The second-order multidimensional IRT model for testlets is formally equivalent to the testlet model, and both are restricted bifactor models¶
Claim · All claims
Evidence · 1 study · theoreticalr3·q2
Currency · newest study 2009 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2012: 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.
q2 i? Algebraic manipulation of the second-order model's equations shows it is formally equivalent to the testlet model, which itself is a bifactor model with proportionality restrictions within each testlet. → Rijmen 2009
Evidence¶
Rijmen 2009¶
Rijmen, F. (2009). Three Multidimensional Models for Testlet-Based Tests: Formal Relations and an Empirical Comparison. ETS Research Report RR-09-37. http://www.ets.org/research/contact.html doi:10.1002/j.2333-8504.2009.tb02194.x
q2 · i? · theoretical · r3
Analytical derivation comparing Equation 7 to the bifactor model's Equation 2 under standard normal latent variables. The derivation shows the second-order model is a restricted bifactor model with the same restrictions that yield the testlet model, and the article notes this equivalence had not been explicitly described before, to the knowledge of the author.
"some further algebraic manipulations of Equation 7 clearly show that the second-order model is formally equivalent to the testlet model"
Discussion¶
Related Claims¶
- In an empirical comparison on a testlet-based English assessment test, the bifactor model is preferred over the second-order and unidimensional 2PL models by both AIC and BIC — related
- Ignoring testlet effects by fitting a unidimensional 2PL model produced mild shrinkage of item intercept and loading estimates toward zero relative to the bifactor model — related
- Multidimensional latent variable models rarely support more than about four to six latent variables, limiting fine-grained SKIVE modeling — related
- The proportionality restrictions that the testlet model imposes on the bifactor model were implausible in the applied dataset, as specific-to-general loading quotients varied far from constant within testlets — related
- A multidimensional IRT model with simple structure fits testlet-based reading data better than both the general polytomous testlet model and the standard 2PL/GPCM — related
- A strict one-factor model fits poorly while four-factor, second-order, and bifactor models fit better, with the bifactor model favored by formal comparisons — related
- Bifactor-(S-1) models of teacher Big Five personality fit student and teacher data better than single-domain or three-facets models — related
- A bifactor model of oral language and reading fits better than one-, three-, and four-factor models in every grade from 4th through 10th — related
- Higher order cognitive skills (inference, perspective taking, comprehension monitoring) fit a bifactor structure with a general cross-language factor and language-specific factors in English and Spanish — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
second-order-model-equivalent-to-testlet-model |
title |
The second-order multidimensional IRT model for testlets is formally equivalent to the testlet model, and both are restricted bifactor models |
description |
The second-order multidimensional IRT model for testlets is formally equivalent to the testlet model, and both are restricted bifactor models |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2009 |
recheck_by |
2012 |
generated |
by process:wiki-ingest on 2026-09-26 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — rijmen-2009 |
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