Cronbach’s alpha is one of the most widely reported statistics relating to reliability in the social sciences. It can be interpreted as an estimate of all possible split-half statistics. It can also be interpreted as the percent of variance that is “true score” variance. Thus, if you have a measure with α = 0.80 in a particular sample, there is about 80% that is “true score” and about 20% error in the measurement for that sample.

Unfortunately, alpha is still not reported in a majority of articles in modern, high-quality research journals. This might stem from the anachronistic assertion that scales, particularly those that are frequently used, are more or less reliable. However, as we demonstrated throughout this chapter, alpha can be influenced by a number of different factors that arespecific to the sample (e.g., sample size, sample bias, etc.). This variability across samples is not something often discussed in the psychometrics literature, but it is important. It highlights the sample-dependent nature of reliability and emphasizes the need to attend to reliability and report it in each study. Estimation of 95% confidence intervals can further aid in the interpretation reliability and offer guidance about the replicability and precision of the estimate.

Although alpha has been the main focus of this chapter, there are modern alternatives to alpha. Rasch modeling, for example, produces interesting information about reliability from that perspective, as does Item Response Theory analysis. Structural equation modeling (SEM) allows us to explicitly model the latent construct and directly analyze it, eliminating the need for alpha altogether. Where possible, it seems desirable to use modern measurement methodologies.