Training course

Overview

Practical Qualitative Data Analysis is a hands-on professional training course designed to equip researchers, analysts, programme practitioners, consultants, and other evidence professionals with practical skills for transforming qualitative information into credible, useful, and actionable findings. The course focuses on the complete qualitative data analysis workflow, from defining a research or evaluation problem and preparing raw data to coding, categorising, developing themes, interpreting findings, validating evidence, and producing professional analytical outputs. Participants will work with realistic examples involving interviews, focus group discussions, observations, open-ended questionnaires, field notes, documents, stakeholder feedback, case studies, and organisational records.

The programme places strong emphasis on learning by doing, enabling participants to practise qualitative data preparation, data management, coding, thematic analysis, framework analysis, content analysis, comparative analysis, case analysis, and narrative approaches. Participants will learn how to develop codebooks, coding protocols, analytical matrices, research memos, evidence tables, and audit trails while working with both manual and computer-assisted approaches. Practical tools including Microsoft Word, Excel, NVivo, ATLAS.ti, MAXQDA, Dedoose, and structured manual coding techniques will be applied to demonstrate how qualitative analysis can be organised efficiently for different datasets, research designs, and professional environments.

The course progressively develops practical analytical skills through exercises, case studies, and real-world scenarios covering customer experience, employee engagement, programme evaluation, service delivery, organisational change, market research, community research, policy analysis, operational performance, and stakeholder engagement. Participants will learn how to identify meaningful patterns, relationships, differences, contradictions, and emerging themes while distinguishing evidence from assumptions and interpretation. Particular attention is given to analytical decision-making, including when to use inductive or deductive coding, how to refine codes, how to compare cases or groups, and how to connect qualitative findings with research questions and practical decisions.

Advanced practical sessions focus on research rigour, validation, triangulation, reflexivity, negative-case analysis, peer review, mixed-methods integration, reproducibility, and responsible qualitative research practice. Participants will learn how to identify bias, assess evidence quality, document analytical decisions, manage sensitive information, and communicate findings through professional reports, thematic summaries, evidence matrices, presentations, and visual displays. The programme concludes with an applied capstone in which participants conduct an end-to-end qualitative analysis using a realistic dataset, validate their findings, develop evidence-based conclusions, and prepare a professional analytical output.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Research officers, research assistants, qualitative researchers, and research analysts

• Monitoring, evaluation, research, and learning (MERL/M&E) professionals

• Programme, project, and development practitioners working with qualitative evidence

• Market research, customer experience, and consumer insights professionals

• Social science, economic, policy, organisational, and community researchers

• Academics, postgraduate students, doctoral researchers, and research assistants

• Human resources, organisational development, employee experience, and people analytics professionals

• Operations, service delivery, quality improvement, and performance professionals

• Consultants and advisory professionals conducting research, evaluations, assessments, and organisational studies

• Policy, planning, governance, and institutional research professionals

• Community and field-based researchers working with interviews, observations, and focus groups

• Professionals analysing employee, customer, stakeholder, beneficiary, or community feedback

• Quantitative analysts and data professionals transitioning into qualitative or mixed-methods analysis

• Research managers and professionals responsible for reviewing or supervising qualitative analysis

• Professionals seeking practical experience with manual and computer-assisted qualitative data analysis tools

Course Objectives

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

• Explain the principles, purposes, and practical applications of qualitative data analysis

• Translate research, evaluation, organisational, programme, or business problems into qualitative analytical questions and objectives

• Select appropriate qualitative data sources, research designs, sampling approaches, and analytical strategies

• Prepare, organise, anonymise, document, and manage qualitative datasets systematically

• Develop practical codebooks, coding protocols, analytical frameworks, metadata structures, and audit trails

• Apply descriptive, in vivo, process, focused, inductive, and deductive coding approaches

• Conduct thematic, framework, content, comparative, narrative, and case-based qualitative analysis

• Identify patterns, relationships, differences, contradictions, exceptions, and emerging themes within qualitative datasets

• Use analytical matrices, evidence tables, theme maps, research memos, and structured displays to organise findings

• Apply manual and computer-assisted analysis using Word, Excel, NVivo, ATLAS.ti, MAXQDA, and Dedoose

• Distinguish description, interpretation, inference, assumption, and causal claims when analysing qualitative evidence

• Conduct within-case, cross-case, subgroup, and comparative analysis using practical analytical techniques

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

• Identify common sources of sampling, response, researcher, interviewer, coding, and interpretation bias

• Integrate qualitative findings with quantitative data and other evidence in mixed-methods research

• Develop professional qualitative findings reports, thematic summaries, evidence matrices, presentations, and visualisations

• Apply ethical principles relating to informed participation, confidentiality, privacy, anonymisation, data security, and responsible data use

• Establish reproducible and auditable qualitative analysis workflows

• Complete an end-to-end practical qualitative analysis and communicate evidence-based findings professionally

Course Content

Day 1: Practical Qualitative Data Foundations, Preparation, and Exploratory Analysis

Module 1: Practical Qualitative Research and Data Preparation Workflow

1.      Foundations of Practical Qualitative Data Analysis and the Complete Analytical Workflow

2.      Translating Research, Evaluation, Business, Programme, and Organisational Problems into Analytical Questions

3.      Qualitative Data Sources: Interviews, Focus Groups, Observations, Open-Ended Surveys, Documents, and Field Notes

4.      Qualitative Research Designs, Purposeful Sampling, Participant Selection, and Data Collection Considerations

5.      Preparing Raw Qualitative Data: Transcription, Cleaning, Formatting, Anonymisation, and Quality Checks

6.      Organising Qualitative Datasets, File Structures, Metadata, Data Dictionaries, and Research Documentation

7.      Data Familiarisation, Repeated Reading, Initial Summaries, Analytical Memos, and Researcher Notes

8.      Developing Initial Analytical Frameworks, Research Questions, Codebook Structures, and Coding Plans

9.      Practical Tools for Qualitative Data Management: Word, Excel, NVivo, ATLAS.ti, MAXQDA, Dedoose, and Manual Workflows

10.  Case Study and Exercise: Preparing, Organising, and Exploring a Realistic Interview, Focus Group, or Open-Ended Survey Dataset

Day 2: Practical Coding, Categorisation, and Thematic Analysis

Module 2: Hands-On Coding and Theme Development

1.      Practical Qualitative Coding Principles and Selecting an Appropriate Coding Strategy

2.      Descriptive, In Vivo, Process, Focused, Inductive, and Deductive Coding in Real-World Datasets

3.      Developing Code Definitions, Inclusion and Exclusion Criteria, Coding Rules, and Practical Codebooks

4.      Applying Initial Coding to Interviews, Focus Groups, Observations, Documents, and Open-Ended Responses

5.      Reviewing, Refining, Merging, Splitting, and Reorganising Codes During Analysis

6.      Categorisation and Moving from Individual Codes to Analytical Categories and Meaningful Patterns

7.      Thematic Analysis: Familiarisation, Coding, Theme Development, Theme Review, Definition, and Interpretation

8.      Framework Analysis for Programme Evaluation, Organisational Research, Customer Experience, and Policy Studies

9.      Content Analysis for Complaints, Feedback, Documents, Incident Reports, Policies, and Open-Ended Responses

10.  Case Study and Exercise: Coding a Realistic Qualitative Dataset and Developing a Structured Set of Themes

Day 3: Applied Qualitative Analysis, Comparison, and Interpretation

Module 3: Practical Analytical Techniques and Evidence-Based Insight

1.      Identifying Recurring Patterns, Relationships, Differences, Contradictions, Exceptions, and Emerging Issues

2.      Developing Analytical Memos and Connecting Codes and Themes to Research Questions and Objectives

3.      Within-Case Analysis for Individual Participants, Organisations, Projects, Communities, Customers, or Programmes

4.      Cross-Case and Comparative Analysis Across Groups, Locations, Departments, Stakeholders, or Participant Categories

5.      Narrative Analysis for Understanding Experiences, Events, Processes, and Participant Perspectives

6.      Case Analysis for Complex Organisational, Programme, Service Delivery, Market, and Community Problems

7.      Developing Analytical Matrices, Theme-by-Group Tables, Evidence Maps, Conceptual Displays, and Comparison Frameworks

8.      Connecting Qualitative Themes to Outcomes, Processes, Contextual Factors, Stakeholder Experiences, and Practical Decisions

9.      Distinguishing Description, Interpretation, Inference, Assumption, and Causal Explanation in Qualitative Findings

10.  Case Study and Exercise: Conducting Comparative Analysis and Developing Evidence-Based Findings from Multi-Group Qualitative Data

Day 4: Advanced Practical Qualitative Analysis, Validation, and Mixed-Methods Integration

Module 4: Qualitative Rigour, Evidence Validation, and Analytical Robustness

1.      Qualitative Trustworthiness: Credibility, Dependability, Confirmability, Transferability, and Practical Quality Standards

2.      Triangulation Across Interviews, Focus Groups, Observations, Documents, Surveys, Administrative Data, and Other Evidence

3.      Negative-Case Analysis, Contradictory Evidence, Exceptions, and Testing Alternative Interpretations

4.      Reflexivity, Researcher Positionality, Analytical Assumptions, and Managing Interpretation Bias

5.      Peer Review, Intercoder Discussion, Coding Consistency, Analytical Challenge, and Quality Assurance

6.      Member Checking, Stakeholder Validation, Participant Feedback, and Verification of Qualitative Findings

7.      Audit Trails, Analytical Memos, Coding Decisions, Version Control, and Transparent Documentation of the Research Process

8.      Missing Perspectives, Sampling Limitations, Nonresponse, Contextual Constraints, and Assessing Evidence Boundaries

9.      Integrating Qualitative and Quantitative Evidence Through Mixed-Methods Analytical Frameworks and Joint Displays

10.  Case Study and Exercise: Validating Qualitative Findings Through Triangulation, Negative Cases, Peer Review, and Mixed-Methods Evidence

Day 5: Practical Qualitative Reporting, Reproducibility, Ethics, and Capstone

Module 5: Professional Qualitative Analysis Delivery and Applied Capstone

1.      From Codes and Themes to Findings, Conclusions, Interpretations, and Evidence-Based Recommendations

2.      Developing Professional Qualitative Findings Tables, Evidence Matrices, Theme Summaries, and Analytical Displays

3.      Writing Qualitative Research Reports: Methodology, Findings, Interpretation, Limitations, Conclusions, and Recommendations

4.      Creating Clear Qualitative Presentations, Visualisations, Conceptual Maps, Theme Networks, and Stakeholder Briefings

5.      Translating Qualitative Findings into Programme Improvements, Organisational Actions, Customer Solutions, and Practical Recommendations

6.      Research Ethics, Informed Participation, Confidentiality, Privacy, Anonymisation, Sensitive Data, and Responsible Data Use

7.      Qualitative Data Governance, Secure Storage, Access Controls, Data Retention, Documentation, and Responsible Evidence Management

8.      Reproducible Qualitative Workflows Using Codebooks, Analytical Memos, Audit Trails, Version Control, Structured File Systems, and Documentation

9.      Integrated Case Study: Conducting an End-to-End Qualitative Analysis and Preparing a Professional Research or Evaluation Report

10.  Capstone Exercise: Defining the Analytical Problem, Preparing the Dataset, Coding Evidence, Developing Themes, Validating Findings, Producing the Final Analysis, and Presenting Evidence-Based Conclusions