Our Methods & Techniques

Cognitive competency modeling is a blended technique used by CPG that combines traditional competency modeling with cognitive task analysis to characterize the behaviors and decisions associated with the successful performance of complex jobs. Cognitive competency models combine necessary knowledge, skills, and cognitive abilities (KSAs) with job-relevant behavioral indicators to define successful performance at the individual, team, or organizational level. We build these models to create a variety of interventions, such as cognitive training scenarios, performance reviews, position descriptions, and assessment tools.

BARS are observation rubrics for assessing proficiency in the cognitive elements of job performance, such as sensemaking, decision making, team coordination, and communication. Our rubrics reflect real-world demands and clearly distinguish levels of expertise. They are applicable regardless of the task or context in which performance is being evaluated. BARS enable organizations to conduct more objective evaluations of job performance, leaning away from observers opinions by using validated descriptors of how people perform when they are more or less proficient. BARS are accompanied by digitized collection, analysis, and reporting tools.

Concept mapping is a structured method for eliciting, organizing, and visually representing knowledge. We use concept mapping to capture how domain experts understand the relationships among key concepts in complex environments. Our cognitive task analysis toolkit enables collaborative knowledge elicitation processes as well as a comparison of understanding across individuals. The result is a clear, navigable representation of complex information that supports analysis, assessment, and knowledge management.

Mastery models are customized descriptions of the developmental progression toward expertise, or mastery, for a particular job in a cognitively complex domain. The mastery modeling approach is grounded in the five stages of cognitive skill acquisition first described by Dreyfus and Dreyfus (1986). Through semi-structured interview techniques, we extract first-hand knowledge of a critical role from individuals who have performed it or mentored the development of others. The contents of a mastery model comprise a roadmap to achieving mastery in a domain or job position. These models codify the qualities and desired performance that must be considered when developing training interventions, creating assessment tools, or selecting individuals for a role.

Mixed methods research combines qualitiatve depth with quantitative rigor. We use tools like interviewing, observation, and cognitive task analysis to develop a grounded understanding. We follow this up with surveys, experiments, or other quantitative measures to test that understanding more broadly. We triangulate evidence across sources to strengthen confidence in our research design and our findings. The result is a fuller, more defensible picture of performance in complex environments.

Many of the problems we address require rigorous, data-driven evaluation. We apply quantitative methods to measure performance, evaluate interventions, and generate defensible evidence to support resourcing decisions. We design fit-for-purpose measurement and analysis strategies, applying experimental and quasi-experimental designs, survey development, and statistical analysis in field environments where creativity is required to establish scientific rigor. The result is analysis that produces credible, actionable insights, even in complex environments with limited data and small sample sizes.

Usability analysis evaluates how effectively users interact with tools, systems, and interfaces in real-world conditions. We go beyond surface-level usability to examine how design impacts decision making, workload, and performance in complex environments. Our approach integrates behavioral observation, user feedback, and cognitive task analysis techniques to identify user decision requirements as well as barriers and opportunities for improvement. The result is actionable insight that drives more intuitive, effective tools aligned to how users actually think and operate.

CTA is a family of established methods used to capture the expertise behind effective performance. Skilled performers often can't explain in precise terms how they make decisions, interpret information, and respond under pressure. CTA is how we uncover that tacit knowledge. Our approach goes beyond task documentation to focus on decision requirements, identifying the cues, judgments, and strategies that account for the difference between good and great. The result is codified knowledge derived from informal practice, which supports the design of training, assessment, and tools aligned to the cognitive demands of a job.

Decision requirements analysis identifies the critical decisions that drive effective performance in complex roles. We define what decisions must be made, under what conditions, and with what information, enabling a clear understanding of performance demands. This approach links cognitive requirements directly to training, assessment, and system design. The result is a structured foundation for improving decision making in real-world environments.

Scenario-based methods are used to assess and develop performance in realistic, context-rich environments. We design scenarios that reflect actual operational conditions, incorporating the decisions, trade-offs, and uncertainties individuals face on the job. These scenarios enable more valid assessment and more effective training than decontextualized methods. The result is a more accurate understanding of performance in conditions that mirror reality.

Performance assessment frameworks provide a structured approach to defining, measuring, and tracking performance over time. We develop frameworks that integrate behavioral and cognitive measures aligned to job demands. These frameworks connect assessment, training, and outcomes into a cohesive system. The result is a more complete and actionable understanding of performance at the individual and organizational level.

Training needs analysis, or front-end analysis, identifies gaps between current and desired performance. We go beyond surface-level skills to identify underlying cognitive and behavioral drivers of performance. Our approach integrates multiple data sources to ensure findings are accurate and actionable. The result is targeted training that addresses the root causes of performance issues.

Structured debrief methods, including After Action Reviews, are used to capture lessons learned and improve future performance. We design and facilitate debriefs that focus on situational assessment, decision making, contextual factors, and outcomes rather than simple task completion. This approach enables deeper learning, more effective knowledge transfer, and modeling of continuous improvement.