Reading Data
library(readxl)
Warning: package 'readxl' was built under R version 4.2.3
Data<-read_excel("E:/STAT 50/Reading.xlsx")
New names:
• `` -> `...1`
Data
# A tibble: 40 × 31
...1 GPRE1 GPOST1 SPRE2 SPOST2 GPRE3 GPOST3 GPRE4 GPOST4 PPRE5 PPOST5 SPRE6
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 5 4 1 3 3 2 1 1 5 1 1
2 2 3 3 2 2 1 2 2 3 5 2 2
3 3 5 3 3 4 1 2 2 3 4 4 2
4 4 5 4 5 5 5 4 5 3 4 5 2
5 5 2 3 3 4 4 2 3 2 2 3 1
6 6 2 2 2 2 1 2 4 4 5 4 3
7 7 3 2 2 2 3 3 4 1 5 2 2
8 8 3 4 3 2 4 3 2 4 3 3 3
9 9 3 3 2 3 3 4 4 4 4 4 2
10 10 2 3 2 2 2 2 1 2 1 3 3
# … with 30 more rows, and 19 more variables: SPOST6 <dbl>, GPRE7 <dbl>,
# GPOST7 <dbl>, SPRE8 <dbl>, SPOST8 <dbl>, PPRE9 <dbl>, PPOST9 <dbl>,
# GPRE10 <dbl>, GPOST10 <dbl>, GPRE11 <dbl>, GPOST11 <dbl>, GPRE12 <dbl>,
# GPOST12 <dbl>, GPRE13 <dbl>, GPOST13 <dbl>, GPRE14 <dbl>, GPOST14 <dbl>,
# PPRE15 <dbl>, PPOST15 <dbl>
library(dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Gupit1<-Data%>%
mutate('Global Strategy 1' = `GPOST1`-`GPRE1` ) %>%
mutate('Global Strategy 3' = `GPOST3`-`GPRE3`) %>%
mutate('Global Strategy 4' = `GPOST4`-`GPRE4`) %>%
mutate('Global Strategy 7' = `GPOST7`-`GPRE7`) %>%
mutate('Global Strategy 10' = `GPOST10`-`GPRE10`) %>%
mutate('Global Strategy 11' = `GPOST11`-`GPRE11`) %>%
mutate('Global Strategy 12' = `GPOST12`-`GPRE12`) %>%
mutate('Global Strategy 13' = `GPOST13`-`GPRE13`) %>%
mutate('Global Strategy 14' = `GPOST14`-`GPRE14`) %>%
mutate('Support Strategy 2' = `SPOST2`-`SPRE2` ) %>%
mutate('Support Strategy 6' = `SPOST6`-`SPRE6`) %>%
mutate('Support Strategy 8' = `SPOST8`-`SPRE8`) %>%
mutate('Problem Solving 15' = `PPOST15`-`PPRE15` ) %>%
mutate('Problem Solving 5' = `PPOST5`-`PPRE5`) %>%
mutate('Problem Solving 9' = `PPOST9`-`PPRE9`)
library(ggpubr)
Warning: package 'ggpubr' was built under R version 4.2.3
Loading required package: ggplot2
library(rstatix)
Warning: package 'rstatix' was built under R version 4.2.3
Attaching package: 'rstatix'
The following object is masked from 'package:stats':
filter
Data2 <- Gupit1%>%
gather(key ="GlobalStrategy", value = "Score", 'Global Strategy 1', 'Global Strategy 3','Global Strategy 4', 'Global Strategy 7','Global Strategy 10', 'Global Strategy 11', 'Global Strategy 12', 'Global Strategy 13', 'Global Strategy 14')%>%
convert_as_factor(GlobalStrategy)
Global Strategy
Data3<-Data2%>%
group_by(GlobalStrategy) %>%
get_summary_stats(Score, type = "mean_sd")
Data3
# A tibble: 9 × 5
GlobalStrategy variable n mean sd
<fct> <fct> <dbl> <dbl> <dbl>
1 Global Strategy 1 Score 40 0.375 1.19
2 Global Strategy 10 Score 40 -0.025 1.37
3 Global Strategy 11 Score 40 0.2 1.47
4 Global Strategy 12 Score 40 0.2 0.992
5 Global Strategy 13 Score 40 0.475 1.01
6 Global Strategy 14 Score 40 0.175 1.26
7 Global Strategy 3 Score 40 -0.225 0.974
8 Global Strategy 4 Score 40 0.25 1.17
9 Global Strategy 7 Score 40 0.325 1.25
Support Strategy
Data5 <- Gupit1%>%
gather(key ="SupportStrategy", value = "Score", "Support Strategy 2", "Support Strategy 6","Support Strategy 8")%>%
convert_as_factor(SupportStrategy)
Data6<-Data5%>%
group_by(SupportStrategy) %>%
get_summary_stats(Score, type = "mean_sd")
Data6
# A tibble: 3 × 5
SupportStrategy variable n mean sd
<fct> <fct> <dbl> <dbl> <dbl>
1 Support Strategy 2 Score 40 0.35 1.23
2 Support Strategy 6 Score 40 0.1 1.37
3 Support Strategy 8 Score 40 0.425 1.20
Problem-Solving
Data8 <- Gupit1%>%
gather(key ="ProblemSolving", value = "Score", "Problem Solving 15", "Problem Solving 5","Problem Solving 9")%>%
convert_as_factor(ProblemSolving)
Data9<-Data8%>%
group_by(ProblemSolving) %>%
get_summary_stats(Score, type = "mean_sd")
Data9
# A tibble: 3 × 5
ProblemSolving variable n mean sd
<fct> <fct> <dbl> <dbl> <dbl>
1 Problem Solving 15 Score 40 0.95 1.36
2 Problem Solving 5 Score 40 -0.25 1.60
3 Problem Solving 9 Score 40 0.075 1.47
Test of Difference between Post test scores and Pre-test Scores
Global Strategy
Significant difference between Post Global Strategy 1 and Pre Global
Strategy 1
t.test(Gupit1$'Global Strategy 1', mu=0)
One Sample t-test
data: Gupit1$"Global Strategy 1"
t = 1.9904, df = 39, p-value = 0.05359
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.006086966 0.756086966
sample estimates:
mean of x
0.375
Significant difference between Post Global Strategy 3 and Pre Global
Strategy 3
t.test(Gupit1$'Global Strategy 3', mu=0)
One Sample t-test
data: Gupit1$"Global Strategy 3"
t = -1.4615, df = 39, p-value = 0.1519
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.53640194 0.08640194
sample estimates:
mean of x
-0.225
Significant difference between Post Global Strategy 4 and Pre Global
Strategy 4
t.test(Gupit1$'Global Strategy 4', mu=0)
One Sample t-test
data: Gupit1$"Global Strategy 4"
t = 1.35, df = 39, p-value = 0.1848
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.1245796 0.6245796
sample estimates:
mean of x
0.25
Significant difference between Post Global Strategy 7 and Pre Global
Strategy 7
t.test(Gupit1$'Global Strategy 7', mu=0)
One Sample t-test
data: Gupit1$"Global Strategy 7"
t = 1.6466, df = 39, p-value = 0.1077
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.07423601 0.72423601
sample estimates:
mean of x
0.325
Significant difference between Post Global Strategy 10 and Pre
Global Strategy 10
t.test(Gupit1$'Global Strategy 10', mu=0)
One Sample t-test
data: Gupit1$"Global Strategy 10"
t = -0.11559, df = 39, p-value = 0.9086
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.462476 0.412476
sample estimates:
mean of x
-0.025
Significant difference between Post Global Strategy 11 and Pre
Global Strategy 11
t.test(Gupit1$'Global Strategy 11', mu=0)
One Sample t-test
data: Gupit1$"Global Strategy 11"
t = 0.85985, df = 39, p-value = 0.3951
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.2704771 0.6704771
sample estimates:
mean of x
0.2
Significant difference between Post Global Strategy 12 and Pre
Global Strategy 12
t.test(Gupit1$'Global Strategy 12', mu=0)
One Sample t-test
data: Gupit1$"Global Strategy 12"
t = 1.2748, df = 39, p-value = 0.2099
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.1173459 0.5173459
sample estimates:
mean of x
0.2
Significant difference between Post Global Strategy 13 and Pre
Global Strategy 13
t.test(Gupit1$'Global Strategy 13', mu=0)
One Sample t-test
data: Gupit1$"Global Strategy 13"
t = 2.9673, df = 39, p-value = 0.005111
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
0.1512115 0.7987885
sample estimates:
mean of x
0.475
Significant difference between Post Global Strategy 14 and Pre
Global Strategy 14
t.test(Gupit1$'Global Strategy 14', mu=0)
One Sample t-test
data: Gupit1$"Global Strategy 14"
t = 0.87942, df = 39, p-value = 0.3846
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.2275072 0.5775072
sample estimates:
mean of x
0.175
Support Strategy
Significant difference between Post Support Strategy 2 and Pre
Support Global Strategy 2
t.test(Gupit1$'Support Strategy 2', mu=0)
One Sample t-test
data: Gupit1$"Support Strategy 2"
t = 1.7982, df = 39, p-value = 0.07989
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.04369597 0.74369597
sample estimates:
mean of x
0.35
Significant difference between Post Support Strategy 6 and Pre
Support Global Strategy 6
t.test(Gupit1$'Support Strategy 6', mu=0)
One Sample t-test
data: Gupit1$"Support Strategy 6"
t = 0.46039, df = 39, p-value = 0.6478
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.3393454 0.5393454
sample estimates:
mean of x
0.1
Significant difference between Post Support Strategy 8 and Pre
Support Global Strategy 8
t.test(Gupit1$'Support Strategy 8', mu=0)
One Sample t-test
data: Gupit1$"Support Strategy 8"
t = 2.2477, df = 39, p-value = 0.03033
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
0.04253912 0.80746088
sample estimates:
mean of x
0.425
Significant difference between Post Support Strategy 5 and Pre
Support Global Strategy 5
t.test(Gupit1$'Problem Solving 5', mu=0)
One Sample t-test
data: Gupit1$"Problem Solving 5"
t = -0.9899, df = 39, p-value = 0.3283
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.7608328 0.2608328
sample estimates:
mean of x
-0.25
Significant difference between Post Support Strategy 9 and Pre
Support Global Strategy 9
t.test(Gupit1$'Problem Solving 9', mu=0)
One Sample t-test
data: Gupit1$"Problem Solving 9"
t = 0.32173, df = 39, p-value = 0.7494
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.3965211 0.5465211
sample estimates:
mean of x
0.075
Significant difference between Post Support Strategy 15 and Pre
Support Global Strategy 15
t.test(Gupit1$'Problem Solving 15', mu=0)
One Sample t-test
data: Gupit1$"Problem Solving 15"
t = 4.4251, df = 39, p-value = 7.533e-05
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
0.5157582 1.3842418
sample estimates:
mean of x
0.95