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Statistical methods for identifying response shift (RS) at the individual level could be of great practical value in interpreting change in PRO data. Guttman errors (GE) may help to identify discrepancies in respondent’s answers to items compared to an expected response pattern and to identify subgroups of patients that are more likely to present response shift. This study explores the benefits of using a GE-based method for RS detection at the subgroup and item levels.
The analysis was performed on the SatisQoL study. The number of GE was determined for each individual at each time of measurement (at baseline T0 and 6 months after discharge M6). Individuals showing discrepancies (with many GE) were suspected to interpret the items differently from the majority of the sample. Patients having a large number of GE at M6 only and not at T0 were assumed to present RS. Patients having a small number of GE at T0 and M6 were assumed to present no RS. The RespOnse Shift ALgorithm in Item response theory (ROSALI) was then applied on the whole sample and on both groups.
Different types of RS (non-uniform recalibration, reprioritization) were more prevalent in the group composed of patients assumed to present RS based on GE. On the opposite, no RS was detected on patients having few GE.
Guttman errors and item response theory models seem to be relevant tools to discriminate individuals affected by RS from the others at the item level.
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Guilleux, A., Blanchin, M., Vanier, A., Guillemin, F., Falissard, B., Hardouin, J. B., & Sébille, V. (2015). RespOnse shift algorithm in item response theory (ROSALI) for response shift detection with missing data in longitudinal patient-reported outcome studies. Quality of Life Research, 24(3), 553–564. CrossRefPubMed
Sijtsma, K., & Molenaar, I. W. (2002). Introduction to nonparametric item response theory (1st ed., Vol. 5). Thousand Oaks: Sage.
Kepka, S., Baumann, C., Anota, A., Buron, G., Spitz, E., Auquier, P., & Mercier, M. (2013). The relationship between traits optimism and anxiety and health-related quality of life in patients hospitalized for chronic diseases: Data from the SATISQOL study. Health and Quality of Life Outcomes, 11(1), 134. doi: 10.1186/1477-7525-11-134. CrossRefPubMedPubMedCentral
Leplège, A., Ecosse, E., Verdier, A. & Perneger, T. V. (1998). The French SF-36 health survey: Translation, cultural adaptation and preliminary psychometric evaluation. Journal of Clinical Epidemiology, 51(11), 1013–1023. doi: 10.1016/S0895-4356(98)00093-6.
Beller, J., & Kliem, S. (2013). GetR: Calculate Guttman error trees in R (version 0.1) [computer software]. Hannover, Germany. http://cran.r-project.org/web/packages/GetR/.
Meijer, R. R. (1994). The number of Guttman errors as a simple and powerful person-fit statistic. Applied Psychological Measurement, 18(4), 311–314. CrossRef
Tendeiro, J. N., & Meijer, R. R. (2014). Detection of invalid test scores: The usefulness of simple nonparametric statistics. Journal of Educational Measurement, 51(3), 239–259. CrossRef
Clogg, C. C. (1995). Latent class models. In G. Arminger, C. C. Clogg, & M. E. Sobel (Eds.), Handbook of statistical modeling for the social and behavioral sciences (pp. 311–359). New York: Springer. CrossRef
van Leeuwen, C. M. C., Post, M. W. M., van der Woude, L. H. V., de Groot, S., Smit, C., van Kuppevelt, D., & Lindeman, E. (2012). Changes in life satisfaction in persons with spinal cord injury during and after inpatient rehabilitation: Adaptation or measurement bias? Quality of Life Research, 21(9), 1499–1508. CrossRefPubMedPubMedCentral
Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12(1), 3–27. CrossRef
Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18(4), 450–469. CrossRef
Kadengye, D. T., Ceulemans, E., & Van den Noortgate, W. (2014). A generalized longitudinal mixture IRT model for measuring differential growth in learning environments. Behavior Research Methods, 46(3), 823–840. PubMed
Rapkin, B. D. & Schwartz, C. E. (2004). Toward a theoretical model of quality-of-life appraisal: Implications of findings from studies of response shift. Health and Quality of Life Outcomes, 2(14). doi: 10.1186/1477-7525-2-14.
Holland, P. W., & Wainer, H. (1993). Differential item functioning. Hillsdale: Erlbaum.
Osterlind, S. J., & Everson, H. T. (2009). Differential item functioning (2nd ed.). Thousand Oaks: Sage. CrossRef
- The Guttman errors as a tool for response shift detection at subgroup and item levels
- Springer International Publishing