IES Blog

Institute of Education Sciences

New International Data Identify “Resilient” Students in Financial Literacy

NCES recently released the results of the Program for International Student Assessment (PISA) 2018 assessment of financial literacy. This assessment measured 15-year-old students’ knowledge and understanding of financial concepts, products, and risks and their ability to apply that knowledge to real-life situations. It found that, on average, U.S. students performed similarly to their peers across the 12 other participating Organization for Economic Cooperation and Development (OECD) countries. 

The assessment also found that 12 percent of U.S. students performed at the highest level of proficiency (level 5). Performance at this level indicates that students can apply their understanding of financial terms and concepts to analyze complex financial products, solve nonroutine financial problems, and describe potential outcomes of financial decisions in the big picture.[1] The U.S. percentage was again similar to the OECD average.

However, this analysis also identified a group of students who might be considered “resilient.” In education research, resilience is defined as the ability to perform well academically despite coming from the disadvantaged backgrounds that have more commonly been associated with lower performance.

High-performing students came from across the spectrum of school poverty levels, as measured by the percentage of students eligible for free or reduced-price lunch (FRPL).[2] In particular, 7 percent of high-performing students in financial literacy came from the highest poverty schools (figure 1).


Figure 1. Percentage distribution of U.S. 15-year-olds in public schools scoring below level 2 and at level 5 of proficiency on the PISA financial literacy scale, by percentage of students eligible for free or reduced-price lunch (FRPL) at their school: 2018

NOTE: Data for percentage of students eligible for FRPL were available for public schools only. An individual student’s level of poverty may vary within schools. Detail may not sum to totals due to rounding.

SOURCE: Organization for Economic Cooperation and Development (OECD), Program for International Student Assessment (PISA), 2018.


It is these 7 percent of students who could be considered “resilient” and may be of interest for further study. For example, research could identify if there are factors that are associated with their high performance when compared to their lower performing peers in similar schools. Research on academically resilient students that used eighth-grade data from TIMSS found, for example, that having high educational aspirations increased the likelihood that students with few home education resources performed at or above the TIMSS Intermediate international benchmark in mathematics.[3] Experiencing less bullying also increased this likelihood.

Examining the “resilient” PISA financial literacy students more closely could also determine the extent to which their individual backgrounds are related to performance. This would be of interest because, even within high-poverty schools, students’ individual circumstances may vary. 

Patterns in Other PISA Subjects

There are similar subsets of “resilient” students in the other PISA 2018 subjects (table 1). Eight percent of high performers in reading were from the highest poverty schools, as were 5 percent of high performers in mathematics and 7 percent of high performers in science.


Table 1. Percentage of U.S. 15-year-olds in public schools scoring at or above level 5 of proficiency, by PISA subject and their schools’ free or reduced-price lunch (FRPL) status: 2018

[Standard errors appear in parentheses]

NOTE: Results are scaled separately; thus, percentages cannot be compared across subjects. Level 5 is the highest level of proficiency in financial literacy; levels 5 and 6 are the highest levels of proficiency in the other PISA subjects. Data for students eligible for FRPL were available for public schools only.

SOURCE: Organization for Economic Cooperation and Development (OECD), Program for International Student Assessment (PISA), 2018.


For more information on the PISA 2018 results in financial literacy and other subjects, visit the NCES International Activities website. To create customized data and charts using PISA and other international assessment data, use the International Data Explorer.

 

By Maria Stephens, AIR


[2] Data for students eligible for FRPL are available for public schools only.

[3] Students at the Intermediate international benchmark can apply basic mathematical knowledge in a variety of situations, and those above this benchmark can do so in increasingly complex situations and, at the highest end, reason with information, draw conclusions, make generalizations, and solve linear equations.

Building Bridges: Increasing the Power of the Civil Rights Data Collection (CRDC) Through Data Linking With an ID Crosswalk

On October 15, 2020, the U.S. Department of Education’s (ED) Office for Civil Rights (OCR) released the 2017–18 Civil Rights Data Collection (CRDC). The CRDC is a biennial survey that has been conducted by ED to collect data on key education and civil rights issues in our nation’s public schools since 1968. The CRDC provides data on student enrollment and educational programs and services, most of which are disaggregated by students’ race/ethnicity, sex, limited English proficiency designation, and disability status. The CRDC is an important aspect of the overall strategy of ED’s Office for Civil Rights (OCR) to administer and enforce civil rights statutes that apply to U.S. public schools. The information collected through the CRDC is also used by other ED offices as well as by policymakers and researchers outside of ED.  

As a standalone data collection, the CRDC provides a wealth of information. However, the analytic power and scope of the CRDC can be enhanced by linking it to other ED and government data collections, including the following:

A Crosswalk to Link CRDC Data to Other Data Collections

To facilitate joining CRDC data to these and other data collections, NCES developed an ID crosswalk. This crosswalk is necessary because there are instances when the CRDC school ID number (referred to as a combo key) does not match the NCES school ID number assigned in other data collections (see the “Mismatches Between ID Numbers” section below for reasons why this may occur). By linking the CRDC to other data collections, researchers can answer questions that CRDC data alone cannot, such as the following:



Mismatches Between ID Numbers

Mismatches between CRDC combo key numbers and NCES ID numbers may occur because of differences in how schools and districts are reported in the CRDC and other collections and because of differences in the timing of collections. Below are some examples.

  • Differences in how schools and school districts are reported in the CRDC and other data collections:
    • New York City Public Schools is reported as a single district in the CRDC but as multiple districts (with one supervisory union and 33 components of the supervisory union) in other data collections. Thus, the district will have one combo key in the CRDC but multiple ID numbers in other data collections.
    • Sometimes charter schools are reported differently in the CRDC compared with other data collections. For example, some charter schools in California are reported as independent (with each school serving as its own school district) in the CRDC but as a single combined school district in other data collections. Thus, each school will have its own combo key in the CRDC, but there will be one ID number for the combined district in other data collections.
    • There are differences between how a state or school district defines a school compared with how other data collections define a school.
  • Differences in the timing of the CRDC and other data collections:
    • There is a lag between when the CRDC survey universe is planned and when the data collection begins. During this time, a new school may open. Since the school has not yet been assigned an ID number, it is reported in the CRDC as a new school.


Interested in using the ID crosswalk to link CRDC data with other data collections and explore a research question of your own? Visit https://www.air.org/project/research-evaluation-support-civil-rights-data-collection-crdc to learn more and access the crosswalk. For more information about the CRDC, visit https://ocrdata.ed.gov/.

 

By Jennifer Sable, AIR, and Stephanie R. Miller, NCES

Data Tools for College Professors and Students

Ever wonder what parts of the country produce the most English majors? Want to know which school districts have the most guidance counselors? The National Center for Education Statistics (NCES) has all the tools you need to dig into these and lots of other data!

Whether you’re a student embarking on a research project or a college professor looking for a large data set to use for an assignment, NCES has you covered. Below, check out the tools you can use to conduct searches, download datasets, and generate your own statistical tables and analyses.

 

Conduct Publication Searches

Two search tools help researchers identify potential data sources for their study and explore prior research conducted with NCES data. The Publications & Products Search Tool can be used to search for NCES publications and data products. The Bibliography Search Tool, which is updated continually, allows users to search for individual citations from journal articles that have been published using data from most surveys conducted by NCES.

Key reference publications include the Digest of Education Statistics, which is a comprehensive library of statistical tabulations, and The Condition of Education, which highlights up-to-date trends in education through statistical indicators.

 

Learn with Instructional Modules

The Distance Learning Dataset Training System (DLDT) is an interactive online tool that allows users to learn about NCES data across the education spectrum. DLDT’s computer-based training introduces users to many NCES datasets, explains their designs, and offers technical considerations to facilitate successful analyses. Please see the NCES blog Learning to Use the Data: Online Dataset Training Modules for more details about the DLDT tool.
 




Download and Access Raw Data Files

Users have several options for conducting statistical analyses and producing data tables. Many NCES surveys release public-use raw data files that professors and students can download and analyze using statistical software packages like SAS, STATA, and SPSS. Some data files and syntax files can also be downloaded using NCES data tools:

  • Education Data Analysis Tool (EDAT) and the Online Codebook allow users to download several survey datasets in various statistical software formats. Users can subset a dataset by selecting a survey, a population, and variables relevant to their analysis.
  • Many data files can be accessed directly from the Surveys & Programs page by clicking on the specific survey and then clicking on the “Data Products” link on the survey website.

 

Generate Analyses and Tables

NCES provides several online analysis tools that do not require a statistical software package:

  • DataLab is a tool for making tables and regressions that features more than 30 federal education datasets. It includes three powerful analytic tools:
    • QuickStats—for creating simple tables and charts.
    • PowerStats—for creating complex tables and logistic and linear regressions.
    • TrendStats—for creating complex tables spanning multiple data collection years. This tool also contains the Tables Library, which houses more than 5,000 published analysis tables by topic, publication, and source.



  • National Assessment of Educational Progress (NAEP) Data Explorer can be used to generate tables, charts, and maps of detailed results from national and state assessments. Users can identify the subject area, grade level, and years of interest and then select variables from the student, teacher, and school questionnaires for analysis.
  • International Data Explorer (IDE) is an interactive tool with data from international assessments and surveys, such as the Program for International Student Assessment (PISA), the Program for the International Assessment of Adult Competencies (PIAAC), and the Trends in International Mathematics and Science Study (TIMSS). The IDE can be used to explore student and adult performance on assessments, create a variety of data visualizations, and run statistical tests and regression analyses.
  • Elementary/Secondary Information System (ElSi) allows users to quickly view public and private school data and create custom tables and charts using data from the Common Core of Data (CCD) and Private School Universe Survey (PSS).
  • Integrated Postsecondary Education Data System (IPEDS) Use the Data provides researcher-focused access to IPEDS data and tools that contain comprehensive data on postsecondary institutions. Users can view video tutorials or use data through one of the many functions within the portal, including the following:
    • Data Trends—Provides trends over time for high-interest topics, including enrollment, graduation rates, and financial aid.
    • Look Up an Institution—Allows for quick access to an institution’s comprehensive profile. Shows data similar to College Navigator but contains additional IPEDS metrics.
    • Statistical Tables—Equips power users to quickly get data and statistics for specific measures, such as average graduation rates by state.