Campbell’s Law and the Net Promoter Score

To make better decisions, you need data. That’s become a truism. But can the process of using the data lead to bad outcomes? It seems like a hypothetical question, but it doesn’t take long to find a few key metrics that, when tracked, can lead to unwanted outcomes: On-time departures: On-time flight departures for airlines

Read More »

Sample Sizes Needed to Exceed NPS Benchmarks

So, you’re planning to collect data and you want to know whether your Net Promoter Score (NPS) is significantly above 50%. Established benchmarks can help research teams know if they’ve reached acceptable thresholds, such as a high Net Promoter Score (e.g., more than 50%). A high NPS is associated with successful product launches. But an NPS

Read More »

How Does Statistical Hypothesis Testing Work?

Statistically significant. p-value. Hypothesis. These terms are not only commonly used in statistics but also have made their way into the vernacular. Making sense of most scientific publications, which can have practical, significant effects on public policy and your life, means understanding a core framework with which we derive much knowledge. That framework is called

Read More »

48 UX Metrics, Methods, & Measurement Articles from 2020

Happy New Year from all of us at MeasuringU! 2020 was a crazy year, but we still managed to post 48 new articles and continued improving MUIQ, our UX testing platform. We hosted our seventh UX Measurement Bootcamp, this time virtually. The change of format was a challenge, but it was fantastic to work with

Read More »

Leading Vs. Lagging Measures in UX

Driving down the road while only looking in the rearview mirror … that gives you a good idea of where you’ve been, but unless the road behind you is exactly like the one in front of you, you may run into some obstacles, to put it mildly. Safe and effective driving means looking forward and

Read More »

Confounded Experimental Designs, Part 1: Incomplete Factorial Designs

UX research and UX measurement can be seen as an extension of experimental design. At the heart of experimental design lie variables. Earlier we wrote about different kinds of variables. In short, dependent variables are what you get (outcomes), independent variables are what you set, and extraneous variables are what you can’t forget (to account

Read More »

Are Cumulative Graphs Misunderstood?

Important decisions should be informed by data. And one of the most common ways of displaying data is by using graphs to better visualize relationships. Graphs can be powerful tools to compactly illustrate patterns. But the type of graph and the visual elements selected can lead to (usually) unintentional misinterpretation. For example, we have written

Read More »

Ten Things to Know about the RITE Method

If you have the budget and time to test with fifteen users, it’s better to break up those fifteen into three groups and make changes between rounds than test all fifteen in one round before making any changes. When you see a user struggle to complete a task because of a poorly labeled field or

Read More »

Does the Von Restorff Effect Influence User Preference?

One of these things is not like the other. That’s the theme of a segment on the long-running US TV show Sesame Street. As children, we learn to identify similarities and differences. And after seeing a group of things that look similar, we tend to remember the differences. Why? Well, one theory describes something called

Read More »
0
    0
    Your Cart
    Your cart is emptyReturn to Shop
    Scroll to Top