Training course

Overview

Practical Mixed Methods Data Analysis is a hands-on professional training course designed for researchers, data analysts, monitoring and evaluation practitioners, programme professionals, consultants, academics, and other evidence-focused professionals who need to analyse and integrate quantitative and qualitative data in real-world settings. The course emphasises learning by doing, enabling participants to work through the complete mixed methods data analysis process from defining research questions and organising datasets to analysing findings, integrating evidence, interpreting results, and producing professional outputs. Practical exercises, realistic datasets, case studies, templates, analytical worksheets, and workplace scenarios are incorporated throughout the programme to build immediately applicable analytical capability.

The course provides intensive practical experience with both quantitative and qualitative analysis techniques. Participants work with descriptive statistics, data cleaning, group comparisons, correlation, basic regression interpretation, trends, and exploratory analysis while simultaneously developing practical skills in qualitative data preparation, coding, categorisation, thematic analysis, framework analysis, content analysis, comparative analysis, narrative analysis, and case analysis. Tools such as Excel, SPSS, Stata, R, Word, Power BI, NVivo, ATLAS.ti, MAXQDA, and Dedoose are considered according to the analytical task and participant requirements. The emphasis is on selecting appropriate techniques, executing analyses systematically, checking results, documenting decisions, and interpreting findings in their practical context.

A major practical focus of Practical Mixed Methods Data Analysis is learning how to bring different forms of evidence together rather than analysing quantitative and qualitative data as disconnected streams. Participants practise using convergent, explanatory sequential, exploratory sequential, and embedded approaches and apply joint displays, evidence matrices, comparison tables, case-ordered displays, data transformation, and narrative integration. Through hands-on case studies involving programme evaluation, customer experience, employee feedback, service delivery, organisational performance, public-sector initiatives, and community research, participants learn to identify convergence, complementarity, divergence, contradiction, and explanatory relationships and transform these findings into useful evidence.

The course progressively introduces advanced practical techniques for validating integrated findings and communicating results responsibly. Participants perform triangulation, investigate negative cases and contradictory evidence, assess missing data and sampling limitations, conduct sensitivity and robustness checks, maintain analytical audit trails, and document reproducible workflows. Practical reporting exercises develop the ability to create professional tables, charts, dashboards, joint displays, evidence summaries, and analytical reports while clearly communicating limitations and uncertainty. The programme culminates in an applied capstone in which participants complete an end-to-end mixed methods analysis using a realistic dataset and produce an integrated evidence-based output suitable for professional or research use.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Researchers and research officers seeking hands-on mixed methods analysis skills

• Data analysts working with quantitative and qualitative datasets

• Monitoring, evaluation, research, and learning professionals

• Programme and project professionals conducting practical evaluations

• Consultants performing research, organisational, market, or social analysis

• Academics and postgraduate researchers undertaking mixed methods studies

• Policy and development practitioners working with multiple forms of evidence

• Business and management analysts supporting evidence-based decisions

• Professionals responsible for analysing surveys, interviews, focus groups, observations, and documents

• Practitioners who want to develop a complete, practical mixed methods analysis workflow

Course Objectives

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

• Explain the practical principles and workflow of mixed methods data analysis

• Convert research and evaluation questions into a practical mixed methods analytical plan

• Select appropriate mixed methods designs and integration strategies

• Prepare, clean, organise, label, and document quantitative and qualitative datasets

• Perform practical descriptive, comparative, exploratory, and basic statistical analyses

• Prepare qualitative data and develop structured codebooks for systematic coding

• Apply inductive, deductive, and hybrid coding techniques

• Conduct thematic, framework, content, comparative, narrative, and case analysis

• Use appropriate software and practical templates to support mixed methods analysis

• Integrate quantitative and qualitative findings through joint displays and evidence matrices

• Identify convergence, complementarity, divergence, contradiction, and explanatory relationships

• Apply data transformation and structured methods for integrating different evidence types

• Develop integrated interpretations and defensible mixed methods conclusions

• Conduct triangulation, negative-case analysis, sensitivity checks, and robustness assessments

• Identify missing data, sampling limitations, measurement issues, and other analytical constraints

• Maintain clear analytical documentation, audit trails, and reproducible workflows

• Create professional charts, tables, dashboards, joint displays, and evidence summaries

• Communicate findings, limitations, uncertainty, and implications clearly

• Complete an end-to-end mixed methods analysis through an applied professional capstone

Course Content

Day 1: Practical Mixed Methods Foundations, Data Preparation, and Exploratory Analysis

Module 1: Hands-On Mixed Methods Data Preparation and Analytical Workflow

1.      Practical Foundations and Workflow of Mixed Methods Data Analysis

2.      Defining Research Questions, Objectives, Variables, Concepts, and Evidence Requirements

3.      Selecting Practical Mixed Methods Designs for Real-World Research and Evaluation

4.      Developing Conceptual Frameworks, Analytical Frameworks, and Data Integration Plans

5.      Quantitative Data Import, Structure, Cleaning, Coding, and Quality Checking

6.      Qualitative Data Collection Files, Transcription, Anonymisation, and Preparation

7.      Organising Metadata, Data Dictionaries, Codebooks, and Analytical Documentation

8.      Exploratory Data Analysis Using Tables, Frequencies, Descriptive Statistics, and Visualisations

9.      Practical Tools: Excel, SPSS, Stata, R, Word, and Structured Analysis Templates

10.  Hands-On Exercise: Preparing and Exploring a Mixed Methods Dataset

Day 2: Practical Quantitative and Qualitative Data Analysis

Module 2: Hands-On Quantitative and Qualitative Analysis Techniques

1.      Practical Descriptive Statistics and Exploratory Quantitative Analysis

2.      Group Comparisons, Cross-Tabulations, Trends, and Subgroup Analysis

3.      Correlation, Basic Regression Interpretation, and Relationship Analysis

4.      Identifying Patterns, Outliers, Missing Values, and Data Quality Problems

5.      Practical Qualitative Coding and Development of a Working Codebook

6.      Inductive, Deductive, and Hybrid Coding Through Hands-On Exercises

7.      Practical Thematic, Framework, and Content Analysis

8.      Comparative, Narrative, and Case Analysis Using Real-World Qualitative Evidence

9.      Practical Software Workflows: NVivo, ATLAS.ti, MAXQDA, Dedoose, and Manual Coding

10.  Case Study Exercise: Analysing Quantitative and Qualitative Data as Separate Evidence Streams

Day 3: Practical Integration, Joint Displays, and Interpretation

Module 3: Hands-On Mixed Methods Integration and Evidence Synthesis

1.      Practical Principles for Integrating Quantitative and Qualitative Findings

2.      Identifying Convergence, Complementarity, Divergence, Contradiction, and Expansion

3.      Building Joint Displays from Quantitative and Qualitative Results

4.      Developing Evidence Matrices and Structured Comparison Tables

5.      Creating Case-Ordered Displays Across Groups, Locations, Cases, or Time Periods

6.      Side-by-Side Comparison and Narrative Weaving of Integrated Findings

7.      Practical Data Transformation and Quantification of Qualitative Categories

8.      Cross-Case, Subgroup, and Longitudinal Integration of Mixed Methods Evidence

9.      Developing Integrated Interpretations, Explanatory Patterns, and Meta-Inferences

10.  Hands-On Exercise: Building a Complete Joint Display and Integrated Findings Matrix

Day 4: Advanced Practical Validation, Robustness, and Evidence Quality

Module 4: Advanced Hands-On Mixed Methods Validation and Quality Assurance

1.      Practical Assessment of Validity, Reliability, Credibility, and Trustworthiness

2.      Triangulation Across Methods, Data Sources, Participants, Cases, and Researchers

3.      Identifying Negative Cases, Contradictions, Outliers, and Unexpected Findings

4.      Practical Assessment of Missing Data, Nonresponse, Sampling, and Measurement Limitations

5.      Distinguishing Association, Explanation, and Causal Claims During Analysis

6.      Testing Alternative Explanations and Contextual Influences

7.      Conducting Sensitivity Analysis and Practical Robustness Checks

8.      Maintaining Audit Trails, Analytical Memos, Version Control, and Reproducible Workflows

9.      Ethics, Confidentiality, Data Governance, and Responsible Mixed Methods Analysis

10.  Applied Exercise: Quality-Assuring and Critically Reviewing an Integrated Mixed Methods Analysis

Day 5: Practical Reporting, Evidence Communication, and Capstone

Module 5: Professional Mixed Methods Reporting and Applied Capstone

1.      Structuring a Professional Mixed Methods Analysis Report

2.      Presenting Quantitative, Qualitative, and Integrated Findings Clearly

3.      Creating Professional Tables, Charts, Dashboards, and Joint Displays

4.      Writing Integrated Findings, Interpretations, Conclusions, and Recommendations

5.      Communicating Limitations, Uncertainty, Contradictions, and Evidence Strength

6.      Developing Evidence Briefs, Executive Summaries, Presentations, and Decision-Support Outputs

7.      Translating Integrated Findings into Practical Research, Programme, Policy, or Organisational Implications

8.      Reviewing Analytical Outputs for Accuracy, Coherence, Transparency, and Reproducibility

9.      Best Practices for Final Data Documentation, Handover, Archiving, and Professional Presentation

10.  Capstone Exercise: Complete an End-to-End Mixed Methods Analysis, Produce an Integrated Report, and Present the Findings