Evidence map›Paper›PMID 39558479›Full record

ArticleInternational journal of psychology : Journal international de psychologie2025

Dimensionality in confirmatory factor analysis is not in the eye of the beholder: Ancillary bifactor statistical indices illuminate dimensionality and reliability.

Tyrone B Pretorius, Anita Padmanabhanunni

Abstract read
In one paragraph

Article in International journal of psychology : Journal international de psychologie, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Tyrone B PretoriusDepartment of Psychology, University of the Western Cape, Cape Town, South Africa.ORCID https://orcid.org/0000-0002-6325-6623
Anita PadmanabhanunniDepartment of Psychology, University of the Western Cape, Cape Town, South Africa.ORCID https://orcid.org/0000-0001-7733-7486

Funding

South African Medical Research Council
6 · The paper itself

Abstract

This tutorial delves into dimensionality assessment within the context of psychological measurement instruments, particularly focusing on bifactor models. It underscores the imperative to move beyond traditional fit indices when evaluating factor structures while highlighting the significance of ancillary bifactor indices such as explained common variance, OmegaH and percentage of uncontaminated correlations in gaining a more comprehensive understanding of the interplay between general and specific group factors. The tutorial offers a step-by-step guide to leveraging the power of R software for confirmatory factor analysis and the acquisition of ancillary bifactor indices. Through practical case studies, it elucidates the potential pitfalls of exclusively relying on fit indices and advocates for a balanced, multifaceted approach to dimensionality assessment. By integrating fit measures and ancillary indices, researchers can draw more informed and nuanced conclusions about measurement instrument dimensionality, ultimately enhancing the precision of psychological assessment.

Indexed as

PsychometricsSoftwareFactor Analysis, StatisticalHumansReproducibility of ResultsAncillary bifactor indicesBifactor modelsConfirmatory factor analysisModel fit indices

Identifiers

PMID39558479
PMCPMC11626227

What OpenQuestion holds

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LicenceCC BY
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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.