In UX surveys, semantic differential scales help measure user attitudes with nuance. This video covers what they are, their pros and cons, and how to write clear, balanced adjective pairs for UX research studies.
UX benchmarking allows us to track the long-term changes in the overall user experience of our product, while UX success metrics help us assess the short-term impact of a specific project or feature launch.
Users’ willingness to share data and their interactions with cookie permission box options vary quite drastically, falling into these five common user types: The Denier, The Skeptic, The Tech-Savvy, The Impatient, and The Enthusiast.
Statistical significance does not always equal practical significance. A difference may be statistically significant without having any meaningful impact in real life. Conversely, practical differences may not always achieve statistical significance.
Presenting study data to stakeholders is a crucial step of most projects. Make sure to present your data truthfully and responsibly to avoid costly negative outcomes and reputation loss. Indicate who your data represents and communicate the limitations of your findings.
Measurement error is the error we introduce when we measure or observe something about our users. It can come from different sources, such as the number of participants, individual variation between participants, testing environment, or other outside factors. This video helps understand and communicate such measurement errors.
False positives and negatives are common errors in quantitative studies that can lead to harmful business decisions. To avoid these mistakes recruit large enough sample sizes, representative participants, and control for confounding variables.
Engagement is an abstract, complex concept used to understand how much people interact with our products. Choosing the right engagement metrics goes beyond time spent.
Pilot testing involves conducting one or two test sessions to evaluate the design of a usability study. It checks for any issues before the actual testing phase, leading to more reliable results.
Confounding variables can affect the validity of data collected during research studies. It's important for researchers to know what they are and how to identify them.
