IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0197503.html
   My bibliography  Save this article

An alternative application of Rasch analysis to assess data from ophthalmic patient-reported outcome instruments

Author

Listed:
  • Richard N McNeely
  • Salissou Moutari
  • Samuel Arba-Mosquera
  • Shwetabh Verma
  • Jonathan E Moore

Abstract

Purpose: To highlight the potential shortcomings associated with the current use Rasch analysis for validation of ophthalmic questionnaires, and to present an alternative application of Rasch analysis to derive insights specific to the cohort of patients under investigation. Methods: An alternative application of Rasch analysis was used to investigate the quality of vision (QoV) for a cohort of 481 patients. Patients received multifocal intraocular lenses and completed a QoV questionnaire one and twelve months post-operatively. The rating scale variant of the polytomous Rasch model was utilized. The parameters of the model were estimated using the joint maximum likelihood estimation. Analysis was performed on data at both post-operative assessments, and the outcomes were compared. Results: The distribution of the location of symptoms altered between assessments with the most annoyed patients completely differing. One month post-operatively, the most prevalent symptom was starbursts compared to glare at twelve months. The visual discomfort from the most annoyed patients is substantially higher at twelve months. The current most advocated approach for validating questionnaires using Rasch analysis found that the questionnaire was “Rasch-valid” one month post-operatively and “Rasch-invalid” twelve months post-operatively. Conclusion: The proposed alternative application of Rasch analysis to questionnaires can be used as an effective decision support tool at population and individual level. At population level, this new approach enables one to investigate the prevalence of symptoms across different cohorts of patients. At individual level, the new approach enables one to identify patients with poor QoV over time. This study highlights some of the potential shortcomings associated with the current use of Rasch analysis to validate questionnaires.

Suggested Citation

  • Richard N McNeely & Salissou Moutari & Samuel Arba-Mosquera & Shwetabh Verma & Jonathan E Moore, 2018. "An alternative application of Rasch analysis to assess data from ophthalmic patient-reported outcome instruments," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-32, June.
  • Handle: RePEc:plo:pone00:0197503
    DOI: 10.1371/journal.pone.0197503
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197503
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0197503&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0197503?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Geoff Masters, 1982. "A rasch model for partial credit scoring," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 149-174, June.
    2. David Andrich, 1978. "A rating formulation for ordered response categories," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 561-573, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ana Rosa Barrio & Mariano González-Pérez & Clara Heredia-Pastor & Jacobo Enríquez-Fuentes & Beatriz Antona, 2022. "Spanish Cross-Cultural Adaptation, Rasch Analysis and Validation of the Ocular Comfort Index (OCI) Questionnaire," IJERPH, MDPI, vol. 19(22), pages 1-14, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. P. A. Ferrari & S. Salini, 2008. "Measuring Service Quality: The Opinion of Europeans about Utilities," Working Papers 2008.36, Fondazione Eni Enrico Mattei.
    2. Chang, Hsin-Li & Yang, Cheng-Hua, 2008. "Explore airlines’ brand niches through measuring passengers’ repurchase motivation—an application of Rasch measurement," Journal of Air Transport Management, Elsevier, vol. 14(3), pages 105-112.
    3. Ivana Bassi & Matteo Carzedda & Enrico Gori & Luca Iseppi, 2022. "Rasch analysis of consumer attitudes towards the mountain product label," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-25, December.
    4. Antonio Caronni & Marina Ramella & Pietro Arcuri & Claudia Salatino & Lucia Pigini & Maurizio Saruggia & Chiara Folini & Stefano Scarano & Rosa Maria Converti, 2023. "The Rasch Analysis Shows Poor Construct Validity and Low Reliability of the Quebec User Evaluation of Satisfaction with Assistive Technology 2.0 (QUEST 2.0) Questionnaire," IJERPH, MDPI, vol. 20(2), pages 1-19, January.
    5. Hua-Hua Chang, 1996. "The asymptotic posterior normality of the latent trait for polytomous IRT models," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 445-463, September.
    6. Curt Hagquist & Raili Välimaa & Nina Simonsen & Sakari Suominen, 2017. "Differential Item Functioning in Trend Analyses of Adolescent Mental Health – Illustrative Examples Using HBSC-Data from Finland," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 10(3), pages 673-691, September.
    7. Salzberger, Thomas & Newton, Fiona J. & Ewing, Michael T., 2014. "Detecting gender item bias and differential manifest response behavior: A Rasch-based solution," Journal of Business Research, Elsevier, vol. 67(4), pages 598-607.
    8. Rasmus A. X. Persson, 2023. "Theoretical evaluation of partial credit scoring of the multiple-choice test item," METRON, Springer;Sapienza Università di Roma, vol. 81(2), pages 143-161, August.
    9. Chang, Hsin-Li & Wu, Shun-Cheng, 2008. "Exploring the vehicle dependence behind mode choice: Evidence of motorcycle dependence in Taipei," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(2), pages 307-320, February.
    10. Genge, Ewa & Bartolucci, Francesco, 2019. "Are attitudes towards immigration changing in Europe? An analysis based on bidimensional latent class IRT models," MPRA Paper 94672, University Library of Munich, Germany.
    11. Jesper Tijmstra & Maria Bolsinova, 2019. "Bayes Factors for Evaluating Latent Monotonicity in Polytomous Item Response Theory Models," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 846-869, September.
    12. Salzberger, Thomas & Koller, Monika, 2013. "Towards a new paradigm of measurement in marketing," Journal of Business Research, Elsevier, vol. 66(9), pages 1307-1317.
    13. Francesca DE BATTISTI & Giovanna NICOLINI & Silvia SALINI, 2008. "Methodological overview of Rasch model and application in customer satisfaction survey data," Departmental Working Papers 2008-04, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    14. Kuan-Yu Jin & Yi-Jhen Wu & Hui-Fang Chen, 2022. "A New Multiprocess IRT Model With Ideal Points for Likert-Type Items," Journal of Educational and Behavioral Statistics, , vol. 47(3), pages 297-321, June.
    15. van der Ark, L. Andries, 2012. "New Developments in Mokken Scale Analysis in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i05).
    16. Piotr Tarka, 2013. "Model of latent profile factor analysis for ordered categorical data," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(1), pages 171-182, March.
    17. Xiaohui Zheng & Sophia Rabe-Hesketh, 2007. "Estimating parameters of dichotomous and ordinal item response models with gllamm," Stata Journal, StataCorp LP, vol. 7(3), pages 313-333, September.
    18. Lai-Fa Hung & Wen-Chung Wang, 2012. "The Generalized Multilevel Facets Model for Longitudinal Data," Journal of Educational and Behavioral Statistics, , vol. 37(2), pages 231-255, April.
    19. Cheng, Yung-Hsiang & Liu, Kuo-Chu, 2012. "Evaluating bicycle-transit users’ perceptions of intermodal inconvenience," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1690-1706.
    20. Ghady El Khoury & Olivier Barbier & Xavier Libouton & Jean-Louis Thonnard & Philippe Lefèvre & Massimo Penta, 2020. "Manual ability in hand surgery patients: Validation of the ABILHAND scale in four diagnostic groups," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-17, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0197503. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.