107
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 108 

GOAL 1: POSTSECONDARY EDUCATION, CAREER AND TECHNICAL EDUCATION, AND ADULT EDUCATION. Increase college access, affordability, quality, 

and completion by improving postsecondary education and lifelong learning opportunities for youths and adults. 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements

1.1.A Federal student loan delinquency rate Federal Student Aid (FSA) Data Center NSLDS is currently the source of the data. Verification and validation occur in three 

aspects of the process: 

(1) Data providers (servicers in this case) transmit data to NSLDS where the data are 

subject to edits and screening. FSA works with the servicers to resolve anomalies. 

(2) Users who analyze the data sometimes identify anomalies in the course of their 

analytic roles and work with experts in NSLDS to resolve. In some cases NSLDS will 

again work with the servicer(s) to resolve. 

(3) The report providing the data for this metric has been validated. This means that 

the programming and logic have been independently reviewed.

1.1.B Web traffic to the College Scorecard Google Analytics data from College 

Scorecard

The data are simply pulled from the GSA Google Analytics account. The data are 

checked for reasonability, and incorporated alongside the existing baseline data. The data 

need to be matched with baseline data and collated accordingly. There is limited analysis 

required.

The data will be matched with prior data for reasonability checks. The Office of Planning, 

Evaluation and Policy Development will pursue solutions to anomalous data, but do not 

anticipate a significant problem, as the data are produced relatively automatically.

1.1.C Percent of High School Seniors Filing a 

FAFSA

The denominator is the number of 

graduating seniors according to the most 

recent projection by NCES (Table 219.10 

within the Digest of Education Statistics). 

The numerator is from Federal Student Aid’s 

(FSA’s) Central Processing System and is 

based on the number of applications during 

the first nine months of the application cycle 

that are—as of September 30 of the first 

year of the application cycle—complete (not 

rejected); first-time filers; incoming 

freshmen, with or without previous college 

attendance; age 18 or less as of June 30 of 

the first year of the application cycle; 

reporting high school diploma attainment; 

and attended a high school in the fifty states 

and Washington, DC.

The denominator is the number of graduating seniors according to the most recent 

projection by National Center for Education Statistics (3,365,560). 

Number of seniors obtained from the most recent release of Digest of Education Statistics 

(2013) obtained here: http://nces.ed.gov/programs/digest/d13/tables/dt13_219.10.asp

The numerator is based on the number of applications during the first nine months of the 

application cycle that are—as of September 30 of the first year of the application cycle—

complete (not rejected; first-time filers; incoming freshmen, with or without previous 

college attendance; age 18 or less as of June 30 of the first year of the application cycle; 

reporting high school diploma attainment; and attended a high school in the 50 states and 

DC. (Data Source: FSA’s Central Processing System. Query timeframe: January–

September 30, 2014: 2,021,691)

Broadly speaking, several individuals and organizations serve as controls, including the 

Customer Analytics Group (for primary calculation), Business Intelligence (for technical 

validation), and the Financial Reporting and Analysis Branch for ensuring documentation 

is complete and archived. 

In addition to the above controls, queries and calculations are simultaneously conducted 

on data from previous years to ensure technical definitions remain consistent and 

calculations are accurate. 

Being that the Central Processing System is the core legacy system used to determine 

aid eligibility and awards and understanding the significance, value and reliability of the 

Digest of Educational Statistics, FSA feels confident that the outcomes are reliable, 

accurate, and valid.

*Metric associated with an Agency Priority Goal. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 109 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements

1.1.D Index of national annual aggregate 

earnings of VR consumers (based on 

the number of competitive integrated 

employment outcomes, hours worked, 

and hourly wages of VR Consumers) 

Rehabilitation Services Administration 

(RSA)- 911

Each grantee’s RSA-911 is submitted as a text file to RSA via an SFTP server. We run an 

edit program that converts the text file into MS Access and verifies the accuracy of the 

data. Each of the 215 data elements is verified. If errors are identified by the application, 

the grantee is requested to correct the errors and resubmit the data. Once the data file is 

corrected and resubmitted, it is checked again using the same process. If anomalies are 

identified, the grantee must provide an explanation and verify that the data is correct. 

RSA provides the agencies with the edit program that we use and are encouraged to run 

their data using the program often, at least quarterly, during the year. In addition, 

agencies have edits in their own systems which run additional checks. 

1.1.E Index of national annual aggregate 

earnings of Transition-Age Youth 

(based on the number of competitive 

integrated employment outcomes, 

hours worked, and hourly wages of VR 

Transition-Age Youth) 

1.1.F *Number of data points or other 

information reports released on the 

FSA Data Center

FSA Data Center The Validation Subject Matter Expert (SME) works with the Request SME to understand 

the solution used to fulfill the request, checking all results, code, and documentation 

produced by the Request SME. Additionally, the Validation SME ensures there are no 

data disclosure issues that need to be addressed. In the event the Validation SME cannot 

validate the request results, they contact the Request SME and the Data Request Team 

(DRT) to resolve any issues necessary to successfully complete their validation. The 

Validation SME completes their portion of the Request Template and notifies the DRT 

that the validation step is complete. Upon receipt of the data that was requested, the DRT 

confirms with the assigned Validation SME that the results have been validated and 

documented per their selected solution and enters it into the Data Request Tracking 

database. 

1.2.A Pell enrollment at IHEs with high 

graduation rates

Data from College Scorecard Validation of the data is conducted by the National Center for Education Statistics (NCES) 

through its annual IPEDS data validation process. NCES will ensure accuracy, while 

Office of Planning, Evaluation and Policy Development / Policy and Program Studies 

Service will incorporate the data into the Scorecard datasets. 

Analysis will be conducted to identify anomalous data. They will be handled on a case-bycase basis, as most anomalies are due to variation from year-to-year or small n-size and 

are already pooled and suppressed before publishing the data. Other data problems may 

necessitate additional runs, qualifications, or suppression for stability reasons. 

1.2.B Number of states that develop or 

strengthen career pathways policies, 

guidance, and or legislation 

Development data from the National Skills 

Coalition, possibly supplemental with data 

from the Association of State Legislators and 

CLASP 

The Department cannot independently verify the data, but rely on the tracking of the 

National Skills Coalition. There are no processes in place to check for anomalous data. 

1.3.A *Degree attainment among 25-34-yearold age cohortNCES Digest of Education Statistics, Table 

104.30 

(https://nces.ed.gov/programs/digest/d15/tab

les/dt15_104.30.asp), Number of persons 

age 18 and over, by highest level of 

educational attainment, sex, race/ethnicity, 

and age: 2015. Tabulated from Current 

Population Survey data, U.S. Census 

Data quality and limitations are documented in: 

http://www.census.gov/prod/2006pubs/tp-66.pdf. 

NCES tabulates the data, which is verified prior to publication according to NCES 

guidelines. 

*Metric associated with an Agency Priority Goal. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 110 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements

1.3.B Enrollment in IHEs below the minimum 

earnings threshold 

Data from College Scorecard Tool Extensive validation is conducted by the Treasury Department before it is shared with the 

Department of Education, both on the earnings file used to construct the measure and on 

the resulting data file by institution. The Department will also continue to validate the unitlevel files that are shared with the Treasury Department to generate the match. 

Analysis will be conducted to identify anomalous data. They will be handled on a case-bycase basis, as most anomalies are due to variation from year-to-year or small n-size and 

are already pooled and suppressed before sharing with the Department of Education. 

Other data problems may necessitate additional runs, qualifications, or suppression for 

stability reasons. 

The median earnings is calculated only on individuals who are employed (i.e., have 

earnings greater than zero). The data are not produced for small years, and are pooled 

across years, to reduce variability or misrepresentative figures. 

1.3.C Persistence among first-time filing aid 

recipients

FSA’s Common Origination and 

Disbursement (COD) system 

Data used for these calculations are based on counts from operational systems (number 

of recipients and number of applicants). Moreover, standardized queries are used to rerun and match calculations for earlier cycles. Therefore, the metric as defined should be 

considered verified and valid. 

Data used for these calculations are based on counts from operational systems (number 

of recipients and number of applicants meeting certain criteria). Anomalous data would 

suggest there are recipients that are not recipients (or vice versa) or applicants that are 

not applicants (or vice versa). A variety of internal controls are in place tracking both of 

these processes. 

*Metric associated with an Agency Priority Goal. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 111 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements

1.4.A Number of STEM postsecondary 

credentials awarded 

Integrated Postsecondary Education Data 

System (IPEDS) Data Center 

Doctor’s Degrees: (324.25, 324.30 and 

324.35) 

http://nces.ed.gov/programs/digest/d14/table

s/dt14_324.25.asp

http://nces.ed.gov/programs/digest/d14/table

s/dt14_324.30.asp

http://nces.ed.gov/programs/digest/d14/table

s/dt14_324.35.asp

Master’s Degrees: (323.30, 323.40, 323.50) 

http://nces.ed.gov/programs/digest/d14/table

s/dt14_323.30.asp

http://nces.ed.gov/programs/digest/d14/table

s/dt14_323.40.asp

http://nces.ed.gov/programs/digest/d14/table

s/dt14_323.50.asp

Bachelor’s Degrees: (322.30, 322.40, 

322.50) 

http://nces.ed.gov/programs/digest/d14/table

s/dt14_322.30.asp

http://nces.ed.gov/programs/digest/d14/table

s/dt14_322.40.asp

http://nces.ed.gov/programs/digest/d14/table

s/dt14_322.50.asp

Associate’s Degrees: (321.30, 321.40, 

321.50) 

http://nces.ed.gov/programs/digest/d14/table

s/dt14_321.30.asp

http://nces.ed.gov/programs/digest/d14/table

s/dt14_321.40.asp

http://nces.ed.gov/programs/digest/d14/table

s/dt14_321.50.asp

Certificates (320.20) 

http://nces.ed.gov/programs/digest/d14/table

s/dt14_320.20.asp

IPEDS collects completions by Classification of Instructional Programs (CIP) Code which 

may be aggregated into STEM and non-STEM counts. The Department of Education 

does not currently have a single definition for which CIP codes are STEM. 

Data quality and limitations are identified in IPEDS First Look Publications, “Data 

Collection Procedures,” and IPEDS methodology available at 

http://nces.ed.gov/pubs2014/2014067.pdf. Data are checked by NCES for consistency.

*Metric associated with an Agency Priority Goal. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 112 

GOAL 2: ELEMENTARY AND SECONDARY EDUCATION. Improve the elementary and secondary education system’s ability to consistently deliver excellent 

instruction aligned with rigorous academic standards while providing effective support services to close achievement and opportunity gaps, and ensure all students 

graduate high school college- and career-ready. 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements 

2.1.A Number of states / territories that have 

adopted college- and career-ready 

standards 

Elementary and Secondary Education Act

(ESEA) Monitoring 

The Office of Elementary and Secondary Education’s (OESE) Office of State Support will 

count the number of states that either (a) have a memorandum of understanding in place 

to implement standards common to a number of states or (b) have a letter from 

Institutions for Higher Education in their state certifying that their state has college- and 

career-ready standards. There are no known data limitations. 

The Office of Elementary and Secondary Education’s (OESE) Office of State Support 

(OSS) monitors that states are meeting requirements under the ESEA waiver. Monitoring 

includes desk monitoring and on-site monitoring. Previously, OSS state leads monitored 

using a monitor protocol and rubric based on ESEA Flexibility waivers to ensure that 

monitoring is consistent across all states with ESEA Flexibility. OSS is revising its 

monitoring practices based on passage of the ESEA, as amended by the ESSA, in 

December 2016, based on statutory revisions, as well as regulatory action. OSS state 

leads work with their Group Leader to finalize monitoring reports. There are no known 

data limitations. 

The Department is no longer conducting ESEA Flexibility monitoring, but states continued 

to implement their ESEA Flexibility requests through August 1, 2016, before beginning the 

transition to the ESSA. 

2.1.B Number of states / territories that are 

implementing next-generation reading 

and mathematics assessments, aligned 

with college- and career-ready standards 

2.2.A Number of states that have fully 

implemented teacher and principal 

evaluation and support systems that 

consider multiple measures of 

effectiveness, with student growth as a 

significant factor 

ESEA Flexibility Applications and 

Monitoring 

In December 2015, the President signed into law the Every Student Succeeds Act

(ESSA), which reauthorizes the Elementary and Secondary Education Act of 1965

(ESEA). Under the ESEA as amended by the ESSA, the Secretary of Education is 

prohibited from, among other prohibitions, prescribing “any aspect or parameter of a 

teacher, principal, or other school leader evaluation system within a State or local 

educational agency” and “indicators or specific measures of teacher, principal, or other 

school leader effectiveness or quality.” The Department has no new data on the number 

of states implementing teacher and leader evaluation and support systems consistent 

with this performance metric and no way to verify the data reported. 

The Department is no longer conducting ESEA Flexibility monitoring, but states continued 

to implement their ESEA Flexibility requests through August 1, 2016, before beginning the 

transition to the ESSA. 

2.3.A Disparity in the rates of out-of-school 

suspensions for students with disabilities 

and youth of color (youth of color metric) 

Civil Rights Data Collection (CRDC) The Office for Civil Rights (OCR) strives to ensure CRDC data are an accurate and 

comprehensive depiction of student access to educational opportunities in school 

districts. The submission system includes a series of embedded edit checks to ensure 

significant data errors are corrected before the district submits its data. Additionally, each 

district is required to certify the accuracy of its submission. Only a district superintendent, 

or the superintendent’s designee, may certify the CRDC submission. Ultimately, the 

quality of the CRDC data depends on accurate collection and reporting by the 

participating districts. 

2.3.B Disparity in the rates of out-of-school 

suspensions for students with disabilities 

and youth of color (SWD, IDEA only 

metric) 

*Metric associated with an Agency Priority Goal. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 113 

2.4.A *Number of persistently low graduation 

rate high schools

NCES EDFacts EDFacts works with the data stewards to determine the appropriate business rule checks for 

these data. The checks that were done on these data include: 

• File validation and format checks: Identifies file submissions that do not conform 

to the accepted file format, the data universe (school/LEA/state directory), or the 

reporting requirements (mandatory reporting fields). 

• Submission Edit Business Rules: Submission edits and warnings ensure that the 

data meet or exceed an acceptable level of reasonability by checking the values 

entered in a field against other similar values in the same file or across files. If a 

discrepancy is found (i.e., a value falls outside of the acceptable range), a 

submission error or warning is issued. Unlike format and validation edits, 

submission edits and warnings are programmed into the EDFacts Submission 

System and applied to the data after they are in the staging database. 

• Coordinated Data Quality Reviews (CDQR): Identifies potential errors and 

anomalies related to completeness, consistency, and comparability in the file 

submissions that would affect the quality and usability of data in the files. 

o CDQR Process: At the end of a collection period, EDFacts runs checks to 

validate data quality of submitted data and presents all results to program 

offices. It is the responsibility of the program offices, in consultation with 

the EDFacts staff, to determine which identified errors to escalate to 

states for further review. The EDFacts Partner Support Center (PSC) 

sends these errors to states for remediation, which take the form of 

explanations for data anomalies, data file submission updates, and data 

corrections. OESE should be considered the point-of-contact for 

identifying which errors were escalated and the result of those 

escalations. 

Data concerns for state, district and school level 2014–15 data submissions are documented 

and available in two places: 

a) State Notes to submitted Adjusted Cohort Graduation Rate (ACGR) data are 

available within the Department’s ED Data Express Tool at 

http://eddataexpress.ed.gov 

b) Identified Data Anomalies are documented at the state level in appendix B to the file 

documentation released along with school and district level data files at 

http://www2.ed.gov/about/inits/ed/edfacts/data-files/index.html 

Additional concerns, if any are identified, related to the calculated national rate or work done 

prior to 2014–15 will be available in future releases of the NCES report “Public High School 

Four-Year On-Time Graduation Rates and Event Dropout Rates: School Year ####-##.” The 

most recent report covers 2010–11 and 2011–12. Reports covering more recent years of data 

are being prepared and once released will be available at 

http://nces.ed.gov/ccd/pub_dropouts.asp. 

Path to public release and national rates: For each collection cycle states report their calculated 

cohort counts and ACGR graduation rates. These counts and rates are tested against a number 

of submission edit business rules and the ACGR Coordinated Data Quality Review for format, 

consistency, completeness, and comparability. Those rule checks are delivered to the Office of 

Elementary and Secondary Education (OESE) for follow-up correspondence through the 

EDFacts Partner Support Center with the states. The program office, in consultation with NCES 

EDFacts staff and other stakeholders, meet to determine which issues identified by the 

business rules should be raised with the state for explanation, update, or correction. Following 

that review, data are then aggregated to the national level for the purpose of calculating and 

publishing a national rate. NCES processes these data to force conformity of reporting 

categories (mapping reported “Major Racial/Ethnic Groups” to the traditional 5 racial/ethnic 

groups) and addresses any issues raised by missing data. After imputations are made, NCES 

produces a national rate for the country as a whole; a rate which is representative of every 

state. Once produced, NCES documents any remaining issues with these data, and the 

aggregation and imputation methodology in a public report. That report, documentation, and the 

associated data tables are put through several stages of review including independent reviews 

at the division (Administrative Data), center (NCES), and principal office (IES) levels prior to 

public dissemination.

*Metric associated with an Agency Priority Goal. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 114 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements

2.4.B Percentage of SIG schools in Cohort 5 

that are above the 25th percentile in 

mathematics, as measured by their state 

assessments 

Analytic dataset produced by the 

contractor for the SIG National Summary, 

because this provides an accurate list of 

SIG schools and flags for different 

exclusions that we are included in the 

analysis. (The analytic dataset is a 

combination of EDFacts student 

achievement files in Math and Reading, 

the NCES Common Core of Data, SIG lists 

provided to EDFacts by Office of State 

Support (OSS), and Exclusions that are 

generated by the contractor that apply to 

these results.) 

To produce these numbers, OESE relies on the analytic dataset produced by the 

contractor for the SIG National Summary, because this provides a list of SIG schools and 

flags for different exclusions that we include in the analysis. The analytic dataset is a 

combination of EDFacts student achievement files in Math and Reading, the NCES 

Common Core of Data, SIG lists provided to EDFacts by OSS, and Exclusions that are 

generated by the contractor that apply to these results. 

For the 2012–13 data, a draft version of the analytic dataset was available in March of 

2015, although OESE hopes that it will be available sooner in future years. 

Information can be found on the EDFacts website 

(http://www2.ed.gov/about/inits/ed/edfacts/index.html).

2.4.C Percentage of SIG schools in Cohort 5 

that are above the 25th percentile in 

reading language arts, as measured by 

their state assessments 

2.5.A Percentage of high school and middle 

school teachers who teach STEM as 

their main assignment who hold a 

corresponding undergraduate degree 

Schools and Staffing Survey (SASS), 

NCES 

The methods report for the 2011–12 SASS is not yet released. Study documentation from 

the 2007–08 survey is available at https://nces.ed.gov/surveys/sass/methods.asp. 

2.5.B Number of External high school 

graduates who have taken at least one 

STEM AP exam 

College Board/AP administrative records College Board Public School List is updated annually by state DOEs; thus small changes 

to the list over time are to be expected as schools open, close, and/or merge. Students 

are assigned to graduating cohorts based on self-reported information (i.e., grade level 

and/or graduation year) provided at the time of registration (in the case of SAT) or test 

administration (in the case of AP and PSAT). The College Board matches students’ data 

across programs to identify the most recent valid value when assigning students to 

cohorts. 

*Metric associated with an Agency Priority Goal. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 115 

GOAL 3: EARLY LEARNING. Improve the health, social-emotional, and cognitive outcomes for all children from birth through 3rd grade, so that all children, 

particularly those with high needs, are on track for graduating from high school college- and career-ready. 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements 

3.1.A *Percent of 4-year olds enrolled in state 

preschool programs

Preschool Development Grants Annual 

Performance Reports (APR), Data 

Workbook, Table A(1)(a) and (b) 

APR data is collected externally. The data are collected primarily through a performance 

report template and a data workbook submitted by State Project Directors for the grants. 

Data are collected by program offices through a technical assistance contractor using a 

performance report template and self-administered electronic submission tool. The data 

collection period for one year is reported in the following year’s APR submission. For 

example, school year 2015–2016 data is reported in February of 2016. During November 

of 2015, State Project Directors are provided with any submission updates and granted 

access to the APR submission tool. The APR data collection period is January 1, 2015–

December 31, 2015. The Technical Assistance (TA) contractor performs an analysis and 

data anomalies are clarified and verified with the State Project Director. 

3.1.B *Number of states with high-quality 

preschool programs standards 

National Institute for Early Education 

Research (NIEER) Yearbook (The State of 

Preschool), Table 1: State Ranking and 

Quality Checklist Sums, Column “ Quality 

Checklist Sums”

NIEER data are collected externally. The data are collected primarily through surveys of 

state preschool administrators using the State of Preschool data collection instrument. 

Data are collected by staff at Rutgers University’s NIEER using a self- administered 

online data instrument. The collection for the 2013–14 school year, for example, began in 

the fall of 2013. During November of 2013, links to the web-based survey instrument were 

sent to administrators of the state-funded preschool programs. The initial listing of 

administrators was drawn from the 2011–12 State of Preschool data collection. NIEER 

project staff updated the list, where appropriate, by recontacting state agencies to 

determine if new programs had been started since the 2013–14 school year, or whether 

any programs had been left out of the previous report (no such programs were identified). 

All states and the District of Columbia responded. Data collection ended in April 2015. All 

programs included in the data collection and current report are those that are funded and 

directed by the states to support group learning experiences for preschool-age children, 

usually ages 3 and 4. A full list of criteria programs must meet for inclusion is available in 

the introduction of this report. Please see Data Verification Overview.

http://nieer.org/yearbook. 

3.2.A Number of states that require a teacher 

with a bachelor’s degree in a state 

preschool program 

National Institute for Early Education 

Research (NIEER) Yearbook (The State of 

Preschool), Workforce Supplement, Table 

3: Preschool Teacher Qualifications and 

Salary, Column “BA Required”

NIEER data are collected externally. The data are collected primarily through surveys of 

state preschool administrators using the State of Preschool data collection instrument. 

Data are collected by staff at Rutgers University’s NIEER using a self- administered 

online data instrument. The collection for the 2013–14 school year, for example, began in 

the fall of 2013. During November of 2013, links to the web-based survey instrument were 

sent to administrators of the state-funded preschool programs. The initial listing of 

administrators was drawn from the 2011–12 State of Preschool data collection. NIEER 

project staff updated the list, where appropriate, by recontacting state agencies to 

determine if new programs had been started since the 2013–14 school year, or whether 

any programs had been left out of the previous report (no such programs were identified). 

All states and the District of Columbia responded. Data collection ended in April, 2015. All 

programs included in the data collection and current report are those that are funded and 

directed by the states to support group learning experiences for preschool-age children, 

usually ages 3 and 4. A full list of criteria programs must meet for inclusion is available in 

the introduction of this report. Please see Data Verification Overview.

http://nieer.org/yearbook. 

*Metric associated with an Agency Priority Goal. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 116 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements

3.3.A Number of states collecting and 

reporting disaggregated data on the 

status of children at kindergarten entry 

using a common measure 

Race to the Top (RTT)-Early Learning 

Challenge (ELC) Technical Assistance 

Center 

The data are limited to the 20 Race to the Top-Early Learning Challenge (RTT-ELC) 

states. 

Department staff has requested Kindergarten Entry Assessment (KEA) data be collected 

in the 2015–16 academic year as part of the State of Preschool data collection and has 

requested to add KEA data reporting in EDFacts. In addition, the new Preschool 

Development Grants will provide data on additional states. 

*Metric associated with an Agency Priority Goal. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 117 

GOAL 4: EQUITY. Increase educational opportunities for underserved students and reduce discrimination so that all students are well-positioned to succeed. 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements 

4.1.A *National high school graduation rate NCES EDFacts EDFacts works with the data stewards to determine the appropriate business rule checks for 

these data. The checks that were done on these data include: 

• File validation and format checks: Identifies file submissions that do not conform 

to the accepted file format, the data universe (school/LEA/state directory), or the 

reporting requirements (mandatory reporting fields). 

• Submission Edit Business Rules: Submission edits and warnings ensure that the 

data meet or exceed an acceptable level of reasonability by checking the values 

entered in a field against other similar values in the same file or across files. If a 

discrepancy is found (i.e., a value falls outside of the acceptable range), a 

submission error or warning is issued. Unlike format and validation edits, 

submission edits and warnings are programmed into the EDFacts Submission 

System and applied to the data after they are in the staging database. 

• Coordinated Data Quality Reviews (CDQR): Identifies potential errors and 

anomalies related to completeness, consistency, and comparability in the file 

submissions that would affect the quality and usability of data in the files. 

o CDQR Process: At the end of a collection period, EDFacts runs checks to 

validate data quality of submitted data and presents all results to program 

offices. It is the responsibility of the program offices, in consultation with 

the EDFacts staff, to determine which identified errors to escalate to 

states for further review. The EDFacts Partner Support Center (PSC) 

sends these errors to states for remediation, which take the form of 

explanations for data anomalies, data file submission updates, and data 

corrections. OESE should be considered the point-of-contact for 

identifying which errors were escalated and the result of those 

escalations. 

Data concerns for state, district and school level 2014–15 data submissions are documented 

and available in two places: 

a) State Notes to submitted Adjusted Cohort Graduation Rate (ACGR) data are 

available within the Department’s ED Data Express Tool at 

http://eddataexpress.ed.gov 

b) Identified Data Anomalies are documented at the state level in appendix B to the file 

documentation released along with school and district level data files at 

http://www2.ed.gov/about/inits/ed/edfacts/data-files/index.html 

Additional concerns, if any are identified, related to the calculated national rate or work done 

prior to 2014–15 will be available in future releases of the NCES report “Public High School 

Four-Year On-Time Graduation Rates and Event Dropout Rates: School Year ####-##.” The 

most recent report covers 2010–11 and 2011–12. Reports covering more recent years of data 

are being prepared and once released will be available at 

http://nces.ed.gov/ccd/pub_dropouts.asp. 

Path to public release and national rates: For each collection cycle states report their calculated 

cohort counts and ACGR graduation rates. These counts and rates are tested against a number 

of submission edit business rules and the ACGR Coordinated Data Quality Review for format, 

consistency, completeness, and comparability. Those rule checks are delivered to the Office of 

Elementary and Secondary Education (OESE) for follow-up correspondence through the 

EDFacts Partner Support Center with the states. The program office, in consultation with NCES 

EDFacts staff and other stakeholders, meet to determine which issues identified by the business 

rules should be raised with the state for explanation, update, or correction. Following that review, 

data are then aggregated to the national level for the purpose of calculating and publishing a 

national rate. NCES processes these data to force conformity of reporting categories (mapping 

reported “Major Racial/Ethnic Groups” to the traditional five racial/ethnic groups) and addresses 

any issues raised by missing data.

*Metric associated with an Agency Priority Goal. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 118 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements

4.1.B *Gap in the graduation rate between all

students and students from low-income 

families

After imputations are made, NCES produces a national rate for the country as a whole; a rate 

which is representative of every state. Once produced, NCES documents any remaining issues 

with these data, and the aggregation and imputation methodology in a public report. That 

report, documentation, and the associated data tables are put through several stages of review 

including independent reviews at the division (Administrative Data), center (NCES), and 

principal office (IES) levels prior to public dissemination. 

4.1.C *Number of schools that do not have a

gap or that decreased the gap between 

students from low-income families and 

the state average for all students

4.2.A Percentage of proactive civil rights 

investigations launched annually that 

address areas of concentration in civil 

rights enforcement 

Office for Civil Rights’ (OCR) Case 

Management System (CMS) and 

Document Management (DM) systems 

The verification and validation of CMS data utilize (1) rules built into the CMS Case 

Management component; (2) periodically checks on questionable data; (3) guidance and 

reference on data entries; and (4) regular training and guidance for primary users 

imputing the data. 

(1) Rules built in CMS: 

 Samples of CMS validations: 

 - Docket number is assigned by the CMS based on four required data (Active 

Office, Case Type, Institution Type, and Case Open Date). 

- Date should be a validated date. 

- When a notification letter is issued, current stage is changed from Evaluation to 

Investigation. 

- Resolution Type is filled by the CMS based on the Resolution Codes of allegations. 

(2) Periodic checks on questionable data: (monthly) 

- Missing entries: such as cases resolved without (a) case resolved/closed dates, (b) 

allegations and (c) resolutions. 

- Inconsistent entries: such as resolution type not matched with resolution code. 

(3) Guidance and reference materials for use by primary users making data entries are 

posted at the OCR SharePoint site and are available to all OCR staff: 

- Lists of issue codes, specific bases, resolution codes, milestones, stage, data 

definition, and etc. 

- CMS Data Entries requirements and checklist. 

- CMS User Manual. 

(4) Users Training: 

 - User training is available as needed. OCR also provides training through a “peer 

helper” or “train the trainer” model for staff in the field offices and headquarters.

4.2.B Percentage of proactive civil rights 

investigations resolved annually that 

address areas of concentration in civil 

rights enforcement 

*Metric associated with an Agency Priority Goal.

NCES EDFacts
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 119 

GOAL 5: CONTINUOUS IMPROVEMENT OF THE U.S. EDUCATION SYSTEM. Enhance the education system’s ability to continuously improve through better 

and more widespread use of data, research and evaluation, evidence, transparency, innovation, and technology. 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements 

5.1.A Number of public data sets included in 

ED Data Inventory and thus linked to 

Data.gov or ED.gov websites 

Data Strategy Team Data Inventory and 

the public ED Data Inventory at 

http://datainventory.ed.gov

The data are validated with a crosswalk between Inventory entries and the listing of public 

Department datasets, ensuring that the data described in the ED Data Inventory is 

publicly available at the identified web address. 

5.1.B Number of states linking K-12 and 

postsecondary data with workforce data 

State Longitudinal Data Systems (SLDS) 

grant monitoring (monthly updates from 

states, annual performance reports, final 

performance reports, and site visits) 

Data are collected through monitoring of states with active SLDS grants. The data are 

limited to this population. While 47 states and territories have received these grants over 

time, by June 2015, there will be fewer than 25 states with active grants, which leads to 

incomplete and not up-to-date data from states that either have not receive grants or that 

do not have active grants. 

5.1.C Number of states linking K-12 with early 

childhood data 

If the state is focusing on an early childhood data system, the state’s reporting might 

reflect only those development efforts, and not statewide P20W (preschool to college 

workforce) development efforts. There are additional sources for information about state 

data linkages in the field. For example the Data Quality Campaign (DQC) conducts 

surveys of state capacity to collect, store, link, and use data. Because the Department 

does not use the same definitions as DQC, our figures tend to be lower than theirs. For 

example, we require that a state possess the capacity to follow its own students in order 

for us to report that the state has the linkage in place. Similarly, we enable states to report 

on whether particular linkages are planned, in progress, or complete, and report that a 

state has a linkage when the state reports that the project is complete; DQC might give a 

state credit for an ‘in progress’ or pilot-stage linkage. 

A survey administered to the universe of states and territories would enable more 

systematically collect data about all states’ capacity for data linkages and data use. There 

is a concern, however, that if those data were to be used for public reporting, states might 

begin to overstate their capacities, particularly on data that are also publicly reported by 

organizations such as DQC. Currently, data from monitoring is used in an iterative, 

formative approach to program improvement; our technical assistance program is 

designed to support states’ efforts to improve their systems. This relies on states being 

honest about their own internal capacities. 

*Metric associated with an Agency Priority Goal. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 120 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements

5.2.A Average time to close “cases” (PTAC + 

FPCO) 

Case Tracking System (CTS) 

Monthly metric reports 

The term “case” refers to requests for quick, informal responses to routine questions 

related to student privacy. These requests are received via email, the Family Policy 

Compliance Office (FPCO) / Privacy Technical Assistance Center (PTAC) resource 

website, or by telephone and subsequently entered into the Case Tracking System (CTS). 

In contrast, “correspondence and complaints” refers to written complaints of alleged 

failures to comply with Family Educational Rights and Privacy Act (FERPA) / Protection of 

Pupil Rights Amendment (PPRA) filed with FPCO; requests for formal written 

guidance/interpretation laws administered by FPCO; and, to the reporting of instances of 

data breaches by educational agencies and institutions. These inquiries are logged into 

the Correspondence Control Manager (CCM) System, given a tracking control number, 

and assigned to FPCO staff. 

The preliminary data for this metric are reviewed at least weekly for verification. If 

anomalous data are identified in the periodic reviews or when anomalies are suspected, 

individual cases are examined individually to identify if they were properly closed or if their 

status was entered incorrectly. When appropriate, corrections are made. Staff responsible 

for entering data into the CTS will continue to be trained on policies and procedures. 

The monthly metric reports are scrutinized by the Director of FPCO, the Contracting 

Officer Representative for the PTAC contractors, and the Department’s Chief Privacy 

Officer, to assure completeness and reliability of data and to recommend any 

improvements to the CTS or modifications to the standard operating procedures. The 

quarter entry represents the fiscal year to date average days to close as of the end of that 

quarter taken from the corresponding monthly report. 

5.3.A *Percentage of select new (noncontinuation) discretionary grant dollars 

that reward evidence

Forecast Report issued by the Office of the 

Chief Financial Officer and final Funding 

Reports from relevant programs 

In determining which discretionary grants are considered “evidence-based” (i.e., the 

numerator when calculating the percentage), the Department includes all programs that 

rewarded applicants with supporting evidence of promise or better (per the EDGAR 

evidence framework). This could be done through a competitive preference or absolute 

priority, an eligibility requirement, or a selection factor. Only the amounts of the grants 

awarded for those projects were counted. In determining what counts as discretionary 

funding (i.e., the denominator when calculating the percentage), the Department includes 

all programs for which the EDGAR evidence framework could conceivably work. In Fiscal 

Year 2015, the Department counted all discretionary grant programs except for those 

programs run through the Institute of Education Sciences (IES), which are already 

evidence-based and would not be candidates for the EDGAR evidence framework). 

5.3.B Number of peer-reviewed, full-text 

resources in the Education Resources 

Information Center (ERIC) 

ERIC To be counted as a full-text, peer-reviewed article in ERIC, the document had to be 

indexed in ERIC and designated with a “peer-reviewed” and “full-text” flag, both of which 

are available on the public ERIC website 

The ERIC contractor uses specified quality assurance procedures. In addition, the IES 

program officer pays close attention to the metric in their review of deliverables. 

5.3.C Number of reviewed studies in the What 

Works Clearinghouse (WWC) database 

WWC To be counted as a reviewed study, the study had to be listed in the What Works 

Clearinghouse’s publicly available Database of Reviewed Studies. 

The What Works Clearinghouse contractors use specified quality assurance procedures. 

In addition, the IES program officers pay close attention to the metric in their review of 

deliverables. 

*Metric associated with an Agency Priority Goal. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 121 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements

5.3.D *Number of completed project 

evaluations from grantees of select 

discretionary grant programs in a given 

fiscal year that meet What Works 

Clearinghouse (WWC) Evidence 

Standards

Discretionary grant slate memoranda, 

discretionary grant financial forecasts and 

reports from OCFO, and the What Works 

Clearinghouse 

Expectation is that it will take about five minutes to search the WWC database for new i3 

studies each quarter. Also considering ways in which Institute of Education Sciences’ staff 

and Office of Innovation and Improvement staff can work closely to report these data 

quickly. 

 

No formal verification or validation processes are in place or to check for anomalous data. 

So far, no anomalous data has been encountered. 

 

The Office will report out on studies that are funded by i3 that are in the WWC database 

and meet WWC Evidence Standards. Please note it is not implied that these studies all 

reflect positive results—in actuality, the WWC Evidence Standards only measure the rigor 

of impact evaluations and do not consider the actual outcomes reported. 

5.4.A Percentage of schools in the country that 

have actual internet bandwidth speeds 

of at least 100 Mbps 

Education Superhighway (for baseline), 

Consortium for School Networking 

(CoSN)/AASA (American Association of 

School Administrators today known as 

AASA, The School Superintendents 

Association) E-rate Infrastructure Survey 

The Department uses an external data source for this metric and relies on the external, 

third party’s verification and validation methodology. Based on the information provided 

(http://cosn.org/about-cosn), the response rate for this survey may not be sufficient to 

ensure that the data are representative of all districts in the country. The Department is 

exploring the feasibility of collecting data on access to and use of education technology 

from a representative sample of schools and districts across the country. In the 

meantime, we believe these data are the best currently available and provide useful 

information to gauge progress on this metric. 

*Metric associated with an Agency Priority Goal. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 122 

GOAL 6: U.S. DEPARTMENT of EDUCATION CAPACITY. Improve the organizational capacities of the Department to implement this strategic plan. 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements 

6.1.A Percent of selections made per job 

opportunity announcement (JOA) 

EDHires (Monster’s electronic hiring 

management system) 

The data are entered into the EDHires system by customers, contractors, and staff, so 

quality of data are only as reliable as the information entered and/or updated in the 

system. 

 

Data are reviewed by HR Specialists for relevance and completeness to ensure correct 

parameters and filters have been applied. 

 

If anomalies are identified, HR Specialists will compare contractor generated reports 

against ad hoc hiring reports generated from the system of record and other linked HR 

systems. Any questionable results would be brought to the contractor’s attention; in turn, 

OHR would work with the contractor to obtain clarity and/or resolve. 

6.1.B EVS Employee Engagement Index Office of Personnel Management (OPM) 

Federal Employee Viewpoint Survey 

(FEVS) 

Any questionable FEVS data points are brought to the attention of OPM. In turn, the OPM 

point of contact responds to the inquiry. Further, if there are questions regarding the 

FEVS data, the Department works with the OPM point of contact to obtain clarity. 

The Engagement index score is calculated by OPM by first determining the percent 

positive for each of the 15 items in the sub-indices (i.e., Leaders Lead, Supervisors, and 

Intrinsic Work Experiences). Then the unrounded percent positive scores are averaged 

across the items in the index to get the index score. The overall index score is then 

rounded for reporting purposes.
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 123 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements

6.1.C Time to Hire Federal Personnel/Payroll System (FPPS) 

Datamart 

The Office of Management (OM) identified a gap between hiring procedures and the 90 

Day Hiring Model’s time to hire calculations which necessitated an adjustment in the way 

time to hire is calculated for some actions. OM found that recruitment work is often 

initiated prior to the receipt of an “official” action. While this is a proactive customer-centric 

approach, especially prevalent in ED’s noncompetitive hiring activities, it can sometimes 

result in a negative time to hire or a time to hire of zero—neither of which reflects a 

realistic or meaningful hiring lead time. 

To account for, track and document this upfront work, OM developed an adjustment 

mechanism to better gauge hiring lead time in these cases. For ease of comparison, the 

adjustment was applied to all FY 2016 hiring actions and resulted in a revised Q1 actual. 

To ensure clarity and consistency in the application of time to hire methodology moving 

forward, OM also clarified time to hire calculation protocols as follows: 

 Individual time to hire: 

o Time to hire for individual hiring actions is determined by calculating 

the number of days between the Request to Recruit (ROE) Date and 

the Verbal (tentative) Offer Date, as reflected in FPPS DataMart. 

 If time to hire results in a negative or zero, it is replaced 

with the value 1. 

 If time to hire cannot be determined (i.e., date not 

available), it is excluded from agency calculations. 

o Time to hire is not calculated for actions In-Progress; it is only 

calculated for Completed actions (i.e., estimated EOD established). 

 Agency time to hire: 

o The Department’s time to hire is an annual rate determined by 

calculating the percent of hiring actions within a fiscal year completed 

within 90 days. 

o Q1, Q2, and Q3 actuals are cumulative, measured from the 

beginning of the fiscal year. 

o Q4 actuals represent the Department’s annual time to hire rate and 

are reported in the Annual Performance Report. 

o Periodic snapshots of time to hire may be computed monthly, 

quarterly or by principal office to help gauge progress toward the 

annual target; however, care should be taken to ensure periodic 

updates are clearly identified as snapshots or progress indicators and 

not represented as the agency’s annual rate.

6.1.D Effective Communication Index Office of Personnel Management (OPM) 

Federal Employee Viewpoint Survey 

(FEVS) 

Any questionable FEVS data points are brought to the attention of OPM. In turn, the OPM 

point of contact responds to the inquiry. Further, if there are questions regarding the 

FEVS data, the Department works with the OPM point of contact to obtain clarity. 

This index score is calculated by the Department by averaging the percent positive scores 

from OPM FEVS questions 53, 58, and 64. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 124 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements

6.2.A. Percentage of A-133 Single Audits 

Overdue for resolution 

Office of the Chief Financial Officer’s 

(OCFO) Audit Accountability & Resolution 

Tracking System (AARTS) 

Calculations for this metric are determined by dividing the total number of audits that are 

overdue at the end of the Department’s fiscal year by the total number of audits in the 

Department’s inventory. 

Access rights to the AARTS database are managed by Office of the Chief Financial 

Officer (OCFO) and all users receive annual IT security training to help ensure data 

integrity. A Quality Control reviewer initiates a weekly upload of A-133 audit data to 

AARTS through a file submission directly from the Federal Audit Clearinghouse (FAC). An 

AARTS administrator (separate from the Quality Control reviewer) must verify the 

uploaded data with the actual audits. Data for individual programs are verified by Principal 

Offices (POs) through periodic review by Responsible Managers and Audit Liaison 

Officials. In addition, the specific data for this metric are verified by POs each month as 

part of monthly Dashboard reports. 

Data are validated by OCFO monthly. Staff work to reconcile data reported on the 

Dashboards with any discrepancies reported by the POs. 

6.2.B. Compliance rate of contractor evaluation 

performance reports 

Past Performance Information Retrieval 

System, www.ppirs.gov, “PPIRS 

Compliance Report”

Compliance rates of contractor performance evaluations are set by OMB and are 

calculated by use of a Government wide reporting tool available in the PPIRS 

(www.ppirs.gov).

Office of the Chief Financial Officer (OCFO) and Federal Student Aid (FSA) Acquisitions 

staff actively monitor the PPIRS report to ensure that each contractor performance 

evaluation reflected on that report should appear on that report, and to rectify any errors 

on the report. 

6.3.A. Overall average impact score of the 

Department’s technical assistance in 

helping build State capacity to 

implement education reforms 

Annual Grantee Satisfaction Survey The contractor has a quality control system and the results are also provided to all of the 

programs that participate in the survey to identify issues/anomalies with the data. 

 

Program staff report anomalies to the contractor to correct. 

 

The data comes from the Annual Grantee Satisfaction Survey Report (See 

https://www2.ed.gov/about/reports/annual/gss/index.html). The report is produced by a 

contractor; the contract is managed by OCFO. 

 

One major issue with the Grantee Satisfaction Survey is the timing of the survey. The 

survey is administered in late spring/early summer when many staff are out of the office, 

which suppresses the response rate. Also, some grant programs do a better job than 

others of promoting the survey and showing how the survey results have been used to 

make improvements. We have been told that the survey is administered late each year for 

the following reasons: 

--The Interagency Agreement can’t be put into clearance until the budget is approved 

each year; 

--Getting the Interagency Agreement through Departmental clearances takes several 

months; program staff are asked to review their customized survey questions and submit 

their grantee contact lists at the busiest time of the year which makes it difficult to turn 

them around quickly. 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 125 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements

6.4.A. Number of ED IT security incidents Cyber Security Assessment and 

Management (CSAM) and RSA Security 

Operations management (SecOps) 

Office of the Chief Information Officer (OCIO) has standard operating procedures (SOP) 

to verify and validate the data: 1) CSAM SOP, 2) Incident Response SOP, and 3) SecOps 

SOP. This process is executed on a weekly basis. Further, the data is pulled from the 

Department’s authoritative source (system of record) for Incident Response and Tracking. 

In addition, SOPs enforce the use of this capability for entering, tracking, and managing 

all incident related activity. The data is also audited on a routine basis to ensure 

consistency and accuracy. 

6.4.B. EVS Results-Based Performance 

Culture Index 

Office of Personnel Management (OPM) 

Federal Employee Viewpoint Survey 

(FEVS) 

Data verification and validation process is integrated into the OPM FEVS survey results 

validation process. Any questionable FEVS results would be brought to the attention of 

OPM; the Department would then work with the OPM point of contact to obtain clarity. 

Data verification and validation process is integrated into the OPM FEVS survey results 

validation process. The Results-Oriented Performance Culture Index score is calculated 

by OPM by first determining the percent positive for each of the 13 items in the index. 

Then the unrounded percent positive scores are averaged across all index items to get 

the index score. The index score is then rounded for reporting purposes. 

6.4.C. EVS Leadership and Knowledge 

Management Index 
APPENDICES

DATA VALIDITY AND VERIFICATION

FY 2016 Annual Performance Report and FY 2018 Annual Performance Plan—U.S. Department of Education 126 

Metric No. Metric (Indicator) Data Source Data Quality, Limitations and Improvements

6.4.D Total usable square footage Department’s Master Space Management 

Plan 

The Department reconciles its rent bills per building on a monthly basis. Every six months 

the usable square footage is verified with GSA. 

Because usable square footage relates directly to rent costs, the Department uses the 

same data verification and validation procedures. Specifically, the data are collected 

directly from Occupancy Agreements and rent bills per building. 

The data are derived from historic examples and relevant experience. Department 

leadership has agreed to a set of assumptions by which the data are based. Leadership 

has reached out to subject matter experts to broaden the scope of the data set, and lower 

risks of missing contingencies that may affect the data. At each step, the data are 

reviewed independently to double check the work of each team member and provide 

quality control. These processes help ensure the data’s completeness and reliability.

For the baseline data, the Department made the following assumptions: 

1) All leased buildings: 2% is applied for anticipation of CPI (Consumer Price Index) 

annual increases on the anniversary date of the active lease/occupancy agreement (OA); 

and 2.5% is applied for anticipation of annual tax increases. 

2) All federal buildings: 2.5% is applied for operating cost escalations on the anniversary 

date of the active OA. 

3) 20% is applied to all federal buildings after an OA has expired and a new OA is 

unavailable. (Projected increase on the appraisal.) 

4) 40% is applied to all leased buildings after an OA has expired and a new OA is 

unavailable. (Projected increase on the market rent.) 

5) If a new OA is unavailable, 3 months’ early rent is applied to all buildings that are 

relocating due to possible Department delays. Example: Changes made to the designs 

after Scope of Work (SOW) is completed. 

6) 3 months’ late rent is applied to all buildings that are relocating due to possible 

Department delays. For example, delays in returning space back to a rentable condition. 

 

The Department reconciles its rent bills per building on a monthly basis. 

 

6.4.E Rent cost Department’s Master Space Management 

Plan 

Data are collected directly from OAs and rent bills per building. The actual rent may vary 

significantly if the Department relocates to a new leased building and/or signs short lease 

extensions. The Department is leveraging the examples and experience of the mobility 

labs and building consolidations programs. 

The Department reconciles its rent bills per building on a monthly basis. Every six 

months, leadership will re-evaluate the data, the assumptions on which it is based, and 

incorporate actual costs and project schedules. These steps will become part of our 

quality assurance program and procedures. Leadership looks to improve completeness, 

reliability, and quality of the data at these milestones. 

Total number of the Department’s 2016 external (public-facing) metrics (indicators): 52