Exercise Number 12

Setup

setwd("D:/stat")

getwd()

## [1] "D:/stat"

Load packages

library(ggplot2)
library(ggpubr)

## Warning: package 'ggpubr' was built under R version 4.2.3

library(rstatix)

## Warning: package 'rstatix' was built under R version 4.2.3

library(readxl)

## Warning: package 'readxl' was built under R version 4.2.3

Data<-read_excel("D:/stat//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)
library(rstatix)
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)

library(ggpubr)
library(rstatix)
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)

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

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

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 13 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

Problem Solving

Significant difference between post problem-solving 5 and pre problem-solving 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 problem-solving 9 and pre problem-solving 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 problem-solving 15 and pre problem-solving 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