Training course

Overview

Qualitative Data Analysis is a professional training course designed to equip researchers, analysts, programme professionals, academics, consultants, and decision-makers with practical skills for systematically analysing non-numerical research data and transforming rich qualitative evidence into credible findings. The course provides a comprehensive foundation in qualitative research methodology, data management, coding, categorisation, thematic analysis, interpretation, and reporting. Participants learn how to work effectively with interviews, focus group discussions, observations, open-ended survey responses, documents, case studies, field notes, and other forms of textual and multimedia qualitative data.

The training begins with the foundations of qualitative data analysis, including research questions, qualitative research designs, theoretical perspectives, sampling approaches, interview and focus group data, transcription, data preparation, anonymisation, metadata, and analytical documentation. Participants learn how to establish a structured qualitative data management workflow and apply systematic approaches to familiarisation, memo writing, coding, categorisation, and theme development. Practical tools such as NVivo, ATLAS.ti, MAXQDA, Dedoose, Microsoft Word, Excel, and structured manual coding techniques are introduced to support different research environments and analytical requirements.

Participants progressively develop advanced qualitative analytical capabilities through thematic analysis, framework analysis, content analysis, narrative analysis, discourse analysis, case study analysis, comparative analysis, and matrix-based approaches. The course examines how to identify patterns, relationships, contradictions, contextual differences, participant perspectives, and emerging explanations while maintaining a clear connection between raw data, codes, categories, themes, interpretations, and research questions. Practical exercises and real-world case studies cover areas such as programme evaluation, customer research, organisational studies, policy research, community development, employee experiences, service delivery, and market research.

The advanced component focuses on analytical rigour, trustworthiness, reflexivity, triangulation, researcher positionality, intercoder processes, negative-case analysis, member checking, audit trails, mixed-methods integration, qualitative data visualisation, and professional reporting. Participants learn how to distinguish description from interpretation, substantiate findings with appropriate evidence, manage analytical bias, document methodological decisions, and communicate nuanced qualitative insights to technical and non-technical audiences. Through practical exercises, software-based activities, case studies, and an integrated capstone, participants develop the ability to conduct a complete qualitative data analysis process and produce credible, transparent, and professionally presented research findings.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Qualitative researchers, research officers, and research assistants

• Monitoring, Evaluation, Research, and Learning (MERL) and Monitoring and Evaluation (M&E) professionals

• Programme, project, and development professionals conducting qualitative research

• Academics, lecturers, postgraduate researchers, and doctoral researchers

• Social science, public policy, development, and organisational researchers

• Market research, customer experience, and consumer insights professionals

• Human resources and organisational development professionals

• Consultants and advisory professionals conducting qualitative studies

• Community development and field research professionals

• Policy analysts and professionals evaluating programmes, services, or interventions

• Journalists, analysts, and professionals working with interviews, documents, and narrative evidence

• Researchers responsible for analysing focus groups, interviews, observations, and open-ended survey responses

• Professionals transitioning from quantitative or mixed-methods research into qualitative analysis

• Professionals seeking practical skills in NVivo, ATLAS.ti, MAXQDA, Dedoose, Excel, and manual qualitative coding

Course Objectives

By the end of the training, participants will be able to:

• Understand the principles, purposes, philosophical foundations, and analytical approaches used in qualitative research

• Translate qualitative research questions and objectives into appropriate analytical strategies

• Prepare, organise, anonymise, and manage interview, focus group, observation, document, and open-ended survey data

• Develop qualitative data management systems, codebooks, metadata structures, and analytical audit trails

• Apply systematic approaches to data familiarisation, initial coding, focused coding, categorisation, and theme development

• Conduct thematic, framework, content, narrative, discourse, case study, and comparative qualitative analysis

• Use NVivo, ATLAS.ti, MAXQDA, Dedoose, Excel, Word, and manual coding techniques for qualitative data analysis

• Identify patterns, relationships, differences, contradictions, explanations, and contextual meanings within qualitative datasets

• Develop credible themes and analytical interpretations that remain grounded in research data

• Apply memo writing, coding comparison, matrix analysis, charting, mapping, and other qualitative analytical techniques

• Assess qualitative research quality using credibility, dependability, confirmability, and transferability principles

• Apply triangulation, negative-case analysis, member checking, reflexivity, peer debriefing, and other quality assurance practices

• Recognise researcher positionality, reflexivity, interpretation bias, and other influences on qualitative analysis

• Integrate qualitative findings with quantitative evidence within mixed-methods research designs

• Develop professional qualitative findings, evidence summaries, analytical narratives, and research reports

• Present qualitative evidence using quotations, thematic structures, matrices, conceptual diagrams, and qualitative data visualisations

• Apply ethical principles relating to informed participation, confidentiality, anonymisation, sensitive information, and responsible data management

• Conduct an integrated qualitative data analysis project from raw research data through coding, interpretation, validation, and final reporting

Course Content

Day 1: Qualitative Research Foundations, Data Preparation, and Analytical Workflow

Module 1: Foundations of Qualitative Data Analysis and Research Data Management

1.      Foundations of Qualitative Research and the Role of Qualitative Data Analysis in Evidence Generation

2.      Qualitative Research Questions, Objectives, Conceptual Frameworks, and Analytical Alignment

3.      Qualitative Research Designs: Phenomenology, Ethnography, Grounded Theory, Case Study, Narrative, and Participatory Approaches

4.      Qualitative Sampling Strategies: Purposive, Maximum Variation, Theoretical, Snowball, Convenience, and Criterion Sampling

5.      Types of Qualitative Data: Interviews, Focus Groups, Observations, Documents, Field Notes, Open-Ended Surveys, and Multimedia

6.      Transcription, Translation, Anonymisation, Data Cleaning, File Organisation, and Qualitative Data Preparation

7.      Qualitative Data Management, Data Dictionaries, Metadata, Naming Conventions, Version Control, and Audit Trails

8.      Data Familiarisation, Repeated Reading, Listening, Observation Review, Analytical Notes, and Initial Reflections

9.      Practical Qualitative Analysis Tools: NVivo, ATLAS.ti, MAXQDA, Dedoose, Excel, Word, and Manual Coding Workflows

10.  Case Study and Exercise: Preparing and Organising an Interview, Focus Group, or Field Research Dataset for Systematic Analysis

Day 2: Coding, Categorisation, and Thematic Analysis

Module 2: Systematic Coding and Development of Qualitative Themes

1.      Principles of Qualitative Coding: Meaning Units, Segments, Codes, Labels, and Analytical Decisions

2.      Open Coding, Initial Coding, Descriptive Coding, In Vivo Coding, and Process Coding Techniques

3.      Focused Coding, Pattern Coding, Axial Coding, Categorisation, and Code Consolidation

4.      Developing a Qualitative Codebook, Code Definitions, Inclusion and Exclusion Criteria, and Coding Rules

5.      Deductive and Inductive Coding Approaches and Combining Theory-Driven and Data-Driven Analysis

6.      Thematic Analysis: Familiarisation, Coding, Theme Generation, Review, Definition, and Reporting

7.      Developing Themes, Subthemes, Relationships, Patterns, Contradictions, and Analytical Explanations

8.      Memo Writing, Reflexive Journaling, Analytical Questions, and Connecting Codes to Research Objectives

9.      Software-Based Coding, Querying, Retrieval, Coding Comparison, and Organising Large Qualitative Datasets

10.  Case Study and Exercise: Coding Interview or Focus Group Data and Developing an Evidence-Based Thematic Framework

Day 3: Advanced Qualitative Analytical Approaches and Interpretation

Module 3: Advanced Qualitative Analysis, Comparison, and Meaning-Making

1.      Framework Analysis for Structured Policy, Programme Evaluation, Health, Social Research, and Organisational Studies

2.      Qualitative Content Analysis: Manifest and Latent Content, Categories, Frequencies, and Interpretive Meaning

3.      Narrative Analysis: Stories, Chronology, Identity, Turning Points, Experiences, and Meaning Structures

4.      Discourse Analysis: Language, Context, Framing, Power, Social Meaning, and Communication Patterns

5.      Case Study Analysis: Within-Case Analysis, Cross-Case Comparison, Contextual Explanation, and Pattern Matching

6.      Comparative Qualitative Analysis Across Groups, Locations, Time Periods, Demographic Categories, or Stakeholder Types

7.      Matrix Analysis, Data Displays, Conceptual Mapping, Relationship Mapping, and Analytical Frameworks

8.      Negative Cases, Contradictory Evidence, Deviant Patterns, Alternative Explanations, and Analytical Refinement

9.      From Description to Interpretation: Developing Explanations, Linking Evidence to Theory, and Avoiding Unsupported Conclusions

10.  Case Study and Exercise: Conducting Cross-Case or Thematic Analysis to Identify Patterns, Differences, Relationships, and Explanations

Day 4: Qualitative Rigour, Validation, Reflexivity, and Mixed-Methods Integration

Module 4: Advanced Qualitative Quality Assurance and Evidence Validation

1.      Trustworthiness in Qualitative Research: Credibility, Dependability, Confirmability, and Transferability

2.      Triangulation Across Data Sources, Researchers, Methods, Perspectives, Locations, and Analytical Approaches

3.      Researcher Reflexivity, Positionality, Assumptions, Interpretation, and Managing Analytical Influence

4.      Intercoder Processes, Coding Consistency, Code Comparison, Peer Review, and Collaborative Qualitative Analysis

5.      Member Checking, Participant Validation, Peer Debriefing, Expert Review, and Stakeholder Validation

6.      Audit Trails, Analytical Documentation, Decision Logs, Version Control, and Transparent Research Practice

7.      Identifying Bias, Selective Interpretation, Confirmation Effects, Over-Generalisation, and Unsupported Qualitative Claims

8.      Integrating Qualitative and Quantitative Evidence in Mixed-Methods Research Designs and Analytical Frameworks

9.      Qualitative Data Visualisation, Concept Maps, Thematic Networks, Matrices, Evidence Displays, and Research Dashboards

10.  Case Study and Exercise: Validating Qualitative Findings Through Triangulation, Reflexivity, Peer Review, Negative-Case Analysis, and Evidence Mapping

Day 5: Professional Qualitative Reporting, Ethics, and Applied Capstone

Module 5: Professional Qualitative Research Practice, Reporting, and Capstone

1.      From Codes and Themes to Qualitative Findings, Interpretations, Conclusions, and Research Implications

2.      Selecting and Presenting Participant Quotations, Evidence Excerpts, Context, and Thematic Support

3.      Writing Qualitative Research Methodology, Analytical Procedures, Findings, Discussion, Conclusions, and Recommendations

4.      Developing Professional Qualitative Reports, Executive Summaries, Evidence Briefs, Presentations, and Stakeholder Outputs

5.      Qualitative Research Ethics: Informed Participation, Confidentiality, Anonymisation, Sensitive Data, and Participant Protection

6.      Data Security, Access Controls, Secure Storage, Responsible Data Sharing, Retention, and Qualitative Research Governance

7.      Reproducible Qualitative Research Workflows Using Structured Project Files, Codebooks, Memos, Audit Trails, Software Projects, and Documentation

8.      Integrated Case Study: Conducting an End-to-End Qualitative Analysis from Raw Interviews or Focus Groups to Validated Themes and Professional Findings

9.      Professional Exercise: Reviewing a Qualitative Analysis for Coding Quality, Analytical Rigour, Evidence Support, Bias, Transparency, and Research Credibility

10.  Capstone Exercise: Designing the Analytical Framework, Preparing Qualitative Data, Coding and Developing Themes, Validating Findings, Interpreting Evidence, and Presenting a Professional Qualitative Research Report