Carl J. Pearson, PhD

TL;DR – outcome impact is most important to an organization, but the hardest for researchers to track. There are some cases where researchers can improve, but in certain situations researchers cannot track outcome impact so they must focus on influence impact. In my framework for research impact, impact includes the research process and how its…

I conducted a thematic analysis of the thirteen research impact frameworks to develop a framework based on a descriptive view of existing frameworks and systems-thinking approach to each component. In this post, I lay out the framework that consists of three types of research impact: execution, influence, outcome. This framework is intended to be heuristically…

Survey weights are a useful tool in a researcher’s tool kit to account for selection bias and accurately generalize results to a population of users. Read my previous post to learn more about why you should use survey weights generally in UX research. That article goes over multiple approaches and strategies for weighting at a…

I love to espouse why human factors psychology is a great path for UX research education. That said, I recognize one blind spot in my training: survey weighting. In human factors, we often rely on experimental design to mitigate concerns about individual differences and biases in our data. In UX research, we don’t often have…

Quantitative UX research is a growing sub-field of UX research. Whether you’re a qualitative UX researcher looking to broaden your skills or someone from a related field looking to pivot, it can be hard to know how to build  your quantitative UX research skills. This shares four pillars to learn quantitative UX research and some…

TL;DR: UX researchers should learn SQL to get their own participant lists and better understand company metrics and product questions. When learning SQL, keep in mind that you have to learn the syntax but also the unique ways your organization has built their data infrastructure. In all tech companies, and even most companies outside of…

tl;dr: When planning a successful survey, ensure the number of invites you need to send is smaller than the number of invites you have available to send by computing the survey viability score yourself or using this calculator. Early in my career, there were a few questions I didn’t always remember to ask when planning…

When was the last time you got a confusing survey from a company or app? If you’re a UX researcher, did you screenshot to discuss with your coworkers (like I always do)?  Here is an in-app survey I got from TikTok that made me feel confused: why were the emojis in black and white? What…

Introduction Why does UX research face the prospect of democratization, while other product functions do not? This question is just the type of problem we as researchers should tackle. By knowing the answer, it unlocks some strategy for how to contend with the threat or opportunity of research democratization.  Research democratization  Unless you’ve been quietly…

tl;dr Strategy/tactics definitions conflate generative/evaluative research with large/small scope research. Evaluative work can be impactful at a strategic level when it appropriately covers the scope of a system. Generative work can be tactical when it’s constrained to be too focused within existing product offerings. Background It’s a common conversation lately in UX that teams need…

I often get asked the question, what does a quantitative UX researcher actually do? There’s no single, correct answer. Rather than discussing educational backgrounds, tools, or methods as others have done before (1, 2, 3), this post outlines the thinking behind the types of projects I’ve typically done as a quantitative UX researcher (UXR). A…

And UX benchmarking can help Note: this article was originally published in UX Collective on Medium.com. tl;dr —With log-based analytics alone, we can’t see users that want to start a task but cannot find it. This leads us to overcount success rates. With behavioral UX benchmarking, we understand a user’s intent so we know how…

Note: this was originally published in UX Collective on Medium.com and is now published more formally in the proceedings for Human Factors and Ergonomics Society Annual Meeting. TL;DR — In the SUM benchmarking method, the calculation of completion rates creates a bias for completion rates which artificially inflates all ‘good’ scores and marks some ‘poor’…