Marketing Letters

, Volume 26, Issue 3, pp 245–255 | Cite as

Appropriate use of single-item measures is here to stay

  • Lars BergkvistEmail author


In their article, Bergkvist and Rossiter (Journal of Marketing Research, 44, 175–184, 2007) recommended marketing academics to use single-item instead of multiple-item measures for doubly concrete constructs. This recommendation was based on a study showing that the predictive validity of single-item measures was comparable to that of multiple-item measures. Kamakura (2014) presents three criticisms of Bergkvist and Rossiter’s study: (1) The correlations used to evaluate predictive validity are inflated by the presence of common-methods variance in the data, (2) the study used concurrent validity as criterion rather than predictive validity, and (3) the multiple-item measures in the study were not corrected for attenuation. A re-analysis of the data from the original study refutes the claims made by Kamakura (2014). The analysis shows that the common-methods variance in the data was negligible and that predicting delayed measures rather than concurrent measures yielded virtually identical results as in the original study. It is also shown that it is possible to estimate single-item reliabilities and correct single-item measures for attenuation, which makes them as predictively valid as multiple-item measures. Thus, there is no reason to change the conclusions and recommendations made in Bergkvist and Rossiter’s (Journal of Marketing Research, 44, 175–184, 2007) article. The present article also shows that Kamakura’s (2014) analysis of consumer panel data has limitations which casts doubts upon the conclusions drawn from the analysis results. In addition, there is a discussion of the cost, in terms of research quality, that researchers unnecessarily using multiple-items measures pay.


Single-item measures Multiple-item measures Psychometrics Reliability Validity 



The author is grateful to Tobias Langner and Saeed Samiee for valuable comments on an earlier version of this manuscript.


  1. Adigüzel, F., & Wedel, M. (2008). Split questionnaire design for massive surveys. Journal of Marketing Research, 45, 608–617.CrossRefGoogle Scholar
  2. 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, 175–184.CrossRefGoogle Scholar
  3. Bergkvist, L., & Rossiter, J. R. (2008). The role of ad likability in predicting an ad’s campaign performance. Journal of Advertising, 37, 85–97.CrossRefGoogle Scholar
  4. Bergkvist, L., & Rossiter, J. R. (2009). Tailor-made single-item measures of doubly concrete constructs. International Journal of Advertising, 28(4), 607–621.CrossRefGoogle Scholar
  5. Bruner, G. C., II. (1998). Standardization & justification: do Aad scales measure up? Journal of Current Issues and Research in Advertising, 20, 1–18.CrossRefGoogle Scholar
  6. Churchill, G. A., Jr. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16, 64–73.CrossRefGoogle Scholar
  7. Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52, 281–302.CrossRefGoogle Scholar
  8. DeVellis, R.F. (2003). Scale development (2nd ed.). Newbury Park, CA: SageGoogle Scholar
  9. Diamantopoulos, A., & Winklhofer, H. (2001). Index construction with formative indicators: an alternative to scale development. Journal of Marketing Research, 38, 269–277.CrossRefGoogle Scholar
  10. Dillman, D. A., Sinclair, M. D., & Clark, J. R. (1993). Effects of questionnaire length, respondent-friendly design, and a difficult question on response rates for occupant-addressed census mail surveys. Public Opinion Quarterly, 57, 289–304.CrossRefGoogle Scholar
  11. Feldman, J. M., & Lynch, J. G., Jr. (1988). Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior. Journal of Applied Psychology, 73, 421–435.CrossRefGoogle Scholar
  12. Griskevicius, V., Tybur, J. M., & Van den Bergh, B. (2010). Going green to be seen: status, reputation, and conspicuous conservation. Journal of Personality and Social Psychology, 98, 392–404.CrossRefGoogle Scholar
  13. 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, 199–218.CrossRefGoogle Scholar
  14. Kamakura, W.A. (2014). Measure twice and cut once: the carpenter’s rule still applies. Marketing Letters, this issue.Google Scholar
  15. Lehmann, D. R., McAllister, L., & Staelin, R. (2011). Sophistication in research in marketing. Journal of Marketing, 75, 155–165.CrossRefGoogle Scholar
  16. Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86, 114–121.CrossRefGoogle Scholar
  17. Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: a comparison of alternative approaches and a reanalysis of past research. Management Science, 52, 1865–1883.CrossRefGoogle Scholar
  18. Norton, M. I., Frost, J. H., & Ariely, D. (2007). Less is more: the lure of ambiguity, or why familiarity breeds contempt. Journal of Personality and Social Psychology, 92, 97–105.CrossRefGoogle Scholar
  19. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.Google Scholar
  20. Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The measurement of meaning. Urbana: University of Illinois Press.Google Scholar
  21. Peterson, R. A. (1994). A meta-analysis of Cronbach’s coefficient alpha. Journal of Consumer Research, 21, 381–391.CrossRefGoogle Scholar
  22. Peterson, R. A., & Kim, Y. (2013). On the relationship between coefficient alpha and composite reliability. Journal of Applied Psychology, 98, 194–198.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. Rossiter, J. R. (2002). The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19, 305–335.CrossRefGoogle Scholar
  25. Rossiter, J. R. (2010). Marketing measurement revolution: C-OAR-SE to replace psychometrics. Transfer - Werbeforschung & Praxis, 56(4), 66–72.Google Scholar
  26. Rossiter, J. R. (2011). Measurement for the social sciences: the C-OAR-SE method and why it must replace psychometrics. Berlin: Springer.CrossRefGoogle Scholar
  27. Sundie, J. M., Kenrick, D. T., Griskevicius, V., Tybur, J. M., Vohs, K. D., & Beal, D. J. (2011). Peacocks, Porsches, and Thorstein Veblen: conspicuous consumption as a sexual signaling system. Journal of Personality and Social Psychology, 100, 664–680.CrossRefGoogle Scholar
  28. Tormala, Z. L., Briñol, P., & Petty, R. E. (2007). Multiple roles for source credibility under high elaboration: it’s all in the timing. Social Cognition, 25, 536–552.CrossRefGoogle Scholar
  29. Wanous, J. P., & Hudy, M. J. (2001). Single-item reliability: a replication and extension. Organizational Research Methods, 4, 361–375.CrossRefGoogle Scholar
  30. Wanous, J. P., & Reichers, A. E. (1996). Estimating the reliability of a single-item measure. Psychological Reports, 78, 631–634.CrossRefGoogle Scholar
  31. Wanous, J. P., Reichers, A. E., & Hudy, M. J. (1997). Overall job satisfaction: how good are single-item measures? Journal of Applied Psychology, 82, 247–252.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.University of Nottingham Ningbo ChinaNingboChina

Personalised recommendations