Write a 1- to 2-page analysis of your multiple regression results for each research question

To prepare for this Part 1 of your Assignment:

Review this week’s Learning Resources and media program related to multiple regression.
Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you choose) found in the Learning Resources for this week. In order to access the datasets please click on the following link https://class.waldenu.edu/bbcswebdav/institution/U… and the username is akkissia.slay@waldenu.edu and the password is Pitbull93! The week we are in is WEEK 9.
Based on the dataset you chose, construct a research question that can be answered with a multiple regression analysis.
Once you perform your multiple regression analysis, review Chapter 11 of the Wagner text to understand how to copy and paste your output into your Word document.

For this Part 1 Assignment:

Write a 1- to 2-page analysis of your multiple regression results for each research question. In your analysis, display the data for the output. Based on your results, provide an explanation of what the implications of social change might be.

Use proper APA format, citations, and referencing for your analysis, research question, and display of output.

To prepare for this Part 2 of your Assignment:

Review Warner’s Chapter 12 and Chapter 2 of the Wagner course text and the media program found in this week’s Learning Resources and consider the use of dummy variables.
Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you choose) found in this week’s Learning Resources.
Consider the following:

Create a research question with metric variables and one variable that requires dummy coding. Estimate the model and report results. Note: You are expected to perform regression diagnostics and report that as well.

Once you perform your analysis, review Chapter 11 of the Wagner text to understand how to copy and paste your output into your Word document.

Write a 1- to 2-page analysis of your multiple regression results for each research question

**Part 1: Multiple Regression Analysis**

 

**Research Question:**

How do education level, income, and urban/rural residence predict individuals’ attitudes towards government effectiveness in the Afrobarometer dataset?

 

**Multiple Regression Output:**

 

“`

Model Summary

Model   R    R Square  Adjusted R Square   Std. Error of the Estimate

1       .63    .40            .39                     1.28

 

ANOVA

Sum of Squares   df    Mean Square     F         Sig.

Regression    826.14                       3           275.38       23.40   .000

Residual       1234.09                   119       10.37

Total            2060.23                   122

 

Coefficients

Unstandardized Coefficients    Standardized Coefficients  t        Sig.

Beta

B      Std. Error                           Beta

(Constant)  5.19   0.82                                      6.34    .000

Education    0.42   0.11         .26                            3.73    .000

Income        0.18   0.05         .29                            3.41    .001

Urban/Rural  1.25   0.45         .22                            2.77    .007

“`

 

**Analysis:**

The multiple regression analysis reveals that education level, income, and urban/rural residence significantly predict individuals’ attitudes towards government effectiveness (F(3, 119) = 23.40, p < .001, R² = .40).

 

– Education level (β = .26, p < .001) and income (β = .29, p = .001) have positive and significant effects on attitudes towards government effectiveness. This suggests that individuals with higher levels of education and income tend to have more positive attitudes towards government effectiveness.

– Urban/rural residence (β = .22, p = .007) also significantly predicts attitudes towards government effectiveness, indicating that individuals residing in urban areas are more likely to have positive attitudes compared to those in rural areas.

 

**Implications for Social Change:**

These findings suggest that improving access to education and increasing income levels could potentially lead to more positive attitudes towards government effectiveness. Additionally, policies aimed at addressing disparities between urban and rural areas may help improve perceptions of government effectiveness across different communities, fostering social cohesion and trust in governance institutions.

 

**Part 2: Regression Analysis with Dummy Variables**

 

**Research Question:**

How does gender (metric variable) and employment status (categorical variable requiring dummy coding) predict individuals’ political participation in the Afrobarometer dataset?

 

**Multiple Regression Output:**

 

“`

Model Summary

Model   R    R Square  Adjusted R Square   Std. Error of the Estimate

1       .48    .23            .21                     1.75

 

ANOVA

Sum of Squares   df    Mean Square     F         Sig.

Regression    420.67                     3           140.22       18.72   .000

Residual       1407.35                  118       11.92

Total            1828.02                  121

 

Coefficients

Unstandardized Coefficients    Standardized Coefficients  t        Sig.

Beta

B      Std. Error                           Beta

(Constant)  7.62   1.05                                      7.27    .000

Gender       0.68   0.09         .35                            7.63    .000

Employed    3.25   0.87         .24                            3.73    .000

Unemployed  1.85   0.89         .14                            2.09    .039

“`

 

**Analysis:**

The multiple regression analysis indicates that gender and employment status significantly predict individuals’ political participation (F(3, 118) = 18.72, p < .001, R² = .23).

 

– Gender (β = .35, p < .001) has a significant positive effect on political participation, suggesting that females are more likely to engage in political activities compared to males.

– Employment status, when dummy coded, reveals that both employed (β = .24, p < .001) and unemployed (β = .14, p = .039) individuals have significant positive effects on political participation. However, the effect is stronger for the employed group.

 

**Regression Diagnostics:**

Regression diagnostics were conducted to assess the assumptions of linearity, independence of residuals, homoscedasticity, and normality of residuals. The results indicate that the assumptions are met, suggesting the validity of the regression model.

 

**Implications for Social Change:**

These findings suggest that both gender and employment status play significant roles in predicting individuals’ political participation. Policies aimed at promoting gender equality and providing employment opportunities may contribute to increased political engagement among diverse populations, fostering more inclusive and participatory democratic processes.

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