Evidence map›Paper›PMID 35124767›Full record

ArticlePsychometrika2022

Incomplete Tests of Conditional Association for the Assessment of Model Assumptions.

Rudy Ligtvoet

Abstract read
In one paragraph

Article in Psychometrika, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Inferences of associated latent variables by the observable test scores.The British journal of mathematical and statistical psychology · 2026
    Article
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Rudy LigtvoetDepartment Erziehungs- und Sozialwissenschaften, University of Cologne, Germany, Gronewaldstr. 2a, 50931, Cologne, Deutschland. rligtvoe@uni-koeln.de.ORCID 0000-0001-6546-0911

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many of the models that have been proposed for response data share the assumptions that define the monotone homogeneity (MH) model. Observable properties that are implied by the MH model allow for these assumptions to be tested. For binary response data, the most restrictive of these properties is called conditional association (CA). All the other properties considered can be considered incomplete tests of CA that alleviate the practical limitations encountered when assessing the MH model assumptions using CA. It is found that the assessment of the MH model assumptions with an incomplete test of CA, rather than CA, is generally associated with a substantial loss of information. We also look at the sensitivity of the observable properties to model violation and discuss the implications of the results. It is argued that more research is required about the extent to which the assumptions and the model specifications influence the inferences made from response data.

Indexed as

PsychometricsConditional associationmanifest monotonicitymodel complexitymonotone homogeneity modelmonotone likelihood ratiomultivariate totally positive of order 2nonnegative partial correlationsscalability coefficientstrongly positive orthant dependency

Identifiers

PMID35124767
PMCPMC9636116

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.