Systematic review approaches for identifying and evaluating assessments: A demonstration advancing strategic talent decision-making in the U.S. Army
David R. Glerum et al.
What the paper says
The 2024 U.S. Army Talent Assessment Strategy (ATAS) established a unified vision for leveraging assessments to transform and modernize how the Army makes personnel decisions across the Soldier lifecycle. Military organizations have a number of talent management tools at their disposal that can identify which talent attributes to capture, such as job analysis to better understand tasks and the attributes required to perform them effectively, competency modeling to identify strategically relevant attributes for organizational success, and taxonomic approaches to developing systems for classifying tasks and attributes. In this article, we suggest that systematic review approaches can build upon these tools to identify, evaluate, and recommend strategically relevant talent assessments to military organizations. We provide a demonstration of this practice to identify valid and reliable assessments capable of measuring talent attributes contained in the Army Talent Attribute Framework (ATAF), the Army's universal framework of knowledge, skills, abilities, and other characteristics (KSAOs) critical to successful performance across positions. For each assessment, we documented information on features that may influence decisions for use (e.g. practical considerations such as response format, number of items). We applied a systematic review approach to academic, commercial, governmental, and public sources, identifying 69 evidence-based assessments that could be used to assess the top 30 most highly rated officer KSAOs identified in a recently conducted Army-wide job analysis. We discuss several directions for future work and implications of this approach (e.g. facilitating automated battery assembly).
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.