Eating Fast Food and Obesity Correlation Analysis

Healthcare problem and research question

In healthcare, it is prudent to assess relationships between healthcare problems and suspected causative factors. If causative factors are determined and positively correlated to healthcare problems, then preventive measures can be put in place (Polit & Lake, 2010). Correlation analysis statistical tests are used to show a relationship between variables (Jackson, 2012). Correlation analysis tests do not accommodate the assumption that the variables are dependent. However, they assume that the variables follow a normal distribution. Correlation analysis shows or disputes correlation between two variables. There are three relationships that are expected in correlation analysis (Jackson, 2012). The first relationship involves a decrease in one variable when the other variable increases. The second relationship is manifested when both variables increase simultaneously. The third relationship shows no correlation between the two variables (r=0.0). The proposed study will attempt to answer this question:

What is the relationship between eating fast food and obesity?

Many research studies have attempted to associate obesity with fast food. Obesity is a global healthcare problem affecting many people. This research question fits to be addressed by the correlation analysis because it has two types of variables, i.e., fast food and obesity. The study will focus on determining whether fast food has causative effects on obesity.

Hypotheses

The study will use both the null hypothesis and alternative hypothesis. The null hypothesis will be as follows:

• H₀: µ_{obesefastfoodconsumers} = µ_{obesenonfastfoodconsumers}

Where:

• H₀ = the null hypothesis
• µ_{obesefastfoodconsumers} = the average number of participants who will be obese and will be fast-food consumers, and
• µ_{obesenonfastfoodconsumers} = the average number of participants who will be obese, but not fast food consumers.

The null hypothesis will imply that there will be no difference in the number of obese persons who consume fast food and those who do not.

The alternative hypothesis will be as follows:

• H₁: µ_{obesefastfoodconsumers} > µ_{obesenonfastfoodconsumers}

Where:

• H₁ = the alternative hypothesis
• µ_{obesefastfoodconsumers} = the average number of participants who will be obese and will be fast-food consumers, and
• µ_{obesenonfastfoodconsumers} = the average number of participants who will be obese, but not fast food consumers.

The alternative hypothesis will imply that the number of obese fast-food consumers will be more than obese persons who will not be consuming fast food.

Variables in the study and expected relationship

The study will assess the relationship between one independent variable (fast food) and one dependent variable (obesity). The independent variable will be expected to have effects on the dependent variable. If the relationship will be negative, then one variable will be found to be increasing while the other variable will be decreasing. If the relationship will be positive then both the variables will be found to be increasing or decreasing simultaneously. When using the correlation analysis, the null or the alternative hypothesis is rejected or accepted depending on the relationships of the variables (Burns & Grove, 2009).

A positive correlation will be expected to exist between the variables. This will imply that the number of obese persons feeding on fast food will be more than obese persons who will not be consuming fast food. A positive correlation between the independent variable (fast food) and the dependent variable (obesity) will lead to rejection of the null hypothesis, but adoption of the alternative hypothesis. Therefore, the study results will give essential information on the causative effects of fast food on obesity.

References

Burns, N., & Grove, S. K. (2009). The practice of nursing research: Appraisal, synthesis, and generation of evidence. Philadelphia, PA: Saunders Elsevier.

Jackson, S. L. (2012). Research methods and statistics: A critical thinking approach (4^th ed.). Belmont, CA: Wadsworth.

Polit, D. F., & Lake, E. (2010). Statistics and data analysis for nursing research. New York, NY: Pearson.