{"id":794,"date":"2023-03-22T12:27:51","date_gmt":"2023-03-22T11:27:51","guid":{"rendered":"https:\/\/site.uit.no\/acqvalab\/?page_id=794"},"modified":"2023-03-23T10:56:38","modified_gmt":"2023-03-23T09:56:38","slug":"workshop-visual-world-eye-tracking-analysis-in-r-with-aine-ito-16-17-02-2023","status":"publish","type":"page","link":"https:\/\/site.uit.no\/acqvalab\/workshop-visual-world-eye-tracking-analysis-in-r-with-aine-ito-16-17-02-2023\/","title":{"rendered":"Workshop: Visual world eye-tracking analysis in R (with Aine\u00a0Ito, 16-17.02.2023)"},"content":{"rendered":"\n

<\/p>\n\n\n\n

Summary<\/strong><\/p>\n\n\n\n

In this workshop, we will look at five different analysis methods for analysing the time-course of visual world eye-tracking data (growth curve analysis, cluster-based permutation analysis, bootstrapped differences of timeseries, generalised additive modelling, and divergence point analysis). I will first introduce these analysis methods and discuss the advantages and disadvantages of each method to help you choose the appropriate method for your research question. In the tutorials, I will guide you through the analysis steps to help you understand what each code is doing. <\/p>\n\n\n\n

R package<\/strong><\/p>\n\n\n\n

If you would like to follow along the analysis during the tutorials, please download the stats.VWP package from my GitHub website (https:\/\/github.com\/aineito\/stats.VWP<\/a>). (Please make sure to do this a couple of days before the workshop as I will still be working on the package until then). The tutorials can be launched anytime, so I hope they will be a useful guide when you apply the analysis to your own data. If you are not very familiar with R but would like to learn how each method works, you are welcome to just watch me going through the codes. Downloading the package is completely optional.<\/p>\n\n\n\n

Schedule<\/strong><\/p>\n\n\n\n

Time<\/strong><\/td>Day 1<\/strong><\/td>Day 2<\/strong><\/td><\/tr>
9:00 \u2013 9:50<\/strong><\/td>Introduction to different methods of analysing visual world eye-tracking data (talk)<\/td>Bootstrapped differences of timeseries (tutorial)<\/td><\/tr>
9:50 \u2013 10:00<\/strong><\/td>Break<\/td>Break<\/td><\/tr>
10:00 \u2013 10:50<\/strong><\/td>Growth curve analysis (tutorial)<\/td>Generalised additive modelling (tutorial)<\/td><\/tr>
10:50 \u2013 11:00<\/strong><\/td>Break<\/td>Break<\/td><\/tr>
11:00 \u2013 11:50<\/strong><\/td>Cluster-based permutation analysis (tutorial)<\/td>Divergence point analysis (tutorial)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Reference<\/strong><\/p>\n\n\n\n

Ito, A. & Knoeferle, P. (2022) Analysing data from the psycholinguistic visual-world paradigm: Comparison of different analysis methods. Behavior Research Methods<\/em>. doi: 10.3758\/s13428-022-01969-3<\/p>\n\n\n\n

Links to Videos<\/strong><\/p>\n\n\n\n

You can watch video recordings from Day 1 and Day 2 of the workshop on our YouTube channel:<\/p>\n\n\n\n

Day 1:
https:\/\/youtu.be\/GIodDcXTaQ8<\/a>
Day 2:
https:\/\/youtu.be\/r3jwo1dE-Ik<\/a>
<\/p>\n","protected":false},"excerpt":{"rendered":"

Summary In this workshop, we will look at five different analysis methods for analysing the time-course of visual world eye-tracking data (growth curve analysis, cluster-based permutation analysis, bootstrapped differences of timeseries, generalised additive modelling, and divergence point analysis). I will first introduce these analysis methods and discuss the advantages and disadvantages of each method to […]<\/p>\n","protected":false},"author":594,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-794","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/site.uit.no\/acqvalab\/wp-json\/wp\/v2\/pages\/794","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/site.uit.no\/acqvalab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/site.uit.no\/acqvalab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/site.uit.no\/acqvalab\/wp-json\/wp\/v2\/users\/594"}],"replies":[{"embeddable":true,"href":"https:\/\/site.uit.no\/acqvalab\/wp-json\/wp\/v2\/comments?post=794"}],"version-history":[{"count":8,"href":"https:\/\/site.uit.no\/acqvalab\/wp-json\/wp\/v2\/pages\/794\/revisions"}],"predecessor-version":[{"id":833,"href":"https:\/\/site.uit.no\/acqvalab\/wp-json\/wp\/v2\/pages\/794\/revisions\/833"}],"wp:attachment":[{"href":"https:\/\/site.uit.no\/acqvalab\/wp-json\/wp\/v2\/media?parent=794"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}