8210 wk11 discussion

Testing for Multiple Regression

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Part 1

It is possible to determine the connection between two variables using regression analysis. Using this method, you may determine how much of an effect the independent variable has on the dependent one (Wagner, 2020). The greatest applications for regression analysis are thus those that include a large number of variables that can be evaluated independently. The major goal of the regression model below was to investigate the link between job status and internet and mobile phone use. According to the data in table 1, this model can account for just 4.2% of the total variance observed. A statistically significant model can be found despite the model's low significance score. ANOVA table 2 is also statistically significant, meaning that there is a statistically significant difference in the mean values of the variables.

Research Question

Is there any impact of cell phone usage and internet usage on employment status?

Table 1: Model Summary
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	.205a	.042	.042	.46251	.042	219.552	2	10012	.000
a. Predictors: (Constant), Internet Usage, Cell Phone Usage

Table 2: ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	93.932	2	46.966	219.552	.000b
	Residual	2141.751	10012	.214
	Total	2235.684	10014
a. Dependent Variable: Employment Status
b. Predictors: (Constant), Internet Usage, Cell Phone Usage

These crucial coefficients may be found in the following table (see row three). The beta readings indicate that the constant is 0.057. Cell phone usage is represented by a score of 0.134, while internet usage is represented by a score of 0.130. Statistically, each of these beta values is significant.

The model is given as:

Employment Status = 0.057 + 0.134 cell phone + 0.13 Internet usage.

Table 3: Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
	B	Std. Error	Beta
1	(Constant)	.057	.014		3.966	.000
	Cell Phone Usage	.134	.013	.102	10.283	.000
	Internet Usage	.130	.008	.160	16.109	.000
a. Dependent Variable: Employment Status

Part 2

Research Question

Does the sampling unit has an impact on the employment status?

There are now three dummy variables created from the sample unit variable, one for each kind of location: urban, rural and semi-urban.

Phase 1

The major goal of this model was to examine the link between the location of the sample units (urban vs. rural) and the participants' job status. This model accounts for 1.1 percent of the variance in the data, according to R square findings. Despite the fact that the result is statistically significant, the value is quite low.

Table 4: Model Summary
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	.106a	.011	.011	.46890	.011	58.229	2	10271	.000
a. Predictors: (Constant), Urban, Rural

Table 5: ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	25.606	2	12.803	58.229	.000b
	Residual	2258.283	10271	.220
	Total	2283.888	10273
a. Dependent Variable: Employment Status
b. Predictors: (Constant), Urban, Rural

The table of regression coefficients may be found in the preceding section, in table 6. In this instance, the Beta values derived from the study are shown. These findings lead us to believe that the beta is always going to be 0.227. A positive beta value was found in the rural and urban regions. However, only the urban factor was shown to be statistically significant. Hence, the model can be given as below:

Employment status = 0.227 + 0.035 rural + 0.17 Urban

Table 6: Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
	B	Std. Error	Beta
1	(Constant)	.227	.041		5.467	.000
	Rural	.035	.021	.072	1.660	.097
	Urban	.170	.042	.175	4.026	.000
a. Dependent Variable: Employment Status

Phase 2

Using regression analysis, a new set of dummy variables was tested in the second phase. Detailed results are shown in Tables 8,9, and 10. In the second model, the urban and semi-urban sample units were the primary focus of attention. Only 1.1 percent of the model's volatility can be explained by this data, according to the model summary. The model's significance is also backed up by the data.

Table 8: Model Summary
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	.106a	.011	.011	.46890	.011	58.229	2	10271	.000
a. Predictors: (Constant), SemiUrban, Urban

There is a breakdown of the regression model's coefficients in the following table 9. The constant beta was determined to be 0.296 in the table. To put it another way: The Beta values for the urban variables were 0.01 and for the semi-urban variables 0.023. Semi-urban was not statistically significant in the model used in this study. The regression equation is given below:

Employment status = 0.296 + 0.1 urban – 0.023 semi-urban.

Table 9: Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
	B	Std. Error	Beta
1	(Constant)	.296	.006		49.734	.000
	Urban	.100	.010	.103	10.474	.000
	SemiUrban	-.023	.014	-.016	-1.660	.097
a. Dependent Variable: Employment Status

Implications for Social Change

In order to affect societal change, the data presented above is crucially important. It goes into great depth on how the dependent and independent variables are connected. Part two involves assessing the impact of different sample units on an individual's employment status. Regression analysis aids in finding the link between variables that are crucial to creating social changes in the environment.

References

Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.