Do you think the variables are appropriately used? Why or why not? Does the addition of the control variables make se

1. Do you think the variables are appropriately used? Why or why not?
2. Does the addition of the control variables make sense to you? Why or why not?
3. Does the analysis answer the research question? Be sure and provide constructive and helpful comments for possible improvement.
4. If there was a significant effect, comments on the strength and its meaningfulness.
5. As a lay reader, were you able to understand the results and their implications? Why or why not?

Variables Entered/Removeda

Model

Variables Entered

Variables Removed

Method

1

Q46a. Level of democracy: today, Q1. Ageb

.

Enter

a. Dependent Variable: Trust in Government Index (higher scores=more trust)

b. All requested variables entered.

Figure 1: Shows a table of the variables entered in the model. All variables are scale variables.

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.407a

.166

.166

3.78746

a. Predictors: (Constant), Q46a. Level of democracy: today, Q1. Age

Figure 2: Shows a table of the model summary form the predictor variables, level of democracy (Today) and Age. Both variables are scale variable.

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

20824.824

2

10412.412

725.864

.000b

Residual

104717.364

7300

14.345

Total

125542.188

7302

a. Dependent Variable: Trust in Government Index (higher scores=more trust)

b. Predictors: (Constant), Q46a. Level of democracy: today, Q1. Age

Figure 3: Shows a table of the AVONA for the variables. All variables are scale variable.

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

3.949

.150

26.408

.000

Q1. Age

.023

.003

.078

7.300

.000

Q46a. Level of democracy: today

.579

.016

.398

37.247

.000

a. Dependent Variable: Trust in Government Index (higher scores=more trust)

Figure 4: Shows a table of the correlation for the variables. All variables are scale variable.

Report

Q1. Age 

Mean

N

Std. Deviation

37.01

10250

14.536

Figure 5: Shows a table of the mean ages of respondents. Age is a scale variable and a continuous variable since it can be an infinite number of values.

Research Question

 As a researcher, I want to determine if there is a correlation between opinions of government, level of government and age in efforts to understand if the government can influence social change among age groups. Therefore, the research question I am interested in answering is, is there a relationship between trust in the government and level of government among age groups? The null hypothesis is, there is no relationship therefore, no correlation between opinions of government, level of government and age.

Analysis

            The research design which would be used to align with this question is correlation. More specifically, multiple regression is the model that will be utilized. As explained by Dr. Matt Jones, multiple regression is similar to bivariate regression however, it allows for more predictor variables to be added (Laureate Education (Producer), 2016g). Therefore, with wanting to understand if a relationship exists between the trust in the government and level of government among age groups, this kind of design is appropriate. The dependent variable that will be used is trust in government, is measured as an interval ratio variable. The higher the score is, the higher the level of trust in the government. Additionally, the independent variable that will be utilized are age and level of democracy (today) which are also measured as interval ratio variable. These variables are justifiable predictor variables due to the nature of the research question. Further, the trust in the government is largely associated with the level of democracy or government which exists therefore, utilizing this variable also helps with establishing the causal relationship which exists.

In figure 2, the model summary provides the statistic R, R-squared, adjusted R-squared and the standard deviation. The important statistic for multiple regression is the adjusted R-squared, which is 0.166. It is important since it provides the modified R-squared value associated with the use of the 2 variables (Laureate Education (Producer), 2016g). Therefore, with taking into consideration the output in figure 2, we can interpret 16.6% of the variability in trust in government index is the result of age and level of democracy (today) combined.

Additionally, the p-value of 0.00 is below the 0.05 threshold, the model demonstrates statistical significance. Additionally, we can reject the null hypothesis that there is no relationship therefore, no correlation between opinions of government, level of government and age. Finally, the coefficients output provides the constant of 3.949 which tells us where our y-intercept is. When interpreting the unstandardized coefficients for the model, we know that for every one unit the independent variable increases, the dependent variable will increase .023 when age is involved and .579 when level of democracy (today) is involved. Even more so, the significance for both independent variables and their associated unstandardized coefficient is 0.000 therefore, demonstrating their statistical significance predictors of trust in government. Overall, based on the data output from the multiple regression and the analysis conducted, there is a relationship that exists