Unobtrusive Measurements

  • Gideon J. MellenberghEmail author


Usually, participants know that they are measured, which implies that they can react to the measurement situation, for example, by satisficing. Unobtrusive measurements are measurements where participants cannot react to the situation. Four measurement modes are distinguished, and for each of these modes examples of unobtrusive measurement procedures are given. First, the self-report mode where participants answer questions themselves (e.g., tests). An unobtrusive self-report disguises the construct that is measured. For example, a writing test to measure aggression instead of writing skill by counting the number of aggressive words in a student’s essay. Second, the other-report mode where an other person answers questions on a participant. For example, a teacher who answers questions on a student’s aggressive behavior. Third, the somatic indicators mode where a participant’s somatic reactions are observed. For example, the observation of a participant’s blushing. Fourth, the physical traces mode where physical traces of a participant are used. For example, traffic offences to assess a participant’s attitude on traffic safety. Random errors decrease the precision of unobtrusive measurements, and systematic errors bias these measurements. The main systematic error is that the unobtrusive measure does not measure the construct that the researcher wants to measure. Unobtrusive measures that involve human judgment (e.g., ratings) are prone to systematic errors, such as, the halo-effect. Unobtrusive measures should not replace reactive measures, but they can complement these measures.


Error of central tendency Error of leniency Halo-effect Other-report mode Physical traces mode Somatic indicators mode Self-report mode 


  1. Achenbach, T. M. (1991). Manual for the child behavior checklist/4–18 and 1991 profile. Burlington, VT: University of Vermont, Department of Psychiatry.Google Scholar
  2. Big data in psychology (2016). [Special issue]. Psychological Methods, 21(4).Google Scholar
  3. Couper, M. P. (2013). Is the sky falling? New technology, changing media, and the future of surveys. Survey Research Methods, 7, 145–156.Google Scholar
  4. Hickendorff, M., Heiser, W. J., van Putten, M., & Verhelst, N. D. (2009). Solution strategies and achievement in Dutch complex arithmetic: Latent variable modeling of change. Psychometrika, 74, 331–350.CrossRefGoogle Scholar
  5. Kerlinger, F. N. (1986). Foundations of behavioral research (3rd ed.). New York, NY: Holt, Rinehart and Winston.Google Scholar
  6. Tan, E. J. J. S., & Westhoff, B. (2014). Unobtrusive measurements. In H. J. Adèr & G. J. Mellenbergh (Eds.), Advising on research methods: Selected topics 2014 (pp. 9–21). Huizen, The Netherlands: van Kessel.Google Scholar
  7. Webb, E. J., Campbell, D. T., Schwartz, R. D., & Sechrest, L. (1966/2000). Unobtrusive measures. Chicago, IL: Rand McNally/Thousand Oaks, CA: SAGE.Google Scholar
  8. Webb, E. J., Campbell, D. T., Schwartz, R. D., Sechrest, L., & Grove, J. B. (1981). Nonreactive measures in the social sciences. Boston: Houghton Mifflin.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Emeritus Professor Psychological Methods, Department of PsychologyUniversity of AmsterdamAmsterdamThe Netherlands

Personalised recommendations