Involves collecting in-depth data using methods such as case studies, focus groups, and interviews and then analyzing them to gain insight into experiences, events, and opinions. Results can be used to provide a deeper understanding of rare events and to provide context to quantitative analyses.
The Office of People Analytics (OPA) provides insightful, evidence-based solutions for total force management. We lead the Department of Defense (DoD) in providing innovative and cost-effective analytical solutions for leadership and decision makers.
Explore our key capabilities and methodologies
Assess the Service Member Life Cycle
OPA discerns how policy and environmental changes impact individuals by analyzing broad-pattern data. We offer insights that help frame considerations of force composition and performance. Results and outcomes are achieved by methodologies drawn from subject matter expertise across several divisions.
Involves standardized data collection from samples of larger groups. This quantitative methodology is used to generate statistically sound population estimates on key attitudes, opinions, and behaviors of interest. Data are used by DoD to understand all aspects of the life cycle, including military propensity, readiness, and retention intention.
Uses techniques such as regression and structural equation modeling to test theories and better understand complex data patterns involving observed and unobserved variables. It can be applied to assess direct and indirect effects of environmental variables on all stages of the military life cycle.
Utilize Cutting-Edge Analytical Methods
OPA uses data-science methods to provide actionable insights that assist policy- and operations-focused decision-making.
Provides basic data insights and population summaries to identify trends and commonalities and highlight structural data relationships, particularly for data quality. Examples of techniques include summary statistics, clustering, text analysis, and geospatial analyses.
Uses historical data and combination of techniques including machine learning and statistical algorithms to estimate future outcomes of interest. It can be used to inform policy by assessing the impact of critical decisions on operations. Techniques most commonly used include regression, network analysis, sentiment, and optimization.
Combines results from descriptive and predictive analyses to provide actionable recommendations for decision makers to consider. Data are analyzed to consider multiple operational outcomes based on courses of action. Techniques include algorithms from applied statistics, machine learning, operations research, and natural language processing.
Employs advanced test-development processes to tailor an examination to the ability of the test taker. Ultimately, this delivers tests with fewer questions and helps us obtain more precise scores.
Provide Data-Driven Recommendations, Solutions, & Tools
OPA provides recommendations and creates tools to improve policy formation for enhanced personnel improvements within the DoD community. We derive results through set methodologies.
Involves collecting in-depth data using methods such as case studies, focus groups, and interviews and then analyzing them to gain insight into experiences, events, and opinions. The findings are used to understand nuances not easily gleaned from quantitative analysis and as a starting point when developing surveys, research questions, or tools.
Establishes documentary evidence — through rigorous research and evaluation — that procedures, processes, or activities carried out in assessment maintain the desired level of compliance with industry standards. This documentation and level of assurance ensure all stages of decision-making and data use are meaningful for their intended purposes. It also helps the future improvement of personnel programs and policies.
Randomly sampled groups are given benign interventions to assess key questions. Data provide information on improving processes and strategies to increase survey response rates.
Uses techniques such as regression and structural equation modeling to test theories and better understand complex data patterns involving observed and unobserved variables. It can be applied to assess the effects of environmental and policy changes on key outcomes.