Data Collection & Management

Course Description

This course provides a practical and comprehensive introduction to data collection and data
management for research, monitoring and evaluation, academic projects, and organizational
decision-making. Participants will learn how to design effective data collection tools, select
appropriate sampling methods, conduct fieldwork, use digital data collection platforms such
as KoboToolbox and Google Forms, clean and manage datasets, ensure data quality, and
prepare basic analysis and reports.
The course combines theory with hands-on exercises to equip learners with real-world skills
needed for high-quality, reliable data collection and management.

Course Objectives
By the end of this course, learners will be able to:
1. Understand different types of data and data collection methods.
2. Design questionnaires, interview guides, and observation checklists.
3. Apply appropriate sampling techniques for various research contexts.
4. Build and deploy digital data collection forms using modern tools.
5. Manage field teams, ensure ethical practices, and maintain data quality.
6. Clean, organize, and manage datasets using Excel/Google Sheets.
7. Conduct basic quantitative and qualitative data analysis.
8. Present findings through reports, charts, and dashboards.

Course Outline
Module 1: Introduction to Data Collection

•  Meaning and types of data
• Primary vs. secondary data
• Quantitative vs. qualitative data
• Ethics, informed consent, and confidentiality

Module 2: Research Design and Sampling

•  Understanding research designs
• Population and sample
• Probability and non-probability sampling methods
• Sample size determination basics

Module 3: Designing Data Collection Tools

• Questionnaires, interview guides, and observation tools
• Writing effective questions
• Open-ended vs. closed-ended items
• Pretesting and piloting tools

Module 4: Digital Data Collection Tools

• Introduction to mobile data collection
• Creating forms in KoboToolbox / ODK / Google Forms
•  Adding skip logic and validation rules
• Deploying forms and managing enumerators

Module 5: Fieldwork and Enumerator Management

• Enumerator roles and responsibilities
• Interviewing techniques and protocols
• Handling difficult respondents
• Daily field reporting and quality checks

Module 6: Data Management and Cleaning

• Data management lifecycle
• Data entry in Excel/Google Sheets
• Data cleaning (missing values, duplicates, outliers)
• Codebooks and variable naming

Module 7: Data Quality Assurance

• Principles of data quality (accuracy, validity, reliability)
• Routine data quality audits
• Source verification and triangulation
• Common data quality risks and solutions

Module 8: Basic Data Analysis

• Descriptive statistics (frequencies, means, charts)
•  Basic qualitative coding
• Summarizing and interpreting results

Module 9: Reporting and Data Visualization

• Writing research findings
• Creating charts in Excel
• Simple dashboards and visual summaries
•  Presenting data to stakeholders

Module 10: Final Assessment Project

• Design a questionnaire
• Build and deploy a digital form
•  Collect sample data
•  Clean, analyze, and present findings

Course Features

•  Step-by-step modules from beginner to intermediate level
• Practical exercises and real-life case studies
• Hands-on training with digital tools (KoboToolbox, ODK, Google Forms)
• Downloadable templates: questionnaires, codebooks, datasets
• Quizzes and assignments after each module
• Instructor support and feedback
• Final project covering the entire data collection workflow
• Certificate of completion

Target Audience

This course is ideal for:

• Beginners interested in research or data work
• Research assistants and enumerators
• Data analysts and monitoring & evaluation officers
• University students and project staff
• NGO staff collecting field data
• Anyone who wants to improve their data skills for academic or professional use

Course Requirements

Learners should have:

Basic computer skills (typing, navigating a browser)

Access to a laptop or desktop computer

Internet connection for using online tools

No prior experience in data collection is required

(Optional) Basic Excel knowledge is helpful but not mandatory