# Testing Methodology

When it comes to testing methodology, one of the most important elements is the selection of the analytical model. Data used in this particular study was largely secondary and time-series. As such, the regression model was selected. The data was based on years in a cumulative way. This meant that data was linearly and normally distributed. With normally distributed data, the regression model is ideal for analysis.

This study utilized two main variables, namely the independent and dependent variables. There is one dependent variable and one independent variable. This means that the study ought to have relied on a simplified regression model to gauge the relationship between the dependent and the independent variables. In this case, the Ordinary Least Square regression model was utilized. The idea was to make sure that the migrants variable was compared directly to the dependent variable to find out how it related.

There were four main assumptions used in this case when conducting the analysis. The four main assumptions were linearity, normality, independence, and homoscedasticity. The linearity assumption held that the relationship between X and Y was linear. In other words, the relationship between migrants’ data and the data on educational attainment was assumed to be linear. The other assumption was homoscedasticity, which means that the variance of the residual will be the same for all values of X or the independent variable. Further, independence was assumed to be the case for each observation. This implies that each of the observations (migrants and education attainment) had high significance each to stand alone. Lastly, the assumption of normality holds that for any fixed value of the independent variable (X), the Y (dependent variable) was normally distributed.

The best thing about using the regression model was that it makes it possible to be able to identify whether these assumptions have been met. The regression model analysis has capabilities to assess whether the data has met assumptions and to measure the variance evidence present in the data. These capacities make the OLS regression model appropriate for this particular study. In the subsequent sections, the empirical model using the OLS regression model will be discussed.

# Empirical Model

The empirical analysis in this report was based on the OLS model. The model makes use of a linear equation, as shown below. The model’s structure is as follows.

¥ = β + β1M + u

Where              ¥ represented education attainment in Canada

β represented the intercept or constant in the equation

β1 represented the coefficients of determinant

M represented the migrants

u represented the error term in the equation

The equation assumes that the relationship between migrants and the education attainment variable in Canada forms a linear structure such that the net effect of the independent variable explains the shifts in the education attainment variable in Canada.

# Data

In this data report, the research opted to use secondary data over primary data. The form was selected because it offers flexibility and efficiency. Flexibility was achieved in the sense that the study could choose from various sources of data. The efficiency was in terms of time and cost. With reasons, the choice of secondary data was made. Additionally, the study chose to use the time series technique. In this technique, data collected is spread out many years. This was done to make it possible to identify movements and shifts in the data. For instance, in investigating public participation, the increases or decreases in this variable could be seen over the years from the data. To ensure that the data collected was reliable, the study elected to use government and official databases as sources. This is because such databases are maintained by experts with the cooperation of many data scientists. Thus, the repute of such data is high. The study used three measures of public participation, namely public campaigns, compliance efforts, and drunk-driving. All these factors were meant to help assess how public participation affects alcohol-related accidents.

# Actual Results

In this section, the results from the regression model analysis will be presented. The accuracy, variance, significance, impact and reliability of the survey data used together with the regression model selected were tested and reported.

Table 1: Regression Model Summary

 Model R R Square Adjusted R Square Std. Error of the Estimate 1 -0.950 0.902 0.901 2.971

The model’s R-value is reported in table 1. The R-value in regression models is used to analyze the variance that the selected variables, both dependent and independent, have on each other (Fletcher 2017, 181-1940. In this case, the effect was found to be negative as designated by the negative R-value. This means that the negative relationship between migration into Canada and the education attainment levels amongst the natives was negative. It also means that the proportion of the education attainment variable that was explained by the data was up to 95%. The R square value was found to be 0.902. This means that the independent variables were able to account for the variance in the dependent variable up to 90.2%. The adjusted R square was found to be 0.901. This shows the proportion that the impact that omitted independent variables had on the results. In this case, the figures were found to be 9.9%. The standard error of the estimate was found to be 2.971. This means that the accuracy of the overall model in making the predictions was high as the figure was low.

Table 2: ANOVA Results

 Model Sum of Squares df Mean Square F Sig. 1 Regression 4245.24 1 4245.240 480.837 0.000 Residual 459.1 52 8.829 Total 4704.34 53 a. Dependent Variable: Education attainment in Canada

Table 2 shows the ANOVA test results. The results were such that the F value and the overall model significance were both indicated. The F value helps to determine the goodness of fit between the model and the data used in the study. The goodness of fit is said to be high whenever the F value is more than the p value (Kumar 2019). The p-value being 0.005, the F value at 480.837 indicated that the model had a high goodness of fit. The overall model significance was found to be high also at 0.000 which is lower than the p-value. Thus, it is clear that the model used is both significant and accurate with a high goodness of fit. Thus, the coefficient of determination for the independent variables were also be accurate owing to the F and significance as shown in the table above.

Table 3: Coefficients of Determination

 Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1.3074 0.373 3.505 0.001 Migrants -0.688 0.279 -0.413 2.466 0.017
1. Dependent Variable: Education attainment in Canada

From the coefficient of determination table, the results show that migrants in Canada have impacted education attainment in Canada negatively. An increase in the number of migrants in Canada in the period surveyed led to a reduction in education attainment amongst the natives by 0.688 units. Further, the significance of this variable was 0.017 which is lower than 0.05. This means that the variable had statistical significance as well. For this reason, the study can deduce that an increase in the number of migrants led to a reduction in education attainment in Canada.

# Interpretation of the Results

The regression results show that the education attainment in Canada is affected by migrants coming to the nation. The relationship was found to be negatively sloped. This means that the increase in migrants led to a drop in educational attainment. The resources that are planned by the education system in Canada accounts for the people in schools already. The other people who come later are the ones that make these resources to be inadequate, especially for the natives. This makes the chances of achieving high education accolades to be extremely low. Thus, with the open borders that Canada has maintained to the rest of the world, the chances of having low education attainment by the natives is high leading to a high educational gap between the natives and migrants.

# Conclusion

In conclusion, it was established that migrants in Canada lead to low education attainment amongst the natives. The analysis revealed that migrants’ data negatively correlated with the data collected on education attainment in Canada. This means that the Canadian government can plan to improve their educational attainment levels, especially amongst the natives, by limiting the entry of the migrants or by planning for the migrants that will join schools later on. This will help to prevent resource inadequacy based on the number of students at a time. By redressing this issue, it will be possible to be ale to raise the educational attainment levels in Canadians schools despite migrants increasing.

This study has helped identify one major problem affecting Canada’s education sector. With the increase in the number of people coming into the country, forcing most students to share available education resources has led to low education uptake and attainment. Thus, the current migration policies need to be revisited to improve education attainment levels of the natives.

The study could have been improved by using more diverse data. The current data focused on inbound migrants only. The outbound migration data should have also been used to show if there is an impact on the number of people living. This is because as people enter Canada’s boundaries and education sector, there are others who leaving it. Thus, this movement needs to also be accounted for. In fact, the study would have been much better had a net migration figure been used instead of migrant data. This would have helped to know what the impact of those leaving Canada has on the education sector in Canada.