AI Article Synopsis
- Exploratory factor analysis helps evaluate large item pools in scale development, but ineffective items can complicate analysis and skew results.
- To address this issue, a straightforward procedure is proposed that identifies potentially problematic items based on their extremeness and consistency before analyzing the data.
- This method, backed by simulations, aids in creating a more effective item subset for normal-range measures, and the procedure is available through a freeware software tool.
Article Abstract
Exploratory factor analysis is widely used for item analysis in the earlier stages of scale development, usually with large pools of items. In this scenario, the presence of inappropriate or ineffective items can hamper the process of analysis, making it very difficult to correctly assess dimensionality and structure. To minimize, this (quite frequent) problem, we propose and implement a simple procedure designed to flag potentially problematic items before we specify any particular factorial solution. The procedure defines regions of item appropriateness and efficiency based on the combined impact of two prior item features: extremeness and consistency. The general proposal is related to the most widely used frameworks for item analysis. The limits of the appropriateness regions are obtained by extensive simulation in conditions that mimic those found in applications. An Item Response Theory index of prior item efficiency is then defined, and a combined approach for selecting the most effective and problem-free item sub-set is proposed. The proposal is useful to normal-range measures, such as questionnaire surveys that elicit reports about non-extreme attitudes, facts, beliefs or states, or personality questionnaires that measure normal-range constructs. The procedure is implemented in a freeware software.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10456160 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0290611 | PLOS |
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