Existing item fit statistics are derived for linear tests under assumptions of no missing responses and normal ability distributions, leaving adaptive testing contexts understudied¶
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? Item fit statistics research has been conducted almost entirely in the context of linear testing, under regular fixed-form assumptions such as no missing responses. → 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?
The authors' framing statement in the presentation description asserts that fit-statistic research to date concerns linear testing, with adaptive-testing contexts unaddressed; the description offers this assertion without reported data.
"To date, all item fit statistics are derived based on linear tests and almost all studies have been done in the context of linear testing."
Discussion¶
Related Claims¶
- 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 — related
- MCAR item and response missingness in linear tests shows no differential effect on model fit across Designs 1 to 4 — related
- Adaptive testing explains why about one third of calibrated Spanish math items carry calibration status 10 — related
- Ability-distribution mismatch with the TESTFACT default does not significantly affect item difficulty estimates but slightly improves discrimination estimates — related
- In a statewide VLE algebra dataset, item missingness is nonignorable: skipping is related to student ability and item difficulty as measured in 2PL-IRT — related
- CAT data cannot be fit into factor models at the item level, making item-level construct validity investigation meaningless for CAT — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
item-fit-statistics-derived-for-linear-tests-only |
title |
Existing item fit statistics are derived for linear tests under assumptions of no missing responses and normal ability distributions, leaving adaptive testing contexts understudied |
description |
Existing item fit statistics are derived for linear tests under assumptions of no missing responses and normal ability distributions, leaving adaptive testing contexts understudied |
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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