A Monte Carlo Study of Eight Confidence Interval Methods for Coefficient Alpha

The purpose of this research is to examine eight of the different methods for computing confidence intervals around alpha that have been proposed to determine which of these, if any, is the most accurate and precise. Monte Carlo methods were used to simulate samples under known and controlled population conditions. In general, the differences in the accuracy and precision of the eight methods examined were negligible in many conditions. For the breadth of conditions examined in this simulation study, the methods that proved to be the most accurate were those proposed by Bonett and Fisher. Larger samples sizes and larger coefficient alphas also resulted in better interval coverage, whereas smaller numbers of items resulted in poorer interval coverage.