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Joy C Macdermid - One of the best experts on this subject based on the ideXlab platform.
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Rasch Analysis of The Shoulder Pain and Disability Index (SPADI) in a postrepair rotator cuff sample.
Journal of hand therapy : official journal of the American Society of Hand Therapists, 2020Co-Authors: Bradley R. Boake, Joshua I. Vincent, Timothy K. Childs, Thomas D. Soules, Daniel L. Zervos, Joy C MacdermidAbstract:Abstract Study Design Clinical measurement study: Level of evidence (N/A) Introduction The Shoulder Pain and Disability Index (SPADI) is a self-reported outcome measure of pain and disability related to shoulder pathology. In comparison to Classical Test Theory (CTT), Rasch Analysis offers a more rigorous examination of the measurement properties of a scale. Purpose of the study: This study utilizes Rasch Analysis to evaluate the psychometric properties of the SPADI to propose potential modifications and avenues for future investigation. Methods SPADI scores (n=212) from participants one-year post rotator cuff repair were collected from an outpatient specialty clinic. Fit to the Rasch model, unidimensionality of the subscales, and areas of bias were evaluated. Results Both the pain and disability subscales satisfied the requirements of the Rasch model with very minimal modifications and demonstrated unidimensionality. The person separation index was found to be high (P>0.80), indicating reliability and internal consistency. Sex and dominant side affected influenced how people scored on the SPADI (Differential item functioning (DIF)). Conclusions The findings suggest some patients in our sample have difficulty discriminating between item responses, particularly within the middle of the scale. Rasch Analysis supports the clinical measurement properties of consistency and reliability, previously determined by CTT methods.
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A comparison of the polytomous Rasch Analysis output of RUMM2030 and R (ltm/eRm/TAM/lordif)
BMC medical research methodology, 2019Co-Authors: Michael Robinson, Andrew M. Johnson, David M. Walton, Joy C MacdermidAbstract:Background Patient-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.
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A comparison of the polytomous Rasch Analysis output of RUMM2030 and R (ltm/eRm/TAM/lordif)
BMC Medical Research Methodology, 2019Co-Authors: Michael Robinson, Andrew M. Johnson, David M. Walton, Joy C MacdermidAbstract:Background Patient-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. Methods Rasch 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. Results There 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. Conclusions While 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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Rasch Analysis of the Patient Rated Elbow Evaluation questionnaire
Health and quality of life outcomes, 2015Co-Authors: Joshua I. Vincent, Joy C Macdermid, Graham J.w. King, Ruby GrewalAbstract:Background The Patient Rated Elbow Evaluation (PREE) was developed as an elbow joint specific measure of pain and disability and validated with classical psychometric methods. More recently, Rasch Analysis has contributed new methods for analyzing the clinical measurement properties of self-report outcome measures. The objective of the study was to determine aspects of validity of the PREE using the Rasch model to assess the overall fit of the PREE data, the response scaling, individual item fit, differential item functioning (DIF), local dependency, unidimensionality and person separation index (PSI).
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measurement properties of the patient rated wrist and hand evaluation Rasch Analysis of responses from a traumatic hand injury population
Journal of Hand Therapy, 2013Co-Authors: Tara L Packham, Joy C MacdermidAbstract:INTRODUCTION: The Patient-Rated Wrist and Hand Evaluation (PRWHE) is a self-reported assessment of pain and disability to evaluate outcome after hand injuries. Rasch Analysis is an alternative strategy for examining the psychometric properties of a measurement scale based in item response theory, rather than classical test theory. PURPOSE OF THE STUDY: This study used Rasch Analysis to examine the content, scoring and measurement properties of the PRWHE. METHODS: PRWHE scores (n = 264) from persons with a traumatic injury or reconstructive surgery to one hand were collected from an outpatient hand rehabilitation facility. Rasch Analysis was conducted to assess how the PRWHE fit the Rasch model, confirms the scaling structure of the pain and disability subscales, and identifies any areas of bias from differential item functioning. RESULTS: Rasch Analysis of the PRWHE supports internal consistency of the scale (α = 0.96) and reliability (as measured by the person separation index) of 0.95. While gender, age, diagnosis, and duration since injury all systematically influenced how people scored the PRWHE, hand dominance and affected side did not. Rasch Analysis supported a 3 subscale structure (pain, specific activities and usual activities) rather than the current divisions of pain and disability. CONCLUSIONS: Initial examination of the PRWHE indicates the psychometric properties of consistency, reliability and responsiveness previously tested by classical methods are further supported by Rasch Analysis. It also suggests the scale structure may be best considered as 3 subscales rather than simply pain and disability. Language: en
Tom Heskes - One of the best experts on this subject based on the ideXlab platform.
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Semi-automated Rasch Analysis using in-plus-out-of-questionnaire log likelihood.
The British journal of mathematical and statistical psychology, 2020Co-Authors: Feri Wijayanto, Karlien Mul, Perry Groot, Baziel G.m. Van Engelen, Tom HeskesAbstract:Rasch Analysis is a popular statistical tool for developing and validating instruments that aim to measure human performance, attitudes and perceptions. Despite the availability of various software packages, constructing a good instrument based on Rasch Analysis is still considered to be a complex, labour-intensive task, requiring human expertise and rather subjective judgements along the way. In this paper we propose a semi-automated method for Rasch Analysis based on first principles that reduces the need for human input. To this end, we introduce a novel criterion, called in-plus-out-of-questionnaire log likelihood (IPOQ-LL). On artificial data sets, we confirm that optimization of IPOQ-LL leads to the desired behaviour in the case of multi-dimensional and inhomogeneous surveys. On three publicly available real-world data sets, our method leads to instruments that are, for all practical purposes, indistinguishable from those obtained by Rasch Analysis experts through a manual procedure.
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Semi-Automated Rasch Analysis using In-plus-out-of-questionnaire Log-likelihood
2020Co-Authors: Feri Wijayanto, Karlien Mul, Perry Groot, Baziel G.m. Van Engelen, Tom HeskesAbstract:Rasch Analysis is a popular statistical tool for developing and validating instruments that aim to measure human performance, attitudes and perceptions. Despite the availability of various software packages, constructing a good instrument based on Rasch Analysis is still considered to be a complex, labor-intensive task, requiring human expertise and bares the possibility results in different but equally suited instruments. In this paper, we propose a semi-automated method for Rasch Analysis based on first principles that reduces the need for human input. To this end, we introduce a novel criterion, called in-plus-out-of-questionnaire log-likelihood (IPOQ-LL). On artificial datasets, we confirm that optimization of IPOQ-LL leads to the desired behavior in the case of multi-dimensional and inhomogeneous surveys. On three publicly available real-world datasets, our method leads to instruments that have, for all practical purposes, similar clinimetrical properties as those obtained by Rasch Analysis experts through a manual procedure.
Leslie Pendrill - One of the best experts on this subject based on the ideXlab platform.
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Rasch Analysis of the Patient Participation in Rehabilitation Questionnaire (PPRQ).
Journal of evaluation in clinical practice, 2019Co-Authors: Jeanette Melin, Robin Fornazar, Martin Spångfors, Leslie PendrillAbstract:Objective: To evaluate the Patient Participation in Rehabilitation Questionnaire (PPRQ) according to Rasch measurement theory. Method: Five hundred twenty-two post-discharge patients from a neurological rehabilitation unit were included. The PPRQ questionnaire comprises 20 items rated by a cohort of 522 patients about their experiences of participating in rehabilitation. The measurement properties of the PPRQ were evaluated by Rasch Analysis of the responses. Results: The Rasch Analysis of 20 items showed some major misfits, particularly three items addressing the involvement of family members. After removing those items, the model fit improved and no significant DIF remained. Despite improvements, person values (−2.96 to 4.86 logits) were not fully matched by the item values (−0.61 to 0.77 logits). Neither did the t test for unidimensionality meet the criterion of 5%, and local dependency was present. The unidimensionality and local dependency could, however, be accommodated for by four testlets. Conclusion: The PPRQ-17 showed that a ruler with a reasonable and clinical hierarchy can be constructed, although the expectations of dimensionality and local dependency need to be evaluated further. Despite room for further development, PPRQ-17 nevertheless shows improved measurement precision in terms of patient leniency compared with previous evaluations with classical test theory. In turn, this can play a crucial role when comparing different rehabilitation programs and planning tailored care development activities. (Less)
Alan Tennant - One of the best experts on this subject based on the ideXlab platform.
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The Impact of Missing Values and Single Imputation upon Rasch Analysis Outcomes: A Simulation Study.
Journal of applied measurement, 2018Co-Authors: Carolina Saskia Fellinghauer, Birgit Prodinger, Alan TennantAbstract:Imputation becomes common practice through availability of easy-to-use algorithms and software. This study aims to determine if different imputation strategies are robust to the extent and type of missingness, local item dependencies (LID), differential item functioning (DIF), and misfit when doing a Rasch Analysis. Four samples were simulated and represented a sample with good metric properties, a sample with LID, a sample with DIF, and a sample with LID and DIF. Missing values were generated with increasing proportion and were either missing at random or completely at random. Four imputation techniques were applied before Rasch Analysis and deviation of the results and the quality of fit compared. Imputation strategies showed good performance with less than 15% of missingness. The Analysis with missing values performed best in recovering statistical estimates. The best strategy, when doing a Rasch Analysis, is the Analysis with missing values. If for some reason imputation is necessary, we recommend using the expectation-maximization algorithm.
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is the pain visual analogue scale linear and responsive to change an exploration using Rasch Analysis
PLOS ONE, 2014Co-Authors: Paula Kersten, Peter J White, Alan TennantAbstract:Objectives Pain visual analogue scales (VAS) are commonly used in clinical trials and are often treated as an interval level scale without evidence that this is appropriate. This paper examines the internal construct validity and responsiveness of the pain VAS using Rasch Analysis. Methods Patients (n = 221, mean age 67, 58% female) with chronic stable joint pain (hip 40% or knee 60%) of mechanical origin waiting for joint replacement were included. Pain was scored on seven daily VASs. Rasch Analysis was used to examine fit to the Rasch model. Responsiveness (Standardized Response Means, SRM) was examined on the raw ordinal data and the interval data generated from the Rasch Analysis. Results Baseline pain VAS scores fitted the Rasch model, although 15 aberrant cases impacted on unidimensionality. There was some local dependency between items but this did not significantly affect the person estimates of pain. Daily pain (item difficulty) was stable, suggesting that single measures can be used. Overall, the SRMs derived from ordinal data overestimated the true responsiveness by 59%. Changes over time at the lower and higher end of the scale were represented by large jumps in interval equivalent data points; in the middle of the scale the reverse was seen. Conclusions The pain VAS is a valid tool for measuring pain at one point in time. However, the pain VAS does not behave linearly and SRMs vary along the trait of pain. Consequently, Minimum Clinically Important Differences using raw data, or change scores in general, are invalid as these will either under- or overestimate true change; raw pain VAS data should not be used as a primary outcome measure or to inform parametric-based Randomised Controlled Trial power calculations in research studies; and Rasch Analysis should be used to convert ordinal data to interval data prior to data interpretation.
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An introduction to Rasch Analysis for Psychiatric practice and research
Journal of psychiatric research, 2012Co-Authors: Neusa Sica Da Rocha, Eduardo Chachamovich, Marcelo Pio De Almeida Fleck, Alan TennantAbstract:This article aims to present the main characteristics of Rasch Analysis in the context of patient reported outcomes in Psychiatry. We present an overview of the main features of the Rasch Analysis, using as an example the latent variable of depressive symptoms, with illustrations using the Beck Depression Inventory. We will show that with fitting data to the Rasch model, we can confirm the structural validity of the scale, including key attributes such as invariance, local dependency and unidimensionality. We also illustrate how the approach can inform on the meaning of the numbers attributed to scales, the amount of the latent traits that such numbers represent, and the consequent adequacy of statistical operations used to analyse them. We would argue that fitting data to the Rasch model has become the measurement standard for patient reported outcomes in general and, as a consequence will facilitate a quality improvement of outcome instruments in psychiatry. Recent advances in measurement technologies built upon the calibration of items derived from Rasch Analysis in the form of computerized adaptive tests (CAT) open up further opportunities for reducing the burden of testing, and/or expanding the range of information that can be collected during a single session.
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Rasch Analysis of the Beck Depression Inventory-II in a neurological rehabilitation sample.
Disability and rehabilitation, 2009Co-Authors: Richard J. Siegert, Alan Tennant, Lynne Turner-stokesAbstract:Purpose. To apply Rasch Analysis to the Beck Depression Inventory-II (BDI-II) responses of a mixed neurorehabilitation sample to determine its suitability for assessing depression among this group.Method. Three hundred and fifteen in-patients undergoing post-acute specialist in-patient rehabilitation at the Regional Rehabilitation Unit (RRU) of Northwick Park Hospital in North London were administered the Beck Depression Inventory. All patients were administered the BDI-II by a clinical psychologist using a large print version and taking as much time as patients required. Rasch Analysis was completed using the RUMM2020 software. Where disordered thresholds were identified item rescoring was undertaken. Each item was also examined for Differential Item Functioning. Where misfit to model expectations was identified items were removed in an iterative fashion. The effect of any deletion upon person estimates was examined. Specific tests of unidimensionality were undertaken at each iterative phase.Results. Res...
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Application of Rasch Analysis in the development and application of quality of life instruments.
Value in Health, 2004Co-Authors: Alan Tennant, Stephen P. Mckenna, Peter HagellAbstract:This paper discusses recent advances that have been made in the field of psychometrics, specifically, the application of Rasch Analysis to the instrument development process. It emphasizes the importance of assessing the fundamental scaling properties of an instrument prior to consideration of traditional psychometric indicators. The paper introduces Rasch Analysis and shows how it has been applied in the development of needs-based measures in order to ensure that they provide unidimensional measurement. By ensuring that scales are based on the same measurement model and that they fit the Rasch model it is possible for QoL scores to be compared across diseases by means of cocalibration and item banking.
Feri Wijayanto - One of the best experts on this subject based on the ideXlab platform.
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Semi-automated Rasch Analysis using in-plus-out-of-questionnaire log likelihood.
The British journal of mathematical and statistical psychology, 2020Co-Authors: Feri Wijayanto, Karlien Mul, Perry Groot, Baziel G.m. Van Engelen, Tom HeskesAbstract:Rasch Analysis is a popular statistical tool for developing and validating instruments that aim to measure human performance, attitudes and perceptions. Despite the availability of various software packages, constructing a good instrument based on Rasch Analysis is still considered to be a complex, labour-intensive task, requiring human expertise and rather subjective judgements along the way. In this paper we propose a semi-automated method for Rasch Analysis based on first principles that reduces the need for human input. To this end, we introduce a novel criterion, called in-plus-out-of-questionnaire log likelihood (IPOQ-LL). On artificial data sets, we confirm that optimization of IPOQ-LL leads to the desired behaviour in the case of multi-dimensional and inhomogeneous surveys. On three publicly available real-world data sets, our method leads to instruments that are, for all practical purposes, indistinguishable from those obtained by Rasch Analysis experts through a manual procedure.
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Semi-Automated Rasch Analysis using In-plus-out-of-questionnaire Log-likelihood
2020Co-Authors: Feri Wijayanto, Karlien Mul, Perry Groot, Baziel G.m. Van Engelen, Tom HeskesAbstract:Rasch Analysis is a popular statistical tool for developing and validating instruments that aim to measure human performance, attitudes and perceptions. Despite the availability of various software packages, constructing a good instrument based on Rasch Analysis is still considered to be a complex, labor-intensive task, requiring human expertise and bares the possibility results in different but equally suited instruments. In this paper, we propose a semi-automated method for Rasch Analysis based on first principles that reduces the need for human input. To this end, we introduce a novel criterion, called in-plus-out-of-questionnaire log-likelihood (IPOQ-LL). On artificial datasets, we confirm that optimization of IPOQ-LL leads to the desired behavior in the case of multi-dimensional and inhomogeneous surveys. On three publicly available real-world datasets, our method leads to instruments that have, for all practical purposes, similar clinimetrical properties as those obtained by Rasch Analysis experts through a manual procedure.
