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Quantitative Research Data Analysis for Education Training Course

Online Training Download PDF
How to Register Click View Schedule for your preferred location, select your training dates, then register as an individual, group, or online participant. You will receive an invitation letter and invoice promptly after submission.
Training Locations Kenya (Nairobi, Mombasa, Malindi, Kisumu, Nakuru, Nanyuki) · Tanzania (Dodoma, Zanzibar, Dar es Salaam) · Dubai UAE · South Africa (Pretoria, Cape Town) · Istanbul · Accra · Banjul more ▾
Groups & Payment Groups of 5+ receive one complimentary place — see group rates. Payment due at least 1 month before (Europe & Asia) or 2 weeks before (Africa programs).
Upcoming Training Schedules 14 locations
Location Duration Next Start Date Dates Available Action
Nairobi, Kenya 10 days Oct 12, 2026 91 dates
Accra, Ghana 10 days Oct 26, 2026 47 dates
Addis Ababa, Ethiopia 10 days Oct 12, 2026 47 dates
Cape Town, South Africa 10 days Oct 26, 2026 45 dates
Dar es Salaam, Tanzania 10 days Oct 12, 2026 45 dates
Dubai, UAE 10 days Oct 12, 2026 62 dates
Istanbul, Turkey 10 days Oct 12, 2026 48 dates
Kampala, Uganda 10 days Oct 12, 2026 45 dates
Kigali, Rwanda 10 days Oct 12, 2026 46 dates
Kuala Lumpur, Malaysia 10 days Oct 19, 2026 45 dates
Mombasa, Kenya 10 days Oct 26, 2026 44 dates
Pretoria, South Africa 10 days Oct 12, 2026 48 dates
Singapore 10 days Oct 19, 2026 47 dates
Zanzibar, Tanzania 10 days Oct 26, 2026 45 dates

Quantitative Research Data Analysis for Education Training Course

Course Overview

Quantitative Research Data Analysis for Education Training Course is designed to equip education professionals, researchers, lecturers, school administrators, university managers, monitoring and evaluation specialists, education planners, policymakers, project managers, quality assurance officers, and development practitioners with advanced knowledge and practical skills in collecting, managing, analyzing, interpreting, and reporting quantitative education data for evidence-based decision-making. As ministries of education, universities, colleges, schools, NGOs, donor agencies, and international development organizations increasingly embrace Results-Based Management (RBM), Monitoring, Evaluation, Accountability and Learning (MEAL), Education Management Information Systems (EMIS), Sustainable Development Goal 4 (SDG 4), educational research, institutional quality assurance, learning analytics, educational assessment, impact evaluation, education policy analysis, digital transformation, evidence-based educational planning, and organizational performance management, quantitative research data analysis has become an essential competency for measuring educational performance, evaluating interventions, improving learning outcomes, and informing strategic policy decisions. This course enables participants to apply internationally recognized quantitative research methodologies to solve complex educational challenges through rigorous statistical analysis and evidence generation.

The course provides participants with practical experience in research design, questionnaire development, sampling techniques, survey data collection, database management, descriptive statistics, inferential statistics, hypothesis testing, regression analysis, correlation analysis, analysis of variance (ANOVA), multivariate analysis, factor analysis, reliability analysis, predictive modeling, education performance measurement, impact evaluation, policy analysis, and data visualization. Participants will learn to integrate Microsoft Excel, SPSS, STATA, R, Python, Microsoft Power BI, Education Management Information Systems (EMIS), Learning Management Systems (LMS), SurveyCTO, KoboToolbox, Open Data Kit (ODK), GIS, cloud computing platforms, business intelligence tools, and digital reporting systems to analyze learner achievement, teacher performance, curriculum implementation, institutional effectiveness, education financing, digital learning initiatives, and donor-funded education programs.

Participants will further explore advanced concepts including statistical modeling, structural equation modeling, predictive analytics, machine learning for education, education data governance, dashboard development, artificial intelligence-assisted statistical analysis, educational data mining, quality assurance, accreditation support, strategic planning, knowledge management, organizational learning, and policy simulation. Practical exercises demonstrate how quantitative research strengthens education monitoring and evaluation systems, enhances institutional accountability, supports evidence-based policy implementation, improves donor reporting, promotes innovation, and drives educational excellence through reliable statistical evidence and analytical insights.

Through instructor-led workshops, statistical analysis laboratories, software demonstrations, collaborative research projects, real-world data analysis exercises, and comprehensive education case studies, participants will gain hands-on experience in designing quantitative studies, managing datasets, performing advanced statistical analyses, interpreting results, preparing research reports, and presenting findings to policymakers and stakeholders. Upon successful completion of the course, participants will possess the competencies required to conduct high-quality quantitative education research, strengthen institutional monitoring and evaluation systems, support Results-Based Management, improve educational planning, enhance research quality, and contribute to improved educational outcomes across ministries of education, universities, colleges, schools, NGOs, research institutions, and international development organizations.

Course Objectives

  1. Understand the principles and applications of quantitative research in education.
  2. Design rigorous quantitative education research studies.
  3. Apply appropriate sampling techniques and data collection methods.
  4. Manage and prepare education datasets for statistical analysis.
  5. Perform descriptive and inferential statistical analyses.
  6. Apply regression, correlation, ANOVA, and multivariate analysis techniques.
  7. Interpret quantitative findings for evidence-based educational decision-making.
  8. Prepare professional statistical reports and research publications.
  9. Strengthen monitoring, evaluation, accountability, and learning systems using quantitative evidence.
  10. Improve institutional performance through data-driven educational research.

Organizational Benefits

  1. Strengthens institutional research and analytical capacity.
  2. Improves evidence-based educational planning and policymaking.
  3. Enhances monitoring, evaluation, accountability, and learning systems.
  4. Supports institutional quality assurance and accreditation.
  5. Improves donor reporting and education project evaluation.
  6. Strengthens strategic decision-making through statistical evidence.
  7. Supports digital transformation in education research and analytics.
  8. Promotes organizational learning and innovation.
  9. Enhances education performance monitoring and predictive planning.
  10. Improves educational outcomes through rigorous quantitative research.

Target Participants

  • Education Researchers
  • University Lecturers
  • School Principals
  • Head Teachers
  • Ministry of Education Officials
  • University Administrators
  • Monitoring and Evaluation Officers
  • Education Planners
  • Institutional Planning Officers
  • Quality Assurance Officers
  • Education Information Management Officers
  • Data Analysts
  • Statisticians
  • Policy Analysts
  • Curriculum Specialists
  • Academic Registrars
  • ICT Officers
  • NGO and Development Project Staff
  • Education Consultants
  • Professionals involved in educational research, monitoring and evaluation, statistics, policy analysis, institutional planning, quality assurance, and educational leadership.

Course Outline

Module 1: Foundations of Quantitative Research in Education

  • Quantitative research principles
  • Research paradigms
  • Education research designs
  • Variables and measurement
  • Research ethics
  • Educational applications

Case Study: Designing a quantitative study to evaluate national literacy achievement.

Module 2: Research Design and Sampling Techniques

  • Experimental design
  • Quasi-experimental design
  • Survey research
  • Probability sampling
  • Sample size determination
  • Sampling bias

Case Study: Developing a representative sampling framework for a nationwide education survey.

Module 3: Data Collection and Database Management

  • Questionnaire design
  • Digital data collection
  • Data coding
  • Data entry
  • Data validation
  • Database management

Case Study: Collecting and managing learner assessment data using SurveyCTO and KoboToolbox.

Module 4: Data Cleaning and Preparation

  • Data screening
  • Missing value treatment
  • Outlier detection
  • Data transformation
  • Variable coding
  • Data quality assurance

Case Study: Preparing examination performance datasets for statistical analysis.

Module 5: Descriptive Statistical Analysis

  • Frequency distributions
  • Measures of central tendency
  • Measures of dispersion
  • Cross-tabulation
  • Graphical presentation
  • Summary statistics

Case Study: Analyzing national examination performance trends across regions.

Module 6: Inferential Statistical Analysis

  • Hypothesis testing
  • Confidence intervals
  • T-tests
  • Chi-square tests
  • Analysis of Variance (ANOVA)
  • Non-parametric tests

Case Study: Comparing student performance across different instructional methods.

Module 7: Correlation and Regression Analysis

  • Correlation analysis
  • Simple linear regression
  • Multiple regression
  • Logistic regression
  • Model diagnostics
  • Prediction models

Case Study: Identifying factors influencing learner achievement using regression analysis.

Module 8: Advanced Quantitative Analysis

  • Factor analysis
  • Cluster analysis
  • Multivariate analysis
  • Structural Equation Modeling
  • Reliability testing
  • Predictive analytics

Case Study: Developing predictive models for student retention in higher education.

Module 9: Statistical Software Applications

  • Microsoft Excel
  • SPSS
  • STATA
  • R programming
  • Python analytics
  • Microsoft Power BI

Case Study: Comparing statistical outputs across SPSS, R, and Python for education research.

Module 10: Monitoring, Evaluation, Accountability and Learning (MEAL)

  • Results-Based Management
  • Education indicators
  • Performance measurement
  • Impact evaluation
  • Data visualization
  • Evidence-based reporting

Case Study: Integrating quantitative research findings into an education MEAL framework.

Module 11: Reporting and Dissemination of Quantitative Research

  • Research reports
  • Statistical interpretation
  • Executive summaries
  • Policy briefs
  • Data storytelling
  • Academic publishing

Case Study: Preparing a statistical education policy report for national education stakeholders.

Module 12: Emerging Trends in Quantitative Education Research

  • Artificial Intelligence
  • Machine learning
  • Learning analytics
  • Big data
  • Education business intelligence
  • Future innovations in quantitative analysis

Case Study: Designing a national education analytics framework integrating Education Management Information Systems (EMIS), Learning Management Systems (LMS), Microsoft Power BI, SPSS, STATA, R, Python, Artificial Intelligence, GIS, cloud computing, predictive analytics, Results-Based Management, and digital reporting to strengthen education policy implementation, institutional performance, accountability, research excellence, and achievement of Sustainable Development Goal 4.

General Information

  1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
  2. Language Proficiency: Participants should have a good command of the English language.
  3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, statistical analysis laboratories, software demonstrations, collaborative group work, research simulations, real-world education datasets, and comprehensive education case studies. Our facilitators are seasoned experts with over a decade of experience in quantitative research, educational statistics, monitoring and evaluation, Results-Based Management, data science, policy analysis, institutional planning, and education performance measurement.
  4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
  5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
  6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
  7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
  8. Additional Services: Accommodation, pickup services, flight booking, and visa processing arrangements are available upon request at discounted rates.
  9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
  10. Post-Training Support: We offer one year of free consultation and coaching after the course.
  11. Group Discounts: Register as a group of more than two participants and enjoy a discount ranging from 10% to 50%.
  12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
  13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.
  14. Website: Visit our website at www.fdc-k.org for more information.

 

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