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Self-serving bias is a cognitive phenomenon in which individuals attribute positive outcomes to internal factors such as their abilities, intelligence, or effort while attributing negative outcomes to external circumstances. This cognitive distortion helps maintain self-esteem but can also impede objective self-assessment.Theoretical Explanations of Self-Serving BiasTwo primary theories explain the self-serving bias: the cognitive explanation and the motivational explanation.The cognitive...
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Measuring bias in self-reported data.

Robert Rosenman1, Vidhura Tennekoon2, Laura G Hill3

  • 1School of Economic Sciences, Washington State University, P.O. Box 646210, Pullman, WA 99164-6210, USA.

International Journal of Behavioural & Healthcare Research
|November 11, 2014
PubMed
Summary
This summary is machine-generated.

Stochastic frontier estimation (SFE) can identify response bias in self-reported data. This method revealed demographic links to bias and showed bias decreased after a family intervention, aiding accurate program effect measurement.

Keywords:
SFEprevention scienceprogramme evaluationresponse biasresponse-shift biasstochastic frontier analysisstochastic frontier estimation

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Area of Science:

  • Behavioral Science
  • Healthcare Research
  • Econometrics

Background:

  • Self-reported data are prevalent in behavioral and healthcare research.
  • Response bias can significantly impact the validity of research findings.
  • Existing methods may not fully capture dynamic changes in response bias.

Purpose of the Study:

  • To demonstrate the utility of stochastic frontier estimation (SFE) for identifying and analyzing response bias.
  • To examine the influence of participant demographics on response bias in a family intervention context.
  • To explore the potential of SFE in addressing response shift bias.

Main Methods:

  • Application of stochastic frontier estimation (SFE) to analyze self-reported data.
  • Examination of response bias before and after a family intervention.
  • Analysis of demographic factors (gender, race/ethnicity) and their relationship with response bias.

Main Results:

  • SFE successfully identified response bias and its covariates.
  • Participant gender and race/ethnicity were associated with the magnitude and change in response bias.
  • Response bias was lower at post-test compared to pre-test, indicating a reduction in bias over time.

Conclusions:

  • SFE provides a robust method for detecting and quantifying response bias in research.
  • Demographic factors play a role in response bias, necessitating careful consideration in data analysis.
  • SFE can help mitigate 'response shift bias,' leading to more accurate estimations of intervention effects.