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- [1] A comparison of the polytomous Rasch analysis output of RUMM2030 and R (ltm/eRm/TAM/lordif)[J]. BMC Medical Research Methodology, 19Michael RobinsonAndrew M. JohnsonDavid M. WaltonJoy C. MacDermid
A comparison of the polytomous Rasch analysis output of RUMM2030 and R (ltm/eRm/TAM/lordif)
被引：32
|作者：
Robinson, Michael
[1
]
Johnson, Andrew M.
[2
]
Walton, David M.
[3
]
MacDermid, Joy C.
[4
,5
,6
]
机构：
[1] Univ Western Ontario, Fac Hlth Sci, London, ON, Canada
[2] Univ Western Ontario, Sch Hlth Studies, London, ON, Canada
[3] Univ Western Ontario, Sch Phys Therapy, London, ON, Canada
[4] Univ Western Ontario, London, ON, Canada
[5] St Josephs Hlth Ctr, Hand & Upper Limb Ctr, Clin Res Lab, London, ON, Canada
[6] Sci McMaster Univ, Hamilton, ON, Canada
基金：
加拿大健康研究院;
关键词：
Rasch;
RUMM2030;
R;
DIF;
IRT;
MEASUREMENT MODEL;
WRIST;
PAIN;
D O I：
10.1186/s12874-019-0680-5
中图分类号：
R19 [保健组织与事业（卫生事业管理）];
学科分类号：
摘要：
BackgroundPatient-reported outcome measures developed using Classical Test Theory are commonly comprised of ordinal level items on a Likert response scale are problematic as they do not permit the results to be compared between patients. Rasch analysis provides a solution to overcome this by evaluating the measurement characteristics of the rating scales using probability estimates. This is typically achieved using commercial software dedicated to Rasch analysis however, it is possible to conduct this analysis using non-specific open source software such a R.MethodsRasch analysis was conducted using the most commonly used commercial software package, RUMM 2030, and R, using four open-source packages, with a common data set (6-month post-injury PRWE Questionnaire responses) to evaluate the statistical results for consistency. The analysis plan followed recommendations used in a similar study supported by the software package's instructions in order to obtain category thresholds, item and person fit statistics, measures of reliability and evaluate the data for construct validity, differential item functioning, local dependency and unidimensionality of the items.ResultsThere was substantial agreement between RUMM2030 and R with regards for most of the results, however there are some small discrepancies between the output of the two programs.ConclusionsWhile the differences in output between RUMM2030 and R can easily be explained by comparing the underlying statistical approaches taken by each program, there is disagreement on critical statistical decisions made by each program. This disagreement however should not be an issue as Rasch analysis requires users to apply their own subjective analysis. While researchers might expect that Rasch performed on a large sample would be a stable, two authors who complete Rasch analysis of the PRWE found somewhat dissimilar findings. So, while some variations in results may be due to samples, this paper adds that some variation in findings may be software dependent.
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