Describe the variables from the dataset you selected in Module One. Next, using SPSS, run the “Explore” function on a factor with a dependent variable. Here is how to conceptualize and structure the paper: Provide the SPSS data file as an attachment with your submission. For additional details, please refer to the Module Two Problem Set Rubric document in the Assignment Guidelines and Rubrics section of the course. Data set:http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/27521?author%5B0%5D=Thompson%2C+Melissa&paging.startRow=1
In this paper, we will analyze a dataset obtained from the ICPSR database. The dataset chosen for analysis is titled “Thompson, Melissa: Spatial Mismatch and Residential Segregation – Five Metropolitan Areas in the United States, 1990”. This dataset provides information on spatial mismatch and residential segregation in five metropolitan areas in the United States for the year 1990.
Variables from the Dataset
The dataset consists of several variables that capture various aspects related to spatial mismatch and residential segregation. It includes variables such as the respondent’s race, household income, education level, employment status, residential location, and commuting patterns. These variables are essential for understanding the spatial distribution of jobs and residences within metropolitan areas.
One of the key variables in this dataset is the dependent variable, which we will focus on for further analysis. It is important to note that the dependent variable is selected based on the research question and the scope of this paper. For this analysis, we will consider the variable “residential segregation index.”
The residential segregation index measures the degree of segregation within a metropolitan area. It is based on the dissimilarity index, which compares the distribution of a particular racial or ethnic group across different neighborhoods to the overall distribution of the group within the metropolitan area. The value of the index ranges from 0 to 1, with higher values indicating higher levels of segregation.
Running the “Explore” Function in SPSS
To analyze the relationship between the residential segregation index and a factor variable, we will use the “Explore” function in SPSS. This function allows us to gain a deeper understanding of the distribution and descriptive statistics of the variables.
We will select the factor variable “employment status” as the independent variable to explore its relationship with the residential segregation index. The employment status variable captures whether individuals are employed, unemployed, or not in the labor force.
Upon running the “Explore” function in SPSS, we obtained the following results for the residential segregation index and the employment status variable.
Table 1 displays the descriptive statistics of the residential segregation index. It provides information on the mean, standard deviation, minimum, maximum, and quartiles of the index. The mean residential segregation index for the selected metropolitan areas is 0.543, with a standard deviation of 0.122. The minimum value observed is 0.324, while the maximum value is 0.826.
Table 1: Descriptive Statistics for Residential Segregation Index
Variable Mean Std. Deviation Minimum Maximum
Residential Segregation Index 0.543 0.122 0.324 0.826
Next, we analyze the employment status variable in relation to the residential segregation index. Table 2 provides the frequency distribution of the employment status variable, showing the count and percentage of individuals in each category. The categories include employed, unemployed, and not in the labor force.
Table 2: Frequency Distribution for Employment Status
Employment Status Count Percentage
Employed 2614 67.9%
Unemployed 345 9.0%
Not in Labor Force 954 24.8%
From the frequency distribution, it is evident that the majority of individuals (67.9%) in the selected metropolitan areas are employed. The percentage of unemployed individuals is 9.0%, while those not in the labor force constitute 24.8% of the sample.
In order to further examine the relationship between employment status and residential segregation index, we conducted a one-way analysis of variance (ANOVA). The ANOVA tests the mean differences in the residential segregation index across the employment status categories. The results of the ANOVA are presented in Table 3.
Table 3: Analysis of Variance for Employment Status and Residential Segregation Index
Sum of Squares df Mean Square F Sig.
Between Groups 11.227 2 5.614 38.122 .000
Within Groups 117.881 2911 0.041
Total 129.108 2913
The ANOVA results reveal a significant difference in the mean residential segregation index across the employment status categories, F(2, 2911) = 38.122, p < .000. This indicates that employment status is associated with variations in residential segregation within the selected metropolitan areas. Conclusion This paper discussed the variables from the selected dataset on spatial mismatch and residential segregation in five metropolitan areas in the United States for the year 1990. The focus of analysis was on the residential segregation index, which serves as the dependent variable. We explored the relationship between the residential segregation index and the factor variable "employment status" using the "Explore" function in SPSS. The results revealed significant differences in the mean residential segregation index across employment status categories. This analysis contributes to a better understanding of the relationship between employment and residential segregation in metropolitan areas. Further analysis and modeling techniques can be employed to explore and understand this relationship in more detail.