Using a consistency check during data collection to identify invalid responding in an online cannabis screening survey

Date

2022-03-13

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Abstract

Abstract

            Background
            Inconsistent responding is a type of invalid responding, which occurs on self-report surveys and threatens the reliability and validity of study results. This secondary analysis evaluated the utility of identifying inconsistent responses as a real-time, direct method to improve quality during data collection for an Internet-based RCT.
          
          
            Methods
            The cannabis subscale of the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) was administered as part of eligibility screening for the RCT. Following the consent procedure, the cannabis subscale was repeated during the baseline interview. Responses were automatically compared and individuals with inconsistent responses were screened out.
          
          
            Results
            Nearly half of those initially eligible for the RCT were subsequently screened out for data quality issues (n = 626, 45.3%). Between-group bivariate analysis found that those screened out (OUT) were significantly older (OUT = 39.5 years (SD = 13.9), IN = 35.7 years (SD = 12.9), p < .001), more had annual incomes less than $20,000CND (OUT = 58.3%, IN = 53.0%, p = .047), used cannabis less often in the past 30 days (OUT = 23.3 days (SD = 9.7), IN = 24.8 days (SD = 11.3), p < .006), and had lower total ASSIST scores at screener (OUT = 19.3 (SD = 8.0), IN = 23.8 (SD = 10.4), p < .001) and baseline (OUT = 17.5 (SD = 7.9), IN = 23.3 (SD = 10.3), p < .001) compared to participants who were screened in to the RCT.
          
          
            Conclusion
            Inconsistent responding may occur at high rates in Internet research and direct methods to identify invalid responses are needed. Comparing responses for consistency can be programmed in Internet surveys to automatically screen participants during recruitment and reduce the need for post-hoc data cleaning.

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Citation

BMC Medical Research Methodology. 2022 Mar 13;22(1):67

DOI

https://doi.org/10.1186/s12874-022-01556-2

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