3a. Preparing for analysis

Preparing for analysis

An introduction to analysis

Analysis is about making sense of what you heard and observed during user research. You'll look for themes and patterns in your data, such as:

  • Cultural or social beliefs that could make certain types of solutions more or less acceptable.
  • An activity that most people are doing today that is difficult or frustrating.
  • Smaller problems that need to be solved before a bigger problem can be addressed.
  • Statements about what people want or wish for.

The patterns that emerge will help you decide which problem you should actually focus on solving, and the design principles for an ideal solution. You may find that the problem you need to focus on differs from the problem you thought you were going to address at the beginning of the process.

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A typical analysis process involves:

  1. Offloading your user research data so it is organized, easy to understand, and easy to manipulate.

  2. Categorizing the data in a systematic way to extract key insights.

  3. Turning your insights into problem statements (“How might we…”) and design principles (“The solution should…”).

Distinguishing between observations and interpretations

As each research session concludes, you’ll have a combination of notes, sketches, photos, videos, and audio recordings to make sense of.

Before you begin offloading your data for analysis, it's important to differentiate between two types of data you may have collected: observations and interpretations.

Characteristics of observations

Characteristics of interpretations

  • A record of something a user said (a direct quote) or did.

  • Involves no additional editing or explanation by the researcher.

  • For example, “Marion walked to the counter and purchased a bottle of water.”

  • Involves judgement, inference, or assumption on behalf of the researcher.

  • Includes ideas, hunches, or explanations the researcher has.

  • For example, “Marion bought a bottle of water because she was thirsty.”

Observations are the raw pieces of data that you will typically begin working with during analysis. Your interpretations are valuable as well, but it’s important to keep them separate from your observations and to recognize that they reflect your own assumptions and biases.

There is a bicycle in Joseph’s hallway outside of his dorm room.

  • Observation
  • Interpretation
Is this statement an observation or interpretation?

Joseph seems to be uncomfortable with the size of his dorm room.

  • Observation
  • Interpretation
Is this statement an observation or interpretation?

Joseph says, “I don’t like storing my bike in the hallway.”

  • Observation
  • Interpretation
Is this statement an observation or interpretation?

If Joseph had a better place to store his bike he would probably feel less stressed.

  • Observation
  • Interpretation
Is this statement an observation or interpretation?

Joseph listens to a podcast while riding his bike to class.

  • Observation
  • Interpretation
Is this statement an observation or interpretation?

Joseph says that the podcast keeps him entertained and makes riding the bike more enjoyable.

  • Observation
  • Interpretation
Is this statement an observation or interpretation?

Try it out: Distinguish between your own observations and interpretations

Differentiate between observations and interpretations in your own user research notes. Highlight or underline interpretations in a different color so that you can easily distinguish them.

Offloading your user research data

Offloading user research data

The first step in data analysis is offloading your data. Offloading allows you to physically manipulate and organize your data as you look for patterns and themes.

Offloading your data involves:

  1. Expanding any abbreviations or shorthand that you used while taking your notes, so that it will make sense to an outside reader.

  2. Writing down individual observations on a sticky note or small piece of paper.

  3. Writing down individual interpretations on a different colored sticky note or piece of paper so that you can easily distinguish them.
  4. For photos, video, and audio, you will select which items or portions of the recording to keep, and which to leave out.

You should expect to spend about 50% of the length of the original interview offloading relevant data. For example, if you conducted a 2-hour interview, you will spend about 1 hour offloading your data.

Do you need to offload every piece of data?

You do not need to offload every single observation and interpretation from your notes. You should focus primarily on those which are relevant to the topics you are interested in, and the problem you think you will be trying to solve.

If you are uncertain about whether to include a particular observation or interpretation in your analysis, go ahead and include it. You never know which piece of data may spark a new idea or help you identify a larger pattern or trend!

Tips for labeling your sticky notes

As you offload your data, be sure to label individual observations and interpretations with a participant identifier (this may be the participant’s initials or a participant number), and the date of the research session. Doing this allows you to go back to the source of the data point quickly.

Tips for offloading other types of media

Offloading photos usually involves printing them on small pieces of paper so that they can be manipulated alongside written observations. Like observations and interpretations, photos should be labeled with a participant identifier and the date of the research session.

Audio and video recordings can be tricky to work with during the analysis process in their raw form, so it is usually easier to use them as a source for direct quotes and observations of participant activities or behavior. 

Keep note of the timestamp associated with these quotes and observations, and include them on the sticky notes alongside the participant identifier and date of the research session.

Try it out: Offload your own user research data

Begin your own analysis process by offloading the data from your own user research onto sticky notes or small pieces of paper.

Don’t lose these pieces of paper! In the next module we’ll practice two different categorization methods using these pieces of paper.