15 Minute Read
In late November, our team came together to discuss the importance and best practices of using tags to make meaning out of mess.
During this 1-hour event our co-founders, Brittany Fulller and Allison Marshall, discussed inductive and deductive tagging, the tagging process and beyond tagging: interpreting insights. Click here to view the full recording, or continue reading for an event recap.
Before we go into the details of tags, let’s quickly introduce Notably.
At its core, Notably is a data-driven product that helps researchers to collect, analyze, and share data.
Notably replaces these products with one designed for your research workflow:
Coding or “Tagging” is the process of taking raw data and assigning meaning to it. Think of coding as a way to give structure to your notes, but something to emphasize here is that while coding is in fact a part of analysis we shouldn’t think of it as a substitute or synonymous with the analysis process itself. It also helps you to make connections between different pieces of data and identify motives, themes, and trends.
Here at Notably we often use this framework, based on the DIKW pyramid, to guide strategic decisions. This framework can also be applied to coding.
When used effectively tags can help you make sense of the mess by providing structure and categorization. They help you to quickly identify patterns in your data as well as uncover insights that could be lost.
Coding qualitative data sounds technical, but really it's a way of layering on meaning to pieces of otherwise disparate information during analysis.
For most projects, there will be a combination of both inductive and deductive tagging. Let's take a look at each of these tags.
Inductive: This is about answering the question, "What is the data telling me?". It is a bottoms-up or grounded theory approach. With inductive tagging, the researcher doesn’t know what the tags will be until they start analyzing the data itself. In discovery research, tags are mostly inductive because you might not even know what the conversation is about or where it will lead until you’re in them.
Deductive: Unlike inductive tagging, here you are answering the question, "What am I setting out to learn?". Here you'll be working backward from key concepts and goals. With deductive tagging, the researcher has a structured interview guide and key concepts that they’re going into the conversation with. For example, in evaluative research, tags are mostly deductive because they are researching key experiences in a journey.
While coding is inductive and deductive, the process of synthesis requires abductive thinking, also known as “generative” or “divergent” thinking. It’s creative and requires at times illogical steps to come up with new and innovative ideas. There is no singular right answer.
Grounded theory, a research method that relies on induction and is popular in qualitative research, is broken down into three areas:
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The goal of tagging isn’t just to categorize data but also to interpret and synthesize this information into a cohesive whole. This is where the power of coding comes in—it helps you to not just classify but also connect, interpret, and contextualize this data for further analysis.
The true goal here isn’t to simply have coded data. We use this coded data to generate ideas that are evidence-based. Analysis is the decisive link between raw data and new, conceptual ideas.
Here are three important things to keep in mind about coding:
When it comes to your data, there is plenty to gain from attempting different analytic angles. New insights can be generated, and you can at times escape from analytic perspectives that have become stereotyped, stale, and boring. We, therefore, want to encourage a modestly playful approach to how you analyze and interpret your data.
There are two distinct types of tags in Notably that are based on the concepts of deductive and inductive tagging. These are project tags and global tags. These two types of tags allow you to code of various degrees of generality. You can then create and retrieve insights at different levels.
As you can see, each type of type will bring about different insights. When used selectively, you have the ability to draw insightful insights, those that are aligned with your goals and objectives.
There are a number of ways that you can use tags in Notably to garner the best results. Here are just three of them.
The biggest difference when using themes (groupings of similar pieces of data) in Notably is that tags are one to many, while themes are many to one. When you think about a theme, consider a special category of an inductive tag. It’s special because most often you only want one piece of data to belong to one theme. On the flip side, one piece of data may have various tags and those tags might be unevenly distributed within the theme.
A theme is also more of a concept than a label. For example, most often our tags could be one or two words maximum. A theme on the other hand might be a few words strung together in a phrase, with less rigid qualifications.
Think of tags as a way to play with tension and likeness across themes.
Tags are a powerful tool to interpret, analyze and draw insights from your data. They help you to move away from objectivity and toward subjectivity.
Notable gives you the opportunity to look at data in an entirely new way, allowing you to uncover new ideas, relationships, and insights.
If you are looking to collect, analyze, and share qualitative research, you need to look no further than Notably. With its powerful tagging system and theme-based analysis, you’ll be able to make meaning out of a mess.
So what are you waiting for? Sign up for free to start creating better, faster insights from your qualitative data.
To rewatch the event recording and highlights, click here.
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