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Using Area Weighting and Confidence Intervals to Improve Quantitative Eye Tracking Data

featured / user experience / eye tracking / quantitative
For this blog post I wanted to address an issue user researchers often encounter when conducting an eye tracking study with different sized areas of interest (AOIs). Specifically, researchers often attempt to identify which AOIs are attracting the most attention. For example, imagine the heat map in Figure 1 represents the results from 30 participants asked to identify how many followers this Twitter profile has. The image on the left shows five main AOIs: Profile, Trends, Feed and Suggestions. Eye Tracking Fors Marsh Group In general, the heat map tells a clear story: most people looked at the "Profile" AOI, which makes sense since that is where followers are listed. However, let's say we wanted to provide... more
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How To Make Your Data Get Along – Data Harmonization

featured / survey research / quantitative / blog
A good researcher seeks multiple sources of data to answer a research question. These data may be results from clinical trials, financial transactions, or even survey data. Often, these do not all stem from a single source, and possess small or large differences in research methods. How does one make sense of these disparate pieces of information and bring them together for better research? In an effort to integrate disparate data and provide better analyses, researchers often rely on a process called data harmonization. What is data harmonization and why is it important?

Data harmonization is the process by which data from heterogeneous sources are combined into a unified, cohesive data product. It is inherently a research activity and success depends on an understanding of... more

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An Introduction to Bayesian Data Analysis for UX Applications

featured / usability / ux / quantitative / blog
Quantitative methods are an essential part of user experience (UX) research. They allow researchers to provide accurate estimates of user performance in terms of errors and response time. However, because of the constraints of traditional statistical approaches, the small sample sizes and high variability of most UX studies preclude meaningful inferences in most situations. In this post I will present Bayesian Data Analysis as a viable alternative and one well suited to address challenges and settings common to UX researchers.

Belief versus ‘the long run’

Traditional, frequentist approaches refer to long run frequency, or how likely an event would occur over N number of replications. To illustrate this, imagine flipping a coin. If the coin is fair, after flipping it 20 times you should get... more
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