Revisiting internal consistency in hospitality research: toward a more comprehensive assessment of scale quality

Abstract

Purpose The purpose of this study is to revisit the measures of internal consistency for multi-item scales in hospitality research and compare the performance of Cronbach’s α, omega total (ωTotal), omega hierarchical (ωH), Revelle’s omega total (ωRT), Minimum Rank Factor Analysis (GLBfa) and GLB algebraic (GLBa). Design/methodology/approach A Monte Carlo simulation was conducted to compare the performance of the six reliability estimators under different conditions common in hospitality research. Second, this study analyzed a data set to complement the simulation study. Findings Overall, ωTotal was the best-performing estimator across all conditions, whereas ωH performed the poorest. α performed well when factor loadings were high with low variability (high/low) and large sample sizes. Similarly, ωRT, GLBfa and GLBa performed consistently well when loadings were high and less variable as well as the sample size and the number of scale items increased. Of the two GLB estimators, GLBa consistently outperformed GLBfa. Practical implications This study provides hospitality managers with a better understanding of what reliability is and the various reliability estimators. Using reliable instruments ensures that organizations draw accurate conclusions that help them move closer to realizing their visions. Originality/value Though popular in other fields, reliability discussions have not yet received substantial attention in hospitality. This study raises these discussions in the context of hospitality research to promote better practices for assessing the reliability of scales used within the hospitality domain.