Training course

Overview

Mixed Methods Data Analysis for Managers is a professional management-focused training course designed to equip managers, department heads, programme leaders, project managers, team leaders, executives, and organisational decision-makers with the practical skills required to interpret and integrate quantitative and qualitative evidence for better management decisions. The course provides a structured approach to understanding survey results, performance indicators, interviews, focus groups, employee feedback, customer insights, operational observations, programme data, and other evidence sources. Participants learn how mixed methods analysis can strengthen organisational assessment, performance improvement, strategic planning, programme management, risk management, and evidence-based decision-making.

The course develops practical managerial capability across the full mixed methods analysis workflow, from defining management questions and selecting appropriate evidence to preparing datasets, analysing quantitative information, coding qualitative data, and interpreting integrated findings. Participants explore descriptive statistics, group comparisons, correlation, regression concepts, trend analysis, and performance analysis alongside thematic, framework, content, comparative, narrative, and case-based qualitative approaches. Practical tools including Excel, Power BI, SPSS, Stata, R, NVivo, ATLAS.ti, MAXQDA, Dedoose, Word, and structured management analysis templates are considered according to organisational needs, with emphasis on selecting tools that support efficient, transparent, and decision-oriented analysis.

A central component of Mixed Methods Data Analysis for Managers is learning how to connect numerical performance evidence with the experiences, explanations, behaviours, and contextual factors behind the numbers. Participants examine convergent, explanatory sequential, exploratory sequential, embedded, transformative, and multiphase mixed methods approaches and learn how to use evidence matrices, joint displays, comparison tables, case-ordered displays, and narrative integration to identify convergence, complementarity, divergence, contradiction, and emerging management issues. Real-world scenarios involving employee engagement, customer satisfaction, service quality, operational performance, programme delivery, organisational change, stakeholder relationships, and resource utilisation enable managers to translate integrated evidence into practical management insight.

The course also emphasises managerial evidence quality, responsible interpretation, governance, and communication. Participants learn how to assess data limitations, sampling issues, missing information, measurement concerns, contradictory findings, and alternative explanations while avoiding unsupported causal conclusions. Practical case studies and exercises develop skills in triangulation, validation, sensitivity analysis, evidence prioritisation, dashboard interpretation, management reporting, and decision-support communication. By completing an applied capstone, participants develop an integrated mixed methods analysis and translate the findings into clear conclusions, actionable recommendations, and professional management outputs.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Managers responsible for evidence-based operational and strategic decisions

• Department heads and functional managers analysing organisational performance

• Programme and project managers evaluating implementation and outcomes

• Team leaders and supervisors working with employee, customer, and operational evidence

• Monitoring, evaluation, research, and learning managers

• Business analysts and management analysts

• Human resources and people managers analysing employee and workforce information

• Operations, service delivery, quality, and performance managers

• Policy and programme managers working with quantitative and qualitative evidence

• Consultants and professionals responsible for management reports and decision support

Course Objectives

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

• Explain the principles and practical value of mixed methods data analysis for management decision-making

• Translate management problems and organisational priorities into clear analytical questions

• Select appropriate mixed methods designs based on management objectives and evidence requirements

• Develop practical conceptual frameworks and analytical plans for organisational and programme analysis

• Prepare, organise, clean, and quality-check quantitative and qualitative management data

• Interpret descriptive statistics, trends, comparisons, relationships, and performance indicators

• Apply structured approaches to coding and analysing employee, customer, stakeholder, and operational feedback

• Use thematic, framework, content, comparative, narrative, and case analysis to identify management insights

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

• Identify convergence, complementarity, divergence, contradictions, and explanatory relationships across evidence sources

• Assess data quality, limitations, missing information, sampling issues, and alternative explanations

• Apply triangulation, validation, sensitivity analysis, and evidence-quality practices to management analysis

• Use practical software and analytical tools to support mixed methods management workflows

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

• Translate integrated evidence into practical, defensible management conclusions and recommendations

• Complete an applied mixed methods management analysis using a realistic organisational case study

Course Content

Day 1: Managerial Mixed Methods Foundations, Evidence Planning, and Data Preparation

Module 1: Foundations of Mixed Methods Analysis for Management Decision-Making

1.      Understanding Mixed Methods Data Analysis and Its Role in Management

2.      Translating Management Problems into Analytical Questions and Evidence Requirements

3.      Aligning Management Objectives, Research Questions, Indicators, and Data Sources

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

5.      Embedded, Transformative, and Multiphase Designs for Organisational and Programme Analysis

6.      Developing Managerial Conceptual Frameworks, Logic Models, and Analytical Frameworks

7.      Selecting and Assessing Quantitative and Qualitative Data Sources

8.      Preparing, Cleaning, Organising, and Documenting Management Datasets

9.      Establishing Data Quality, Governance, Confidentiality, and Responsible Evidence Practices

10.  Practical Exercise: Developing a Mixed Methods Analysis Plan for a Management Problem

Day 2: Quantitative and Qualitative Analysis for Managers

Module 2: Applied Quantitative and Qualitative Analysis for Management Evidence

1.      Descriptive Statistics, Performance Indicators, and Management Dashboards

2.      Trend Analysis, Group Comparisons, Benchmarking, and Performance Patterns

3.      Correlation, Regression Concepts, and Interpreting Relationships in Management Data

4.      Subgroup Analysis, Segmentation, and Identifying Differences Across Teams, Units, or Locations

5.      Foundations of Qualitative Coding for Employee, Customer, and Stakeholder Evidence

6.      Developing Managerial Codebooks and Applying Inductive, Deductive, and Hybrid Coding

7.      Thematic, Framework, and Content Analysis for Management Decision-Making

8.      Comparative, Narrative, and Case-Based Analysis of Organisational Evidence

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

10.  Practical Case Study: Analysing Quantitative Performance Data and Qualitative Feedback

Day 3: Integration, Interpretation, and Management Insight

Module 3: Integrated Mixed Methods Analysis for Managerial Decision-Making

1.      Principles and Practical Logic of Integrating Management Evidence

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

3.      Building Joint Displays to Connect Performance Metrics with Qualitative Explanations

4.      Developing Evidence Matrices and Comparison Tables for Management Decisions

5.      Using Case-Ordered Displays to Compare Teams, Departments, Locations, or Customer Segments

6.      Side-by-Side Comparison and Narrative Integration of Quantitative and Qualitative Findings

7.      Data Transformation and Structured Quantification of Qualitative Management Evidence

8.      Linking Themes and Patterns to Key Performance Indicators and Management Priorities

9.      Developing Integrated Interpretations, Explanatory Models, and Management Meta-Inferences

10.  Practical Exercise: Developing a Management Evidence Matrix and Integrated Findings

Day 4: Advanced Managerial Evidence Quality, Validation, and Risk

Module 4: Advanced Mixed Methods Quality Assurance and Evidence-Based Management

1.      Assessing Validity, Reliability, Credibility, and Trustworthiness of Management Evidence

2.      Triangulating Employee, Customer, Operational, Financial, and Stakeholder Information

3.      Analysing Negative Cases, Contradictions, Outliers, and Unexplained Management Findings

4.      Assessing Missing Data, Nonresponse, Sampling Limitations, and Measurement Issues

5.      Interpreting Statistical Relationships Without Overstating Causality

6.      Assessing Alternative Explanations, Confounding Factors, and Contextual Influences

7.      Sensitivity Analysis, Robustness Checks, and Testing the Stability of Management Findings

8.      Reflexivity, Analytical Transparency, Audit Trails, and Peer Review in Management Analysis

9.      Ethics, Confidentiality, Data Governance, Reproducibility, and Responsible Use of Organisational Evidence

10.  Applied Case Study: Critically Reviewing Mixed Methods Evidence Before a Major Management Decision

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

Module 5: Professional Mixed Methods Reporting and Management Application

1.      Designing Management-Focused Mixed Methods Reports and Analytical Narratives

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

3.      Developing Executive Summaries, Management Briefs, and Evidence-Based Decision Papers

4.      Translating Integrated Findings into Practical Management Conclusions and Recommendations

5.      Using Dashboards, Charts, Tables, Joint Displays, and Evidence Maps for Decision Support

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

7.      Applying Mixed Methods Evidence to Performance Improvement, Change Management, and Strategic Planning

8.      Using Integrated Evidence for Risk Identification, Problem Diagnosis, and Resource Prioritisation

9.      Reviewing Management Analysis for Coherence, Quality, Governance, and Reproducibility

10.  Capstone Exercise: Complete a Mixed Methods Management Analysis, Develop an Evidence-Based Report, and Present Decision-Support Recommendations