# Data Blocks

Data blocks are containers for the questions you ask and your source for answers.

A study is a container for your research project. Every SoundingBox study consists of two elements: tasks (the things you want people to try) and questions (survey-like questions that you ask people answer after they do their activity).

You build your study using the SoundingBox study builder. When you're ready you launch it. We handle getting responses for you, or you have your own participants complete it. When your study is complete your next challenge is to analyze your results and get your insights.

This is where data blocks come in. Data blocks are containers for the questions you ask. They provide built-in analytics that help you get answers.

Data blocks come in a handful of flavors. The most common is the scale question data block. Scale blocks summarize the results for each scale question you ask.

Each scale data block has the following elements:

  • A score
  • An emoticon that shows how the result compares to benchmarks
  • The progress toward the benchmarking target, expressed as a percentage

Anatomy of a data block
Most blocks consist of a score, an emoticon and progress toward a goal (the benchmarking target).

The score has a little more nuance than just being the average of a scale result (say the choice of between 1 and 5 with 5 being best). That's why it's not a value like 2.5 for example. Instead of just doing an average of answer choices between 1 and 5, we convert the score to an 100 point scale. This conversion makes it easier to compare scale results across questions and studies, no matter how many item choices your scale has.

Another thing we do to the score is ensure that the top end of the result (100) always means something "good" or desirable and the bottom end of the result (0) always means something not desirable or bad. You configure the semantic meaning of the scale when you design your study. This makes it easy to glance at the tile and know what the number means. Is this something good that happened or something bad? Either way it's something you're going to want to explore.

# Exploring with data blocks

Data blocks are a great way to see at a glance how your study went. But they're not an end in themselves. They're a starting point for going deeper to get your insights. Did people like version A or version B? What's behind that? Data blocks can answer the first question instantly. But the second question likely requires some exploration of your task replays.

Let's say a data block shows something especially good or bad and you want to know more. Clicking on the block sorts all the responses by that block from worst to best and jumps to the first replay. Chances are this person will have something to say or do something that provides important clues for the reasons behind their answers, clues about what you may want to change in your design and why.

Data blocks in action
Data blocks are your jumping off point to deeper insights.