SLDS Data Use Issue Brief 3: Turning Administrative Data into Research-Ready Longitudinal Datasets, July 2012 1

Data Use Issue Brief 3

Turning Administrative Data into Research-Ready 

Longitudinal Datasets

July 2012

This publication is part of a 

series of Data Use Issue Briefs 

designed to share best practices 

on data use by states. Issue 

Brief 1 provides an overview of 

the different types of data use. 

Briefs 2-4 share best practices for 

conducting education research with 

longitudinal administrative data. 

Look for forthcoming issue briefs on 

instructional data use.

Data Use Issue Brief 1:

The Data Use Landscape 

Data Use Issue Brief 2:

Forming Research Partnerships with 

State and Local Education Agencies

Data Use Issue Brief 3:

Turning Administrative Data into 

Research-Ready Longitudinal Datasets

Data Use Issue Brief 4:

Techniques for Analyzing 

Longitudinal Administrative Data

This brief is excerpted from the 

following working paper: 

Cratty, Dorothyjean (2010). “Conducting 

Responsible Education Research with 

Longitudinal Administrative Data.” 

For more information on the IES SLDS 

Grant Program or for support with system

development and use, please visit 

http://nces.ed.gov/programs/SLDS.

As SLDS Data Use Issue Brief 2, “Forming Research Partnerships with State and District Education 

Agencies,” discusses, statewide longitudinal data systems (SLDSs) have provided state education agencies and 

local education agencies (SEAs and LEAs) with valuable data for education research. However, because 

administrative data files are not created specifically for representative longitudinal research, there may be multiple 

steps involved in preparing the data in order to make it “research ready.” This issue brief (and the next) share 

important information about the SLDS data collection process that is relevant for analysis and may be of 

particular interest to researchers outside of the education agencies. 

An SLDS consists of administrative data primarily collected for the following purposes:

• record student enrollment and assessments; 

• manage teaching staff and financial resources;

• assign and schedule classes and record grades;

• exchange information with parents; and

• meet federal reporting requirements for EDFacts, Civil Rights Data Collection 

(CRDC), Common Core of Data-Fiscal (CCD-Fiscal), etc.

Because these purposes are separate from research, administrative data may look different 

from the type of data researchers would want. For example, some administrative data 

files may be longitudinal but not representative, and others may be representative but not 

longitudinal. Student assessment files may be both longitudinal and representative, but not 

linked to other student, teacher, course, or household files. 

As states continue to build their SLDSs, more of these links between files are becoming 

available. However, to take advantage of the longitudinal nature of the data, research 

partners and in-house analysts often must first construct representative research-ready 

datasets. While data systems are unique to a state or district, a number of best practices 

have emerged. This brief discusses some key challenges and potential solutions when 

working with administrative data.

Important Considerations when Selecting Administrative 

Data Files for Longitudinal Analysis

Make sure you know why and how each record was collected. 

Find ways to get as much information about the data collection process as possible 

without burdening SEA and LEA data personnel. Familiarize yourself with the Common 

Education Data Standards (CEDS) and federal reporting requirements using the National 

Center for Education Statistics (NCES) and other U.S. Department of Education websites. 

Find metadata/paradata (data about the data) on SEA and LEA websites.

Example: Suspension files may include all students—some with one or more suspensions 

recorded, and others who appear to have not been suspended. However, these suspensions 

may only be the more serious Legally Reportable Offenses, or those meeting the Alternative 

Learning Program reporting requirements for special education students. Similarly, it is 

important to confirm whether English Language Learners variables indicate that testing 

was warranted, services were needed, or services were provided.
SLDS Data Use Issue Brief 3: Turning Administrative Data into Research-Ready Longitudinal Datasets, July 2012 2

Do not assume linked subsets of data are 

representative: every additional file or year 

linked leads to additional non-random 

attrition, leaving a particularly stable—but nonrepresentative—subset of students and teachers.

Example: The state enrolls 100,000 5th graders each year. An 

analysis sample for teacher value-added estimates requires 4th 

and 5th grade math and/or reading scores and teacher record 

linkages, which may reduce the sample to as low as 60,000 5th 

graders. Adding teacher certification variables requires two or 

more years of teacher matching, which could further reduce 

the sample to less than 50,000 students. The omitted students 

are those who move, miss a test, take an alternative test one 

year, and/or are in pull-out classes that are harder to match to 

teachers (see Issue Brief 4 for techniques regarding the use of 

analysis subsamples).

Do not assume that data files are the same 

across years. States often have to change what 

data they collect and/or how they collect them.

Example: Variable codings that can change from year to year 

include: 1) parents’ education changes from four categories to 

six; 2) school lunch eligibility changes from yes/no to free/

reduced/paid; and 3) a variable for raw test scores changes 

from recording only standard test scores to recording both 

standard and alternative test scores, which have different 

scales. In this last case, the files ideally include a new variable 

indicating which test a student takes, but that information may 

not be obvious or included in the documentation.

Potential Challenges to Preparing 

Administrative Data for Research

Longitudinal analysis requires a representative sample of 

unique observations with a complete panel of consistently 

coded variables with few missing values. School administrative 

records pose many challenges that must be addressed in order 

to meet these requirements. Ideally, solutions should be 

tailored to each specific research context, but if single, allpurpose decision rules are made, details of the rationale and 

any sensitivity tests should be well documented. The following 

are examples of potential problems and solutions.

Non-unique observations for individual students 

in a given year

Problem: Multiple, identical annual student course records 

for elementary grades in a course file set up to accommodate 

semester and block schedules of middle school and high 

school courses.

Solution: Confirm and delete duplicate or superfluous records 

(this is important to do within each individual file in each 

year—before merging them into a longitudinal analysis file).

Problem: Multiple assessment records for the same student in 

the same year, but with different test score values.

Solution: Find out why there are two scores: were two students 

matched to the same ID, or did one student change schools 

mid-year or take a make-up test? Decide how to aggregate to 

one score per student per year (i.e., take the mean, highest, or 

latest score).

Non-unique values for the same variable across 

student-year records

Problem: In the same year, some students have both an 8th 

grade math score and a 9th grade Algebra I score.

Solution: Determine whether students are 8th graders taking a 

nearby high school algebra class, or 9th grade algebra students 

with 8th grade scores carried over in the records.

Problem: In the same year, some students are recorded as 

being in different schools.

Solution: Determine whether two students are sharing the 

same ID, students moved mid-year, or students are dualenrolled. Decide which school to assign to the student-year 

observation: the school with the test scores, the school with 

the most days attended, or other. Alternatively, create two 

student-year school variables: “main school” and “other.”

Missing values within panel

Problem: In some years, important student values are missing. 

There may have been a problem with the source file in a given 

year, or the data may not be collected at every grade level. For 

example, school lunch eligibility and special education status 

are often more frequently recorded for early grades.

Solution: Rather than dropping all student-year records with 

incomplete longitudinal values from the analysis, decide on 

an aggregation method to complete the panel of variables. 

For example, replace missing student-year values for race and 

gender with the student’s modal value. For variables that were 

not recorded at every grade, consider recoding designations 

such as “ever school lunch eligible” or “ever learning disabled,” 

as appropriate.

Visit the SLDS website for additional resources on 

administrative data use 

(http://nces.ed.gov/programs/slds).