Evidence map›Paper›PMID 41673943›Full record

ArticlePsychometrika2026

The Co-Varying Ties between Networks and Item Responses via Latent Variables.

Selena Wang, Tracy Morrison Sweet, Subhadeep Paul

Abstract read
In one paragraph

Article in Psychometrika, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Selena Wanghttps://ror.org/02ets8c94Indiana University School of Medicine, USA.ORCID 0000-0002-4358-5382
Tracy Morrison SweetHuman Development and Quantitative Methodology, https://ror.org/047s2c258University of Maryland, USA.
Subhadeep Paulhttps://ror.org/00rs6vg23The Ohio State University, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Relationships among teachers are known to influence their teaching-related perceptions. We study whether and how teachers' advising relationships (networks) are related to their perceptions of satisfaction, students, and influence over educational policies, recorded as their responses to a questionnaire (item responses). We propose a novel joint model of network and item responses (JNIRM) with correlated latent variables to understand these co-varying ties. This methodology allows the analyst to test and interpret the dependence between a network and item responses. Using the JNIRM, we discover that teachers' advising relationships contribute to their perceptions of satisfaction and students more often than their perceptions of influence over educational policies. In addition, we observe that the complementarity principle applies in certain schools, where teachers tend to seek advice from those who are different from them. The JNIRM has the potential to improve the cost efficiency of network studies by integrating network and item response information during model estimation.

Indexed as

School TeachersHumansPersonal SatisfactionPsychometricsStudentsSurveys and Questionnaireshigh-dimensional analysislatent space modelsocial network analysis

Identifiers

PMID41673943
PMCPMC13294630

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.