FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3194386754> ?p ?o ?g. }

• W3194386754 endingPage "154851292110369" @default.
• W3194386754 startingPage "154851292110369" @default.
• W3194386754 abstract "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." @default.
• W3194386754 created "2021-08-30" @default.
• W3194386754 creator A5076228355 @default.
• W3194386754 creator A5078718861 @default.
• W3194386754 date "2021-08-18" @default.
• W3194386754 modified "2023-10-16" @default.
• W3194386754 title "The importance of identifying the dimensionality of constructs employed in simulation and training for AI" @default.
• W3194386754 cites W1851043932 @default.
• W3194386754 cites W1975799495 @default.
• W3194386754 cites W1981882373 @default.
• W3194386754 cites W1990109387 @default.
• W3194386754 cites W2033636223 @default.
• W3194386754 cites W2052140079 @default.
• W3194386754 cites W2129375743 @default.
• W3194386754 cites W2136407476 @default.
• W3194386754 cites W2139593703 @default.
• W3194386754 cites W2403516908 @default.
• W3194386754 cites W2415393931 @default.
• W3194386754 cites W2740497975 @default.
• W3194386754 cites W2940161247 @default.
• W3194386754 cites W2943383840 @default.
• W3194386754 cites W2991029543 @default.
• W3194386754 cites W3008046637 @default.
• W3194386754 cites W3011309374 @default.
• W3194386754 cites W3013809454 @default.
• W3194386754 cites W3031514878 @default.
• W3194386754 cites W3033348861 @default.
• W3194386754 cites W3033402143 @default.
• W3194386754 cites W597066122 @default.
• W3194386754 doi "https://doi.org/10.1177/15485129211036936" @default.
• W3194386754 hasPublicationYear "2021" @default.
• W3194386754 type Work @default.
• W3194386754 sameAs 3194386754 @default.
• W3194386754 citedByCount "0" @default.
• W3194386754 crossrefType "journal-article" @default.
• W3194386754 hasAuthorship W3194386754A5076228355 @default.
• W3194386754 hasAuthorship W3194386754A5078718861 @default.
• W3194386754 hasConcept C105795698 @default.
• W3194386754 hasConcept C111030470 @default.
• W3194386754 hasConcept C119857082 @default.
• W3194386754 hasConcept C121955636 @default.
• W3194386754 hasConcept C139945424 @default.
• W3194386754 hasConcept C144133560 @default.
• W3194386754 hasConcept C151730666 @default.
• W3194386754 hasConcept C154945302 @default.
• W3194386754 hasConcept C196083921 @default.
• W3194386754 hasConcept C199360897 @default.
• W3194386754 hasConcept C2779343474 @default.
• W3194386754 hasConcept C2780801425 @default.
• W3194386754 hasConcept C33923547 @default.
• W3194386754 hasConcept C40722632 @default.
• W3194386754 hasConcept C41008148 @default.
• W3194386754 hasConcept C71104824 @default.
• W3194386754 hasConcept C86803240 @default.
• W3194386754 hasConceptScore W3194386754C105795698 @default.
• W3194386754 hasConceptScore W3194386754C111030470 @default.
• W3194386754 hasConceptScore W3194386754C119857082 @default.
• W3194386754 hasConceptScore W3194386754C121955636 @default.
• W3194386754 hasConceptScore W3194386754C139945424 @default.
• W3194386754 hasConceptScore W3194386754C144133560 @default.
• W3194386754 hasConceptScore W3194386754C151730666 @default.
• W3194386754 hasConceptScore W3194386754C154945302 @default.
• W3194386754 hasConceptScore W3194386754C196083921 @default.
• W3194386754 hasConceptScore W3194386754C199360897 @default.
• W3194386754 hasConceptScore W3194386754C2779343474 @default.
• W3194386754 hasConceptScore W3194386754C2780801425 @default.
• W3194386754 hasConceptScore W3194386754C33923547 @default.
• W3194386754 hasConceptScore W3194386754C40722632 @default.
• W3194386754 hasConceptScore W3194386754C41008148 @default.
• W3194386754 hasConceptScore W3194386754C71104824 @default.
• W3194386754 hasConceptScore W3194386754C86803240 @default.
• W3194386754 hasLocation W31943867541 @default.
• W3194386754 hasOpenAccess W3194386754 @default.
• W3194386754 hasPrimaryLocation W31943867541 @default.
• W3194386754 hasRelatedWork W143968899 @default.
• W3194386754 hasRelatedWork W2030108766 @default.
• W3194386754 hasRelatedWork W2030580200 @default.
• W3194386754 hasRelatedWork W2057036262 @default.
• W3194386754 hasRelatedWork W2363289403 @default.
• W3194386754 hasRelatedWork W2793492196 @default.
• W3194386754 hasRelatedWork W2902653534 @default.
• W3194386754 hasRelatedWork W3033996450 @default.
• W3194386754 hasRelatedWork W4283166889 @default.
• W3194386754 hasRelatedWork W4312707344 @default.
• W3194386754 isParatext "false" @default.
• W3194386754 isRetracted "false" @default.
• W3194386754 magId "3194386754" @default.
• W3194386754 workType "article" @default.