Evaluating Longitudinal Anchoring Methods for Rasch Models.
J Appl Meas
; 21(3): 294-312, 2020.
Article
en En
| MEDLINE
| ID: mdl-33983901
ABSTRACT
Because modern, simultaneously estimated longitudinal Rasch models are unable to handle many timepoints, new methods of producing person and item estimates and evaluating test function are necessary. Longitudinal anchoring, in which a common scale of item parameters is used to estimate trait levels over multiple occasions, is a potential solution. With proper anchoring procedures, person and item estimates can be obtained without limiting the number of timepoints that can be analyzed. A simulation study examining the performance of six longitudinal anchoring methods (Floated, Racked, Time One, Mean, Random, and Stacked) was conducted. The Mean and the Stacked anchoring methods best recovered the population change over time, person and item estimates, and model fit. The Racked method could not produce reliable change estimates and should be avoided. Longitudinal anchoring is an easily implemented solution when analyzing large longitudinal datasets and shows promise as a low-computation method of producing latent trait estimates.
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Bases de datos:
MEDLINE
Asunto principal:
Proyectos de Investigación
/
Simulación por Computador
/
Modelos Estadísticos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
J Appl Meas
Año:
2020
Tipo del documento:
Article