Statistical assumptions
In this analysis, the labor force status will be the explained variable while the number of people married will be the explanatory variable. In the analysis, it will be assumed that there is a linear relationship between the two variables and that the data follow a normal distribution (Green & Salkind, 2014). The data is presented in appendix 1.
Hypothesis
- Null hypothesis: The number of married people does not have an impact on the labor force status.
- Alternative hypothesis: The number of married people has an impact on the labor force status.
Scatter plots
In the diagram, the points tend to slope upwards. This indicates that there is a positive relationship between the two variables.
Correlation coefficient
The results presented in appendix 2 below show that there is a strong positive relationship between labor force and the number of married people.
Regression
The regression line will take the form Y = b0 + b1X
- Y = Labor force status
- X = Number of people married
The theoretical expectations are b0 can take any value and b1 > 0.
Results
The results are presented in the appendix 3 below. The regression equation will take the form Y = -403.81 + 2.246X. The coefficient of the number of married people is positive. This implies that marriage increases chances of being in the labor force.
Test of hypothesis
A two-tailed t-test will be used to test the significance of the explanatory variable. The t-statistics for number of people married is 13.075, while the p-value is 0.000. Since the p-value is less than α=0.05, then reject the null hypothesis and conclude that number of married people is a significant determinant of the labor force. The value of f-calculated is 170.961. The value of significance f (0.0000) is less than α (0.05). This shows that the overall regression line is significant. Based on the tests above, the null hypothesis will be rejected (FrankfortNachmias & Nachmias, 2008). This implies that the number of married people has an impact on the labor force.
Progress and Skills
The progress and skills with quantitative reasoning and analysis was very good. However, gathering data and analysis through the SPSS package was challenging. Specifically, regression and correlation analysis was challenging in terms of relating the variables to the results of the data analysis. In the end, I was able to overcome this challenge by treating each set of data independently during correlation and regression analysis. The outcome of the analysis was satisfactory since the findings connoted to the expected relationship between the independent and dependent variables; despite the fact the data set was fairly large. From the quantitative analysis, I need to improve on applying different statistical tools in correlation and regression analysis, especially when the coefficient is complex. Besides, I need to improve on how to go about the steps in creating a simple linear regression. In order to improve on these skills, I intend to enroll for an online tutorial on using statistical tools in data analysis. Specifically, I will concentrate on how to code and analyze data using the SPSS software.
My original topic, Ethnic and Racial Disportionality the Criminal Justice System, and approach have not changed since it was in line with the course work on dissertation approach. I only need to adjust the suggested quantitative research to ensure that the data collected is expansive enough to be passed through the SPSS software. Using a statistical package has informed my understanding of research in general in terms of how to carry out quantitative reasoning and analysis by applying appropriate tools to make sense out of a set of data. Besides, I have learned the distinct ways of interpreting data from analysis in any research article.
References
FrankfortNachmias, C., & Nachmias, D. (2008). Research methods in the social sciences. New York: Worth.
Green, S. B., & Salkind, N. J. (2014). Using SPSS for Windows and Macintosh: Analyzing and understanding data. Upper Saddle River, NJ: Pearson.