{"id":1242,"date":"2023-07-27T04:14:56","date_gmt":"2023-07-27T04:14:56","guid":{"rendered":"https:\/\/statorials.org\/tr\/rde-test-yoneticisi\/"},"modified":"2023-07-27T04:14:56","modified_gmt":"2023-07-27T04:14:56","slug":"rde-test-yoneticisi","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/rde-test-yoneticisi\/","title":{"rendered":"R'de scheffe testi nas\u0131l yap\u0131l\u0131r"},"content":{"rendered":"

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\u00dc\u00e7 veya daha fazla ba\u011f\u0131ms\u0131z grubun ortalamalar\u0131 aras\u0131nda istatistiksel olarak anlaml\u0131 bir fark olup olmad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in tek y\u00f6nl\u00fc ANOVA<\/a> kullan\u0131l\u0131r.<\/span><\/p>\n

ANOVA tablosunun genel p de\u011feri belirli bir anlaml\u0131l\u0131k d\u00fczeyinin alt\u0131ndaysa, grup ortalamalar\u0131ndan en az birinin di\u011ferlerinden farkl\u0131 oldu\u011funu s\u00f6ylemek i\u00e7in yeterli kan\u0131t\u0131m\u0131z var demektir.<\/span><\/p>\n

Ancak bu bize hangi<\/em> gruplar\u0131n birbirinden farkl\u0131 oldu\u011funu s\u00f6ylemez. Bu bize basit\u00e7e t\u00fcm grup ortalamalar\u0131n\u0131n e\u015fit olmad\u0131\u011f\u0131n\u0131 s\u00f6yler.<\/span><\/p>\n

Hangi gruplar\u0131n birbirinden farkl\u0131 oldu\u011funu tam olarak bilmek i\u00e7in<\/a><\/span> aile ba\u015f\u0131na hata oran\u0131n\u0131<\/a> kontrol edebilecek post-hoc bir test yapmam\u0131z gerekiyor.<\/span><\/p>\n

En s\u0131k kullan\u0131lan post-hoc testlerden biri Scheffe testidir.<\/span><\/p>\n

Bu e\u011fitimde R’de Scheffe testinin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n

\u00d6rnek: R’de Scheffe testi<\/strong><\/span><\/h3>\n

Bir \u00f6\u011fretmenin \u00fc\u00e7 farkl\u0131 \u00e7al\u0131\u015fma tekni\u011finin \u00f6\u011frenciler aras\u0131nda farkl\u0131 test puanlar\u0131na yol a\u00e7\u0131p a\u00e7mad\u0131\u011f\u0131n\u0131 bilmek istedi\u011fini varsayal\u0131m. Bunu test etmek i\u00e7in rastgele 10 \u00f6\u011frenciyi her \u00e7al\u0131\u015fma tekni\u011fini kullanmalar\u0131 i\u00e7in g\u00f6revlendiriyor ve s\u0131nav sonu\u00e7lar\u0131n\u0131 kaydediyor.<\/span><\/p>\n

\u00dc\u00e7 grup aras\u0131ndaki ortalama s\u0131nav puanlar\u0131 farkl\u0131l\u0131klar\u0131n\u0131 test etmek amac\u0131yla tek y\u00f6nl\u00fc bir ANOVA uydurmak i\u00e7in R’de a\u015fa\u011f\u0131daki ad\u0131mlar\u0131 kullanabiliriz ve tam olarak hangi gruplar\u0131n farkl\u0131 oldu\u011funu belirlemek i\u00e7in Scheffe testini kullanabiliriz.<\/span><\/p>\n

Ad\u0131m 1: Veri k\u00fcmesini olu\u015fturun.<\/span><\/strong><\/p>\n

A\u015fa\u011f\u0131daki kod, 30 \u00f6\u011frencinin tamam\u0131n\u0131n s\u0131nav sonu\u00e7lar\u0131n\u0131 i\u00e7eren bir veri k\u00fcmesinin nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n

 #create data frame<\/span>\ndata <- data.frame(technique = rep<\/span> (c(\"tech1\", \"tech2\", \"tech3\"), each<\/span> = 10<\/span> ),\n                   score = c(76, 77, 77, 81, 82, 82, 83, 84, 85, 89,\n                             81, 82, 83, 83, 83, 84, 87, 90, 92, 93,\n                             77, 78, 79, 88, 89, 90, 91, 95, 95, 98))\n\n#view first six rows of data frame<\/span>\nhead(data)\n\n  technical score\n1 tech1 76\n2 tech1 77\n3 tech1 77\n4 tech1 81\n5 tech1 82\n6 tech1 82\n<\/strong><\/pre>\n
 Ad\u0131m 2: Her grubun s\u0131nav sonu\u00e7lar\u0131n\u0131 g\u00f6r\u00fcnt\u00fcleyin.<\/span><\/strong><\/p>\n
 A\u015fa\u011f\u0131daki kod, her grup i\u00e7in s\u0131nav sonu\u00e7lar\u0131n\u0131n da\u011f\u0131l\u0131m\u0131n\u0131 g\u00f6rselle\u015ftirmek amac\u0131yla kutu grafiklerinin nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span> <\/p>\n
 boxplot(score ~ technique,\n        data = data,\n        main = \"Exam Scores by Studying Technique\",\n        xlab = \"Studying Technique\",\n        ylab = \"Exam Scores\",\n        col = \"steelblue\",\n        border = \"black\")\n<\/strong><\/pre>\n
\"R'de<\/p>\n
 Ad\u0131m 3: Tek y\u00f6nl\u00fc ANOVA ger\u00e7ekle\u015ftirin.<\/span><\/strong><\/p>\n
 A\u015fa\u011f\u0131daki kod, her gruptaki ortalama s\u0131nav puanlar\u0131 aras\u0131ndaki farklar\u0131 test etmek i\u00e7in tek y\u00f6nl\u00fc ANOVA’n\u0131n nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini g\u00f6sterir:<\/span><\/p>\n
 #fit the one-way ANOVA model<\/span>\nmodel <- aov(score ~ technique, data = data)\n\n#view model output\n<\/span>summary(model)\n\n            Df Sum Sq Mean Sq F value Pr(>F)  \ntechnical 2 211.5 105.73 3.415 0.0476 *\nResiduals 27 836.0 30.96                 \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<\/strong><\/pre>\n
 Genel p de\u011feri ( 0,0476<\/strong> ) 0,05’ten k\u00fc\u00e7\u00fck oldu\u011fundan bu durum her grubun ayn\u0131 ortalama s\u0131nav puan\u0131na sahip olmad\u0131\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n
 Daha sonra hangi gruplar\u0131n farkl\u0131 oldu\u011funu belirlemek i\u00e7in Scheffe testi yapaca\u011f\u0131z.<\/span><\/p>\n
 Ad\u0131m 4: Scheffe testini ger\u00e7ekle\u015ftirin.<\/strong><\/span><\/p>\n
 Scheffe testini ger\u00e7ekle\u015ftirmek i\u00e7in DescTools<\/a> paketindeki ScheffeTest()<\/strong> fonksiyonunu kullanaca\u011f\u0131z.<\/span><\/p>\n
 A\u015fa\u011f\u0131daki kod, \u00f6rne\u011fimiz i\u00e7in bu i\u015flevin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n
 #load DescTools package<\/span>\nlibrary(DescTools)\n\n#perform Scheffe's test\n<\/span>ScheffeTest(model)\n\n  Posthoc multiple comparisons of means: Scheffe Test \n    95% family-wise confidence level\n\n$technical\n            diff lwr.ci upr.ci pval    \ntech2-tech1 4.2 -2.24527202 10.645272 0.2582    \ntech3-tech1 6.4 -0.04527202 12.845272 0.0519 .  \ntech3-tech2 2.2 -4.24527202 8.645272 0.6803    \n\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n
 Sonucun yorumlanma \u015fekli \u015fu \u015fekildedir:<\/span><\/p>\n