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Multiple regression

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Multiple Regression

**Research question**

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

**Null Hypothesis**

H_{0}: 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 |
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Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |

1 | .659^{a} |
.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

ANOVA^{a} |
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Model | Sum of Squares | df | Mean Square | F | Sig. | |

1 | Regression | 2628.403 | 4 | 657.101 | 6.142 | .001^{b} |

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

Coefficients^{a} |
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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) |