Social Entrepreneurship

rm(list=ls())

1. Importing the data into R:

my.ds <- read_excel("~/Documents/Onedrive/Social Entrepreneurship/Final Paper/finalsocialentrepreneurship2.xlsx")
my.df <- as.data.frame(my.ds)

2. Finding the variance, correlation and summary statistics

# Variance
cat("The output of variance analysis is :\n ")

The output of variance analysis is :
 

myCov <- cov(my.df, method = c("pearson"))
round(myCov, 3)

         SVC     RP     AA AA*RP     DI    TR
SVC    0.282 -0.015  0.039 0.020  0.038 0.001
RP    -0.015  0.035 -0.003 0.033 -0.004 0.001
AA     0.039 -0.003  0.020 0.016  0.019 0.001
AA*RP  0.020  0.033  0.016 0.050  0.015 0.002
DI     0.038 -0.004  0.019 0.015  0.018 0.001
TR     0.001  0.001  0.001 0.002  0.001 0.000

#Correlation Matrix and Test
cat("\n\nThe output of correlation analysis is :\n ")

The output of correlation analysis is :
 

myCorr <- cor(my.df, method = c("pearson"))  
round(myCorr, 3)

         SVC     RP     AA AA*RP     DI    TR
SVC    1.000 -0.151  0.524 0.172  0.535 0.189
RP    -0.151  1.000 -0.118 0.783 -0.152 0.258
AA     0.524 -0.118  1.000 0.514  0.997 0.603
AA*RP  0.172  0.783  0.514 1.000  0.482 0.603
DI     0.535 -0.152  0.997 0.482  1.000 0.539
TR     0.189  0.258  0.603 0.603  0.539 1.000

library(psych)
corr.test(my.df)

Call:corr.test(x = my.df)
Correlation matrix 
        SVC    RP    AA AA*RP    DI   TR
SVC    1.00 -0.15  0.52  0.17  0.54 0.19
RP    -0.15  1.00 -0.12  0.78 -0.15 0.26
AA     0.52 -0.12  1.00  0.51  1.00 0.60
AA*RP  0.17  0.78  0.51  1.00  0.48 0.60
DI     0.54 -0.15  1.00  0.48  1.00 0.54
TR     0.19  0.26  0.60  0.60  0.54 1.00
Sample Size 
[1] 15
Probability values (Entries above the diagonal are adjusted for multiple tests.) 
       SVC   RP   AA AA*RP   DI   TR
SVC   0.00 1.00 0.42  1.00 0.42 1.00
RP    0.59 0.00 1.00  0.01 1.00 1.00
AA    0.04 0.68 0.00  0.42 0.00 0.22
AA*RP 0.54 0.00 0.05  0.00 0.48 0.22
DI    0.04 0.59 0.00  0.07 0.00 0.42
TR    0.50 0.35 0.02  0.02 0.04 0.00

 To see confidence intervals of the correlations, print with the short=FALSE option

#Summary Statistics
cat("\n\nFinding the summary and description of the data :\n ")

Finding the summary and description of the data :
 

summary(my.df)

      SVC                 RP                 AA                AA*RP                DI                 TR            
 Min.   :-1.58418   Min.   :-0.24503   Min.   :-0.220896   Min.   :-0.28030   Min.   :-0.19424   Min.   :-0.0166113  
 1st Qu.:-0.33063   1st Qu.:-0.11866   1st Qu.:-0.097563   1st Qu.:-0.17052   1st Qu.:-0.07914   1st Qu.:-0.0166113  
 Median :-0.07505   Median :-0.07893   Median : 0.001705   Median :-0.02529   Median : 0.03597   Median : 0.0099668  
 Mean   :-0.14320   Mean   : 0.00000   Mean   : 0.000000   Mean   : 0.00000   Mean   : 0.01679   Mean   :-0.0006645  
 3rd Qu.: 0.14402   3rd Qu.: 0.07405   3rd Qu.: 0.071643   3rd Qu.: 0.12796   3rd Qu.: 0.09353   3rd Qu.: 0.0099668  
 Max.   : 0.75254   Max.   : 0.36214   Max.   : 0.314549   Max.   : 0.56159   Max.   : 0.32374   Max.   : 0.0099668  

library(psych)  # Call the pysch library
psych::describe(my.df)

      vars  n  mean   sd median trimmed  mad   min  max range  skew kurtosis   se
SVC      1 15 -0.14 0.53  -0.08   -0.10 0.36 -1.58 0.75  2.34 -0.97     1.33 0.14
RP       2 15  0.00 0.19  -0.08   -0.01 0.21 -0.25 0.36  0.61  0.59    -0.77 0.05
AA       3 15  0.00 0.14   0.00   -0.01 0.13 -0.22 0.31  0.54  0.39    -0.48 0.04
AA*RP    4 15  0.00 0.22  -0.03   -0.02 0.23 -0.28 0.56  0.84  0.77     0.10 0.06
DI       5 15  0.02 0.14   0.04    0.01 0.17 -0.19 0.32  0.52  0.44    -0.44 0.03
TR       6 15  0.00 0.01   0.01    0.00 0.00 -0.02 0.01  0.03 -0.37    -1.98 0.00

corr.test(my.df)

Call:corr.test(x = my.df)
Correlation matrix 
        SVC    RP    AA AA*RP    DI   TR
SVC    1.00 -0.15  0.52  0.17  0.54 0.19
RP    -0.15  1.00 -0.12  0.78 -0.15 0.26
AA     0.52 -0.12  1.00  0.51  1.00 0.60
AA*RP  0.17  0.78  0.51  1.00  0.48 0.60
DI     0.54 -0.15  1.00  0.48  1.00 0.54
TR     0.19  0.26  0.60  0.60  0.54 1.00
Sample Size 
[1] 15
Probability values (Entries above the diagonal are adjusted for multiple tests.) 
       SVC   RP   AA AA*RP   DI   TR
SVC   0.00 1.00 0.42  1.00 0.42 1.00
RP    0.59 0.00 1.00  0.01 1.00 1.00
AA    0.04 0.68 0.00  0.42 0.00 0.22
AA*RP 0.54 0.00 0.05  0.00 0.48 0.22
DI    0.04 0.59 0.00  0.07 0.00 0.42
TR    0.50 0.35 0.02  0.02 0.04 0.00

 To see confidence intervals of the correlations, print with the short=FALSE option

3 Regression Model 1:

Regression Anslysis:

Model1 <- lm(SVC~RP, data=my.df)
summary(Model1)

Call:
lm(formula = SVC ~ RP, data = my.df)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.47459 -0.18721  0.09488  0.22544  0.84521 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  -0.1432     0.1405  -1.019    0.327
RP           -0.4259     0.7737  -0.550    0.591

Residual standard error: 0.5443 on 13 degrees of freedom
Multiple R-squared:  0.02277,   Adjusted R-squared:  -0.0524 
F-statistic: 0.303 on 1 and 13 DF,  p-value: 0.5914

4 Regression Model 2:

Regression Anslysis:

Model2 <- lm(SVC~AA, data=my.df)
summary(Model2)

Call:
lm(formula = SVC ~ AA, data = my.df)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.00381 -0.26269  0.05129  0.28553  0.88200 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  -0.1432     0.1211  -1.183   0.2580  
AA            1.9790     0.8916   2.220   0.0448 *
---
Signif. codes:  
0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4689 on 13 degrees of freedom
Multiple R-squared:  0.2748,    Adjusted R-squared:  0.219 
F-statistic: 4.927 on 1 and 13 DF,  p-value: 0.04485

5 Regression Model 3:

Model1 <- lm(SVC~RP*AA, data=my.df)
summary(Model1)

Call:
lm(formula = SVC ~ RP * AA, data = my.df)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.96985 -0.28250  0.07162  0.19024  0.92400 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  -0.1545     0.1285  -1.202   0.2546  
RP           -0.1944     0.7121  -0.273   0.7899  
AA            1.8464     0.9545   1.935   0.0792 .
RP:AA        -3.8627     5.1253  -0.754   0.4669  
---
Signif. codes:  
0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4943 on 11 degrees of freedom
Multiple R-squared:  0.3181,    Adjusted R-squared:  0.1321 
F-statistic:  1.71 on 3 and 11 DF,  p-value: 0.2224

6 Regression Model 4:

Model1 <- lm(SVC~DI, data=my.df)
summary(Model1)

Call:
lm(formula = SVC ~ DI, data = my.df)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.99780 -0.26506  0.07571  0.27211  0.87727 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  -0.1785     0.1211  -1.474   0.1643  
DI            2.1000     0.9189   2.285   0.0397 *
---
Signif. codes:  
0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4651 on 13 degrees of freedom
Multiple R-squared:  0.2866,    Adjusted R-squared:  0.2318 
F-statistic: 5.223 on 1 and 13 DF,  p-value: 0.03972

6 Regression Model 5:

Model1 <- lm(SVC~TR, data=my.df)
summary(Model1)

Call:
lm(formula = SVC ~ TR, data = my.df)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.32252 -0.16768 -0.01082  0.28262  0.81677 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  -0.1383     0.1398  -0.989    0.341
TR            7.4283    10.7227   0.693    0.501

Residual standard error: 0.5407 on 13 degrees of freedom
Multiple R-squared:  0.0356,    Adjusted R-squared:  -0.03858 
F-statistic: 0.4799 on 1 and 13 DF,  p-value: 0.5006

centered.model <- lm(SVC ~ RP*AA, data = my.df)
library(visreg)
visreg(centered.model, "RP", by="AA", 
       overlay=TRUE, partial=FALSE)