{"id":681,"date":"2022-02-27T13:31:59","date_gmt":"2022-02-27T18:31:59","guid":{"rendered":"https:\/\/pressbooks.library.ryerson.ca\/criticaldataliteracy\/?post_type=chapter&#038;p=681"},"modified":"2022-02-27T17:04:06","modified_gmt":"2022-02-27T22:04:06","slug":"introductio","status":"publish","type":"chapter","link":"https:\/\/pressbooks.library.torontomu.ca\/criticaldataliteracy\/chapter\/introductio\/","title":{"raw":"Introduction","rendered":"Introduction"},"content":{"raw":"As we learned in the first module, data visualizations are an effective way to present information because they allow us to discover insights relatively quickly and easily--at least compared to sifting through the original data ourselves.\r\n\r\nBut are data visualizations always easy to understand? Can they always be interpreted quickly?\r\n\r\nNot necessarily. Some data visualizations will require more time to analyze and understand, either because they are complex or confusing or just not what we were expecting.\r\n\r\nIn this module,\u00a0 we will discuss strategies and tools for critically assessing data visualizations and will identify features of data visualizations that can be confusing or manipulative.\r\n<div class=\"textbox textbox--learning-objectives\"><header class=\"textbox__header\">\r\n<p class=\"textbox__title\"><strong>Learning Objectives<\/strong><\/p>\r\n\r\n<\/header>\r\n<div class=\"textbox__content\">\r\n\r\nBy the end of this module, you should be able to:\r\n<ol>\r\n \t<li>Analyze and break down a data visualization.<\/li>\r\n \t<li>Identify misleading features in a data visualization.<\/li>\r\n \t<li>Evaluate a data visualization using data literacy strategies.<\/li>\r\n<\/ol>\r\n<\/div>\r\n<\/div>","rendered":"<p>As we learned in the first module, data visualizations are an effective way to present information because they allow us to discover insights relatively quickly and easily&#8211;at least compared to sifting through the original data ourselves.<\/p>\n<p>But are data visualizations always easy to understand? Can they always be interpreted quickly?<\/p>\n<p>Not necessarily. Some data visualizations will require more time to analyze and understand, either because they are complex or confusing or just not what we were expecting.<\/p>\n<p>In this module,\u00a0 we will discuss strategies and tools for critically assessing data visualizations and will identify features of data visualizations that can be confusing or manipulative.<\/p>\n<div class=\"textbox textbox--learning-objectives\">\n<header class=\"textbox__header\">\n<p class=\"textbox__title\"><strong>Learning Objectives<\/strong><\/p>\n<\/header>\n<div class=\"textbox__content\">\n<p>By the end of this module, you should be able to:<\/p>\n<ol>\n<li>Analyze and break down a data visualization.<\/li>\n<li>Identify misleading features in a data visualization.<\/li>\n<li>Evaluate a data visualization using data literacy strategies.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n","protected":false},"author":362,"menu_order":1,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-681","chapter","type-chapter","status-publish","hentry"],"part":186,"_links":{"self":[{"href":"https:\/\/pressbooks.library.torontomu.ca\/criticaldataliteracy\/wp-json\/pressbooks\/v2\/chapters\/681","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pressbooks.library.torontomu.ca\/criticaldataliteracy\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/pressbooks.library.torontomu.ca\/criticaldataliteracy\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/pressbooks.library.torontomu.ca\/criticaldataliteracy\/wp-json\/wp\/v2\/users\/362"}],"version-history":[{"count":3,"href":"https:\/\/pressbooks.library.torontomu.ca\/criticaldataliteracy\/wp-json\/pressbooks\/v2\/chapters\/681\/revisions"}],"predecessor-version":[{"id":748,"href":"https:\/\/pressbooks.library.torontomu.ca\/criticaldataliteracy\/wp-json\/pressbooks\/v2\/chapters\/681\/revisions\/748"}],"part":[{"href":"https:\/\/pressbooks.library.torontomu.ca\/criticaldataliteracy\/wp-json\/pressbooks\/v2\/parts\/186"}],"metadata":[{"href":"https:\/\/pressbooks.library.torontomu.ca\/criticaldataliteracy\/wp-json\/pressbooks\/v2\/chapters\/681\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/pressbooks.library.torontomu.ca\/criticaldataliteracy\/wp-json\/wp\/v2\/media?parent=681"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/pressbooks.library.torontomu.ca\/criticaldataliteracy\/wp-json\/pressbooks\/v2\/chapter-type?post=681"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/pressbooks.library.torontomu.ca\/criticaldataliteracy\/wp-json\/wp\/v2\/contributor?post=681"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/pressbooks.library.torontomu.ca\/criticaldataliteracy\/wp-json\/wp\/v2\/license?post=681"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}