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Predicting the Validity and Reliability of Survey Questions

Author

Listed:
  • Felderer, Barbara
  • Repke, Lydia
  • Weber, Wiebke
  • Schweisthal, jonas
  • Bothmann, Ludwig

Abstract

The Survey Quality Predictor (SQP) is an open-access system to predict the quality, i.e., the reliability and validity, of survey questions based on the characteristics of the questions. The prediction is based on a meta-regression of many multitrait-multimethod (MTMM) experiments in which characteristics of the survey questions were systematically varied. The release of SQP 3.0 that is based on an expanded data base as compared to previous SQP versions raised the need for a new meta-regression. To find the best method for analyzing the complex data structure of SQP (e.g., the existence of various uncorrelated predictors), we compared four suitable machine learning methods in terms of their ability to predict both survey quality indicators: LASSO, elastic net, boosting and random forest. The article discusses the performance of the models and illustrates the importance of the individual item characteristics in the random forest model, which was chosen for SQP 3.0.

Suggested Citation

  • 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.
    • Handle: RePEc:osf:osfxxx:hkngd
    DOI: 10.31219/osf.io/hkngd
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    References listed on IDEAS

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    1. Willem Saris & Theresia van Wijk & Annette Scherpenzeel, 1998. "Validity and Reliability of Subjective Social Indicators: The effect of different measures of association," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 45(1), pages 173-199, November.
    2. 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.
    3. 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.
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