This blog post deals with The National Center on Education Statistics annual education data digest, the Condition of Education 2013.
Sarah D. Sparks writes in the Education Week article, Education Department Launches Overhauled Education Digest:
The National Center on Education Statistics this morning releases its annual education data digest, the Condition of Education 2013.
It finds a steady increase in the concentration of poverty in American schools. One in five public schools in 2011 had 75 percent or more of their students qualify for free- or reduced-price meals, up from only one in eight schools a decade ago.
And in the wake of the economic downturn, Americans who don’t attain higher education are the most likely to be unemployed: Among adults ages 25-34 who started but did not complete a high school degree, 30 percent were unemployed, making them only slightly better off than those with just a high school diploma, a group with a 32 percent unemployment rate. However, high school dropouts still lag far behind, with unemployment among this group at 44 percent.
On a brighter note, the Condition also finds higher enrollment in preschool—more than 60 percent of children ages 3-5 now attend, a majority of them in full-day classes&mdashand 15 states now require kindergarten for all students.
New Report Format
This year marks the start of a new format for the Condition of Education, according to NCES Commissioner Sean P. “Jack” Buckley. Only a handful of print issues of the report will be published going forward, but the website has been overhauled to make the data easier to use. NCES also—for those extreme edu-data junkies out there—is rolling out Condition of Education apps for smartphones and tablets.
The report itself, which has historically been a digest of all manner of education data released in a given year, has been pared down to 42 indicators that will be gauged annually, in the areas of population characteristics, participation in education, elementary and secondary education, and postsecondary education. http://blogs.edweek.org/edweek/inside-school-research/2013/05/education_department_launches_overhauled_education_digest.html?intc=es
Citation:
The Condition of Education
Elementary and Secondary Education
This website has the key indicators of the condition of education in the United States. These indicators summarize important developments and trends using the latest statistics and are updated every year or every other year. A Congressionally mandated annual report on these indicators is provided to the White House and Congress each year.
In addition, this website has Spotlights on issues of current policy interest. These Spotlights take a more in-depth look at the issues through text, graphics and short videos.
Chapter 1: Trends in Employment Rates by Educational Attainment
Chapter 2: Kindergarten Entry Status: On-Time, Delayed-Entry, and Repeating Kindergartners
Chapter 3: The Status of Rural Education
Chapter 4: Financing Postsecondary Education in the United States
Here is the Readers Guide from the National Center for Education Statistics:
Reader’s Guide
The Condition of Education is available in three forms: a print volume for 2013; on the National Center for Education Statistics (NCES) website as a full pdf, as individual pdfs, and in html; and on our mobile website. All reference tables are hyperlinked within the pdf and html versions, as are the sources for each of the graphics. The reference tables can be found in other NCES publications—primarily the Digest of Education Statistics. A pdf that contains all of the reference tables used in The Condition of Education 2013 is available on the NCES website.
Data Sources and Estimates
The data in these indicators were obtained from many different sources—including students and teachers, state education agencies, local elementary and secondary schools, and colleges and universities—using surveys and compilations of administrative records. Users should be cautious when comparing data from different sources. Differences in aspects such as procedures, timing, question phrasing, and interviewer training can affect the comparability of results across data sources.
Most indicators summarize data from surveys conducted by NCES or by the Census Bureau with support from NCES. Brief explanations of the major NCES surveys used in these indicators can be found in the Guide to Sources. More detailed explanations can be obtained on the NCES website under “Surveys and Programs.”
The Guide to Sources also includes information on non-NCES sources used to compile indicators, such as the American Community Survey (ACS) and the Current Population Survey (CPS). These are Census Bureau surveys used extensively in the indicators. For further details on the ACS, see http://www.census.gov/acs/www/. For further details on the CPS, see http://www.census.gov/cps/.
Data for indicators are obtained primarily from two types of surveys: universe surveys and sample surveys. In universe surveys, information is collected from every member of the population. For example, in a survey regarding certain expenditures of public elementary and secondary schools, data would be obtained from each school district in the United States. When data from an entire population are available, estimates of the total population or a subpopulation are made by simply summing the units in the population or subpopulation. As a result, there is no sampling error, and observed differences are reported as true.
Since a universe survey is often expensive and time consuming, many surveys collect data from a sample of the population of interest (sample survey). For example, the National Assessment of Educational Progress (NAEP) assesses a representative sample of students rather than the entire population of students. When a sample survey is used, statistical uncertainty is introduced, because the data come from only a portion of the entire population. This statistical uncertainty must be considered when reporting estimates and making comparisons.
Various types of statistics derived from universe and sample surveys are reported in the indicators. Many indicators report the size of a population or a subpopulation, and often the size of a subpopulation is expressed as a percentage of the total population. In addition, the average (or mean) value of some characteristic of the population or subpopulation may be reported. The average is obtained by summing the values for all members of the population and dividing the sum by the size of the population. An example is the annual average salaries of full-time instructional faculty at degree-granting postsecondary institutions. Another measure that is sometimes used is the median. The median is the midpoint value of a characteristic at or above which 50 percent of the population is estimated to fall, and at or below which 50 percent of the population is estimated to fall. An example is the median annual earnings of young adults who are full-time, full-year wage and salary workers.
Standard Errors
Using estimates calculated from data based on a sample of the population requires consideration of several factors before the estimates become meaningful. When using data from a sample, some margin of error will always be present in estimations of characteristics of the total population or subpopulation because the data are available from only a portion of the total population. Consequently, data from samples can provide only an approximation of the true or actual value. The margin of error of an estimate, or the range of potential true or actual values, depends on several factors such as the amount of variation in the responses, the size and representativeness of the sample, and the size of the subgroup for which the estimate is computed. The magnitude of this margin of error is measured by what statisticians call the “standard error” of an estimate.
When data from sample surveys are reported, the standard error is calculated for each estimate. The standard errors for all estimated totals, means, medians, or percentages are reported in the reference tables.
In order to caution the reader when interpreting findings in the indicators, estimates from sample surveys are flagged with a “!” when the standard error is between 30 and 50 percent of the estimate, and suppressed with a “‡” when the standard error is 50 percent of the estimate or greater.
Data Analysis and Interpretation
When estimates are from a sample, caution is warranted when drawing conclusions about one estimate in comparison to another, or about whether a time series of estimates is increasing, decreasing, or staying the same. Although one estimate may appear to be larger than another, a statistical test may find that the apparent difference between them is not reliably measurable due to the uncertainty around the estimates. In this case, the estimates will be described as having no measurable difference, meaning that the difference between them is not statistically significant.
Whether differences in means or percentages are statistically significant can be determined using the standard errors of the estimates. In these indicators and other reports produced by NCES, when differences are statistically significant, the probability that the difference occurred by chance is less than 5 percent, according to NCES standards.
Data presented in the indicators do not investigate more complex hypotheses, account for interrelationships among variables, or support causal inferences. We encourage readers who are interested in more complex questions and in-depth analysis to explore other NCES resources, including publications, online data tools, and public- and restricted-use datasets at http://nces.ed.gov.
For all indicators that report estimates based on samples, differences between estimates (including increases and decreases) are stated only when they are statistically significant. To determine whether differences reported are statistically significant, two-tailed t tests at the .05 level are typically used. The t test formula for determining statistical significance is adjusted when the samples being compared are dependent. The t test formula is not adjusted for multiple comparisons, with the exception of statistical tests conducted using the NAEP Data Explorer. When the variables to be tested are postulated to form a trend, the relationship may be tested using linear regression, logistic regression, or ANOVA trend analysis instead of a series of t tests. These alternate methods of analysis test for specific relationships (e.g., linear, quadratic, or cubic) among variables. For more information on data analysis, please see the NCES Statistical Standards, Standard 5-1, available at http://nces.ed.gov/statprog/2002/std5_1.asp.
A number of considerations influence the ultimate selection of the data years to feature in the indicators. To make analyses as timely as possible, the latest year of available data is shown. The choice of comparison years is often also based on the need to show the earliest available survey year, as in the case of the NAEP and the international assessment surveys. In the case of surveys with long time frames, such as surveys measuring enrollment, the decade’s beginning year (e.g., 1980 or 1990) often starts the trend line. In the figures and tables of the indicators, intervening years are selected in increments in order to show the general trend. The narrative for the indicators typically compares the most current year’s data with those from the initial year and then with those from a more recent period. Where applicable, the narrative may also note years in which the data begin to diverge from previous trends.
Rounding and Other Considerations
All calculations within the indicators are based on unrounded estimates. Therefore, the reader may find that a calculation, such as a difference or a percentage change, cited in the text or figure may not be identical to the calculation obtained by using the rounded values shown in the accompanying tables. Although values reported in the supplemental tables are generally rounded to one decimal place (e.g., 76.5 percent), values reported in each indicator are generally rounded to whole numbers (with any value of 0.50 or above rounded to the next highest whole number). Due to rounding, cumulative percentages may sometimes equal 99 or 101 percent rather than 100 percent.
Race and Ethnicity
The Office of Management and Budget (OMB) is responsible for the standards that govern the categories used to collect and present federal data on race and ethnicity. The OMB revised the guidelines on racial/ ethnic categories used by the federal government in October 1997, with a January 2003 deadline for implementation (Office of Management and Budget 1997). The revised standards require a minimum of these five categories for data on race: American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, and White. The standards also require the collection of data on the ethnicity categories Hispanic or Latino and Not Hispanic or Latino. It is important to note that Hispanic origin is an ethnicity rather than a race, and therefore persons of Hispanic origin may be of any race. Origin can be viewed as the heritage, nationality group, lineage, or country of birth of the person or the person’s parents or ancestors before their arrival in the United States. The race categories White, Black, Asian, Native Hawaiian or Other Pacific Islander, and American Indian or Alaska Native, as presented in these indicators, exclude persons of Hispanic origin unless noted otherwise.
The categories are defined as follows:
American Indian or Alaska Native: A person having origins in any of the original peoples of North and South America (including Central America) and maintaining tribal affiliation or community attachment.
Asian: A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent, including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.
Black or African American: A person having origins in any of the black racial groups of Africa.
Native Hawaiian or Other Pacific Islander: A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.
White: A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.
- Hispanic or Latino: A person of Mexican, Puerto Rican, Cuban, South or Central American, or other Spanish culture or origin, regardless of race.
Within these indicators, some of the category labels have been shortened in the indicator text, tables, and figures. American Indian or Alaska Native is denoted as American Indian/Alaska Native (except when separate estimates are available for American Indians alone or Alaska Natives alone); Black or African American is shortened to Black; and Hispanic or Latino is shortened to Hispanic. When discussed separately from Asian estimates, Native Hawaiian or Other Pacific Islander is shortened to Native Hawaiian/Pacific Islander.
The indicators draw from a number of different sources. Many are federal surveys that collect data using the OMB standards for racial/ethnic classification described above; however, some sources have not fully adopted the standards, and some indicators include data collected prior to the adoption of the OMB standards. This report focuses on the six categories that are the most common among the various data sources used: White, Black, Hispanic, Asian, Native Hawaiian/Pacific Islander, and American Indian/Alaska Native. Asians and Native Hawaiians/Pacific Islanders are combined into one category in indicators for which the data were not collected separately for the two groups.
Some of the surveys from which data are presented in these indicators give respondents the option of selecting either an “other” race category, a “Two or more races” or “multiracial” category, or both. Where possible, indicators present data on the “Two or more races” category; however, in some cases this category may not be separately shown because the information was not collected or due to other data issues. The “other” category is not separately shown. Any comparisons made between persons of one racial/ethnic group to “all other racial/ ethnic groups” include only the racial/ethnic groups shown in the indicator. In some surveys, respondents are not given the option to select more than one race. In these surveys, respondents of two or more races must select a single race category. Any comparisons between data from surveys that give the option to select more than one race and surveys that do not offer such an option should take into account the fact that there is a potential for bias if members of one racial group are more likely than members of the others to identify themselves as “Two or more races.”1 For postsecondary data, foreign students are counted separately and are therefore not included in any racial/ethnic category.
The American Community Survey (ACS), conducted by the U.S. Census Bureau, collects information regarding specific racial/ethnic ancestry. Selected indicators include Hispanic ancestry subgroups (such as Mexican, Puerto Rican, Cuban, Dominican, Salvadoran, Other Central American, and South American) and Asian ancestry subgroups (such as Asian Indian, Chinese, Filipino, Japanese, Korean, and Vietnamese). In addition, selected indicators include “Two or more races” subgroups (such as White and Black, White and Asian, and White and American Indian/Alaska Native).
For more information on the ACS, see the Guide to Sources. For more information on race/ ethnicity, see the Glossary.
Limitations of the Data
The relatively small sizes of the American Indian/Alaska Native and Native Hawaiian/Pacific Islander populations pose many measurement difficulties when conducting statistical analysis. Even in larger surveys, the numbers of American Indians/Alaska Natives and Native Hawaiians/ Pacific Islanders included in a sample are often small. Researchers studying data on these two populations often face small sample sizes that reduce the reliability of results. Survey data for American Indians/Alaska Natives often have somewhat higher standard errors than data for other racial/ethnic groups. Due to large standard errors, differences that seem substantial are often not statistically significant and, therefore, not cited in the text.
Data on American Indians/Alaska Natives are often subject to inaccuracies that can result from respondents self-identifying their race/ethnicity. Research on the collection of race/ethnicity data suggests that the categorization of American Indian and Alaska Native is the least stable self-identification (U.S. Department of Labor, Bureau of Labor Statistics [BLS] 1995). The racial/ ethnic categories presented to a respondent, and the way in which the question is asked, can influence the response, especially for individuals who consider themselves of mixed race or ethnicity. These data limitations should be kept in mind when reading this report.
As mentioned above, Asians and Native Hawaiians/Pacific Islanders are combined into one category in indicators for which the data were not collected separately for the two groups. The combined category can sometimes mask significant differences between subgroups. For example, prior to 2011, the National Assessment of Educational Progress (NAEP) collected data that did not allow for separate reporting of estimates for Asians and Native Hawaiians/Pacific Islanders. Information from the Digest of Education Statistics, 2011 (table 21), based on the Census Bureau Current Population Reports, indicates that 96 percent of all Asian/Pacific Islander 5- to 24-year-olds are Asian. This combined category for Asians/Pacific Islanders is more representative of Asians than Native Hawaiians/Pacific Islanders.
Symbols
In accordance with the NCES Statistical Standards, many tables in this volume use a series of symbols to alert the reader to special statistical notes. These symbols, and their meanings, are as follows:
— Not available.
† Not applicable.
# Rounds to zero.
! Interpret data with caution. The coefficient of variation (CV) for this estimate is between 30 and 50 percent.
‡ Reporting standards not met. Either there are too few cases for a reliable estimate or the coefficient of variation (CV) for this estimate is 50 percent or greater.
* p < .05 Significance level.
1 Such bias was found by a National Center for Health Statistics study that examined race/ethnicity responses to the 2000 Census. This study found, for example, that as the percentage of multiple-race respondents in a county increased, the likelihood of respondents stating Black as their primary race increased among Black/White respondents but decreased among American Indian or Alaska Native/Black respondents. See Parker, J. et al. (2004). Bridging Between Two Standards for Collecting Information on Race and Ethnicity: An Application to Census 2000 and Vital Rates. Public Health Reports, 119(2): 192–205. Available through http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1497618.
For those who are interested in education, this report is a goldmine.
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