Quantifying Effects of Item Wording and Multiple Sources of Measurement Error Using Extended Bifactor Models

Walter P. Vispoel; Hyeryung Lee; Tingting Chen

doi:10.3390/bs16091636

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Quantifying Effects of Item Wording and Multiple Sources of Measurement Error Using Extended Bifactor Models

Journal article

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Quantifying Effects of Item Wording and Multiple Sources of Measurement Error Using Extended Bifactor Models

Walter P. Vispoel, Hyeryung Lee and Tingting Chen

Behavioral sciences, Vol.16(9), 1636

09/13/2026

DOI: 10.3390/bs16091636

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Abstract

Researchers have long known that scores from Likert-style self-report measures of psychological traits are affected by both item wording (negative and positive) and multiple sources of measurement error (specific-factor, transient, and random-response), but the magnitude of such effects is rarely separated and individually estimated within research studies. When these effects are not distinguished, as is the case when reporting conventional reliability estimates (e.g., alpha, omega, split-half, test–retest), they are confounded with trait variance and/or each other, typically resulting in overestimation of score accuracy. We demonstrate how to quantify and separate trait, wording, and multiple measurement error effects within extended bifactor models using data from a large sample of respondents (n = 1796) who completed the Physical Appearance subscale from the Self-Description Questionnaire-III on two occasions. Results revealed that models including item wording effects for both single and multiple occasion designs provided noticeably superior fits to the data in comparison to models excluding such effects, with item wording accounting for 8.5% to 8.8% of total score variance across designs. We further demonstrate how to extend analyses to the individual item level to identify items that are most and least affected by wording and measurement error effects and provide code in R to enable readers to apply these techniques to their own data.

reliability

item wording effects

confirmatory factor analysis

bifactor models

Self-Description Questionnaire-III

R programming

Details

Title: Subtitle: Quantifying Effects of Item Wording and Multiple Sources of Measurement Error Using Extended Bifactor Models
Creators: Walter P. Vispoel - University of Iowa
Hyeryung Lee - Oklahoma State University
Tingting Chen - University of Iowa
Resource Type: Journal article
Publication Details: Behavioral sciences, Vol.16(9), 1636
DOI: 10.3390/bs16091636
ISSN: 2076-328X
eISSN: 2076-328X
Publisher: MDPI
Grant note: Iowa Testing Programs
This project received internal university research assistant support from the Iowa Testing Programs.
Language: English
Date published: 09/13/2026
Academic Unit: Psychological and Quantitative Foundations
Record Identifier: 9985230927402771

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