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Is it quality, is it redundancy, or is model inadequacy? Some strategies for judging the appropriateness of high-discrimination items
Is it quality, is it redundancy, or is model inadequacy? Some strategies for judging the appropriateness of high-discrimination items
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Is it quality, is it redundancy, or is model inadequacy? Some strategies for judging the appropriateness of high-discrimination items
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Is it quality, is it redundancy, or is model inadequacy? Some strategies for judging the appropriateness of high-discrimination items
Is it quality, is it redundancy, or is model inadequacy? Some strategies for judging the appropriateness of high-discrimination items
Journal Article

Is it quality, is it redundancy, or is model inadequacy? Some strategies for judging the appropriateness of high-discrimination items

2023
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Overview
When developing new questionnaires, it is traditionally assumed that the items should be as discriminative as possible, as if this was always indicative of their quality. However, in some cases these high discriminations may be masking some problems such as redundancies, shared residuals, biased distributions, or model limitations which may contribute to inflate the discrimination estimates. Therefore, the inspection of these indices may lead to erroneous decisions about which items to keep or eliminate. To illustrate this problem, two different scenarios with real data are described. The first focuses on a questionnaire that contains an item apparently highly discriminant, but redundant. The second focuses on a clinical questionnaire administered to a community sample, which gives place to highly right-skewed item response distributions and inflated discriminant indices, despite the items do not discriminate well among the majority of participants. We propose some strategies and checks to identify these situations, so that the items that are inappropriate may be identified and removed. Therefore, this article seeks to promote a critical attitude, which may involve going against routine stablished principles when they are not appropriate. Cuando se desarrollan nuevos cuestionarios, tradicionalmente se asume que los ítems deben ser lo más discriminativos posible, como si esto fuera siempre indicativo de su calidad. Pero en algunos casos estas discriminaciones elevadas pueden estar ocultando algunos problemas como redundancias, residuales compartidos, distribuciones sesgadas o limitaciones del modelo que pueden contribuir a inflar las estimaciones de la discriminación. Por lo tanto, la inspección de estos índices puede llevar a decisiones erróneas sobre qué ítems mantener o eliminar. Para ilustrar este problema, se describen dos escenarios diferentes con datos reales. El primero se centra en un cuestionario que contiene un ítem aparentemente muy discriminante, pero redundante. El segundo se centra en un cuestionario clínico administrado a una muestra comunitaria, lo que da lugar a distribuciones de respuesta de los ítems muy sesgadas y a índices de discriminación inflados, a pesar de que los ítems no discriminan bien entre la mayoría de los sujetos. Proponemos algunas estrategias y comprobaciones para identificar estas situaciones, para facilitar la identificación y eliminación de los ítems inapropiados. Por lo tanto, este artículo pretende promover una actitud crítica, que puede implicar ir en contra de los principios rutinarios establecidos cuando no son apropiados.
Publisher
Servicio de Publicaciones, Universidad de Murcia

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