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SPECIAL COMMUNICATION| Volume 103, ISSUE 7, P1487-1498, July 2022

Reporting Guideline for RULER: Rasch Reporting Guideline for Rehabilitation Research: Explanation and Elaboration

Published:April 14, 2022DOI:https://doi.org/10.1016/j.apmr.2022.03.019

      Highlights

      • The Rasch Reporting Guideline for Rehabilitation Research (RULER) guideline, checklist, and article set uniform expectations on reporting Rasch Measurement (RM) studies.
      • The RULER guideline includes a framework and checklist of 59 recommendations.
      • Rehabilitation science will advance by transparent reporting of RM studies.
      • The RULER guideline, checklist, and article will evolve as the field develops consensus on best practices.
      • Consistent use of RM terminology will be a focus of a future task force.

      Abstract

      The Rasch Reporting Guideline for Rehabilitation Research (RULER) provides peer-reviewed, evidence-based, transparent, and consistent recommendations for reporting studies that apply Rasch Measurement (RM) Theory in a rehabilitation context. The purpose of the guideline is to ensure that authors, reviewers, and editors have uniform guidance about how to write and evaluate research on rehabilitation outcome assessments. The RULER statement includes an organizing framework and a checklist of 59 recommendations. This companion article supports the RULER statement by providing details about the framework, rationale for the domains and recommendations in the checklist and explaining why these considerations are important for improving consistency and transparency in reporting the results of RM studies. This article is not intended to describe how to conduct RM studies but provides rationale for the essential elements that authors should address in each domain. Consistency and transparency in reporting RM studies will advance rehabilitation research if authors consider these issues when planning their study and include the checklist when they submit their manuscript for peer review. A copy of the checklist can be found at [table 2 in https://doi.org/10.1016/j.apmr.2022.03.013].

      Keywords

      List of abbreviations:

      ACRM (American Congress of Rehabilitation Medicine), cMDC (conditional minimally detectable change), CTT (classical test theory), DIF (differential item functioning), LID (local item dependence), MDC (minimally detectable change), M-ISIG (Measurement Interdisciplinary Special Interest Group), PCAR (Principal Component Analysis of Residuals), PROM (patient-reported outcome measure), PSI (person separation index), PSR (person separation reliability), RM (Rasch Measurement), RP (relative precision index), RULER (Rasch Reporting Guideline for Rehabilitation Research), RUMM (Rasch Unidimensional Measurement Model)
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      References

        • Andrich D
        • Marais I.
        A course in Rasch Measurement Theory: measuring in the educational, social and health sciences.
        Springer, New York2019
        • Malec JF.
        Editorial: an end to ordinal misrule?.
        Arch Phys Med Rehabil. 2020; 101: 166-167
        • Bond TG
        • Fox CM.
        Applying the Rasch model: fundamental measurement in the human sciences.
        Erlbaum, Mahwax, NJ2001
        • U.S. Department of Health and Human Services Food and Drug Administration
        Guidance for industry: patient-reported outcome measures: use in medical product development to support labeling claims.
        Fed Regist. 2009; 74: 65132-65133
      1. American Institutes for Research. Principles for making health care measurement patient-centered. Available at: https://www.air.org/sites/default/files/Patient-Centered-Measurement-Principles-April-2017.pdf. Accessed January 12, 2020.

        • Cano SJ
        • Pendrill LR
        • Melin J
        • Fisher WP.
        Towards consensus measurement standards for patient-centered outcomes.
        Measurement. 2019; 141: 62-69
        • Khadka J
        • Gothwal VK
        • McAlinden C
        • Lamoureux EL
        • Pesudovs K.
        The importance of rating scales in measuring patient-reported outcomes.
        Health Qual Life Outcomes. 2012; 10: 80
        • Van de Winckel A
        • Ottiger B
        • Bohlhalter S
        • Nyffeler T
        • Vanbellingen T.
        Comprehensive ADL outcome measurement after stroke: Rasch validation of the Lucerne ICF-Based Multidisciplinary Observation Scale (LIMOS).
        Arch Phys Med Rehabil. 2019; 100: 2314-2323
        • Van de Winckel A
        • Gauthier L.
        A Revised Motor Activity log following Rasch validation (Rasch-Based MAL-18) and consensus methods in chronic stroke and multiple sclerosis.
        Neurorehabil Neural Repair. 2019; 33: 787-791
        • Wilson M.
        Constructing measures: an item response modeling approach. Routledge, New York2004
        • Reise SP
        • Yu J.
        Parameter recovery in the graded response model using MULTILOG.
        J Educ Meas. 1990; 27: 133-144
        • Smith AB
        • Rush R
        • Fallowfield LJ
        • Velikova G
        • Sharpe M.
        Rasch fit statistics and sample size considerations for polytomous data.
        BMC Med Res Methodol. 2008; 8: 33
        • Chen WH
        • Lenderking W
        • Jin Y
        • Wyrwich KW
        • Gelhorn H
        • Revicki DA.
        Is Rasch model analysis applicable in small sample size pilot studies for assessing item characteristics? An example using PROMIS pain behavior item bank data.
        Qual Life Res. 2014; 23: 485-493
        • Linacre JM.
        Sample size and item calibration stability.
        Rasch Mes Trans. 1994; 7: 328
        • Wright BD.
        Misunderstanding the Rasch model.
        J Educ Meas. 1977; 14: 219-225
        • Linacre JM.
        Understanding Rasch measurement: estimation methods for Rasch measures.
        J Outcome Meas. 1999; 3: 382-405
        • Linacre JM.
        Investigating rating scale category utility.
        J Outcome Meas. 1999; 3: 103-122
        • Linacre JM.
        Optimizing rating scale category effectiveness.
        J Appl Meas. 2002; 3: 85-106
        • McHorney CA
        • Tarlov AR.
        Individual-patient monitoring in clinical practice: are available health status surveys adequate?.
        Qual Life Res. 1995; 4: 293-307
      2. Linacre JM. DIF - DPF - bias - interactions concepts. Help for Winsteps Rasch measurement and Rasch analysis software: www.winsteps.com. Available at: https://www.winsteps.com/winman/difconcepts.htm. Accessed March 26, 2020.

        • Zwick R.
        A review of ETS differential item functioning assessment procedures: flagging rules, minimum sample size requirements, and criterion refinement.
        ETS Res Rep Ser. 2012; 2012: i-30
        • Myers ND
        • Wolfe EW
        • Feltz DL
        • Penfield RD.
        Identifying differential item functioning of rating scale items with the Rasch model: an introduction and an application.
        Meas Phys Educ Exerc Sci. 2006; 10: 215-240
      3. Linacre JM. Dimensionality: contrasts & variances. A user's guide to Winsteps Ministep Rasch-model computer programs (version 3. 81. 0). Available at: https://www.winsteps.com/winman/principalcomponents.htm. Accessed January 27, 2022.

        • Hackshaw A
        • Kirkwood A.
        Interpreting and reporting clinical trials with results of borderline significance.
        BMJ. 2011; 343: d3340
        • Simone A
        • Rota V
        • Tesio L
        • Perucca L.
        Generic ABILHAND questionnaire can measure manual ability across a variety of motor impairments.
        Int J Rehabil Res. 2011; 34: 131-140
      4. Linacre JM. Reliability and separation of measures. Available at: https://www.winsteps.com/winman/reliability.htm. Accessed May 21, 2020.

        • Linacre JM.
        Standard errors and reliabilities: Rasch and raw score.
        Rasch Meas Trans. 2007; 20: 1086
      5. Fisher Jr W. Rasch Measurement Transactions. Reliability, separation, strata statistics. Available at: https://www.rasch.org/rmt/rmt63i.htm. Accessed May 7, 2020.

      6. Linacre JM. Rasch Measurement Transactions. Rasch-based generalizability theory: reliability and precision (S.E.) nomogram. Available at: https://www.rasch.org/rmt/rmt71h.htm. Accessed May 7, 2020.

      7. Linacre JM. Rasch Measurement Transactions. Sample size and item calibration or person measure stability. Available at: https://www.rasch.org/rmt/rmt74m.htm. Accessed June 25, 2020.

      8. Linacre JM. Help for Winsteps Rasch measurement and Rasch analysis software: displacement measures. Available at: https://www.winsteps.com/winman/displacement.htm. Accessed January 12, 2020.

        • Mallinson T
        • Schepens Niemiec SL
        • Carlson M.
        • et al.
        Development and validation of the Activity Significance Personal Evaluation (ASPEn) scale.
        Aust Occup Ther J. 2014; 61: 384-393
        • Kerlinger FN
        • Lee HB.
        Foundations of behavioral research.
        Wadsworth Cengage Learning, Belmont, CA2000
        • Tennant A
        • Conaghan PG.
        The Rasch measurement model in rheumatology: what is it and why use it? When should it be applied, and what should one look for in a Rasch paper?.
        Arthritis Rheum. 2007; 57: 1358-1362
        • Luppescu S.
        Comparing measures: scatterplots.
        Rasch Meas Trans. 1995; 9: 410
        • Bland JM
        • Martin Bland J
        • Altman DG.
        Measuring agreement in method comparison studies.
        Stat Methods Med Res. 1999; 8: 135-160
        • Messick S.
        Validity of psychological assessment: validation of inferences from persons’ responses and performances as scientific inquiry into score meaning.
        Am Psychol. 1995; 50: 741-749
        • Mokkink LB
        • Terwee CB
        • Patrick DL.
        • et al.
        The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes.
        J Clin Epidemiol. 2010; 63: 737-745
        • EMGO Institute for Health and Care Research
        Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN).
        COSMIN, 2022 (Available at: http://www.cosmin.nl/. Accessed January 12, 2020)
        • Liang MH.
        Longitudinal construct validity: establishment of clinical meaning in patient evaluative instruments.
        Med Care. 2000; 38 (II84-90)
        • Kozlowski AJ
        • Cella D
        • Nitsch KP
        • Heinemann AW.
        Evaluating individual change with the Quality of Life in Neurological Disorders (Neuro-QoL) Short Forms.
        Arch Phys Med Rehabil. 2016; 97: 650-654
        • Khan A
        • Chien CW
        • Brauer SG.
        Rasch-based scoring offered more precision in differentiating patient groups in measuring upper limb function.
        J Clin Epidemiol. 2013; 66: 681-687
        • Briggs MS
        • Rethman KK
        • Crookes J.
        • et al.
        Implementing patient-reported outcome measures in outpatient rehabilitation settings: a systematic review of facilitators and barriers using the Consolidated Framework for Implementation Research.
        Arch Phys Med Rehabil. 2020; 101: 1796-1812
        • Hartman ML
        • Fisher AG
        • Duran L.
        Assessment of functional ability of people with Alzheimer’s disease.
        Scand J Occup Ther. 1999; 6: 111-118
        • Magasi S
        • Harniss M
        • Tulsky DS
        • Cohen ML
        • Heaton RK
        • Heinemann AW.
        Test accommodations for individuals with neurological conditions completing the NIH Toolbox-Cognition Battery: an evaluation of frequency and appropriateness.
        Rehabil Psychol. 2017; 62: 455-463
      9. Linacre JM. Test validity and Rasch measurement: construct, content, etc. Available at: https://www.rasch.org/rmt/rmt181h.htm. Accessed June 25, 2020.

        • Magasi S
        • Harniss M
        • Heinemann AW.
        Interdisciplinary approach to the development of accessible computer-administered measurement instruments.
        Arch Phys Med Rehabil. 2018; 99: 204-210
        • Christensen KB
        • Makransky G
        • Horton M.
        Critical values for Yen’s Q3: identification of local dependence in the Rasch model using residual correlations.
        Appl Psychol Meas. 2017; 41: 178-194
        • Marais I.
        Response dependence and the measurement of change.
        J Appl Meas. 2009; 10: 17-29
        • Mallinson T.
        Rasch analysis of repeated measures.
        Rasch Meas Trans. 2011; 25: 13-17
        • Weaver J
        • Liu J
        • Guernon A
        • Bender-Pape T
        • Mallinson T.
        Psychometric properties of the Coma Near-Coma Scale in adults in disordered states of consciousness: a Rasch analysis.
        Arch Phys Med Rehabil. 2021; 102: 591-597
        • Stelmack JA
        • Stelmack TR
        • Massof RW.
        Measuring low-vision rehabilitation outcomes with the NEI VFQ-25.
        Invest. Ophthalmol Vis Sci. 2002; 43: 2859-2868
      10. Luppescu S. Rasch Measurement Transactions. DIF: graphical diagnosis. Available at: https://www.rasch.org/rmt/rmt51j.htm. Accessed March 26, 2020.

      11. Luppescu S., Rasch Measurement Transactions. Comparing measures: scatterplots. Available at: https://www.rasch.org/rmt/rmt91c.htm. Accessed March 26, 2020.

      12. Kilmen S. Effect of DIF magnitudes, focal group sample size, and DIF ratio on the performance of SIBTEST. Available at: http://ijsse.com/sites/default/files/issues/2016/v6i1/paper-11.pdf. Accessed January 27, 2022.

        • Rouquette A
        • Hardouin JB
        • Vanhaesebrouck A
        • Sébille V
        • Coste J.
        Differential item functioning (DIF) in composite health measurement scale: recommendations for characterizing DIF with meaningful consequences within the Rasch model framework.
        PLoS One. 2019; 14e0215073
        • Christensen KB
        • Thorborg K
        • Hölmich P
        • Clausen MB.
        Rasch validation of the Danish version of the Shoulder Pain and Disability Index (SPADI) in patients with rotator cuff-related disorders.
        Qual Life Res. 2019; 28: 795-800
      13. Linacre JM. Table 23.99. Largest residual correlations for items. Help for Winsteps Rasch measurement and Rasch analysis software: www.winsteps.com. Available at: https://www.winsteps.com/winman/table23_99.htm. Accessed March 26, 2020.

      14. Linacre JM. A user's guide to WINSTEPS & MINISTEP Rasch-model computer programs. Program manual. Available at: http://www.winsteps.com/winman. Accessed. January 27, 2022.

        • Schougaard LMV
        • de Thurah A
        • Bech P
        • Hjollund NH
        • Christiansen DH.
        Test-retest reliability and measurement error of the Danish WHO-5 Well-being Index in outpatients with epilepsy.
        Health Qual Life Outcomes. 2018; 16: 175
        • Harniss M
        • Magasi S
        • Sabat D.
        Accessibility considerations in the National Children’s.
        Study. Front Pediatr. 2021; 9624175
      15. Shirley Ryan AbilityLab. Rehabilitation measures database. Available at: https://www.sralab.org/rehabilitation-measures. Accessed July 9, 2020.

      16. National Library of Medicine. Home. Available at: http://www.nlm.nih.gov/cde/index.html. Accessed July 9, 2020.

      17. Heart and Stroke Foundation-Canadian Partnership for Stroke Recovery. Find an assessment.
        Stroke engine. 2020; (Available at:) (Accessed May 7, 2020.)
      18. Spinal Cord Injury Research Evidence. Home. Available at: http://scireproject.com. Accessed July 9, 2020.

      19. Lexile. For researchers. Available at: https://lexile.com/for-researchers/. Accessed July 9, 2020.

      20. Hemingway. Hemingway Editor. Available at: http://www.hemingwayapp.com/. Accessed July 9, 2020.

      Linked Article

      • Rasch Reporting Guideline for Rehabilitation Research (RULER): the RULER Statement
        Archives of Physical Medicine and RehabilitationVol. 103Issue 7
        • Preview
          The application of Rasch Measurement (RM) Theory to rehabilitation assessments has proliferated in recent years. RM Theory helps design and refine assessments so that items reflect a unidimensional construct in an equal interval metric that distinguishes among persons of different abilities in a manner that is consistent with the underlying trait. Rapid growth of RM in rehabilitation assessment studies has led to inconsistent results reporting. Clear, consistent, transparent reporting of RM Theory results is important for advancing rehabilitation science and practice based on precise measures.
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