安裝需要的packages

packages = c("dplyr","ggplot2", "data.table", "scales", "tidytext","ltm")
existing = as.character(installed.packages()[,1])
for(pkg in packages[!(packages %in% existing)]) install.packages(pkg)

載入需要的packages以及資料

require(dplyr)
require(ggplot2)
require(data.table)
require(scales)
require(wordcloud2)
require(tidytext)
require(ltm)

載入資料

TB = read.csv('data.csv')
TB$單因子組別= factor(TB$單因子組別)
TB$雙因子組別= factor(TB$雙因子組別)

檢視前6筆資料

head(TB)

單因子分析：切換場景(S) VS 不切換場景(N)

加總主題分數 - 單因子分析

3.加總值平均(沉浸感) -> 顯著性與boxplot

# cronbach alpha

head(TB[6:9])

cronbach.alpha(TB[6:9], CI=TRUE, standardized=TRUE)


Standardized Cronbach's alpha for the 'TB[6:9]' data-set

Items: 4
Sample units: 120
alpha: 0.655

Bootstrap 95% CI based on 1000 samples
 2.5% 97.5% 
0.524 0.752

new_tb <- data.frame(TB$沉浸感1,TB$沉浸感3,TB$沉浸感4)
head(new_tb)

cronbach.alpha(new_tb,CI= TRUE, standardized = TRUE)


Standardized Cronbach's alpha for the 'new_tb' data-set

Items: 3
Sample units: 120
alpha: 0.7

Bootstrap 95% CI based on 1000 samples
 2.5% 97.5% 
0.572 0.794


# 根據上面cronbach.alpha的結果，拿掉沉浸感2

TB <- transform(TB, all_immer = (TB$沉浸感1 + TB$沉浸感3 + TB$沉浸感4)/3)
Result = aov(TB$all_immer~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)  
TB$單因子組別   1   5.49   5.490   4.558 0.0348 *
Residuals     118 142.13   1.205                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$all_immer ~ TB$單因子組別)

$`TB$單因子組別`
         diff        lwr       upr     p adj
S-N 0.4277778 0.03098075 0.8245748 0.0348385

plot(TukeyHSD(Result),las=1)


boxplot(formula = TB$all_immer ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "沉浸感) -> 顯著性",
        col = "gray")


ggplot(TB, aes(x = 單因子組別, y = all_immer)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))

NA
NA
NA

4.加總值平均(自我創造力) -> 顯著性與boxplot

# cronbach alpha

head(TB[16:18])

cronbach.alpha(TB[16:18], CI=TRUE, standardized=TRUE)


Standardized Cronbach's alpha for the 'TB[16:18]' data-set

Items: 3
Sample units: 120
alpha: 0.823

Bootstrap 95% CI based on 1000 samples
 2.5% 97.5% 
0.752 0.874

TB <- transform(TB, all_creative = (TB$想法數量 + TB$品質 + TB$創造力)/3)
Result = aov(TB$all_creative~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)  
TB$單因子組別   1   5.49   5.490   5.482 0.0209 *
Residuals     118 118.18   1.001                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$all_creative ~ TB$單因子組別)

$`TB$單因子組別`
         diff        lwr       upr     p adj
S-N 0.4277778 0.06596177 0.7895938 0.0208946

boxplot(formula = TB$all_creative ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "自我創造力) -> 顯著性",
        col = "gray")


ggplot(TB, aes(x = 單因子組別, y = all_creative)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))

5.加總值平均(社交互動 Social Interaction) -> 顯著性與boxplot

# cronbach alpha

head(TB[19:27])

cronbach.alpha(TB[19:27], CI=TRUE, standardized=TRUE)


Standardized Cronbach's alpha for the 'TB[19:27]' data-set

Items: 9
Sample units: 120
alpha: 0.842

Bootstrap 95% CI based on 1000 samples
 2.5% 97.5% 
0.797 0.878

TB <- transform(TB, all_SI = (TB$QoI1 + TB$QoI2 + TB$QoI3 + TB$QoI4 + TB$SM1 + TB$SM2 + TB$SM3 + TB$SM4 + TB$SM5)/9)
Result = aov(TB$all_SI~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1   0.91  0.9091    1.03  0.312
Residuals     118 104.11  0.8823

boxplot(formula = TB$all_SI ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "社交互動 Social Interaction) -> 顯著性",
        col = "gray")

6.加總值平均(社交互動 Social Interaction：QOI) -> 顯著性與boxplot

# cronbach alpha

head(TB[19:22])

cronbach.alpha(TB[19:22], CI=TRUE, standardized=TRUE)


Standardized Cronbach's alpha for the 'TB[19:22]' data-set

Items: 4
Sample units: 120
alpha: 0.834

Bootstrap 95% CI based on 1000 samples
 2.5% 97.5% 
0.779 0.877

TB <- transform(TB, all_QOI = (TB$QoI1 + TB$QoI2 + TB$QoI3 + TB$QoI4)/4) 
Result = aov(TB$all_QOI~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1    0.6  0.6021   0.447  0.505
Residuals     118  159.0  1.3474

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$all_QOI ~ TB$單因子組別)

$`TB$單因子組別`
         diff       lwr       upr     p adj
S-N 0.1416667 -0.278003 0.5613364 0.5051362

boxplot(formula = TB$all_QOI ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "社交互動 Social Interaction：QOI) -> 顯著性",
        col = "gray")


ggplot(TB, aes(x = 單因子組別, y = all_QOI)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))

NA
NA
NA
NA

7.加總值平均(社交互動 Social Interaction：SM) -> 顯著性與boxplot

# cronbach alpha

head(TB[23:27])

cronbach.alpha(TB[23:27], CI=TRUE, standardized=TRUE)


Standardized Cronbach's alpha for the 'TB[23:27]' data-set

Items: 5
Sample units: 120
alpha: 0.707

Bootstrap 95% CI based on 1000 samples
 2.5% 97.5% 
 0.61  0.78

TB <- transform(TB, all_SM = (TB$SM1 + TB$SM2 + TB$SM3 + TB$SM4 + TB$SM5)/5)
Result = aov(TB$all_SM~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1    1.2  1.2000   1.285  0.259
Residuals     118  110.2  0.9335

boxplot(formula = TB$all_SM ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "社交互動 Social Interaction：SM) -> 顯著性",
        col = "gray")

各項項目分數 - 單因子分析

3.沉浸感 Immersion -> 顯著性與boxplot


Result = aov(TB$沉浸感1~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1   0.53  0.5333   0.248  0.619
Residuals     118 253.83  2.1511

Result = aov(TB$沉浸感2~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value   Pr(>F)    
TB$單因子組別   1  22.53  22.533   14.49 0.000224 ***
Residuals     118 183.43   1.555                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Result = aov(TB$沉浸感3~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)  
TB$單因子組別   1   5.21   5.208   3.686 0.0573 .
Residuals     118 166.72   1.413                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Result = aov(TB$沉浸感4~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)  
TB$單因子組別   1  16.13  16.133   6.742 0.0106 *
Residuals     118 282.37   2.393                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

boxplot(formula = TB$沉浸感1 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我可以像在現實世界中一樣與環境互動",
        col = "gray")


boxplot(formula = TB$沉浸感2 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我感覺與外界隔絕",
        col = "gray")


boxplot(formula = TB$沉浸感3 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我完全沉浸其中",
        col = "gray")


boxplot(formula = TB$沉浸感4 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我忘記了我的日常煩惱",
        col = "gray")

4.認知壓力 NASA-TLX-> 顯著性與boxplot


Result = aov(TB$精神需求~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)  
TB$單因子組別   1    4.8   4.800   3.506 0.0636 .
Residuals     118  161.6   1.369                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Result = aov(TB$體力需求~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1    0.1   0.075   0.026  0.871
Residuals     118  335.9   2.846

Result = aov(TB$時間壓力~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1    2.7   2.700   1.168  0.282
Residuals     118  272.8   2.312

Result = aov(TB$時間壓力~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1    2.7   2.700   1.168  0.282
Residuals     118  272.8   2.312

Result = aov(TB$成功~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1   1.01   1.008   0.784  0.378
Residuals     118 151.78   1.286

Result = aov(TB$努力~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)  
TB$單因子組別   1   4.03   4.033    3.89 0.0509 .
Residuals     118 122.33   1.037                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Result = aov(TB$不安~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1   3.01   3.008   1.226   0.27
Residuals     118 289.58   2.454

boxplot(formula = TB$精神需求 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "精神需求",
        col = "gray")


boxplot(formula = TB$體力需求 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "體力需求",
        col = "gray")


boxplot(formula = TB$時間壓力 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "時間壓力",
        col = "gray")


boxplot(formula = TB$成功 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "成功",
        col = "gray")


boxplot(formula = TB$努力 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "努力",
        col = "gray")


boxplot(formula = TB$不安 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "不安",
        col = "gray")

5.創造力-> 顯著性與boxplot


Result = aov(TB$想法數量~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1   4.03   4.033   2.713  0.102
Residuals     118 175.43   1.487

Result = aov(TB$品質~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)  
TB$單因子組別   1   6.08   6.075   5.518 0.0205 *
Residuals     118 129.92   1.101                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Result = aov(TB$創造力~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)  
TB$單因子組別   1   6.53   6.533   4.249 0.0415 *
Residuals     118 181.43   1.538                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

boxplot(formula = TB$想法數量 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "想法數量",
        col = "gray")


boxplot(formula = TB$品質 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "品質",
        col = "gray")


boxplot(formula = TB$創造力 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "創造力",
        col = "gray")

6 社交互動 Social Interaction：QOI-> 顯著性與boxplot


Result = aov(TB$QoI1~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1    0.3   0.300    0.12   0.73
Residuals     118  295.7   2.506

Result = aov(TB$QoI2~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1    1.2   1.200   0.593  0.443
Residuals     118  238.8   2.023

Result = aov(TB$QoI3~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1    0.3   0.300   0.182   0.67
Residuals     118  194.1   1.645

Result = aov(TB$QoI4~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1   0.83  0.8333   0.428  0.514
Residuals     118 229.67  1.9463

boxplot(formula = TB$QoI1 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我可以判斷對方是否在聽我說話。",
        col = "gray")


boxplot(formula = TB$QoI2 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "我能充分理解對方在VR頭腦風暴時中做的任何行為。",
        col = "gray")


boxplot(formula = TB$QoI3 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我確信我的搭檔明白我在說什麼。",
        col = "gray")


boxplot(formula = TB$QoI4 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我專注於和對方互動。",
        col = "gray")

6 社交互動 Social Interaction：SM-> 顯著性與boxplot


Result = aov(TB$SM1~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)  
TB$單因子組別   1    4.8   4.800    2.81 0.0963 .
Residuals     118  201.6   1.708                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Result = aov(TB$SM2~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)  
TB$單因子組別   1   9.07   9.075    4.83 0.0299 *
Residuals     118 221.72   1.879                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Result = aov(TB$SM3~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1   4.03   4.033   2.144  0.146
Residuals     118 221.93   1.881

Result = aov(TB$SM4~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1   3.33   3.333   1.479  0.226
Residuals     118 265.97   2.254

Result = aov(TB$SM5~TB$單因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$單因子組別   1   0.01  0.0083   0.003  0.955
Residuals     118 313.32  2.6552

boxplot(formula = TB$SM1 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "我從VR頭腦風暴的體驗中獲得了很多的滿足感。",
        col = "gray")


boxplot(formula = TB$SM2 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "我很享受和我的夥伴一起度過的時光。",
        col = "gray")


boxplot(formula = TB$SM3 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "當我在進行VR頭腦風暴時，我常常覺得自己是孤身一人。",
        col = "gray")


boxplot(formula = TB$SM4 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在VR中，當周遭有動靜時，我觀察到我的夥伴們容易分心。",
        col = "gray")


boxplot(formula = TB$SM5 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "當我在進行VR頭腦風暴時，時間似乎過得很慢。",
        col = "gray")

雙因子分析： NC NB SC SB

NC -> 不切換場景 + 電子鐘 NB -> 不切換場景 + 炸彈 SC -> 切換場景 + 電子鐘 SB -> 切換場景 + 炸彈

加總主題分數 - 雙因子分析

3.加總值平均(沉浸感) -> 顯著性與boxplot

# cronbach alpha

head(TB[6:9])

cronbach.alpha(TB[6:9], CI=TRUE, standardized=TRUE)


Standardized Cronbach's alpha for the 'TB[6:9]' data-set

Items: 4
Sample units: 120
alpha: 0.655

Bootstrap 95% CI based on 1000 samples
 2.5% 97.5% 
0.509 0.757

new_tb <- data.frame(TB$沉浸感1,TB$沉浸感3,TB$沉浸感4)
head(new_tb)

cronbach.alpha(new_tb,CI= TRUE, standardized = TRUE)


Standardized Cronbach's alpha for the 'new_tb' data-set

Items: 3
Sample units: 120
alpha: 0.7

Bootstrap 95% CI based on 1000 samples
 2.5% 97.5% 
0.575 0.787

# 根據上面cronbach.alpha的結果，拿掉沉浸感2
TB <- transform(TB, all_immer = (TB$沉浸感1 + TB$沉浸感3 + TB$沉浸感4)/4)
Result = aov(TB$all_immer~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   3.47  1.1561   1.685  0.174
Residuals     116  79.57  0.6859

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$all_immer ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff        lwr       upr     p adj
NC-NB -0.1166667 -0.6740858 0.4407524 0.9475692
SB-NB  0.3166667 -0.2407524 0.8740858 0.4523339
SC-NB  0.2083333 -0.3490858 0.7657524 0.7643641
SB-NC  0.4333333 -0.1240858 0.9907524 0.1842976
SC-NC  0.3250000 -0.2324191 0.8824191 0.4290363
SC-SB -0.1083333 -0.6657524 0.4490858 0.9573703

plot(TukeyHSD(Result),las=1)


boxplot(formula = TB$all_immer ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "沉浸感) -> 顯著性",
        col = "gray")


ggplot(TB, aes(x = 雙因子組別, y = all_immer)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))

NA
NA
NA

4.加總值平均(自我創造力) -> 顯著性與boxplot

TB <- transform(TB, all_creative = (TB$想法數量 + TB$品質 + TB$創造力)/3)
Result = aov(TB$all_creative~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   6.11   2.038   2.011  0.116
Residuals     116 117.55   1.013

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$all_creative ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff        lwr       upr     p adj
NC-NB -0.1777778 -0.8553028 0.4997472 0.9030170
SB-NB  0.3888889 -0.2886361 1.0664139 0.4431012
SC-NB  0.2888889 -0.3886361 0.9664139 0.6832345
SB-NC  0.5666667 -0.1108583 1.2441917 0.1348794
SC-NC  0.4666667 -0.2108583 1.1441917 0.2807724
SC-SB -0.1000000 -0.7775250 0.5775250 0.9805459

boxplot(formula = TB$all_creative ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "自我創造力) -> 顯著性",
        col = "gray")


ggplot(TB, aes(x = 雙因子組別, y = all_creative)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))

NA
NA

5.加總值平均(社交互動 Social Interaction) -> 顯著性與boxplot

TB <- transform(TB, all_SI = (TB$QoI1 + TB$QoI2 + TB$QoI3 + TB$QoI4 + TB$SM1 + TB$SM2 + TB$SM3 + TB$SM4 + TB$SM5/9))
Result = aov(TB$all_SI~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3    134   44.50   0.702  0.552
Residuals     116   7348   63.35

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$all_SI ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff       lwr      upr     p adj
NC-NB -1.3333333 -6.690131 4.023464 0.9157721
SB-NB  1.6444444 -3.712353 7.001242 0.8541890
SC-NB  0.1259259 -5.230872 5.482723 0.9999169
SB-NC  2.9777778 -2.379020 8.334575 0.4716703
SC-NC  1.4592593 -3.897538 6.816057 0.8929214
SC-SB -1.5185185 -6.875316 3.838279 0.8811735

boxplot(formula = TB$all_SI ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "社交互動 Social Interaction) -> 顯著性",
        col = "gray")


ggplot(TB, aes(x = 雙因子組別, y = all_SI)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))

NA
NA
NA

6.加總值平均(社交互動 Social Interaction：QOI) -> 顯著性與boxplot

TB <- transform(TB, all_QOI = (TB$QoI1 + TB$QoI2 + TB$QoI3 + TB$QoI4)/4) 
Result = aov(TB$all_QOI~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3    1.6  0.5319   0.391   0.76
Residuals     116  158.0  1.3620

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$all_QOI ~ TB$雙因子組別)

$`TB$雙因子組別`
             diff        lwr       upr     p adj
NC-NB -0.12500000 -0.9104769 0.6604769 0.9758367
SB-NB  0.19166667 -0.5938102 0.9771435 0.9201581
SC-NB -0.03333333 -0.8188102 0.7521435 0.9995130
SB-NC  0.31666667 -0.4688102 1.1021435 0.7198789
SC-NC  0.09166667 -0.6938102 0.8771435 0.9901782
SC-SB -0.22500000 -1.0104769 0.5604769 0.8779082

boxplot(formula = TB$all_QOI ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "社交互動 Social Interaction：QOI) -> 顯著性",
        col = "gray")


ggplot(TB, aes(x = 雙因子組別, y = all_QOI)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))

NA
NA
NA
NA

7.加總值平均(社交互動 Social Interaction：SM) -> 顯著性與boxplot

TB <- transform(TB, all_SM = (TB$SM1 + TB$SM2 + TB$SM3 + TB$SM4 + TB$SM5)/5)
Result = aov(TB$all_SM~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   1.97  0.6564   0.696  0.556
Residuals     116 109.39  0.9430

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$all_SM ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff        lwr       upr     p adj
NC-NB -0.1666667 -0.8202456 0.4869123 0.9101222
SB-NB  0.1933333 -0.4602456 0.8469123 0.8673524
SC-NB  0.0400000 -0.6135790 0.6935790 0.9985466
SB-NC  0.3600000 -0.2935790 1.0135790 0.4797822
SC-NC  0.2066667 -0.4469123 0.8602456 0.8428825
SC-SB -0.1533333 -0.8069123 0.5002456 0.9282306

boxplot(formula = TB$all_SM ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "社交互動 Social Interaction：SM) -> 顯著性",
        col = "gray")


ggplot(TB, aes(x = 雙因子組別, y = all_SM)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))

各項項目分數 - 雙因子分析

3.沉浸感 Immersion -> 顯著性與boxplot


Result = aov(TB$沉浸感1~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3    1.1  0.3667   0.168  0.918
Residuals     116  253.3  2.1833

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$沉浸感1 ~ TB$雙因子組別)

$`TB$雙因子組別`
               diff        lwr       upr     p adj
NC-NB  1.000000e-01 -0.8944881 1.0944881 0.9936597
SB-NB  2.666667e-01 -0.7278215 1.2611548 0.8972857
SC-NB  1.000000e-01 -0.8944881 1.0944881 0.9936597
SB-NC  1.666667e-01 -0.8278215 1.1611548 0.9719785
SC-NC  1.776357e-15 -0.9944881 0.9944881 1.0000000
SC-SB -1.666667e-01 -1.1611548 0.8278215 0.9719785

Result = aov(TB$沉浸感2~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value  Pr(>F)   
TB$雙因子組別   3  22.83   7.611   4.821 0.00336 **
Residuals     116 183.13   1.579                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$沉浸感2 ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff         lwr       upr     p adj
NC-NB  0.1000000 -0.74565708 0.9456571 0.9897915
SB-NB  0.9666667  0.12100959 1.8123237 0.0182147
SC-NB  0.8666667  0.02100959 1.7123237 0.0423448
SB-NC  0.8666667  0.02100959 1.7123237 0.0423448
SC-NC  0.7666667 -0.07899041 1.6123237 0.0900841
SC-SB -0.1000000 -0.94565708 0.7456571 0.9897915

Result = aov(TB$沉浸感3~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   7.76   2.586   1.827  0.146
Residuals     116 164.17   1.415

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$沉浸感3 ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff        lwr       upr     p adj
NC-NB -0.4000000 -1.2006691 0.4006691 0.5633784
SB-NB  0.2666667 -0.5340025 1.0673358 0.8212559
SC-NB  0.1666667 -0.6340025 0.9673358 0.9483579
SB-NC  0.6666667 -0.1340025 1.4673358 0.1376779
SC-NC  0.5666667 -0.2340025 1.3673358 0.2578586
SC-SB -0.1000000 -0.9006691 0.7006691 0.9880224

Result = aov(TB$沉浸感4~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)  
TB$雙因子組別   3  16.97   5.656    2.33  0.078 .
Residuals     116 281.53   2.427                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$沉浸感4 ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff        lwr       upr     p adj
NC-NB -0.1666667 -1.2151837 0.8818504 0.9759169
SB-NB  0.7333333 -0.3151837 1.7818504 0.2677889
SC-NB  0.5666667 -0.4818504 1.6151837 0.4964747
SB-NC  0.9000000 -0.1485171 1.9485171 0.1193105
SC-NC  0.7333333 -0.3151837 1.7818504 0.2677889
SC-SB -0.1666667 -1.2151837 0.8818504 0.9759169

boxplot(formula = TB$沉浸感1 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我可以像在現實世界中一樣與環境互動",
        col = "gray")


boxplot(formula = TB$沉浸感2 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我感覺與外界隔絕",
        col = "gray")


boxplot(formula = TB$沉浸感3 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我完全沉浸其中",
        col = "gray")


boxplot(formula = TB$沉浸感4 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我忘記了我的日常煩惱",
        col = "gray")

4.認知壓力 NASA-TLX-> 顯著性與boxplot


Result = aov(TB$精神需求~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   5.37   1.789   1.289  0.282
Residuals     116 161.00   1.388

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$精神需求 ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff        lwr       upr     p adj
NC-NB -0.1000000 -0.8929093 0.6929093 0.9876771
SB-NB  0.4333333 -0.3595760 1.2262427 0.4866914
SC-NB  0.2666667 -0.5262427 1.0595760 0.8169323
SB-NC  0.5333333 -0.2595760 1.3262427 0.3012427
SC-NC  0.3666667 -0.4262427 1.1595760 0.6246785
SC-SB -0.1666667 -0.9595760 0.6262427 0.9469420

Result = aov(TB$體力需求~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3    0.6  0.1861   0.064  0.979
Residuals     116  335.4  2.8911

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$體力需求 ~ TB$雙因子組別)

$`TB$雙因子組別`
               diff        lwr       upr     p adj
NC-NB  1.666667e-01 -0.9777141 1.3110474 0.9812817
SB-NB  6.666667e-02 -1.0777141 1.2110474 0.9987454
SC-NB  1.776357e-15 -1.1443807 1.1443807 1.0000000
SB-NC -1.000000e-01 -1.2443807 1.0443807 0.9958121
SC-NC -1.666667e-01 -1.3110474 0.9777141 0.9812817
SC-SB -6.666667e-02 -1.2110474 1.0777141 0.9987454

Result = aov(TB$時間壓力~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3    4.2   1.400   0.599  0.617
Residuals     116  271.3   2.338

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$時間壓力 ~ TB$雙因子組別)

$`TB$雙因子組別`
      diff        lwr       upr     p adj
NC-NB  0.1 -0.9292214 1.1292214 0.9942702
SB-NB  0.5 -0.5292214 1.5292214 0.5861367
SC-NB  0.2 -0.8292214 1.2292214 0.9573866
SB-NC  0.4 -0.6292214 1.4292214 0.7421292
SC-NC  0.1 -0.9292214 1.1292214 0.9942702
SC-SB -0.3 -1.3292214 0.7292214 0.8722848

Result = aov(TB$成功~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   6.09   2.031   1.606  0.192
Residuals     116 146.70   1.265

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$成功 ~ TB$雙因子組別)

$`TB$雙因子組別`
             diff        lwr       upr     p adj
NC-NB -0.13333333 -0.8902109 0.6235443 0.9677003
SB-NB  0.03333333 -0.7235443 0.7902109 0.9994559
SC-NB -0.53333333 -1.2902109 0.2235443 0.2615121
SB-NC  0.16666667 -0.5902109 0.9235443 0.9396616
SC-NC -0.40000000 -1.1568776 0.3568776 0.5159010
SC-SB -0.56666667 -1.3235443 0.1902109 0.2125795

Result = aov(TB$努力~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   5.97   1.989   1.916  0.131
Residuals     116 120.40   1.038

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$努力 ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff         lwr       upr     p adj
NC-NB -0.3333333 -1.01901705 0.3523504 0.5855937
SB-NB  0.2666667 -0.41901705 0.9523504 0.7417309
SC-NB  0.1333333 -0.55235038 0.8190170 0.9573056
SB-NC  0.6000000 -0.08568371 1.2856837 0.1084581
SC-NC  0.4666667 -0.21901705 1.1523504 0.2910509
SC-SB -0.1333333 -0.81901705 0.5523504 0.9573056

Result = aov(TB$不安~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   7.43   2.475   1.007  0.392
Residuals     116 285.17   2.458

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$不安 ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff       lwr       upr     p adj
NC-NB -0.5333333 -1.588595 0.5219279 0.5537753
SB-NB -0.5333333 -1.588595 0.5219279 0.5537753
SC-NB -0.6333333 -1.688595 0.4219279 0.4029462
SB-NC  0.0000000 -1.055261 1.0552612 1.0000000
SC-NC -0.1000000 -1.155261 0.9552612 0.9946776
SC-SB -0.1000000 -1.155261 0.9552612 0.9946776

boxplot(formula = TB$精神需求 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "精神需求",
        col = "gray")


boxplot(formula = TB$體力需求 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "體力需求",
        col = "gray")


boxplot(formula = TB$時間壓力 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "時間壓力",
        col = "gray")


boxplot(formula = TB$成功 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "成功",
        col = "gray")


boxplot(formula = TB$努力 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "努力",
        col = "gray")


boxplot(formula = TB$不安 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "不安",
        col = "gray")

5.創造力-> 顯著性


Result = aov(TB$想法數量~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3    7.0   2.333   1.569  0.201
Residuals     116  172.5   1.487

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$想法數量 ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff        lwr       upr     p adj
NC-NB -0.4333333 -1.2539932 0.3873265 0.5166459
SB-NB  0.2000000 -0.6206598 1.0206598 0.9204289
SC-NB  0.1000000 -0.7206598 0.9206598 0.9888549
SB-NC  0.6333333 -0.1873265 1.4539932 0.1896463
SC-NC  0.5333333 -0.2873265 1.3539932 0.3314946
SC-SB -0.1000000 -0.9206598 0.7206598 0.9888549

Result = aov(TB$品質~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   6.49   2.164   1.938  0.127
Residuals     116 129.50   1.116

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$品質 ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff        lwr       upr     p adj
NC-NB -0.1333333 -0.8444576 0.5777909 0.9614651
SB-NB  0.4333333 -0.2777909 1.1444576 0.3892237
SC-NB  0.3333333 -0.3777909 1.0444576 0.6142971
SB-NC  0.5666667 -0.1444576 1.2777909 0.1667158
SC-NC  0.4666667 -0.2444576 1.1777909 0.3228791
SC-SB -0.1000000 -0.8111242 0.6111242 0.9830876

Result = aov(TB$創造力~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3    6.7   2.233   1.429  0.238
Residuals     116  181.3   1.563

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$創造力 ~ TB$雙因子組別)

$`TB$雙因子組別`
             diff        lwr       upr     p adj
NC-NB  0.03333333 -0.8080028 0.8746695 0.9996034
SB-NB  0.53333333 -0.3080028 1.3746695 0.3536891
SC-NB  0.43333333 -0.4080028 1.2746695 0.5378950
SB-NC  0.50000000 -0.3413362 1.3413362 0.4118208
SC-NC  0.40000000 -0.4413362 1.2413362 0.6032619
SC-SB -0.10000000 -0.9413362 0.7413362 0.9896372

boxplot(formula = TB$想法數量 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "想法數量",
        col = "gray")


boxplot(formula = TB$品質 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "品質",
        col = "gray")


boxplot(formula = TB$創造力 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "創造力",
        col = "gray")

6 社交互動 Social Interaction：QOI-> 顯著性與boxplot


Result = aov(TB$QoI1~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   4.83   1.611   0.642   0.59
Residuals     116 291.13   2.510

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$QoI1 ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff        lwr       upr     p adj
NC-NB -0.1333333 -1.1995772 0.9329106 0.9879792
SB-NB  0.3000000 -0.7662439 1.3662439 0.8834661
SC-NB -0.2333333 -1.2995772 0.8329106 0.9406875
SB-NC  0.4333333 -0.6329106 1.4995772 0.7148090
SC-NC -0.1000000 -1.1662439 0.9662439 0.9948379
SC-SB -0.5333333 -1.5995772 0.5329106 0.5623616

Result = aov(TB$QoI2~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   1.77  0.5889   0.287  0.835
Residuals     116 238.20  2.0534

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$QoI2 ~ TB$雙因子組別)

$`TB$雙因子組別`
             diff        lwr       upr     p adj
NC-NB -0.10000000 -1.0644539 0.8644539 0.9930602
SB-NB  0.23333333 -0.7311206 1.1977872 0.9219841
SC-NB  0.06666667 -0.8977872 1.0311206 0.9979116
SB-NC  0.33333333 -0.6311206 1.2977872 0.8043533
SC-NC  0.16666667 -0.7977872 1.1311206 0.9694181
SC-SB -0.16666667 -1.1311206 0.7977872 0.9694181

Result = aov(TB$QoI3~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   1.17  0.3889   0.233  0.873
Residuals     116 193.20  1.6655

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$QoI3 ~ TB$雙因子組別)

$`TB$雙因子組別`
             diff        lwr       upr     p adj
NC-NB -0.13333333 -1.0019220 0.7352553 0.9782144
SB-NB  0.13333333 -0.7352553 1.0019220 0.9782144
SC-NB -0.06666667 -0.9352553 0.8019220 0.9971492
SB-NC  0.26666667 -0.6019220 1.1352553 0.8541544
SC-NC  0.06666667 -0.8019220 0.9352553 0.9971492
SC-SB -0.20000000 -1.0685887 0.6685887 0.9317992

Result = aov(TB$QoI4~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3    1.1  0.3667   0.185  0.906
Residuals     116  229.4  1.9776

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$QoI4 ~ TB$雙因子組別)

$`TB$雙因子組別`
            diff        lwr       upr     p adj
NC-NB -0.1333333 -1.0798043 0.8131376 0.9829997
SB-NB  0.1000000 -0.8464710 1.0464710 0.9926650
SC-NB  0.1000000 -0.8464710 1.0464710 0.9926650
SB-NC  0.2333333 -0.7131376 1.1798043 0.9179177
SC-NC  0.2333333 -0.7131376 1.1798043 0.9179177
SC-SB  0.0000000 -0.9464710 0.9464710 1.0000000

boxplot(formula = TB$QoI1 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我可以判斷對方是否在聽我說話。",
        col = "gray")


boxplot(formula = TB$QoI2 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "我能充分理解對方在VR頭腦風暴時中做的任何行為。",
        col = "gray")


boxplot(formula = TB$QoI3 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我確信我的搭檔明白我在說什麼。",
        col = "gray")


boxplot(formula = TB$QoI4 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我專注於和對方互動。",
        col = "gray")

6 社交互動 Social Interaction：SM-> 顯著性與boxplot


Result = aov(TB$SM1~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3  10.03   3.344   1.976  0.121
Residuals     116 196.33   1.693

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$SM1 ~ TB$雙因子組別)

$`TB$雙因子組別`
             diff        lwr       upr     p adj
NC-NB -0.56666667 -1.4422704 0.3089371 0.3352081
SB-NB  0.20000000 -0.6756037 1.0756037 0.9332826
SC-NB  0.03333333 -0.8422704 0.9089371 0.9996481
SB-NC  0.76666667 -0.1089371 1.6422704 0.1081158
SC-NC  0.60000000 -0.2756037 1.4756037 0.2851847
SC-SB -0.16666667 -1.0422704 0.7089371 0.9597929

Result = aov(TB$SM2~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   9.23   3.075    1.61  0.191
Residuals     116 221.57   1.910

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$SM2 ~ TB$雙因子組別)

$`TB$雙因子組別`
      diff       lwr      upr     p adj
NC-NB -0.1 -1.030171 0.830171 0.9922805
SB-NB  0.5 -0.430171 1.430171 0.5011866
SC-NB  0.5 -0.430171 1.430171 0.5011866
SB-NC  0.6 -0.330171 1.530171 0.3381411
SC-NC  0.6 -0.330171 1.530171 0.3381411
SC-SB  0.0 -0.930171 0.930171 1.0000000

Result = aov(TB$SM3~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3    7.5   2.500   1.327  0.269
Residuals     116  218.5   1.883

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$SM3 ~ TB$雙因子組別)

$`TB$雙因子組別`
             diff        lwr       upr     p adj
NC-NB -0.26666667 -1.1903076 0.6569743 0.8753957
SB-NB  0.43333333 -0.4903076 1.3569743 0.6136111
SC-NB  0.03333333 -0.8903076 0.9569743 0.9997001
SB-NC  0.70000000 -0.2236410 1.6236410 0.2032249
SC-NC  0.30000000 -0.6236410 1.2236410 0.8320044
SC-SB -0.40000000 -1.3236410 0.5236410 0.6725115

Result = aov(TB$SM4~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3    3.5   1.167   0.509  0.677
Residuals     116  265.8   2.291

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$SM4 ~ TB$雙因子組別)

$`TB$雙因子組別`
             diff       lwr       upr     p adj
NC-NB  0.10000000 -0.918798 1.1187980 0.9940955
SB-NB -0.26666667 -1.285465 0.7521313 0.9036505
SC-NB -0.30000000 -1.318798 0.7187980 0.8688933
SB-NC -0.36666667 -1.385465 0.6521313 0.7843976
SC-NC -0.40000000 -1.418798 0.6187980 0.7360861
SC-SB -0.03333333 -1.052131 0.9854647 0.9997764

Result = aov(TB$SM5~TB$雙因子組別)
summary(Result)

               Df Sum Sq Mean Sq F value Pr(>F)
TB$雙因子組別   3   0.43  0.1417   0.053  0.984
Residuals     116 312.90  2.6974

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$SM5 ~ TB$雙因子組別)

$`TB$雙因子組別`
               diff       lwr       upr     p adj
NC-NB -8.881784e-16 -1.105385 1.1053845 1.0000000
SB-NB  1.000000e-01 -1.005385 1.2053845 0.9953597
SC-NB -6.666667e-02 -1.172051 1.0387178 0.9986087
SB-NC  1.000000e-01 -1.005385 1.2053845 0.9953597
SC-NC -6.666667e-02 -1.172051 1.0387178 0.9986087
SC-SB -1.666667e-01 -1.272051 0.9387178 0.9793114

boxplot(formula = TB$SM1 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "我從VR頭腦風暴的體驗中獲得了很多的滿足感。",
        col = "gray")


boxplot(formula = TB$SM2 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "我很享受和我的夥伴一起度過的時光。",
        col = "gray")


boxplot(formula = TB$SM3 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "當我在進行VR頭腦風暴時，我常常覺得自己是孤身一人。",
        col = "gray")


boxplot(formula = TB$SM4 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在VR中，當周遭有動靜時，我觀察到我的夥伴們容易分心。",
        col = "gray")


boxplot(formula = TB$SM5 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "當我在進行VR頭腦風暴時，時間似乎過得很慢。",
        col = "gray")

差異分析- 時間方式呈現喜歡程度 t test


TB$time= factor(TB$time)
p <- ggplot(data =TB, aes(x = like,fill = time)) +  geom_bar(position = "dodge")
p



var.test(TB$like~TB$time,  data = TB) #函數 var.test()進行變異數同質性檢定


    F test to compare two variances

data:  TB$like by TB$time
F = 1.0481, num df = 59, denom df = 59, p-value = 0.8576
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
 0.626026 1.754574
sample estimates:
ratio of variances 
           1.04805

# 炸彈 和 電子鐘 差異程度
t.test(TB$like~TB$time , var.equal = TRUE,data = TB) # t.test 結果顯示雙尾 TWO sample t-test , p-value 要小於0.05 才有顯著差異


    Two Sample t-test

data:  TB$like by TB$time
t = 0.42491, df = 118, p-value = 0.6717
alternative hypothesis: true difference in means between group B and group C is not equal to 0
95 percent confidence interval:
 -0.3660421  0.5660421
sample estimates:
mean in group B mean in group C 
       4.833333        4.733333

t.test(TB$like~TB$time , var.equal = TRUE,alt= "greater",data = TB) # 單尾檢定


    Two Sample t-test

data:  TB$like by TB$time
t = 0.42491, df = 118, p-value = 0.3358
alternative hypothesis: true difference in means between group B and group C is greater than 0
95 percent confidence interval:
 -0.2901671        Inf
sample estimates:
mean in group B mean in group C 
       4.833333        4.733333

Result = aov(TB$like~TB$time)
summary(Result)

             Df Sum Sq Mean Sq F value Pr(>F)
TB$time       1    0.3   0.300   0.181  0.672
Residuals   118  196.1   1.662

TukeyHSD(Result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = TB$like ~ TB$time)

$`TB$time`
    diff        lwr       upr     p adj
C-B -0.1 -0.5660421 0.3660421 0.6716741

boxplot(formula = TB$like~TB$time,
        data =TB,
        xlab = "組別",
        ylab = "喜歡程度。",
        col = "gray")

NA
NA

---
title: "data analysis"
author: "YuehChi"
date: "2022/12/13"
output:
  html_notebook:
    toc: yes
    toc_float: yes
    highlight: pygments
    theme: flatly
    css: style.css
  html_document:
    toc: yes
    df_print: paged
---



### 安裝需要的packages

```{r}
packages = c("dplyr","ggplot2", "data.table", "scales", "tidytext","ltm")
existing = as.character(installed.packages()[,1])
for(pkg in packages[!(packages %in% existing)]) install.packages(pkg)
```

### 載入需要的packages以及資料

```{r echo = T, results = 'hide'}
require(dplyr)
require(ggplot2)
require(data.table)
require(scales)
require(wordcloud2)
require(tidytext)
require(ltm)
```

### 載入資料
```{r}
TB = read.csv('data.csv')
TB$單因子組別= factor(TB$單因子組別)
TB$雙因子組別= factor(TB$雙因子組別)

```


### 檢視前6筆資料
```{r}
head(TB)
```

# 單因子分析： 切換場景(S) VS 不切換場景(N)

## 加總主題分數 - 單因子分析

### 3.加總值平均(沉浸感) -> 顯著性與boxplot

```{r}
# cronbach alpha

head(TB[6:9])
cronbach.alpha(TB[6:9], CI=TRUE, standardized=TRUE)

new_tb <- data.frame(TB$沉浸感1,TB$沉浸感3,TB$沉浸感4)
head(new_tb)
cronbach.alpha(new_tb,CI= TRUE, standardized = TRUE)

```


```{r}

# 根據上面cronbach.alpha的結果，拿掉沉浸感2

TB <- transform(TB, all_immer = (TB$沉浸感1 + TB$沉浸感3 + TB$沉浸感4)/3)
Result = aov(TB$all_immer~TB$單因子組別)
summary(Result)
TukeyHSD(Result)
plot(TukeyHSD(Result),las=1)

boxplot(formula = TB$all_immer ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "沉浸感) -> 顯著性",
        col = "gray")

ggplot(TB, aes(x = 單因子組別, y = all_immer)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))



```

### 4.加總值平均(自我創造力) -> 顯著性與boxplot

```{r}
# cronbach alpha

head(TB[16:18])
cronbach.alpha(TB[16:18], CI=TRUE, standardized=TRUE)

```

```{r}
TB <- transform(TB, all_creative = (TB$想法數量 + TB$品質 + TB$創造力)/3)
Result = aov(TB$all_creative~TB$單因子組別)
summary(Result)
TukeyHSD(Result)

boxplot(formula = TB$all_creative ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "自我創造力) -> 顯著性",
        col = "gray")

ggplot(TB, aes(x = 單因子組別, y = all_creative)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))

```

### 5.加總值平均(社交互動 Social Interaction) -> 顯著性與boxplot

```{r}
# cronbach alpha

head(TB[19:27])
cronbach.alpha(TB[19:27], CI=TRUE, standardized=TRUE)

```

```{r}
TB <- transform(TB, all_SI = (TB$QoI1 + TB$QoI2 + TB$QoI3 + TB$QoI4 + TB$SM1 + TB$SM2 + TB$SM3 + TB$SM4 + TB$SM5)/9)
Result = aov(TB$all_SI~TB$單因子組別)
summary(Result)

boxplot(formula = TB$all_SI ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "社交互動 Social Interaction) -> 顯著性",
        col = "gray")

```



### 6.加總值平均(社交互動 Social Interaction：QOI) -> 顯著性與boxplot


```{r}
# cronbach alpha

head(TB[19:22])
cronbach.alpha(TB[19:22], CI=TRUE, standardized=TRUE)

```

```{r}
TB <- transform(TB, all_QOI = (TB$QoI1 + TB$QoI2 + TB$QoI3 + TB$QoI4)/4) 
Result = aov(TB$all_QOI~TB$單因子組別)
summary(Result)
TukeyHSD(Result)

boxplot(formula = TB$all_QOI ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "社交互動 Social Interaction：QOI) -> 顯著性",
        col = "gray")

ggplot(TB, aes(x = 單因子組別, y = all_QOI)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))




```

### 7.加總值平均(社交互動 Social Interaction：SM) -> 顯著性與boxplot

```{r}
# cronbach alpha

head(TB[23:27])
cronbach.alpha(TB[23:27], CI=TRUE, standardized=TRUE)

```

```{r}
TB <- transform(TB, all_SM = (TB$SM1 + TB$SM2 + TB$SM3 + TB$SM4 + TB$SM5)/5)
Result = aov(TB$all_SM~TB$單因子組別)
summary(Result)

boxplot(formula = TB$all_SM ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "社交互動 Social Interaction：SM) -> 顯著性",
        col = "gray")

```



## 各項項目分數 - 單因子分析

### 3.沉浸感 Immersion -> 顯著性與boxplot

```{r}

Result = aov(TB$沉浸感1~TB$單因子組別)
summary(Result)

Result = aov(TB$沉浸感2~TB$單因子組別)
summary(Result)

Result = aov(TB$沉浸感3~TB$單因子組別)
summary(Result)

Result = aov(TB$沉浸感4~TB$單因子組別)
summary(Result)


boxplot(formula = TB$沉浸感1 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我可以像在現實世界中一樣與環境互動",
        col = "gray")

boxplot(formula = TB$沉浸感2 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我感覺與外界隔絕",
        col = "gray")

boxplot(formula = TB$沉浸感3 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我完全沉浸其中",
        col = "gray")

boxplot(formula = TB$沉浸感4 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我忘記了我的日常煩惱",
        col = "gray")

```



### 4.認知壓力 NASA-TLX-> 顯著性與boxplot

```{r}

Result = aov(TB$精神需求~TB$單因子組別)
summary(Result)

Result = aov(TB$體力需求~TB$單因子組別)
summary(Result)

Result = aov(TB$時間壓力~TB$單因子組別)
summary(Result)

Result = aov(TB$時間壓力~TB$單因子組別)
summary(Result)

Result = aov(TB$成功~TB$單因子組別)
summary(Result)

Result = aov(TB$努力~TB$單因子組別)
summary(Result)

Result = aov(TB$不安~TB$單因子組別)
summary(Result)

boxplot(formula = TB$精神需求 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "精神需求",
        col = "gray")

boxplot(formula = TB$體力需求 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "體力需求",
        col = "gray")

boxplot(formula = TB$時間壓力 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "時間壓力",
        col = "gray")

boxplot(formula = TB$成功 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "成功",
        col = "gray")

boxplot(formula = TB$努力 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "努力",
        col = "gray")

boxplot(formula = TB$不安 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "不安",
        col = "gray")

```




### 5.創造力-> 顯著性與boxplot


```{r}

Result = aov(TB$想法數量~TB$單因子組別)
summary(Result)

Result = aov(TB$品質~TB$單因子組別)
summary(Result)

Result = aov(TB$創造力~TB$單因子組別)
summary(Result)

boxplot(formula = TB$想法數量 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "想法數量",
        col = "gray")

boxplot(formula = TB$品質 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "品質",
        col = "gray")

boxplot(formula = TB$創造力 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "創造力",
        col = "gray")

```

### 6 社交互動 Social Interaction：QOI-> 顯著性與boxplot


```{r}

Result = aov(TB$QoI1~TB$單因子組別)
summary(Result)

Result = aov(TB$QoI2~TB$單因子組別)
summary(Result)

Result = aov(TB$QoI3~TB$單因子組別)
summary(Result)

Result = aov(TB$QoI4~TB$單因子組別)
summary(Result)

boxplot(formula = TB$QoI1 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我可以判斷對方是否在聽我說話。",
        col = "gray")

boxplot(formula = TB$QoI2 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "我能充分理解對方在VR頭腦風暴時中做的任何行為。",
        col = "gray")

boxplot(formula = TB$QoI3 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我確信我的搭檔明白我在說什麼。",
        col = "gray")

boxplot(formula = TB$QoI4 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我專注於和對方互動。",
        col = "gray")

```


### 6 社交互動 Social Interaction：SM-> 顯著性與boxplot

```{r}

Result = aov(TB$SM1~TB$單因子組別)
summary(Result)

Result = aov(TB$SM2~TB$單因子組別)
summary(Result)

Result = aov(TB$SM3~TB$單因子組別)
summary(Result)

Result = aov(TB$SM4~TB$單因子組別)
summary(Result)

Result = aov(TB$SM5~TB$單因子組別)
summary(Result)


boxplot(formula = TB$SM1 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "我從VR頭腦風暴的體驗中獲得了很多的滿足感。",
        col = "gray")

boxplot(formula = TB$SM2 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "我很享受和我的夥伴一起度過的時光。",
        col = "gray")

boxplot(formula = TB$SM3 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "當我在進行VR頭腦風暴時，我常常覺得自己是孤身一人。",
        col = "gray")

boxplot(formula = TB$SM4 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在VR中，當周遭有動靜時，我觀察到我的夥伴們容易分心。",
        col = "gray")

boxplot(formula = TB$SM5 ~TB$單因子組別,
        data =TB,
        xlab = "組別",
        ylab = "當我在進行VR頭腦風暴時，時間似乎過得很慢。",
        col = "gray")

```





# 雙因子分析： NC NB SC SB
NC -> 不切換場景 + 電子鐘
NB -> 不切換場景 + 炸彈
SC -> 切換場景 + 電子鐘
SB -> 切換場景 + 炸彈

## 加總主題分數 - 雙因子分析

### 3.加總值平均(沉浸感) -> 顯著性與boxplot
```{r}
# cronbach alpha

head(TB[6:9])
cronbach.alpha(TB[6:9], CI=TRUE, standardized=TRUE)

new_tb <- data.frame(TB$沉浸感1,TB$沉浸感3,TB$沉浸感4)
head(new_tb)
cronbach.alpha(new_tb,CI= TRUE, standardized = TRUE)

```


```{r}
# 根據上面cronbach.alpha的結果，拿掉沉浸感2
TB <- transform(TB, all_immer = (TB$沉浸感1 + TB$沉浸感3 + TB$沉浸感4)/4)
Result = aov(TB$all_immer~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)
plot(TukeyHSD(Result),las=1)

boxplot(formula = TB$all_immer ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "沉浸感) -> 顯著性",
        col = "gray")

ggplot(TB, aes(x = 雙因子組別, y = all_immer)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))



```

### 4.加總值平均(自我創造力) -> 顯著性與boxplot

```{r}
TB <- transform(TB, all_creative = (TB$想法數量 + TB$品質 + TB$創造力)/3)
Result = aov(TB$all_creative~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

boxplot(formula = TB$all_creative ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "自我創造力) -> 顯著性",
        col = "gray")

ggplot(TB, aes(x = 雙因子組別, y = all_creative)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))


```

### 5.加總值平均(社交互動 Social Interaction) -> 顯著性與boxplot

```{r}
TB <- transform(TB, all_SI = (TB$QoI1 + TB$QoI2 + TB$QoI3 + TB$QoI4 + TB$SM1 + TB$SM2 + TB$SM3 + TB$SM4 + TB$SM5/9))
Result = aov(TB$all_SI~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

boxplot(formula = TB$all_SI ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "社交互動 Social Interaction) -> 顯著性",
        col = "gray")

ggplot(TB, aes(x = 雙因子組別, y = all_SI)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))



```



### 6.加總值平均(社交互動 Social Interaction：QOI) -> 顯著性與boxplot

```{r}
TB <- transform(TB, all_QOI = (TB$QoI1 + TB$QoI2 + TB$QoI3 + TB$QoI4)/4) 
Result = aov(TB$all_QOI~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

boxplot(formula = TB$all_QOI ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "社交互動 Social Interaction：QOI) -> 顯著性",
        col = "gray")

ggplot(TB, aes(x = 雙因子組別, y = all_QOI)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))




```

### 7.加總值平均(社交互動 Social Interaction：SM) -> 顯著性與boxplot

```{r}
TB <- transform(TB, all_SM = (TB$SM1 + TB$SM2 + TB$SM3 + TB$SM4 + TB$SM5)/5)
Result = aov(TB$all_SM~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

boxplot(formula = TB$all_SM ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "社交互動 Social Interaction：SM) -> 顯著性",
        col = "gray")

ggplot(TB, aes(x = 雙因子組別, y = all_SM)) +   # Draw ggplot2 boxplot
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", col = "red") +  # Add points to plot
  stat_summary(fun = mean, geom = "text", col = "red",     # Add text to plot
               vjust = 1.5, aes(label = paste("Mean:", round(after_stat(y), digits = 1))))

```



## 各項項目分數 - 雙因子分析

###  3.沉浸感 Immersion -> 顯著性與boxplot

```{r}

Result = aov(TB$沉浸感1~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$沉浸感2~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$沉浸感3~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$沉浸感4~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)


boxplot(formula = TB$沉浸感1 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我可以像在現實世界中一樣與環境互動",
        col = "gray")

boxplot(formula = TB$沉浸感2 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我感覺與外界隔絕",
        col = "gray")

boxplot(formula = TB$沉浸感3 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我完全沉浸其中",
        col = "gray")

boxplot(formula = TB$沉浸感4 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我忘記了我的日常煩惱",
        col = "gray")

```


### 4.認知壓力 NASA-TLX-> 顯著性與boxplot

```{r}

Result = aov(TB$精神需求~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$體力需求~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$時間壓力~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$成功~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$努力~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$不安~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

boxplot(formula = TB$精神需求 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "精神需求",
        col = "gray")

boxplot(formula = TB$體力需求 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "體力需求",
        col = "gray")

boxplot(formula = TB$時間壓力 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "時間壓力",
        col = "gray")

boxplot(formula = TB$成功 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "成功",
        col = "gray")

boxplot(formula = TB$努力 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "努力",
        col = "gray")

boxplot(formula = TB$不安 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "不安",
        col = "gray")

```


### 5.創造力-> 顯著性 


```{r}

Result = aov(TB$想法數量~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$品質~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$創造力~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

boxplot(formula = TB$想法數量 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "想法數量",
        col = "gray")

boxplot(formula = TB$品質 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "品質",
        col = "gray")

boxplot(formula = TB$創造力 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "創造力",
        col = "gray")

```
### 6 社交互動 Social Interaction：QOI-> 顯著性與boxplot


```{r}

Result = aov(TB$QoI1~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$QoI2~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$QoI3~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$QoI4~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

boxplot(formula = TB$QoI1 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我可以判斷對方是否在聽我說話。",
        col = "gray")

boxplot(formula = TB$QoI2 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "我能充分理解對方在VR頭腦風暴時中做的任何行為。",
        col = "gray")

boxplot(formula = TB$QoI3 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我確信我的搭檔明白我在說什麼。",
        col = "gray")

boxplot(formula = TB$QoI4 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在進行VR頭腦風暴時，我專注於和對方互動。",
        col = "gray")

```


### 6 社交互動 Social Interaction：SM-> 顯著性與boxplot

```{r}

Result = aov(TB$SM1~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$SM2~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$SM3~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$SM4~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)

Result = aov(TB$SM5~TB$雙因子組別)
summary(Result)
TukeyHSD(Result)


boxplot(formula = TB$SM1 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "我從VR頭腦風暴的體驗中獲得了很多的滿足感。",
        col = "gray")

boxplot(formula = TB$SM2 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "我很享受和我的夥伴一起度過的時光。",
        col = "gray")

boxplot(formula = TB$SM3 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "當我在進行VR頭腦風暴時，我常常覺得自己是孤身一人。",
        col = "gray")

boxplot(formula = TB$SM4 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "在VR中，當周遭有動靜時，我觀察到我的夥伴們容易分心。",
        col = "gray")

boxplot(formula = TB$SM5 ~TB$雙因子組別,
        data =TB,
        xlab = "組別",
        ylab = "當我在進行VR頭腦風暴時，時間似乎過得很慢。",
        col = "gray")

```






##  差異分析- 時間方式呈現喜歡程度 t test
```{r}

TB$time= factor(TB$time)
p <- ggplot(data =TB, aes(x = like,fill = time)) +  geom_bar(position = "dodge")
p


var.test(TB$like~TB$time,  data = TB) #函數 var.test()進行變異數同質性檢定

# 炸彈 和 電子鐘 差異程度
t.test(TB$like~TB$time , var.equal = TRUE,data = TB) # t.test 結果顯示雙尾 TWO sample t-test , p-value 要小於0.05 才有顯著差異

t.test(TB$like~TB$time , var.equal = TRUE,alt= "greater",data = TB) # 單尾檢定


Result = aov(TB$like~TB$time)
summary(Result)
TukeyHSD(Result)

boxplot(formula = TB$like~TB$time,
        data =TB,
        xlab = "組別",
        ylab = "喜歡程度。",
        col = "gray")


```



data analysis

YuehChi

2022/12/13

安裝需要的packages

載入需要的packages以及資料

載入資料

檢視前6筆資料

單因子分析：切換場景(S) VS 不切換場景(N)

加總主題分數 - 單因子分析

3.加總值平均(沉浸感) -> 顯著性與boxplot

4.加總值平均(自我創造力) -> 顯著性與boxplot

各項項目分數 - 單因子分析

3.沉浸感 Immersion -> 顯著性與boxplot

4.認知壓力 NASA-TLX-> 顯著性與boxplot

5.創造力-> 顯著性與boxplot

雙因子分析： NC NB SC SB

加總主題分數 - 雙因子分析

3.加總值平均(沉浸感) -> 顯著性與boxplot

4.加總值平均(自我創造力) -> 顯著性與boxplot

各項項目分數 - 雙因子分析

3.沉浸感 Immersion -> 顯著性與boxplot

4.認知壓力 NASA-TLX-> 顯著性與boxplot

5.創造力-> 顯著性

差異分析- 時間方式呈現喜歡程度 t test

data analysis

YuehChi

2022/12/13

安裝需要的packages

載入需要的packages以及資料

載入資料

檢視前6筆資料

單因子分析： 切換場景(S) VS 不切換場景(N)

加總主題分數 - 單因子分析

3.加總值平均(沉浸感) -> 顯著性與boxplot

4.加總值平均(自我創造力) -> 顯著性與boxplot

5.加總值平均(社交互動 Social Interaction) -> 顯著性與boxplot

6.加總值平均(社交互動 Social Interaction：QOI) -> 顯著性與boxplot

7.加總值平均(社交互動 Social Interaction：SM) -> 顯著性與boxplot

各項項目分數 - 單因子分析

3.沉浸感 Immersion -> 顯著性與boxplot

4.認知壓力 NASA-TLX-> 顯著性與boxplot

5.創造力-> 顯著性與boxplot

6 社交互動 Social Interaction：QOI-> 顯著性與boxplot

6 社交互動 Social Interaction：SM-> 顯著性與boxplot

雙因子分析： NC NB SC SB

加總主題分數 - 雙因子分析

3.加總值平均(沉浸感) -> 顯著性與boxplot

4.加總值平均(自我創造力) -> 顯著性與boxplot

5.加總值平均(社交互動 Social Interaction) -> 顯著性與boxplot

6.加總值平均(社交互動 Social Interaction：QOI) -> 顯著性與boxplot

7.加總值平均(社交互動 Social Interaction：SM) -> 顯著性與boxplot

各項項目分數 - 雙因子分析

3.沉浸感 Immersion -> 顯著性與boxplot

4.認知壓力 NASA-TLX-> 顯著性與boxplot

5.創造力-> 顯著性

6 社交互動 Social Interaction：QOI-> 顯著性與boxplot

6 社交互動 Social Interaction：SM-> 顯著性與boxplot

差異分析- 時間方式呈現喜歡程度 t test

單因子分析：切換場景(S) VS 不切換場景(N)