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Quality of Life Research
Published in: Quality of Life Research 2/2008

Open Access 01-03-2008

Nonparametric IRT analysis of Quality-of-Life Scales and its application to the World Health Organization Quality-of-Life Scale (WHOQOL-Bref)

Authors: Klaas Sijtsma, Wilco H. M. Emons, Samantha Bouwmeester, Ivan Nyklíček, Leo D. Roorda

Published in: Quality of Life Research | Issue 2/2008

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Abstract

Background

This study investigates the usefulness of the nonparametric monotone homogeneity model for evaluating and constructing Health-Related Quality-of-Life Scales consisting of polytomous items, and compares it to the often-used parametric graded response model.

Methods

The nonparametric monotone homogeneity model is a general model of which all known parametric models for polytomous items are special cases. Merits, drawbacks, and possibilities of nonparametric and parametric models and available software are discussed. Particular attention is given to the monotone homogeneity model (also known as the Mokken model), and the often-used parametric graded response model.

Results

Data from the WHOQOL-Bref were analyzed using both the monotone homogeneity model and the graded response model. The monotone homogeneity model analysis yielded unidimensional scales for each content domain. Scalability coefficients further showed that some items have limited scalability with respect to the other items in the same scale. The parametric IRT analyses lead to the rejection of some of the items.

Conclusions

The nonparametric monotone homogeneity model is highly suited for data analysis in a health-related quality-of-life context, and the parametric graded response model may add interesting features to measurement provided the model fits the data well.
Literature
1.
Hambleton, R. K., & Swaminathan, H. (1985). Item response theory. Principles and applications. Boston: Kluwer Nijhoff.
2.
Mokken, R. J. (1971). A theory and procedure of scale analysis. Berlin: De Gruyter.
3.
Mokken, R. J. (1997). Nonparametric models for dichotomous responses. In W. J. v. d. Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 351–367). New York: Springer.
4.
Sijtsma, K., & Molenaar, I. W. (2002). Introduction to nonparametric item response theory. Thousand Oaks, CA: Sage.
5.
Petersen, M. A. (2004). Book review: Introduction to nonparametric iterm response theory. Quality of Life Research, 14, 1201–1202.CrossRef
6.
Ringdal, K., Ringdal, G. I., Kaasa, S., Bjordal, K., Wisløff, F., Sundstrøm, S., & Hermstad, M.J. (1999). Assessing the consistency of psychometric properties of the HRQOL scales within the EORTC QLC-C30 across populations by means of the Mokken scaling model. Quality of Life Research, 8, 25–41.PubMedCrossRef
7.
Moorer, P., Suurmeijer, Th. P. B. M., Foets, M., & Molenaar, I. W. (2001). Psychometric properties of the RAND-36 among three chronic diseases (multiple sclerosis, rheumatic diseases and COPD) in the Netherlands. Quality of Life Research, 10, 637–645.PubMedCrossRef
8.
Van der Heijden, P. G. M., Van Buuren, S., Fekkes, M., Radder, J., & Verrips, E. (2003). Unidimensionality and reliability under Mokken scaling of the dutch language version of the SF-36. Quality of Life Research, 12, 189–198.PubMedCrossRef
9.
Roorda, L. D., Roebroeck, M. E., Van Tilburg, T., Molenaar, I. W., Lankhorst, G. J., Bouter, L.M., & the Measuring Mobility Studying Group (2005). Measuring activity limitations in walking: Development of a hierarchical scale for patients with lower-extremity disorders who live at home. Archives of Physical Medicine and Rehabilitation, 86, 2277–2283.PubMedCrossRef
10.
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Nielsen & Lydische.
11.
Muraki, E. (1997). A generalized partial credit model. In W. J. v. d. Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 153–164). New York: Springer.
12.
Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561–573.CrossRef
13.
Barley, E. A., & Jones, P. W. (2006). Repeatability of a Rasch model of the AQ20 over five assessments. Quality of Life Research, 15, 801–809.PubMedCrossRef
14.
Fitzpatrick, R., Norquist, J. M., Jenkinson, C., Reeves, B. C., Morris, R. W., Murray, D. W., & Gregg, P. J. (2004). A comparison of Rasch with likert scoring to discriminate between patients’ evaluations of total hip replacement surgery. Quality of Life Research, 13, 331–338.PubMedCrossRef
15.
Kosinski, M., Bjorner, J. B., Ware, J. E. Jr, Batenhorst, A., & Cady, R. K. (2003). The responsiveness of headache impact scales scored using ‘classical’ and ‘modern’ psychometric methods: A re-analysis of three clinical trials. Quality of Life Research, 12, 903–912.
16.
Junker, B. W., & Sijtsma, K. (2001). Nonparametric item response theory in action: An overview of the special issue. Applied Psychological Measurement, 25, 211–220.CrossRef
17.
Stout, W. F. (2002). Psychometrics: From practice to theory and back. Psychometrika, 67, 485–518.CrossRef
18.
Sijtsma, K., & Meijer, R. R. (2007). Nonparametric item response theory and related topics. In C. R. Rao & S. Sinharay (Eds.), Handbook of statistics, vol. 26: Psychometrics (pp. 719–746). Amsterdam: Elsevier.
19.
The WHOQoL Group (1998). Development of the World Health Organisation WHOQOL-Bref QoL assessment. Psychological Medicine, 28, 551–559.CrossRef
20.
Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.
21.
Reckase, M. D. (1997). A linear logistic multidimensional model for dichotomous item response data. In W. J. v. d. Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 271–286). New York: Springer.
22.
Mellenbergh, G. J. (1995). Conceptual notes on models for discrete polytomous item responses. Applied Psychological Measurement, 19, 91–100.CrossRef
23.
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika, Monograph supplement No. 17.
24.
Hemker, B. T., Sijtsma, K., Molenaar, I. W., & Junker, B. W. (1997). Stochastic ordering using the latent trait and the sum score in polytomous IRT models. Psychometrika, 62, 331–347.CrossRef
25.
Van Engelenburg, G. (1997). On psychometric models for polytomous items with ordered categories within the framework of item response theory. Ph.D. Thesis, Amsterdam, The Netherlands: University of Amsterdam.
26.
Samejima, F. (1997). Graded response model. In W. J. v. d. Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 85–100). New York: Springer.
27.
Molenaar, I. W. (1997). Nonparametric models for polytomous responses. In W. J. v. d. Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory. (pp. 369–380). New York: Springer.
28.
Van der Ark, L. A. (2005). Stochastic ordering of the latent trait by the sum score under various polytomous IRT models. Psychometrika, 70, 283–304.CrossRef
29.
Junker, B. W., & Sijtsma, K. (2000). Latent and manifest monotonicity in item response models. Applied Psychological Measurement, 24, 65–81.CrossRef
30.
Mokken, R. J., Lewis, C., & Sijtsma, K. (1986). Rejoinder to ‘The Mokken scale: A critical discussion’. Applied Psychological Measurement, 10, 279–285.CrossRef
31.
Fan, X. (1998). Item response theory and classical test theory: An empirical comparison of their item/person statistics. Educational and Psychological Measurement, 58, 357–382.CrossRef
32.
Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Erlbaum.
33.
Molenaar, I. W., & Sijtsma, K. (2000). User’s manual MSP5 for Windows. Groningen, the Netherlands: iecPROGAMMA.
34.
Ramsay, J. O. (2000). Testgraf. A program for the analysis of multiple choice test and questionnaire data. Montreal, Canada: Department of Psychology, McGill University.
35.
Ramsay, J. O. (1991). Kernel smoothing approaches to nonparametric item characteristic curve estimation. Psychometrika, 56, 611–630.CrossRef
36.
Fox, J. (1997). Applied regression analysis, linear models, and related methods. Thousand Oaks, CA: Sage.
37.
Rossi, N., Wang, X., & Ramsay, J. O. (2002). Nonparametric item response function estimates with the EM algorititm. Journal of Educational and Behavioral Statistics, 27, 291–317.CrossRef
38.
Van Ginkel, J. R., & Van der Ark, L. A. (2005). SPSS syntax for missing value imputation in test and questionnaire data. Applied Psychological Measurement, 29, 152–153.CrossRef
39.
Reise, S. P., & Waller, N. G. (1990). Fitting the two-parameter model to personality data. Applied Psychological Measurement, 14, 45–58.CrossRef
40.
Hemker, B. T., Sijtsma, K., & Molenaar, I. W. (1995). Selection of unidimensional scales from a multidimensional itembank in the polytomous IRT model. Applied Psychological Measurement, 19, 337–352.CrossRef
41.
Thissen, D., Chen, W.-H., & Bock, R. D. (2003). Multilog (version 7) [computer sotware]. Lincolnwood, IL: Scientific Software International.
42.
Ostini, R., & Nering, M. L. (2006). Polytomous item response theory models. Thousand Oaks, CA: Sage.
43.
Sinharay, S., Johnson, M. S., & Stern, H. S. (2006). Posterior predictive assessment of item response theory models. Applied Psychological Measurement, 30, 298–321.CrossRef
44.
Allen, M.J., & Yen, W. M. (1979). Introduction to measurement theory. Belmont, CA: Wadsworth.
45.
Hays, R.D., Brodsky, M., Johnston, M. F., Spritzer, K. L., & Hui, K. (2005). Evaluating the statistical significance of health-related quality of life change in individual patients. Evaluation and the Health Professions, 28, 160–171.CrossRef
46.
Hemker, B. T., Van der Ark, L. A., & Sijtsma, K. (2001). On measurement properties of continuation ratio models. Psychometrika, 66, 487–506.CrossRef
47.
Crane, P. K., Gibbons, L. E., Narasimhalu, K., Lai, J.-S., & Cella, D. (2007). Rapid detection of differential item functioning in assessments of health-related quality of life: The functional assessment of cancer therapy. Quality of Life Research, 16, 101–114.PubMedCrossRef
48.
Emons, W. H. M., Sijtsma, K., & Meijer, R. R. (2005). Global, local, and graphical person-fit analysis using person response functions. Psychological Methods, 10, 101–119.PubMedCrossRef
49.
Bjorner, J. B., Kosinski, M., & Ware, J. E. jr. (2003). Calibration of an item pool for assessing the burden of headaches: An application of item response theory to the Headache Impact Test (HIT™). Quality of Life Research, 12, 913–933.
50.
Bjorner, J. B., Kosinski, M., & Ware, J. E. jr. (2003). The feasibility of applying item response theory to measures of migraine impact: A re-analysis of three clinical studies. Quality of Life Research, 12, 887–902.
51.
Fliege, H., Becker, J., Walter, O. B., Bjorner, J. B., Klapp, B. F., & Rose, M. (2005). Development of a computer-adaptive test for depression (d-cat). Quality of Life Research, 14, 2277–2291.PubMedCrossRef
52.
Lai, J.-S., Cella, D., Chang, C.-H., Bode, R. K., & Heinemann, A. W. (2003). Item banking to improve, shorten and computerize self-reported fatigue: An illustration of steps to create a core item bank from the facit-fatigue scale. Quality of Life Research, 12, 485–501.PubMedCrossRef
53.
Petersen, M. A., Groenvold, M., Aaronson, N., Fayers, P., Sprangers, M., Bjorner, J. B., et al. (2006). Multidimensional computerized adaptive testing of the EORTC QLQ-C30: Basic developments and evaluations. Quality of Life Research, 15, 315–329.PubMedCrossRef
54.
Schwartz, C., Welch, G., Santiago-Kelley, P., Bode, R., & Sun, X. (2006). Computerized adaptive testing of diabetes impact: A feasibility study of hispanics and non-hispanics in an active clinic population. Quality of Life Research, 15, 1503–1518.PubMedCrossRef
Metadata
Title
Nonparametric IRT analysis of Quality-of-Life Scales and its application to the World Health Organization Quality-of-Life Scale (WHOQOL-Bref)
Authors
Klaas Sijtsma
Wilco H. M. Emons
Samantha Bouwmeester
Ivan Nyklíček
Leo D. Roorda
Publication date
01-03-2008
Publisher
Springer Netherlands
Published in
Quality of Life Research / Issue 2/2008
Print ISSN: 0962-9343
Electronic ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-007-9281-6

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