Course Details

Data Collection Methods for M&E Training Course

INTRODUCTION

This essential training course provides comprehensive knowledge and practical skills for selecting, designing, and implementing effective data collection methods for Monitoring and Evaluation (M&E). Recognizing that high-quality data is the bedrock of credible M&E, enabling robust analysis and informed decision-making, this program equips participants with a systematic understanding of various data collection approaches, tools, and ethical considerations. Participants will gain deep insights into quantitative and qualitative techniques, sampling strategies, data quality assurance, and practical application, all crucial for generating reliable evidence of project progress and impact. This course is ideal for M&E specialists, project managers, program staff, researchers, and anyone involved in gathering data to assess program effectiveness.

DURATION

10 days

TARGET AUDIENCE

This course is specifically designed for professionals involved in monitoring, evaluation, research, and program implementation who need to collect data effectively, including:

• Monitoring and Evaluation (M&E) Specialists.
• Project and Program Managers.
• Researchers and Data Analysts.
• Field staff involved in data gathering.
• NGO and Development Practitioners.

OBJECTIVES

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

• Understand the diverse range of data collection methods for M&E.
• Select appropriate data collection methods based on M&E questions and context.
• Design effective data collection tools for both quantitative and qualitative data.
• Implement ethical and robust data collection processes in the field.
• Ensure data quality and prepare data for analysis.

MODULES

Module 1: Foundations of M&E Data Collection

• Define data collection in the context of Monitoring and Evaluation.
• Explore the purpose and importance of reliable data for M&E and decision-making.
• Understand the link between M&E questions, indicators, and data collection methods.
• Discuss the distinction between primary and secondary data sources.
• Examine ethical principles in data collection, including informed consent and confidentiality.

Module 2: Sampling Strategies for Data Collection

• Understand the purpose of sampling in M&E and when it is appropriate.
• Differentiate between probability and non-probability sampling techniques.
• Explore common sampling methods (e.g., simple random, stratified, cluster, convenience, snowball).
• Discuss factors influencing sample size determination and representation.
• Learn about potential biases in sampling and how to minimize them.

Module 3: Quantitative Data Collection Methods: Surveys

• Understand the principles of survey design for M&E purposes.
• Discuss different survey administration modes (e.g., face-to-face, phone, online, self-administered).
• Explore questionnaire design principles, including question types and wording.
• Examine best practices for pre-testing and piloting survey instruments.
• Learn about managing survey data collection processes effectively.

Module 4: Quantitative Data Collection Methods: Direct Measurement & Observation

• Understand the use of direct observation as a data collection method.
• Discuss the design of observation checklists and structured observation protocols.
• Explore the use of physical measurements, counts, and performance tests.
• Examine the application of administrative data sources (e.g., service records, attendance logs).
• Learn about technological tools for quantitative data collection (e.g., mobile data collection apps).

Module 5: Qualitative Data Collection Methods: Interviews

• Understand the purpose and principles of qualitative inquiry in M&E.
• Discuss different types of interviews (e.g., in-depth, semi-structured, key informant).
• Explore techniques for developing effective interview guides and probing questions.
• Examine best practices for conducting interviews (e.g., building rapport, active listening).
• Learn about transcribing and organizing interview data.

Module 6: Qualitative Data Collection Methods: Focus Group Discussions (FGDs) & Case Studies

• Understand the rationale and application of Focus Group Discussions (FGDs) in M&E.
• Discuss techniques for planning and facilitating effective FGDs.
• Explore the use of case studies for in-depth understanding of specific situations.
• Examine methods for selecting case study subjects and gathering diverse perspectives.
• Learn about participatory qualitative data collection approaches.

Module 7: Data Quality Assurance and Management

• Define data quality and its key dimensions (e.g., accuracy, completeness, consistency, timeliness).
• Discuss strategies for ensuring data quality at each stage of the collection process.
• Explore methods for data validation, cleaning, and verification.
• Understand the importance of data security, storage, and backup.
• Learn about data management plans and standard operating procedures.

Module 8: Integrating Methods and Practical Application

• Understand the benefits of using mixed methods (combining quantitative and qualitative) in M&E.
• Discuss how to integrate different data collection methods effectively within a project.
• Explore practical considerations for resource allocation, budgeting, and team management for data collection.
• Examine common challenges in data collection and strategies for troubleshooting.
• Practice developing a comprehensive data collection plan for a given M&E scenario.

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com