Repositori URV

Identificador: imarina:9329488

Handle: https://hdl.handle.net/20.500.11797/imarina9329488

Autors:
Ferrando, Pere JMorales-Vives, Fabia

Resum:
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.