Statistics for Research - L14 - How to Perform Reliability Analysis using Cronbach Alpha in R?

Cronbach’s alpha is presented as a go-to statistic for checking whether a multi-item scale measures a latent construct consistently—an essential step for building credible research measures. Reliability is defined as the consistency or stability of a test/measurement: the same construct should yield similar results across time, under different conditions, and when administered to different people. In practice, researchers often operationalize a construct (like organizational commitment) using several survey items answered on a shared agreement scale (e.g., “I love my job,” “I believe in my organization,” “I like to tell people that I love my job,” “I am not looking for another job”). When those items move together in a stable way, the measurement is considered reliable.

The session distinguishes reliability from validity: reliability is necessary but not sufficient for validity. A measure can produce consistent results without actually capturing the intended construct well. For internal consistency reliability—where the focus is how items within the same scale correlate—Cronbach’s alpha is highlighted as the most common approach. The core idea is that alpha summarizes the degree to which items in a scale are interrelated, using the scale’s total score variance and the variance attributable to measurement error.

The walkthrough then shifts to implementation in R. After loading a CSV dataset from the working directory, the psych package is used. Cronbach’s alpha is computed with the alpha() function by supplying the set of items that represent the latent variable (for example, a five-item organizational performance or commitment scale). The results are stored in an object and printed, with attention typically placed on standardized alpha.

Several alpha-related outputs are unpacked. Raw alpha is the basic Cronbach’s alpha coefficient (0 to 1), while standardized alpha adjusts for the number of items. The output may also include “G6(smc),” described as a Guilford-Benjamin modification of alpha corrected for item count. The average inter-item correlation (average r) is reported as well, ranging from -1 to +1, with values closer to 1 indicating stronger item alignment. Additional diagnostics include mean score, standard deviation, median, and a signal-to-noise ratio (true-score variance relative to error variance), where higher values indicate better internal consistency.

Acceptable alpha thresholds are presented as guidance rather than universal law. Common rules of thumb include alpha ≥ 0.70 as acceptable, George and Mallery (2003) suggesting ≥ 0.60, and a more optimistic interpretation where ≥ 0.80 is good and ≥ 0.90 is very good. The session then shows how to probe item contribution using alpha.drop(). By examining what happens to standardized alpha when each item is removed, it becomes clear whether any item is weakening internal consistency. In the example, removing a single item (op1) barely changes alpha, while removing other items would reduce it—leading to the conclusion that item deletion is unnecessary when alpha is already above the desired threshold.

Finally, the session explains how to report results: compute Cronbach’s alpha separately for each latent variable/construct, then report the number of items, sample size, and the alpha value (e.g., a five-item scale with alpha around 0.912 described as high internal consistency). The emphasis throughout is practical: check reliability, interpret alpha outputs carefully, and avoid deleting items without considering how that could harm content validity.