IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v165y2002i2p233-253.html
   My bibliography  Save this article

Causal variables, indicator variables and measurement scales: an example from quality of life

Author

Listed:
  • Peter M. Fayers
  • David J. Hand

Abstract

There is extensive literature on the development and validation of multi‐item measurement scales. Much of this is based on principles derived from psychometric theory and assumes that the individual items form parallel tests, so that simple weighted or unweighted summation is an appropriate method of aggregation. More recent work either continues to promulgate these methods or places emphasis on modern techniques centred on item response theory. In fact, however, clinical measuring instruments often have different underlying principles, so adopting such approaches is inappropriate. We illustrate, using health‐related quality of life, that clinimetric and psychometric ideas need to be combined to yield a suitable measuring instrument. We note the fundamental distinction between indicator and causal variables and propose that this distinction suffices to explain fully the need for both clinimetric and psychometric techniques, and identifies their respective roles in scale development, validation and scoring.

Suggested Citation

  • Peter M. Fayers & David J. Hand, 2002. "Causal variables, indicator variables and measurement scales: an example from quality of life," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 233-253, June.
  • Handle: RePEc:bla:jorssa:v:165:y:2002:i:2:p:233-253
    DOI: 10.1111/1467-985X.02020
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-985X.02020
    Download Restriction: no

    File URL: https://libkey.io/10.1111/1467-985X.02020?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. J. L. Hutton & Paula R. Williamson, 2000. "Bias in meta‐analysis due to outcome variable selection within studies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 359-370.
    2. Mary Dupuis Sammel & Louise M. Ryan & Julie M. Legler, 1997. "Latent Variable Models for Mixed Discrete and Continuous Outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 667-678.
    Full references (including those not matched with items on IDEAS)

    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. D. J. Bartholomew, 2002. "Discussion on the paper by Fayers and Hand," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 253-261, June.
    2. Yang Lu, 2019. "Flexible (panel) regression models for bivariate count–continuous data with an insurance application," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1503-1521, October.
    3. Emilio Augusto Coelho-Barros & Jorge Alberto Achcar & Josmar Mazucheli, 2010. "Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 865-880.
    4. Nikolaos Pandis & Padhraig S Fleming & Helen Worthington & Kerry Dwan & Georgia Salanti, 2015. "Discrepancies in Outcome Reporting Exist Between Protocols and Published Oral Health Cochrane Systematic Reviews," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-10, September.
    5. Chen Yuqi & Wang Yuedong & Guo Wensheng & Kotanko Peter & Usvyat Len, 2016. "Joint Model for Mortality and Hospitalization," The International Journal of Biostatistics, De Gruyter, vol. 12(2), pages 1-11, November.
    6. Zhang, Q. & Ip, E.H., 2014. "Variable assessment in latent class models," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 146-156.
    7. Zhenzhen Zhang & Thomas M. Braun & Karen E. Peterson & Howard Hu & Martha M. Téllez-Rojo & Brisa N. Sánchez, 2018. "Extending Tests of Random Effects to Assess for Measurement Invariance in Factor Models," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 634-650, December.
    8. Luo, Chongliang & Liang, Jian & Li, Gen & Wang, Fei & Zhang, Changshui & Dey, Dipak K. & Chen, Kun, 2018. "Leveraging mixed and incomplete outcomes via reduced-rank modeling," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 378-394.
    9. Leila Amiri & Mojtaba Khazaei & Mojtaba Ganjali, 2018. "A mixture latent variable model for modeling mixed data in heterogeneous populations and its applications," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 95-115, January.
    10. Wynanda A van Enst & Rob J P M Scholten & Lotty Hooft, 2012. "Identification of Additional Trials in Prospective Trial Registers for Cochrane Systematic Reviews," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-5, August.
    11. Zhang, Xiao & Boscardin, W. John & Belin, Thomas R. & Wan, Xiaohai & He, Yulei & Zhang, Kui, 2015. "A Bayesian method for analyzing combinations of continuous, ordinal, and nominal categorical data with missing values," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 43-58.
    12. Samson B. Adebayo & Ludwig Fahrmeir & Christian Seiler & Christian Heumann, 2011. "Geoadditive Latent Variable Modeling of Count Data on Multiple Sexual Partnering in Nigeria," Biometrics, The International Biometric Society, vol. 67(2), pages 620-628, June.
    13. Hoshino, Takahiro, 2008. "A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1413-1429, January.
    14. Jamie J Kirkham & Doug G Altman & Paula R Williamson, 2010. "Bias Due to Changes in Specified Outcomes during the Systematic Review Process," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-5, March.
    15. Nussbaum, Frank & Giesen, Joachim, 2020. "Pairwise sparse + low-rank models for variables of mixed type," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    16. Jane Osburn, 2011. "A Latent Variable Approach to Examining the Effects of HR Policies on the Inter- and Intra-Establishment Wage and Employment Structure: A Study of Two Precision Manufacturing Industries," Working Papers 451, U.S. Bureau of Labor Statistics.
    17. Piia K. Peura & Janne A. Martikainen & Timo T. Purmonen & Juha H. O. Turunen, 2012. "Sponsorship-Related Outcome Selection Bias in Published Economic Studies of Triptans," Medical Decision Making, , vol. 32(2), pages 237-245, March.
    18. Kerry Dwan & Douglas G Altman & Juan A Arnaiz & Jill Bloom & An-Wen Chan & Eugenia Cronin & Evelyne Decullier & Philippa J Easterbrook & Erik Von Elm & Carrol Gamble & Davina Ghersi & John P A Ioannid, 2008. "Systematic Review of the Empirical Evidence of Study Publication Bias and Outcome Reporting Bias," PLOS ONE, Public Library of Science, vol. 3(8), pages 1-31, August.
    19. Takahiro Hoshino & Hiroshi Kurata & Kazuo Shigemasu, 2006. "A Propensity Score Adjustment for Multiple Group Structural Equation Modeling," Psychometrika, Springer;The Psychometric Society, vol. 71(4), pages 691-712, December.
    20. Jun Zhu & Jens C. Eickhoff & Mark S. Kaiser, 2003. "Modeling the Dependence between Number of Trials and Success Probability in Beta-Binomial–Poisson Mixture Distributions," Biometrics, The International Biometric Society, vol. 59(4), pages 955-961, 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:bla:jorssa:v:165:y:2002:i:2:p:233-253. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.