Type II diabetes affects any person, though people in a given age bracket are at an increased risk of contracting it. Young people with strong immune systems and exercise a lot do not face the risk of getting diabetes type II, unlike the elderly with a low immunity and do not exercise regularly (Chen et al., 2016). The study aims to establish the existence of a correlation between age and diabetes type II risk. SPSS would enable the study to conduct the research and subsequent analysis. Pearson correlation coefficient would let the study determine if collinearity exists between the variables. A Pearson correlation coefficient above of 1 means a perfect positive correlation while a Pearson correlation coefficient of -1 means there is a perfect downhill correlation between age and the risk of getting Type II diabetes.
The study believed that there is a possible correlation between age and one being diabetic. Alva et al. (2017) argued that blood sugar levels allow medics to predict whether a person suffers or is at risk of getting diabetic. Glucose levels of 100 mg/dL preempts that one’s blood sugar is normal. If the person’s glucose level ranges 100 mg/dL to 125 mg/dL, the individual is presumed pre-diabetic. However, if the person’s blood sugar level exceeds 126 mg/dL on at least two tests, then the person is considered diabetic. The study aims to determine whether age plays a key role in predicting if one is at risk or suffers from diabetes Type II. In the case, the age becomes the independent variable, whereas the glucose levels or blood sugar acts as the dependent variable.
Delegate your assignment to our experts and they will do the rest.
The study being primarily quantitative, then tests must follow the data collection to determine the collinearity. Pearson correlation test offers the best grounds for determining if the two variables correlate. Age and glucose levels correlation outcome form the study’s basis. Age being the independent variable, and glucose level being the dependent variable, the study aims to predict one’s glucose levels from the person’s age (Alva et al., 2017). The null hypothesis states that no correlation exists between age and glucose level, while the alternate hypothesis states that age and glucose levels bear a correlation.
Dependent variable: glucose level
Independent variable: age
Null hypothesis: There is no correlation between age and glucose level
Alternate hypothesis: a correlation exists between age and glucose level
Statistical test: Pearson correlation test
|
Descriptive Statistics |
|||
|
Mean |
Std. Deviation |
N |
|
| GLUC |
153.81 |
65.893 |
280 |
| AGE |
42.92 |
13.553 |
280 |
Figure 1: descriptive statistics
|
Correlations |
|||
|
GLUC |
AGE |
||
| Pearson Correlation | GLUC |
1.000 |
-0.008 |
| AGE |
-0.008 |
1.000 |
|
| Sig. (1-tailed) | GLUC |
0.446 |
|
| AGE |
0.446 |
||
| N | GLUC |
280 |
280 |
| AGE |
280 |
280 |
|
Figure 2: Correlations
Figure 3: ANOVA Tests
The correlation finding based on figure 2 reveals a correlation coefficient of -0.008, which means there could be no collinearity between glucose levels and age. The correlation coefficient value of 0.446 is less than 0.005, thus not statistically significant. A correlation coefficient of -0.008 is relatively negligible, thus the study cannot reject the null hypothesis. While it was initially predicted that age could determine if one suffers diabetes, but the findings revealed otherwise (Atlas, 2015).
References
Alva, M. L., Hoerger, T. J., Zhang, P., & Gregg, E. W. (2017). Identifying risk for type 2 diabetes in different age cohorts: does one size fit all?. BMJ Open Diabetes Research and Care , 5 (1), e000447.
Atlas, D. (2015). International diabetes federation. IDF Diabetes Atlas, 7th edn. Brussels, Belgium: International Diabetes Federation .
Chen, L., Magliano, D. J., & Zimmet, P. Z. (2016). The worldwide epidemiology of type 2 diabetes mellitus—present and future perspectives. Nature reviews endocrinology , 8 (4), 228-236.
