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Numerous traditional assessments have been developed to determine suitability of US military recruits for cyber careers. Cyber career field managers expressed a concern there may be well-qualified candidates that lack cyber knowledge, and therefore are not identified with knowledge-based tests. Technological advances such as serious gaming may provide opportunities to assess constructs traditional methods do not effectively measure. The purpose of this effort was to identify potential gains in validity that could be achieved beyond traditional methods through the use of serious games for several cyber jobs (both for enlisted and officer positions). Throughout this phase of research, an extensive literature review of military and civilian assessments targeted cyber occupations. Then, military subject matter experts in these career fields provided input and guidance (e.g., focus on aptitudes and traits as knowledge and skill are rapidly outdated). A gap analysis between all measures of such constructs identified a short list of candidates for measurement in a serious game. A survey of 800 airmen in the 1N4X1A, 3D1X2 and 17DEX/SX career fields was conducted; 290 respondents identified six constructs to be the focus for serious game assessment. The game was developed, and constructs validated on a sample chosen to model Air Force enlisted recruits. Additional psychometric data from enlistees and cyber trainees will be gathered once COVID-19 restrictions are lifted. [ABSTRACT FROM AUTHOR]

Advances at the intersection of artificial intelligence (AI) and education and training are occurring at an ever-increasing pace. On the education and training side, psychological and performance constructs play a central role in both theory and application. It is essential, therefore, to accurately determine the dimensionality of a construct, as it is often employed during both the assessment and development of theory, and its practical application. Traditionally, both exploratory and confirmatory factor analyses have been employed to establish the dimensionality of data. Due in part to inconsistent findings, methodologists recently resurrected the bifactor approach for establishing the dimensionality of data. The bifactor model is pitted against traditional data structures, and the one with the best overall fit (according to chi-square, root mean square error of approximation (RMSEA), comparative fit index (CFI), Tucker–Lewis index (TLI), and standardized root mean square residual (SRMR)) is preferred. If the bifactor structure is preferred by that test, it can be further examined via a suite of emerging coefficients (e.g., omega, omega hierarchical, omega subscale, H, explained common variance, and percent uncontaminated correlations), each of which is computed from standardized factor loadings. To examine the utility of these new statistical tools in an education and training context, we analyze data where the construct of interest is trust. We chose trust as it is central, among other things, to understanding human reliance upon and utilization of AI systems. We utilized the above statistical approach and determined the two-factor structure of widely employed trust scale is better represented by one general factor. Findings like this hold substantial implications for theory development and testing, prediction as in structural equation modeling (SEM) models, as well as the utilization of scales and their role in education, training, and AI systems. We encourage other researchers to employ the statistical measures described here to critically examine the construct measures used in their work if those measures are thought to be multidimensional. Only through the appropriate utilization of constructs, defined in part by their dimensionality, are we to advance the intersection of AI and simulation and training. [ABSTRACT FROM AUTHOR]

INTRODUCTION The role of human judgment and the factors associated with the 'fallibility' of decision making have been central facets in many areas of human performance research (Guion, 1998, p. […], This research focuses on a modeling approach and set of mathematical tools that were derived from research on intelligence systems, namely fuzzy system modeling. This study systematically evaluates these tools as an approach for modeling human decision making, contrasting the approach with more traditional methods based on regression. The research was conducted using experts and a simulated task environment related to allocating rewards in the form of merit pay. The results indicate that fuzzy system models generally perform as well as or better than both linear and nonlinear regression methods in terms of model fit. These results are discussed in terms of issues regarding modeling precision versus parsimony, the value of adaptive modeling techniques, empirical versus subjective approaches to model building, and individual differences in judgment strategies. Potential applications of this research include using the modeling approach studied to build higher-fidelity models that yield new insights and a better understanding of decision-making strategies and environments.

Covariance structure modeling analyses have been used to investigate the possibility that appearance comparisons influence the link between developmental issues (early maturational timing, teasing), body image and eating disturbance. This research supports the key role of appearance-based comparisons as a mediational connection between social feedback (teasing), body image and eating disturbance. It was established that body image mediates the effects of teasing on eating disturbance. Maturational timing was not an important predictor of comparison.

Most job-related decisions are based on information concerning the nature or structure of work. For example, the extent to which certain aspects of the job are separable and critical for successful performance often serve as inputs to an organization's performance management and compensation systems. Because the information needs to be as veridical as possible, it is common to have this information provided on multiple perspectives of the job and from multiple sources. This multimethod-multirater structuring of job data is typically analyzed to determine such things as convergent and discriminant validity. It is imperative that the user select the correct model for these analyses, as each approach makes very different assumptions about the composition of the data. The authors describe the 2 most common approaches - the additive and direct product models. Data from 7 different jobs are analyzed. The researchers argue the direct product model provides a better representation of the data.

Negative verbal commentary (teasing), obesity level, and perceived weight have a direct impact on the development of body image and eating disturbance in teenage girls. Maturation timing has no effect on these factors. Covariance structure modelling and longitudinal analysis shows the effect of different factors on the development of body image and eating disturbance. The major risk factor for the teasing about body weight, size and overall appearance is the excess weight gain by the teenage girls.

The relation between sensation-seeking and alcohol consumption is partially mediated by alcohol expectancy. The family history of alcohol abuse may not affect drinking outcomes. The relation may be falsely inflated due to overlap of items on sensation-seeking and alcohol consumption measures. Covariance structure modeling techniques are used for the analysis.

The article presents a preface from the guest editor of this special issue on the intersection of information technology and human resources.