C., Kinicki, A. J., & Clark, M. A. (2002). Substantive and operational issues of response bias across levels of analysis: An example of climate-satisfaction relationships. Journal of Applied Psychology, 87(2), 355–368.

    Article  Google Scholar 

  • Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision making, 5(5), 411–419.

    Google Scholar 

  • Phillips, L. W. (1981). Assessing measurement error in key informant reports: A methodological note on Organizational analysis in marketing. Journal of Marketing Research, 18, 395–415.

    Article  Google Scholar 

  • Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in Organizational research: Problems and prospects. Journal of Management, 12(4), 531–544.

    Article  Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in Behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

    Article  Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social Science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569.

    Article  Google Scholar 

  • Richardson, H. A., Simmering, M. J., & Sturman, M. C. (2009). A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods, 12(4), 762–800.

    Article  Google Scholar 

  • Rindfleisch, A, & Antia, K. D. (2012). Survey research in B2B marketing: Current challenges and emerging opportunities. In G. L. Lilien, & R. Grewal (Eds.), Handbook of Business-to-Business marketing (pp 699–730). Northampton: Edward Elgar.

  • Rindfleisch, A., Malter, A. J., Ganesan, S., & Moorman, C. (2008). Cross-sectional versus longitudinal survey research: Concepts, findings, and guidelines. Journal of Marketing Research, 45(3), 261–279.

    Article  Google Scholar 

  • Rossiter, J. R. (2002). The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19(4), 305–335.

    Article  Google Scholar 

  • Schaller, T. K., Patil, A., & Malhotra, N. K. (2015). Alternative techniques for assessing common method variance: An analysis of the theory of planned behavior research. Organizational Research Methods, 18(2), 177–206.

    Article  Google Scholar 

  • Schriesheim, C. A. (1979). The similarity of individual directed and group directed leader behavior descriptions. Academy of Management Journal., 22(2), 345–355.

    Article  Google Scholar 

  • Schuman, H., & Presser, N. (1981). Questions and answers in attitude surveys. New York: Academic.

    Google Scholar 

  • Schwarz, N., Groves, R., & Schuman, H. (1998). Survey methods. In D. Gilbert, S. Fiske, & G. Lindzey (Eds.), Handbook of social psychology (Vol. 1, 4th ed., pp. 143–179). New York: McGraw Hill.

    Google Scholar 

  • Simmering, M. J., Fuller, C. M., Richardson, H. A., Ocal, Y., & Atinc, G. M. (2015). Marker variable choice, reporting, and interpretation in the detection of common method variance: A review and demonstration. Organizational Research Methods, 18(3), 473–511.

    Article  Google Scholar 

  • Song, M., Di Benedetto, C. A., & Nason, R. W. (2007). Capabilities and financial performance: The moderating effect of Strategic type. Journal of the Academy of Marketing Science, 35(1), 18–34.

    Article  Google Scholar 

  • Stock, R. M., & Zacharias, N. A. (2011). Patterns and performance outcomes of innovation orientation. Journal of the Academy of Marketing Science, 39(6), 870–888.

    Article  Google Scholar 

  • Sudman, S., Bradburn, N. M., & Schwarz, N. (1996). Thinking about answers: The application of cognitive processes to survey methodology. San Francisco: Jossey-Bass.

    Google Scholar 

  • Summers, J. O. (2001). Guidelines for conducting research and publishing in marketing: From conceptualization through the review process. Journal of the Academy of Marketing Science, 29(4), 405–415.

    Article  Google Scholar 

  • The American Association for Public Opinion Research. (2016). Standard definitions: Final dispositions of case codes and outcome rates for surveys (9th ed.) AAPOR.

    Google Scholar 

  • Tourangeau, R., Rips, L. J., & Rasinski, K. (2000). The psychology of survey response. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119–134.

    Article  Google Scholar 

  • Wall, T. D., Michie, J., Patterson, M., Wood, S. J., Sheehan, M., Clegg, C. W., & West, M. (2004). On the validity of subjective measures of company performance. Personnel Psychology, 57(1), 95–118.

    Article  Google Scholar 

  • Wei, Y. S., Samiee, S., & Lee, R. P. (2014). The influence of organic Organizational cultures, market responsiveness, and product strategy on firm performance in an emerging market. Journal of the Academy of Marketing Science, 42(1), 49–70.

    Article  Google Scholar 

  • Weijters, B., Baumgartner, H., & Schillewaert, N. (2013). Reversed item bias: An integrative model. Psychological Methods, 18(3), 320–334.

    Article  Google Scholar 

  • Weisberg, H. F. (2005). The Total survey error approach: A guide to the new Science of survey research. Chicago: Chicago University Press.

    Book  Google Scholar 

  • Wells, W. D. (1993). Discovery-oriented consumer research. Journal of Consumer Research, 19(4), 489–504.

    Article  Google Scholar 

  • Williams, L. J., Hartman, N., & Cavazotte, F. (2010). Method variance and marker variables: A review and comprehensive CFA marker technique. Organizational Research Methods, 13(3), 477–514.

    Article  Google Scholar 

  • Winship, C., & Mare, R. D. (1992). Models for sample selection bias. Annual Review of Sociology, 18(1), 327–350.

    Article  Google Scholar 

  • Wittink, D. R. (2004). Journal of marketing research: 2 Ps. Journal of Marketing Research, 41(1), 1–6.

    Article  Google Scholar 

  • Zinkhan, G. M. (2006). From the Editor: Research traditions and patterns in marketing scholarship. Journal of the Academy of Marketing Science, 34, 281–283.

    Article  Google Scholar 

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

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Aric Rindfleisch served as Guest Editor for this article.

Appendix

Appendix

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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