Both income and obesity are related in some non-linear ways. In most poor countries or third world countries, obesity level usually increase with a rise in come, while in developed nations, it decreases with income (Pee, et.al, 2017). The aim of this paper is to determine the relationship between poverty and obesity. In particular, we would like to know whether low income earners are at a higher risk of being affected by obesity. Our research question is therefore, “Are people living in poverty more likely to be affected by obesity?”. We therefore calculated the Body Mass Index of the individuals who participated in the research.
Null hypothesis: There is a significant relationship between obesity and poverty level.
Alternative hypothesis: There is no statistically significant relationship.
In my case, I assumed that poor people are those with a value less than 4 in terms of income level. I then ran a regression analysis of all the participants with an income level of less than 4 and their Body Mass Index in order to determine whether there is any association between the two variables (Chaterjee & Hadi, 2006). The total number of the poor individuals is 1867 out of the 7689 of the whole population considered during the study.
Results and Analysis
Descriptive Statistics
Mean
Std. Deviation
N
BMI
44.1789
153.06134
1867
INCOME2
2.08
.822
1867
Correlations
BMI
INCOME2
Pearson Correlation
BMI
1.000
.000
INCOME2
.000
1.000
Sig. (1-tailed)
BMI
.
.495
INCOME2
.495
.
N
BMI
1867
1867
INCOME2
1867
1867
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Change Statistics
R Square Change
F Change
df1
df2
Sig. F Change
1
.000a
.000
.000
153.10236
.000
.000
1
1865
.989
a. Predictors: (Constant), INCOME2
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
4.210
1
4.210
.000
.989a
Residual
4.372E7
1865
23440.334
Total
4.372E7
1866
a. Predictors: (Constant), INCOME2
b. Dependent Variable: BMI
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
95% Confidence Interval for B
Correlations
B
Std. Error
Beta
Lower Bound
Upper Bound
Zero-order
Partial
Part
1
(Constant)
44.059
9.652
4.565
.000
25.129
62.989
INCOME2
.058
4.312
.000
.013
.989
-8.400
8.515
.000
.000
.000
a. Dependent Variable: BMI
Findings
Correlation Table indicates that the correlation between BMI and income is significant since the p-value is less than the 0.05 significant level. From the Model Summary and ANOVA tables above, it can be deduced that the p-value (0.989) is greater than the 0.05 significance level. We therefore fail to reject the null hypothesis and conclude that there is statistically significance relationship between obesity and the level of poverty (Rubi, 2009). Thus, it can be alluded that people living in poverty are more likely to be affected by obesity. Some of the reasons for the rise in obesity cases among the poor individuals could be: irregular meals, lower education level, as well as higher rate of unemployment (Boison, 2017). Another factor is low physical activity since most poor people lack enough money to purchase sport equipment.
References
Boison, C. D. (2017). Relationship Between Family Income And Obesity. MA: Book Venture Publishing LLC.
Chatterjee, S., & Hadi, A. S. (2006). Regression Analysis by Example. Hoboken, NJ: John Wiley & Sons.
Pee, S. D., Taren, D., & Bloem, M. W. (2017). Nutrition and Health in a Developing World. New York, NY: Humana Press.
Rubin, A. (2009). Statistics for Evidence-Based Practice and Evaluation. Boston, MA: Cengage Learning.