Question

User stories is one of methods of information gathering in requirements discovery. Because they are based on a practical situation, stakeholders can relate to them and can comment on their situation with respect to the story. How would soliciting user stories before designing a questionnaire make it more relevant?

Answer 1

How would soliciting user stories before designing a questionnaire make it more relevant?

1. How to Design a Story

Before creating a survey, it's important to think about its purpose. Common purposes include:

Compiling market research

Soliciting feedback

Monitoring performance

2. The Best Survey Question and Answer Styles

The way you structure questions and answers will define the limits of analysis that are available to you when summarizing results. These limits can make or break your ability to gain insights about your key questions. So it's important to think about how you'll summarize the response to questions as you design them not afterwards.

There are four main question and answer styles, and therefore four main response data types:

Categorical - Unordered labels like colors or brand names; also known as "nominal

Ordinal - Likert scales like "strongly disagree to strongly agree" or "never to often"

Interval - Ranges like "number of employee"

Ratio - Numbers like inches of rain

Survey apps provide a wide range of data-collection tools, but every data type falls into at least one of these four buckets.

3. How to Select Stories Respondents

Most surveys are sent to a small subset of a larger population. Using such samples to make general statements about the population is called inference. Descriptive statistics are statements about just the sample; inferential statistics are statements about a population using a sample.

It's worth noting that inferential statistics with surveys is difficult and commonly impossible, even for experts. Sometimes you just can't generalize the sample to the population in a reliable way you're stuck making statements about people who actually filled out the survey. Most of the time, you can chalk this up to sampling bias: when your sample is not reflective of the population that you're interested in. Avoiding sampling bias is particularly important if you intend to analyze the results by segment.

4. Focus on the High-Points

Visualizing data is one of the most important activities I carry out at Zapier. It's a passion of mine because graphs can elicit a wide variety of emotional responses. People have very different reactions to data based on how it's graphed, so it's important to be thoughtful when creating visualizations.

Knowing the challenges with measurement, I guide my coworkers at Zapier to focus on trends and avoid reading too much into small differences in data. It's easy to lose the big picture when looking at statistics and graphs, so it's important to remember that some error exists with any method. Don't miss the forest through the trees; when interpreting results, start with the largest differences first, not the most unusual. If you notice an unusual result, be skeptical and see if the result can be replicated in another survey.