https://realitybending.github.io/tag/rt/ RT Hugo Blox Builder (https://hugoblox.com)en-usMon, 18 May 2020 00:00:00 +0000 https://realitybending.github.io/media/icon_hu_82f4b62152eab490.png https://realitybending.github.io/tag/rt/ https://realitybending.github.io/post/2020-05-18-analyze_rt/ Mon, 18 May 2020 00:00:00 +0000 https://realitybending.github.io/post/2020-05-18-analyze_rt/ <p>This is a very, very important topic given the widespread usage of reaction times in psychology. Most of the time, we analyze it as a regular variable, using traditional models such as <em>linear models</em>, <em>ANOVAs</em> etc. The problem is that these models <strong>assume that the RT is normally distributed, which is false</strong>.</p> <p>This leads us to adjustements like <strong>outliers removal</strong> or <strong>log-transformation</strong>, distorting the data because of our non-appropriate models.</p> <p>The good news is, it&rsquo;s very easy to use better models, that account for the non-normal distribution of RT. And these alternatives are beautifully presented by <a href="https://vbn.aau.dk/da/persons/117060" target="_blank" rel="noopener">Jonas K. Lindeløv</a> in the guide below:</p> <p>👉 <a href="https://lindeloev.github.io/shiny-rt/" target="_blank" rel="noopener"><strong>Reaction time distributions: an interactive overview</strong></a> 👈</p> <p>It is a must-read for all psychologist. Do check-it out!!</p> <hr> <p><em>Thanks for reading! Do not hesitate to tweet and share this post, and leave a comment below</em> &#x1f917;</p> <p>🐦 <em>And don&rsquo;t forget to join me on X</em> <a href="https://x.com/Dom_Makowski" target="_blank" rel="noopener">@Dom_Makowski</a></p>