Training course on Data Collection Tools and Techniques for Social Protection M and E

Training Course on Data Collection Tools and Techniques for Social Protection M and E 

Introduction

Data Collection Tools and Techniques for Social Protection Monitoring and Evaluation (M and E) is a foundational and practical discipline essential for generating reliable evidence on the performance and impact of social protection programs. The effectiveness of any M and E system hinges on its ability to collect high-quality, relevant, and timely data. In the complex landscape of social protection, this requires a diverse toolkit of methodologies, from rigorous quantitative surveys to in-depth qualitative inquiries and the strategic utilization of administrative data. Mastering these tools and techniques is paramount for accurately tracking program implementation, assessing outcomes, and informing evidence-based decisions that ultimately improve the lives of vulnerable populations.

Training Course on Data Collection Tools and Techniques for Social Protection M and E is meticulously designed to equip with the advanced theoretical insights and intensive practical tools necessary to excel in Data Collection Tools and Techniques for Social Protection M and E. We will delve into the foundational concepts of data types and their appropriate uses, master the intricacies of designing robust survey instruments, conducting effective qualitative interviews, and leveraging administrative data, and explore cutting-edge approaches to digital data collection, quality assurance, and ethical considerations. A significant focus will be placed on hands-on exercises, practical application, and analyzing real-world challenges in diverse social protection contexts. By integrating industry best practices, analyzing complex case studies, and engaging in practical simulations, attendees will develop the strategic acumen to confidently lead and implement high-quality data collection efforts, fostering unparalleled data integrity, evidence generation, and program effectiveness.

Course Objectives

Upon completion of this course, participants will be able to:

1. Analyze the fundamental concepts of data types and their relevance for social protection M and E.
2. Comprehend the entire data collection planning process for social protection programs.
3. Master the design of robust survey instruments for quantitative data collection.
4. Develop expertise in implementing quantitative data collection techniques, including household surveys.
5. Formulate strategies for effectively utilizing administrative data for M and E purposes.
6. Understand the critical role of qualitative data collection tools (FGDs, KIIs) and techniques.
7. Implement robust approaches to digital data collection tools and platforms.
8. Explore key strategies for ensuring data quality assurance and validation.
9. Apply methodologies for effective data management, storage, and security.
10. Understand and address ethical considerations in all stages of data collection.
11. Develop preliminary skills in basic data analysis for M and E insights.
12. Design a comprehensive data collection plan for a social protection program.
13. Examine global best practices and lessons learned in social protection data collection.

Target Audience

This course is essential for professionals involved in data collection and M and E for social protection:

1. M and E Specialists & Officers: Responsible for designing and implementing M and E activities.
2. Program Managers & Coordinators: Overseeing program implementation and data needs.
3. Field Staff & Data Collectors: Directly involved in data gathering.
4. Data Analysts & Information Management Specialists: Working with social protection data.
5. Government Officials: From ministries responsible for social welfare, planning, and statistics.
6. Development Practitioners: From NGOs and international organizations.
7. Researchers & Academics: Conducting studies on social protection.
8. Civil Society Organizations: Engaged in monitoring and advocacy.

Course Duration: 10 Days

Course Modules

Module 1: Foundations of Data Collection in Social Protection M and E

• Explain the importance of data for effective social protection M and E.
• Differentiate between various types of data: quantitative, qualitative, and administrative.
• Discuss the role of data collection at different stages of the program cycle.
• Understand how M and E frameworks inform data needs and indicators.
• Outline the key steps in the data collection planning process.

Module 2: Designing Survey Instruments for Quantitative Data

• Master the principles of effective survey instrument design for social protection.
• Learn to formulate different types of survey questions (e.g., closed-ended, open-ended, Likert scales).
• Understand the importance of questionnaire structure, flow, and skip logic.
• Develop skills in wording questions to minimize bias and ensure clarity.
• Conduct pre-testing and piloting of survey instruments for refinement.

Module 3: Quantitative Data Collection Techniques (Household Surveys)

• Explore various sampling methods for household surveys (e.g., simple random, stratified, cluster sampling).
• Learn best practices for training and managing survey enumerators effectively.
• Understand fieldwork logistics, including planning, supervision, and quality control.
• Discuss different data collection modes (e.g., face-to-face, phone, online surveys).
• Identify common challenges in collecting household survey data in social protection contexts.

Module 4: Administrative Data for Social Protection M and E

• Identify key sources of administrative data within social protection systems (e.g., beneficiary registries, payment records, service delivery logs).
• Analyze the advantages and limitations of using administrative data for M and E.
• Learn techniques for data extraction and integration from Management Information Systems (MIS).
• Discuss common data quality issues in administrative records and how to address them.
• Explore best practices for maximizing the utility of existing administrative data.

Module 5: Qualitative Data Collection Tools (FGDs, KIIs)

• Understand the purpose and unique role of qualitative data in social protection M and E.
• Develop skills in designing effective interview guides for Key Informant Interviews (KIIs).
• Master facilitation techniques for conducting productive Focus Group Discussions (FGDs).
• Learn observation techniques and systematic methods for taking field notes.
• Discuss ethical considerations specific to qualitative data collection, such as power dynamics.

Module 6: Other Qualitative and Participatory Techniques

• Explore the application of case studies and narrative approaches in social protection evaluation.
• Learn about participatory tools like Photo-voice and participatory mapping.
• Understand the Most Significant Change (MSC) technique for capturing emergent impacts.
• Introduce Participatory Rural Appraisal (PRA) tools for community engagement.
• Discuss ethical considerations when using participatory methods, ensuring genuine participation.

Module 7: Digital Data Collection Tools and Platforms

• Gain hands-on experience with popular digital data collection platforms (e.g., ODK, SurveyCTO, KoboToolbox).
• Learn to design digital forms, implement skip logic, and integrate multimedia.
• Understand capabilities for offline data collection in remote areas.
• Explore real-time data transmission, synchronization, and monitoring dashboards.
• Analyze the advantages and challenges of transitioning to digital data collection.

Module 8: Data Quality Assurance and Validation

• Emphasize the paramount importance of data quality (accuracy, completeness, consistency, timeliness).
• Implement various data validation techniques (e.g., range checks, consistency checks, cross-checks).
• Learn systematic data cleaning procedures and protocols.
• Understand the crucial role of field supervision and back-checks in ensuring data quality.
• Develop strategies for building data quality into every stage of the M and E cycle.

Module 9: Data Management and Storage

• Master principles of effective data management for large and complex datasets.
• Learn best practices for data entry, coding, and variable naming conventions.
• Explore different data storage solutions (e.g., cloud-based platforms, local servers).
• Implement robust data security and privacy protocols (e.g., anonymization, access control, compliance with GDPR/national regulations).
• Understand strategies for data archiving and ensuring long-term accessibility for future analysis.

Module 10: Ethical Considerations in Data Collection

• Deepen understanding of the informed consent process (verbal, written, adapted for vulnerable groups).
• Ensure strict adherence to principles of confidentiality and anonymity of collected data.
• Comply with national and international data protection and privacy regulations.
• Learn to minimize potential harm and maximize beneficence for research participants.
• Address special ethical considerations for sensitive data (e.g., related to disability, gender-based violence, child protection).

Module 11: Basic Data Analysis for M and E Insights

• Introduce fundamental descriptive statistics for quantitative data (e.g., frequencies, means, percentages).
• Learn basic qualitative data analysis techniques, such as thematic coding and categorization.
• Understand how to link preliminary data analysis directly to M and E questions.
• Gain an introduction to practical statistical software for basic analysis (e.g., Excel, basic functionalities of Stata/R).
• Practice presenting preliminary findings clearly and concisely.

Module 12: Practical Application and Case Studies

• Engage in a comprehensive group exercise to develop a detailed data collection plan for a social protection program.
• Participate in role-playing scenarios to practice various data collection techniques.
• Analyze real-world data collection challenges encountered in social protection programs and brainstorm practical solutions.
• Discuss lessons learned from diverse country contexts regarding data collection successes and failures.
• Collaborate on designing a mixed-methods data collection strategy for a complex evaluation.

 

Training Methodology

• Interactive Workshops: Facilitated discussions, group exercises, and problem-solving activities.
• Case Studies: Real-world examples to illustrate successful community-based surveillance practices.
• Role-Playing and Simulations: Practice engaging communities in surveillance activities.
• Expert Presentations: Insights from experienced public health professionals and community leaders.
• Group Projects: Collaborative development of community surveillance plans.
• Action Planning: Development of personalized action plans for implementing community-based surveillance.
• Digital Tools and Resources: Utilization of online platforms for collaboration and learning.
• Peer-to-Peer Learning: Sharing experiences and insights on community engagement.
• Post-Training Support: Access to online forums, mentorship, and continued learning resources.

 

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

• Participants must be conversant in English.
• Upon completion of training, participants will receive an Authorized Training Certificate.
• The course duration is flexible and can be modified to fit any number of days.
• Course fee includes facilitation, training materials, 2 coffee breaks, buffet lunch, and a Certificate upon successful completion.
• One-year post-training support, consultation, and coaching provided after the course.