Training course

Overview

Advanced Mixed Methods Data Analysis is an advanced professional training course designed for experienced researchers, quantitative and qualitative analysts, monitoring and evaluation specialists, academics, programme and policy professionals, consultants, and evidence leaders who need to design, analyse, integrate, and interpret complex mixed methods evidence. The course develops advanced capabilities for combining statistical and qualitative analysis in a methodologically coherent way, enabling participants to address complex research, evaluation, organisational, policy, market, and development questions that cannot be adequately answered through a single analytical approach. Emphasis is placed on rigorous integration, advanced inference, methodological alignment, and production of defensible evidence for high-stakes professional and research settings.

The programme examines advanced mixed methods research designs, including convergent, explanatory sequential, exploratory sequential, embedded, transformative, multiphase, and complex intervention designs, with particular attention to how timing, priority, sequencing, sampling, and integration affect analytical quality. Participants will develop advanced analytical plans covering research questions, conceptual and theoretical frameworks, sampling architectures, measurement, data preparation, quantitative modelling, qualitative coding, integration points, and meta-inference. Quantitative techniques include advanced regression, generalized linear models, interaction and heterogeneous-effects analysis, longitudinal and panel approaches, causal inference concepts, missing-data strategies, and robustness analysis, while qualitative techniques include advanced thematic, framework, comparative, case, narrative, and cross-case analysis.

Advanced integration techniques form the core of the training. Participants will learn to construct and interpret joint displays, data matrices, case-ordered displays, matrix displays, weaving and contiguous approaches, data transformation procedures, integrated typologies, and meta-inference frameworks. The course addresses convergence, complementarity, expansion, discordance, contradiction, and explanatory relationships between quantitative and qualitative findings. Practical applications include programme and policy evaluation, organisational transformation, customer and market research, public services, health and development programmes, employee and stakeholder research, implementation studies, and complex operational assessments. Participants will use practical analytical environments and tools such as R, Python, SPSS, Stata, Excel, NVivo, ATLAS.ti, MAXQDA, Dedoose, and structured integration matrices.

The final stage focuses on advanced mixed methods validity, inference quality, triangulation, methodological coherence, integration quality, causal interpretation, sensitivity analysis, robustness, missing data, complex sampling, ethical governance, reproducibility, and transparent reporting. Participants will learn how to assess whether integrated conclusions are supported by the evidence, investigate contradictory results, evaluate alternative explanations, and communicate uncertainty without weakening analytical usefulness. The course culminates in an advanced capstone in which participants design an integrated mixed methods study, analyse quantitative and qualitative evidence, construct advanced joint displays, investigate convergence and divergence, develop defensible meta-inferences, conduct robustness and validity assessments, and produce a professional mixed methods research or evaluation report.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Experienced quantitative and qualitative researchers and senior research analysts

• Advanced monitoring, evaluation, research, and learning (MERL/M&E) specialists

• Senior programme, project, development, and policy research professionals

• Academic researchers, postgraduate researchers, doctoral candidates, and research supervisors

• Senior market research, customer insights, consumer research, and business intelligence professionals

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

• Research and evaluation consultants conducting complex or multi-phase studies

• Senior data analysts and statisticians working with integrated evidence

• Programme evaluation, impact assessment, implementation research, and learning specialists

• Public-sector and institutional research, policy, and performance professionals

• Human resources, organisational development, workforce research, and employee experience specialists

• Strategy, transformation, operations, and performance improvement professionals using mixed evidence

• Research managers and technical leads responsible for designing, commissioning, reviewing, or governing mixed methods studies

• Professionals integrating survey, administrative, experimental, observational, interview, focus group, documentary, and longitudinal evidence

• Experienced researchers transitioning from single-method designs to advanced mixed methods research

Course Objectives

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

• Explain advanced mixed methods research principles, philosophical foundations, methodological assumptions, strengths, limitations, and applications

• Design sophisticated mixed methods studies aligned with complex research, evaluation, policy, programme, organisational, and strategic questions

• Select and justify convergent, explanatory sequential, exploratory sequential, embedded, transformative, multiphase, and other advanced mixed methods designs

• Develop advanced conceptual frameworks, theoretical frameworks, research questions, analytical models, sampling architectures, and integration plans

• Design quantitative and qualitative data structures that support systematic integration and meta-inference

• Apply advanced quantitative analytical techniques including regression, generalized linear models, interaction analysis, subgroup analysis, longitudinal approaches, causal inference concepts, and robustness testing

• Apply advanced qualitative approaches including thematic, framework, content, comparative, narrative, case, cross-case, and contextual analysis

• Develop sophisticated codebooks, data dictionaries, analytical matrices, evidence maps, research memos, and audit trails

• Determine appropriate points of integration across research design, sampling, data collection, analysis, interpretation, and reporting

• Construct and interpret joint displays, matrix displays, case-ordered displays, integrated typologies, and other mixed methods integration tools

• Analyse convergence, complementarity, expansion, discordance, contradiction, and explanatory relationships between quantitative and qualitative findings

• Apply data transformation techniques for integrating qualitative and quantitative evidence where methodologically appropriate

• Conduct cross-case, subgroup, longitudinal, multilevel, and comparative mixed methods integration

• Develop and evaluate meta-inferences from integrated quantitative and qualitative evidence

• Apply triangulation, negative-case analysis, alternative explanation testing, sensitivity analysis, and robustness assessment

• Evaluate missing data, nonresponse, sampling limitations, measurement limitations, selection effects, and other threats to mixed methods inference

• Assess methodological coherence and the quality of integration between research questions, methods, datasets, analyses, and conclusions

• Use advanced analytical tools including R, Python, SPSS, Stata, Excel, NVivo, ATLAS.ti, MAXQDA, Dedoose, and structured integration frameworks

• Develop transparent, reproducible, auditable, and ethically governed mixed methods workflows

• Produce advanced mixed methods reports, joint displays, evidence summaries, research presentations, and decision-support outputs

• Communicate uncertainty, contradictory evidence, methodological limitations, and evidence boundaries appropriately

• Complete an advanced mixed methods capstone involving study design, parallel analysis, integration, validation, meta-inference, and professional reporting

Course Content

Day 1: Advanced Mixed Methods Research Design, Integration Architecture, and Data Preparation

Module 1: Advanced Mixed Methods Methodology and Analytical Planning

1.      Advanced Foundations of Mixed Methods Data Analysis, Methodological Paradigms, and Integration Logic

2.      Complex Research Problems, Research Questions, Objectives, Hypotheses, and Mixed Methods Meta-Inference

3.      Advanced Mixed Methods Designs: Convergent, Explanatory Sequential, Exploratory Sequential, Embedded, Transformative, and Multiphase Designs

4.      Developing Advanced Conceptual and Theoretical Frameworks for Integrated Quantitative and Qualitative Research

5.      Advanced Sampling Architecture: Probability, Purposeful, Sequential, Stratified, Maximum Variation, Case-Based, and Integrated Sampling

6.      Quantitative and Qualitative Measurement Alignment, Construct Development, Operationalisation, and Data Source Integration

7.      Advanced Data Preparation: Cleaning, Coding, Transcription, Anonymisation, Metadata, Linking, and Dataset Architecture

8.      Developing Advanced Codebooks, Data Dictionaries, Analytical Matrices, Integration Protocols, and Audit Trails

9.      Advanced Mixed Methods Analysis Planning: Timing, Priority, Sequencing, Integration Points, Data Dependencies, and Quality Controls

10.  Case Study and Exercise: Designing an Advanced Mixed Methods Study and Developing an End-to-End Integrated Analysis Plan

Day 2: Advanced Quantitative and Qualitative Analysis for Mixed Methods Research

Module 2: Advanced Parallel Analysis and Method-Specific Modelling

1.      Advanced Quantitative Exploratory Analysis, Distributional Assessment, Data Visualisation, and Multivariable Relationships

2.      Advanced Statistical Inference, Effect Sizes, Confidence Intervals, Multiple Comparisons, and Evidence Interpretation

3.      Advanced Regression Modelling: Multiple Regression, Generalized Linear Models, Logistic Models, and Model Specification

4.      Interaction Effects, Heterogeneous Effects, Subgroup Analysis, Nonlinearity, Model Diagnostics, and Robust Inference

5.      Longitudinal, Panel, Repeated-Measures, and Multilevel Quantitative Analysis for Mixed Methods Research

6.      Advanced Qualitative Coding: Hierarchical, Inductive, Deductive, Abductive, Focused, and Process Coding

7.      Advanced Thematic and Framework Analysis: Theme Architecture, Thematic Networks, Matrices, and Conceptual Development

8.      Advanced Comparative, Cross-Case, Narrative, Content, Discourse, and Case-Based Qualitative Analysis

9.      Analytical Memos, Negative Cases, Contradictory Evidence, Alternative Interpretations, and Cross-Method Analytical Preparation

10.  Case Study and Exercise: Conducting Advanced Quantitative and Qualitative Analyses of a Complex Programme, Organisational, Policy, or Market Dataset

Day 3: Advanced Integration, Joint Displays, and Meta-Inference

Module 3: Advanced Mixed Methods Integration and Interpretation

1.      Advanced Mixed Methods Integration Principles and Identifying Strategic Integration Points Across the Research Lifecycle

2.      Connecting Statistical Results with Qualitative Themes, Cases, Mechanisms, Experiences, Contexts, and Explanatory Evidence

3.      Convergence, Complementarity, Expansion, Discordance, Contradiction, and Silence in Integrated Findings

4.      Advanced Joint Displays: Matrix Displays, Side-by-Side Displays, Case-Ordered Displays, Theme-by-Variable Matrices, and Integrated Tables

5.      Weaving, Contiguous Presentation, Narrative Integration, and Other Advanced Approaches to Mixed Methods Interpretation

6.      Data Transformation: Quantitising Qualitative Data, Qualitising Quantitative Findings, and Evaluating Transformation Assumptions

7.      Cross-Case, Subgroup, Longitudinal, Multilevel, and Comparative Integration of Quantitative and Qualitative Evidence

8.      Developing Integrated Typologies, Profiles, Configurations, Explanatory Models, and Conceptual Representations

9.      Meta-Inference Development: Connecting Integrated Evidence to Research Questions, Theories, Explanations, and Practical Conclusions

10.  Case Study and Exercise: Constructing Advanced Joint Displays, Resolving Divergent Evidence, and Developing Defensible Mixed Methods Meta-Inferences

Day 4: Advanced Mixed Methods Validity, Causal Interpretation, Robustness, and Evidence Quality

Module 4: Advanced Validation, Inference, and Research Quality

1.      Advanced Mixed Methods Validity: Design Quality, Methodological Coherence, Integration Quality, and Inference Consistency

2.      Triangulation Across Methods, Datasets, Sampling Frames, Cases, Stakeholders, Locations, Time Periods, and Analytical Perspectives

3.      Investigating Divergent Findings Through Negative Cases, Contradictory Evidence, Alternative Explanations, and Contextual Analysis

4.      Advanced Causal Interpretation: Connecting Quantitative Associations with Qualitative Mechanisms, Context, Processes, and Implementation Evidence

5.      Advanced Missing Data, Nonresponse, Selection Effects, Sampling Limitations, Measurement Error, and Their Effects on Integrated Conclusions

6.      Sensitivity Analysis, Robustness Checks, Alternative Specifications, Model Comparison, and Stability of Mixed Methods Findings

7.      Advanced Subgroup and Heterogeneity Analysis for Identifying Differences in Effects, Experiences, Contexts, and Mechanisms

8.      Evaluating Transferability, Generalisability, Contextual Boundaries, Scope Conditions, and Evidence Limitations

9.      Advanced Research Governance: Ethics, Confidentiality, Privacy, Data Protection, Reproducibility, Transparency, and Analytical Accountability

10.  Case Study and Exercise: Conducting an Advanced Mixed Methods Validity and Robustness Assessment of Integrated Research Findings

Day 5: Advanced Mixed Methods Reporting, Governance, and Capstone

Module 5: Advanced Mixed Methods Reporting, Decision Support, and Applied Capstone

1.      From Integrated Evidence to Meta-Inferences, Advanced Conclusions, Implications, and Evidence-Based Recommendations

2.      Advanced Mixed Methods Reporting Structures: Research Design, Methods, Analysis, Integration, Interpretation, and Validation

3.      Developing Advanced Joint Displays, Evidence Matrices, Integrated Visualisations, Conceptual Models, and Research Dashboards

4.      Reporting Convergence, Complementarity, Discordance, Contradictions, Uncertainty, Evidence Gaps, and Analytical Limitations

5.      Communicating Advanced Mixed Methods Findings to Academic, Executive, Policy, Programme, Organisational, and Technical Audiences

6.      Translating Integrated Evidence into Programme Design, Policy Development, Organisational Transformation, Strategic Planning, and Decision Support

7.      Reproducible Mixed Methods Research: Scripts, Syntax, Codebooks, Data Dictionaries, Analytical Memos, Integration Records, and Version Control

8.      Advanced Mixed Methods Governance: Research Ethics, Data Protection, Quality Assurance, Auditability, Transparency, and Responsible Evidence Use

9.      Integrated Advanced Case Study: Conducting an End-to-End Mixed Methods Analysis, Integrating Findings, Validating Meta-Inferences, and Preparing a Professional Research Report

10.  Capstone Exercise: Designing the Research Strategy, Analysing Quantitative and Qualitative Evidence, Constructing Advanced Integration Displays, Evaluating Robustness, Developing Meta-Inferences, and Presenting Defensible Mixed Methods Conclusions