Angry_Moods
Akshay_Ratnawat
9/7/2020
## Loading required package: tidyverse## -- Attaching packages -------------------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
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## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Analysis of Ways to improve an angry mood: A look at gender and sports participation
People have different ways of improving their mood when angry. Striegel (1994) found that anger often hurts an athlete’s performance and that capability to control anger is what makes good athletes even better. This study adds to the past research and examines the difference in ways to improve an angry mood by gender and sports participation.
The participants were 78 Rice University undergraduates, ages 17 to 23. Of these 78 participants, 48 were females and 30 were males and 25 were athletes and 53 were non-athletes.
The participants were asked to respond to a questionnaire that asked about what they do to improve their mood when angry or furious. Then they filled out a demographics questionnaire.
## Gender Sports Anger.Out Anger.In Control.Out Control.In Anger_Expression
## 1 2 1 18 13 23 20 36
## 2 2 1 14 17 25 24 30
## 3 2 1 13 14 28 28 19
## 4 2 1 17 24 23 23 43
## 5 1 1 16 17 26 28 27
## 6 1 1 16 22 25 23 38Lets see the structure and Summary of Data
## 'data.frame': 78 obs. of 7 variables:
## $ Gender : int 2 2 2 2 1 1 1 2 2 2 ...
## $ Sports : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Anger.Out : int 18 14 13 17 16 16 12 13 16 12 ...
## $ Anger.In : int 13 17 14 24 17 22 12 16 16 16 ...
## $ Control.Out : int 23 25 28 23 26 25 31 22 22 29 ...
## $ Control.In : int 20 24 28 23 28 23 27 31 24 29 ...
## $ Anger_Expression: int 36 30 19 43 27 38 14 24 34 18 ...## Gender Sports Anger.Out Anger.In
## Min. :1.000 Min. :1.000 Min. : 9.00 Min. :10.00
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:13.00 1st Qu.:15.00
## Median :2.000 Median :2.000 Median :16.00 Median :18.50
## Mean :1.615 Mean :1.679 Mean :16.08 Mean :18.58
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:18.00 3rd Qu.:22.00
## Max. :2.000 Max. :2.000 Max. :27.00 Max. :31.00
## Control.Out Control.In Anger_Expression
## Min. :14.00 Min. :11.00 Min. : 7.00
## 1st Qu.:21.00 1st Qu.:18.25 1st Qu.:27.00
## Median :24.00 Median :22.00 Median :36.00
## Mean :23.69 Mean :21.96 Mean :37.00
## 3rd Qu.:27.00 3rd Qu.:24.75 3rd Qu.:44.75
## Max. :32.00 Max. :32.00 Max. :68.00The summary stats of variables like Gender and Sports does not make sense as these are categorical variables having a numeric structure.
Lets Explore the Control Out Score
Control out high scores demonstrate that people control the outward expression of angry feelings. The below histogram shows the distribution of Control Out scores of all the people. From the distribution it is visible that more people have a higher control out scores.But this does not tell us much about how the control out score differ among the two groups Athlete and Non Athlete 
The Mean Control out score of Athletes, Non Athletes and Overall is as below respectively
## [1] 24.68## [1] 23.22642## [1] 23.69231The above data shows that there is some difference between the mean control out scores of the two groups.But we are not sure whether this difference is significant or not or its just a sampling bias. Further this difference might not be reliable also because this study has an extremely unbalanced design. There were a lot more non-athletes than athletes in the sample.
Now lets explore the Anger Expression Index
This is an Index of general anger expression: (Anger-Out) + (Anger-In) - (Control-Out) - (Control-In) + 48, This shows the level of anger expression.
The below boxplot shows that there are few outliers in the Non Athlete group. Further it is visible that on average the Non Athlete have a higher Anger Expression index among the two. The below boxplot also shows that the spread or variation of Anger expression index is more in Non_Athlete than in Athletes.
The below is boxplot of Anger Expression index among Males and Females. We see that there are few outliers among Females. The below boxplot also shows that the spread or variation of Anger expression index is more in Males than in Females.
Analysing the Control-In and Control-Out scores
Control-in score shows how people control their feelings. A high score demonstrate that people control angry feelings by calming down or cooling off.
On the other hand the Control-Out shows the expression of angry feelings. A high scores demonstrate that people control the outward expression of angry feelings.
Thus we expect a positive correlation between the Control-In and Control-Out scores. As people who control their angry feelings by calming down or cooling off will also be the people with a high Control-Out score because they will be able to control their outward expression of Angry feelings. To test it lets find the Correlation and test its significance.
cor.test(angry_moods$`Control.In`,angry_moods$`Control.Out`)##
## Pearson's product-moment correlation
##
## data: angry_moods$Control.In and angry_moods$Control.Out
## t = 9.0261, df = 76, p-value = 1.19e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5914163 0.8118649
## sample estimates:
## cor
## 0.7192834From the above results we see that correlation between the Control-In and Control-Out score is 0.71. Thus as expected there is positive correlation between the two scores. We can also see that the P-value is quite low, which means that we can easily reject the Null Hypothesis that the correlation is equal to zero at 1% level of significance.Thus the positive correlation we see among the Control-In and Control-Out score is significant correlation.
Analysing the Anger-Out and Control-Out Scores
Anger-Out score shows how people express anger. A high score demonstrate that people deal with anger by expressing it in a verbally or physically aggressive fashion.
Thus we expect a negative correlation between the Anger-Out score and Control-Out score. This is true because the people having a high Anger-Out score would be having a low Control-Out score. Lets check whether this is statistically true or not.
cor.test(angry_moods$`Anger.Out`,angry_moods$`Control.Out`)##
## Pearson's product-moment correlation
##
## data: angry_moods$Anger.Out and angry_moods$Control.Out
## t = -6.2504, df = 76, p-value = 2.183e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7127903 -0.4138093
## sample estimates:
## cor
## -0.5826834The above results show that the correlation between the Anger-Out and Control-Out score is -0.58. This is as per our expectation. Further we also see that p-value of the correlation is very low. We can easily reject the Null Hypothesis that the correlation is equal to zero at 1% level of significance.Thus the negative correlation we see among the Anger-Out and Control-Out score is significant correlation.