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A Publication of the National Center for Education Statistics at IES

What is the capacity for linking K–12 student data in the SLDS to other data? How are the data linked?

More than three-quarters of states and territories responding to the survey (79 percent) reported that they collect data across multiple agencies in a P-20W+ environment (figure 3). The type of data system model used for P-20W+ SLDSs varies, with 41 percent of states and territories reporting using a centralized data system model,2 18 percent reporting using a federated model,3 and 20 percent reporting using a hybrid model.4 Eighteen percent of states and territories reported that the question was not applicable, and 4 percent did not respond to the question.


Figure 3. Percentage of states and territories with P-20W+ data collections, by model type: 2018

Figure 3. Percentage of states and territories with P-20W+ data collections, by model type: 2018

NOTE: N = 51. Detail may not sum to total due to rounding. P-20W+ refers to data from prekindergarten (early childhood), K–12, and postsecondary through postgraduate education, along with workforce and other outcomes data (e.g., public assistance and corrections data). The specific agencies and other organizations that participate in the P-20W+ initiative vary from state to state.
SOURCE: U.S. Department of Education, National Center for Education Statistics, Statewide Longitudinal Data Systems (SLDS) Survey, Fall 2018.


A little more than half of states and territories reported having operational automated infrastructure to link K–12 student data to postsecondary data (51 percent), to Perkins CTE data (53 percent), and to early childhood data (53 percent) (figure 4).

A smaller percentage of states and territories have operational automated links between K–12 student data and K–12 teacher data (43 percent) and workforce data (31 percent). Less than one-third of states and territories do not plan to link K–12 student data to data from all the other sectors. Almost threequarters of states and territories reported planned, in progress, or operational automated linkages between K–12 student and K–12 teacher, postsecondary, Perkins CTE, and early childhood data.


Figure 4. Percentage of states and territories with other sector data linked to K–12 student data, by operational status: 2018

Figure 4. Percentage of states and territories with other sector data linked to K–12 student data, by operational status: 2018

NOTE: N = 51. Detail may not sum to total due to rounding. CTE refers to career and technical education.
SOURCE: U.S. Department of Education, National Center for Education Statistics, Statewide Longitudinal Data Systems (SLDS) Survey, Fall 2018.


How Are Data Directly Linked to K–12 Student Data?

It is important to be able to connect information about students to nonstudent entities like teachers or to long-term outcomes like workforce participation. These connections are possible only with direct linkages between K–12 student data and other data types. K–12 student data are linked with data from other sectors using a variety of strategies (figure 5).

For K–12 teacher data, 71 percent of states and territories reported having operational linkages to K–12 student data through course assignments. Sixty-three percent of states and territories reported using statewide unique teacher identification numbers (IDs) as part of their strategy to link K–12 student data to K–12 teacher data. Data linking methods are not mutually exclusive; for example, many of the states and territories that reported linking K–12 teacher and student data through course assignments also use statewide unique IDs.


Figure 5. Percentage of states and territories with direct K–12 student data links to other data sectors, by linking method and operational status: 2018

Figure 5. Percentage of states and territories with direct K–12 student data links to other data sectors, by linking method and operational status: 2018

NOTE: N = 51. Detail may not sum to total due to rounding. CTE refers to career and technical education. ID refers to unique identifier.
SOURCE: U.S. Department of Education, National Center for Education Statistics, Statewide Longitudinal Data Systems (SLDS) Survey, Fall 2018.


States and territories most commonly reported using an element match process5 to connect K–12 student data to postsecondary data (operational in 65 percent of responding states and territories) and workforce data (operational in 37 percent of responding states and territories). Assigned unique identifiers were the method most commonly reported as operational for connecting K–12 student data to Perkins CTE data (73 percent) and to early childhood data (61 percent).

Overall, respondents reported few significant changes to how they link data between the 2017 and 2018 surveys.

States and territories use linked data to enable several replicable, automated processes (figure 6). The processes most commonly reported as operational by states and territories included moving student data from K–12 to in-state postsecondary institutions through e-transcripts (39 percent), moving student data from LEAs to the SEA through Student Records Exchange6 (SRE or SREx) (35 percent), and moving student data to other states’ postsecondary entities via e-transcripts (31 percent). Less commonly reported as operational were moving student data across LEAs in the state through Student Records Exchange (24 percent); cross-state data sharing with the Southeast Education Data Exchange, the Midwest Education Information Consortium, the Wage Record Interchange System (WRIS) or WRIS 2 (14 percent); and moving student data to other states’ SEAs via Student Records Exchange (4 percent).


Figure 6. Percentage of states and territories that move student data through replicable, automated processes, by process: 2018

Figure 6. Percentage of states and territories that move student data through replicable, automated processes, by process: 2018

NOTE: N = 51. Detail may not sum to total due to rounding.
SOURCE: U.S. Department of Education, National Center for Education Statistics, Statewide Longitudinal Data Systems (SLDS) Survey, Fall 2018.

2 In a centralized data model, all participating source data systems periodically copy their data to a single, centrally located data repository that organizes, integrates, and stores them using a common data standard. Users can query the system to access the data that they have been authorized to view and use.
3 In a federated data model, individual source data systems maintain control over their own data but agree to share some or all of their data with other participating systems upon request. Users submit queries via a shared intermediary interface that then searches the independent source systems. Data from source systems are located and matched to fulfill a specific data request. The linked data are not stored but rather are removed once cached and delivered.
4 A hybrid data model combines features of the centralized and federated models. For example, hybrid models may establish and maintain data linkages through common identifiers such as Social Security number, name, date of birth, and student identifier, while data such as enrollment, attainment, and assessment information are kept separate until requested by researchers or other users.
5 An element match process uses one or more data elements to link or connect records or datasets. For example, a state or territory may use student characteristics such as date of birth, last name, and grade level to connect records between postsecondary and K–12 data systems
6 A Student Record Exchange application facilitates the secure and efficient electronic exchange of student records as students move between schools.