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Assessing item-feature effects with item response tree models.

Ulf Böckenholt1

  • 1Kellogg School of Management, Northwestern University, Evanston, Illinois, USA.

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Summary
This summary is machine-generated.

This study extends item response tree models to identify item-feature effects, revealing they influence directional responses but not scale usage biases. Accounting for these effects improves psychological test measurement and interpretation.

Keywords:
item-feature effectsitem-response-tree modelsresponse styles

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Area of Science:

  • Psychometrics
  • Psychological Measurement
  • Statistical Modeling

Background:

  • Item response tree models effectively detect response style effects (e.g., midpoint, extremity).
  • Existing models primarily focus on measuring response styles, not item characteristics.
  • Understanding item-feature interactions is crucial for accurate psychological assessment.

Purpose of the Study:

  • To extend item response tree models to analyze item-feature effects.
  • To investigate how item features (e.g., wording) influence response patterns.
  • To improve the interpretation and fit of psychological measurement models.

Main Methods:

  • Reanalysis of three published psychological test datasets.
  • Application of an extended item response tree modeling approach.
  • Utilizing maximum likelihood estimation for models with missing data.

Main Results:

  • The proposed extension successfully captures item-feature effects, particularly across affirmative and reverse-worded items.
  • Item-feature effects were found to impact directional responses.
  • These effects did not significantly influence midpoint or extremity response preferences.
  • Incorporating item-feature effects substantially enhanced model fit and construct interpretation.

Conclusions:

  • The extended item response tree model provides a valuable tool for examining item-feature effects in psychological measurement.
  • Accounting for item-feature effects leads to more accurate construct measurement and interpretation.
  • The proposed method is readily implementable with existing statistical software.