Data Collection
About Data Collection
Data
collection is the process of gathering quantitative and qualitative
information on specific variables with the aim of evaluating outcomes or
gleaning actionable insights. Good data collection requires a clear
process to ensure the data you collect is clean, consistent, and
reliable.
Establishing that process, however, can be tricky. It
involves taking stock of your objectives, identifying your data
requirements, deciding on a method of data collection, and finally
organizing a data collection plan that synthesizes the most important
aspects of your program.
Data collection involves steps such as
• Define your project objectives
• Identify your data requirements
• Determine your method of data collection
• Organize your data collection plan
Why is Data Collection important?
Data
collection enables a person or organization to answer relevant
questions, evaluate outcomes and make predictions about future
probabilities and trends. Accurate data collection is essential to
maintaining the integrity of research, making informed business
decisions, and ensuring quality assurance.
• Data empowers you to make informed decisions
• Data helps you identify problems
• Data allows you to develop accurate theories
• Data will back up your arguments
• Data tells you what you’re doing well
• Find Solutions To Problems
• Keep Track Of It All
Who should take the Data Collection Exam?
• Professionals engaged in research or quality management
• Innovators
• Anyone who wants to assess their data collection skills
• Research managers and senior executives
•
Professionals working in outsourced companies responsible for market
research, consumer research, or any research-related activity
• Anyone interested in data collection
• Individuals who encounter data collection within their day-to-day job
• Students
Data Collection Certification Course Outline
1. The Research Process
2. Scientific Method and Research Design
3. Research and Data Objectives
4. Research Communication
5. Primary Data Collection
6. Secondary Data Collection
7. Sampling and Hypothesis
8. Data Processing and Analysis