Revisiting internal consistency in hospitality research: toward a more comprehensive assessment of scale quality
通过蒙特卡洛模拟和实证数据分析，比较了六种信度估计量在酒店研究中的表现，发现omega total整体最优，而Cronbach's alpha在高载荷、低变异和大样本时表现良好。
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 ( ω Total ), omega hierarchical ( ω H ), Revelle’s omega total ( ω RT ), Minimum Rank Factor Analysis (GLB fa ) and GLB algebraic (GLB a ). 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, ω Total was the best-performing estimator across all conditions, whereas ω H performed the poorest. α performed well when factor loadings were high with low variability (high/low) and large sample sizes. Similarly, ω RT , GLB fa and GLB a 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, GLB a consistently outperformed GLB fa . 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.