In the article, the author claims that children growing up in low-income families tend to have higher rate of obesity, which implies a negative correlation between income and obesity rate.
To support his point, the writer used the model built by University of Michigan Health System based on the data from 111,799 Massachusetts students. The model presents that “every 1 percent increasing in low-income status is associated with 1.17 percent increase in rates of obese students”, which strongly advocates the correlation between the two factors: the lower the family’s income is, the higher possibility for the child to be overweight.
There are several explanations can help to illustrate this correlation.
Firstly, according to the article, the reason …show more content…
The companies often prefer to hire or promote people who have good figures to people who are obese, so overweight people are more likely to have lower income. Also, researches suggest that if parents are overweight, their children are inclined to be fat as well.
Furthermore, there might be a third factor like education which affect both income and obesity. For one, people who have higher education degree are more competitive in job market and therefore have higher income. For another, more educated a person is, he or she may be more aware of the danger of obesity and know how to keep …show more content…
The experiment consists of two parts. First, we need to randomly select children from low income families and randomly assign them into two groups: A and B, recording their fitness like BMI data. Second, we have to manipulate one and the only one independent variable which is the presence versus the absence of exercise and measure the dependent variable which is the children’s BMI data. To be specific, we keep one group of children do exercise every day and another group be more sedentary as well as control all other conditions or variables equal. Also, to avoid the experimenter expectancy effect and the placebo effect, we should let other researchers assign the groups without knowing which one is the experimental group or control group by ourselves. Neither should the participants know whether they are in certain group. After 10 years, we can collect the participants’ BMI data and compare the how their BMI change through years as well as the difference of the rate of their BMI change between the two groups. If the result shows that the BMI from the group who have no exercise increases faster than the group do exercise frequently, we can conclude that lack of exercise causes higher rate of obesity. Otherwise, the explanation is not well
To support his point, the writer used the model built by University of Michigan Health System based on the data from 111,799 Massachusetts students. The model presents that “every 1 percent increasing in low-income status is associated with 1.17 percent increase in rates of obese students”, which strongly advocates the correlation between the two factors: the lower the family’s income is, the higher possibility for the child to be overweight.
There are several explanations can help to illustrate this correlation.
Firstly, according to the article, the reason …show more content…
The companies often prefer to hire or promote people who have good figures to people who are obese, so overweight people are more likely to have lower income. Also, researches suggest that if parents are overweight, their children are inclined to be fat as well.
Furthermore, there might be a third factor like education which affect both income and obesity. For one, people who have higher education degree are more competitive in job market and therefore have higher income. For another, more educated a person is, he or she may be more aware of the danger of obesity and know how to keep …show more content…
The experiment consists of two parts. First, we need to randomly select children from low income families and randomly assign them into two groups: A and B, recording their fitness like BMI data. Second, we have to manipulate one and the only one independent variable which is the presence versus the absence of exercise and measure the dependent variable which is the children’s BMI data. To be specific, we keep one group of children do exercise every day and another group be more sedentary as well as control all other conditions or variables equal. Also, to avoid the experimenter expectancy effect and the placebo effect, we should let other researchers assign the groups without knowing which one is the experimental group or control group by ourselves. Neither should the participants know whether they are in certain group. After 10 years, we can collect the participants’ BMI data and compare the how their BMI change through years as well as the difference of the rate of their BMI change between the two groups. If the result shows that the BMI from the group who have no exercise increases faster than the group do exercise frequently, we can conclude that lack of exercise causes higher rate of obesity. Otherwise, the explanation is not well