# Is there a statistical justification for removing items from a scale with good reliability?

So, we were reviewing a manuscript where the research group was developing an anxiety survey for upper level biology students. The survey had 25 items; was administered via paper and online formats (N = 200-250 each time) and reliability was > 0.8 with either format. Secondly, they got 4 components using PCA, loadings were fairly high (> 0.7) and subscale reliability was pretty high too.

Based on their high reliability and loadings, the researchers were contemplating on whether items needed to be excluded from the next administration of the survey. From what they had stated, there were no items with cross loadings, no items that consistently showed up without loadings and their 4 components were meaningful. So, why would high reliability, high loadings, a 25 item survey and a decent item:case ratio bring up the question of dropping any items at all? Is there justification for that decision?

Note that PCA (and Cronbach $\alpha$, if that's what they use to estimate reliability) is generally not recommended for scale building. Results are often not too different but factor analysis makes more sense in principle.