Unit 11: Intro to Photoshop
Unit 12: Design Accessibility
Unit 13: Product Design Thinking
Unit 14: User Experience Design
Unit 16: Introduction to Design Portfolios
Unit 17: Portfolio Development
Unit 18: Personal Branding
Unit 19: Case Studies
Unit 20: Portfolio Website Design
Unit 21: Career Coaching
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Synthesizing Research with Affinity Mapping

After you have done your user research, you need to sit down and analyze your research data to make them have informative value to your design.

1. Make a plan and set objectives for analysis.

Setting goals and analysis objectives ahead of time helps focus your study—and prevents you from collecting too much “noise.” By setting learning goals and carefully designing your study, you can collect just enough data to find meaningful insights without overwhelming yourself during analysis. 

2. Take notes immediately after sessions. 

If you’re conducting data analysis for interviews, multiple rounds of focus groups, or ethnographic fieldwork—you can improve your efficiency by reviewing notes, videos, transcripts, or other materials from each session and jotting down initial impressions immediately after. 

This process is called periodic analysis and its benefits include:

  • Preventing the wasted time and effort of redundant work 
  • Preventing your memory of different sessions from blending together
  • Reducing the risk of you missing important details that might become the bedrock of your work’s final quality
  • Identifying what was most important to you and other stakeholders in the moment

Ultimately, these benefits help you answer important research questions as efficiently and thoroughly as possible. Work smarter, not harder!

If you’re conducting user interviews with other team members, reconvene with your team after each conversation. Have a discussion about how your participants’ responses fit into your research questions. Does the whole team agree? Maybe you missed something that your teammate picked up on. 

If you’re conducting user interviews with other team members, reconvene with your team after each conversation. Have a discussion about how your participants’ responses fit into your research questions. Does the whole team agree? Maybe you missed something that your teammate picked up on. 

Periodic analysis for quantitative research

Periodic analysis is also useful in quantitative research, as going into a study with the wrong questions, metrics, or ranges can lead to big headaches down the line during analysis. By analyzing your variables for analysis, the assumptions behind them, and your data, as you go, you’ll be able to catch mistakes and anomalies early on, some of which may lead you to adjust your study.

For example, say your final data should be a bimodal distribution like the graph below. 

See how the graph has two peaks with a range of -4 to 4? Well, what if you assumed that the range you should be testing is -4 to 0? You would just have a normal distribution curve and be missing one of your peaks because you limited your range from the get-go.

3. Review all the data upfront. 

Before tagging, organizing, or analyzing anything, scan through the entirety of your dataset to see what jumps out at you. 

Just looking through your data ≠ analysis. But taking a moment to slow down and orient yourself to what’s there can make a massive difference in your ability to understand and apply analytical frameworks to the data. 

Once you’ve gotten familiar with what’s there, you can start to sort it into an easier, more manageable form. 

4. Organize your data.

Considering the implications of certain anomalies and outliers early on in the process can save you lots of time and money. But the only way to catch these things early on, or sometimes at all, is if you are analyzing your data at each phase of the study.

Qualitative data tends to yield a wealth of information, but not all of it is meaningful to your research goals. As the evaluator, it’s your job to sift through the raw data and find patterns, themes, and stories that are significant in the context of your research question.

This process of organizing your data is known as “qualitative data reduction.”

What is qualitative data reduction?

Data reduction is the process of thickening and intensifying the flavor of a qualitative data by simmering or boiling.

Basically, you’re reducing the volume of data into a summarized and more meaningful format… kind of like turning juice into a flavorful syrup by boiling away all the boring water.

There are several common ways to organize qualitative research data. The most common methods in a UX research context are thematic analysis, content analysis, and narrative analysis. Discourse analysis, framework analysis, and grounded theory, while less commonly used in user research, are two other methods worth noting.

Thematic analysis 

Thematic analysis is a systematic approach to grouping data into themes that represent user needs, motivations, and behaviors. In some cases, these themes may be directly adapted from your learning goals and research questions, while in others, you may see these themes emerge after the data is collected. 

Thematic Analysis process and examples for UX research

Thematic analysis is also known as affinity mapping. After putting research data into groups based on themes, you need to name each theme.

However, there is a really important thing to note here – do not just put a few categorization words to it. Use something called an “I” statement which reads like a person making a statement. For example, “I don’t like to get up early”. This is a very specific sentence that summarizes an “affinity”, or attributes, preferences, habits, personalities, etc. The trick is to make your thematic titles read like a complete sentence so that people don’t need to go into each one of your research data points to understand what theme it was about.

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