hkr.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Sample size and statistical conclusions from tests of fit to the Rasch model according to the Rasch Unidimensional Measurement Model (RUMM) program in health outcome measurement
Kristianstad University, Research Environment PRO-CARE, Patient Reported Outcomes - Clinical Assessment Research and Education. Kristianstad University, School of Health and Society, Avdelningen för Hälsovetenskap I. Kristianstad University, Research Platform for Collaboration for Health. (PRO-CARE)ORCID iD: 0000-0003-2174-372X
Kristianstad University, Research Environment PRO-CARE, Patient Reported Outcomes - Clinical Assessment Research and Education. Kristianstad University, School of Health and Society, Avdelningen för Sjuksköterskeutbildningarna. Kristianstad University, Research Platform for Collaboration for Health.ORCID iD: 0000-0003-4820-6203
2016 (English)In: Journal of Applied Measurement, ISSN 1529-7713, Vol. 17, no 4, 416-431 p.Article in journal (Refereed) Published
Abstract [en]

Sample size is a major factor in statistical null hypothesis testing, which is the basis for many approaches to testing Rasch model fit. Few sample size recommendations for testing fit to the Rasch model concern the Rasch Unidimensional Measurement Models (RUMM) software, which features chi-square and ANOVA/F-ratio based fit statistics, including Bonferroni and algebraic sample size adjustments. This paper explores the occurrence of Type I errors with RUMM fit statistics, and the effects of algebraic sample size adjustments. Data with simulated Rasch model fitting 25-item dichotomous scales and sample sizes ranging from N=50 to N=2500 were analysed with and without algebraically adjusted sample sizes. Results suggest the occurrence of Type I errors with N≥500, and that Bonferroni correction as well as downward algebraic sample size adjustment are useful to avoid such errors, whereas upward adjustment of smaller samples falsely signal misfit. Our observations suggest that sample sizes around N=250 to N=500 may provide a good balance for the statistical interpretation of RUMM fit statistics studied here with respect to Type I errors and under the assumption of Rasch model fit within the examined framed of reference (i.e., about 25 item parameters well targeted to the sample).

Place, publisher, year, edition, pages
2016. Vol. 17, no 4, 416-431 p.
Keyword [en]
ANOVA, Chi-square, fit statistics, F-ratio, RUMM, sample size, sample size adjustment, simulation, Type I error
National Category
Health Sciences
Identifiers
URN: urn:nbn:se:hkr:diva-15970PubMedID: 28009589OAI: oai:DiVA.org:hkr-15970DiVA: diva2:970593
Available from: 2016-09-14 Created: 2016-09-14 Last updated: 2017-04-10Bibliographically approved

Open Access in DiVA

No full text

PubMed

Search in DiVA

By author/editor
Hagell, PeterWestergren, Albert
By organisation
Research Environment PRO-CARE, Patient Reported Outcomes - Clinical Assessment Research and EducationAvdelningen för Hälsovetenskap IResearch Platform for Collaboration for HealthAvdelningen för Sjuksköterskeutbildningarna
In the same journal
Journal of Applied Measurement
Health Sciences

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 258 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf