Study2
108096022
Variable name:
Gender:Sex
Age:Age
Recgroup: 1=Mindfulness; 2=The sun; 9=Can’t hear
Decentering: The 3-items scale of mean decentering
posregscore: The correct recognition of positive emotion during stimulation task
negregscore: The correct recognition of negative emotion during stimulation task
totreg: The total correct recognition of all emotion during stimulation task (posregscore+negregscore)
posright: The correct choosen of the targer positive emotion
negright: The correct choosen of the target negative emotion
score: The correct choosen of the all emotion (posright+negright)
Descriptive stat
## Gender Age SS RECGROUP Decentering posregscore negregscore totreg posright
## 1 1 20 3.175 2 2.000000 18 14 32 7
## 2 1 26 2.775 1 4.000000 18 16 34 16
## 3 1 19 3.975 9 2.666667 20 21 41 15
## 4 1 22 3.700 9 3.000000 17 20 37 11
## 5 1 30 3.350 1 3.333333 17 19 36 6
## 6 2 31 2.775 2 4.000000 20 19 39 13
## negright score
## 1 8 15
## 2 6 22
## 3 14 29
## 4 12 23
## 5 12 17
## 6 15 28## Gender Age SS RECGROUP
## Min. :1.000 Min. :18.00 Min. :1.350 Min. :1.000
## 1st Qu.:1.000 1st Qu.:23.00 1st Qu.:2.775 1st Qu.:1.000
## Median :1.000 Median :29.00 Median :3.175 Median :2.000
## Mean :1.425 Mean :32.75 Mean :3.181 Mean :2.411
## 3rd Qu.:2.000 3rd Qu.:38.00 3rd Qu.:3.625 3rd Qu.:2.000
## Max. :4.000 Max. :70.00 Max. :4.700 Max. :9.000
## Decentering posregscore negregscore totreg posright
## Min. :1.000 Min. :15.00 Min. : 9.00 Min. :27.00 Min. : 3.0
## 1st Qu.:3.000 1st Qu.:18.00 1st Qu.:18.00 1st Qu.:37.00 1st Qu.:11.0
## Median :3.333 Median :19.00 Median :19.00 Median :38.00 Median :13.0
## Mean :3.326 Mean :18.91 Mean :18.95 Mean :37.86 Mean :12.8
## 3rd Qu.:4.000 3rd Qu.:20.00 3rd Qu.:20.00 3rd Qu.:39.00 3rd Qu.:15.0
## Max. :5.000 Max. :21.00 Max. :21.00 Max. :41.00 Max. :18.0
## negright score
## Min. : 1.00 Min. : 5.00
## 1st Qu.: 9.00 1st Qu.:20.25
## Median :12.00 Median :25.00
## Mean :11.49 Mean :24.29
## 3rd Qu.:14.00 3rd Qu.:29.00
## Max. :19.00 Max. :37.00
正確辨識正向的分數與去中心化在1,2組算微正相關; 在3組負相關
正確辨識負向的分數與去中心化在1,3組負相關; 2組零相關
正確辨識正負向總和的分數與去中心化在1,3組負相關; 2組微正相關
正確答對目標正向情緒的分數與去中心化在1,2,3組負相關
正確答對目標負向情緒的分數與去中心化在1,2,3組負相關
正確答對所有目標情緒的分數與去中心化在1,2,3組負相關
正確辨識正向的分數與人際技巧在1,2組算微正相關; 在3組負相關
正確辨識負向的分數與人際技巧在1,2組算負相關; 在3組正相關
正確辨識正負向總和的分數與人際技巧在1,3組負相關; 在2組正相關
正確答對目標正向情緒的分數與人際技巧在1,2,3組負相關
正確答對目標負向情緒的分數與人際技巧在1,2,3組負相關
正確答對所有目標情緒的分數與人際技巧在1,2,3組負相關
Correlation of Mindfulness group
study2_1<-subset(study2,RECGROUP==1,c(4,9,13,14,15,16,17,18))
rcorr(as.matrix(study2_1),type="pearson")## SS Decentering posregscore negregscore totreg posright negright
## SS 1.00 0.45 0.12 -0.23 -0.12 -0.09 -0.20
## Decentering 0.45 1.00 0.07 -0.33 -0.23 -0.02 -0.22
## posregscore 0.12 0.07 1.00 -0.07 0.57 0.44 0.09
## negregscore -0.23 -0.33 -0.07 1.00 0.78 0.36 0.59
## totreg -0.12 -0.23 0.57 0.78 1.00 0.57 0.55
## posright -0.09 -0.02 0.44 0.36 0.57 1.00 0.37
## negright -0.20 -0.22 0.09 0.59 0.55 0.37 1.00
## score -0.18 -0.15 0.31 0.58 0.67 0.80 0.85
## score
## SS -0.18
## Decentering -0.15
## posregscore 0.31
## negregscore 0.58
## totreg 0.67
## posright 0.80
## negright 0.85
## score 1.00
##
## n= 59
##
##
## P
## SS Decentering posregscore negregscore totreg posright negright
## SS 0.0004 0.3703 0.0766 0.3799 0.5203 0.1267
## Decentering 0.0004 0.6067 0.0111 0.0834 0.8615 0.0993
## posregscore 0.3703 0.6067 0.5909 0.0000 0.0006 0.4927
## negregscore 0.0766 0.0111 0.5909 0.0000 0.0054 0.0000
## totreg 0.3799 0.0834 0.0000 0.0000 0.0000 0.0000
## posright 0.5203 0.8615 0.0006 0.0054 0.0000 0.0042
## negright 0.1267 0.0993 0.4927 0.0000 0.0000 0.0042
## score 0.1813 0.2593 0.0180 0.0000 0.0000 0.0000 0.0000
## score
## SS 0.1813
## Decentering 0.2593
## posregscore 0.0180
## negregscore 0.0000
## totreg 0.0000
## posright 0.0000
## negright 0.0000
## scoreCorrelation of the sun group
study2_2<-subset(study2,RECGROUP==2,c(4,9,13,14,15,16,17,18))
rcorr(as.matrix(study2_2),type="pearson")## SS Decentering posregscore negregscore totreg posright negright
## SS 1.00 0.30 0.20 -0.09 0.04 -0.19 -0.25
## Decentering 0.30 1.00 0.06 0.02 0.05 -0.19 -0.10
## posregscore 0.20 0.06 1.00 -0.01 0.59 0.43 0.15
## negregscore -0.09 0.02 -0.01 1.00 0.80 0.26 0.52
## totreg 0.04 0.05 0.59 0.80 1.00 0.47 0.51
## posright -0.19 -0.19 0.43 0.26 0.47 1.00 0.57
## negright -0.25 -0.10 0.15 0.52 0.51 0.57 1.00
## score -0.25 -0.16 0.32 0.45 0.56 0.86 0.91
## score
## SS -0.25
## Decentering -0.16
## posregscore 0.32
## negregscore 0.45
## totreg 0.56
## posright 0.86
## negright 0.91
## score 1.00
##
## n= 70
##
##
## P
## SS Decentering posregscore negregscore totreg posright negright
## SS 0.0113 0.0969 0.4409 0.7131 0.1189 0.0352
## Decentering 0.0113 0.6303 0.8794 0.6804 0.1071 0.4044
## posregscore 0.0969 0.6303 0.9097 0.0000 0.0002 0.2012
## negregscore 0.4409 0.8794 0.9097 0.0000 0.0267 0.0000
## totreg 0.7131 0.6804 0.0000 0.0000 0.0000 0.0000
## posright 0.1189 0.1071 0.0002 0.0267 0.0000 0.0000
## negright 0.0352 0.4044 0.2012 0.0000 0.0000 0.0000
## score 0.0356 0.1854 0.0076 0.0000 0.0000 0.0000 0.0000
## score
## SS 0.0356
## Decentering 0.1854
## posregscore 0.0076
## negregscore 0.0000
## totreg 0.0000
## posright 0.0000
## negright 0.0000
## scoreCorrelation of the can’t hear group
study2_9<-subset(study2,RECGROUP==9,c(4,9,13,14,15,16,17,18))
rcorr(as.matrix(study2_9),type="pearson")## SS Decentering posregscore negregscore totreg posright negright
## SS 1.00 0.48 -0.39 0.21 -0.11 -0.06 -0.15
## Decentering 0.48 1.00 -0.22 -0.23 -0.38 -0.16 -0.33
## posregscore -0.39 -0.22 1.00 -0.31 0.49 0.37 -0.06
## negregscore 0.21 -0.23 -0.31 1.00 0.68 0.46 0.55
## totreg -0.11 -0.38 0.49 0.68 1.00 0.70 0.46
## posright -0.06 -0.16 0.37 0.46 0.70 1.00 0.61
## negright -0.15 -0.33 -0.06 0.55 0.46 0.61 1.00
## score -0.14 -0.29 0.14 0.56 0.62 0.87 0.92
## score
## SS -0.14
## Decentering -0.29
## posregscore 0.14
## negregscore 0.56
## totreg 0.62
## posright 0.87
## negright 0.92
## score 1.00
##
## n= 17
##
##
## P
## SS Decentering posregscore negregscore totreg posright negright
## SS 0.0497 0.1217 0.4257 0.6694 0.8177 0.5534
## Decentering 0.0497 0.4039 0.3674 0.1315 0.5404 0.2019
## posregscore 0.1217 0.4039 0.2299 0.0455 0.1493 0.8141
## negregscore 0.4257 0.3674 0.2299 0.0028 0.0639 0.0216
## totreg 0.6694 0.1315 0.0455 0.0028 0.0017 0.0647
## posright 0.8177 0.5404 0.1493 0.0639 0.0017 0.0089
## negright 0.5534 0.2019 0.8141 0.0216 0.0647 0.0089
## score 0.5912 0.2631 0.5897 0.0193 0.0077 0.0000 0.0000
## score
## SS 0.5912
## Decentering 0.2631
## posregscore 0.5897
## negregscore 0.0193
## totreg 0.0077
## posright 0.0000
## negright 0.0000
## scoreT test
var.test(study2$posregscore,study2$negregscore)##
## F test to compare two variances
##
## data: study2$posregscore and study2$negregscore
## F = 0.5928, num df = 145, denom df = 145, p-value = 0.001777
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4275395 0.8219487
## sample estimates:
## ratio of variances
## 0.5928031t.test(study2$posregscore,study2$negregscore,var.equal=FALSE)##
## Welch Two Sample t-test
##
## data: study2$posregscore and study2$negregscore
## t = -0.17917, df = 272.21, p-value = 0.8579
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4105396 0.3420464
## sample estimates:
## mean of x mean of y
## 18.91096 18.94521boxplot(study2$posregscore,study2$negregscore,xlab='posregscore negregscore',col="darkblue")
var.test(study2$posright,study2$negright)##
## F test to compare two variances
##
## data: study2$posright and study2$negright
## F = 0.70252, num df = 145, denom df = 145, p-value = 0.03429
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5066712 0.9740802
## sample estimates:
## ratio of variances
## 0.7025229t.test(study2$posright,study2$negright,var.equal=FALSE)##
## Welch Two Sample t-test
##
## data: study2$posright and study2$negright
## t = 3.3943, df = 281.41, p-value = 0.000787
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.5524334 2.0777036
## sample estimates:
## mean of x mean of y
## 12.80137 11.48630boxplot(study2$posright,study2$negright,xlab='posright negright',col="darkred")
所有人在辨識正向情緒與負向情緒沒有差異，在正確答對正向目標與負向目標有差異(正向優於負向)。
Anova test
aov1<-aov(study2$posregscore~study2$RECGROUP)
summary(aov1)## Df Sum Sq Mean Sq F value Pr(>F)
## study2$RECGROUP 1 2.1 2.098 1.057 0.306
## Residuals 144 285.7 1.984ggboxplot(study2,x="RECGROUP",y="posregscore",ylab="posregscore",xlab="recgroup")
aov2<-aov(study2$negregscore~study2$RECGROUP)
summary(aov2)## Df Sum Sq Mean Sq F value Pr(>F)
## study2$RECGROUP 1 0.0 0.003 0.001 0.975
## Residuals 144 485.6 3.372ggboxplot(study2,x="RECGROUP",y="negregscore",ylab="negregscore",xlab="recgroup")
aov3<-aov(study2$posright~study2$RECGROUP)
summary(aov3)## Df Sum Sq Mean Sq F value Pr(>F)
## study2$RECGROUP 1 0.8 0.846 0.093 0.761
## Residuals 144 1310.4 9.100ggboxplot(study2,x="RECGROUP",y="posright",ylab="posright",xlab="recgroup")
aov4<-aov(study2$negright~study2$RECGROUP)
summary(aov4)## Df Sum Sq Mean Sq F value Pr(>F)
## study2$RECGROUP 1 0.9 0.892 0.069 0.793
## Residuals 144 1865.6 12.955ggboxplot(study2,x="RECGROUP",y="negright",ylab="negright",xlab="recgroup")
aov5<-aov(study2$score~study2$RECGROUP)
summary(aov5)## Df Sum Sq Mean Sq F value Pr(>F)
## study2$RECGROUP 1 0 0.20 0.006 0.938
## Residuals 144 4782 33.21ggboxplot(study2,x="RECGROUP",y="score",ylab="score",xlab="recgroup")
所有ANOVA MODEL都不顯著，在5%信心水準中，正向辨識、負向辨識、正確目標正向、正確目標負向在RECGROUP中沒有差異。