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Correction for Measurement Errors in Survey Research: Necessary and Possible


Survey research is the most frequently used data collection method in many disciplines. Nearly, everybody agrees that such data contain serious measurement errors. However, only few researchers try to correct for them. If the measurement errors in the variables vary, the comparison of the sizes of effects of these variables on each other will be wrong. If the sizes of the measurement errors are different across countries, cross-national comparisons of relationships between variables cannot be made. There is ample evidence for these differences in measurement errors across variables, methods and countries (Saris and Gallhofer in Design, evaluation and analysis of questionnaires for survey. Wiley, Hoboken, 2007; Oberski in Measurement errors in comparative surveys. PhD thesis, University of Tilburg, 2011). Therefore, correction for measurement errors is essential for the social sciences. The correction for measurement errors can be made in a simple way, but it requires that the sizes of the error variances are known for all observed variables. Many experiments are carried out to determine the quality of questions. The relationship between the quality and the characteristics of the questions has been studied. Because this relationship is rather strong, one can also predict the quality of new questions. A program SQP has been developed to predict the quality of questions. Using this program, the quality of the questions (complement of error variance) can be obtained for nearly all questions measuring subjective concepts. For objective variables, other research needs to be used (e.g., Alwin in Margins of error: a study of reliability in survey measurement. Wiley, Hoboken, 2007). Using these two sources of information, making correction for measurement error in survey research is possible. We illustrate here that correction for measurement errors can and should be performed.

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  1. 1.

    We are grateful to Wiebke Weber of RECSM for collecting the data in Table 1. Pei-Shan Liao of the Academia Sinica and Zih-Wei Wang of the National Taipei University reported to us that in the Taiwanese Sociological Journal and the Political science review, the situation was very similar: Together these journals published in 2010–2012 in total 67 papers of which 18 used survey research and 9 mentioned the problem of measurement errors but only one makes corrections for measurement errors. We are very grateful for this information.

  2. 2.

    y i  = f i  + e i and cov(f i e i ) = 0 then var(y i ) = var(f i ) + var(e i ) and dividing by var(y i ) gives 1 = [var(f i )/var(y i )] + [var(e i )/var(y i )] or 1 = q i 2  + var(e i ) ,so the total variance of y i , which is equal to 1, can be decomposed into the quality of the question i plus the proportion error in the variance in the observed variable y i . Normally, q i 2 has been called the reliability of a measurement instrument (Lord and Novick 1968) but we prefer the term quality for reason that will become clear in Sect. 4.

  3. 3.


  4. 4.

    However, this information is not enough to correct for measurement errors in all ESS questions because at the same time in the ESS more than 60,000 questions were asked with respect to values, opinions, attitudes, preferences, feelings, etc. This illustrates that a different approach is required.

  5. 5.

    SQP is free of change available at sqp.upf.edu.

  6. 6.

    It can be shown that the CMV = r 1j m ij m 2j r 2j .


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Correspondence to Willem E. Saris.


Appendix 1

See Table 7.

Table 7 Procedure to correct for measurement error using LISREL

Appendix 2: The different questions


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Saris, W.E., Revilla, M. Correction for Measurement Errors in Survey Research: Necessary and Possible. Soc Indic Res 127, 1005–1020 (2016). https://doi.org/10.1007/s11205-015-1002-x

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  • Correction for measurement errors
  • Quality
  • SQP 2.0
  • European Social Survey