Respond to at least one of your colleaguesâ€™ posts and comment on the following:
 Do you think the variables are appropriately used? Why or why not?
 Does the addition of the control variables make sense to you? Why or why not?
 Does the analysis answer the research question? Be sure and provide constructive and helpful comments for possible improvement.
 If there was a significant effect, comments on the strength and its meaningfulness.
 As a lay reader, were you able to understand the results and their implications? Why or why not?
Using the General Social Survey. I came up with the research question,
â€œis there an affect between highest year of school completed and
respondent income on respondentâ€™s socioeconomic status? The null
hypothesis would be the opposite of this, that there is no affect
between these two independent variables on the dependent variable. The
dependent variable is the respondentâ€™s socioeconomic status. The two
independent variables are the respondentâ€™s income in constant dollars
and the highest year of school completed.
Multiple regression builds on bivariate regression by adding more predictor variables to the equation. It is also referred to as â€œfit a multiple regression model. In order to calculate this multiple regression model on SPSS, all of the variables must be interval ratio variables. They must be metric (Laureate Education, 2016).
Olvera Astivia & Kroc (2019), explain that multiple regression can be used as a term of mean centering. The original assumption of expectationindependence among predictors can be described by multiple regression and centering. The correlation between the main effects are determined by this testing and can help to provide a stronger analysis of effects and their interactions.
Looking at the Model Index below, it can be determined that 41% of the variability of the respondentâ€™s socioeconomic status can be explained by the combination of the two independent variables by looking at the R square result. Looking at the ANOVA box, it can be determined that the model is significant because the significance level is .000 because it is below the .05 threshold, showing that the model has statistical significance. Looking at the coefficient box, when there is an increase in the independent variable, there will be an increase in the dependent variable by looking at the standardized coefficient. The significance level is .000 for both independent variables. The null hypothesis can be rejected in saying that there is no relationship between the two independent variables and dependent variable.
The independent variables can be predictors of the dependent variable. From the multiple regression model, it can be determined that respondentâ€™s income and highest year of school completed can be a predictor for respondentâ€™s socioeconomics status. Since significance was found, the strength of the effect can be seen below in the results. Income in constant dollars (b=.000). Highest year of school completed (b=3.435). For every increase in the independent variables, the dependent variable increases by the numbers calculated below. To answer the research question, the null hypothesis can be rejected, and it can be determined that respondentâ€™s income in constant dollars and highest year of school completed has an effect on socioeconomic status.
Variables Entered/Removed^{a} 

Model 
Variables Entered 
Variables Removed 
Method 
1 
HIGHEST YEAR OF SCHOOL COMPLETED, RESPONDENT INCOME IN CONSTANT DOLLARS^{b} 
. 
Enter 
a. Dependent Variable: R’s socioeconomic index (2010) 

b. All requested variables entered. 
Table 1. Variables entered as the highest year of school completed and respondent income in constant dollars created in SPSS from the General Social Survey Data Set 2014.
Model Summary 

Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 
1 
.643^{a} 
.413 
.412 
17.2475 
a. Predictors: (Constant), HIGHEST YEAR OF SCHOOL COMPLETED, RESPONDENT INCOME IN CONSTANT DOLLARS 
Table 2. R square output for highest year of school completed and respondent income in constant dollars created in SPSS from the General Social Survey Data Set 2014.
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
318142.017 
2 
159071.008 
534.735 
.000^{b} 
Residual 
452164.393 
1520 
297.477 

Total 
770306.410 
1522 

a. Dependent Variable: R’s socioeconomic index (2010) 

b. Predictors: (Constant), HIGHEST YEAR OF SCHOOL COMPLETED, RESPONDENT INCOME IN CONSTANT DOLLARS 
Table 3. Oneway ANOVA test using highest year of school completed and respondent income in constant dollars as predictors in SPSS from the General Social Survey Data Set 2014.
Coefficients^{a} 

Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 

B 
Std. Error 
Beta 

1 
(Constant) 
8.413 
2.161 
3.894 
.000 

RESPONDENT INCOME IN CONSTANT DOLLARS 
.000 
.000 
.320 
15.233 
.000 

HIGHEST YEAR OF SCHOOL COMPLETED 
3.435 
.158 
.456 
21.697 
.000 

a. Dependent Variable: R’s socioeconomic index (2010) 
Table 4. Coefficients using respondent income in constant dollars and highest year of school completed created in SPSS from the General Social Survey Data Set 2014.
References
Laureate Education (Producer). (2016g). Multiple regression [Video file]. Baltimore, MD: Author.
Olvera Astivia, O.L. & Kroc. E. (2019). Centering in Multiple Regression Does Not Always Reduce Multicollinearity: How to Tell When Your Estimates Will Not Benefit from Centering. Educational and Psychological Measurement, 79(5), 813826.