Model-data fit evaluation in IRT is claimed to be crucial for both linear and adaptive tests because item response model goodness-of-fit is relevant to any test purpose¶
Claim · All claims
Evidence · 1 study · causalr?·q1
Currency · newest study 2013 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2016: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q1 i? Evaluating model-data fit matters for both linear and adaptive tests because IRT model goodness-of-fit is relevant to any purpose of a test. → Shudong Wang 2013
Evidence¶
Shudong Wang 2013¶
Shudong Wang, Hong Jiao. (2013). The effect of nonignorable missing data in computerized adaptive test on item fit statistics for polytomous item response models. Presentation paper. https://www.nwea.org/research/publication/the-effect-of-nonignorable-missing-data-in-computerized-adaptive-test-on-item-fit-statistics-for-polytomous-item-response-models/
q1 · i? · causal · r?
A stated position from the presentation description, offered as an unargued assertion; the available text presents no evidence or data supporting it.
"it is crucial to evaluate model-data fit because the goodness-of-fit of item response theory (IRT) models are relevant to any purpose of a test."
Discussion¶
Related Claims¶
- Existing item fit statistics are derived for linear tests under assumptions of no missing responses and normal ability distributions, leaving adaptive testing contexts understudied — related
- Item parceling at the testlet level substantially improves model fit, allowing partial (Rasch, 2PL) or full (bifactor) recovery of CAT constructs — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
irt-model-fit-crucial-any-test-purpose |
title |
Model-data fit evaluation in IRT is claimed to be crucial for both linear and adaptive tests because item response model goodness-of-fit is relevant to any test purpose |
description |
Model-data fit evaluation in IRT is claimed to be crucial for both linear and adaptive tests because item response model goodness-of-fit is relevant to any test purpose |
status |
review |
evidence_strength |
weak |
newest_evidence |
2013 |
recheck_by |
2016 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — shudong-wang-2013 |
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