Global Posts

Cortnie King- Student Family Support Specialist Appomattox County Schools

434-221-8454

CRking@ACPS.com

Julie Allard-Former Court Advocate- Social Worker

540-373-9372

Casey Vandegrift- Former Intervarsity Christian Fellowship Leader

434-610-6917

I completed Domestic Violence 101 training with Empower House Domestic Violence Agency in 2022

I have completed PCI security awareness training for 2021 and 2022

I completed psychological first aid training in 2023

Throughout the course of the semester, I have been performing tests in order to answer the research question, “How does household income affect family involvement?” This question comes from research conducted with The Andy Taylor Center and Head Start. These are both child care centers whom we sent activities and surveys in order to better understand family involvement. Three of the tests that were run in order to answer the research question are the ANOVA test, the Regression test and the Chi-squared test. These were conducted using both the R-Studio and SPSS statistical systems.

The independent variable question for the research conducted is, “How involved was your family throughout the activity?” This question could be answered by participants on a scale of 0 to 10 with 0 meaning not at all and 10 meaning a great amount. This question was recoded for some tests due to their being multiple questions involving parent involvement levels and the necessity in order to run the test.

The dependent variable question for the research that was conducted is, “What is your annual household income?” This question could be answered with the categories, “Less than $10,000”, “$31,000 to $50,999”, “$51,000 to $70,999”, “$71,000 to $90,999”, “$91,000 or more” and “Prefer not to answer.” The option of “Prefer not to answer” was missing data and was recoded to not be included in the tests that were conducted.

Table 1.

Chi-squared

This table shows that in the “Not Engaged” category there were four respondents that were in the low income bracket and four respondents that were in the high income bracket. The  “Moderate Engagement” category shows that there were 16 respondents in the low income bracket and four respondents in the high income bracket. In the “Engaged” category there were 12 respondents in the low income bracket and 12 respondents in the high income bracket. We also see that the chi-squared statistic is 4.68. The degrees of freedom for this test is 2. The p-value shows us that there is no significant difference at the .05 level. This test was recoded to only include low and high income levels removing the six different categories of answer choices. This test was conducted using R-studio.

Table 2.

Basic Linear Regression

This table shows a Basic Linear regression in R-studio. V36 is standing in place for the dependent variable, “What is your annual household income?” Family involvement has a negative correlation with household income. For every one unit family involvement increases, household income decreases by -0.1034. There is a significant finding at the .001 level. The R-squared statistic is 0.005917 which means that income has explained .59% of the variation in family involvement. This test was conducted using R-studio.

Table 3.

ANOVA

This is an ANOVA test run in the statistical system SPSS. Family involvement was measured using the question, “How involved was your family throughout the activity?” We can see that the significant value is .003 which below the .01 level and therefore is significant at the .01 level. This means there is a significant difference between the means.

In order to answer the research question, “How does household income affect family involvement?”, these three tests were conducted. The independent variable being family involvement and the dependent variable being household income. In the Chi-squared test we see that there is no significant difference at the .05 level between family involvement and household income. With the Regression test, it shows that family involvement has a negative correlation with household income. Finally, the ANOVA test shows that there is significance at the .01 level for family involvement. Overall, we see that household income does not have a significant effect on household income.

Welcome to my sociological theory e-portfolio! My name is Ky’Leah Walls. I took the Sociology Theory course in the spring of ’23. This e-portfolio aims to engage with my earlier thoughts about sociological theory. I will make revisions and add explanations for my changes. I will also learn different skills and techniques for writing.

Welcome back to my personal and professional e-portfolio! Throughout the next couple weeks, please be aware there will be some new changes and updates to the site.

Here is my interpretation of what this means:

I hypothesized that families who have a better relationships would enjoy the Cupcake Flower activity more involvement. In other words, the self-ranking of the family race would influence the involvement of an activity. 

The selection of the child’s race decreases by 0.2100 units for every unit increase in how much they enjoyed the Cupcake Flower Activity. Therefore, it appears that subjective race status and enjoyment of activities are inversely related. However, this finding is significant at the point 0.05 > (p = 0.013). The R2 statistic is 0.08113, meaning that this model explains 0.8 percent of the variation in the dependent variable (enjoyment of activity). No relationship can be discerned between self-ranked of a child’s race and enjoyment of the activity.

-0.21049 is the coefficient for v11xv31. Because it is negative we know there is an inverse relationship. As one increases, the other decreases.

The p-value is what you compare to our alpha levels (0.05*, 0.01**, 0.001***) to see if there is a significant difference/finding. Here there is significance at the point 0.05 > p. We do not interpret the intercept (the line above that is significant). Therefore, our p-value is 0.0126.

Lastly, our R2 statistic shows us how much of the variation in our dependent variable we have explained with our independent variable(s). In this case, our R2 is 0.08113 – BUT we must move the decimal two places to the left so we can interoperate it as a percentage and not a probability.

Example 2:

Here is my interpretation of what this means:

I hypothesized that families who have a better enjoyment of the activity would have more involvement. In other words, how much you enjoy the activities would influence your involvement in the activity. 

For every one-unit increase in enjoyment, involvement increases by 0.45973 units. Therefore, it appears that involvement in the activity is positively related to enjoyment. However, this finding is not significant (p<0.001). The R2 statistic is 0.348, meaning that this model explains 34.8 percent of the variation in the dependent variable (involvement). In this case, relationships can be discerned between self-ranked family involvement and enjoyment of the activity.

0.45973 is the coefficient for v17xv18. Because it is positive we know there is a direct relationship. As one increases, the other increases.

The p-value is what you compare to our alpha levels (0.05*, 0.01**, 0.001***) to see if there is a significant difference/finding. Here there is no significance. We do not interpret the intercept (the line above that is significant). Therefore, our p-value is 2.055e-08.

Lastly, our R2 statistic shows us how much of the variation in our dependent variable we have explained with our independent variable(s). In this case, our R2 is 0.348 – BUT we must move the decimal two places to the left so we can interpret it as a percentage and not a probability.

Hi, my name is Robert and welcome to my E-Portfolio! I am currently a senior at Longwood University and pursuing to get my degree in Psychology with a minor in Sociology. Throughout my years at Longwood, I have gained experience in doing research, customer service, time-management, organization, and more. I want to be an individual that can help make a difference for everybody in the future and doing it the best way possible! I work well in groups/teams and give my all for the part I am given. Besides school, I have work experience as well that has helped me gain other skills that would be beneficial for future employment.