Evidence map›Paper›PMID 37622768›Full record

ArticleBehavioral sciences (Basel, Switzerland)2023

Measuring State and Trait Anxiety: An Application of Multidimensional Item Response Theory.

Leonardo Carlucci, Marco Innamorati, Melissa Ree, Giorgia D'Ignazio, Michela Balsamo

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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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

5 authors.

Leonardo CarlucciLearning Science Hub, University of Foggia, 71121 Foggia, Italy.ORCID 0000-0002-9853-5606
Marco InnamoratiDepartment of Human Sciences, Università Europea di Roma, 00163 Rome, Italy.ORCID 0000-0003-1389-2290
Melissa ReeSchool of Psychological Science, University of Western Australia, Perth 6009, Australia.ORCID 0000-0002-2287-3297
Giorgia D'IgnazioDepartment of Psychological, Health and Territorial Sciences, University of "G. d'Annunzio" Chieti-Pescara, 66100 Chieti, Italy.
Michela BalsamoDepartment of Psychological, Health and Territorial Sciences, University of "G. d'Annunzio" Chieti-Pescara, 66100 Chieti, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA) is a widely used measure of state and trait anxiety. Within the Classical Testing Theory model, consistent findings provide support for its multidimensional factor structure, discriminant, convergent, and nomological validity, as well as age and gender invariance, across healthy and clinical samples. Nevertheless, some issues regarding STICSA dimensionality and item-scale composition remain unresolved (e.g., both bifactor and two-factor models were found to fit data equally well). The goal of this study was to investigate the STICSA's dimensionality within the Item Response Theory, and to assess the tenability of the bifactor model as a plausible model over the multidimensional model. The sample consisted of 3338 Italian participants (58.21% females; 41.79% males) with an average age of 35.65 years (range: 18-99; SD = 20.25). Both bifactor and two-correlated dimensions of the STICSA scales were confirmed to fit data by applying the multidimensional Item Response Theory (mIRT). While the bifactor model showed better fit indices, the multidimensional model was more accurate and precise (0.86-0.88) in estimating state and trait latent anxiety. A further comparison between multidimensional item parameters revealed that the multidimensional and bifactor models were equivalent. Findings showed that the STICSA is an accurate and precise instrument for measuring somatic and cognitive symptomatology dimensions within state and trait anxiety. The use of the state/trait total score requires special attention from the clinicians and researchers to avoid bias in the psychodiagnostic assessment.

Indexed as

anxietyassessmentbifactorcognitivepsychometricssomatic

Identifiers

PMID37622768
PMCPMC10451624

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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.