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Gepubliceerd in: Quality of Life Research 1/2022

27-05-2021 | Special Section: Non-parametric IRT

More flexible response functions for the PROMIS physical functioning item bank by application of a monotonic polynomial approach

Auteurs: Carl F. Falk, Felix Fischer

Gepubliceerd in: Quality of Life Research | Uitgave 1/2022

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Abstract

Purpose

In developing item banks for patient reported outcomes (PROs), nonparametric techniques are often used for investigating empirical item response curves, whereas final banks usually use parsimonious parametric models. A flexible approach based on monotonic polynomials (MP) provides a compromise by modeling items with both complex and simpler response curves. This paper investigates the suitability of MPs to PRO data.

Method

Using PROMIS Wave 1 data (N = 15,725) for Physical Function, we fitted an MP model and the graded response model (GRM). We compared both models in terms of overall model fit, latent trait estimates, and item/test information. We quantified possible GRM item misfit using approaches that compute discrepancies with the MP. Through simulations, we investigated the ability of the MP to perform well versus the GRM under identical data collection conditions.

Results

A likelihood ratio test (p < 0.001) and AIC (but not BIC) indicated better fit for the MP. Latent trait estimates and expected test scores were comparable between models, but we observed higher information for the MP in the lower range of physical functioning. Many items were flagged as possibly misfitting and simulations supported the performance of the MP. Yet discrepancies between the MP and GRM were small.

Conclusion

The MP approach allows inclusion of items with complex response curves into PRO item banks. Information for the physical functioning item bank may be greater than originally thought for low levels of physical functioning. This may translate into small improvements if an MP approach is used.
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Voetnoten
1
For a recent discussion on the merits of collapsing categories, see Harel and Steele [25].
 
2
Estimation options were changed slightly to increase computational speed and are described in Supplementary Materials.
 
Literatuur
1.
go back to reference Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Addison-Wesley. Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Addison-Wesley.
2.
go back to reference Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Lawrence Erlbaum Associates. Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Lawrence Erlbaum Associates.
3.
go back to reference Fries, J. F., Bruce, B., & Cella, D. (2005). The promise of PROMIS: Using item response theory to improve assessment of patient-reported outcomes. Clinical and Experimental Rheumatology, 23(5 Suppl 39), S53–S57.PubMed Fries, J. F., Bruce, B., & Cella, D. (2005). The promise of PROMIS: Using item response theory to improve assessment of patient-reported outcomes. Clinical and Experimental Rheumatology, 23(5 Suppl 39), S53–S57.PubMed
6.
go back to reference Samejima, F. (1972). A general model of free-response data. Psychometric Monographs No. 18. Psychometric Society. Samejima, F. (1972). A general model of free-response data. Psychometric Monographs No. 18. Psychometric Society.
7.
go back to reference Samejima, F. (2010). The general graded response model. In M. Nering & R. Ostini (Eds.), Handbook of polytomous item response theory models: Developments and applications (pp. 77–107). Taylor & Francis. Samejima, F. (2010). The general graded response model. In M. Nering & R. Ostini (Eds.), Handbook of polytomous item response theory models: Developments and applications (pp. 77–107). Taylor & Francis.
17.
go back to reference Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates.
18.
go back to reference Feuerstahler, L. M. (2016). Exploring alternate latent trait metrics with filtered monotonic polynomial IRT models (PhD thesis). Department of Psychology, University of Minnesota. Feuerstahler, L. M. (2016). Exploring alternate latent trait metrics with filtered monotonic polynomial IRT models (PhD thesis). Department of Psychology, University of Minnesota.
25.
go back to reference Harel, D., & Steele, R. J. (2018). An information matrix test for the collapsing of categories under the partial credit model. Journal of Educational and Behavioral Statistics, 43, 721–750.CrossRef Harel, D., & Steele, R. J. (2018). An information matrix test for the collapsing of categories under the partial credit model. Journal of Educational and Behavioral Statistics, 43, 721–750.CrossRef
29.
go back to reference van der Ark, L. A., & Sijtsma, K. (2005). The effect of missing data imputation on Mokken scale analysis. In L. A. van der Ark, M. A. Croon, & K. Sijtsma (Eds.), New developments in categorical data analysis for the social and behavioral sciences (pp. 147–166). Lawrence Erlbaum. van der Ark, L. A., & Sijtsma, K. (2005). The effect of missing data imputation on Mokken scale analysis. In L. A. van der Ark, M. A. Croon, & K. Sijtsma (Eds.), New developments in categorical data analysis for the social and behavioral sciences (pp. 147–166). Lawrence Erlbaum.
39.
go back to reference Organization for Economic Cooperation and Development. (2017). PISA 2015 technical report. Organization for Economic Cooperation and Development. Organization for Economic Cooperation and Development. (2017). PISA 2015 technical report. Organization for Economic Cooperation and Development.
46.
go back to reference Maydeu-Olivares, A. (2005). Further empirical results on parametric versus nonparametric IRT modeling of Likert-type personality data. Multivariate Behavioral Research, 40, 261–279.CrossRef Maydeu-Olivares, A. (2005). Further empirical results on parametric versus nonparametric IRT modeling of Likert-type personality data. Multivariate Behavioral Research, 40, 261–279.CrossRef
47.
go back to reference R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
Metagegevens
Titel
More flexible response functions for the PROMIS physical functioning item bank by application of a monotonic polynomial approach
Auteurs
Carl F. Falk
Felix Fischer
Publicatiedatum
27-05-2021
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 1/2022
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-021-02873-7

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