Impact of Occupation, Education Level, Alcohol Consumption, Glucose Intake, Age, and Weight on Diabetes Diagnosis

INTRODUCTION
RESEARCH QUESTIONS
The research question for this study is: Is the diagnosis of diabetes affected by the occupation,
education level, alcohol consumption, glucose intake, age and weight of an individual?
RESEARCH OBJECTIVES
The main objective of this research study is:
To determine whether the diagnosis of diabetes is affected by the occupation, education level,
alcohol consumption, glucose intake, age and weight of an individual.
The other objectives in this study are:
1. To determine whether there is a difference between the categories of diabetes diagnosis
regarding age.
2. To determine whether the categories of diabetes diagnosis significantly differ from each other
based on counts.
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RESEARCH HYPOTHESES
This research study will test three hypotheses:
HYPOTHESIS 1
Null Hypothesis (H0): The diagnosis of diabetes categories would significant differs from each
other based on age.
Alternative Hypothesis (H1): There isn’t any difference between the categories of diabetes
diagnosis regarding age.
In order to test this hypothesis, the Independent Sample t-test is applied. The t-test is a statistical
test that specifically provides information on the relationship between two variables with the
dependent being metric (measured on interval or ratio scale) and the independent being non
metric with two categories (measured on either nominal or ordinal scale) (Barbara & Susan,
2014; Ren & Ying, 2010).
The assumptions of the Independent Sample t-test are (Witten, 2011; Everitt &
Skrondal,2010):
1. The dependent variable should be continuous in nature. This implies that it should be
measured on either interval or ratio scales.
2. The independent variable should be categorical in nature with two or more groups.
3. The observations should be independent of each other.
4. The data should not contain outliers that can be considered as significant.
5. The dependent variable should follow a normal distribution.
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HYPOTHESIS 2
Null Hypothesis (H0): The categories of diabetes diagnosis significantly differ from each other.
Alternative Hypothesis (H1): The categories of diabetes diagnosis do not significantly differ
from each other.
In order to test this hypothesis, the Binomial test is applied. The Binomial test is a form of a
probabilistic statistical test for testing difference between the categories of a dichotomous
variable (Usama & Padhraic, 2008). The Binomial test is a non-parametric statistical test and
therefore the assumptions that apply for the parametric tests do not apply for this test (Corder &
Foreman, 2009). The only assumption for the Binomial test is that the sample used is a random
sample (Oscar, 2009).
HYPOTHESIS 3
Null Hypothesis (H0): The diagnosis of diabetes is affected by the occupation, education level,
alcohol consumption, glucose intake, age and weight of an individual.
Alternative Hypothesis (H1): The diagnosis of diabetes is not affected by the occupation,
education level, alcohol consumption, glucose intake, age and weight of an individual.
In order to test this hypothesis, the Logistic Regression is applied. Regression analysis can
broadly be defined as a statistical analysis method whose aim is to establish the presence,
absence and type of relationship between variables (Galit, Peter, Inbal, Patel, & Kenneth, 2018;
Han & Jaiwei, 2011). The Logistic Regression is a type of regression analysis in which the
dependent variable is categorical in nature (Hosmer, 2013). The dependent variable(s) can be
either continuous or categorical. For this study, the Binomial Logistic Regression will be
specifically used.
RESULTS
DESCRIPTIVE STATISTICS
From Table 1: Descriptive Statistics for Continuous Variables we observe the summary statistics
for the continuous data variables in the study.
Table 1: Descriptive Statistics for Continuous Variables
The histograms for the continuous variables are as shown in the plots in Figure 1: Histogram for
Weight Variable to Figure 4: Histogram for Age below. In all the plots, the distributions appear
to be skewed to the left except for the age variable which displays a bell shaped density curve
and it’s hence normally distributed from visual inspection.
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Figure 1: Histogram for Weight Variable Figure 2: Histogram for Glucose Variable
Figure 3: Histogram for Alcohol Variable Figure 4: Histogram for Age
Table 2: Overall Descriptive Statistics for Categorical Variables shows the count of the valid and
missing observations in the data for the categorical variables in the study.
The diabetes variable, education and occupation had both valid and missing values, the table 2
shows the number of count that were observed and the number of count that were missing on all
the three variable categories.
Table 2: Overall Descriptive Statistics for Categorical Variables
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