A Complete Guide to Data Collection Methods

Our map of reality is defined by it. It can make or break the future of a small business. It’s the evolutionary secret of every creature on the planet, and it works behind the scenes aligning your products and services to your customers’ needs.

What is this mysterious agent of fortune that holds the keys to destiny? It’s data. More specifically, the methodology of data collection.

As a small business owner or entrepreneur, you’re the shot-caller. It’s you that makes product and marketing decisions, manages time and assets, and needs to understand customer behaviour.

Larger companies can rely on an engineering team to make critical data-driven decisions, but smaller businesses tend to have less wiggle room in their payroll. Without a firm grasp of data collection, your decision-making is in the dark.

In this article, we'll explore the process of gathering data, and how each method of data collection provides the insight you need to tolerate the challenge of evolving consumer preferences and markets.

The different types of data

Data is a vast domain. In fact, data is an infinitely expanding world of information. It’s everything, everywhere, all at once (great movie, by the way)—being collected, filtered, stored and transformed.

Fortunately, raw data can be sorted into a system that makes it easier to handle. Yep, data has an intrinsic division. Like the lateralization of the brain, each type of data is either qualitative or quantitative.

There’s another grand division in the sea of data: its origin. If data comes from first-hand experience, it’s considered primary data. If it comes from someone else's research publication—literally secondhand information—then it’s considered to be secondary data.

To make the absolute most out of data, you need to learn how and when different types are useful.

Qualitative vs quantitative data

Qualitative research discovers qualities like hair colour, favourite salad dressing, or music genre; quantitative research is more concerned about the number of blondes that like blue cheese salad dressing and listen to Van Halen.

Qualitative data gives context and describes phenomena. It continuously answers the question: what more can we know about this?

For example, you can get qualitative data from feedback forms on your website that help you get to know your customers. The more you listen to them, the better you can tune into your customers’ needs.

The key difference between qualitative and quantitative data is what you intend to do with them.

Qualitative data has no intentions of performing mathematical feats. Now, it’s certainly possible to do math with qualitative data, but if you did - it would become quantitative data. Instead of organizing by numbers, qualitative data just wants to get to know the subject better.

On the other hand, quantitative data always intends to ‘math the hell out of things’. The idea is to find patterns, statistics, and prove something about your customers with numbers.

Both types of data sets are important to flesh out the whole picture. Qualitative data is subjective, interpretive, and exploratory. Quantitative data is objective, to the point, and conclusive. The former helps you to sync with customers' dynamics, and the latter determines precise answers about them.

What is primary data?

Primary data is the new stuff. It’s unaltered, unpublished, and untouched by the greasy fingers of interpretation. It is more valid, reliable, authentic, and objective than secondary data.

However, primary data isn’t always easy to come by. It demands more investment in time, focus, and resources, and small businesses may not be in a position to hire a team of scientists and private investigators.

But there’s no need to get discouraged—you can collect primary data online. Online forms are easy to set up, and cost but a fraction in time and money.

👍 Pros:

• Primary data is always relevant to the study
• Allows for useful limits on data parameters (who, when, how, what, and where)
• High-quality, reliable, and authentic data
• You can personally verify the data
• Possible to collect additional data during the research

👎 Cons:

• Very expensive and time-consuming
• A huge hassle to organize (what to look for, how to collect data, finding participants, setting objectives, etc)
• Ethical and legal considerations
• The time and effort making sure that the data is legitimate

What is secondary data?

If primary data is all about going to the source, then secondary data is about consulting a resource.

Secondary data is second-hand knowledge, already published and available as a resource for future studies. Although secondary data has limits on its capacity for precise revelation, it can still provide you with insight and context.

In fact, primary data is rarely useful without a framework of existing knowledge (secondary data). In other words: if we had no history of experience, we wouldn’t understand our current experience.

Examples of secondary data:

• Population census
• Housing statistics
• Social security database
• Electoral statistics
• Focus group transcripts
• Fieldnotes
• Journals of experimental research

👍 Pros:

• The hard work that it takes to develop, implement, and analyse the study has been done for you
• It takes less time
• Cost-effective
• Likely comprehensive
• Thanks to the internet, the research can be done from your swivel chair

👎 Cons:

• The information you find may not fully satisfy your research
• Your study’s reliability rests on someone else’s claims
• It’s at least a little bit outdated
• The possibility of copyright infringements
• You can’t teleport through space and time to clarify the results. (Well, maybe you can, but no one would believe you)

Primary data collection methods

Together, primary and secondary data tag-team the mysteries of the unknown world with seamless chemistry. But - it’s all about the tools.

Both types of data collection are essential, but primary data collection methods are your portal to first-hand, up-to-date information. They can organize and assess the fluctuations of market behaviour and customer feedback that inform decision-making with high precision.

With the right tools, it’s easy to use both qualitative and quantitative methods to collect primary data that aligns with your research purpose.

1. Surveys and questionnaires

You’re probably familiar with surveys and questionnaires. They seem to pop up everywhere.

A questionnaire is just a set of questions. It doesn't necessarily conduct a survey, but sometimes it does (if it’s feeling cute).

On the other hand, surveys always use questions, and so by default they always are questionnaires. However, question sets in a survey are used to extract statistics about a population (a census for example).

A simple form on your website that asks for feedback can also include questions that measure various features of your customers. After you receive a good chunk of feedback, analysis can reveal hard facts about your audience.

Surveys use only closed-ended questions (most of the time). To simplify the process of data collection and analysis, surveys use closed-ended questions to get standardized answers.

The time commitment is so little that people are more likely to share a quick opinion—especially if your online form is fun and direct.

Questionnaires use closed and open-ended questions. Apart from multiple-choice style questions that focus on quantitative data, questionnaires also use open-ended questions. Open-ended questions elicit unique, descriptive and sometimes lengthy answers for use as qualitative data.

As far as data collection methods go, questionnaires and surveys are highly accessible because you can conduct them online. With the help of data collection forms, there is a little-to-no effort between you and collecting online data that reports on customer feedback, market diagnostics, behavioural trends, and more.

Online surveys and questionnaires also have the advantage of:

• Reducing the time it takes to set up research studies
• Significantly reducing research expenses
• Respondents being able to access survey forms through mobile devices
• Intelligent online forms that can direct questions according to each response
• Adjusting questions anytime to capture useful information
• Precise targeting when selecting population profiles
• Complex data analytics that is always up to date and instantly computes

No matter the length of a questionnaire or survey, most people won’t spend more than ten minutes filling it out, so it’s best to keep these short.

👍 Pros:

• Easy to refine, expand, and make changes
• Easy to distribute, collect, and analyse
• Specific and practical data you can act on
• Anonymity increases the likelihood of candid responses
• Can target specific population samples
• Relatively inexpensive
• Easy to use with a smaller or larger population

👎 Cons:

• Context limitations can reduce the study depth
• Minimal qualitative data
• Respondents may answer dishonestly
• Questionnaires may not be completed if respondents lose interest
• Can be time-consuming

2. Focus groups

Focus groups are field studies - usually with a group of 6-12 people together in a room. At least one moderator is present to lead the group through a series of questions and prompts about the subject.

The idea is to stimulate group discussion that reveals any underlying attitudes, opinions, perceptions, and feelings about the subject. Focus groups can explore topics in-depth from the unique perspectives of people who share qualities such as sex, age, race, profession, etc.

Focus groups collect qualitative data through the use of open-ended questions. The moderator will usually have a list of questions, but the order and structure is not a necessary control.  

There are two common types of focus groups:

• Dueling-Moderator: as the questions are answered by the group, new ways of thinking are introduced by two opposing moderators. This kind of focus group dynamic can stimulate a deeper probe into the psyche of the participants.  
• Two-Way: instead of a second moderator, a second group acts as another perspective to stimulate deeper investigation. After one group listens to the other group answer questions posed by the moderator, the group that listens is able to facilitate more discussion.

👍 Pros:

• Immediate reception and synthesis of ideas
• Less expensive and time-consuming than one-on-one interviews
• Generates fresh ideas from each group of individuals
• Allows for clarification and exploration of questions

👎 Cons:

• Group members and moderators may demonstrate some bias
• Some training may be required
• Difficult to analyse or quantify the data
• The researcher has very little control over the outcome
• Some group members may dominate the discussion
• A lack of anonymity may suppress some discussion
• A lack of confidentiality

3. Interviews

Interviews bring up all sorts of imagery—the nervousness of a job interview, or maybe even a fireman sharing the story of a wildly ambitious cat he rescued from the top of an apple tree.

We usually picture the traditional interview as one-on-one and in-person. However, it’s not uncommon to interview more than one person at a time, over the phone, or even online.

Telephone interviews are efficient but have certain drawbacks. Online interviews have become increasingly popular with the emergence of accessible, easy-to-use, and inexpensive video calling. You can even record and save the interview material with the click of a button.

You can also conduct interviews with online forms. Previously a huge time burden, it’s now possible for you to automate the entire process by creating a pre-set form that you can send out to your desired interviewee.

👍 Pros:

• Allows for clarification and expansion of answers
• Able to gather in-depth information and pursue hunches
• Can tailor the discussion to each person
• Opportunity to break the ice
• Highly effective qualitative method
• Can observe behaviours face-to-face

👎 Cons:

• Bias may result from the interviewer’s presence and perspective
• Requires strong interviewing skills or training
• Tedious and slow to collect information
• Responses may be less honest
• Smaller population samples
• Data analysis and quantification is difficult

4. Observation

Observation seems to be pretty straightforward. You pick something that you want to learn about, place yourself in view of it, and then take notes. However, there are some obstacles to consider:

1. Anyone who tries to observe their own thoughts will find out it’s not as easy as you’d think. When a phenomenon is observed, it changes behaviour. This is known as the Hawthorne Effect.
2. There’s a lot going on in the world, and we are prone to distraction. To really discover the underlying mechanics of phenomena, you need to identify where to focus, and limit distraction.

The common types of observation methods deal with these considerations:

• Natural Observation is observing the behaviour of phenomena in their natural setting without any controls on the environment or impact from the presence of an observer.
• Participant Observation is when the observed is aware of the intent and presence of the observer. This is where you’d have to account for the behaviour changes in the presence of an observer.
• Non Participant Observation is when the researcher is incognito. The observed phenomenon is not aware of the presence or function of the observer.
• Structured Observation is when the observer has a plan of what to observe, how to observe it, what to record, and how to record what is observed. This is the least natural form of observation, but it’s easy to repeat and control variables.
• Unstructured Observation allows the researcher to observe at their leisure and decide on the go what is important to record.

👍 Pros:

• Easy to organize
• There’s a high level of accuracy in the results
• It is the most natural form of data collection
• Structured observation can reduce bias
• Can be combined with other methods of data collection
• It is the only way to get data in certain situations

👎 Cons:

• Some phenomena aren't possible to observe
• Bias is likely in unstructured observation
• Requires a skilled observer
• It can be expensive and time-consuming
• It's difficult to verify the data’s application

5. Case studies

A case study is not a data collection method in itself. It employs a variety of research methods to construct a detailed illustration of the subject.

Case studies use depth interviews, direct observations, secondary data collection (photos, videos, journals, clinical notes, official documents) and any other reliable sources of information to examine a person, group, event, or community.

Information on nearly every aspect of the subject is gathered to find patterns and causes of behaviour. Case studies are often used in clinical studies like psychotherapy, legal cases, and social work.

The major categories of case studies include:

• Prospective: this type of case study involves the careful observation of a subject over time. For example, a group of people who’ve had the flu may be monitored to observe the effects of their disease.
• Retrospective: this type of case study focuses on the historical narrative of the subject. For example, the researcher may look at a patient’s lifestyle history to determine the cause of their illness.
• Explanatory: this type of case study forms a theory based on a comprehensive examination of data in order to explain the phenomena.
• Descriptive: this type of case study only seeks to describe the phenomena as it occurs and manifests as a narrative.

👍 Pros:

• Uses a variety of qualitative data collection methods to the investigation
• Develops an in-depth image of the subject from multiple perspectives
• Provides a strong foundation for quantitative data collection methods
• Relatively inexpensive

👎 Cons:

• Data collection and analysis are very time-consuming
• Vulnerable to bias
• Difficult to standardize the data of a small sample size

Time to demystify your data

We hope that data collection is no longer a big mystery for you. You have begun to equip yourself with vital knowledge to navigate the endless tides of information. With the right tools, you can optimize your research efforts and easily hone in on data that makes the difference.

If you’re looking for help along the way—Paperform is a great tool to assist your data collection needs. With Paperform you can automate your data collection with smart surveys and forms, and get the insights you need.

Get started with our 14-day free trial, no credit card is required.

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