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The Link Between Sleep and Weight

Correlation is the association between two variables, when there is an increase or decrease in one variable what effect does it has to the other variable (Triola, 2010). In this research the author looks on the relationship between a person who do not sleep or get quality sleep and their body weight. There was a study which highlighted a correlation between lack of sleep and increase in body weight. In the study with the women 40 – 60 years old, it was concluded that after studying their eating and sleeping pattern for 5-7 years women who had trouble falling asleep gained approximately 11 pounds. In the other study with the younger men, they studied their sleeping patterns for two consecutive days one day eight hour sleep and the other day four hour sleep. The researchers reported an increase in calorie intake (approximately 560 more) after sleeping for four days significantly more than the person who slept for eight hours.

The two variables we have in this scenario is lack of sleep and increase in body weight, for these two variables to be correlated they must be linked or dependent on each other. Although the writer shows studies to show that when there is lack of sleep there is increase in appetite there can be other variables that determine the results such as location, body mass, individuals’ state of mind and age. According to the text the relationship between these two variables is weak and negative. The study is not conclusive because there is no data to prove that an increase in sleep will reduce weight.

Although there is a correlation between not getting quality sleep and gaining weight, there is not enough information to prove causality, the researchers did not provide data to prove that getting more quality sleep will reduce your weight. The data provided is not sufficient proof to change one’s lifestyle by sleeping more to reduce weight. There is no evidence to prove that people who lose weight will have more quality sleep.

The researchers need to gather information from a wider population and also do the reverse by orchestrating studies that shows the relationship between getting quality sleep and losing weight.

Why Going to Church Can Make You Fat

The two variables been studied are going to church and obesity, those who take part in religious activities are more prone to gaining weight. In this article researchers studied a group of 2400 people for 18 years, they came to the conclusion that people who goes to church are twice likely to gain weight than those who did not attend. In this study there is a correlation between the two variables church and obesity as indicated by the results of the studies. Correlation is the relationship between two variables, in this case the two variables are church attendance and obesity (Atmanspacher, H. 2014). According to the research people who goes to church are more likely to get obese.

In interpreting correlation there must four factors present; the numerical value of the correlation, the sign of the correlation coefficient, the statistical significance of the correlation, and the effect size of the correlation. The numerical value of the correlation is normally a number between -1 and 1, the closer the coefficient number is to 1 the stronger the correlation which could strong negative or strong positive. The sign of the correlation coefficient dictates the relationship between the two variables a positive correlation means as one variable increases the other increase also and if one decreases the other decreases also. The statistical significance of the correlation must have a probability less than 0.05 which is the probability of getting a correlation is less than 5 times out of a 100.

Although there is a correlation between the two variables, this does not prove causation. There is not sufficient data to prove that going to church makes you obese and for this relationship to a cause and effect the reverse has to agree also. The reverse is those who do not go to church will lose weight. There is no evidence to prove this relationship. There can be other variables involved in this relationship that will allow persons going to church to gain weight. These variables can include person’s peace of mind, contentment, and sitting for long hours.

The augment’s for causal relationship is not convincing because of the availability of other variables involved. Two variables maybe correlated but unless the reverse is true and can be proven there is not enough evidence to prove causality. In order to make this argument more convincing more studies need to be with a bigger population and there must be some data on those who do not go to church. Causation would be strong if there was evidence to prove that staying away from church will reduce weight gain. The evidence are not convincing to change one’s lifestyle based on this article. There need to studies done on the other factors that contribute to weight gain in persons going to church.

Why Having Kids is Bad for Your Health

In a recent study done by the University of Minnesota they looked at the relationship between mothers and women without children and fathers and men without children. The population was 838 women and 682 men. They wanted to find out if having children is bad for your health, the results show that mothers had a higher body mass index than women without children, women eat healthier than mothers, and mothers and fathers exercise less than women without children. Mothers exercise 1.5 hours less and consumed 368 more calories. They both had similar quantity of food but mother’s food was less in quality. It was also noted that there was no difference between the body index of the fathers and men without children (Rochman, B. 2011).

The correlation is between mothers having children and bad health, in this study the results are showing that having children is bad for your health. There is a correlation because there is a relationship between having children and bad health according to the research (Bitter & Wilder, 1946). The research suggest that having children is bad for your health because it causes increase in body index, less exercise and eating more calories. These three results is an indication that mothers are living a less heathy lifestyle.

This correlation is a weak negative correlation because when a woman has a child it negatively affects their health according to the research. The study is not conclusive in terms of the difference the results would be if you had bring in another variable of the amount of children. There is not enough information to assert the difference between one child and 10 children, would the results be the same?

Causal relationship indicates that one variable has a direct influence over the next variable, so in this study the aim is to determine if this relationship is causal. Does the increase in one variable led to the increase or decrease in the next variable. Are they synchronized so much so that if one moves there is a reaction to the other. Correlation is symmetrical but causal relationships are not, if a male is positively correlated with running then it is true that running is positively related to a male. In casual relations there is a difference if lack of exercise causes heart disease it does not holds through that heart disease cause lack of exercise.

In this scenario there is evidence for causal relationship although the evidence could be challenged for statistical significance. The proof of increase body mass, less exercise and eating more calories are evidence that there is a causal relationship. Although the relationship is casual there are other variables that are involved in the statistics such as the mother’s state of mind, health consciousness, and her overall self-awareness. In this study there is not enough evidence or the difference between a mother and a women is not significant to warrant a change in lifestyle. The study is good however as it allows mothers to watch their calorie intake and remind them to exercise more.

Reference

Atmanspacher, H. (2014), Roles of causation and meaning for interpreting correlations. Journal of Analytical Psychology, 59: 429–434. doi: 10.1111/1468-5922.12086

Bitter, R. B., & Wilder, C. E. (1946). Expectancy Tables: A Method of Interpreting Correlation Coefficients. The Journal of Experimental Education Vol. 14, No. 3, 245-252.http://www.jstor.org/stable/20150855

McCoy, K. The link between sleep and weight. http://www.everydayhealth.com/sleep/101/tips/snooze-control-suggested-for-overweight-children.aspx

Park, A. (2011). Why going to church can make you fat.http://healthland.time.com/2011/03/24/why-going-to-church-can-make-you-fat/

Rochman, B. (2011). Why having kids is bad for your healthhttp://healthland.time.com/2011/04/11/is-parenthood-bad-for-your-health/?iid=WBeditorspicks

Triola, M. (2010). Elementary Statistics using Excel. Boston: Pearson Educational Company.

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