Just as the framing of a question determines how someone might respond to you, the way you perform research dictates what your efforts reveal. Choosing the optimal data collection methodologies ensures research outcomes meet the intended goals. The choice for a data collection method in research should reflect the constraints, needs, and goals of the project, but there's no one-size-fits-all answer. Here are five different data collection methods to add to your research toolkit:

 1. Observation

Observation is a type of study in which the behaviors and actions of study participants are observed. The four main types of observation studies are: Participant, Naturalistic, Structured and Case Study. 

  • Participant Observation, the researcher actively engages with the environment or group under observation. 
  • Naturalistic Observation, the researcher maintains some distance and observes the participants in their natural environment.
  • Structured Observation, the researcher will still observe natural behavior, but in a more structured setting. For example, introducing a specific task for the participant to complete. This allows the researcher to gather more quantitative data rather than qualitative. 
  • Case Study, the researcher will take a more in-depth look at an individual, social group or even an event to provide a detailed analysis. 

User researchers may often work in groups, relying on consensus, teamwork, and mutual observation to improve the quality of research data.

2. Surveys and Focus Groups

Conducting surveys can be a cost-effective way to gather data, but it is a less personal or interactive data collection method. It's widely used in the digital arena, and it works well in situations that might benefit from anonymous responses. Focus groups offer a similarly quick route to gleaning impressions, but they can pose more logistical challenges.

Both methods run the risk of participant bias. So to minimize bias, be cautious of how you format user research questions, present material in a judgment-free manner, and incentivize participation. When you analyze your data in Notably, be sure to create a tag for each of your participants. This will allow you to quickly spot bias and see where one participant’s voice may be influencing a data set more than another’s.

To minimize bias, be cautious of how you format questions, present material in a judgment-free manner, and incentivize participation.

3. Interviews

Interviewing a participant helps build a rapport that can break down barriers. Interview Analysis often leads to deep insights based on the perspectives participants bring to a conversation. With Notably, being able to tag and theme your digital stickies allows you to see patterns as they emerge, which makes synthesizing data from interviews quick and easy.

In the data section of Notably, you can create a template to use during an interview for notes, upload audio/video from the field and/or Zoom calls to be transcribed. From here, you can view data from all of your interviews in one place, and filter between different tags to see recurring patterns, themes and insights.

Create your own AI-powered templates for better, faster research synthesis. Discover new customer insights from data instantly.

4. Analytics Tools

Analytics tools are a hallmark of the modern digital era. Whether you're running a business or in charge of a public entity, there are plenty of ways to track user behavior, satisfaction, and performance in realtime, numerical terms.

Using analytics tools might seem like a quick route to aggregating data at scale. However, it's extremely easy to be overwhelmed by information. Some of the figures that technology provides for you may not reveal useful information. It's important to remember that although analytics can reveal correlations, they can't reveal underlying causes without further analysis. For example, analytics tools might tell you the actions someone takes, but it doesn’t necessarily tell you why they took an action or how it felt when they took it.

5. Document Reviews

Reviewing existing literature is a time-honored data collection method in research. By exploring what other people have said, done, and written about a topic, researchers can expand horizons, learn faster, and avoid duplicating work.

As with other methods, reviewing documents has its limitations. Building on a prior result just because it seems appropriate may result in an incorrect conclusion if you lose sight of the original context. With inductive study methods, like grounded theory research, literature reviews may even bog your work down with inappropriate biases or preconceptions.

Quantitative vs. Qualitative Data Collection

Some data collection methods, like using analytics tools, are strictly quantitative. Others can offer a mix of qualitative and quantitative results. For instance, a survey with close-ended questions will tell you how many people answered each way, even if the questions aren't numerical themselves.

It's important to pick a method that fits the research you want to explore. On the other hand, be open to adjusting based on what you discover. A study that starts off focusing on qualitative information might benefit from switching to a quantitative approach, but only once you've explored the concept enough to know which data points deserve focus.

Data collection is only one small (but important) step in the research process. The next step is to synthesize the information you've gathered, to form insights and share what is important and why it matters. 

Your choice of note-taking tools goes a long way towards simplifying synthesis. Although there'll always be a place for pen and paper, digital canvases like Notably help you discover relationships between data as it appears. Get started for free to make your customer research data easier to collect, comprehend, and share.

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