Assessing item-feature effects with item response tree models.

Recent applications of item response tree models demonstrate that this model class is well suited to detect midpoint and extremity response style effects in both attitudinal and personality measurements. This paper proposes an extension of this approach that goes beyond measuring response styles and allows us to examine item-feature effects. In a reanalysis of three published data sets, it is shown that the proposed extension captures item-feature effects across affirmative and reverse-worded items in a psychological test. These effects are found to affect directional responses but not midpoint and extremity preferences. Moreover, accounting for item-feature effects substantially improves model fit and interpretation of the construct measurement. The proposed extension can be implemented readily with current software programs that facilitate maximum likelihood estimation of item response models with missing data.

• Publication year

  2019

• Venue

  British Journal of Mathematical & Statistical Psychology

• Publication date

  2019-03-26

• Fields of study

  Medicine, Computer Science, Psychology

• Identifiers
  DOI 10.1111/bmsp.12163PMID 30912584
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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