A general framework for planning the number of items/subjects for evaluating Cronbach’s alpha: Integration of hypothesis testing and confidence intervals

Abstract

Cronbach’s alpha, widely used for measuring reliability, often operates within studies with sample information, suffering insufficient sample sizes to have sufficient statistical power or precise estimation. To address this challenge and incorporate considerations of both confidence intervals and cost-effectiveness into statistical inferences, our study introduces a novel framework. This framework aims to determine the optimal configuration of measurements and subjects for Cronbach’s alpha by integrating hypothesis testing and confidence intervals. We have developed two R Shiny apps capable of considering up to nine probabilities, which encompass width, validity, and/or rejection events. These apps facilitate obtaining the required number of measurements/subjects, either by minimizing overall cost for a desired probability or by maximizing probability for a predefined cost.