Drexel Co-Op: Measurement Science Assistant

NBME offers a versatile selection of high-quality assessments and educational services for students, professionals, educators, regulators and institutions dedicated to the evolving needs of medical education and health care. To ensure our assessments meet the highest standards of quality, stay relevant and align to the current curriculum in medical schools and training programs, we rely on a wide network of collaborators. These include the volunteers who help develop our exam questions, the committees and panels who represent various groups within the medical education community, external researchers and health profession organizations. NBME views diversity, equity and inclusion (DEI) as foundational and enduring to our strategy and vision. We continue to focus on ensuring that our DEI work is impactful and ingrained in everything we do, including with our staff, culture, products and services, the Philadelphia community and the broader medical education landscape. Our commitment manifests in our hiring and staff development, recruitment for committees, grants programs, design and review of our assessments, and involvement in our local and national communities. Learn more about NBME at NBME.org. Co-ops must be within a commutable distance of university city during the duration of their co-op. Please note, this co-op assignment will begin on September 28, 2026. The Office of Research Strategy (ORS) at NBME is seeking a motivated Drexel University Co op student to support the Measurement Science team. This role provides hands on experience in educational and psychological measurement, applied data analysis, and research supporting automated scoring of constructed responses-particularly related to the Communication Learning Assessment (CLA).The Co op will work closely with measurement scientists and collaborators across data science and research teams, contributing to ongoing research, documentation, and reporting activities.Programs and Initiatives Supported:Communication Learning Assessment (CLA)Research related to automated scoring of open ended, constructed response dataStudies examining scoring quality, accuracy, and fairnessAutomated Scoring and Annotation ResearchSupport for analyses involving human annotations, model outputs, and response level dataThe Co-op student will support research and analytic activities, including:Data Preparation and CleaningClean and organize research datasets (e.g., constructed responses, annotations, scoring outputs)Identify and resolve data quality issues such as missing values, duplicates, or formatting inconsistenciesPrepare analytic datasets following established project protocolsDescriptive and Preliminary AnalysesConduct basic descriptive statistics (e.g., frequencies, distributions, summary statistics)Create tables and figures to support internal research discussionsPerform preliminary comparisons and exploratory analyses under guidance from measurement scientistsDocumentation and Research SupportDraft and maintain documentation such as data dictionaries, README files, and analysis notesContribute to project documentationAssist in organizing materials for reviewsReporting and CommunicationDevelop brief written summaries of analytic findings for internal audiencesSupport preparation of internal research reports, presentations, or technical appendicesClearly document analytic decisions and assumptions in a reproducible mannerRecommended Qualifications:Completion of at least one course involving statistical analysis or programming in R and/or PythonAbility to perform basic data cleaning, data validation, and descriptive statistical analysesFamiliarity with working with structured datasets (e.g., CSV, Excel)Ability to document analytic workflows clearly and accurately, including data dictionaries and analysis notesStrong attention to detail and commitment to data quality and reproducibilityAbility to communicate findings clearly in written summaries and tablesInterest in measurement, assessment, data science, or applied researchCoursework or experience in statistics, data science, psychology, education, public health, or a related quantitative fieldExperience working with:R and/or Python for data analysis (coursework, projects, or prior work experience)Pandas, tidyverse, or similar data analysis librariesReproducible analysis practices (e.g., well commented code, versioned files)Exposure to:Open ended or text based data (e.g., survey responses, written responses, annotations)Research or academic project environmentsAbility to work independently while also collaborating with a research team