﻿{"id":1666,"date":"2013-01-22T16:55:03","date_gmt":"2013-01-22T07:55:03","guid":{"rendered":"http:\/\/fujiitoshiki.com\/improvesociety\/?p=1666"},"modified":"2013-01-24T11:19:37","modified_gmt":"2013-01-24T02:19:37","slug":"how-to-execute-multiple-comparison","status":"publish","type":"post","link":"https:\/\/www.fujiitoshiki.com\/improvesociety\/?p=1666","title":{"rendered":"How to execute multiple comparison"},"content":{"rendered":"<div class=\"theContentWrap-ccc\"><p>Student&#8217;s t-test would be executed to compare average between two groups. Then if you would like to compare average between 3 or more groups, what do you do? The test needs 2 steps process. <\/p>\n<ol>\n<li><span style=\"line-height: 16px;\">Analysis of variance (ANOVA)<\/span><\/li>\n<li>Compare between each 2 groups<\/li>\n<\/ol>\n<h3>1. Analysis of variance<\/h3>\n<p>On analysis of variance, null hypothesis is that all groups belong to one population. Therefore, if null hypothesis has been rejected, all groups would not belong to one population. <\/p>\n<p>If all groups belong to one population, the average of all groups, called as grand mean (MG), would be close to average of population. Furthermore, if all groups belong to one population, each average of each groups, for example, M1, M2, M3, would be close to grand mean. However, if any group doesn&#8217;t belong to one population, the average of other population would be far from MG. Then we need the indicator that represents how far each average of each groups from grand mean, corrected with number of sample, n. It is called as mean square among groups (MSA). <\/p>\n<p><img src='https:\/\/s0.wp.com\/latex.php?latex=%5Cdisplaystyle+MSA+%3D+%5Cfrac%7B%5Csum_%7Bi%3D1%7D%5Ek+n_i+%28M_i+-+MG%29%5E2%7D%7Bk-1%7D+&#038;bg=T&#038;fg=000000&#038;s=0' alt='\\displaystyle MSA = \\frac{\\sum_{i=1}^k n_i (M_i - MG)^2}{k-1} ' title='\\displaystyle MSA = \\frac{\\sum_{i=1}^k n_i (M_i - MG)^2}{k-1} ' class='latex' \/><\/p>\n<p>MSA; mean square among groups. n; number of sample in each groups. i; number of group (incremental variable). k; number of groups.<\/p>\n<p>Then calculate variances of each samples, correct with number of each sample and you would take index of variances in samples. It is mean square of error (MSE), average of variance in group. <\/p>\n<p><img src='https:\/\/s0.wp.com\/latex.php?latex=%5Cdisplaystyle+MSE+%3D+%5Cfrac%7B%5Csum_%7Bi%3D1%7D%5Ek+%28n_i+-+1%29V_i%7D%7B%5Csum_%7Bi%3D1%7D%5E%7Bk%7D%28n_i+-+1%29%7D&#038;bg=T&#038;fg=000000&#038;s=0' alt='\\displaystyle MSE = \\frac{\\sum_{i=1}^k (n_i - 1)V_i}{\\sum_{i=1}^{k}(n_i - 1)}' title='\\displaystyle MSE = \\frac{\\sum_{i=1}^k (n_i - 1)V_i}{\\sum_{i=1}^{k}(n_i - 1)}' class='latex' \/><\/p>\n<p>MSE; mean square of error. n; number of sample in each groups. i; number of group (incremental variable). k; number of groups. V; variance.<\/p>\n<p><img src='https:\/\/s0.wp.com\/latex.php?latex=%5Cdisplaystyle+V+%3D+%5Cfrac%7B%5Csum%28x+-+%5Cbar+x%29%5E2%7D%7Bn-1%7D&#038;bg=T&#038;fg=000000&#038;s=0' alt='\\displaystyle V = \\frac{\\sum(x - \\bar x)^2}{n-1}' title='\\displaystyle V = \\frac{\\sum(x - \\bar x)^2}{n-1}' class='latex' \/><\/p>\n<p>x; each value of samples in each groups. n; number of sample.<\/p>\n<p>F statistics, calculated as ratio MSA to MSE, follows F distribution. When F statistics would be over a value, null hypothesis would be rejected and you could compare average between each groups. <\/p>\n<p><img src='https:\/\/s0.wp.com\/latex.php?latex=%5Cdisplaystyle+F%3D%5Cfrac%7BMSA%7D%7BMSE%7D&#038;bg=T&#038;fg=000000&#038;s=0' alt='\\displaystyle F=\\frac{MSA}{MSE}' title='\\displaystyle F=\\frac{MSA}{MSE}' class='latex' \/><\/p>\n<h3>2. Compare between each 2 groups<\/h3>\n<p>If null hypothesis would be rejected with ANOVA, you could compare between each groups with following method. <\/p>\n<ul>\n<li><span style=\"line-height: 16px;\">Bonferroni method<\/span><\/li>\n<li>Tukey&#8217;s HSD<\/li>\n<li>Dunnet&#8217;s procedure<\/li>\n<li>Hsu&#8217;s MCB tests<\/li>\n<li>Scheffe&#8217;s procedure<\/li>\n<\/ul>\n<p>Bonferroni method may be easy to understand and use. Divided significance level <img src='https:\/\/s0.wp.com\/latex.php?latex=%5Calpha&#038;bg=T&#038;fg=000000&#038;s=0' alt='\\alpha' title='\\alpha' class='latex' \/> by k, number of pairs, would be Bonferroni corrected significance level. See following chart. <\/p>\n<p><a href=\"\/\/fujiitoshiki.com\/improvesociety\/wp-content\/uploads\/20130123BonferroniCorrectedSignificanceLevel.jpg\"><img loading=\"lazy\" decoding=\"async\" src=\"\/\/fujiitoshiki.com\/improvesociety\/wp-content\/uploads\/20130123BonferroniCorrectedSignificanceLevel-300x300.jpg\" alt=\"Bonferroni Corrected Significance Level\" width=\"300\" height=\"300\" class=\"alignnone size-medium wp-image-1687\" srcset=\"https:\/\/www.fujiitoshiki.com\/improvesociety\/wp-content\/uploads\/20130123BonferroniCorrectedSignificanceLevel-300x300.jpg 300w, https:\/\/www.fujiitoshiki.com\/improvesociety\/wp-content\/uploads\/20130123BonferroniCorrectedSignificanceLevel-150x150.jpg 150w, https:\/\/www.fujiitoshiki.com\/improvesociety\/wp-content\/uploads\/20130123BonferroniCorrectedSignificanceLevel.jpg 302w\" sizes=\"auto, (max-width: 300px) 85vw, 300px\" \/><\/a><\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Student&#8217;s t-test would be executed to compare average between two groups. Then if you would like to comp &hellip; <a href=\"https:\/\/www.fujiitoshiki.com\/improvesociety\/?p=1666\" class=\"more-link\"><span class=\"screen-reader-text\">&#8220;How to execute multiple comparison&#8221; \u306e<\/span>\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_crdt_document":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-1666","post","type-post","status-publish","format-standard","hentry","category-statistics"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.fujiitoshiki.com\/improvesociety\/index.php?rest_route=\/wp\/v2\/posts\/1666","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.fujiitoshiki.com\/improvesociety\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.fujiitoshiki.com\/improvesociety\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.fujiitoshiki.com\/improvesociety\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.fujiitoshiki.com\/improvesociety\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1666"}],"version-history":[{"count":16,"href":"https:\/\/www.fujiitoshiki.com\/improvesociety\/index.php?rest_route=\/wp\/v2\/posts\/1666\/revisions"}],"predecessor-version":[{"id":1682,"href":"https:\/\/www.fujiitoshiki.com\/improvesociety\/index.php?rest_route=\/wp\/v2\/posts\/1666\/revisions\/1682"}],"wp:attachment":[{"href":"https:\/\/www.fujiitoshiki.com\/improvesociety\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1666"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.fujiitoshiki.com\/improvesociety\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1666"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.fujiitoshiki.com\/improvesociety\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1666"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}