Multiple regression

 

 

 

 

 

Multiple regression

 

Name

 

Institution

 

 

 

 

 

 

 

 

Multiple Regression

Research question

How well does family income in constant dollars predict the respondent’s prestige score?

Null Hypothesis

H0: Family income inconstant dollars is not a significant predictor of the respondent’sprestige score.

Research design

The research design used for this question is causal design. Causaldesign can be used to measure the impact a certain change has on an existing assumption (Labaree, 2016).

Dependent variable and how it is measured

The dependent variable for the study was the respondent’s prestige score.  The variable is measured using a ratio scale

Independent variable and how it is measured

The independent variable is family income in constant dollar. The variable is measured using a ratio scale

Control variables added in the model

The control variables added in the study included:

  • Age of respondent
  • Number of hours usually work a week
  • Respondent’s highest degree

Justification for adding the variables

The researcher was not particularly interested in the control variables but acknowledges some relationship to the dependent variable. Zhou’s (2005) study on occupational prestige uses college education and work hours as control variables, and this justifies their uses as control variables. The researcher was also interested in establishing whether the dependent variable can be controlled for age.

Significance and stregth of effect

From table 2 in the appendix section, since the p-value is less than 0.05 this means that the model has reached statistical significance. However, since the variable family income in constant dollars has a p-value of 0.794 as shown in Table 3. The independent variable is not significant in predicting the respondent’s prestige score. Therefore, the researcher did not find any significance.

Explanation and research question

Table 1 from the appendix shows that the Adjusted R Square is 0.364. This means that 36.4% of the variation in the dependent variable can be explained by the variation in the independent variables.  Table 3 also shows that only the variable respondent’s highest degree had a significant contribution to the model. Given this result, we fail to reject the null hypothesis and conclude that family income does not predict prestige score

 

 

 

References

Labaree, R. (2016, October 21). Organizing Your Social Sciences Research Paper: Types of Research Designs. Retrieved October 24, 2016, from USC Libraries: http://libguides.usc.edu/writingguide/researchdesigns

Zhou, X. (2005). The Institutional Logic of Occupational Prestige Ranking: Reconceptualization and Reanalyses1. American Journal of Sociology, 90-140.

 

 

 

 

 

 

 

 

 

 

 

 

Appendix

Table 1

 

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .659a .434 .364 10.343
a. Predictors: (Constant), RS HIGHEST DEGREE, NUMBER OF HOURS USUALLY WORK A WEEK, AGE OF RESPONDENT, FAMILY INCOME IN CONSTANT DOLLARS

 

Table 2

 

ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 2628.403 4 657.101 6.142 .001b
Residual 3423.489 32 106.984    
Total 6051.892 36      
a. Dependent Variable: Rs occupational prestige score (2010)
b. Predictors: (Constant), RS HIGHEST DEGREE, NUMBER OF HOURS USUALLY WORK A WEEK, AGE OF RESPONDENT, FAMILY INCOME IN CONSTANT DOLLARS

Table 3

 

Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 24.578 10.190   2.412 .022
FAMILY INCOME IN CONSTANT DOLLARS -1.239E-005 .000 -.048 -.263 .794
AGE OF RESPONDENT .074 .146 .079 .508 .615
NUMBER OF HOURS USUALLY WORK A WEEK .211 .157 .202 1.340 .190
RS HIGHEST DEGREE 5.980 1.446 .628 4.134 .000
a. Dependent Variable: Rs occupational prestige score (2010)

 

 

 

 

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