adding references for data reliabilty and issues in equity and blurb …

…about statistical signif + pvalues

abstract.md

@@ -1,3 +1,3 @@
 # Abstract
 
-Over the last fifty years, trends in educational attainment have reflected simultaneous movements towards closing and widening disparities between different identity groups. Studying educational attainment, specifically revolved around studying disparities in education, is vital because of the implications for future work opportunities, financial security, and resource access. **EduAttain** identifies and investigates the role certain demographic factors play as determinants of educational attainment, namely, sex, race, and Hispanic ethnicity. Leveraging data from *IPUMS*, and using *R*, *R Shiny*, and *SQLite*, trends in educational attainment across different identity groups are studied through the use of pie charts to display results and draw comparisons displayed on a **[web-based dashboard](https://donizk.shinyapps.io/EduAttain/)**. The statistical relationship between these factors and educational attainment are studied using a *binary logistic regression*, to determine what populations had a higher odds of having a high school diploma or greater. The findings of this project affirm some of the findings presented in the literature, while providing new insight into certain racial and Hispanic ethnic subgroups rates of educational attainment. In general, the highest attaining populations in educational attainment were the White, Non Hispanic, and Female populations, compared to all other respective identity groups. Within the Hispanic ethnic group, the Cuban population maintained the highest level of educational attainment, relative to all other Hispanic ethnic subgroups. Furthermore, these results establish that the *Human Capital Model* fails to consider certain aspects of identity that may greatly influence the level of education an individual attains, outside of the influence of income and financial investments into education.
+Over the last fifty years, trends in educational attainment have reflected simultaneous movements towards closing and widening disparities between different identity groups. Studying educational attainment, specifically revolved around studying educational inequity, is vital because of the implications for future work opportunities, financial security, and resource access. **EduAttain** identifies and investigates the role certain demographic factors play as determinants of educational attainment, namely, sex, race, and Hispanic ethnicity. Leveraging data from *IPUMS*, and using *R*, *R Shiny*, and *SQLite*, trends in educational attainment across different identity groups are studied through the use of pie charts to display results and draw comparisons displayed on a **[web-based dashboard](https://donizk.shinyapps.io/EduAttain/)**. The statistical relationship between these factors and educational attainment are studied using a *binary logistic regression*, to determine what populations had a higher odds of having a high school diploma or greater. The findings of this project affirm some of the findings presented in the literature, while providing new insight into certain racial and Hispanic ethnic subgroups rates of educational attainment. In general, the highest attaining populations in educational attainment were the White, Non Hispanic, and Female populations, compared to all other respective identity groups. Within the Hispanic ethnic group, the Cuban population maintained the highest level of educational attainment, relative to all other Hispanic ethnic subgroups. Furthermore, these results establish that the *Human Capital Model* fails to consider certain aspects of identity that may greatly influence the level of education an individual attains, outside of the influence of income and financial investments into education.

config.yaml

@@ -4,7 +4,7 @@
 # Project-specific values
 title: 'EduAttain: A Statistical Analysis of the Impact of Different Demographic Indicators on Educational Attainment'
 author: 'Kyrie Doniz'
-date: '22 July 2022'
+date: '5 May 2023'
 firstreader: 'Janyl Jumadinova'
 secondreader: 'Timothy Bianco'
 logo: 'images/logo'

references.bib

@@ -268,4 +268,62 @@ @misc{r
   publisher={The R Foundation},
   url={https://www.r-project.org/about.html},
   urldate={2023-03-21}
+}
+@misc{microdata,
+  title={What do we mean by microdata?},
+  publisher={The World Bank},
+  url={https://datahelpdesk.worldbank.org/knowledgebase/articles/228873-what-do-we-mean-by-microdata},
+  urldate={2023-05-03}
+}
+@misc{source,
+  title={About IPUMS CPS},
+  publisher={IPUMS CPS},
+  url={https://cps.ipums.org/cps/about.shtml},
+  urldate={2023-05-03}
+}
+@misc{survey,
+  title={The Use of Self-Report Data in Psychology},
+  publisher={Very Well Mind},
+  url={https://www.verywellmind.com/definition-of-self-report-425267},
+  urldate={2023-05-03}
+}
+@misc{cps,
+  title={Frequently Asked Questions},
+  publisher={United States Census Bureau},
+  url={https://www.census.gov/programs-surveys/cps/about/faqs.html#:~:text=About%2059%2C000%20households%20are%20selected,of%20other%20addresses%20and%20people.},
+  urldate={2023-05-03}
+}
+@misc{nyt,
+  title={Census Miscounted the Population of 14 State, a Review Shows},
+  author={Wines, M.},
+  publisher={The New York Times},
+  url={https://www.nytimes.com/2022/05/19/us/2020-census-miscount-states.html},
+  urldate={2023-05-03}
+}
+@misc{brook,
+  title={Why census undercounts are problematic for political representation},
+  author={Sanchez, G.R.},
+  publisher={Brookings},
+  url={https://www.brookings.edu/blog/how-we-rise/2022/03/28/why-census-undercounts-are-problematic-for-political-representation/},
+  urldate={2023-05-03}
+}
+@misc{npr,
+  title={The 2020 census had big undercounts of Black people, Latinos, and Native Americans},
+  author={Lo Wang, H.},
+  publisher={National Public Radio},
+  url={https://www.npr.org/2022/03/10/1083732104/2020-census-accuracy-undercount-overcount-data-quality},
+  urldate={2023-05-03}
+}
+@misc{pew,
+  title={Key facts about the quality of the 2020 census},
+  author={Cohn, D. & Passel, J.S.},
+  publisher={Pew Research Center},
+  url={https://www.pewresearch.org/short-reads/2022/06/08/key-facts-about-the-quality-of-the-2020-census/},
+  urldate={2023-05-03}
+}
+@misc{jli,
+  title={Data Equity: What Is It, and Why Does It Matter?},
+  publisher={JLI Consulting},
+  url={https://www.jliconsultinghawaii.com/blog/2020/7/10/data-equity-what-is-it-and-why-does-it-matter},
+  urldate={2023-05-03}
 }