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Correction for Measurement Errors in Survey Research: Necessary and Possible

Author

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  • Willem E. Saris

    (Universitat Pompeu Fabra)

  • Melanie Revilla

    (Universitat Pompeu Fabra)

Abstract

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.

Suggested Citation

  • Willem E. Saris & Melanie Revilla, 2016. "Correction for Measurement Errors in Survey Research: Necessary and Possible," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 127(3), pages 1005-1020, July.
    • Handle: RePEc:spr:soinre:v:127:y:2016:i:3:d:10.1007_s11205-015-1002-x
    DOI: 10.1007/s11205-015-1002-x
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    References listed on IDEAS

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    1. Jacques A. Hagenaars, 1988. "Latent Structure Models with Direct Effects between Indicators," Sociological Methods & Research, , vol. 16(3), pages 379-405, February.
    2. repec:ucp:bkecon:9780226316529 is not listed on IDEAS
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    Cited by:

    1. Felderer, Barbara & Repke, Lydia & Weber, Wiebke & Schweisthal, jonas & Bothmann, Ludwig, 2024. "Predicting the Validity and Reliability of Survey Questions," OSF Preprints hkngd, Center for Open Science.
    2. Marc Asensio & Melanie Revilla, 2022. "Number of answer categories for bipolar item specific scales in face-to-face surveys: Does more mean better?," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(3), pages 1413-1433, June.
    3. Piotr Tarka, 2018. "An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 313-354, January.
    4. Melanie Revilla & Jan Karem Höhne & Tobias Rettig, 2023. "Differences in measurement quality depending on recall: results for a question about trust in the parliament," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2125-2146, June.
    5. Maria Rubio Juan & Melanie Revilla, 2021. "Support for mitigation and adaptation climate change policies: effects of five attitudinal factors," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 26(6), pages 1-22, August.
    6. Anna DeCastellarnau, 2018. "A classification of response scale characteristics that affect data quality: a literature review," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1523-1559, July.
    7. André Pirralha & Wiebke Weber, 2020. "Correction for measurement error in invariance testing: An illustration using SQP," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-18, October.

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