Using an iterative design along with multiple methodological approaches and a large representative sample, this study presents reliability, validity, and fairness evidence for two surveys measuring student attitudes toward science, technology, engineering, and math (S-STEM) and interest in STEM careers for (a) 4th- through 5th-grade students (Upper Elementary S-STEM) and (b) 6th- through 12th-grade students (Middle/High S-STEM). Findings from exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) suggested the use of a four-factor structure to measure student attitudes toward science, math, engineering/technology, and 21st century skills. Subject matter experts and literature reviews provided evidence of content validity. Reliability levels were high for both versions. Furthermore, both the Upper Elementary S-STEM and Middle/High S-STEM Surveys demonstrated evidence of configural, metric, and scalar invariance across grade levels, races/ethnicities, and genders. The findings support the validity of interpretations and inferences made from scores on the instruments’ items and subscales.

We examine the extent to which g and specific abilities predict learning in a training context by juxtaposing contrasting theories of cognitive ability (i.e., Spearmanian theory and nested-factors theory) and using an analytical strategy (i.e., multidimensional importance analysis) that aligns with underlying theoretical assumptions of both theories. When conducting incremental validity analysis (Spearmanian approach), specific abilities did not add much to the prediction of learning. However, when conducting relative weights analysis (nested-factors approach), specific abilities were the dominant predictors of learning. Results suggest different theoretical and analytical approaches can lead to different results. Results also suggest specific abilities may provide predictive utility beyond g in the training context, but recognizing this utility depends on one’s theoretical assumptions and methodological approach.

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Understanding the processes through which trainee characteristics influence learning is important for identifying mechanisms that drive training effectiveness. We examine the direct and indirect paths through which core self-evaluations (CSE) impact learning. We also include general cognitive ability (GCA) to explore whether CSE's paths to effectiveness differ from those of a well-documented predictor of learning. We proposed a model in which CSE contributes to training effectiveness through its influence on motivational intervening mechanisms, and we tested this model empirically with military personnel (N = 638) who participated in job-required training. The data supported a partially mediated model. Irrespective of inclusion of GCA as a control variable, motivation and effort allocation (MEA) process variables (i.e., training motivation, midtraining self-efficacy, and midtraining goal setting) mediated (or partially mediated) the relationship between CSE and training outcomes that included affective (e.g., intentions to transfer), cognitive (e.g., declarative knowledge), and skill-based (e.g., proficiency) learning. Conversely, GCA had neither direct nor indirect effects on affective learning but did demonstrate direct effects on cognitive and skill-based learning. Results support the utility of including CSE in training research and practice, suggest that MEA serves as an explanatory mechanism for CSE's relation to learning outcomes, and demonstrate that CSE and GCA differentially influence training effectiveness and do so through different explanatory mechanisms.

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