Careless responding in internet-based quality of life assessments.

Purpose: Quality of life (QoL) measurement relies upon participants providing meaningful responses, but not all respondents may pay sufficient attention when completing self-reported QoL measures. This study examined the impact of careless responding on the reliability and validity of Internet-based...

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Published in:Quality of Life Research Vol. 27; no. 4; pp. 1077 - 1089
Main Authors: Schneider, Stefan, May, Marcella, Stone, Arthur A.
Format: research tables/charts Journal Article
Published: Springer Nature Apr2018
Online Access:View this record in EBSCOhost
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      dt: Apr2018
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s11136-017-1767-2
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        atl: Careless responding in internet-based quality of life assessments.
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          Schneider, Stefan
          May, Marcella
          Stone, Arthur A.
        affil: University of Southern California, Los Angeles, USA
      sug:
        subj:
          Quality of Life
          Internet
          Patient-Reported Outcomes
          Questionnaires Utilization
          Funding Source
          Human
          Data Analysis
          Psychometrics
          Reliability and Validity
          Surveys
          Male
          Adult
          Middle Age
          Aged
          Female
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Aged: 65+ years
          Male
          Female
      ab: Purpose: Quality of life (QoL) measurement relies upon participants providing meaningful responses, but not all respondents may pay sufficient attention when completing self-reported QoL measures. This study examined the impact of careless responding on the reliability and validity of Internet-based QoL assessments.Methods: Internet panelists (n = 2000) completed Patient-Reported Outcomes Measurement Information System (PROMIS®) short-forms (depression, fatigue, pain impact, applied cognitive abilities) and single-item QoL measures (global health, pain intensity) as part of a larger survey that included multiple checks of whether participants paid attention to the items. Latent class analysis was used to identify groups of non-careless and careless responders from the attentiveness checks. Analyses compared psychometric properties of the QoL measures (reliability of PROMIS short-forms, correlations among QoL scores, "known-groups" validity) between non-careless and careless responder groups. Whether person-fit statistics derived from PROMIS measures accurately discriminated careless and non-careless responders was also examined.Results: About 7.4% of participants were classified as careless responders. No substantial differences in the reliability of PROMIS measures between non-careless and careless responder groups were observed. However, careless responding meaningfully and significantly affected the correlations among QoL domains, as well as the magnitude of differences in QoL between medical and disability groups (presence or absence of disability, depression diagnosis, chronic pain diagnosis). Person-fit statistics significantly and moderately distinguished between non-careless and careless responders.Conclusions: The results support the importance of identifying and screening out careless responders to ensure high-quality self-report data in Internet-based QoL research.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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