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Detection and Treatment of Careless Responses to Improve Item Parameter Estimation

Author

Listed:
  • Jeffrey M. Patton

    (Financial Industry Regulatory Authority (FINRA))

  • Ying Cheng
  • Maxwell Hong

    (University of Notre Dame)

  • Qi Diao

    (Educational Testing Service)

Abstract

In psychological and survey research, the prevalence and serious consequences of careless responses from unmotivated participants are well known. In this study, we propose to iteratively detect careless responders and cleanse the data by removing their responses. The careless responders are detected using person-fit statistics. In two simulation studies, the iterative procedure leads to nearly perfect power in detecting extremely careless responders and much higher power than the noniterative procedure in detecting moderately careless responders. Meanwhile, the false-positive error rate is close to the nominal level. In addition, item parameter estimation is much improved by iteratively cleansing the calibration sample. The bias in item discrimination and location parameter estimates is substantially reduced. The standard error estimates, which are spuriously small in the presence of careless responses, are corrected by the iterative cleansing procedure. An empirical example is also presented to illustrate the proposed procedure. These results suggest that the proposed procedure is a promising way to improve item parameter estimation for tests of 20 items or longer when data are contaminated by careless responses.

Suggested Citation

  • Jeffrey M. Patton & Ying Cheng & Maxwell Hong & Qi Diao, 2019. "Detection and Treatment of Careless Responses to Improve Item Parameter Estimation," Journal of Educational and Behavioral Statistics, , vol. 44(3), pages 309-341, June.
    • Handle: RePEc:sae:jedbes:v:44:y:2019:i:3:p:309-341
    DOI: 10.3102/1076998618825116
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    References listed on IDEAS

    as
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    8. Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).
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    Cited by:

    1. Yue Liu & Hongyun Liu, 2021. "Detecting Noneffortful Responses Based on a Residual Method Using an Iterative Purification Process," Journal of Educational and Behavioral Statistics, , vol. 46(6), pages 717-752, December.

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