Evidence map›Paper›PMID 36172568›Full record

ArticleSAGE open medicine2022

Modelling variance in the multidimensional Health Literacy Questionnaire: Does a General Health Literacy factor account for observed interscale correlations?

Gerald R Elsworth, Sandra Nolte, Christina Cheng, Melanie Hawkins, Richard H Osborne

Abstract read
In one paragraph

Article in SAGE open medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Developing a validity testing plan for a patient-reported outcome measure in a new context using the argument-based approach to validity.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026
    Article
  4. Article
  5. [Measuring health literacy: methods and tools for assessing general health literacy in adults].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025
    Review
  6. 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

5 authors.

Gerald R ElsworthCentre for Global Health and Equity, Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, VIC, Australia.ORCID https://orcid.org/0000-0002-1037-9590
Sandra NolteCentre for Global Health and Equity, Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, VIC, Australia.ORCID https://orcid.org/0000-0001-6185-9423
Christina ChengCentre for Global Health and Equity, Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, VIC, Australia.
Melanie HawkinsCentre for Global Health and Equity, Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, VIC, Australia.
Richard H OsborneCentre for Global Health and Equity, Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, VIC, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Self-report measures of health literacy frequently encompass a group of conceptually distinct but related domains scored as either a single scale or separately for each domain. Psychometric studies of the 44-item Health Literacy Questionnaire, scored as nine separate scales, typically report the results of correlated factors confirmatory factor analyses as validity evidence. However, some scales are quite strongly correlated, raising the question of whether there is sufficient discriminant validity to warrant separate scoring. This psychometric study aims to contrast the results of fitting higher-order and bifactor models as alternative options to illuminate the issue. Methods: Correlated factors, higher-order and bifactor confirmatory factor analysis models were fitted to an Australian sample of responses to the Health Literacy Questionnaire (N = 813) using Bayesian confirmatory factor analysis methods. Results: All models representing a nine-factor structure for the Health Literacy Questionnaire fitted well. The correlated factors model replicated previous findings, showing inter-factor correlations between 0.19 and 0.93. A higher-order model showed relatively high loadings of all nine first-order factors on the second-order factor with particularly high loadings (⩾0.97) for three. Two bifactor models showed that the majority of Health Literacy Questionnaire items were multifactorial, each containing systematic variance from both a General Health Literacy factor and a domain-specific factor. Seven items from four scales were identified as strongly associated with the General Health Literacy factor, with item content suggesting that this factor indexes a broad sense of agency and efficacy in interacting with health-related information and healthcare providers. Conclusion: Contrasting correlated factors, higher-order and bifactor models fitted to the Health Literacy Questionnaire suggest that constituent items in self-report health literacy questionnaires might be anticipated to represent at least two sources of reliable and substantive common factor variance: variance associated with General Health Literacy and variance associated with a more specific domain, suggesting that items may be 'irreducibly' heterogeneous. Implications for test development and validation practice are discussed.

Indexed as

Bayesian structural equation modellingbifactor modelHealth literacyHealth Literacy Questionnairevalidity

Identifiers

PMID36172568
PMCPMC9511310

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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.