Observations have always been one of my favorite ways to collect data. Watching how people move about and behave in a space is inherently addictive—there is so much you can discover if you pay close attention to what is happening around you, and it feels a bit like going undercover as a spy.

While there is often a bias toward quantitative data, numbers aren’t everything. I love the complexity and nuance revealed through qualitative data. Because qualitative data is open-ended, it helps you understand peoples’ thoughts and experiences in their own words–this can reveal interesting, profound, funny, and unexpected insights that would be lost in quantitative methods.

This post will help you determine—if indeed you are conducting human subjects research—whether it is exempt from IRB review.

This third post defines criteria for human subjects research by breaking down the term “human subjects research.”

In my first post in this IRB 101 series, I described what IRBs are and why they exist. IRBs exist to protect research participants. In this second post, I focus on risks to research participants.

My hope in leading the IRB 101 workshop and writing these posts is to help others overcome their nervousness about IRBs. Let’s start with a shared, foundational understanding of IRB.

Since the reality of coronavirus set in back in March, our team has been having a lot of conversations about study design.

Based on my experience presenting quantitative results to museums, I came up with a list of 10 things to know about quantitative results so you can better assess their meaningfulness.

This article describes critical appraisal, a method applied by us to help a museum’s education department make data-driven decisions about programming.

Figuring out sample size for interviews can sometimes feel trickier than for quantitative methods, like questionnaires because there aren’t tools like sample size calculators to use. However, there are several important questions to consider that can help guide your decision-making.

Sampling is a very important consideration for all types of data collection. For audience research and summative evaluations in particular, it is important that the sample from which data is collected represents the actual population.

Sample size is a standard question we are asked, particularly for questionnaires since we will be using statistical analyses. Read about what we recommend and why.

I’d like to piggy-back on the slow movement and talk about Institutional Review Board (IRB) and school district review, which is Slow with a capital ‘s’—for better or worse.

This article describes how the statistical procedure K-means cluster analysis can be used to deconstruct the complex nature of museum visitors.