library(readxl)
## Warning: package 'readxl' was built under R version 4.0.5
DataBFinalRS <- read_excel("C:/1. School/3rd Year/2nd sem/Statistical Software/4.  Sample Data AnalysisBlessi R Markdown/DataBFinalRS.xlsx")
## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...7
## * ...
DataBFinalRS$Gender <-factor(DataBFinalRS$Gender)
DataBFinalRS$Education<-factor(DataBFinalRS$Education)
Gender= DataBFinalRS$Gender
Gender.freq = table(Gender)
Gender.relfreq = Gender.freq 
Gender.freq
## Gender
##  1  2 
## 62 55
Gender.relfreq = Gender.freq / nrow(DataBFinalRS)
cbind(Gender.relfreq)
##   Gender.relfreq
## 1      0.5299145
## 2      0.4700855
Education = DataBFinalRS$Education
Education.freq = table(Education)
Education.relfreq = Education.freq 
Education.freq
## Education
##  1  2  3  4  5  6  7  8 
##  9 17 19 14 10 13 33  2
Education.relfreq = Education.freq / nrow(DataBFinalRS)
cbind(Education.relfreq)
##   Education.relfreq
## 1        0.07692308
## 2        0.14529915
## 3        0.16239316
## 4        0.11965812
## 5        0.08547009
## 6        0.11111111
## 7        0.28205128
## 8        0.01709402
summary(DataBFinalRS$AgeC)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3.00    3.00    4.00    4.12    5.00    5.00
summary(DataBFinalRS$BMI)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     6.8    14.5    15.7    15.3    16.3    17.2
AgeCoded <-factor(DataBFinalRS$AgeC)
library(plyr)
## Warning: package 'plyr' was built under R version 4.0.5
ddply(DataBFinalRS, .(AgeCoded), summarize,  BMI=mean(BMI))
DataBFinalRS$M1 <- factor(DataBFinalRS$M1)
DataBFinalRS$M2 <- factor(DataBFinalRS$M2)
DataBFinalRS$M2<-factor(DataBFinalRS$M2)
DataBFinalRS$M2<-factor(DataBFinalRS$M2)
DataBFinalRS$M2<-factor(DataBFinalRS$M2)
M1 = DataBFinalRS$M1
M1.freq = table(M1)
M1.relfreq = M1.freq 
M1.freq
## M1
##  0  1  2  3  4  5 
##  7 13 27 32 32  6
M1.relfreq = M1.freq / nrow(DataBFinalRS)
cbind(M1.relfreq)
##   M1.relfreq
## 0 0.05982906
## 1 0.11111111
## 2 0.23076923
## 3 0.27350427
## 4 0.27350427
## 5 0.05128205
M2 = DataBFinalRS$M2
M2.freq = table(M2)
M2.relfreq = M2.freq 
M2.freq
## M2
##  0  1  2  3  4  5 
## 40 25 25 14  7  6
M2.relfreq = M2.freq / nrow(DataBFinalRS)
cbind(M2.relfreq)
##   M2.relfreq
## 0 0.34188034
## 1 0.21367521
## 2 0.21367521
## 3 0.11965812
## 4 0.05982906
## 5 0.05128205
M3 = DataBFinalRS$M3
M3.freq = table(M3)
M3.relfreq = M3.freq 
M3.freq
## M3
##  0  1  2  3  4 
## 40 36 21 14  6
M3.relfreq = M3.freq / nrow(DataBFinalRS)
cbind(M3.relfreq)
##   M3.relfreq
## 0 0.34188034
## 1 0.30769231
## 2 0.17948718
## 3 0.11965812
## 4 0.05128205
M4 = DataBFinalRS$M4
M4.freq = table(M4)
M4.relfreq = M4.freq 
M4.freq
## M4
##  0  1  2  3  4 
## 61 32 11  9  4
M4.relfreq = M4.freq / nrow(DataBFinalRS)
cbind(M4.relfreq)
##   M4.relfreq
## 0 0.52136752
## 1 0.27350427
## 2 0.09401709
## 3 0.07692308
## 4 0.03418803
M5 = DataBFinalRS$M5
M5.freq = table(M5)
M5.relfreq = M5.freq 
M5.freq
## M5
##  0  1  2  3 
## 72 31 12  2
M5.relfreq = M5.freq / nrow(DataBFinalRS)
cbind(M5.relfreq)
##   M5.relfreq
## 0 0.61538462
## 1 0.26495726
## 2 0.10256410
## 3 0.01709402
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.0.5
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
summarise(group_by(DataBFinalRS,Media,U1),count =n())
## `summarise()` has grouped output by 'Media'. You can override using the `.groups` argument.
summarise(group_by(DataBFinalRS,Media,U2),count =n())
## `summarise()` has grouped output by 'Media'. You can override using the `.groups` argument.
summarise(group_by(DataBFinalRS,Media,U3),count =n())
## `summarise()` has grouped output by 'Media'. You can override using the `.groups` argument.
summarise(group_by(DataBFinalRS,Media,U4),count =n())
## `summarise()` has grouped output by 'Media'. You can override using the `.groups` argument.
summarise(group_by(DataBFinalRS,Media,U5),count =n())
## `summarise()` has grouped output by 'Media'. You can override using the `.groups` argument.
summarise(group_by(DataBFinalRS,Media,U6),count =n())
## `summarise()` has grouped output by 'Media'. You can override using the `.groups` argument.
summarise(group_by(DataBFinalRS,Media,U7),count =n())
## `summarise()` has grouped output by 'Media'. You can override using the `.groups` argument.

Physical Development

Pre-school’s Play

mean(DataBFinalRS$Pl1)
## [1] 3.17094
mean(DataBFinalRS$Pl2)
## [1] 2.547009
mean(DataBFinalRS$Pl3)
## [1] 2.794872
mean(DataBFinalRS$Pl4)
## [1] 3.102564
mean(DataBFinalRS$Pl5)
## [1] 3.145299
mean(DataBFinalRS$OverallPl)
## [1] 2.577778

Eating Habits Pre-school’s Play

mean(DataBFinalRS$E1)
## [1] 2.179487
mean(DataBFinalRS$E2)
## [1] 2.264957
mean(DataBFinalRS$E3)
## [1] 2.897436
mean(DataBFinalRS$E4)
## [1] 2.752137
mean(DataBFinalRS$E5)
## [1] 2.564103
mean(DataBFinalRS$OverallE)
## [1] 2.6

Activities of Daily Living

mean(DataBFinalRS$Ac1)
## [1] 2.367521
mean(DataBFinalRS$Ac2)
## [1] 2.606838
mean(DataBFinalRS$Ac3)
## [1] 2.188034
mean(DataBFinalRS$OverallAc)
## [1] 2.612536

Overall Physical Development

mean(DataBFinalRS$OverallPD)
## [1] 2.596771

Psychological Development

Relationship with others/family

mean(DataBFinalRS$Rel1)
## [1] 2.649573
mean(DataBFinalRS$Rel2)
## [1] 2.700855
mean(DataBFinalRS$Rel3)
## [1] 2.820513
mean(DataBFinalRS$Rel4)
## [1] 2.444444
mean(DataBFinalRS$Rel5)
## [1] 2.675214
mean(DataBFinalRS$OverallRel)
## [1] 2.599512

Imitation and Adaptation

mean(DataBFinalRS$Ad1)
## [1] 3.017094
mean(DataBFinalRS$Ad2)
## [1] 3.145299
mean(DataBFinalRS$Ad3)
## [1] 2.683761
mean(DataBFinalRS$Ad4)
## [1] 2.794872
mean(DataBFinalRS$Ad5)
## [1] 2.666667
mean(DataBFinalRS$Ad6)
## [1] 2.735043
mean(DataBFinalRS$Ad7)
## [1] 2.410256
mean(DataBFinalRS$Overallad)
## [1] 2.80464

Overall Psychosocial Development

mean(DataBFinalRS$OverallPsy)
## [1] 2.702076

Cognitive Development

mean(DataBFinalRS$La1)
## [1] 2.931624
mean(DataBFinalRS$La2)
## [1] 3.034188
mean(DataBFinalRS$La3)
## [1] 2.324786
mean(DataBFinalRS$OverallLa)
## [1] 2.880342

Leasrning

mean(DataBFinalRS$Le1)
## [1] 2.794872
mean(DataBFinalRS$Le2)
## [1] 3.08547
mean(DataBFinalRS$Le3)
## [1] 3.034188
mean(DataBFinalRS$Le4)
## [1] 2.982906
mean(DataBFinalRS$Le5)
## [1] 2.888889
mean(DataBFinalRS$Le6)
## [1] 2.974359
mean(DataBFinalRS$Le7)
## [1] 2.957265
mean(DataBFinalRS$Le8)
## [1] 3.08547
mean(DataBFinalRS$Le9)
## [1] 3.153846
mean(DataBFinalRS$Le10)
## [1] 2.820513
mean(DataBFinalRS$Le11)
## [1] 2.854701
mean(DataBFinalRS$Le12)
## [1] 2.581197
mean(DataBFinalRS$OverallLe)
## [1] 2.808405

Overall Cognitive Development

mean(DataBFinalRS$OverallCo)
## [1] 2.844373

Overall Physical Development

mean(DataBFinalRS$OverallDevelopmentaleffects)
## [1] 2.714407

Significant Relationship between the extent of media use and developmental effects among preschoolers

Spearman Correlation

Physical Development

Pre-schools Play

cor.test(DataBFinalRS$Extent, DataBFinalRS$OverallPl, method="spearman")
## Warning in cor.test.default(DataBFinalRS$Extent, DataBFinalRS$OverallPl, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  DataBFinalRS$Extent and DataBFinalRS$OverallPl
## S = 355477, p-value = 0.0002577
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.3317926

Eating Habits

cor.test(DataBFinalRS$Extent, DataBFinalRS$OverallE, method="spearman")
## Warning in cor.test.default(DataBFinalRS$Extent, DataBFinalRS$OverallE, : Cannot
## compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  DataBFinalRS$Extent and DataBFinalRS$OverallE
## S = 291909, p-value = 0.3153
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##         rho 
## -0.09363785

Activities of Daily Living

cor.test(DataBFinalRS$Extent, DataBFinalRS$OverallAc, method="spearman")
## Warning in cor.test.default(DataBFinalRS$Extent, DataBFinalRS$OverallAc, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  DataBFinalRS$Extent and DataBFinalRS$OverallAc
## S = 389418, p-value = 1.947e-07
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.4589522

Overall Physical Development

cor.test(DataBFinalRS$Extent, DataBFinalRS$OverallPD, method="spearman")
## Warning in cor.test.default(DataBFinalRS$Extent, DataBFinalRS$OverallPD, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  DataBFinalRS$Extent and DataBFinalRS$OverallPD
## S = 396358, p-value = 3.008e-08
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.4849534

Psychological Development

Relationship with others/family

cor.test(DataBFinalRS$Extent, DataBFinalRS$OverallRel, method="spearman")
## Warning in cor.test.default(DataBFinalRS$Extent, DataBFinalRS$OverallRel, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  DataBFinalRS$Extent and DataBFinalRS$OverallRel
## S = 280486, p-value = 0.5862
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##         rho 
## -0.05083984

Imitation and adaptation

cor.test(DataBFinalRS$Extent, DataBFinalRS$Overallad, method="spearman")
## Warning in cor.test.default(DataBFinalRS$Extent, DataBFinalRS$Overallad, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  DataBFinalRS$Extent and DataBFinalRS$Overallad
## S = 260914, p-value = 0.8098
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.02248775

Overall Psychological Development

cor.test(DataBFinalRS$Extent, DataBFinalRS$OverallPsy, method="spearman")
## Warning in cor.test.default(DataBFinalRS$Extent, DataBFinalRS$OverallPsy, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  DataBFinalRS$Extent and DataBFinalRS$OverallPsy
## S = 276806, p-value = 0.6916
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##         rho 
## -0.03705248

Cognitive Development

Language

cor.test(DataBFinalRS$Extent, DataBFinalRS$OverallLa, method="spearman")
## Warning in cor.test.default(DataBFinalRS$Extent, DataBFinalRS$OverallLa, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  DataBFinalRS$Extent and DataBFinalRS$OverallLa
## S = 292803, p-value = 0.2982
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##         rho 
## -0.09698574

Learning

cor.test(DataBFinalRS$Extent, DataBFinalRS$OverallLe, method="spearman")
## Warning in cor.test.default(DataBFinalRS$Extent, DataBFinalRS$OverallLe, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  DataBFinalRS$Extent and DataBFinalRS$OverallLe
## S = 315136, p-value = 0.05127
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.1806571

Overall Psychological Development

cor.test(DataBFinalRS$Extent, DataBFinalRS$OverallCo, method="spearman")
## Warning in cor.test.default(DataBFinalRS$Extent, DataBFinalRS$OverallCo, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  DataBFinalRS$Extent and DataBFinalRS$OverallCo
## S = 300140, p-value = 0.1812
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## -0.124474

Overall Developmental Effects

cor.test(DataBFinalRS$Extent, DataBFinalRS$OverallDevelopmentaleffects, method="spearman")
## Warning in cor.test.default(DataBFinalRS$Extent,
## DataBFinalRS$OverallDevelopmentaleffects, : Cannot compute exact p-value with
## ties
## 
##  Spearman's rank correlation rho
## 
## data:  DataBFinalRS$Extent and DataBFinalRS$OverallDevelopmentaleffects
## S = 365651, p-value = 4.047e-05
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.3699099

Using Point Biserial Correlation Point Biserial Correlation is estimated by Pearson Correlation when the variables correlated are continuous and categorical variables where the categorical variable having only two responses.

Physical Development

Playing Educational Games

cor.test(DataBFinalRS$U1, DataBFinalRS$OverallPD)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U1 and DataBFinalRS$OverallPD
## t = 0.27531, df = 115, p-value = 0.7836
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1565981  0.2062366
## sample estimates:
##        cor 
## 0.02566446

Playing uneducational games

cor.test(DataBFinalRS$U2, DataBFinalRS$OverallPD)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U2 and DataBFinalRS$OverallPD
## t = -1.6575, df = 115, p-value = 0.1001
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.32526450  0.02960295
## sample estimates:
##        cor 
## -0.1527509

Watching vides and movies

cor.test(DataBFinalRS$U3, DataBFinalRS$OverallPD)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U3 and DataBFinalRS$OverallPD
## t = -1.4338, df = 115, p-value = 0.1543
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.30667987  0.05021616
## sample estimates:
##        cor 
## -0.1325249

watching educational videos

cor.test(DataBFinalRS$U4, DataBFinalRS$OverallPD)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U4 and DataBFinalRS$OverallPD
## t = 0.0018153, df = 115, p-value = 0.9986
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1813693  0.1816967
## sample estimates:
##          cor 
## 0.0001692819

listening to music

cor.test(DataBFinalRS$U5, DataBFinalRS$OverallPD)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U5 and DataBFinalRS$OverallPD
## t = -0.12169, df = 115, p-value = 0.9034
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1924835  0.1705373
## sample estimates:
##         cor 
## -0.01134699

watching television

cor.test(DataBFinalRS$U6, DataBFinalRS$OverallPD)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U6 and DataBFinalRS$OverallPD
## t = 0.28229, df = 115, p-value = 0.7782
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1559633  0.2068595
## sample estimates:
##        cor 
## 0.02631469

Social Media

cor.test(DataBFinalRS$U7, DataBFinalRS$OverallPD)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U7 and DataBFinalRS$OverallPD
## t = -1.1971, df = 115, p-value = 0.2337
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2867011  0.0720417
## sample estimates:
##        cor 
## -0.1109422

Psychological Development

Playing Educational Games

cor.test(DataBFinalRS$U1, DataBFinalRS$OverallPsy)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U1 and DataBFinalRS$OverallPsy
## t = 3.2008, df = 115, p-value = 0.001772
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1101967 0.4444646
## sample estimates:
##       cor 
## 0.2860082

Playing uneducational games

cor.test(DataBFinalRS$U2, DataBFinalRS$OverallPsy)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U2 and DataBFinalRS$OverallPsy
## t = -1.7706, df = 115, p-value = 0.07928
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.33454053  0.01920023
## sample estimates:
##        cor 
## -0.1629005

Watching vides and movies

cor.test(DataBFinalRS$U3, DataBFinalRS$OverallPsy)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U3 and DataBFinalRS$OverallPsy
## t = -0.047891, df = 115, p-value = 0.9619
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1858481  0.1772108
## sample estimates:
##          cor 
## -0.004465805

watching educational videos

cor.test(DataBFinalRS$U4, DataBFinalRS$OverallPsy)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U4 and DataBFinalRS$OverallPsy
## t = 0.54509, df = 115, p-value = 0.5868
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1319853  0.2301758
## sample estimates:
##        cor 
## 0.05076394

listening to music

cor.test(DataBFinalRS$U5, DataBFinalRS$OverallPsy)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U5 and DataBFinalRS$OverallPsy
## t = 1.6791, df = 115, p-value = 0.09584
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.02761363  0.32704366
## sample estimates:
##       cor 
## 0.1546948

watching television

cor.test(DataBFinalRS$U6, DataBFinalRS$OverallPsy)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U6 and DataBFinalRS$OverallPsy
## t = -3.7629, df = 115, p-value = 0.000266
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4835668 -0.1591303
## sample estimates:
##        cor 
## -0.3310987

Social Media

cor.test(DataBFinalRS$U7, DataBFinalRS$OverallPsy)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U7 and DataBFinalRS$OverallPsy
## t = 1.294, df = 115, p-value = 0.1982
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.06310545  0.29491910
## sample estimates:
##       cor 
## 0.1197999

Cognitive Development

Playing Educational Games

cor.test(DataBFinalRS$U1, DataBFinalRS$OverallCo)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U1 and DataBFinalRS$OverallCo
## t = 3.8331, df = 115, p-value = 0.0002069
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1651409 0.4882821
## sample estimates:
##       cor 
## 0.3365836

Playing uneducational games

cor.test(DataBFinalRS$U2, DataBFinalRS$OverallCo)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U2 and DataBFinalRS$OverallCo
## t = -0.31412, df = 115, p-value = 0.754
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2096979  0.1530670
## sample estimates:
##         cor 
## -0.02927952

Watching vides and movies

cor.test(DataBFinalRS$U3, DataBFinalRS$OverallCo)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U3 and DataBFinalRS$OverallCo
## t = -0.34157, df = 115, p-value = 0.7333
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2121420  0.1505681
## sample estimates:
##         cor 
## -0.03183505

watching educational videos

cor.test(DataBFinalRS$U4, DataBFinalRS$OverallCo)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U4 and DataBFinalRS$OverallCo
## t = 2.6135, df = 115, p-value = 0.01016
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.05772472 0.40107039
## sample estimates:
##       cor 
## 0.2367765

listening to music

cor.test(DataBFinalRS$U5, DataBFinalRS$OverallCo)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U5 and DataBFinalRS$OverallCo
## t = 2.3245, df = 115, p-value = 0.02185
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.03152442 0.37881018
## sample estimates:
##       cor 
## 0.2118451

watching television

cor.test(DataBFinalRS$U6, DataBFinalRS$OverallCo)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U6 and DataBFinalRS$OverallCo
## t = -1.3211, df = 115, p-value = 0.1891
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.29720185  0.06061296
## sample estimates:
##        cor 
## -0.1222653

Social Media

cor.test(DataBFinalRS$U7, DataBFinalRS$OverallCo)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U7 and DataBFinalRS$OverallCo
## t = 1.0533, df = 115, p-value = 0.2944
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.0852917  0.2744181
## sample estimates:
##        cor 
## 0.09775484

Overall Developmental Effects

Playing Educational Games

cor.test(DataBFinalRS$U1, DataBFinalRS$OverallDevelopmentaleffects)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U1 and DataBFinalRS$OverallDevelopmentaleffects
## t = 3.7812, df = 115, p-value = 0.0002492
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1607033 0.4848025
## sample estimates:
##       cor 
## 0.3325352

Playing uneducational games

cor.test(DataBFinalRS$U2, DataBFinalRS$OverallDevelopmentaleffects)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U2 and DataBFinalRS$OverallDevelopmentaleffects
## t = -1.8319, df = 115, p-value = 0.06955
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.33954149  0.01355988
## sample estimates:
##        cor 
## -0.1683876

Watching vides and movies

cor.test(DataBFinalRS$U3, DataBFinalRS$OverallDevelopmentaleffects)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U3 and DataBFinalRS$OverallDevelopmentaleffects
## t = -1.0753, df = 115, p-value = 0.2845
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.27629767  0.08327233
## sample estimates:
##         cor 
## -0.09976882

watching educational videos

cor.test(DataBFinalRS$U4, DataBFinalRS$OverallDevelopmentaleffects)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U4 and DataBFinalRS$OverallDevelopmentaleffects
## t = 1.8278, df = 115, p-value = 0.07017
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.01393521  0.33920932
## sample estimates:
##       cor 
## 0.1680228

listening to music

cor.test(DataBFinalRS$U5, DataBFinalRS$OverallDevelopmentaleffects)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U5 and DataBFinalRS$OverallDevelopmentaleffects
## t = 2.005, df = 115, p-value = 0.04732
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.002324437 0.353518706
## sample estimates:
##       cor 
## 0.1837799

watching television

cor.test(DataBFinalRS$U6, DataBFinalRS$OverallDevelopmentaleffects)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U6 and DataBFinalRS$OverallDevelopmentaleffects
## t = -2.001, df = 115, p-value = 0.04774
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.353203951 -0.001964774
## sample estimates:
##        cor 
## -0.1834323

Social Media

cor.test(DataBFinalRS$U7, DataBFinalRS$OverallDevelopmentaleffects)
## 
##  Pearson's product-moment correlation
## 
## data:  DataBFinalRS$U7 and DataBFinalRS$OverallDevelopmentaleffects
## t = 0.43319, df = 115, p-value = 0.6657
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1422126  0.2202813
## sample estimates:
##        cor 
## 0.04036238