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A psychometric model for respondent-level anchoring on self-report rating scale instruments.

Weicong LyuDaniel M Bolt
Published in: The British journal of mathematical and statistical psychology (2021)
Among the various forms of response bias that can emerge with self-report rating scale assessments are those related to anchoring, the tendency for respondents to select categories in close proximity to the rating category used for the immediately preceding item. In this study we propose a psychometric model based on a multidimensional nominal model for response style that also simultaneously accommodates a respondent-level anchoring tendency. The model is estimated using a fully Bayesian estimation procedure. By applying this model to a real test data set measuring extraversion, we explore a theory that both response styles and anchoring might be viewed as evidence of a lack of effortful responding. Empirical results show that there is a positive correlation between the strength of midpoint response style and the anchoring effect; further, responses indicative of either anchoring or response style both negatively correlate with response time, consistent with a theory that both phenomena reflect reduced respondent effort. The results support attending to both anchoring and midpoint response style as ways of assessing respondent engagement.
Keyphrases
  • machine learning
  • deep learning
  • tertiary care