Introduction
Overweight and obesity prevalence rates have been on a sharp increase in the United States and across the globe since the early 1980’s (Morrill & Chinn, 2004). Because weight gains show no signs of abatement, these two health conditions have become a major public health issue because they increase the risks for the development of other medical problems such as type 2 diabetes, hypertension and some cancer types in addition to premature death.
For the last three decades there has been an increase in the portion of foods consumed by the American households especially away from home. This trend has been in parallel with the increases in body weight and has been cited as a key factor leading to the rising obesity prevalence rates. Young and Nestle (2007) argue that “portion sizes offered by fat-food chains are often two to five times larger than when first introduced” (p. 239).
The United States food supply currently provides 3,900 kcal per day, an increase of 700 kcal/day per head since the early 1980’s. On the other hand, the dietary intake has increased by a mere 200-300 kcal/day within the same period of time. Studies also show that Americans’ food budget make up almost fifty percent of their entire household budget. Americans also consume approximately thirty percent of their daily calories away from home, much of it from the fast food restaurants.
Research (Bowman, Gortmakr, Ebbeling, Pereira & Ludwig, 2004) shows that regular consumption of fast foods is related to gains in weight and obesity in both children and adults. The purpose of this study is to examine the relationship between obesity prevalence and fast food restaurant prevalence in the United States.
Research question
The study will address the following question: is there any relationship between the obesity prevalence and fast food restaurant prevalence in the United States?
Research hypothesis
- Alternative hypothesis: there is a positive relationship between obesity and fast food restaurant prevalence in the United States.
- Null hypothesis: there is no relationship between obesity prevalence and fast food restaurant prevalence in the United States.
Method
Measures
The study has two variables: obesity prevalence rates and fast food prevalence rates.
Data collection
The obesity rates were collected through the Center for Disease Control’s Behavioral Risk Factor Surveillance System (BRFSS), on the basis of self-reported weight and height. Each year, state health departments use standard procedures to collect data through a series of monthly telephone interviews with the Americans. Also the fast food rate was computed 1 restaurants to100,000 residents. The following websites were used to obtain the data.
Results
The test conducted in the Pearson product moment correlation coefficient. The Pearson product moment correlation coefficient is also known as the Pearson correlation coefficient is used to test for the existence of a relationship between two or more variables. In testing for the existence of such a relationship, the Pearson correlation coefficient checks for the direction as well as the magnitude of the relationship. The direction tells us whether the variables in question are positively or negatively related while the magnitude tells us the strength of such a relationship.
The Pearson correlation coefficient ranges from -1 to +1. A negative Pearson correlation coefficient indicates a negative relationship between variables while a positive Pearson correlation coefficient indicates a positive relationship between variables. Two variables are said to be strongly correlated if the correlation coefficient is close to 1 (or -1). Variables are said to be weakly correlated if the correlation coefficient is close to 0. If the correlation coefficient is 1 (or -1), the variables are said to be perfectly correlated. On the other hand, if the correlation coefficient is 0, the variables are said to have no correlation at all (Crawford, 1995).
In order to use the Pearson correlation coefficient test, various issues must be taken into consideration. The first issue is the scale of measurement of the variables under investigation. The variables must be either in interval or ratio scale of measurement. Nevertheless, the two variables need not be measured on the same scale of measurement. That is, the two variables need not be measured on interval or ratio scale; one can be on interval and the other one can be measured on ratio scale. Variables that are measured on ordinal scale cannot be tested using the Pearson correlation coefficient. Instead, the Spearman’s Rank Order correlation test will be more applicable.
The second issue that must be taken into consideration is the issue of unit of measurement. The Pearson correlation coefficient test can be used on variables that have different units of measurement. Third, the test treats all variables under consideration equally regardless of whether they are dependent or independent variables. The test therefore does not make use of any theory underlying the relationship between the variables under investigation.
In addition to the above-mentioned issues, the Pearson correlation coefficient test has some underlying assumptions. The first assumption is that all the variables must be measured either on interval or ratio scale. The second assumption is that the variables should have a normal distribution. The third assumption is that the variables should have a linear relationship. Fourth, any outliers present in the data are reduced or removed altogether. Fifth, the data is homoscedastic, implying that the variance of the error terms is constant throughout the data.
Because of these assumptions, it is important to test the data before conducting the Pearson correlation coefficient test to check whether these assumptions hold or they are violated. In case one or more of the assumptions are violated, corrective measures should be taken after which the Pearson correlation coefficient test can be conducted (Chandran, 2004).
Discussion
Descriptive statistics
The descriptive statistics show that the sample size for both the respondents and the fast food restaurants was 50 each. The mean of the adult overweight rate per 100000 was 62.94 while that for the fast food rate over 100000 residents was 8.528. The skewness for the adult overweight rate per 100000 was -.52, implying that the data for weight rate was negatively skewed. In addition, this is confirmed by the histogram for overweight rate which shows that the data is skewed to the left, implying a negatively skewed data.
On the other hand, the skewness of the fast food rate over 100000 residents was 0.126, implying that the data for fast food rate was positively skewed. This is also confirmed by the histogram for fast food rate which shows that the data is a bit skewed to the right, implying a positively skewed data. The minimum weight rate per 100000 was 55 while the maximum was 69. On the other hand, the minimum fast food rate per 100000 was 5 while the maximum was 12.9.
The frequency of adult over weight rate per 100000
Majority of the respondents (16%) had a weight rate of 62, followed by 64 (14%), 63 and 66 (12% each), 60 (10%), 65 (8%), 58 and 67 (6%), 55, 68 and 61 (4% each) and lastly 55 and 69 (2% each).
The frequency of fast food rate per 100000
Majority of the fast food rate was 7.6, 7.7, 8.2, 8.5 and 10.7 (6% each), followed by 5.8, 6.1, 8.1, 8.4, 8.6, 8.8, 9.0 and 9.9 (4% each), followed by 5, 5.1, 6.4, 6.9, 7.3, 7.4, 7.8, 8.3, 8.9, 9.1, 9.4, 9.7, 10.1, 10.3, 10.5, 10.8, 11.4, 11.6 and 12.9 (2% each).
Correlations
The correlations output shows the results for the pair-wise correlation test for the two variables. The results show that the Pearson correlation coefficient was 0.384. This coefficient tells us two things about the nature of the relationship between adult overweight rate and fast food rate per 100000 residents. To begin with, there is a positive relationship between adult overweight rate and fast food rate per 100000 residents. This implies that as the adult overweight rate per 100000 increases, the fast food rate per 100000 also increases. Similarly, if the adult overweight rate per 100000 decreases, the fast food rate per 100000 also decreases.
Therefore, adult overweight rate per 100000 and fast food rate per 100000 change in the same direction. This supports the alternative hypothesis of this study which stated that: there is a positive relationship between obesity and fast food restaurant prevalence in the United States.
The second point is the magnitude of the relationship between adult overweight rate and fast food rate per 100000 residents, which informs us about the strength of the relationship. In this case, the magnitude is 0.384. This figure lies between 0.3 and 0.5, which implies that the strength of the relationship between adult overweight rate and fast food rate per 100000 residents is medium. That is, the relationship between the two variables is neither weak nor strong.
Besides the correlation coefficient, the output from the Pearson correlation coefficient test also gives a value on the significance of the test. To determine the significance of the relationship between the two variables under investigation, the significance value is usually compared with the conventional alpha levels. A relationship is said to be statistically significant if the reported significance value is less than the alpha.
On the other hand, if the reported significance value is greater than the alpha, the relationship is said to be statistically insignificant. Using an alpha value of 0.05 (5%), the significance value reported in the output is 0.006, which is less than our alpha of 0.05.
The conclusion is that the relationship between adult overweight rate and fast food rate per 100000 residents is statistically significant. The results of this study therefore support the previously reviewed studies, which found that overweight is positively related with the rate of fast foods consumption.
Conclusion
This study has examined the relationship between obesity and fast food prevalence rates. Data was obtained from three different websites on overweight rates and fast food rates. Like the studies conducted by Young and Nestle (2007) and Bowman et al. (2004), this study found that being overweight (obese) is positively related with the rate at which people eat fast foods. This study has important implications for public health programs that are aimed at addressing the obesity calamity. In particular, the programs should focus on public education and awareness creation on the dangers of fast foods (Seiders & Petty, 2004).
Reference List
Bowman, S. A., Gortmakr, S. I., Ebbeling, C. B., Pereira, M. A., & Ludwig, D. S. (2004). Effects of fast-food consumption on energy intake and diet quality among children in a national household survey. Pediatrics, 113, 112-8.
Chandran, E. (2004). Research Methods: A Quantitative Approach. Nairobi: Daystar University.
Crawford, I. (1995). Marketing Research and Information Systems. Rome: FAO.
Morrill, A. C., & Chinn, C. D. (2004). The obesity epidemic in the United States. Journal of Public Health Policy, 25(3/4), 353-366.
Seiders, K., & Petty, R. D. (2004). Obesity and the role of food marketing: A policy analysis of issues and remedies. Journal of Public Policy & Marketing, 23(2), 153-169.
Young, L. R., & Nestle, M. (2007). Portion sizes and obesity: Responses of fast-food companies. Journal of Public Health Policy, 28(2), 238-248.