A readily applicable procedure for point and interval estimation of coefficient alpha is outlined for complex sample designs, in cases where this coefficient is close to composite reliability. As a by-product, point and interval estimation of the change in alpha due to measuring instrument revision is also possible. This approach can be used as an aid for scale construction and development in empirical investigations involving analyses of complex sample survey data, in particular for secondary data analyses from large-scale studies. The procedure is illustrated with data from an educational survey.

The representation gap in gifted and talented education poses a persistent challenge in educational systems worldwide. This theoretical manuscript presents the Bull’s Eye Model for Affective Development—Expansion (BEM-e) an innovative framework designed to address this gap. By incorporating elements from positive psychology, the BEM-e aims to identify and nurture traditionally underrepresented students who possess camouflaged gifted and talented abilities. Drawing upon the Engagement, Perseverance, Optimism, Connectedness, and Happiness model (EPOCH), along with measures of hope and metacognition, BEM-e provides a comprehensive approach to talent identification and service. The model emphasizes the holistic development of individuals by considering affective factors, engagement, perseverance, optimism, connectedness, happiness, hope, and metacognition. Additionally, dynamic assessment is integrated during the implementation of BEM-e modules, allowing for personalized and adaptive identification processes.

Cognitive abilities are related to job performance. However, there is less agreement about the relative contribution of general versus specific cognitive abilities to job performance. Similarly, it is not clear how cognitive abilities operate in the context of complex occupations. This study assessed the role of cognitive abilities on the performance of three aviation-related jobs: flying, navigation, and air battle management (ABM). Correlated-factor and bifactor models were used to draw a conclusion about the predictive relations between cognitive abilities and job performance. Overall, the importance of particular cognitive abilities tends to vary across the three occupations, and each occupation has different sets of essential abilities. Importantly, the interplay of general versus specific abilities is different across occupations, and some specific abilities also show substantial predictive power.

During the development of large-scale school achievement tests, panels of independent subject-matter experts use systematic judgmental methods to rate the correspondence between a given test’s items and performance objective statements. The individual experts’ ratings may then be used to compute summary indices to quantify the match between a given test and its target item domain. The magnitude of alignment index variability across experts within a panel, and randomly-sampled panels, is largely unknown, however. Using rater-by-item data from alignment reviews of 14 US states’ achievement tests, we examine observed distributions and estimate standard errors for three alignment indices developed by Webb. Our results suggest that alignment decisions based on the recommended criterion for the balance-of-representation index may often be uncertain, and that the criterion for the depth-of-knowledge consistency index should perhaps be reconsidered. We also examine current recommendations about the number of expert panelists required to compute these alignment indices.