Relationship between Teenagers Usage of the Internet and Amount of Sleep - Assignment Example

STATISTICS ANALYSIS The relationship between the average amount of sleep a high school teenager gets per school night and the total average amount of Internet used in one school week Abstract Today’s school teenagers have unprecedented access to modern technology and use them in different ways (both positive and negative). Teenagers spend a lot of time using the technology, and the vast majority of the current technologies have access to Internet. A number of concerns have been raised on the effects of internet use and the amount spent to sleep which subsequently affects the academic performance of the students. This paper provides an overview of the impact of Internet use on the amount spent to sleep among the teenagers. The purpose was to examine the relationship between teenagers’ usage of the Internet and amount of sleep. 50 teenage students were selected and quantitative data in regard to amount of time they spend on internet and sleeping was gathered. Introduction Vast expansion in the sector of technology has been experienced in the recent past and has been embraced by almost all the people regardless of age. For a younger generation of people, technology has taken a very substantial role in both their educational and social lives. A large number of teenagers have access to cell phones, the Internet, desktops, laptops, and many other forms of modern technology. A matter of concern has been raised on the increased role played by the modern technology in the teenagers’ lives about how these children might be influenced. Currently, technology is greatly changing the process and content to a level that young children/teenagers are heavily immersed in a world that is surrounded with a lot of information. The ever increasing amount of time spent by teenagers on modern technology has raised a number of questions about the use and the trends of the technology. Dehmler (2009) states that the children of today are growing up in a very interconnected and networked world. The teenagers have unlimited access to the modern technologies and use them in the ways they deem fit for them. This paper provides an evaluation of the impact of Internet on the sleeping hours of teenagers going to school. Data Data was collected among the high school teenage students. The following table gives the results given by the students; S/No. Gender  Amount of time spent using internet Amount hours of hours slept S/No. Gender  Amount of time spent using internet Amount hours spent in sleeping 1 Male 8.3 6 26 Male 12.5 6 2 Female 10.0 6 27 Male 17.5 5 3 Male 20.0 4 28 Male 2.5 8 4 Male 16.7 4 29 Female 16.7 5 5 Female 12.5 6 30 Female 12.5 5 6 Male 10.0 6 31 Male 7.5 8 7 Male 11.7 6 32 Female 10.0 6 8 Female 12.5 6 33 Female 8.3 6 9 Female 16.7 5 34 Female 5.0 8 10 Female 5.0 8 35 Female 5.0 7 11 Female 2.5 9 36 Male 7.5 6 12 Female 2.5 10 37 Male 10.0 5 13 Male 5.0 9 38 Female 8.3 5 14 Male 6.7 8 39 Male 8.7 5 15 Male 7.5 9 40 Male 2.5 9 16 Female 20.0 5 41 Male 5.0 8 17 Female 25.0 4 42 Female 5.0 8 18 Male 20.0 4 43 Female 8.3 6 19 Female 16.7 5 44 Male 10.0 5 20 Male 20.0 4 45 Male 12.5 5 21 Female 20.0 4 46 Male 12.5 5 22 Male 2.5 8 47 Female 10.0 5 23 Female 5.0 9 48 Female 5.0 8 24 Female 7.5 8 49 Female 2.5 9 25 Female 10.0 7 50 Male 2.5 9 Methodology This study was undertaken to present an understanding of the impact of Internet on the amount of hours spent on sleeping by the teenage students. Quantitative research methodology was used to guide the study. Two variables were used to test for the quantitative impact of the study. The variables included, the amount of time spent on the Internet (explanatory variable) and the amount of time spent in sleeping (dependent/response variable). The following regression model was used to predict the dependent variable; Where, is the coefficient for the constant variable is the coefficient for the variable Internet hours is the error term. Results Descriptive statistics The following table gives the summary statistics;  Amount of time spent using internet Statistics Amount of time spent in sleeping Statistics Mean 10.04 Mean 6.44 Standard Error 0.818654 Standard Error 0.246046 Median 9.333333 Median 6 Mode 5 Mode 5 Standard Deviation 5.788757 Standard Deviation 1.73981 Sample Variance 33.50971 Sample Variance 3.026939 Kurtosis -0.34866 Kurtosis -1.20469 Skewness 0.63671 Skewness 0.298548 Range 22.5 Range 6 Minimum 2.5 Minimum 4 Maximum 25 Maximum 10 Sum 502 Sum 322 Count 50 Count 50 From the table, it can clearly be seen that the average hours spent by the teenage students on the internet in a week (school working days) is 10.04 hours. However, the average amount of hours spent in sleeping by the same teenagers is 6.44. The most frequent hours spent in both the internet and in sleeping is 5 hours. Correlation In order to test for significance relationship between the two variables, we conducted a correlation test. The following table gives the correlation matrix between the two variables;    Amount of time spent using internet Amount of time spent sleeping Amount of time spent using internet 1 Amount of time spent sleeping -0.86197 1 From the above, we observe that the Pearson correlation coefficient between amount of time spent using internet and Amount of time spent sleeping is -0.86197; this is a clear indication of a strong negative linear relationship between the two variables. That is to say, an increase in the Amount of time spent using internet results to a decrease in the Amount of time spent sleeping. To further confirm the relationship between the two variables, a scatterplot was constructed; Linear regression We constructed a linear model in order to predict the dependent variable. Regression Statistics Multiple R 0.861973402 R Square 0.742998145 Adjusted R Square 0.73764394 Standard Error 0.891142935 Observations 50 ANOVA   df SS MS F Significance F Regression 1 110.2015 110.2015 138.7691 9.10076E-16 Residual 48 38.11852 0.794136 Total 49 148.32         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 9.041 0.254 35.562 0.000 8.530 9.552 8.530 9.552 Amount of time spent using internet -0.259 0.022 -11.780 0.000 -0.303 -0.215 -0.303 -0.215 The p-value for the F-statistic is 0.000 (a value less than 5% significance level), we therefore reject the null hypothesis and conclude that the model is appropriate/fit at 5% significance level. The value of R-squared is given as 0.7430; this implies that 74.3% of the variation in the dependent variable (Amount hours spent in sleeping) is explained by the explanatory variable (Amount of time spent using internet) in the model. In terms of significance of the explanatory variables, we observe that the explanatory variable (Amount of time spent using internet) is significant in the model at 5% significance level. Similarly, the constant/intercept is also significant at 5% significance level. Lastly, we observe that the coefficient for the amount of time spent using internet is -0.259, this implies that a unit increase in the explanatory variable (Amount of time spent using internet) results to a decrease in the dependent variable (Amount hours spent in sleeping) by 0.259. Chi-Square We also looked at the association between gender and amount of time spent using internet. The following table gives the Chi-square test results; Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 10.422a 12 .579 Likelihood Ratio 12.572 12 .401 N of Valid Cases 50 a. 26 cells (100.0%) have expected count less than 5. The minimum expected count is .48. Clearly, it can be observed that there is no significant statistical evidence of any association between gender and the amount of time spent using the internet by the teenage students. Next, we look at the association between gender and amount of time in sleeping. The following table gives the Chi-square test results; Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 3.904a 6 .690 Likelihood Ratio 5.070 6 .535 N of Valid Cases 50 a. 9 cells (64.3%) have expected count less than 5. The minimum expected count is .48. The p-value for the Pearson Chi-Square is 0.690 (a value greater than 5% significance level), we therefore accept the null hypothesis and conclude that there is no evidence of any association between gender and amount of time spent sleeping by the teenage students. Conclusion From the above findings, it is evident that there exists a strong relationship between the amount of hours spent by the teenage students in using the internet and the amount of hours they spend to sleep. This could have negative repercussions on the students’ academic performance since they may tend to be sleepy during class hours as they had little sleep over night. Lastly, the results shows that there is no significant evidence of any association between gender and amount of time spent by the teenage students in either sleeping or using internet. Works Cited Dehmler, M. K. (2009). Adolescent Technology Usage, Sleep, Attention and Academics. Read More