1 

 

Data Quality Assessment Tool for Administrative Data 

William Iwig 

National Agricultural Statistics Services 

Michael Berning 

U.S. Census Bureau 

Paul Marck 

U.S. Census Bureau 

Mark Prell 

Economic Research Service 

 

 

February 2013 
2 

Contents 

Introduction ................................................................................................................................ 1 

I. Discovery Phase ........................................................................................................................ 6 

II. Initial Acquisition Phase .................................................................................................... 13 

III. Repeated Acquisition Phase ............................................................................................... 18

Appendix A—Compendium of Questions Across All Three Phases ..................................... 24 

Appendix B—Date Dictionary Template ................................................................................ 27 

 
Data Quality Assessment Tool for Administrative Data 

William Iwig, Michael Berning, Paul Marck, Mark Prell1

Introduction 

Government agencies maintain data for the administration of government programs. Unlike survey data, 

which are collected for statistical purposes, administrative data are collected as part of a program 

agency’s routine operations. Through a Memorandum of Understanding (MOU) a program agency may 

share its administrative records, or a data file extract of those records, with a statistical agency (under 

provisions that protect data confidentiality). The extent and effectiveness of data-sharing are impeded by 

various barriers. One of these barriers is limited information on how to assess the quality of the data. 

Broadly defined, data quality means “fitness for use.” Different users of the same data can have different 

assessments of its quality. Administrative data were gathered for a particular purpose—running a 

program—and can have qualities that are well-suited for that purpose. When the data are adapted to a 

new purpose, issues of data quality become especially salient. 

Assessment of data quality can benefit both program and statistical agencies. Statistical agencies that 

seek more routine use of administrative records in their statistical programs benefit from understanding 

the quality of the administrative data they receive. Upon receipt, a statistical agency produces various 

reports, analyses and data products that may be informative for program managers, policy officials and 

the program’s various stakeholders. The benefits to a program of these statistical analyses can be 

substantial. In addition, the program agency may face the need to increase understanding of its 

administrative records system among its own staff. As senior staff transition to different jobs or retire, 

ongoing staff responsible for managing the agency’s data system can benefit from fuller understanding 

and more complete documentation of key aspects of the system including the quality of the data. 

In July 2011, the Interagency Council on Statistical Policy asked the Statistical Uses of Administrative 

Records Subcommittee (of the Federal Committee on Statistical Methodology (FCSM)) to examine the 

 

1

 This paper represents part of the ongoing work of an interagency subcommittee under the auspices of the Federal 

Committee on Statistical Methodology (FCSM). The views expressed represent the individual authors and not 

necessarily the full FCSM or the agencies at which the authors are employed, which are: Economic Research 

Service (Mark Prell), National Agricultural Statistics Service (William Iwig) and the U.S. Census Bureau (Michael 

Berning, Paul Marck).

1 
quality of administrative data gathered by government agencies. The Subcommittee’s Data Quality 

Working Group had already begun foundational cross-agency work that developed into the current Data 

Quality Assessment Tool for Administrative Data presented here (hereafter referred to as the Tool). This 

Tool is developed to support a conversation between a user of an agency’s administrative data —either a 

user who may be initially unfamiliar with the structure, content and meaning of the records or a user who 

repetitively acquires the data but may not be aware of recent changes to the system—and a 

knowledgeable supplier or provider of administrative data. The Tool provides questions that are pertinent 

to helping a user assess the fitness for their intended use. These broad questions can prompt more 

particular, data-specific questions in the discussion between the data receiver and the data provider. 

The Tool is designed to help promote data quality in two ways. First, by helping a user better understand 

the data’s attributes, the Tool enables the data to be used more appropriately and meaningfully for their 

new applications—ones that differ from their original administrative uses. Second, the Tool can help 

promote data quality over time, both for administrative purposes and statistical purposes. While the Tool 

does not describe all the steps needed for improving quality, the Tool can help a data provider evaluate 

current quality conditions. Which aspects of data quality are already strong? In what ways? Which other 

aspects of data quality could benefit from strengthening? Assessing and understanding current quality is 

the critical first step by which a data provider would begin a long-term process by which to improve data 

quality over time. 

A key feature of data quality assessment that is built into this Tool is the recognition that data quality is 

not homogenous but instead has several dimensions (or “characteristics” or “features”). This multidimensional structure is a common feature of the data quality frameworks for other national statistical 

offices. To draw from their expertise and build on their previous work, the Data Quality Working Group 

examined the frameworks of the Australia Bureau of Statistics2, the United Kingdom’s Office of National 

Statistics3, Statistics Canada4, and Eurostat5. While these frameworks each focus on the quality of 

statistical products and output, they do differ to some degree in scope, terminology, and application. 

Some frameworks were designed to be applied for the various steps in the statistical production process, 

including the use of administrative data sources. Based on this review, the Working Group developed a 

 

2

 ABS Data Quality Framework, May 2009 ( http://www.abs.gov.au/ausstats/abs@.nsf/mf/1520.0 ) 

3

 ONS Guidelines for Measuring Statistical Quality ( http://www.ons.gov.uk/ons/guide-method/methodquality/quality/guidelines-for-measuring-statistical-quality/index.html ) 

4

 Statistics Canada Quality Guidelines ( http://www.statcan.gc.ca/pub/12-539-x/12-539-x2009001-eng.htm ) 

5

 European Statistics Code of Practice ( http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-32-11-955/EN/KS32-11-955-EN.PDF ) 

2 
tool containing six quality dimensions: Relevance, Accessibility, Coherence, Interpretability, Accuracy, 

and Institutional Environment. The Tool is specifically designed to assess the quality characteristics of an 

administrative data source. It provides a definition of each of its six dimensions and a set of questions 

that help the data receiver and the data provider evaluate the quality of an administrative data source 

relative to each dimension. Some of the dimensions overlap but each question is only included under one 

dimension. Timeliness is a common separate dimension in many of the prior frameworks but it is 

covered here under the Relevance dimension of the Tool. Coherence and Comparability are frequently 

included as separate dimensions in other frameworks but are both included under the Coherence 

dimension in the tool, covering consistency over time periods and comparability over domains. 

Using the Tool does not result in a single overall numerical measure (or metric or index) for data quality. 

Instead, the Tool provides questions for which some answers are quantitative and others qualitative. The 

usefulness of the Tool lies in providing a well-developed set of questions that prompt the user to consider 

certain key attributes of data quality; the Tool does not result in a judgment or recommendation apart 

from what the user develops. It is the user’s own interpretation of the answers—and the user’s 

prioritization of which ones are especially germane for the data application at hand—that constitutes the 

user’s own assessment of data quality. . 

The Tool contains 43 questions organized by three “phases” of the data sharing process. Then, within 

each phase, the questions are organized by the dimensions of data quality that are relevant to that phase. 

Consequently, only a subset of the questions must be answered at any one time. The 12 questions in the 

initial Discovery phase cover information needed to determine the feasibility and desirability of a possible 

data sharing project and ultimately, to approve the development of an MOU. The data quality dimensions 

of Relevance, Accessibility, and Interpretability are central to the Discovery phase. The Initial 

Acquisition phase begins with the approval for developing an MOU, includes finalizing and signing of the 

MOU, and ends with the first-time receipt of the data. During this phase, more detailed information will 

be needed by statistical staff on data quality issues, such as recoding methods, known sources of errors, 

and proportion of missing values for each data field. The data quality dimensions of Accessibility and 

Interpretability are important in this phase, as they were in the Discovery phase, each with new questions 

that arise with Initial Acquisition. This phase also has questions for the dimensions of Coherence, 

Accuracy, and Institutional Environment. Altogether this phase has 29 questions. Questions in the 

Repeated Acquisition phase cover periodic receipt of data. These questions would not be answered unless 

and until a second installment of the data is planned. The Repeated Acquisition phase has 11 questions, 

two of which are new while nine are repeated from the Discovery or Initial Acquisition phase. 

3 
 

Questions listed in the Tool under one phase may, in an actual instance, be helpful in a different phase. 

The ordering or sequencing of questions in the Tool need not be binding. Appendix A—Compendium 

of Questions Across All Three Phases provides a table containing the Tool’s full set of questions for 

easy reference. While the Tool contains 43 questions, only some of the questions are answered in any one 

of the three phases. Those questions that are not helpful for a user are ignorable, while other questions 

may prompt a user to think of issues or questions in addition to those provided by the Tool. 

Based on pilot test results, the time required for the staff of the data provider to complete the Tool (for the 

early phases) is predicted to be about six to ten hours—if a data dictionary is already available. If a data 

provider does not have a data dictionary, then a much larger but unknown amount of time would be 

involved with preparing one. The Tool’s Appendix B—Data Dictionary Template provides an example 

of a core set of information that is typically contained in a data dictionary. 

As suggested previously, good communication between the program agency and the statistical agency 

regarding the quality characteristics of the administrative data file is very important. Depending on the 

formality of the data sharing arrangement, communication protocols and contact persons may be specified 

in the data sharing agreement. Both agencies will likely continue to discover new findings about the 

quality of the data during the data sharing arrangement which need to be communicated to the other 

agency. 

To facilitate completion of the Tool, a hypothetical example is provided with “Example answers”

involving files from a generic State agency administering the Supplemental Nutrition Assistance Program 

(SNAP, formerly the Food Stamp Program). SNAP is a federal-state partnership administered at the 

federal level by the Food and Nutrition Service of the U.S. Department of Agriculture, and at the State 

level by separate State SNAP agencies. 

In SNAP, the group of persons who usually purchase and prepare foods together is known officially as 

the “SNAP unit.” The example below substitutes the more common term “family.” 

When using this Tool, it is important to recognize that the number and content of the administrative data 

files maintained in a program agency’s data system may differ from the number and content of the data 

file extracts that are derived or taken from the agency’s administrative data files and sent to the datareceiving statistical agency to conduct a research project. The Tool is flexible enough to be completed: 

4 
 once, by discussing each separate file as needed in the answer; or 

 separately for each separate file. 

What is key is that a conscious decision be made to adopt one of those two approaches, that the answers 

be written clearly for that approach, and that (if necessary) clear distinctions be provided between the 

administrative data files and the data file extracts. The hypothetical example we provide for SNAP 

happens to involve two administrative files. Each file is discussed, as needed, in the answers in the 

Discovery phase in anticipation of sending two data file extracts, one from each original administrative 

file, in the Initial Acquisition phase. The example also includes detailed data dictionaries for the 

Discovery and Repeated Acquisition phases. 

 

5 
Discovery 

I. Discovery Phase 

Questions in the Discovery phase cover information needed to approve the development of an MOU. The 

data quality dimensions of Relevance, Accessibility, and Interpretability are central to the Discovery 

phase. (12 questions) 

RELEVANCE. Relevance refers to how well the administrative data file meets the needs of the 

user in regards to data level (person, family, household, establishment, company, etc.), broad 

data definitions and concepts, population coverage, time period and timeliness. 

1. What are the general contents of the administrative data file(s) maintained by the program? 

Example answer. In our SNAP data system, we organize data into two administrative data files. 

Administrative File 1 is a “Program Beneficiaries” file that contains address information for the family. 

Administrative File 2 is a “Benefits” file that contains information on the amount of SNAP benefits for 

the family and personal information about each of the family’s members (e.g., birth date, gender, etc.). If 

we were to complete an MOU with you, it would be convenient for us to prepare two data files extracts, 

one for each of our original administrative files. 

2. What entity is represented at the record level of the administrative data file (person, household, 

family, business, etc.)? 

Example answer. Administrative File 1 is organized by family (the Benefit Case Number is a family’s 

unique identifier). Administrative File 2 is organized by individuals receiving benefits (the Case Number, 

Benefit Month, and Client ID serve as a unique identifier). The data file extracts would be organized the 

same way. 

3. Describe the scope of the records included on the administrative data file (program participants, 

geographic areas, etc.). 

Example answer. The administrative files contain data for those who officially begin the application 

process, including (a) applicants who are determined to be eligible for benefits and become SNAP 

participants for some period, (b) applicants who are ineligible for SNAP benefits and are not participants, 

and (c) applicants who do not complete the application process. The application process is officially 

begun with the submission of a SNAP application form that contains the names of the persons in the 

family, a signature, and an SSN. The geographic scope covers all applications filed in any program office 

in the State. If you want a data file extract for a particular sub-State area, or you want data fields (zip 

codes) that indicate such areas, the MOU should specify. 

4. What are the earliest and latest dates for which administrative data are available (including in 

archive)? 

Example answer. Our system contains active data for the calendar year-to-date and for the 5 previous 

calendar years. As each year ends, the earliest year of active data is moved to archive where they are 

retrievable, although with difficulty. Archives extend to 1998. Data earlier than 1998 have been purged 

and are not retrievable. 

5. How often are the data collected at the program office? (e.g., Daily for flow data, or a particular 

date such as April 1)? 

Example answer. Daily, at least for days when program offices are open. 

6 
Discovery 

6. What is the “reference time period” by which the data are organized in the administrative data 

file? 

Example answer. Although data are collected daily (question 5), in our State’s database there is not a 

separate file for each business day. Instead, we organize the data by month in our administrative data 

system. If an MOU is written, it would be good if it specified whether you want the data in your data file 

extract to be organized by month, or if it should be aggregated to the quarter or the year before we send it 

to you. 

7. How soon after a reference time period ends can a data file extract be prepared and provided? 

Example answer. After the end of a month, about 3-4 weeks would be needed to prepare a data file 

extract that includes that months’ data. 

ACCESSIBILITY. Accessibility refers to the ease with which the data file extract can be 

obtained from the administrative agency. This includes the suitability of the form or medium for 

transferring the data, confidentiality constraints, and cost. 

administrative data file meets the

8. Describe any legal, regulatory or administrative restrictions on access to the data file extract. 

Example answer. There are two restrictions on any data file extract. First, if an MOU is written by 

which the data can be shared, the MOU will describe the project(s) for which the data can be used with 

the restriction that the data cannot be used for any other project (unless the MOU is amended). Second, 

the data are to be kept confidential. The MOU, or a supplementary Data Security Agreement, would need 

to specify the technical and procedural safeguards by which the agency that receives the data will limit 

access to the data and maintain confidentiality. In particular, the MOU would need to describe how 

Personally Identifiable Information (PII) will be protected and how access to the PII will be permitted 

only to authorized persons. 

9. Describe any records or fields that were collected but cannot be included (for confidentiality or 

other reasons) in the data file extract provided to the user. 

Example answer. There are no restrictions on the availability of data fields, provided they are specified 

in the MOU. 

10. What is the typical reimbursement for sharing your data? Are reports, a data product, or a 

monetary fee given in return? 

Example answer. In exchange for providing data access to the statistical agency, the State SNAP agency 

requests that the statistical agency provide summary reports showing the results of analysis that includes 

the State SNAP file and tabulations quantifying the quality of the State administrative records data. 

INTERPRETABILITY. Interpretability refers to the clarity of information to ensure that the 

administrative data are utilized in an appropriate way. This includes evaluation of data 

collection forms, data collection instructions, and a data dictionary.

7 
Discovery 

13. Describe each variable on the administrative data file and note their valid values. (See template 

for data dictionary). 

Example answer. See attached data dictionary. 

14. If a complete data dictionary is not available, describe the primary identification and response 

fields on the administrative data file and note their valid values. 

Example answer. See attached data dictionary. 

8 
Example Data Dictionary for Discovery Phase 

File Name: Program Beneficiaries.por Description: 2004 Beneficiary Contact Information 

File Date: February 14, 2005 File Format: SAS portable dataset 

Field Name Description Req. Type Len Format Units Valid Values Definitions Notes:

case Case ID Y Num 6 1 - 999999 Sequential number assigned to each 

case 

Primary Key 

yrmo First benefit month case 

appeared at address 

Y Num 4 yymm 04

01 - 12

yy – two digit year 

mm – two digit month 

resaddr Residential address Y Char 1 Null 

Y 

N 

No response (Default) 

Yes this is a residential address 

No this is not a residential address 

HHName Name for household Y Char 40 Alphabetic 

addrln1 Mailing address – line 1 Y Char 22 Alphanumeric 

addrln2 Mailing address – line 2 N Char 22 Null 

Alphanumeric 

No response (Default)

city City N Char 16 Alphabetic 

St State N Char 2 Alphabetic See Notes Definitions are contained the 

USPS standard for ZIP 

codes dated Jan 1, 2005 

Zip 5 digit zip code Y Num 4 See Notes See Notes Valid values and definitions 

are contained in USPS 

standard for ZIP codes dated 

Jan 1, 2005 

fips_county FIPS County code N Num 3 See Notes See Notes Valid values and definitions 

are contained in FIPS 

standard dated Oct 24, 1998. 

 

9 
Example Data Dictionary for Discovery Phase 

File Name: Benefits_2004.por Description: Benefits data for 2004.

File Date: February 14, 2005 File Format: SAS portable dataset 

Field Name Description Req. Type Len Format Units Valid Values Definitions Notes: 

case Case ID Y Num 6 0 - 999999 Sequential number assigned to each 

case 

Primary Key 

yrmo Benefit month Y Num 6 yyyymm 2004 

01-12

yyyy – 4 digit year 

mm – 2 digit month 

Primary Key 

CID Client ID Y Char 6 0 - 999999 Sequential number assigned to each 

client 

Primary Key 

client Name of client Y Char 40 Alphabetic Full name of client 

ssn Social Security Number N Num 9 ###-##-#### Null 

Numeric 

(Default) 

birthdt Birthdate Y Num 6 mmddyy 01-12

01-31

00-99

mm – 2 digit month 

dd – 2 digit day 

yy – 2 digit year 

race Ethnic Group N Char 1 1 

2 

3 

4 

5 

6 

White 

Black 

Hispanic 

American Indian or Alaskan Native 

Asian or Pacific Islander 

Omitted (Default)

sex Client gender N Char 1 M 

F 

U 

Male 

Female 

Unknown (Default)

sig1 Code which identifies 

the client's status in 

group 

Y Char 1 A 

B 

C 

D 

E 

F 

G 

H 

J 

K 

M 

Head of Household member 

Student 

Striker 

Military member 

Eligible legalized alien 

Treatment center residents 

Head of Household - nonmember 

Refugee 

Disqualified as ineligible alien 

Disqualified failed to meet SSN 

requirement 

10 
Example Data Dictionary for Discovery Phase 

Field Name Description Req. Type Len Format Units Valid Values Definitions Notes:

R 

S 

T 

U 

X 

W 

Z 

Migrant, out of work stream 

DHS employee 

Alien with acceptable alien status 

Disqualified for intentional program 

violation 

Seasonal farm worker 

Non primary wage disqualified for 

noncompliance 

Migrant, in work stream 

Alien granted temporary resident 

status 

educat Client Education Level Y Char 1 1 

2 

3 

4 

5 

6 

7 

8 

9 

A 

B 

C 

D 

E 

F 

N 

First Grade 

Second Grade 

Third Grade 

Fourth Grade 

Fifth Grade 

Sixth Grade 

Seventh Grade 

Eighth Grade 

Ninth Grade 

Tenth Grade 

Eleventh Grade 

High School Graduate/ GED 

Currently Attends JR, High, GED 

Attending or completed some college 

Graduate of a 4 yr college 

No Formal Education 

casestat Case status code Y Num 3 1 

2 

3 

4 

Active 

Denied 

Suspended 

Hold (Default)

coupon Benefit amount Y Num 3 $### Dollar 0 - 999 $0 (Min) (Default)

$999 (Max) 

serlvl The client's time limited 

benefits tier level 

Y Char 1 1 

2 

Likely to obtain above min. wage 

within 6 mo. 

Can obtain above min. wage 

employment within 1 yr with case 

11 
Example Data Dictionary for Discovery Phase 

12 

Field Name Description Req. Type Len Format Units Valid Values Definitions Notes:

3 

4 

5 

 

6 

7 

8 

management 

Client has severe impediment to 

employment 

Client refused to cooperate 

Client has 18 months of work 

experience or completion of HS, GED, 

Post Sec., Tech or Voc. School 

Client has 6-17 months recent work 

experience or completion of 11th 

grade 

Client has < 6 months of work 

experience and completion of less than 

3 years of 

Plucker assigned default for SIG 7,8 

clients 
Initial Acquisition 

II. Initial Acquisition Phase 

The Initial Acquisition phase begins with the approval for developing the MOU, includes the finalizing 

and signing of the MOU, and ends with the first-time receipt of the data. The data quality dimensions of 

Accessibility and Interpretability are important in this phase, as they were in the Discovery phase, each 

with new questions that arise with Initial Acquisition. This phase also has questions for the dimensions of 

Coherence, Accuracy, and Institutional Environment. (29 questions) 

ACCESSIBILITY. Accessibility refers to the ease with which the data file extract can be 

obtained from the administrative agency. This includes the suitability of the form or medium for 

transferring the data, confidentiality constraints, and cost. 

11. Describe any disclosure control methods or other modifications of the administrative data file 

used to protect the confidentiality of the data (i.e., Topcoding, Replacing names with identification 

numbers), or will non-modified data from the program agency’s database(s) be provided and 

accessible to the statistical agency? 

Example answer. No disclosure control methods are used to suppress or alter the data when the data are 

drawn from the State’s administrative data files and placed into data file extracts. The data file extracts 

are protected via an encryption key that will be provided to the statistical agency under separate 

correspondence. 

12. How will the data file extract be transferred to the user? 

Example answer. Because data file extracts are encrypted, they can be on DVD shipped via traceable 

mail. 

INTERPRETABILITY. Interpretability refers to the clarity of information to ensure that the 

administrative data are utilized in an appropriate way. This includes evaluation of data

collection forms, data collection instructions, and a data dictionary.

15. If a complete data dictionary is not available, describe any remaining fields (not already 

documented in the Discovery phase). 

Example answer. Data Dictionary provided 

16. Provide a copy of any forms and instructions to the applicant used to collect data. 

Example answer. See our State’s website at www.SNAPInstructions.GenericState.gov for a copy of the 

form used to collect data and the instructions. 

17. Describe the methodology used to recode original data to create a value for a new variable, 

such as assigning a reported age to an age range. 

Example answer. No recodes—data are keyed either from paper forms or as captured during interview. 

18. Describe the data architecture and relationship between key variables. For example, are data 

stored in a spreadsheet with one row for each person/entity, a relational database, or some other 

format? 

13 
Initial Acquisition 

Example answer. Data are extracted from a state database and written to DVD as two flat files. One file 

uses case id as a primary key and contains information related to the case such as the address for the 

family. The other file uses a composite case id-benefit month-client id as a primary key and contains data 

related to the individual client, benefit amounts, and payment dates. 

19. Does the dataset contain records for those who are denied eligibility to the program or benefits 

and how can those records be identified? 

Example answer. Yes, the SNAP database contains four types of cases, as indicated by the variable 

“casestat,” which is the case status code. There are four codes: active, denied, suspended and on hold 

cases. If the statistical agency wants data only for a subset of cases in the State’s database (e.g., only the 

“active” cases) then the MOU should specify which subset is wanted; otherwise it is likely to be 

presumed that the statistical agency wants data for all four types of cases. 

COHERENCE. Coherence refers to the degree to which the administrative data are comparable with 

other data sources and consistent over time and across geographical areas. This includes evaluation of 

data concepts, classifications, questionnaire wording, data collection methodologies, reference period, and 

the target population. 

20. Please describe any classification systems used for categorizing or classifying the data. 

For example: Do questions asked about race use the current race and ethnicity categories defined 

by the Office of Management and Budget (OMB)? Do questions asked about industry 

classifications use the current North American Industry Classification System (NAICS)? 

Example answer. We use a classification system for race and ethnicity in a single database field. We 

combine 4 standard racial categories (White, Black, American Indian or Alaskan Native, Asian or Pacific 

Islander) with an ethnic category (Hispanic) into a set of 5 either/or alternatives. In this system, a person 

could be classified as, for example, either White or Hispanic but not both. 

21. Were there changes made during the file extract period (e.g., January 2006 through December 

2008) or differences across the geographical areas covered by the file in the questions asked that 

would cause a break in consistency, such as different questions, revised questions, questions in 

different languages, or deleted questions? 

Example answer. Based on our conversations of what the file extract period is likely to be for your 

statistical study (October 2003- September 2007), there were no changes in this period for the examples 

you list. 

22. Were changes made during the file extract period to how the data are processed, such as 

changes to mode of data collection, changes to instructions for completing the application form, 

changes to the edit, changes to classification codes, or changes to the query system used to retrieve 

the data? 

Example answer. In 2005 the State introduced an option by which a SNAP applicant could directly enter 

(some) of the family’s information on-line; a limited amount of paper-based information, including a 

signature, continued to be required of all persons submitting a SNAP application. The previous paperbased form (combined with computer-assisted personal interviews) continued to be available for those 

who did not choose to use the on-line option at all. 

14 
Initial Acquisition 

23. Were there changes during the file extract period to geographical boundaries? 

Example answer. In June 2005, the geographic area for zip code 12345 was reclassified into zip codes 

12344 and 12345. The records for the number of participants in zip code 12345 shows a sharp drop in 

June 2005. 

24. Briefly, describe substantial changes during the file extract period or differences across 

geographical areas covered by the file that influenced who participated in the program, such as 

legislative changes, changes to eligibility requirements, expansions of the program, or natural 

disasters impacting program participation? 

Example answer. Households where all members benefit from cash assistance provided through the 

Temporary Assistance for Needy Families (TANF) Program are categorically eligible for SNAP. State 

SNAP agencies have the option to expand categorical eligibility so that households receiving TANF 

services other than cash assistance are categorically eligible for SNAP. In January 2004, our State 

adopted the option of expanded categorical eligibility. As a result of adopting this option, the number of 

families in the State eligible for SNAP increased. 

25. Were there any other changes during the file extract period or differences across the 

geographical areas covered by the file that would cause a break in consistency in regards to 

comparing data over time? If so, describe the change/difference and when/where it occurred. 

Example answer. Yes. Described in question 24 above. The number of families in SNAP could be 

higher following January 2004 than it would have been. 

ACCURACY. Accuracy refers to the closeness of the administrative record data values to their 

(unknown) true values. This includes information on any known sources of errors in the 

administrative data such as missing records, missing values of individual data items, 

misinterpretation of questions, and keying, coding, and duplication errors. 

26. What investigations/analyses have been conducted that reveal data quality characteristics (such 

as Government Accountability Office reports, Office of Inspector General audits, internal agency 

reviews, etc.)? 

Example answer. SNAP agencies use identification (such as driver’s license or photo I.D.), wage stubs, 

and other documents to check on personal, earnings and residential information reported by the family 

applying for SNAP. Is the person really who the person claims to be? Does the employer’s report of the 

person’s income agree with the person’s report? These questions and SNAP agency procedures reflect 

one type of data accuracy that can fall under your category of “analyses.” Let us know if you need more 

detail on the documentation we require from SNAP applicants. 

A separate type of data accuracy is the accuracy of State eligibility and benefit determinations which is 

monitored by the SNAP Quality Control (QC) System. See materials on FNS website about QC at 

http://www.fns.usda.gov/snap/qc/default.htm. Payment error rates are calculated at the national and State 

levels. In Fiscal 2004, the national payment error rate was 5.88 percent including both overpayments of 

4.48 percent and underpayments of 1.41 percent. 

15 
Initial Acquisition 

27. What percentage of eligibles are not included on the data file or what percentage of those 

mandated are not compliant? What is known about their characteristics? 

Example answer. An annual report issued by Food and Nutrition Service provides estimates at the State 

level for participation rates among eligibles. The national rate in Fiscal 2004 was 61.8 percent 

(http://www.fns.usda.gov/ORA/menu/Published/SNAP/FILES/Participation/Trends2002-09.pdf. 

28. What is the proportion of duplicate records on the data file extract? 

Example answer. None. The file extracted for benefit data contains a composite primary key of Case IDBenefit Month-Client ID that ensures one unique occurrence for each combination of these fields. There 

are regular quality control processes that prevent a single client from being assigned to multiple client 

ID’s and a single Client ID’s from being assigned to multiple Case ID’s for a given benefit month . 

29. What is the proportion of missing values for each field? (Feel free to provide an attachment.) 

Example answer. Ethnic group is the primary field with missing values. Item nonresponse for the ethnic 

group field in fiscal year 2004 is 72%. The reason for this high percentage of missing values is that 

SNAP applicants can voluntarily respond whether they want to answer race/ethnicity questions (that is, an 

answer is not required in order to receive SNAP benefits) 

30. Describe other metadata provided with the administrative data file such as record counts, range 

of values, and frequencies of responses. 

Example answer. Metadata will include record counts and a data dictionary. The frequency of missing 

values for each field in the fiscal 2004 data is attached. 

31. What are the known sources of errors in the administrative data (e.g. non-response, keying, 

coding errors)? 

Example answer. Item nonresponse for the race/ethnicity variable in fiscal year 2004 is 72%. Keying 

errors are known to be less than 1%. 

32. What questions are most often misinterpreted? 

Example answer. In cases of joint custody there is often confusion about household composition and 

which Case ID a client should be assigned. 

33. Which items are subject to revision either by editing or updating data values? What items are 

revised the most? 

Example answer. Case status can change. Also, addresses are updated as people move and old addresses 

are overwritten. 

34. If a value is revised from what was originally reported, describe any flags in the dataset that 

would indicate that the value was changed as well as explain why the value was changed. 

Example answer. No flags are set. Change in case status can only be noted by comparing the case

number across file months. 

16 
Initial Acquisition 

INSTITUTIONAL ENVIRONMENT. Institutional Environment refers to the credibility of the 

administrative agency for producing high quality and reliable administrative data. This includes 

an evaluation of the agency’s quality standards and processes for assuring adherence to 

standards. 

 

35. Describe the purpose of the administrative program. 

Example answer. The purposes of SNAP are to strengthen the agricultural economy; to help to achieve a 

fuller and more effective use of food abundances; and to provide for improved levels of nutrition among 

low-income households through a cooperative Federal-State program of food assistance to be operated 

through normal channels of trade. 

36. Describe your processes for data collection, editing, review, correction, dissemination, and 

retention. 

Example answer. Data collection is done via a client interview with periodic quality control from firstline supervisors. Clients sit face to face with an intake worker while the intake worker keys in the 

responses. Editing a case file after the fact can only be done with management concurrence. A 10-

percent sample of cases is reviewed by management on a daily basis to determine the completeness of 

data. Data is maintained in the system for five years after which non-active files are archived. 

37. Who is source data collected from (self-reported, interview a third party)? 

Example answer. The client or someone representing the client. 

38. How is source data collected (paper questionnaire, computer assisted person interview, 

computer assisted telephone interview, web data collection form)?

Example answer. A combination of paper questionnaire and computer-assisted person interview 

39. Describe the checks the administrative agency performs to ensure the quality of the data and 

the typical results for your production processes. 

Example answer. See our State’s website at www.SNAPInstructions.GenericState.gov/quality.htm for a 

description of our processes and the key performance indicators for the quality of our work. 

40. Describe the principles, standards, or guidelines the agency uses as a basis to determine what is 

considered acceptable quality. 

Example answer. See our State’s website at www.SNAPInstructions.GenericState.gov/quality.htm for 

the policies, principles, standards, and guidelines that guide our work. 

41. Describe the findings and corrective actions of studies, evaluations or audits to assess 

compliance with quality standards. 

Example answer. Copies of routine inspections results from the state comptroller are posted at 

www.SNAPInstructions.GenericState.gov/quality.htm Attached is an evaluation of our processes for the 

2009 Malcolm Baldrige National Quality Award. 

17 
Repeated Acquisition 

III. Repeated Acquisition Phase 

Questions in the Repeated Acquisition phase cover periodic receipt of data. (11 questions, including 9 

questions repeated from Initial Acquisition)

 

INTERPRETABILITY. Interpretability refers to the clarity of information to ensure that the 

administrative data are utilized in an appropriate way. This includes evaluation of data

collection forms, data collection instructions, and a data dictionary.

[11.] Describe each field on the data file and note their valid values. (See template for data 

dictionary). 

Example answer. A data dictionary is attached. 

COHERENCE. Coherence refers to the degree to which the administrative data are comparable with 

other data sources and consistent over time and across geographical areas. This includes evaluation of 

data concepts, classifications, questionnaire wording, data collection methodologies, reference period, and 

the target population. 

[21.] Were there changes made during the file extract period (e.g., January 2006 through 

December 2008) or differences across the geographical areas covered by the file in the questions 

asked that would cause a break in consistency, such as different questions, revised questions, 

questions in different languages, or deleted questions? 

Example answer. A fingerprint status question was added in 2006. Files for benefit months earlier than

2006 do not have this field. 

[22.] Were changes made during the file extract period to how the data are processed, such as 

changes to mode of data collection, changes to instructions for completing the application form, 

changes to the edit, changes to classification codes, or changes to the query system used to retrieve 

the data? 

Example answer. In January 2007, a new database system was brought online to support the program 

and pre 2007 data was ported to the new system. To our knowledge, this transition caused no 

degradation to the data quality. 

[23.] Were there changes during the file extract period to geographical boundaries? 

Example answer. Coding for geography was refined in June 2006 with the inclusion of a ZIP + 4 address 

field. 

[24.] Were there changes during the file extract period or differences across geographical areas 

covered by the file that influenced who participated in the program, such as legislative changes, 

changes to eligibility requirements, expansions of the program, or natural disasters impacting 

program participation? 

Example answer. In August 2006, tornadoes destroyed several large businesses in one county leading to 

unemployment/reduced income in that area which increased the number of participants meeting program 

participation requirements. 

18 
Repeated Acquisition 

[25.] Were there any other changes during the file extract period or differences across the 

geographical areas covered by the file that would cause a break in consistency in regards to 

comparing data over time? If so, describe the change/difference and when/where it occurred. 

Example answer. None 

ACCURACY. Accuracy refers to the closeness of the administrative record data values to their 

(unknown) true values. This includes information on any known sources of errors in the 

administrative data such as missing records, missing values of individual data items, 

misinterpretation of questions, and keying, coding, and duplication errors. 

[28.] What is the proportion of duplicate records on the data file? 

Example answer. None. The file extracted for benefit data contains a composite primary key of Case IDBenefit Month-Client ID that ensures one unique occurrence for each combination of these fields. There 

are regular quality control processes that prevent a single client from being assigned to multiple client 

ID’s and a single Client ID’s from being assigned to multiple Case ID’s for a given benefit month . 

[29.] What is the proportion of missing values for each field? (Feel free to provide an attachment.) 

Example answer. Ethnic group is the primary field with missing values. Item nonresponse for the ethnic 

group field in fiscal year 2005-2010 was less than 1%. 

[30.] Describe other metadata provided with the administrative data file such as record counts, 

range of values, and frequencies of responses. 

Example answer. Metadata will include record counts and a data dictionary. The frequency of missing 

values for each field in the fiscal 2004 data is attached. 

INSTITUTIONAL ENVIRONMENT. Institutional Environment refers to the credibility of the 

administrative agency for producing high quality and reliable administrative data. This includes 

an evaluation of the agency’s quality standards and processes for assuring adherence to 

standards. 

42. Describe corrective actions taken to improve the quality of your processes and data. 

Example answer. None 

43. For the file extract period, describe any new records or revisions to existing records that may 

occur after data acquisition.

Example answer. Records do get added to the system for a particular month after the end of the month 

passes, but the lag is at most a day or two. Therefore, the file extract we send you will contain all the 

records we have and will ever have for the month. However, various fields such as income are subject to 

revision as new information comes to the SNAP office. After you receive the data file, there could be 

revisions to records for these fields that you would not have in the file extract we send you.

19 
Example Data Dictionary for Repeated Acquisition Phase 

20 

File Name: Program Beneficiaries.por Description: 2011 Beneficiary Contact Information 

File Date: January 12, 2012 File Format: SAS portable dataset 

Field Name Description Req. Type Len Format Units Valid Values Definitions Notes:

case Case ID Y Num 6 1 - 999999 Sequential number assigned to each 

case 

Primary Key 

yrmo First benefit month case 

appeared at address 

Y Num 4 yymm 04

01 - 12

yy – two digit year 

mm – two digit month 

resaddr Residential address Y Char 1 Null 

Y 

N 

No response (Default) 

Yes this is a residential address 

No this is not a residential address 

HHName Name for household Y Char 40 Alphabetic 

addrln1 Mailing address – line 

1 

Y Char 22 Alphanumeric 

addrln2 Mailing address – line 

2 

N Char 22 Null 

Alphanumeric 

No response (Default)

city City N Char 16 Alphabetic 

St State N Char 2 Alphabetic See Notes Definitions are contained 

the USPS standard for ZIP 

codes dated Jan 1, 2005 

Zip 5 digit zip code Y Num 4 See Notes See Notes Valid values and definitions 

are contained in USPS 

standard for ZIP codes 

dated Jan 1, 2005 

Zip4 Additional 4 numbers 

to 5 digit zip code 

N Char 4 See Notes See Notes Valid values and definitions 

are contained in USPS 

standard for ZIP codes 

dated Jan 1, 2005 

fips_county FIPS County code N Num 3 See Notes See Notes Valid values and definitions 

are contained in FIPS 

standard dated Oct 24, 

1998.
Example Data Dictionary for Repeated Acquisition Phase 

21 

File Name: Benefits_2011.por Description: Benefits data for 2011.

File Date: January 12, 2012 File Format: SAS portable dataset 

Field Name Description Req. Type Len Format Units Valid Values Definitions Notes:

case Case ID Y Num 6 0 - 999999 Sequential number assigned to each 

case 

Primary Key 

yrmo Benefit month Y Num 6 yyyymm 2006 

01-12

yyyy – 4 digit year 

mm – 2 digit month 

Primary Key 

CID Client ID Y Char 6 0 - 999999 Sequential number assigned to each 

client 

Primary Key 

client Name of client Y Char 40 Alphabetic Full name of client 

ssn Social Security Number N Num 9 ###-##-#### Null 

Numeric 

(Default) 

birthdt Birthdate Y Num 6 mmddyy 01-12

01-31

00-99

mm – 2 digit month 

dd – 2 digit day 

yy – 2 digit year 

race Ethnic Group N Char 1 1 

2 

3 

4 

5 

6 

White 

Black 

Hispanic 

American Indian or Alaskan Native 

Asian or Pacific Islander 

Omitted (Default)

sex Client gender N Char 1 M 

F 

U 

Male 

Female 

Unknown (Default)

sig1 Code which identifies 

the client's status in 

group 

Y Char 1 A 

B 

C 

D 

E 

F 

G 

H 

J 

K 

M 

Head of Household member 

Student 

Striker 

Military member 

Eligible legalized alien 

Treatment center residents 

Head of Household - nonmember 

Refugee 

Disqualified as ineligible alien 

Disqualified failed to meet SSN 

requirement 
Example Data Dictionary for Repeated Acquisition Phase 

22 

Field Name Description Req. Type Len Format Units Valid Values Definitions Notes:

R 

S 

T 

U 

X 

W 

Z 

Migrant, out of work stream 

DHS employee 

Alien with acceptable alien status

Disqualified for intentional program 

violation 

Seasonal farm worker 

Non primary wage disqualified for 

noncompliance 

Migrant, in work stream 

Alien granted temporary resident status 

educat Client Education Level Y Char 1 1 

2 

3 

4 

5 

6 

7 

8 

9 

A 

B 

C 

D 

E 

F 

N 

First Grade 

Second Grade 

Third Grade 

Fourth Grade 

Fifth Grade 

Sixth Grade 

Seventh Grade 

Eighth Grade 

Ninth Grade 

Tenth Grade 

Eleventh Grade 

High School Graduate/ GED 

Currently Attends JR, High, GED 

Attending or completed some college 

Graduate of a 4 yr college 

No Formal Education 

fingprcd Finger print status Y Char 1 A 

B 

C 

D 

 

E 

F 

I 

Client has filed an appeal and is 

continuing to receive benefits while 

appeal is in progress 

Religious exemption 

Certified out of office 

Physically unable to have fingerprints 

taken 

Equipment failure 

Disqualified from the program 

Fingerprint images have been taken. 

Client has since been denied. Client 

record is in inactive file. 
Example Data Dictionary for Repeated Acquisition Phase 

23 

Field Name Description Req. Type Len Format Units Valid Values Definitions Notes: 

Y 

Z 

Fingerprint images have been taken. 

Client currently receiving benefits. 

Client record is in active file 

Fingerprint images of one finger has 

been taken. The image of the other 

finger is still required. Client record 

may be in either active or inactive file. 

casestat Case status code Y Num 3 1 

2 

3 

4 

Active 

Denied 

Suspended 

Hold (Default)

coupon Benefit amount Y Num 3 $### Dollar 0 - 999 $0 (Min) (Default)

$999 (Max) 

serlvl The client's time limited 

benefits tier level 

Y Char 1 1 

2 

3 

4 

5 

6 

7 

8 

Likely to obtain above min. wage 

within 6 mo. 

Can obtain above min. wage 

employment within 1 yr with case 

management 

Client has severe impediment to 

employment 

Client refused to cooperate 

Client has 18 months of work 

experience or completion of HS, GED, 

Post Sec., Tech or Voc. School 

Client has 6-17 months recent work 

experience or completion of 11th grade 

Client has < 6 months of work 

experience and completion of less than 

3 years of 

Plucker assigned default for SIG 7,8 

clients 
Appendix A—Compendium of Questions Across All Three Phases

24 

No. Phase of MOU process Questions, by Dimension 

Discovery Initial 

Acquisition 

Repeated 

Acquisition 

 

Relevance. Relevance refers to how well the administrative data file meets the needs of the user in regards to data level (person, family, 

household, establishment, company, etc.), broad data definitions and concepts, population coverage, time period and timeliness. 

1  What are the general contents of the of the administrative data file(s) maintained by the program? 

2  What entity is represented at the record level on the administrative data file (person, household, 

family, business, etc.)? 

3  Describe the scope of the records included on the administrative data file (program participants, 

geographic areas, etc.). 

4  What are the earliest and latest dates for which administrative data are available (including in 

archive)? 

5  How often are the data collected at the program office? (Daily for flow data, or a particular date such 

as April 1)? 

6  What is “reference time period” by which the data are organized in the administrative file(s)? 

7  How soon after a reference period ends can a data file extract be prepared and provided? 

Accessibility. Accessibility refers to the ease with which the data file extract can be obtained from the administrative agency. This includes the 

suitability of the form or medium for transferring the data, confidentiality constraints, and cost. 

8  Describe any legal, regulatory or administrative restrictions on access to the data file extract. 

9  Describe any records or fields that were collected but cannot be included (for confidentiality or other 

reasons) in the data file extract provided to the user. 

10  What is the typical reimbursement for sharing your data? Are reports, a data product, or a monetary 

fee given in return? 

11  Describe any disclosure control methods or other modifications of the data file used to protect the 

confidentiality of the data (i.e., Topcoding, Replacing names with identification numbers) or will 

non-modified data from the program agency’s database(s) be provided and accessible to the statistical 

agency?. 

12  How will the data file extract be transferred to the user? 

Interpretability. Interpretability refers to the clarity of information to ensure that the administrative data are utilized in an appropriate way. This 

includes evaluation of data collection forms, data collection instructions, and a data dictionary. 

13   Describe each variable on the administrative data file and note their valid values. (See template for 

data dictionary). 

14  If a complete data dictionary is not available, describe the primary identification and response fields 

on the administrative data file and note their valid values. 
Appendix A—Compendium of Questions Across All Three Phases

25 

No. Phase of MOU process Questions, by Dimension 

Discovery Initial 

Acquisition 

Repeated 

Acquisition 

Interpretability. Interpretability refers to the clarity of information to ensure that the administrative data are utilized in an appropriate way. This 

includes evaluation of data collection forms, data collection instructions, and a data dictionary. (continued) 

15  If a complete data dictionary is not available, describe any remaining fields (not already documented 

in the Discovery phase). 

16  Provide a copy of any forms and instructions to the applicant used to collect data. 

17  Describe the methodology used to recode original data to create a value for a new variable, such as 

assigning a reported age to an age range. 

18  Describe the data architecture and relationship between key variables. For example, are data stored in 

a spreadsheet with one row for each person/entity, a relational database, or some other format? 

19  Does the dataset contain records for those who are denied eligibility to the program or benefits and 

how can those records be identified? 

Coherence. Coherence refers to the degree to which the administrative data are comparable with other data sources and consistent over time and 

across geographical areas. This includes evaluation of data concepts, classifications, questionnaire wording, data collection methodologies, the 

file extract period, and the target population. 

20  Please describe any classification systems used for categorizing or classifying the data. 

For example: Do questions asked about race use the current race and ethnicity categories defined by 

the Office of Management and Budget (OMB)? Do questions asked about industry classifications 

use the current North American Industry Classification System (NAICS)? 

21   Were there changes made during file extract period (e.g., January 2006 through December 2008) or 

differences across the geographical areas covered by the file in the questions asked that would cause 

a break in consistency, such as different questions, revised questions, questions in different 

languages, or deleted questions? 

22   Were changes made during the file extract period to how the data are processed, such as changes to 

mode of data collection, changes to instructions for completing the application form, changes to the 

edit, changes to classification codes, or changes to the query system used to retrieve the data? 

23   Were there changes during the file extract period to geographical boundaries? 

24   Briefly, describe substantial changes during the file extract period or differences across the 

geographical areas covered by the file that influenced who participated in the program, such as 

legislative changes, changes to eligibility requirements, expansions of the program, or natural 

disasters impacting program participation? 

25   Were there any other changes during the file extract period or differences across the geographical 

areas covered by the file that would cause a break in consistency in regards to comparing data over 

time? If so, describe the change/difference and when/where it occurred. 
Appendix A—Compendium of Questions Across All Three Phases

26 

No. Phase of MOU process Questions, by Dimension

Discovery Initial 

Acquisition 

Repeated 

Acquisition 

Accuracy. Accuracy refers to the closeness of the administrative record data values to their (unknown) true values. This includes information on 

any known sources of errors in the administrative data such as missing records, missing values of individual data items, misinterpretation of

questions, and keying, coding, and duplication errors. 

26  What investigations/analyses have been conducted that reveal data quality characteristics (such as 

GAO reports, Office of Inspector General audits, internal agency reviews, etc.)? 

27  What percentage of eligibles are not included on the data file or what percentage of those mandated 

are not compliant? What is known about their characteristics? 

28   What is the proportion of duplicate records on the data file extract? 

29   What is the proportion of missing values for each field? (Feel free to provide an attachment.) 

30   Describe other metadata provided with the administrative data file such as record counts, range of 

values, and frequencies of responses. 

31  What are the known sources of errors in the administrative data (e.g. non-response, keying, coding 

errors)? 

32  What questions are most often misinterpreted? 

33  Which items are subject to revision either by editing or updating data values? What items are revised 

the most? 

34  If a value is revised from what was originally reported, describe any flags in the dataset that would 

indicate that the value was changed as well as explain why the value was changed. 

Institutional Environment. Institutional Environment refers to the credibility of the administrative agency for producing high quality and 

reliable administrative data. This includes an evaluation of the agency’s quality standards and processes for assuring adherence to standards. 

35  Describe the purpose of the administrative program. 

36  Describe your processes for data collection, editing, review, correction, dissemination, and retention. 

37  Who is source data collected from (self-reported, interview a third party)? 

38  How is source data collected (paper questionnaire, computer assisted person interview, computer 

assisted telephone interview, web data collection form)? 

39  Describe quality control checks and the typical results for your production processes. 

40  Describe the agency’s quality standards. 

41  Describe the findings and corrective actions of studies, evaluations or audits to assess your 

compliance with quality standards. 

42  Describe corrective actions taken to improve the quality of your processes and data. 

43  For the file extract period, describe any new records or revisions to existing records that may occur 

after data acquisition. 
Appendix B—Data Dictionary Template 

Name Description Examples

File Name Provide the filename of the table Applicants.txt 

Providers.csv 

File Date Date the file was saved. 3/4/12 

Description Enter a description of the file contents indicating the 

file extract period for the information 

2004 program beneficiaries 

File Format Describe how the data is stored on the file. Comma separated, Quote 

delimited values 

For each column in the table, provide the following information: 

Name Description Examples

Field Name Name of each field/column as it appears in the table FName, LName, Address, 

Income, 

Description Description of the field/column. "Applicants First Name" 

Type Data type of the fields/columns in the table and 

field/column size 

Text, Character, Number, 

Integer, Double, Yes/No, 

Date/Time, Currency, 

Hyperlink 

Req. Is a response required? Y, N 

Len The length of the field. 8.2, 20, 256 

Format The format that the data is stored in the table. ###-##-####, 

(###) ###-#### 

###.## 

Units Physical units for the value of the field/column. 

Leave blank if not relevant. 

Hours, Dollars, Acres, 

Thousands 

Valid 

Values 

The values that would be accepted as a valid 

response. 

Null 

"1" 

"2" 

Definitions Define the meaning for the codes that are used. 

Identify which value is the default value 

Null – No response (Default) 

1 - Active 

2 - Denied 

Notes Note any other items of special consideration. 

 Is this field a primary or secondary key? 

 Is this field indexed? 

 Are the valid values and definitions based on a 

recognized standard like ZIP codes? If so, note 

the standard and version used. 

 Note other considerations. 

Primary Key 

Indexed 

USPS ZIP Code Standard 

1/1/05 

27