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Percentile ranks and benchmark estimates of change for the Health Education Impact Questionnaire: Normative data from an Australian sample

OBJECTIVE: Participant self-report data play an essential role in the evaluation of health education activities, programmes and policies. When questionnaire items do not have a clear mapping to a performance-based continuum, percentile norms are useful for communicating individual test results to us...

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Detalles Bibliográficos
Autores principales: Elsworth, Gerald R, Osborne, Richard H
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5435365/
https://www.ncbi.nlm.nih.gov/pubmed/28560039
http://dx.doi.org/10.1177/2050312117695716
Descripción
Sumario:OBJECTIVE: Participant self-report data play an essential role in the evaluation of health education activities, programmes and policies. When questionnaire items do not have a clear mapping to a performance-based continuum, percentile norms are useful for communicating individual test results to users. Similarly, when assessing programme impact, the comparison of effect sizes for group differences or baseline to follow-up change with effect sizes observed in relevant normative data provides more directly useful information compared with statistical tests of mean differences and the evaluation of effect sizes for substantive significance using universal rule-of-thumb such as those for Cohen’s ‘d’. This article aims to assist managers, programme staff and clinicians of healthcare organisations who use the Health Education Impact Questionnaire interpret their results using percentile norms for individual baseline and follow-up scores together with group effect sizes for change across the duration of typical chronic disease self-management and support programme. METHODS: Percentile norms for individual Health Education Impact Questionnaire scale scores and effect sizes for group change were calculated using freely available software for each of the eight Health Education Impact Questionnaire scales. Data used were archived responses of 2157 participants of chronic disease self-management programmes conducted by a wide range of organisations in Australia between July 2007 and March 2013. RESULTS: Tables of percentile norms and three possible effect size benchmarks for baseline to follow-up change are provided together with two worked examples to assist interpretation. CONCLUSION: While the norms and benchmarks presented will be particularly relevant for Australian organisations and others using the English-language version of the Health Education Impact Questionnaire, they will also be useful for translated versions as a guide to the sensitivity of the scales and the extent of the changes that might be anticipated from attendance at a typical chronic disease self-management or health education programme.