High School Completion

Narrative

Education plays a critical role in the health and well-being of young adults in the United States. Previous studies have found that education is associated with better health outcomes. For example, those who graduate from high school have lower death rates and an average life expectancy 6–9 years greater than those who do not graduate from high school.1, 2 Individuals who do not complete high school have higher rates of illness and earlier deaths.

In 2013, more than 90 percent of 18- to 24-year-olds not enrolled in high school had received a high school diploma or equivalent credential (e.g., General Educational Development certificate). High school completion was highest among non-Hispanic Asians (95.8 percent), non-Hispanic Native Hawaiians and other Pacific Islanders (95.3 percent), and non-Hispanic Whites (93.7 percent; Figure 1). High school completion was lower among other racial and ethnic groups, including non-Hispanic persons of multiple races (92.5 percent), non-Hispanic Blacks (89.3 percent), non-Hispanic American Indians and Alaska Natives (86.2 percent), and Hispanics (81.8 percent).

young adults with high school degree or equivalent by race

Figure 1 Source

High school completion also varies by age and sex. In 2013, a higher percentage of females had a high school degree or equivalent than their male counterparts (91.9 versus 89.4 percent, respectively; Figure 2). These differences were also evident at specific ages. High school completion was highest among females who were 23 years of age (94.5 percent), and lowest among 18-year-old males and females (76.5 and 83.6 percent, respectively). High school completion programs for students at high risk of non-completion show strong evidence of effectiveness for all students and for the subset of students at risk for non-completion because they are pregnant or have children.3

young adults with high school degree or equivalent by age and sex

Figure 2 Source

Data Sources

Figure 1. U.S. Census Bureau and Bureau of Labor Statistics, Current Population Survey, Annual Social and Economic Supplement. Analysis conducted by the Maternal and Child Health Epidemiology and Statistics Program.

Figure 2. U.S. Census Bureau and Bureau of Labor Statistics, Current Population Survey, Annual Social and Economic Supplement. Analysis conducted by the Maternal and Child Health Epidemiology and Statistics Program.

Endnotes

1 Allensworth D, Lewallen TC, Stevenson B, Katz S. Addressing the needs of the whole child: what public health can do to answer the education sector’s call for a stronger partnership. Preventing Chronic Disease. 2011;8(2):A44. Accessed September 5, 2014.

2 Wong MD, Shapiro MF, Boscardin WJ, Ettner SL. Contribution of major diseases to disparities in mortality. New England Journal of Medicine. 2002;347(20):1585–1592.

3 The Community Guide. Promoting Health Equity Through Education Programs and Policies: High School Completion Programs. Accessed March 2, 2015.

Data

Statistical Significance Test

Calculate the difference between two estimates:

Calculated Z-Test Result 0.9567433 Not statistically significant

We follow statistical conventions in defining a significant difference by a p-value less than 0.05 where there is a less than 5% probability of observing a difference of that magnitude or greater by chance alone if there were really no difference between estimates. The 95% confidence interval includes a plausible range of values for the observed difference; 95% of random samples would include the true difference with fewer than 5% of random samples failing to capture the true difference.

This website allows comparisons between two estimates using the independent z-test for differences in rates or proportions. This test is appropriate for comparing independent populations across years (e.g., 2011 versus 2012) or subgroups (e.g., Male versus Female) on corresponding measures. To the extent possible, the functionality of this application has limited estimate comparisons based on appropriate use of the independent z-test. However, some tables present subgroup categories within broader categories that will allow comparisons between non-independent populations (e.g., low birth weight and very low birth weight). Users should exercise caution when interpreting these test results, which will frequently overstate statistical significance.

For some tables, the website does not allow for comparisons between two estimates, even though the data represent independent populations. Generally, this is because the standard errors were not publicly available at the time this website was created.

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