Tv Time Versus Sleeping Quality for Children

We like watching television, watching free videos online, or playing video games. However, I am worried about that our children have too much screen times that is the time they spend on TV, computer, smartphone, tablets, mobile devices, or hand-held games. Thus, I am going to investigate the effects of TV time on the children. After searching on this topic for some time, I do find that TV time has significant effects on our children on several aspects.

Bulck (2004) found that children who watched more TV, spent more time on computer games and internet, would sleep less both weekdays and weekend, and have higher overall levels of being tired. Bulck also found that setting up an ending time would reduce the negative effect of the screen time on sleep behavior.

Bodenlos (2013) confirmed the previous findings that food advertisements affect how people choose which foods to eat. Bodenlos did not find significant relationships between the TV time and the calorie-intake. However, he did suggest that the longer exposure to food advertisements would make people eat more and more likely have obesity problems.

Dahl (1996) found that if children have inadequate sleep, the children would more likely lose control of attention and emotions.

Vioque (2000) found that if people watched more TV, then, they spent less time on sleep, and more likely to have obesity problems.

Sidney (1996) found that the TV viewing time is positively related to the body mass index, hostility and depression. However, TV viewing times was not significantly related to blood pressure, and cholesterol levels for young adults.

Foley (2012) found that if the children were on the screen (TV, games, computers, etc.) 90 minutes before they went to bed, they would need longer time to fall to sleep.  

In this Paper, I would focus on the article of Foley and dig into it more carefully. Foley had collected a huge amount of data with 2017 participants aged from 5 to 18. I would take some data from Foley and perform [pic 1] analysis. We learned from this class that the [pic 2] test is used to test independence or homogeneity. It is for two nominal variables. In the [pic 3]test for independence, the null hypothesis is that the samples are independent. If the p-value is smaller than the significant level which is normally set to[pic 4], or if the [pic 5] statistics is greater than the upper critical [pic 6] value or less than the lower critical[pic 7] value , then, we reject the null hypothesis and we can conclude that the data support that they are dependent.