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<p>The quality of self-report data has long been a concern, with increasing attention to the issue of insufficient effort responding (IER). Researchers have made considerable progress in developing IER detection techniques (Huang et al., Journal of Business and Psychology 30:299–311, 2014; Meade and Craig, Psychological Methods 17:437, 2012). Yet, when researchers identify IER, the typical solution is to drop respondents who engage in it, which can limit sample size and statistical power. In this paper, we propose and demonstrate a new way to limit the effects of IER on data quality by treating IER as a measured method factor which can be utilized to control for IER. This method presents an alternative to conventional strategies, offering researchers the means to address various manifestations of IER while minimizing sample loss and statistical power reduction. This study contributes meaningfully to survey research methodology by providing a systematic procedure for discerning and addressing IER in data. It offers a nuanced consideration of the trade-offs between retaining or discarding potentially flawed data, thereby empowering researchers to make informed decisions to enhance data quality and integrity.</p>

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I Caught It, Now What Do I Do With It? Controlling For Insufficient Effort Responding

  • 期刊论文

  • 发表日期：

    2025年5月5日

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  • Elizabeth Ragland,
  • Brian K. Miller,
  • Marcia J. Simmering,
  • Christie M. Fuller

摘要

The quality of self-report data has long been a concern, with increasing attention to the issue of insufficient effort responding (IER). Researchers have made considerable progress in developing IER detection techniques (Huang et al., Journal of Business and Psychology 30:299–311, 2014; Meade and Craig, Psychological Methods 17:437, 2012). Yet, when researchers identify IER, the typical solution is to drop respondents who engage in it, which can limit sample size and statistical power. In this paper, we propose and demonstrate a new way to limit the effects of IER on data quality by treating IER as a measured method factor which can be utilized to control for IER. This method presents an alternative to conventional strategies, offering researchers the means to address various manifestations of IER while minimizing sample loss and statistical power reduction. This study contributes meaningfully to survey research methodology by providing a systematic procedure for discerning and addressing IER in data. It offers a nuanced consideration of the trade-offs between retaining or discarding potentially flawed data, thereby empowering researchers to make informed decisions to enhance data quality and integrity.