{"schemaVersion":"1.0","exportedAt":"2026-05-15T12:38:46.724Z","occupation":{"soc":"19-4061.00","title":"Social Science Research Assistants","group":"Life, Physical & Social Science","sector":"52","jobZone":4,"jobZoneInferred":false},"framework":{"version":"v.26.05","description":"","contextCovered":"This framework covers social science research environments — including universities, policy institutes, and government agencies — where research assistants design data systems, conduct statistical analyses, ensure data quality, and contribute to scholarly and policy-relevant research outputs.","levels":{"emerging":{"label":"Emerging","statements":["Data entry tasks — execute accurately and consistently under direct supervision in a structured research project environment.","Existing database records — verify for accuracy and flag apparent errors for review by a senior researcher in a university or policy research setting.","Statistical software packages — operate using provided scripts to run basic descriptive analyses on cleaned social science datasets.","Research results — compile into draft summary tables and graphs following established templates under close guidance from project leadership.","Project-related manuscripts and presentations — gather source materials and format reference sections in compliance with style guidelines specified by supervising researchers.","Structured data files — import, sort, and filter using spreadsheet software to support ongoing research project tasks.","Research protocols and procedural documentation — read and apply to assigned tasks, seeking clarification when ambiguities arise in a supervised team context.","Geographic information system tools — use at a basic level to locate and retrieve spatial datasets relevant to an assigned social science study.","Team meetings and research briefings — participate in attentively, taking notes and summarizing key points to support project documentation.","Quality control checklists — apply to completed data entry batches, identifying discrepancies and escalating unresolved issues to a supervising researcher."]},"developing":{"label":"Developing","statements":["Custom data entry and cleaning scripts — design and test using statistical or scripting software to automate repetitive processing tasks on mid-sized social science datasets.","Descriptive and basic multivariate statistical analyses — perform independently using software such as SPSS, Stata, or R, interpreting output in the context of a defined research question.","Database tables and relational structures — prepare, manipulate, and maintain to support longitudinal or multi-wave research projects with reduced oversight.","Fact sheets, written summaries, and visualizations — produce from analyzed data for inclusion in project reports, adapting presentation style to target audiences such as policymakers or academic reviewers.","Research quality control procedures — implement systematically across assigned project phases, documenting deviations and recommended corrective actions.","Manuscript drafts and conference presentations — contribute substantive sections to, incorporating correctly cited findings and coordinating revisions with co-authors.","Analytical or scientific software — configure and troubleshoot in routine research computing environments, resolving common errors without escalation.","Incoming datasets from multiple sources — assess for validity and consistency, applying established criteria to determine fitness for analysis in a multi-site study.","Research team members and external stakeholders — communicate findings clearly in both written and oral formats during project status updates and departmental meetings.","Data governance and file management conventions — apply consistently across shared project drives and database systems to ensure reproducibility and regulatory compliance."]},"proficient":{"label":"Proficient","statements":["Complex multivariate and inferential statistical analyses — design and execute autonomously, selecting appropriate methods based on research design and data characteristics in a professional research environment.","Purpose-built analytical programs — develop and document using object-oriented or scripting languages to handle non-routine data transformations and statistical modeling tasks.","Large-scale relational databases — architect, optimize, and manage end-to-end across multi-year research projects, ensuring data integrity and accessibility for interdisciplinary teams.","Comprehensive research quality control frameworks — develop and enforce across all project phases, diagnosing systemic data issues and implementing validated corrective procedures.","Peer-reviewed manuscripts and technical reports — lead the drafting and revision of, synthesizing quantitative findings with contextual interpretation suitable for academic and policy audiences.","Ambiguous or conflicting datasets — evaluate critically using inductive and deductive reasoning to resolve analytical challenges without supervisory input in fast-paced research contexts.","Geographic information system analyses — conduct independently to integrate spatial variables into social science research designs and enrich multivariate models.","Research presentations — deliver authoritatively to diverse audiences including academic conferences, funding agencies, and government clients, defending methodological choices under scrutiny.","Junior research staff — mentor in statistical methods, software use, and scientific writing, providing structured feedback that accelerates professional development.","Cross-functional project timelines and data workflows — manage and coordinate across team members and institutional partners to ensure on-schedule delivery of research outputs."]},"advanced":{"label":"Advanced","statements":["Organizational research strategy and methodology standards — define and champion across a division or institute, aligning social science projects with institutional priorities and funder requirements.","Enterprise-level data infrastructure and quality assurance systems — design and oversee, establishing policies that govern data collection, storage, and validation across multiple concurrent research programs.","Research program portfolios — lead from conception through dissemination, making high-stakes methodological and resource allocation decisions that shape the direction of the organization's scientific output.","Statistical and analytical innovation initiatives — spearhead by evaluating and integrating emerging technologies and methods into the research pipeline, positioning the organization at the cutting edge of quantitative social science.","Senior scientists, policy clients, and funding bodies — communicate and negotiate with at the executive level, translating complex research findings into strategic recommendations that influence policy or practice.","Research staff competency development programs — architect and implement organization-wide, incorporating learning strategies and performance benchmarks that build sustained institutional capacity.","Interdisciplinary research collaborations — initiate and govern across academic institutions, government agencies, and private sector partners, establishing data-sharing agreements and co-authorship frameworks.","Ethics, integrity, and scientific rigor standards — model and institutionalize across all research activities, reviewing protocols and publications to ensure compliance with professional and regulatory requirements.","Grant applications and large-scale funding proposals — lead the development of, authoring methodology and capacity sections that secure multi-year resources for the research enterprise.","Organizational knowledge management systems — design and steward, ensuring that institutional expertise in data analytics, database management, and research methods is documented, accessible, and continuously improved."]}}},"sources":{"onet":"v30.2 (CC BY 4.0)","crosswalk":"https://skillscrosswalk.com","generator":"LER.me"},"attribution":"© EBSCOed"}