Health, Public Health & Healthcare Management · Physical, Online
Health Research Data Management & Analysis Training Workshop/ Seminar
10 Days4 sessions available Certificate on completion
The Health Research Data Management & Analysis Training Workshop equips health researchers, public health professionals, clinical research teams and monitoring and evaluation specialists with practical skills for managing, processing, analysing and reporting health research data. The training covers research data collection, database design, data entry and validation, data cleaning, coding, data security, descriptive and inferential statistics, questionnaire analysis, qualitative data management and research reporting. Participants learn to develop reliable data management workflows, maintain data quality, analyse quantitative and qualitative findings, interpret research results and prepare evidence-based reports. The workshop emphasises data confidentiality, ethical research practices, reproducibility and the responsible use of research data to strengthen health studies, programme evaluation and public health decision-making.
The Health Research Data Management & Analysis Training Workshop provides practical training in managing, cleaning, analysing and reporting health research data. Participants learn to develop data collection tools, design databases, validate datasets, conduct statistical analysis, manage qualitative research data and interpret findings for evidence-based decision-making. The course covers research data quality assurance, confidentiality, quantitative and qualitative analysis, clinical research data management and research reporting using standard software. Designed for health researchers, epidemiologists, clinical research coordinators, public health professionals and data managers, the workshop strengthens research integrity, analytical capacity and the production of reliable evidence for healthcare and public health programmes.
Course objectives
Design effective health research data management systems aligned with study objectives and research protocols.
Produce well-documented, reproducible research datasets and analytical outputs suitable for publication and evidence-based decision-making.
Interpret quantitative and qualitative findings and communicate results through tables, charts and research reports.
Apply appropriate data protection, confidentiality, access control and ethical research practices.
Manage and analyse qualitative research data, including interview transcripts and focus group discussions.
Conduct descriptive and inferential statistical analyses to answer health research questions.
Apply data entry, validation, cleaning, coding and quality assurance procedures to research datasets.
Develop data collection tools, codebooks, data dictionaries and database structures for health studies.
Who should attend
Health researchers, public health officers, epidemiologists, clinical research coordinators, research assistants, data managers, biostatisticians, medical researchers, monitoring and evaluation specialists, NGO research teams, healthcare programme managers, university lecturers, postgraduate students, health information officers, survey coordinators, research consultants and professionals working on clinical trials, health surveys and population health studies.
Course content
Module 1: Introduction to Health Research Data Management Principles of research data management, quantitative and qualitative health data, the research data lifecycle, data management plans, research protocols and analytical requirements, roles and respo
Module 2: Health Research Design and Data Requirements Research questions and hypotheses, quantitative and qualitative study designs, cross-sectional studies, cohort studies, case-control studies, clinical trials, mixed-methods research, outcome and expos
Module 3: Data Collection Tools and Instrument Development Structured questionnaires, interview guides, focus group discussion guides, observation checklists, clinical assessment forms, electronic data collection tools, question sequencing, variable defin
Module 4: Database Design and Research Data Organisation Database structure and principles, relational databases, tables and records, primary and foreign keys, unique participant identifiers, data dictionaries, codebooks, variable naming conventions, data
Module 5: Data Entry, Validation and Quality Assurance Manual and electronic data entry, validation rules, range checks, consistency checks, duplicate detection, missing data identification, skip logic, cross-field validation, double data entry, discrepan
Module 6: Data Cleaning and Preprocessing Identifying and correcting errors, standardising formats, handling inconsistent categories, managing missing values, detecting outliers, checking duplicate records, recoding variables, preparing derived variables,
Module 7: Health Research Data Security, Ethics and Confidentiality Research ethics principles, informed consent, confidentiality, data minimisation, de-identification and pseudonymisation, secure data storage, access permissions, encryption, data sharing
Module 8: Quantitative Data Management and Statistical Analysis Preparing quantitative datasets, descriptive statistics, frequencies and percentages, measures of central tendency and variability, cross-tabulation, comparing groups, confidence intervals, h
Module 9: Advanced Statistical Analysis for Health Research Correlation analysis, linear regression, logistic regression, confounding and interaction, analysis of variance, non-parametric tests, survival analysis concepts, repeated-measures data, multivar
Module 10: Qualitative Research Data Management and Analysis Transcription and translation workflows, organising interview and focus group data, anonymising transcripts, coding frameworks, deductive and inductive coding, thematic analysis, content analysi
Module 11: Mixed-Methods Research Data Integration Principles of mixed-methods research, integration of quantitative and qualitative datasets, triangulation, connecting survey findings with interviews, comparing convergent and divergent results, joint dis
Module 12: Survey Data Management and Analysis Survey design considerations, sampling frames, sample size and response rates, questionnaire coding, survey weights, stratified and cluster sampling, non-response analysis, data completeness, population estim
Module 13: Clinical Research and Trial Data Management Clinical research data workflows, participant screening and enrolment records, case report forms, participant identification, visit tracking, adverse event data handling, protocol deviations, data que
Module 14: Research Data Visualisation and Interpretation Tables and charts for research findings, distribution plots, trend charts, comparison charts, visualising categorical and continuous variables, presenting subgroup findings, selecting appropriate v
Module 15: Research Data Management Using Standard Software Managing datasets using Excel, organising data in SQL-based database systems, conducting statistical analysis with SPSS, Stata or R, managing qualitative research using NVivo or comparable tools,
Module 16: Research Reporting, Reproducibility and Data Archiving Preparing research methods and results sections, reporting statistical findings, documenting data cleaning and analysis decisions, maintaining research files, reproducible analytical workfl
Module 17: Research Data Quality Audits and Compliance Developing data quality assessment plans, completeness and accuracy checks, consistency reviews, source document verification, audit trails, data discrepancy logs, corrective action plans, monitoring
Module 18: Capstone Project and Practical Implementation Developing a research data management plan, designing a data dictionary, preparing a data collection tool, importing and cleaning a sample health research dataset, conducting quantitative or qualita
General notes
Health Research Data Analysis Course, Medical Research Data Management, Public Health Data Analysis Training, Clinical Research Data Management, Statistical Analysis for Health Research, Research Data Quality Assurance, Qualitative Data Analysis Training, Health Research Methods, Clinical Trial Data Management, Public Health Research Training.
. Practical Exercises and Expected Outputs Data management planning exercise — research data management plan, questionnaire development exercise — structured data collection tool, database design exercise — database structure and data dictionary, data validation exercise — validation rules and data quality checklist, data cleaning exercise — cleaned and documented research dataset, quantitative analysis exercise — statistical summary and interpretation report, regression exercise — model output and adjusted effect estimates, qualitative analysis exercise — coding framework and thematic analysis report, mixed-methods exercise — integrated findings matrix, survey analysis exercise — population estimates and subgroup comparison report, clinical research exercise — participant tracking and discrepancy management log, data security exercise — access control and data protection checklist, research reporting exercise — formatted results tables and figures, capstone project — documented dataset, analysis outputs and final health research report.
Frequently asked questions
Basic familiarity with research methods, spreadsheets and descriptive statistics is helpful. The course introduces foundational concepts before progressing to more advanced analytical techniques.
Yes. The workshop covers statistical analysis of numerical data, thematic analysis of interviews and focus groups, and integration of findings in mixed-methods research.
Depending on the training arrangement, participants may use Excel, KoboToolbox, SPSS, Stata, R, NVivo, Power BI and SQL-based database systems.
Yes. Dedicated content addresses participant tracking, case report forms, data queries, protocol deviations, data integrity and audit readiness in clinical research.
Yes. The training covers informed consent, de-identification, secure storage, access controls, confidentiality, data sharing and responsible research data management.
Yes. Participants will develop a research data management workflow, prepare a dataset, conduct relevant analyses and produce a report with interpreted findings.