CoChat
Sign Up
Cited by 9Psychometric Methodologies and TestingPsychological Well-being and Life SatisfactionEducational Technology and Assessment

A computerized adaptive testing advancing the measurement of subjective well-being

Open access
Wu, Yifang, Cai, Yan, Tu, Dongbo·2019·Journal of Pacific Rim Psychology
DOI 10.1017/prp.2019.6
Open this paper in CoChat — free accountOpen PDF

Abstract

This article aimed at developing an adaptive version of the subjective well-being (SWB) scale to measure a comprehensive concept of SWB among Chinese university students. Item response theory was employed to formulate the item bank of the SWB scale and computerized adaptive testing (CAT) for SWB (CAT-SWB), based on several commonly used SWB scales, after unidimensionality testing, model selection, local dependence testing, parameter estimation, item fit test and differential item functioning (DIF) analysis were performed. Finally, two CAT simulations using simulated-data and real-data were carried out to verify and evaluate the CAT-SWB. Results indicated that the proposed CAT-SWB had an excellent performance in that it largely reduces the number of test items and the length of test time without losing measurement precision.

Integrity signals

Registry

Crossref · 10.1017/prp.2019.6

Cites

64 of 64 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Randomly selecting the first item from the full bank ensures an unbiased starting point.

    “the CAT was initiated by randomly selecting a first item from the full bank, ensuring an unbiased starting point15,29.”

    From Leveraging computerized adaptive testing for cost-effective evaluation of large language models in medical benchmarking (2026) · cites this paper as [29]

  • Simulating 3600 examinees with true ability parameters drawn from a uniform distribution across 36 discrete ability levels from −3.5 to +3.5 provides a more stringent test of the CAT algorithm's robustness across the entire ability spectrum.

    “We simulated 3600 examinees with true ability parameters drawn from a uniform distribution, spanning 36 discrete ability levels from −3.5 to +3.5 at intervals of 0.2. Each ability level was replicated 100 times. This uniform sampling, as opposed to a normal distribution, provides a more stringent test of the CAT algorithm’s robustness across the entire ability spectrum15,29,33.”

    From Leveraging computerized adaptive testing for cost-effective evaluation of large language models in medical benchmarking (2026) · cites this paper as [29]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Wu, Y., Cai, Y., & Tu, D.
Affiliations
  • School of Psychology, Jiangxi Normal University, Nanchang, China
  • Jiangxi Normal University (CN)
Corresponding author
Tu, Dongbo
ORCID
Venue
Journal of Pacific Rim Psychology
Publisher
SAGE Publications
Published
2019-01
Volume
13
ISSN
1834-4909 (ISSN-L)
DOI
10.1017/prp.2019.6
OpenAlex
W2921700832
Cited by
8
References
62
Citations by year
2026: 2 · 2023: 1 · 2022: 3 · 2021: 1 · 2020: 1
FWCI
0.90
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Pacific Rim Psychology (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Social Sciences › Psychology › Social Psychology › Psychological Well-being and Life Satisfaction
  • Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
Keywords
computerized adaptive testing, subjective well-being, item response theory, item bank, Chinese university students, differential item functioning, local dependence, test length reduction, measurement precision

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review