Data is any information collected from text or numbers to help conclude.1 It’s a powerful tool for personal and professional use, as it helps you choose market segments, ideal marketing mix, correct decision-making strategies, and more.
In this article, you’ll learn what data collection is, the various methods of collecting data, and the most frequently asked questions (FAQs).
Definition: Data Collection
Data collection is gathering information from relevant sources to help solve research problems. Most organizations use it to evaluate the outcomes of problems, foresee future trends or probabilities, and answer relevant questions. Before you start data collection, consider your aim, the data types, and the procedure and methods you’ll use for storage, collection, and processing.
Data collection largely depends on two types of information, primary and secondary data. Primary data is collected through first-hand means like surveys, experiments, or observations. On the other hand, secondary data is information acquired through second-party sources or sources that aren’t the actual user. This data is already available for analysis and may include sources like magazines, books, newspapers, journals, and more.
Step 1: Defining the aim of the research
Identify your goals or the result of your task before data collection. If you’re unsure where to begin, start by creating a problem statement;2 ask yourself, what are the problems you’re addressing, and why do they matter? Then, create one or more research questions3 that help you define what you want to know.
Depending on your findings, you may formulate either type of data collection:
- Quantitative data – written as graphs or numbers for analysis through statistical techniques.
- Qualitative data – written as words for analysis through categorizations and interpretations.
Collecting quantitative and qualitative data requires ideal situations to use the data effectively. For instance, testing a hypothesis,4 gaining large-scale statistical insights, or measuring something precisely requires collecting quantitative data. On the other hand, collecting qualitative data is ideal for exploring ideas, gaining detailed insights into specific contexts, or understanding experiences.
However, if you have a research problem with several aims, you can use a mixed-method approach that collects both, quantitative and qualitative, data.
Step 2: Choosing a data collection method
Depending on the data you’re collecting, choose one of the following methods that best suits your research:
- Experimental research primarily uses a quantitative approach.
- Interviews, focus groups, and ethnographies form part of the qualitative method approach.
- Surveys, archival research, observations, and secondary data collection may be qualitative or quantitative.
The following are some methods that can help you answer your research questions:
|Method||When to use the data||How to collect the data|
|Experiment||Testing a causal relationship||Manipulate variables,
Measure effects of variables on others
|Survey||Understanding the general opinions or characteristics of groups of people||Use in-person, virtual, or over-the-phone questionnaires|
|Interview/focus group||Understanding more profound opinions or perceptions of a topic||Ask open-ended questions in focus group discussions or interviews|
|Observation||Understanding something in their natural setting||Survey or measure a sample without interfering with them|
|Ethnography||Understanding the culture of an organization, business, or community first-hand||Participate and join a community group, Record your reflections and observations
|Archival research||Understanding historical or current conditions, events, or practices||Access documents, manuscripts, or records from depositories, libraries, or the internet|
|Secondary data collection||Analyzing data from populations that you can't get first-hand||Finding existing datasets already collected from sources like research organizations or government agencies|
Step 3: Planning the data collection procedures
After knowing the method(s) you’ll use, plan how you’ll implement them. Understand the procedures for accurate measurements or observations of the necessary variables.5 Also, know the form your interviews and surveys will take: for instance, experimenting requires you to decide on your experimental design.6
Operationalization entails converting concepts from abstract ideas into measurable observations. The process allows you to translate the conceptual definition of your research to the operational definition of what you’ll measure.
Suppose you want to collect quantitative data by using surveys and decide to measure supervisors’ leadership concepts. You can operationalize this concept in the following ways:
- Ask supervisors to rate their leadership skills on a scale of 1-10, judging their ability in decision-making, delegation, and dependability.
- Ask their employees to give anonymous supervisor feedback regarding the same topics.
Multiple ratings allow you to cross-check your data and assess the test validity of your research measures.
A sampling plan is a process of obtaining a data system that involves a defined population and a sample. The population is the group you plan to conclude on, while a sample is a group you will collect data from.
How you recruit participants for your sample determines your sampling method. Finding the correct method depends on factors like timeframe, the needed sample size, and the accessibility of the collected data sample.
Having a detailed manual that standardizes procedures used in data collection when working with multiple researchers is essential. However, ensure you lay out specific step-by-step instructions for collecting data for everyone to remain consistent. This way, your data will be reliable, and you can replicate it for future studies.
Creating a data management plan
Before data collection, organize and store your data by:
- Finding ways to safeguard and anonymize data to prevent theft of sensitive information like identity numbers and names if you’re gathering information from people.
- Formulating a systematic way of performing data entry or transcriptions to reduce distortions when collecting data through interviews.
- Having an organization system that routinely backs up data to prevent losses.
Step 4: Collecting the data
Before you proceed with data collection, implement your preferred methods to observe or measure the variables you’re researching on. Here’s an example of how you can collect quantitative and qualitative data:
You formulate a survey with closed-ended and open-ended questions and hand it out to 300 company employees to gather information on manager perceptions across different locations and departments. Closed-ended questions ask the employees to rate their managers on a scale of 1 – 10. This survey creates numerical data you can statistically analyze for patterns and averages.
In comparison, the open-ended questions seek employees’ opinions on what their managers are doing well and possible future improvements. These questions produce qualitative data you can categorize through content analysis to get more feedback.
Record high-quality data using the following practices:
- Always double-check your manuals for errors
- Get an indication of your quantitative data quality by accessing the validity and reliability of your data
- Record all relevant data when and as you collected them
In data collection, using the appropriate methods is crucial when searching for the right solutions for your research problem.
The four steps of data collection are:
- Defining the aim of the research
- Choosing a data collection method
- Planning the data collection procedures
- Collecting the data
1 systems thinking “Data, Information, Knowledge, and Wisdom.” Accessed on October, 19, 2022. http://www.systems-thinking.org/dikw/dikw.htm.
2 interaction design. “Problem Statements.” Accessed on October, 19, 2022. https://www.interaction-design.org/literature/topics/problem-statements.
3 writing center. “How to Write a Research Question.” Accessed on October, 19, 2022. https://writingcenter.gmu.edu/guides/how-to-write-a-research-question.
4 collegeessay. “How to Write a Hypothesis for a Research Paper.” Accessed on October, 19, 2022. https://www.collegeessay.org/blog/how-to-write-a-hypothesis.
5 fluxnet. “Data Variables.” Accessed on October, 19, 2022. https://fluxnet.org/data/aboutdata/data-variables/.
6 bioversityinternational. “EXPERIMENTAL DESIGNS AND DATA ANALYSIS.” Accessed on October, 19, 2022. https://www.bioversityinternational.org/fileadmin/bioversity/publications/Web_version/108/ch06.htm.