• Guttman error graphs: a visual...

*Result*: Guttman error graphs: a visual approach to scalability analysis.

Title:
Guttman error graphs: a visual approach to scalability analysis.
Authors:
Reichenheim ME; Universidade do Estado do Rio de Janeiro. Instituto de Medicina Social Hésio Cordeiro. Departamento de Epidemiologia. Rio de Janeiro, RJ, Brasil., Moraes CL; Universidade do Estado do Rio de Janeiro. Instituto de Medicina Social Hésio Cordeiro. Departamento de Epidemiologia. Rio de Janeiro, RJ, Brasil.; Universidade Estácio de Sá. Programa de Pós-graduação em Saúde da Família. Rio de Janeiro, RJ, Brasil., Bastos JL; Universidade Federal de Santa Catarina. Departamento de Saúde Pública. Florianópolis, SC, Brasil.
Source:
Revista de saude publica [Rev Saude Publica] 2026 Feb 09; Vol. 60, pp. e1. Date of Electronic Publication: 2026 Feb 09 (Print Publication: 2026).
Publication Type:
Journal Article
Language:
English; Portuguese
Journal Info:
Publisher: Universidade De Sao Paulo. Faculdade De Higiene E Saude Publica Country of Publication: Brazil NLM ID: 0135043 Publication Model: eCollection Cited Medium: Internet ISSN: 1518-8787 (Electronic) Linking ISSN: 00348910 NLM ISO Abbreviation: Rev Saude Publica Subsets: MEDLINE
Imprint Name(s):
Original Publication: Sao Paulo : Universidade De Sao Paulo. Faculdade De Higiene E Saude Publica
MeSH Terms:
Computer Graphics*, Humans ; Data Interpretation, Statistical
References:
J Clin Epidemiol. 2015 Apr;68(4):360-9. (PMID: 24084448)
Health Psychol Behav Med. 2018 May 10;6(1):136-161. (PMID: 34040826)
Rev Saude Publica. 2021 Aug 09;55:40. (PMID: 34378771)
J Community Psychol. 2025 Jan;53(1):e23146. (PMID: 39171502)
Entry Date(s):
Date Created: 20260211 Date Completed: 20260211 Latest Revision: 20260214
Update Code:
20260214
PubMed Central ID:
PMC12885155
DOI:
10.11606/s1518-8787.2026060007015
PMID:
41670163
Database:
MEDLINE

*Further Information*

*Objective: To develop an innovative graphical tool to represent Guttman errors and facilitate scalability analysis of measurement instruments in epidemiology.
Methods: Implemented in R (RStudio), the guttemap function was developed to fill this gap. It provides an intuitive visual representation of Guttman errors, with color gradients that facilitate the assessment of measurement instruments, revealing internal patterns of inconsistency. The rationale underlying the proposed Guttman error map is presented, along with an annotated summary of the routine for its implementation.
Results: Seven synthetic examples show the potential of graphical representation in identifying problem areas and how this helps to inform adjustments and develop more robust instruments.
Conclusions: With guttemap, Guttman error analysis becomes more accessible and interpretable, contributing to the improvement of measurement instruments and the advancement of epidemiological research.*