# Testing Methodology

Income inequalities form the biggest challenge when it comes to accessibility of health services. In many countries, income inequalities remain high. Over the years, such inequalities have impacted the way in which different population cohorts’ access to medical services. In this study, the focus was on secondary data collected in order to establish the impact of income inequalities on healthcare services accessibility in Canada. This study will analyze the two variables, namely, income inequalities and healthcare services accessibility. The idea is to assess whether the two are related in any way.

To answer the research questions implied in this study, the linear regression model will be used. This model assumes that the data used is linearly distributed. This will be determined by conducting preliminary computations to determine if the variances detected are spread across a line. If so, the overall significance will be high for the model allowing the researcher to compare the variables. In this study, two main variables will be compared. Since they are only two, using a simplified regression model is appropriate to avoid confusion on how the variables relate to each other. These analyses will be done from the Excel software utilizing the data analysis toolkit.

# Empirical Model

The study will utilize a linear regression equation in measuring possible relationships in the data used. The equation will appear as follows:

∑ = β + β1I+ u

Where              ∑ represented healthcare services accessibility

β represented the intercept or constant in the equation

β1 represented the coefficients of determinant

I represented the income inequalities

u represented the error term in the equation

The equation is based on the assumption that other possible factors that can affect the relationship between income inequalities and healthcare services accessibility will remain constant. This is important for the study to be able to measure the net effect that income inequalities have on the healthcare accessibility variable in the context of Canada. Mediating and moderating factors will be measured to find out if, when applied, they would change the final outcome of the analysis. This will be critical in identifying where the study could be improved or the suggestions for future studies.

# Data

The study used secondary data primarily. The reason for using secondary data is because it makes the whole process of getting the inferences much efficient. This is because secondary data is often collected by other parties and stored in categorized ways for easy access. This data saves time and money that the current researcher would otherwise spend seeking the same data. The fact that this study relied on time series data also rationalizes the use of secondary data. Time-series data were used so that the outliers in the data over the years can be eliminated. The analysis was done, and the results were as revealed below.

# Hypothesized Results

The study was based on two major hypotheses. The first one, the null hypothesis, holds that income inequalities do not affect the healthcare accessibility in Canada. The other one is the alternative hypothesis, which holds that income inequalities affect healthcare services accessibility in Canada. Further, it is expected that income inequalities affect the accessibility of healthcare services in a negative way. This means that an increase in the inequalities leads to a reduction in accessibility. This expectation is based on the insights obtained from the literature review. When people have low incomes, they tend to have low access to medical covers, which means that their overall access to medical services is curtailed. Thus, it is expected that people with high incomes have higher access to medical covers and hence medical services than those with low incomes. This is where income inequalities come in. The gap between the rich and the poor in Canada is expected to reflect on how people access medical services where the rich are favored at the expense of the poor. This negative relationship between the independent and dependent variables is expected to hold true after the analysis is done. The results are as shown in the subsequent sections.

# Actual Results

In this section, the analysis was done focusing on the model’s relevance to the data, the accuracy of the predictions, and the statistical significance of the variables used. There were three tables produced from the analysis. The first table reveals the model’s relevance to the variables selected and the data used. The second one measures the accuracy of the model to account for the variances in the sample data. The third table gives the coefficient of determinants of the predictor and criterion variables.

Table 1: Regression Model Summary

 Model R R Square Adjusted R Square Std. Error of the Estimate 1 -0.967 0.936 0.934 2.672

In table 1, the R-value is given. It is used to measure the overall impact that the predictor variable has on the criterion variable (Austin and Merlo 2017, 3257-3277). In this case, the effect is negative. This means that the predictor variable affects the dependent variable negatively at 0.967. The R square value was found to be 0.936, which means that the proportion that the independent variable explains the variances in the dependent one is 93.6%. The remaining 6.4% can be explained by other independent variables, mediating, and moderating variables. Moreover, the adjusted R square value, which gives the proportion that additional variables (Ahlgren and Walberg 2017, 285) will have on the current results, stood at 0.934. It means that the introduction of other variables will have an impact of 93.4% on the current results. As such, further studies should be done to explain the remaining variances. Further, the standard error of the estimate, which measures the overall prediction accuracy of the variables and the model used, stood at 2.672. This estimate means that the analytical model used was highly accurate in explaining the variances in the data used testing the relationship between healthcare services accessibility and income inequalities.

Table 2: ANOVA Results

 Model Sum of Squares df Mean Square F Sig. 1 Regression 5394.522 1 5394.522 755.861 0.001 Residual 371.12 52 7.137 Total 5765.642 53 a. Dependent Variable: Healthcare services accessibility in Canada

From table 2, the ANOVA results reveal the F value and the overall model significance. The study’s p-value stood at 0.05. Based on the p-value, it can be established that the sample data used has enough evidence to explain the variances identified since it is much higher than the p-value at 755.861. As such, the coefficients of determination can be analyzed.

Table 3: Coefficients of Determination

 Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1.253 0.373 3.359 0.001 Income inequalities -0.803 0.275 -0.256 2.920 0.005
1. Dependent Variable: Healthcare services accessibility in Canada

Table 3 gives the individual coefficients of determination. The results showed that income inequalities had a negative relationship with healthcare services accessibility in Canada. As such, it was revealed that a unit increase in income inequalities led to a drop in healthcare services accessibility in Canada by 0.803. The significance of this variable stood at 0.005, which was below the p-value at 0.05. This means that it was highly influential in determining the accessibility of healthcare services in Canada.

# Interpretation of the Results

The study found that the alternative hypothesis holds true that income inequalities affect healthcare services accessibility in Canada. The results above demonstrated that the expected income inequalities effect on the accessibility of healthcare services was true. It affected the dependent variable in a negative way. This means that an increase in the inequalities leads to a reduction in accessibility. When people have low incomes, they tend to have low access to medical covers, which means that their overall access to medical services is curtailed. Thus, it is expected that people with high incomes have higher access to medical covers and hence medical services than those with low incomes. Thus, it is true that the gap between the rich and the poor in Canada is expected to reflect on how people access medical services where the rich are favored at the expense of the poor. This negative relationship between the independent and dependent variables is accurate as the study found the data and model used to be highly significant and accurate. With these results, it can be deduced that if the government of Canada is seeking to improve healthcare access, it has to work on the prevailing income inequalities.

# Conclusion

On the whole, the income inequalities variable was found to be highly significant in influencing the level of healthcare services accessibility in the Canadian population. The relationship was found to be negative. This means that as the income inequalities increased in the period studied in this paper, the healthcare services accessibility increased. However, the study found that the accessibility to healthcare services in Canada increased in that period. This, therefore, means that this increase was not influenced by the income inequalities. This variable was the reason why accessibility grew at a slow pace. Therefore, the Canadian government should ensure that the inequalities are low to be able to improve the healthcare services accessibility.

The study could be improved by introducing the mediating and moderating factors such as government intervention and the cost of medical services. In addition, the study could have been more accurate had it used more variables other than income accessibilities; this is because this variable only explained the reduction in the accessibility of healthcare services. Since the net effect was an increase in accessibility, it means that this variable was not enough to explain the variances around the accessibility of healthcare services in Canada. However, the results show that the government of Canada should pay more attention to income inequalities to help more people access medical services.