RECODE Q46A (99=0) (ELSE=1) INTO Democracy10.
VARIABLE LABELS Democracy10 ‘Satisfaction with democracy’.
EXECUTE.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT ADULT_CT
/METHOD=ENTER Q1 Democracy10
/SCATTERPLOT=(*ZRESID ,*ZPRED)
/RESIDUALS DURBIN HISTOGRAM(ZRESID)
/SAVE COOK ZRESID.
| Variables Entered/Removeda | |||
| Model | Variables Entered | Variables Removed | Method |
| 1 | Satisfaction with democracy, Q1. Ageb | . | Enter |
| a. Dependent Variable: ADULTCT: Number of adults in household | |||
| b. All requested variables entered. |
| Model Summaryb | |||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Durbin-Watson |
| 1 | .057a | .003 | .003 | 2.465 | 1.391 |
| a. Predictors: (Constant), Satisfaction with democracy, Q1. Age | |||||
| b. Dependent Variable: ADULTCT: Number of adults in household |
| ANOVAa | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 999.038 | 2 | 499.519 | 82.228 | .000b |
| Residual | 309929.057 | 51019 | 6.075 | |||
| Total | 310928.095 | 51021 | ||||
| a. Dependent Variable: ADULTCT: Number of adults in household | ||||||
| b. Predictors: (Constant), Satisfaction with democracy, Q1. Age |
| Coefficientsa | ||||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | Collinearity Statistics | |||
| B | Std. Error | Beta | Tolerance | VIF | ||||
| 1 | (Constant) | 3.198 | .048 | 66.850 | .000 | |||
| Q1. Age | 4.055E-005 | .001 | .000 | .054 | .957 | .995 | 1.005 | |
| Satisfaction with democracy | .496 | .039 | .057 | 12.794 | .000 | .995 | 1.005 | |
| a. Dependent Variable: ADULTCT: Number of adults in household |
| Collinearity Diagnosticsa | ||||||
| Model | Dimension | Eigenvalue | Condition Index | Variance Proportions | ||
| (Constant) | Q1. Age | Satisfaction with democracy | ||||
| 1 | 1 | 2.846 | 1.000 | .01 | .02 | .01 |
| 2 | .121 | 4.854 | .01 | .65 | .28 | |
| 3 | .034 | 9.183 | .99 | .34 | .71 | |
| a. Dependent Variable: ADULTCT: Number of adults in household |
| Residuals Statisticsa | |||||
| Minimum | Maximum | Mean | Std. Deviation | N | |
| Predicted Value | 3.20 | 3.70 | 3.65 | .140 | 51022 |
| Std. Predicted Value | -3.239 | .329 | .000 | 1.000 | 51022 |
| Standard Error of Predicted Value | .011 | .057 | .017 | .008 | 51022 |
| Adjusted Predicted Value | 3.19 | 3.70 | 3.65 | .140 | 51022 |
| Residual | -2.698 | 50.304 | .000 | 2.465 | 51022 |
| Std. Residual | -1.095 | 20.410 | .000 | 1.000 | 51022 |
| Stud. Residual | -1.095 | 20.410 | .000 | 1.000 | 51022 |
| Deleted Residual | -2.700 | 50.306 | .000 | 2.465 | 51022 |
| Stud. Deleted Residual | -1.095 | 20.494 | .000 | 1.000 | 51022 |
| Mahal. Distance | .096 | 26.551 | 2.000 | 3.371 | 51022 |
| Cook’s Distance | .000 | .008 | .000 | .000 | 51022 |
| Centered Leverage Value | .000 | .001 | .000 | .000 | 51022 |
| a. Dependent Variable: ADULTCT: Number of adults in household |
What is your research question? Answer: how can we determine whether real assumptions exist between number of adults in the household, their age and their satisfaction with the level of democracy today?
Using Afrobarometer data set (IMB SPSS Statistics 21, n.d.), I have created a dummy variable using interval/ratio variable, labelled as level of democracy: today. The categorical dummy variable tells us two things: you are either satisfy by your level of democracy today or not satisfy by your level of democracy today. If you elect 1, you are satisfied, and if you elect 0, you are not satisfied.
The coefficient table tells us more information about individual independent variables. Another important consideration to look into is the variance inflation factor (VIF). VIF is the number that shows the level of severity of multicollinearity in an ordinary least-squares regression analysis (Warner, 2012)— values within 10 and above 10 indicate serious multicollinearity or high probability of correlation in the model (Wagner, 2016). However, 1.005 for both the predictors indicate normal level of correspondence or assumption. In my Model Summary table, the Durbin-Watson statistic, which tells us about the independence of errors (Laureate Education, 2016j), is showing a value of 1.391. This value is an example of an absolute absent of correlation between the residuals (Laureate Education, 2016j).
The Anova table shows the overall statistical significant of the calculated variables. In this case, we have a statistical significant of 0.000, indicating the rejection of the null hypothesis when conventional P-value as set to P<0.05.Our Cook’s distance shows an unnecessary relationship on the model ranging from 0.0- 0.008, with value of 1.0 or greater showing possible influence of correlation. Our scatter plot provides strange and slightly uniform display of homoscedasticity, which give some linearity slip details in the relationship of the variables. However, the histogram indicates how the distribution of correlation or no errors exists (Wagner, 2016). Looking at the histogram, the distribution display of the frequency and regression standardized residual shows an insignificant deviation from normalcy.
In terms of the positive implication for social change and after analyzing and reviewing all tables and data of the applicable variables, there seem to exhibit little possible violations on the assumptions of the resulted data. Therefore, majority of assumptions were possibly made. I have used the assumption, under the SPSS value label of interval ratio (i.e. the level of democracy: today) to conclude the notion that if you don’t know or never heard about democracy, we will assume that you are not satisfy, or have not specifically participated in the democratic process. While all others who refused or kept quit means you are satisfy. This tells us the basic concept of creating dummy variable; that is the provision or creation of predictor variable (categorical variable) that answers the question of yes or no about group participation (Warner, 2012). The assumptions of my resulted data, tell us how implication for positive change relies on how community involvement can increase and strengthen transparency in democratic process. Last year, I attended a 3-day workshop in the city of DeKalb, Iowa State. The mission of the workshop was to increase public awareness and community involvement in politics and democracy. The speeches were given by famous politicians on how community participation on democratic process could yield positive outcome in electoral process or electing apparent leader.
Reference
IMB SPSS Statistics 21. (n.d.). Afrobarometer [Data file]. Retrieved from Walden University my student Account.
Laureate Education (Producer). (2016j). Regression diagnostics, model evaluation, and dummy variables [Video file]. Baltimore, MD: Author.
Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th ed.). Thousand Oaks, CA: Sage Publications
Warner, R. M. (2012). Applied Statistics from bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: Sage Publications.
