HW0208

inputting the data

dat1 = read.table(textConnection("
Acad_K Soc_K Total_K Qtime QL1 QL2 QL3 QL4 QL5 QL6 QL7 QL8 QI1 QI2 QI3 QI4 QI5 QI6 QI7 QA1 QA2 QA3 QA4 QA5 QA6 QA7
21  17  38  2   5   4   3   5   5   2   4   5   5   5   4   3   3   4   4   5   4   2   1   4   1   1
20  16  36  2   4   5   1   4   5   5   4   4   5   4   4   4   5   4   4   4   4   2   4   2   1   4
15  11  26  2   3   3   2   1   4   1   3   2   5   2   3   1   1   2   3   2   1   5   4   4   4   4
17  19  36  1   5   3   1   2   5   1   2   5   5   5   3   4   4   4   5   2   2   3   5   5   4   5
15  13  28  2   1   4   1   1   3   2   1   5   5   5   2   3   2   2   3   3   2   3   4   3   2   4
20  14  34  1   5   3   2   4   3   5   5   5   4   5   4   2   4   5   3   5   4   5   3   5   4   5
18  14  32  1   1   2   1   1   2   1   2   4   4   3   3   2   2   3   5   4   3   1   4   3   3   5
21  20  41  1   4   5   4   3   4   4   3   3   4   5   3   5   4   4   3   5   5   4   3   5   4   4
24  19  43  1   1   3   4   5   5   2   5   5   5   5   3   3   4   5   5   2   5   2   5   5   1   4
23  19  42  1   4   5   4   3   4   4   4   5   5   5   5   1   3   5   2   5   5   5   2   5   1   2
20  14  34  2   1   1   1   1   5   1   1   5   5   5   1   1   1   4   4   2   2   2   4   2   1   4
21  11  32  1   4   1   4   1   5   1   5   5   4   4   5   5   4   4   4   5   4   1   1   5   1   1
24  20  44  2   2   4   4   4   4   4   5   5   5   5   4   3   4   4   4   4   4   1   5   5   1   4
16  10  26  2   5   2   2   2   3   2   3   3   4   4   3   2   1   4   5   4   2   2   4   3   2   5
19  14  33  1   4   4   1   3   5   1   2   5   5   5   4   4   1   2   2   3   3   1   4   2   1   4
17  13  30  3   2   1   2   3   3   1   3   4   4   4   3   1   2   3   3   3   2   3   4   3   2   3
20  15  35  3   4   4   4   4   4   2   3   4   4   4   4   2   2   4   2   4   3   2   4   4   2   4
17  15  32  3   5   5   5   4   5   2   5   5   5   5   4   2   1   5   5   2   4   2   5   5   4   5
16  12  28  1   5   2   5   2   4   2   5   5   5   5   5   1   2   5   3   4   4   3   3   4   2   3
20  14  34  1   5   1   4   1   5   1   5   5   5   5   5   3   1   5   5   1   1   5   5   5   5   5
19  12  31  1   3   2   4   4   5   3   4   5   4   5   4   3   2   5   4   4   2   3   4   5   1   5
17  13  30  1   1   1   1   1   5   1   1   5   4   5   1   1   1   4   5   3   4   4   4   4   3   5
10  9   19  2   2   3   4   4   5   2   4   4   5   4   2   1   1   3   5   3   2   5   5   3   4   5
15  16  31  2   3   4   4   4   5   3   4   4   4   5   3   3   4   4   3   4   4   3   4   4   2   4
18  16  34  3   4   1   2   1   5   1   3   4   5   5   4   1   4   3   5   4   3   5   4   5   1   5
11  13  24  2   5   1   1   1   5   1   4   5   5   5   2   1   1   2   5   2   2   5   5   5   4   5
19  14  33  2   3   4   3   3   3   3   4   5   5   5   4   5   3   3   5   5   5   3   5   3   5   5
19  16  35  3   4   1   1   1   4   1   1   3   4   5   2   1   3   5   5   1   1   4   4   2   3   5
21  14  35  2   5   1   1   1   5   2   2   4   5   4   1   1   1   4   4   2   2   5   5   2   1   5
20  18  38  2   5   1   3   2   5   5   4   5   5   5   5   1   1   5   5   5   2   5   5   4   5   5
15  13  28  2   4   4   4   4   5   4   5   4   5   5   5   5   3   4   4   5   4   4   4   4   3   4
23  20  43  2   5   4   2   2   5   3   2   5   5   5   3   1   1   2   5   4   3   4   5   2   3   5
18  18  36  2   1   1   1   1   5   1   1   1   3   3   3   4   1   5   3   3   2   4   4   4   3   4
21  14  35  3   1   2   1   1   2   1   1   1   3   5   4   3   2   4   3   4   4   3   4   2   2   4
20  17  37  2   3   5   5   5   5   2   5   5   5   5   2   4   2   5   4   4   4   2   5   4   1   4
19  15  34  2   2   3   3   2   3   2   5   5   4   4   4   3   1   5   4   4   4   2   4   5   2   4
16  21  37  1   1   2   1   1   4   2   2   5   5   5   2   3   2   5   5   1   1   3   5   5   5   5
18  19  37  3   2   1   1   1   3   1   2   2   3   3   3   3   3   3   3   4   4   4   4   4   2   3
15  13  28  2   3   3   3   3   3   2   3   3   4   4   4   1   3   4   3   4   3   4   4   4   2   4
24  19  43  2   1   1   1   1   5   1   5   5   5   5   2   2   1   5   5   4   5   3   4   4   2   5
19  19  38  2   2   2   3   4   2   2   4   4   4   4   4   2   4   4   3   4   4   3   2   4   2   3
21  19  40  2   3   4   4   4   2   4   4   4   5   4   4   5   3   4   3   4   4   2   2   5   2   2
19  14  33  2   5   5   4   4   5   2   5   5   5   5   5   3   5   4   3   5   5   2   4   5   2   3
20  15  35  2   5   5   3   4   5   2   4   5   5   5   3   3   2   4   5   3   3   2   5   4   1   5
16  18  34  2   3   2   1   2   5   1   4   5   5   4   3   5   4   5   4   4   4   2   3   5   2   3
19  15  34  1   4   2   3   4   5   4   4   5   5   4   4   3   1   4   4   4   4   3   3   5   3   4
14  14  28  3   5   4   5   5   5   5   5   5   5   5   5   2   1   5   5   2   2   4   4   1   5   5
21  14  35  3   5   5   5   4   3   4   4   5   5   5   5   1   3   5   4   5   4   3   3   5   1   4
15  14  29  2   3   1   1   1   2   1   2   3   5   4   4   1   1   4   3   2   2   1   5   1   5   5
20  14  34  2   1   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   4   5   4   5   5   5
25  21  46  1   5   5   2   5   1   5   5   5   5   5   5   5   1   5   5   5   5   5   3   5   5   5
17  13  30  2   2   3   2   1   5   2   4   5   5   4   4   2   1   3   3   3   4   3   3   4   2   4
21  18  39  2   2   4   2   1   4   5   4   5   4   5   3   1   2   4   4   2   1   4   5   2   2   4
17  10  27  2   5   1   4   3   4   2   4   5   5   4   4   1   3   4   4   3   2   4   4   5   2   5
16  18  34  2   4   5   4   5   4   5   4   5   5   4   4   3   3   4   4   5   3   5   4   5   4   5
21  23  44  2   4   5   5   5   4   3   5   4   5   5   5   5   5   5   4   4   3   4   4   4   2   4
24  18  42  1   3   4   3   4   2   5   3   4   4   4   3   5   4   5   3   5   5   3   4   4   2   4
24  18  42  2   4   3   4   2   4   3   5   3   4   5   3   5   4   4   3   4   4   3   4   4   2   2
23  20  43  3   3   4   4   4   3   4   5   5   4   4   5   2   4   5   4   4   4   3   3   4   1   4
17  21  38  2   4   2   4   3   4   4   4   5   4   5   3   5   3   5   5   3   5   4   5   4   3   4
17  23  40  3   4   5   5   4   3   5   4   5   4   4   5   5   4   2   3   3   2   4   3   4   3   4
10  11  21  3   5   3   3   2   5   1   2   4   4   4   4   2   3   2   5   2   2   4   5   4   5   4
15  12  27  2   4   3   4   2   4   1   3   4   5   5   4   1   2   1   3   4   2   4   4   4   4   3
10  15  25  2   5   3   4   3   3   1   2   4   5   5   3   3   1   2   4   2   3   4   3   4   5   3
"), header = TRUE)

Finding the most important reasons for picking BDEs

MainE <- sort(colMeans(dat1[, 5:26]))
print(MainE)

##      QI5      QL6      QA6      QI4      QL4      QL3      QL2      QA2 
## 2.500000 2.515625 2.656250 2.718750 2.781250 2.890625 3.000000 3.218750 
##      QA3      QL1      QA1      QL7      QI3      QA4      QA5      QI7 
## 3.281250 3.406250 3.531250 3.546875 3.578125 3.921875 3.921875 3.937500 
##      QI6      QL5      QA7      QL8      QI2      QI1 
## 3.953125 4.046875 4.078125 4.343750 4.531250 4.578125

lbM <- mean(MainE) - sd(MainE)
upM <- mean(MainE) + sd(MainE)
sigMainE <- MainE[MainE < lbM | MainE > upM]
print(sigMainE)

##      QI5      QL6      QA6      QI4      QL4      QL8      QI2      QI1 
## 2.500000 2.515625 2.656250 2.718750 2.781250 4.343750 4.531250 4.578125

inspecting all best fit lines

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.5.3

library(cowplot)

## Warning: package 'cowplot' was built under R version 4.5.3

library(ggpubr)

## Warning: package 'ggpubr' was built under R version 4.5.3

## 
## Attaching package: 'ggpubr'

## The following object is masked from 'package:cowplot':
## 
##     get_legend

#Different Kiasu metrics

ggplot(dat1, aes(x = Total_K, y = Acad_K)) +
geom_point() +
geom_smooth(method=lm) +
stat_regline_equation(label.x = 20, label.y = 20, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 18, color = "red")

## `geom_smooth()` using formula = 'y ~ x'

ggplot(data=dat1, aes(x = Total_K, y = Soc_K)) +
geom_point() +
geom_smooth(method=lm) +
stat_regline_equation(label.x = 20, label.y = 20, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 18, color = "red")

## `geom_smooth()` using formula = 'y ~ x'

#Both Kiasu methods correlate with the total kiasu

Comparisons with Total Kiasu

How long each individual looked

ggplot(data=dat1, aes(x = Total_K, y = Qtime)) +
geom_point() +
geom_smooth(method=lm, se=FALSE)  +
stat_regline_equation(label.x = 20, label.y = 2.7, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 2.3, color = "red")

## `geom_smooth()` using formula = 'y ~ x'

Questions regarding how long they looked and

PL1 <- ggplot(data=dat1, aes(x = Total_K, y = QL1)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 4.6, color = "red")

PL2 <- ggplot(data=dat1, aes(x = Total_K, y = QL2)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 4.6, color = "red")

PL3 <- ggplot(data=dat1, aes(x = Total_K, y = QL3)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 4.6, color = "red")

PL4 <- ggplot(data=dat1, aes(x = Total_K, y = QL4)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 4.6, color = "red")

PL5 <- ggplot(data=dat1, aes(x = Total_K, y = QL5)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 1, color = "red")

PL6 <- ggplot(data=dat1, aes(x = Total_K, y = QL6)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 4.6, color = "red")

PL7 <- ggplot(data=dat1, aes(x = Total_K, y = QL7)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 1, color = "red")

PL8 <- ggplot(data=dat1, aes(x = Total_K, y = QL8)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 1, color = "red")

plot_grid(
  PL1, PL2, PL3, PL4, PL5, PL6, PL7, PL8,
  ncol = 2,
  nrow = 4,
  labels = "AUTO",
  align = "vh"
)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

How Important to BDE selection

PI1 <- ggplot(data=dat1, aes(x = Total_K, y = QI1)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 1, color = "red")

PI2 <- ggplot(data=dat1, aes(x = Total_K, y = QI2)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 1, color = "red")

PI3 <- ggplot(data=dat1, aes(x = Total_K, y = QI3)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 4.6, color = "red")

PI4 <- ggplot(data=dat1, aes(x = Total_K, y = QI4)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 4.6, color = "red")

PI5 <- ggplot(data=dat1, aes(x = Total_K, y = QI5)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 4.6, color = "red")

PI6 <- ggplot(data=dat1, aes(x = Total_K, y = QI6)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 4.6, color = "red")

PI7 <- ggplot(data=dat1, aes(x = Total_K, y = QI7)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 1, color = "red")

plot_grid(
  PI1, PI2, PI3, PI4, PI5, PI6, PI7,
  ncol = 2,
  nrow = 4,
  labels = "AUTO",
  align = "vh"
)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

Extent of agreeability questions

PA1 <- ggplot(data=dat1, aes(x = Total_K, y = QA1)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 4.6, color = "red")

PA2 <- ggplot(data=dat1, aes(x = Total_K, y = QA2)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 4.6, color = "red")

PA3 <- ggplot(data=dat1, aes(x = Total_K, y = QA3)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 1, color = "red")

PA4 <- ggplot(data=dat1, aes(x = Total_K, y = QA4)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 1, color = "red")

PA5 <- ggplot(data=dat1, aes(x = Total_K, y = QA5)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 1, color = "red")

PA6 <- ggplot(data=dat1, aes(x = Total_K, y = QA6)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 1, color = "red")

PA7 <- ggplot(data=dat1, aes(x = Total_K, y = QA7)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 20, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 20, label.y = 1, color = "red")

plot_grid(
  PA1, PA2, PA3, PA4, PA5, PA6, PA7,
  ncol = 2,
  nrow = 4,
  labels = "AUTO",
  align = "vh"
)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

Social Kiasu scales

Questions regarding how long they looked and

SL1 <- ggplot(data=dat1, aes(x = Soc_K, y = QL1)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 1, color = "red")

SL2 <- ggplot(data=dat1, aes(x = Soc_K, y = QL2)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 4.6, color = "red")

SL3 <- ggplot(data=dat1, aes(x = Soc_K, y = QL3)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 1, color = "red")

SL4 <- ggplot(data=dat1, aes(x = Soc_K, y = QL4)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 4.6, color = "red")

SL5 <- ggplot(data=dat1, aes(x = Soc_K, y = QL5)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 1, color = "red")

SL6 <- ggplot(data=dat1, aes(x = Soc_K, y = QL6)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 4.6, color = "red")

SL7 <- ggplot(data=dat1, aes(x = Soc_K, y = QL7)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 1, color = "red")

SL8 <- ggplot(data=dat1, aes(x = Soc_K, y = QL8)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 1, color = "red")

plot_grid(
  SL1, SL2, SL3, SL4, SL5, SL6, SL7, SL8,
  ncol = 2,
  nrow = 4,
  labels = "AUTO",
  align = "vh"
)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

How Important to BDE selection

SI1 <- ggplot(data=dat1, aes(x = Soc_K, y = QI1)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 3.4, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 3, color = "red")

SI2 <- ggplot(data=dat1, aes(x = Soc_K, y = QI2)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 2.4, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 2, color = "red")

SI3 <- ggplot(data=dat1, aes(x = Soc_K, y = QI3)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 4.6, color = "red")

SI4 <- ggplot(data=dat1, aes(x = Soc_K, y = QI4)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 4.6, color = "red")

SI5 <- ggplot(data=dat1, aes(x = Soc_K, y = QI5)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 4.6, color = "red")

SI6 <- ggplot(data=dat1, aes(x = Soc_K, y = QI6)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 4.6, color = "red")

SI7 <- ggplot(data=dat1, aes(x = Soc_K, y = QI7)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 2.4, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 2, color = "red")

plot_grid(
  SI1, SI2, SI3, SI4, SI5, SI6, SI7,
  ncol = 2,
  nrow = 4,
  labels = "AUTO",
  align = "vh"
)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

Extent of agreeability questions

SA1 <- ggplot(data=dat1, aes(x = Soc_K, y = QA1)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 1, color = "red")

SA2 <- ggplot(data=dat1, aes(x = Soc_K, y = QA2)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 4.6, color = "red")

SA3 <- ggplot(data=dat1, aes(x = Soc_K, y = QA3)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 4, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 3.6, color = "red")

SA4 <- ggplot(data=dat1, aes(x = Soc_K, y = QA4)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 1, color = "red")

SA5 <- ggplot(data=dat1, aes(x = Soc_K, y = QA5)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 1, color = "red")

SA6 <- ggplot(data=dat1, aes(x = Soc_K, y = QA6)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 4.6, color = "red")

SA7 <- ggplot(data=dat1, aes(x = Soc_K, y = QA7)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 7.5, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 7.5, label.y = 1, color = "red")

plot_grid(
  SA1, SA2, SA3, SA4, SA5, SA6, SA7,
  ncol = 2,
  nrow = 4,
  labels = "AUTO",
  align = "vh"
)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

Academic Kiasu Scales

Questions regarding how long they looked and

AL1 <- ggplot(data=dat1, aes(x = Acad_K, y = QL1)) +
geom_point() +
geom_smooth(method=lm, se=FALSE)

AL2 <- ggplot(data=dat1, aes(x = Acad_K, y = QL2)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 1, color = "red")

AL3 <- ggplot(data=dat1, aes(x = Acad_K, y = QL3)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 4.6, color = "red")

AL4 <- ggplot(data=dat1, aes(x = Acad_K, y = QL4)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 4.6, color = "red")

AL5 <- ggplot(data=dat1, aes(x = Acad_K, y = QL5)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 4.6, color = "red")

AL6 <- ggplot(data=dat1, aes(x = Acad_K, y = QL6)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 1, color = "red")

AL7 <- ggplot(data=dat1, aes(x = Acad_K, y = QL7)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 4.6, color = "red")

AL8 <- ggplot(data=dat1, aes(x = Acad_K, y = QL8)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 4.6, color = "red")

plot_grid(
  AL1, AL2, AL3, AL4, AL5, AL6, AL7, AL8,
  ncol = 2,
  nrow = 4,
  labels = "AUTO",
  align = "vh"
)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

How long each individual looked

ggplot(data=dat1, aes(x = Acad_K, y = Qtime)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 2.7, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 2.3, color = "red")

## `geom_smooth()` using formula = 'y ~ x'

How Important to BDE selection

AI1 <- ggplot(data=dat1, aes(x = Acad_K, y = QI1)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 3.4, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 3, color = "red")

AI2 <- ggplot(data=dat1, aes(x = Acad_K, y = QI2)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 2.4, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 2, color = "red")

AI3 <- ggplot(data=dat1, aes(x = Acad_K, y = QI3)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 4.6, color = "red")

AI4 <- ggplot(data=dat1, aes(x = Acad_K, y = QI4)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 4.6, color = "red")

AI5 <- ggplot(data=dat1, aes(x = Acad_K, y = QI5)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 4.6, color = "red")

AI6 <- ggplot(data=dat1, aes(x = Acad_K, y = QI6)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 4.6, color = "red")

AI7 <- ggplot(data=dat1, aes(x = Acad_K, y = QI7)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 2.4, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 2, color = "red")

plot_grid(
  AI1, AI2, AI3, AI4, AI5, AI6, AI7,
  ncol = 2,
  nrow = 4,
  labels = "AUTO",
  align = "vh"
)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

Extent of agreeability questions

AA1 <- ggplot(data=dat1, aes(x = Acad_K, y = QA1)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 4.6, color = "red")

AA2 <- ggplot(data=dat1, aes(x = Acad_K, y = QA2)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 5, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 4.6, color = "red")

AA3 <- ggplot(data=dat1, aes(x = Acad_K, y = QA3)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 1, color = "red")

AA4 <- ggplot(data=dat1, aes(x = Acad_K, y = QA4)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 1, color = "red")

AA5 <- ggplot(data=dat1, aes(x = Acad_K, y = QA5)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 1, color = "red")

AA6 <- ggplot(data=dat1, aes(x = Acad_K, y = QA6)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 1, color = "red")

AA7 <- ggplot(data=dat1, aes(x = Acad_K, y = QA7)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
stat_regline_equation(label.x = 10, label.y = 1.4, color = "blue") +
stat_cor(method = "pearson", label.x = 10, label.y = 1, color = "red")

plot_grid(
  AA1, AA2, AA3, AA4, AA5, AA6, AA7,
  ncol = 2,
  nrow = 4,
  labels = "AUTO",
  align = "vh"
)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'