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Short scales are time‐efficient for participants and cost‐effective in research. However, researchers often mistakenly expect short scales to have the same reliability as long ones without considering the effect of scale length. We argue that applying a universal benchmark for alpha is problematic as the impact of low‐quality items is greater on shorter scales. In this study, we proposed simple guidelines for item reduction using the "alpha‐if‐item‐deleted" procedure in scale construction. An item can be removed if alpha increases or decreases by less than.02, especially for short scales. Conversely, an item should be retained if alpha decreases by more than.04 upon its removal. For reliability benchmarks,.80 is relatively safe in most conditions, but higher benchmarks are recommended for longer scales and smaller sample sizes. Supplementary analyses, including item content, face validity, and content coverage, are critical to ensure scale quality. [ABSTRACT FROM AUTHOR]

We examined the performance of coefficient alpha and its potential competitors (ordinal alpha, omega total, Revelle's omega total [omega RT], omega hierarchical [omega h], greatest lower bound [GLB], and coefficient H) with continuous and discrete data having different types of non-normality. Results showed the estimation bias was acceptable for continuous data with varying degrees of non-normality when the scales were strong (high loadings). This bias, however, became quite large with moderate strength scales and increased with increasing non-normality. For Likert-type scales, other than omega h, most indices were acceptable with non-normal data having at least four points, and more points were better. For different exponential distributed data, omega RT and GLB were robust, whereas the bias of other indices for binomial-beta distribution was generally large. An examination of an authentic large-scale international survey suggested that its items were at worst moderately non-normal; hence, non-normality was not a big concern. We recommend (a) the demand for continuous and normally distributed data for alpha may not be necessary for less severely non-normal data; (b) for severely non-normal data, we should have at least four scale points, and more points are better; and (c) there is no single golden standard for all data types, other issues such as scale loading, model structure, or scale length are also important. [ABSTRACT FROM AUTHOR]

We compared coefficient alpha with five alternatives (omega total, omega RT, omega h, GLB, and coefficient H) in two simulation studies. Results showed for unidimensional scales, (a) all indices except omega h performed similarly well for most conditions; (b) alpha is still good; (c) GLB and coefficient H overestimated reliability with small samples and short scales, and (d) sensitivity to scale quality reduced with longer scales. For contaminated scales, (a) all indices except omega h were reasonably unbiased with non-severe contamination; (b) alpha, omega total, and GLB were more sensitive in picking up contamination with shorter scales, whereas omega RT and omega h were not; and (c) coefficient H could not pick up contaminated items among high-quality items. For applied researchers, (a) supplementary information of scale characteristics helps choose the appropriate index; (b) comparing different scales with one golden standard is inappropriate; (c) omega h should not be used alone. [ABSTRACT FROM AUTHOR]