{"id":1899,"date":"2023-04-17T15:36:01","date_gmt":"2023-04-17T15:36:01","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/?post_type=chapter&#038;p=1899"},"modified":"2024-10-18T20:54:45","modified_gmt":"2024-10-18T20:54:45","slug":"advanced-experimental-design-fresh-take","status":"web-only","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/chapter\/advanced-experimental-design-fresh-take\/","title":{"raw":"Advanced Experimental Design: Fresh Take","rendered":"Advanced Experimental Design: Fresh Take"},"content":{"raw":"<section class=\"textbox learningGoals\">\r\n<ul>\r\n\t<li>Review an experiment and explain if it has been designed well<\/li>\r\n\t<li>Use randomized block design to create a hypothetical experiment to answer a research question<\/li>\r\n<\/ul>\r\n<\/section>\r\n<h2>Randomized Block Design<\/h2>\r\n<div class=\"textbox shaded\">\r\n<p><strong>The Main Idea<\/strong><\/p>\r\n<p>A <b>Blocking Variable\u00a0<\/b><span class=\"TextRun SCXW238077237 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW238077237 BCX0\">is a variable that a researcher knows is a nuisance factor. Variables like age, gender, income, and education level are controllable and are often accounted for in a study via blocking.<\/span><\/span><\/p>\r\n<p><strong>First block, then randomly assign<\/strong>.<strong>\u00a0Blocking\u00a0<\/strong>occurs when a researcher first divides a random sample into homogeneous groups before randomly assigning the individuals in each group to treatment and control groups.<\/p>\r\n<p><strong>Keep blocking to a minimum<\/strong>. We should\u00a0<span class=\"TextRun SCXW238084103 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW238084103 BCX0\">block unwanted variables that can be measured and might influence the outcome, but we should keep blocking to a minimum. Just block the most important nuisance variables and let randomization handle the rest. <\/span><\/span><span class=\"EOP SCXW238084103 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\r\n<p>Use a <strong>randomized block design<\/strong> when a known nuisance factor is measurable and controllable or when the sample size is not large enough to ensure equal groups in a random sample. Otherwise, allow randomization to handle the confounding factors.<\/p>\r\n<\/div>\r\n<p>The video below provides a good example and explanation of blocking and randomized block design.<\/p>\r\n<section class=\"textbox watchIt\"><iframe src=\"\/\/plugin.3playmedia.com\/show?mf=10356052&amp;p3sdk_version=1.10.1&amp;p=20361&amp;pt=375&amp;video_id=W4CKKk7hJZ4&amp;video_target=tpm-plugin-huhmrihp-W4CKKk7hJZ4\" width=\"800px\" height=\"450px\" frameborder=\"0\" marginwidth=\"0px\" marginheight=\"0px\"><\/iframe><br \/>\r\n<p>You can view the\u00a0<a href=\"https:\/\/course-building.s3.us-west-2.amazonaws.com\/Quantitative+Reasoning+-+2023+Build\/Transcriptions\/1+3+09+Randomized+Block+Design.txt\" target=\"_blank\" rel=\"noopener\">transcript for \u201c1 3 09 Randomized Block Design\u201d here (opens in new window).<\/a><\/p>\r\n<\/section>","rendered":"<section class=\"textbox learningGoals\">\n<ul>\n<li>Review an experiment and explain if it has been designed well<\/li>\n<li>Use randomized block design to create a hypothetical experiment to answer a research question<\/li>\n<\/ul>\n<\/section>\n<h2>Randomized Block Design<\/h2>\n<div class=\"textbox shaded\">\n<p><strong>The Main Idea<\/strong><\/p>\n<p>A <b>Blocking Variable\u00a0<\/b><span class=\"TextRun SCXW238077237 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW238077237 BCX0\">is a variable that a researcher knows is a nuisance factor. Variables like age, gender, income, and education level are controllable and are often accounted for in a study via blocking.<\/span><\/span><\/p>\n<p><strong>First block, then randomly assign<\/strong>.<strong>\u00a0Blocking\u00a0<\/strong>occurs when a researcher first divides a random sample into homogeneous groups before randomly assigning the individuals in each group to treatment and control groups.<\/p>\n<p><strong>Keep blocking to a minimum<\/strong>. We should\u00a0<span class=\"TextRun SCXW238084103 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW238084103 BCX0\">block unwanted variables that can be measured and might influence the outcome, but we should keep blocking to a minimum. Just block the most important nuisance variables and let randomization handle the rest. <\/span><\/span><span class=\"EOP SCXW238084103 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p>Use a <strong>randomized block design<\/strong> when a known nuisance factor is measurable and controllable or when the sample size is not large enough to ensure equal groups in a random sample. Otherwise, allow randomization to handle the confounding factors.<\/p>\n<\/div>\n<p>The video below provides a good example and explanation of blocking and randomized block design.<\/p>\n<section class=\"textbox watchIt\"><iframe loading=\"lazy\" src=\"\/\/plugin.3playmedia.com\/show?mf=10356052&amp;p3sdk_version=1.10.1&amp;p=20361&amp;pt=375&amp;video_id=W4CKKk7hJZ4&amp;video_target=tpm-plugin-huhmrihp-W4CKKk7hJZ4\" width=\"800px\" height=\"450px\" frameborder=\"0\" marginwidth=\"0px\" marginheight=\"0px\"><\/iframe><\/p>\n<p>You can view the\u00a0<a href=\"https:\/\/course-building.s3.us-west-2.amazonaws.com\/Quantitative+Reasoning+-+2023+Build\/Transcriptions\/1+3+09+Randomized+Block+Design.txt\" target=\"_blank\" rel=\"noopener\">transcript for \u201c1 3 09 Randomized Block Design\u201d here (opens in new window).<\/a><\/p>\n<\/section>\n","protected":false},"author":15,"menu_order":24,"template":"","meta":{"_candela_citation":"[]","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"part":86,"module-header":"fresh_take","content_attributions":[],"internal_book_links":[],"video_content":null,"cc_video_embed_content":{"cc_scripts":"","media_targets":[]},"try_it_collection":null,"_links":{"self":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/1899"}],"collection":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/users\/15"}],"version-history":[{"count":6,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/1899\/revisions"}],"predecessor-version":[{"id":15402,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/1899\/revisions\/15402"}],"part":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/parts\/86"}],"metadata":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/1899\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/media?parent=1899"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapter-type?post=1899"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/contributor?post=1899"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/license?post=1899"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}