{"schemaVersion":"1.0","exportedAt":"2026-05-15T12:38:23.846Z","occupation":{"soc":"43-9111.00","title":"Statistical Assistants","group":"Office & Administrative Support","sector":"55","jobZone":4,"jobZoneInferred":false},"framework":{"version":"v.26.05","description":"","contextCovered":"This framework covers statistical support and analytical work performed in office, research, government, and administrative environments where practitioners compute, verify, manage, and communicate data-driven findings across the full range of Job Zone 4 preparation and experience.","levels":{"emerging":{"label":"Emerging","statements":["Statistical formulas and calculators — apply under direct supervision to compute basic descriptive statistics for assigned datasets in an office or research support environment.","Source data completeness and accuracy — verify by cross-referencing original survey forms or records against entered values under guidance from a senior analyst.","Data entry tasks — execute with attention to detail by inputting coded records into statistical software or database systems following established protocols.","Data coding lists — interpret and apply to categorize raw survey responses prior to computer entry in a structured administrative workflow.","Standard office suite software — use to organize and format tabular data outputs in support of team reporting tasks.","Database user interface tools — navigate under supervision to retrieve and file data records according to departmental data management procedures.","Survey forms and printed reports — sort, label, and organize for distribution or analysis following defined paperwork-handling procedures.","Basic statistical charts and graphs — produce using analytical software templates to illustrate preliminary findings for supervisor review.","Written comprehension skills — demonstrate by reading and following detailed procedural manuals and codebooks relevant to assigned statistical support tasks.","Time management principles — apply by prioritizing routine data entry and verification tasks to meet established project deadlines in a team-based setting."]},"developing":{"label":"Developing","statements":["Statistical computations and trend analyses — perform routinely using analytical software such as SAS or SPSS to support research or operational reporting with reduced oversight.","Source data audits — conduct independently by designing and executing completeness and accuracy checks across multiple data files in a production database environment.","Data compilation reports — prepare by integrating results from multiple analyses into coherent charts, graphs, and narrative summaries for internal or external stakeholders.","Database query tools — use proficiently to extract, update, and maintain large datasets in support of ongoing statistical projects.","Data coding schemes — develop and refine for recurring survey instruments, ensuring consistent classification across data collection cycles.","Publication-ready data tables — assemble by formatting and validating statistical outputs for inclusion in departmental or government reports.","Complex problem-solving techniques — apply when reconciling discrepancies between source records and database entries across high-volume data sets.","Graphics and photo imaging software — utilize to enhance the visual clarity of analytical charts and figures prepared for presentation or publication.","Critical thinking skills — employ to evaluate the appropriateness of selected statistical methods relative to project data types and research objectives.","Document management software — use to organize, version-control, and archive project files and analytical records within established information governance frameworks."]},"proficient":{"label":"Proficient","statements":["Advanced statistical analyses — design and execute autonomously using programming languages such as Python or R to address non-routine analytical questions across the full project lifecycle.","Multi-source data integration — lead by merging and reconciling datasets from disparate systems to ensure analytical integrity for complex research or policy projects.","Comprehensive analytical reports — author independently, translating statistical findings into clear, evidence-based narratives suitable for diverse professional audiences.","Data quality assurance frameworks — implement by establishing validation rules and audit workflows that systematically detect and resolve data errors organization-wide.","Database architecture and query optimization — apply using SQL or equivalent tools to maintain high-performance data environments supporting concurrent analytical workloads.","Statistical publication processes — manage end-to-end by coordinating data preparation, peer review, and formatting for official release in accordance with agency standards.","Judgment and decision-making competency — exercise when selecting appropriate modeling approaches, interpreting ambiguous results, and recommending corrective analytical actions.","Active learning strategies — employ by independently evaluating emerging statistical methodologies and integrating relevant techniques into current project work.","CRM and financial analysis software — leverage to align statistical outputs with operational or business intelligence objectives across cross-functional teams.","Inductive and deductive reasoning — apply systematically to identify patterns in complex datasets and draw valid, defensible conclusions for organizational decision support."]},"advanced":{"label":"Advanced","statements":["Organizational statistical strategy — set direction for by defining standards, methodologies, and technology roadmaps that govern data analysis practices across the enterprise.","Competency development programs — design and deliver for statistical assistant teams, mentoring staff in advanced analytical techniques, software proficiency, and quality assurance.","Cross-departmental data governance — lead by establishing policies for data integrity, security, and lifecycle management that align with regulatory and organizational requirements.","Enterprise-scale publication initiatives — oversee by directing the end-to-end production of statistical reports, datasets, and public-facing data releases for large agencies or organizations.","Senior stakeholder communication — drive by translating complex statistical insights into strategic recommendations presented to executive leadership and external partners.","Institutional analytical frameworks — architect by integrating development environment software, object-oriented programming tools, and database platforms into unified, scalable data pipelines.","Quality and performance standards — establish for statistical support functions, defining measurable benchmarks and audit mechanisms to ensure consistent accuracy and reliability.","Research partnerships and contracts — negotiate and manage by representing the organization's statistical capabilities with academic institutions, government bodies, or industry clients.","Workforce planning for statistical operations — lead by assessing team capacity, identifying skill gaps, and directing recruitment and training strategies to meet evolving analytical demands.","Innovation in statistical methodology — champion at the organizational level by evaluating advanced modeling techniques, piloting new analytical tools, and institutionalizing best practices across the field."]}}},"sources":{"onet":"v30.2 (CC BY 4.0)","crosswalk":"https://skillscrosswalk.com","generator":"LER.me"},"attribution":"© EBSCOed"}