Training course

Overview

Strategic Mixed Methods Data Analysis is an advanced professional training course designed for researchers, analysts, managers, strategists, policy professionals, programme leaders, consultants, and senior decision-makers who need to combine quantitative and qualitative evidence to support complex strategic decisions. The course focuses on applying mixed methods analysis to strategic planning, organisational transformation, policy development, programme evaluation, market intelligence, risk assessment, customer and stakeholder analysis, workforce strategy, and performance improvement. Participants develop a structured understanding of how different evidence sources can be designed, analysed, integrated, and interpreted to generate strategically relevant insights.

The course provides a comprehensive strategic analytical workflow covering research questions, conceptual and theoretical frameworks, evidence architecture, sampling, data preparation, quantitative analysis, qualitative analysis, and integrated interpretation. Participants examine descriptive and exploratory statistics, comparisons, trends, relationships, regression concepts, segmentation, and longitudinal evidence alongside thematic, framework, content, comparative, narrative, and case analysis. Practical tools including Excel, Power BI, SPSS, Stata, R, NVivo, ATLAS.ti, MAXQDA, Dedoose, evidence matrices, analytical frameworks, and strategic dashboards are considered as part of a professional evidence workflow. Emphasis is placed on analytical alignment, methodological transparency, evidence quality, and the practical relevance of findings.

A central focus of Strategic Mixed Methods Data Analysis is the systematic integration of quantitative and qualitative evidence to understand complex strategic issues. Participants explore convergent, explanatory sequential, exploratory sequential, embedded, transformative, and multiphase designs and learn to use joint displays, evidence matrices, case-ordered displays, comparison frameworks, data transformation, narrative integration, and evidence maps. Strategic case studies involving organisational change, market expansion, programme effectiveness, customer experience, workforce performance, policy implementation, stakeholder engagement, operational risk, and resource allocation provide opportunities to identify convergence, complementarity, divergence, contradiction, and explanatory relationships across evidence sources.

The course progressively develops advanced capabilities in strategic evidence validation, scenario assessment, analytical robustness, governance, and decision support. Participants learn to evaluate triangulation, negative cases, contradictory evidence, missing information, sampling limitations, measurement issues, selection effects, alternative explanations, and causal interpretation. The programme also addresses audit trails, reproducibility, research ethics, confidentiality, data governance, and transparent communication of uncertainty. Through practical exercises, strategic case studies, evidence-review activities, and an applied capstone, participants develop the ability to transform integrated evidence into strategic insights, structured decision-support outputs, professional reports, and actionable recommendations.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Strategic planners and strategy professionals responsible for evidence-based planning

• Researchers and analysts conducting complex mixed methods studies

• Senior monitoring, evaluation, research, and learning professionals

• Managers and programme leaders working with strategic evidence

• Policy analysts and development professionals

• Business and organisational analysts supporting strategic decisions

• Consultants conducting market, organisational, programme, or policy analysis

• Risk, governance, performance, and transformation professionals

• Academic and postgraduate researchers undertaking applied mixed methods research

• Senior professionals responsible for integrating multiple evidence sources into strategic decision-making

Course Objectives

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

• Explain the strategic principles, applications, and analytical logic of mixed methods data analysis

• Translate complex strategic priorities into clear analytical questions and evidence requirements

• Select appropriate mixed methods designs for organisational, policy, programme, and business challenges

• Develop conceptual, theoretical, and strategic analytical frameworks

• Design integrated evidence architectures linking multiple data sources and analytical approaches

• Assess and prepare quantitative and qualitative datasets for strategic analysis

• Apply quantitative techniques for trends, comparisons, segmentation, relationships, and strategic performance analysis

• Apply qualitative techniques including thematic, framework, content, comparative, narrative, and case analysis

• Integrate quantitative and qualitative evidence using joint displays, matrices, evidence maps, and structured comparison

• Identify convergence, complementarity, expansion, divergence, contradiction, and unexplained findings

• Develop strategic interpretations, explanatory models, typologies, configurations, and meta-inferences

• Evaluate evidence quality using triangulation, validation, sensitivity analysis, and robustness assessment

• Assess sampling limitations, missing data, measurement problems, selection effects, and alternative explanations

• Distinguish descriptive, associative, explanatory, and causal interpretations appropriately

• Apply scenario-oriented analysis to assess strategic implications under different conditions

• Use appropriate analytical software and professional evidence-management tools

• Communicate complex integrated findings through strategic reports, dashboards, evidence briefs, and presentations

• Apply ethical, transparent, reproducible, and well-governed approaches to strategic evidence

• Translate integrated evidence into defensible strategic conclusions and decision-support recommendations

• Complete an applied strategic mixed methods analysis through a professional capstone

Course Content

Day 1: Strategic Mixed Methods Foundations, Research Design, and Data Quality

Module 1: Strategic Research Design and Integrated Evidence Architecture

1.      Strategic Principles and Applications of Mixed Methods Data Analysis

2.      Translating Strategic Priorities into Research Questions and Evidence Requirements

3.      Aligning Strategic Objectives, Indicators, Concepts, and Analytical Questions

4.      Mixed Methods Designs: Convergent, Explanatory Sequential, and Exploratory Sequential Approaches

5.      Embedded, Transformative, and Multiphase Designs for Complex Strategic Analysis

6.      Developing Conceptual, Theoretical, Logic Model, and Strategic Analytical Frameworks

7.      Designing Integrated Evidence Architectures and Data Source Strategies

8.      Sampling, Measurement, Data Quality, and Evidence Relevance for Strategic Research

9.      Data Preparation, Documentation, Governance, Confidentiality, and Analytical Readiness

10.  Strategic Exercise: Developing an Integrated Mixed Methods Evidence and Analysis Plan

Day 2: Quantitative and Qualitative Analysis for Strategic Evidence

Module 2: Strategic Quantitative and Qualitative Data Analysis

1.      Descriptive Statistics, Strategic KPIs, Trends, and Exploratory Analysis

2.      Comparative Analysis, Benchmarking, Segmentation, and Strategic Performance Patterns

3.      Correlation, Regression Concepts, and Interpretation of Strategic Relationships

4.      Longitudinal, Subgroup, Heterogeneity, and Scenario-Oriented Quantitative Analysis

5.      Strategic Qualitative Coding, Codebook Development, and Analytical Frameworks

6.      Inductive, Deductive, and Hybrid Coding for Complex Strategic Evidence

7.      Thematic, Framework, and Content Analysis for Strategic Research

8.      Comparative, Narrative, Discourse, and Case Analysis for Strategic Insight

9.      Using Excel, Power BI, SPSS, Stata, R, NVivo, ATLAS.ti, MAXQDA, and Dedoose

10.  Strategic Case Study: Analysing Quantitative Performance Evidence and Qualitative Stakeholder Data

Day 3: Advanced Integration, Strategic Insight, and Evidence Synthesis

Module 3: Integrated Mixed Methods Analysis and Strategic Interpretation

1.      Principles and Architecture of Strategic Quantitative and Qualitative Evidence Integration

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

3.      Developing Strategic Joint Displays and Integrated Evidence Matrices

4.      Case-Ordered Displays for Comparing Markets, Programmes, Business Units, and Stakeholder Groups

5.      Side-by-Side Comparison and Narrative Weaving of Strategic Findings

6.      Data Transformation and Structured Integration of Qualitative and Quantitative Evidence

7.      Cross-Case, Subgroup, Longitudinal, and Multi-Level Integration

8.      Developing Strategic Typologies, Configurations, Explanatory Models, and Evidence Maps

9.      Meta-Inference and Development of Defensible Strategic Conclusions

10.  Practical Exercise: Building a Strategic Joint Display, Evidence Map, and Integrated Insight Framework

Day 4: Advanced Strategic Evidence Rigour, Validation, and Scenario Assessment

Module 4: Advanced Mixed Methods Validation, Robustness, and Strategic Evidence Quality

1.      Assessing Validity, Reliability, Credibility, Trustworthiness, and Strategic Evidence Quality

2.      Triangulation Across Financial, Operational, Market, Customer, Employee, Programme, and Stakeholder Evidence

3.      Negative Cases, Contradictions, Outliers, and Strategic Evidence Gaps

4.      Missing Data, Nonresponse, Sampling Limitations, and Measurement Risk

5.      Distinguishing Association, Explanation, and Causal Interpretation

6.      Alternative Explanations, Confounding Factors, Selection Effects, and Contextual Influences

7.      Sensitivity Analysis, Robustness Checks, and Competing Strategic Explanations

8.      Scenario Assessment, Uncertainty Analysis, and Strategic Implications of Alternative Evidence

9.      Analytical Transparency, Audit Trails, Reproducibility, Ethics, and Strategic Data Governance

10.  Strategic Case Study: Validating Integrated Evidence and Assessing Its Implications for a Complex Decision

Day 5: Strategic Reporting, Decision Support, and Applied Capstone

Module 5: Strategic Mixed Methods Reporting and Decision Application

1.      Designing Strategic Mixed Methods Reports and Integrated Evidence Narratives

2.      Presenting Quantitative, Qualitative, and Integrated Findings for Strategic Audiences

3.      Developing Strategic Briefs, Executive Summaries, Evidence Papers, and Decision Memos

4.      Translating Integrated Findings into Strategic Conclusions and Evidence-Based Recommendations

5.      Using Dashboards, Charts, Tables, Joint Displays, Evidence Maps, and Strategic Scorecards

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

7.      Applying Mixed Methods Evidence to Strategy, Transformation, Programme Evaluation, and Policy Development

8.      Using Integrated Evidence for Risk Assessment, Resource Allocation, Scenario Planning, and Performance Improvement

9.      Reviewing Strategic Analytical Outputs for Coherence, Quality, Governance, and Reproducibility

10.  Capstone Exercise: Complete a Strategic Mixed Methods Analysis, Develop an Integrated Strategic Evidence Report, and Present Decision-Support Findings