====== ANOVA ====== Read [[:ANOVA]] for the logics of ANOVA also read: [[:r:oneway_anova|Oneway ANOVA R]] {{:anova_egs.xlsx}} Eysenck는 (1974) 단어자료를 기억하는 정도는 단어를 외울 때 사용한 처리방법에 따라서 다르게 나타날 것이라고 예측 (p. 426) 55-65세 50명을 다섯 그룹에 무작위배치(random assignment) - 의도학습 - **의도.** 목록을 따라 읽도록 지시 - 나중에 회상을 요구받을 것이라고 하여 일부러 암기하도록 지시 - 우연학습 - **낱자세기.** 낱자세기 - 각 단어의 숫자를 세어보도록 지시 - **운율.** 운율이 같은 단어 생각해보도록 지시 - **형용사.** 형용사를 사용하여 단어를 수식해보도록 지시 - **심상.** 단어를 심상(머리)에 세겨보도록 지시 GROUP RECALL 1 9 1 8 1 6 1 8 1 10 1 4 1 6 1 5 1 7 1 7 2 7 2 9 2 6 2 6 2 6 2 11 2 6 2 3 2 8 2 7 3 11 3 13 3 8 3 6 3 14 3 11 3 13 3 13 3 10 3 11 4 12 4 11 4 16 4 11 4 9 4 23 4 12 4 10 4 19 4 11 5 10 5 19 5 14 5 5 5 10 5 11 5 14 5 15 5 11 5 11 {{:Tab16-1.csv}} ^ ^ 낱자세기 ^ 운율 ^ 형용사 ^ 심상 ^ 의도 ^ | | 9 | 7 | 11 | 12 | 10 | | | 8 | 9 | 13 | 11 | 19 | | | 6 | 6 | 8 | 16 | 14 | | | 8 | 6 | 6 | 11 | 5 | | | 10 | 6 | 14 | 9 | 10 | | | 4 | 11 | 11 | 23 | 11 | | | 6 | 6 | 13 | 12 | 14 | | | 5 | 3 | 13 | 10 | 15 | | | 7 | 8 | 10 | 19 | 11 | | | 7 | 7 | 11 | 11 | 11 | | 평균 | 7 | 6.9 | 11 | 13.4 | 12 | | 표준편차 | 1.83 | 2.13 | 2.49 | 4.50 | 3.74 | | 변량 | 3.33 | 4.54 | 6.22 | 20.27 | 14.00 | | SS | 30 | 40.9 | 56 | 182.4 | 126 | | | | | | | 435.3 | | 전체평균 | 10.06 | | 전체표준편차 | 4.01 | | 전체변량 | 16.06 | | 전체n | 50 | | 전체SS | 786.82 | | BetweenVar | 351.52 | 가정. - 정상성 - 변량동질성 - 관찰독립성 > a.data <- read.csv("http://commres.net/wiki/_media/tab16-1.csv") > a.data$GROUP <- factor(a.data$GROUP) > a.data GROUP RECALL 1 1 9 2 1 8 3 1 6 4 1 8 5 1 10 6 1 4 7 1 6 8 1 5 9 1 7 10 1 7 11 2 7 12 2 9 13 2 6 14 2 6 15 2 6 16 2 11 17 2 6 18 2 3 19 2 8 20 2 7 21 3 11 22 3 13 23 3 8 24 3 6 25 3 14 26 3 11 27 3 13 28 3 13 29 3 10 30 3 11 31 4 12 32 4 11 33 4 16 34 4 11 35 4 9 36 4 23 37 4 12 38 4 10 39 4 19 40 4 11 41 5 10 42 5 19 43 5 14 44 5 5 45 5 10 46 5 11 47 5 14 48 5 15 49 5 11 50 5 11 > a.out <- aov(RECALL~GROUP, data=a.data) > a.out Call: aov(formula = RECALL ~ GROUP, data = a.data) Terms: GROUP Residuals Sum of Squares 351.52 435.30 Deg. of Freedom 4 45 Residual standard error: 3.110198 Estimated effects may be unbalanced > > summary(a.out) Df Sum Sq Mean Sq F value Pr(>F) GROUP 4 351.5 87.88 9.085 1.82e-05 *** Residuals 45 435.3 9.67 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > TukeyHSD(a.out) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = RECALL ~ GROUP, data = a.data) $GROUP diff lwr upr p adj 2-1 -0.1 -4.05223799 3.852238 0.9999937 3-1 4.0 0.04776201 7.952238 0.0460196 4-1 6.4 2.44776201 10.352238 0.0003180 5-1 5.0 1.04776201 8.952238 0.0068354 3-2 4.1 0.14776201 8.052238 0.0385792 4-2 6.5 2.54776201 10.452238 0.0002524 5-2 5.1 1.14776201 9.052238 0.0055623 4-3 2.4 -1.55223799 6.352238 0.4291513 5-3 1.0 -2.95223799 4.952238 0.9510451 5-4 -1.4 -5.35223799 2.552238 0.8510119 ====== eg. p.473 ====== > ns <- c(27, 34, 19, 20, 56, 35, 23, 37, 4, 30, 4, 42, 34, 19, 49) > ds <- c(48, 29, 34, 6, 18, 63, 9, 54, 28, 71, 60, 54, 51, 25, 49) > as <- c(34, 65, 55, 33, 42, 54, 21, 44, 61, 38, 75, 61, 51, 32, 47) > > smoke <- data.frame(ns,ds,as) > smoke ns ds as 1 27 48 34 2 34 29 65 3 19 34 55 4 20 6 33 5 56 18 42 6 35 63 54 7 23 9 21 8 37 54 44 9 4 28 61 10 30 71 38 11 4 60 75 12 42 54 61 13 34 51 51 14 19 25 32 15 49 49 47 > smoke.st <- stack(smoke) > smoke.st values ind 1 27 ns 2 34 ns 3 19 ns 4 20 ns 5 56 ns 6 35 ns 7 23 ns 8 37 ns 9 4 ns 10 30 ns 11 4 ns 12 42 ns 13 34 ns 14 19 ns 15 49 ns 16 48 ds 17 29 ds 18 34 ds 19 6 ds 20 18 ds 21 63 ds 22 9 ds 23 54 ds 24 28 ds 25 71 ds 26 60 ds 27 54 ds 28 51 ds 29 25 ds 30 49 ds 31 34 as 32 65 as 33 55 as 34 33 as 35 42 as 36 54 as 37 21 as 38 44 as 39 61 as 40 38 as 41 75 as 42 61 as 43 51 as 44 32 as 45 47 as > smoke.mod <- aov(values~ind,data=smoke.st) > smoke.mod Call: aov(formula = values ~ ind, data = smoke.st) Terms: ind Residuals Sum of Squares 2643.378 11700.400 Deg. of Freedom 2 42 Residual standard error: 16.69074 Estimated effects may be unbalanced > summary(smoke.mod) Df Sum Sq Mean Sq F value Pr(>F) ind 2 2643 1321.7 4.744 0.0139 * Residuals 42 11700 278.6 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(smoke.mod) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = values ~ ind, data = smoke.st) $ind diff lwr upr p adj ds-as -7.60000 -22.40679 7.206788 0.4327383 ns-as -18.66667 -33.47345 -3.859878 0.0104480 ns-ds -11.06667 -25.87345 3.740122 0.1768164 >