Administration for Community Living

Office of Performance Evaluation

SEPTEMBER 2020

ACL’s Data Quality 

Guidance
TABLE OF CONTENTS

Overview 1

What is Quality Data? 3

Benefits of High-Quality Data 5

Improving Data Quality 6

Data Types 7

Data Components 8

Building a New Data Set 10

Implement a data collection plan 10

Set data quality standards 10

Data files 11

Documentation Files 12

Create a plan for data correction 13

Plan for widespread data integration and distribution 13

Set goals for ongoing data collection 13

Maintaining a Data Set 14

Data formatting and standardization 14

Data confidentiality and privacy 14

Data extraction 15

Preservation of data authenticity 15

Metadata to support data documentation 15

Evaluating an Existing Data Set 16

Data Dissemination 18

Data Extraction 18

Example of High-Quality Data 19

Exhibit 1. Older Americans Act Title III Nutrition Services: 

 Number of Home-Delivered Meals 21

Exhibit 2. Participants in Continuing Education/Community Training 22

Summary 23

Glossary of Terms 24

Data Quality Resources 25

Citations 26
1

Overview

The Government Performance and Results Modernization Act of 2010, along with other 

legislation, requires federal agencies to report on the performance of federally funded 

programs and grants. The data the Administration for Community Living (ACL) collects 

from programs, grants, and research are used internally to make critical decisions 

concerning program oversight, agency initiatives, and resource allocation. Externally, 

ACL’s data are used by the U.S. Department of Health & Human Services (HHS) headquarters to inform Congress of progress toward programmatic and organizational 

goals and priorities, and its impact on the public good. 

The Office of Performance and Evaluation (OPE) developed this Data Quality Guide

as an introduction to the topic of data quality and to present strategies to enhance 

data utility. This guide, along with ACL’s Performance Strategy1, aims to promote 

high-quality transparent information that supports sound decision-making. The Data 

Quality Guide begins by defining data quality, explaining the importance of improving 

data, describing data types, and providing recommendations for building, maintaining, and evaluating data sets. It then describes the role of data quality in dissemination and data extraction. It closes with examples of high-quality data and recommendations for additional resources. As you review this guide, remember, developing 

high-quality data is a journey and not a destination. Incremental change is worthwhile.

The Office of Management and Budget (OMB, 2001) defines “quality” as a broad 

term that encompasses the notions of “utility,” “objectivity,” and “integrity.” “Utility” 

refers to the usefulness of the information to the intended users. “Objectivity” refers 

to whether the disseminated information is being presented in an accurate, clear, 

complete, and unbiased manner and, as a matter of substance, is accurate, reliable, 

1 https://acl.gov/sites/default/files/programs/2018-07/OPE%20PM%20Strategy%20FINAL%206-1-2018.docx
2

and unbiased. “Integrity” refers to security or the protection of information from 

unauthorized access or revision, to ensure that the information is not compromised 

through corruption or falsification. The General Accounting Office (GAO, 2000) adds 

to this definition by stating that quality data has validity, in that it adequately represents actual performance, and that it is consistent (i.e., reliable), in that it is collected repeatedly, using the same processes over time, allowing for comparisons 

between findings. 

OPE is also available to support your office with assessing the quality of your data.
3

What is Quality Data?

Data collection is the process of gathering and measuring information on variables 

of interest in an established systematic fashion that enables one to answer stated 

research questions, test hypotheses, and evaluate outcomes (Faculty Development 

and Instructional Center at Northern Illinois University, 2005). Data quality refers to 

the utility of a data set and its ability to be easily processed and analyzed.

Data must be consistent and unambiguous 

to be of high quality. Data quality issues are 

often the result of data set mergers or data 

integration processes in which data fields are 

incompatible due to schema or format inconsistencies. Data that are not of high quality can 

undergo data cleansing to raise their quality 

(Informatica, 2020).

The Office of Management and Budget (OMB, 2001) defines “quality” as a broad term 

that encompasses the notions of “utility,” “objectivity,” and “integrity.” “Utility” 

refers to the usefulness of the information to the intended users. “Objectivity” refers 

to whether the disseminated information is being presented in an accurate, clear, 

complete, and unbiased manner and, as a matter of substance, is accurate, reliable, 

and unbiased. “Integrity” refers to security or the protection of information from 

unauthorized access or revision, to ensure that the information is not compromised 

through corruption or falsification.2

 

2 Office of Management and Budget. (2001). Guidelines for ensuring and maximizing the quality, objectivity, 

utility, and integrity of information disseminated by federal agencies. Retrieved from https://obamawhitehouse.archives.gov/omb/fedreg_final_information_quality_guidelines/

High-quality data 

is consistent, 

unambiguous, 

and accurate.
4

High-quality data can be easily processed and analyzed with minimal explanations 

or data cleansing. High-quality data are essential to business intelligence efforts and 

data analytics, as well as operational efficiency. Once the data are collected, the numeric data can be used to establish performance measures that monitor the number 

of inputs, outputs, and outcomes (See ACL’s Performance Strategy3

). Performance 

measures monitor progress toward predetermined performance goals and standards, 

enabling program managers to make evidence-based decisions that can improve or 

sustain progress toward programmatic goals. 

3 https://acl.gov/sites/default/files/programs/2018-07/OPE%20PM%20Strategy%20FINAL%206-1-2018.docx
5

Benefits of High-Quality Data

There are several benefits of high-quality data. First among them is the ability 

to communicate the relationship between inputs, action, outputs, and impact. 

Accurate data can “tell a story” by demonstrating to stakeholders measurable 

consequences of action or inaction. This information leads to informed 

decision-making and future action. 

High-quality data lead to better decision-making across an organization. ACL’s data impact both policy and programmatic decisions. 

If we provide quality data, ACL leadership will have a higher level of 

confidence when making decisions guiding the future of ACL. 

Data document the delivery of federal services and can be used to 

gauge effectiveness. 

High-quality data enable the research community to answer 

research questions that have an impact on the community. 

Furthermore, ACL, other federal organizations, and state and local 

agencies depend on quality data for their policy and program planning. These policies and programs, in turn, impact many additional 

stakeholder groups. They, along with various advocacy groups, 

depend on ACL’s data to pursue funding for services and initiatives 

necessary to serve their constituencies.

High-quality data lead to increased consistency and coordination 

between existing and new data programs or grants, the elimination 

of redundant data (see the Paperwork Reduction Act), and the capability to analyze and visualize data. 

$
6

High-quality data sets may be linked together to explore innovative 

and complex research topics and inform decision-making. Uniformity 

in design, data definitions, and identity protections make 

this possible. 

Data advance progress on shared or similar objectives while 

fulfilling broader federal information needs.

Improving Data Quality

It is important to develop a systematic, standardized data collection strategy that 

includes a defined process for building, maintaining, and evaluating new data sets 

(see below). When developing or implementing an approach to improving data, 

give careful consideration to the needs and capacity of your program or grant. 

Consider addressing data challenges. Perform an in-depth analysis of existing 

legislation to ensure all mandatory data elements are collected, the appropriate 

categories are represented, and the results can be easily interpreted. 
7

Data Types

To fulfill ACL’s mission, robust, accurate information is required to inform policy and 

practice. There are two types of data, quantitative and qualitative. Each form of data 

has its advantages and disadvantages. Quantitative data provide information about 

quantities, i.e., numbers. Qualitative data are descriptive and describe observed 

phenomena, such as language. These data are nonnumerical and collected through 

observations, interviews, and focus groups (McLeod, 2019). Most government data, 

particularly performance measure data, are quantitative. 

Quantitative data are typically collected using replicable, standardized processes 

that allow for large numbers of participants to be studied. These data are used 

to derive generalizations about the phenomenon being examined and, since the 

collection processes are standardized, it is possible to make comparisons across 

respondent categories over time. This approach allows for a large volume of data to 

be collected, but the breadth of the responses is narrower. A limitation of quantitative data is that they do not provide a rich narrative of a phenomenon. Quantitative 

data do not capture individual feelings and perceptions in the way that qualitative 

techniques can. Most population-level data collection by ACL uses quantitative data.

Qualitative data provide more depth and detail by speaking with or observing 

individuals rather than focusing on large groups. This approach allows for nuanced 

responses that, when compared to quantitative data, are more difficult to generalize and systematically compare. Due to the time required to build rapport with 

a respondent, a less structured data collection process, and the need to code and 

interpret the data, collecting qualitative data can be more time consuming (and 

expensive) than collecting quantitative data. Qualitative data can be transformed 

into quantitative data through coding procedures, though they will remain more 

subjective than quantitative data.
8

Data Components 

Several key components are necessary when collecting, reporting, or assessing 

data, each of which can individually or cumulatively impact data quality. Understanding these components and their definitions will help to structure and evaluate 

the quality of data.

Validity refers to how the data are collected rather than the data themselves. 

Data validation is conducted to determine which data meet a defined format, type, 

and range. Data are valid if they are 

in the right format and of the correct 

type and fall within the proper range. 

These characteristics help in organization and analysis. Some software 

automatically converts data to the 

correct format. For example, if you 

are collecting data about the time of day users visit your site, you must decide on 

the format you will use. You might choose to use 24-hour time and use two digits 

for minutes and two for hours. Examples of this data format would include 14:34, 

17:05, and 08:42. Data that do not follow this format would be invalid. 

Data are valid if they are in 

the right format and of the 

correct type and fall within 

the proper range.
9

Key Components When Collecting, Reporting, or Assessing Quantitative Data

Data 

Dictionary

A data dictionary, as defined in the IBM Dictionary of Computing (IBM, 1993), is a 

“centralized repository of information about data such as meaning, relationships to 

other data, origin, usage, and format.” Data dictionaries assist data users in understanding data set content and the meaning and relationships between variables. 

Data Format Formatting is an important data quality consideration due to its influence on access, 

preservation, and interoperability. Selecting a standard format for data allows users 

to access findings with off-the-shelf software, such as Excel, rather than requiring file 

manipulation that may introduce unwanted data errors or artifacts. The use of a standard format reduces the likelihood of data becoming unreadable as time and computer 

software advance. Standard formatting also facilitates research across data sets, allowing for new questions to be explored by ACL staff and external researchers. Some types 

of basic formatting that can improve data quality are formatting concerning currency, 

percentages, decimals, and dates.

Data Rules Data rules are specific, documented standards for data entry, explaining what quality 

data are. An example of data quality is the use of a marital status code requiring 

entry of a value for single, married, widowed, or divorced. Blank entries would 

considered invalid.

Percentages 

vs. Raw 

Numbers

Percentages are commonly used to describe mathematical findings. Mathematically, 

a percentage represents a numerator divided by a denominator (i.e., the total). A raw 

number is a count that has not been statistically manipulated. Raw numbers sometimes 

provide more transparent information than percentages. For example, you could say that 

5% of a group declined an intervention. Alternatively, you could say that one out of the 20 

participants declined an intervention. Both may be accurate statements, but highlighting 

the small number of refusals (numerator) and the small size of the group (denominator) 

improves the clarity of the data and the implications for the findings.

Missing Data Missing data (or missing values) is defined as the data value that is not stored for a 

variable in the observation of interest. 

Data Analysis Data analysis is the process of systematically applying statistical and/or logical 

techniques to describe and illustrate, condense and recap, and evaluate data.

Variance Variance measures how far a data set is spread out. It is mathematically defined as the 

average of the squared differences from the mean (Statistics How To, 2020). Small

variances indicate few outliers (i.e., unusually high or low numbers) in the data. 

Outliers can indicate errors in data collection or documentation.
10

Building a New Data Set 

Implement a data collection plan

Using a logic model and performance measurement strategies is essential to 

developing a data collection plan. These practices require you to identify inputs, 

outputs, and outcomes; clarify definitions for each; and strategize how to collect 

required data and measure progress. Your data collection plan should identify the 

information you need to collect and additional useful information. This approach 

may require the establishment of additional indicators, beyond those gathered at 

the individual project or grantee level, and expand into the development 

of data-sharing agreements. Data 

encryption and transmission processes receive and store sensitive data, 

particularly data that may include 

personally identifiable information 

(PII). Explicitly state your requirements and procedures to minimize confusion and ensure timely and effective data 

collection. When building your data set, be mindful of the Paperwork Reduction Act 

(PRA) of 1995, which gives the OMB authority over the collection of information by 

federal agencies. The PRA is a law governing how federal agencies collect information from the public and aims to reduce the burden of collecting unnecessary data 

(U.S. Office of Performance Management, 2011).

Set data quality standards

It is important to set data quality standards to assist in identifying erroneous data. 

This step should include clear instructions for the format of data entry, such as 

percentiles versus raw numbers, and explanations for any formulas or calculations 

necessary to produce data required for collection. Data quality standards should 

also consider outliers that may indicate data entry errors. Historical precedent, 

It is essential to set goals for 

your data collection prior to 

developing a data system.
11

analysis of variance, and general programmatic knowledge should inform the 

identification of such errors. 

Data quality standards are used by ACL to certify data submissions and develop 

advisories for ACL’s data presentation website regarding possible data quality 

issues. As ACL accepts data through the various grantee systems, are there clear 

guidelines, and are all of the grant/contract officers using the same standards for 

looking at the data and accepting them as complete?

One approach to establishing a data quality standard is to develop a data quality 

checklist. Data quality checklists can be used by a grantee to format data and reduce 

quality concerns for recipients and users. They can also be used by recipients, such 

as ACL, to identify formatting errors that may introduce incorrect or erroneous data 

into a data set, limiting their usefulness and accuracy. The example below, from 

the Oak Ridge National Laboratory Distributed Active Archive Center (n.d.), presents 

vital components that can be used by ACL. 

Example: Data Checklist

Data files

1. Check for integrity of files (Checksum, file size, number of files)

2. Filenames are descriptive and consistent

 Action: Rename data files, if needed

3. Check if file format is appropriate and can be opened

 Action: Modify to archive format (non-proprietary) if needed

4. File organization is consistent and appropriate

5. Table header information complete and consistent with documentation

6. Properly versioned, if needed
12

Documentation Files

Documentation files describe and clarify the contents of a data file. When developing a documentation file, you should ensure that the following criteria are met.

1. Documentation matches files received

2. Data set and its contents are clearly described

3. Geospatial and temporal information are complete and described

4. Variables and units follow standards or are well defined

5. Publication or manuscript describing the data is provided

6. Methodology, calibrations, and algorithms provided

7. Known issues/limitations clearly described

8. Statements are properly referenced

The U.S. Census Bureau, as an example, provides code lists of acceptable variable 

responses and subject definitions for population, housing, and group quarters for 

when using the American Community Survey (ACS) for multiple years. It also provides 

guidance for comparing ACS data to 2000 and 2010 Census and instructions for 

applying statistical testing to ACS 1-year and 5-year data. Also, the U.S. Census 

Bureau provides sample design, estimation methodology, and data accuracy 

explanations for 1-year and multi-year datasets4

. 

4 https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html

Example 1. 

Housing Subjects 

Summarized 

to the Block Level 

(Repeated by Race and 

Hispanic Origin)
13

Parameter Values

Parameter values describe the expected range and form of data, and help identify 

potential data errors. When developing parameter values documentation, you should 

ensure that the following actions are taken and criteria defined.

1. Check to ensure valid range

2. Visualize (plot, map, or both)

3. Code(s) for missing values defined and used

4. Values for coded fields defined

5. Are accuracy and precision stated and reasonable?

Example 1 from the 2018 U.S. Census shows how many of these parameter values can 

be communicated to a data user.

Create a plan for data correction

When identified, data errors should be investigated, corrected, or removed from 

the data set. Procedures should include defining the person(s) responsible for correcting data and methods for correction. This ensures a consistent process, reducing the likelihood of additional errors entering the data set.

Plan for widespread data integration and distribution 

ACL is developing processes to link data sets, allowing analyses across data sets to 

answer new questions and provide previously unavailable insights. You should design 

your data set with interoperability in mind, using standardized data fields, definitions, 

and file formatting to allow data sets to be linked, as well as accessed and transformed using most statistical software. These practices also reduce the likelihood of 

introducing errors due to required data transformation.

Set goals for ongoing data collection

It is vital to remember improving data quality is a process, not an event. It may not 

be possible to address all data quality issues at one time. The documentation of 

processes and corrective actions ensure errors are addressed and not repeated.
14

Maintaining a Data Set 

Developing and maintaining a data set takes forethought and a commitment to 

oversight. The results of this work are accurate data that improve organizational 

efficiency and productivity. For these reasons, it is crucial to investigate and assess 

data content, structure, relationships, and quality on an ongoing basis. Standardizing your data to meet organizational needs, as well as implementing and enforcing 

standardized data format rules across your organization, will establish a foundation 

for additional data set rules concerning data confidentiality and privacy, integrity, 

authenticity, and documentation. Standardized formats also enhance interoperability across data sets, allowing data to be cross-referenced and additional analyses 

to be performed. Data set rules can then guide data cleansing, e.g., the removal of 

incorrect, incomplete, duplicate, and improperly formatted data.

Data formatting and standardization

Data formatting and standardization have been previously introduced as critical 

components of data set development, as has planning data correction strategies. 

Knowing when to implement these strategies requires regular, systematic checks of 

data quality to ensure that data entry is adhering to formatting standards and that 

any data outliers are identified and checked against historical precedent, analysis 

of variance, and general programmatic knowledge. These strategies protect the integrity of the data. Data cleansing can then occur to remove incorrect, incomplete, 

duplicate, and improperly formatted data. Correcting these errors manually is inefficient and costly. Data cleansing software can systematically examine and cleanse 

data in a manner of minutes rather than hours, allowing program staff to focus on 

data monitoring, profiling, and standardization.

Data confidentiality and privacy 

The federal government has a responsibility to protect the confidentiality and privacy

of individuals whose data it collects, stores, and uses for analysis and programmatic 
15

planning. HHS and ACL have strict safety, security, and privacy requirements. To ensure 

ACL’s data maintains confidentiality and privacy, consult with ACL’s Director of the Office 

of Information Resources Management and the Chief Information Security Officer on all 

safety, security, and privacy requirements. If your data is managed by a third party (a 

contractor or grantee), provide them with all HHS and ACL requirements.

Data extraction

Processes to access and extract information from full data sets should include mechanisms to log users and their actions within the data sets (i.e., data entry, data alteration, data downloads). A previous version of the data set should be automatically 

archived so that an unaltered version is available if errors are introduced during the 

extraction. These processes not only protect the integrity of the data but also establish 

an information trail useful in tracking data usage and alteration over time.

Preservation of data authenticity

Data authenticity requires content being unchanged during transmission from point to 

point. Inaccuracies can be introduced during transmission of a file and may not necessarily be due to tampering but merely to unattributable technology failures. The use of 

encryption software to authenticate that the file sent is the one received is one way to 

check for data origin authenticity. This data encryption also protects the data from file 

corruption, whether intentional or unintentional.

Metadata to support data documentation

In addition to the development of a data dictionary (previously described), data set 

developers should consider the use of metadata to support data documentation. 

Metadata provide information that describes data and, at a minimum, contains the 

information necessary to understand and reuse data created by others. A metadata 

schema is a formal framework for recording and describing data. Schemas, along 

with data dictionaries and readme files, which contain instructions and information 

about file updates, often accompany large data files and data sets (Iowa State 

University, University Library, n.d.).
16

ARE THE DATA RELEVANT?

Relevance is the level of consistency between data content and the area of interest 

of the user. To maximize the use of ACL’s 

data, those data should not only be accurate; they must be relevant to the program’s 

or grant’s overarching goals and objectives. 

To ensure your data meet this requirement, 

link the data collection to the outcomes 

identified in your logic model (see ACL’s 

Logic Model Guidance). It is imperative that 

as time evolves, your data collection evolves 

as well. Consider a conscientious design 

that anticipates future uses of data. 

According to the PRA, federal agencies must 

aim to reduce the total amount of paperwork 

burden the federal government imposes 

through the collection of data. To achieve adherence to PRA requirements, we should only 

collect data that are useful and necessary.

ARE THE DATA ACCURATE?

Accuracy refers to how well the data describe 

real-world conditions. Accurate data convey 

true information. If there are errors in the 

data, making a data-driven decision is impossible, and your data have the potential 

to damage ACL’s reputation. Inaccurate data 

create clear problems, as they can cause you 

to come to incorrect conclusions. 

ARE THE DATA TIMELY?

Timeliness refers to how recently the event 

the data represent occurred. Using outdated data can lead to inaccurate results and 

taking actions that don’t reflect the current 

reality. Generally, data should be recorded as 

soon after the real-world event as possible. 

Data that reflect events that happened more 

recently are more likely to reflect the current 

reality. Data typically become less useful and 

less accurate as time goes on.

Evaluating an Existing Data Set
17

DO YOUR DATA RELATE TO THE 

GOALS IDENTIFIED IN YOUR 

LOGIC MODEL? 

Identify data needs to answer key agency 

questions and questions tailored to the 

needs of your program or grant. 

ARE THE DATA COLLECTED 

VALUED BY STAKEHOLDERS?

Assess and balance the needs of stakeholders with the requirements and intent of 

the program/grant and ACL’s goals and 

objectives. 

ARE THE DATA COMPARABLE? 

Data among various groups (e.g., grantees, 

states, etc.) should be comparable. When 

comparing data across multiple data sets, 

consistency is vital. A data item should be consistent in both its content and its format. Data 

items should possess the same definition and 

format across data collection activities and 

data sets. If your data aren’t consistent, different groups may be operating under different 

assumptions about what is true. Inconsistent 

data can lead to departments within ACL 

unknowingly working against one another.

ARE THE DATA COMPLETE? 

To get a full picture of the needs of programs/

grants ACL supports, as well as maintain open 

channels of communication, our data must 

include all up-to-date, pertinent information. 

If the data are complete, there are no gaps. 

Everything that was supposed to be collected 

was successfully collected. Incomplete data 

may make the data reported less useful. If 

data are incomplete, you might have trouble 

gathering accurate insights. For instance, if 

a respondent doesn’t include his or her age, 

it will be harder to target content to people 

based on their age.
18

Data Dissemination

ACL has a responsibility to collect, analyze, and disseminate high-quality data. The 

collection and analysis of high-quality data are required for programmatic oversight, evaluations of effectiveness, and mandated federal reporting. Dissemination 

is essential to allow additional stakeholders to benefit from data collected using 

federal funds. State stakeholders, including the aging and disability network, local 

Area Agencies on Aging, Councils on Developmental Disabilities, and other social 

service providers, depend on high-quality ACL data to fund and provide quality 

services to the community. Researchers depend on ACL’s data for examining local, 

regional, and national service usage and service effectiveness trends and findings. 

Advocates and other private citizens also rely on the accuracy and access to ACL’s 

data to persuade lawmakers and other service funders to introduce and improve 

funding and services for older adults, people with disabilities across the lifespan, 

and their families and caregivers. Ultimately, we have a responsibility to share 

federal agency data with state, local, and tribal governments.

Data Extraction

Data extraction involves selecting 

distinct data set variables and then 

recoding them into a format useful 

for analysis or presentation. Data 

extraction and accurate, meaningful 

presentation are critical to the dissemination of information and the use of 

collected data by stakeholders. 

Be sure when developing a data 

collection system to discuss 

the required format of the data 

upon extraction. Consider both 

the type (currency, percentages, 

time, etc.) and the format (Excel, 

Pdf, Word, etc.)
19

The American Community Survey (ACS)5 is a standardized data collection administered by the U.S. Census Bureau since 2005. Data are collected quarterly via the Internet, mail, telephone, and in-person interviews. The ACS addresses many of the topics 

within the census, but its more frequent data collection (quarterly vs. every 10 years) 

allows for more current data and trend analysis. The questions used in the ACS are 

well defined and used by many federal agencies in their data collection efforts. They 

can also be used at the local or regional level, as well as by researchers. ACS uses the 

following definition of poverty:

5 https://www.census.gov/programs-surveys/acs

Poverty status is determined by 

comparing annual income to a set of 

dollar values (called poverty thresholds) 

that vary by family size, number of children, and the age of the householder. 

If a family’s before-tax money income is 

less than the dollar value of their threshold, then that family and every individual 

in it are considered to be in poverty. For 

people not living in families, poverty 

status is determined by comparing 

the individual’s income to his or her 

poverty threshold. 

The poverty thresholds are updated 

annually to account for changes in the cost 

of living using the Consumer Price Index 

(CPI). They do not vary geographically. 

The ACS is a continuous survey, and people 

respond throughout the year. Since income 

is reported for the previous 12 months, 

the appropriate poverty threshold for each 

family is determined by multiplying the 

base-year poverty threshold from 1982 by 

the average of monthly CPI values for the 

12 months preceding the survey month. 

For more information, see page 107 of 

“American Community Survey and Puerto 

Rico Community Survey 2018 Subject 

Definitions” here. 

Example of High-Quality Data

How Poverty Is Measured
20

As an example of data usage, the U.S. Census Bureau examined 2017 and 2018 ACS 

poverty data to determine change in the national poverty rate, as well as to identify 

states with lower and higher poverty rates than the national average. This annual 

schedule allows the U.S. Census Bureau to show that there was an increase in the 

average poverty rate during 2007–2011, but that the rate decreased during 2012–

2018. Exhibit 1 shows the year-to-year change in the poverty rate from 2005 to 2018.

University Centers for Excellence in Developmental Disabilities (UCEDDs) are interdisciplinary education, research and public service units of a university or not-forprofit entity associated with universities. UCEDDs advise federal, state, and community policymakers and promote opportunities for individuals with developmental 

disabilities to exercise self-determination and to be independent, productive, 

integrated and included in all facets of community life. Funding from ACL provided 

the infrastructure support for the Centers to engage in interdisciplinary preservice 

training, continuing education, community services, research, and information 

dissemination activities. From FY 2015 to FY 2018, there was an overall increase in 

the number of participants receiving continued education and/or training. 

ACL’s Administration on Disabilities (AoD) exemplifies high-quality data through 

the annual use of a Tier I Review Tool to conduct high-level program compliance 

and outcome reviews of the Statement of Goals and Priorities (SGP) and Program 

Performance Report (PPR). The outcome review uses narratives and measures to 

evaluate performance based on data analysis to understand results. In addition 

to ensuring Developmental Disabilities Assistance Act compliance, AoD’s Tier I 

Review Tool is also used to determine if a Protection and Advocacy System (P&A) 

needs a higher level of review such as a Tier II review. 
21

Unlike the ACS, grantees collect the Older Americans Act Title III data. The exhibit 

below presents information on the number of home-delivered meals distributed 

across the nation by federal fiscal year. Markers of quality include the clear and 

succinct definitions for the data, the annual collection cycle occurring in each state 

and territory contributing to large sample size, and the analysis and reviews occurring at multiple levels before the submission process, which enhance the accuracy 

of the data.

*Home-Delivered Meal (one meal) — A meal provided to a qualified individual in his or her place of residence. The meal is 

served in a program administered by State Units on Aging (SUAs) and/or Area Agencies on Aging (AAAs) and meets all of the 

requirements of the Older Americans Act and state/local laws. As noted in Section IIA, meals provided to individuals through 

means-tested programs such as Medicaid Title XIX waiver meals or other programs such as state-funded means-tested programs are excluded from the Nutrition Service Incentive Program (NSIP) meals figure in line 4a; they are included in the meal 

total reported on line 4 of Section IIA. Certain Title III-E funded home-delivered meals may also be included.

2019

2018

2017

2016

2015

50,000,000 100,000,000 150,000,000

FISCAL YEAR

nnnnnnnnnnnnnnnnnnnnnnn

nnnnnnnnnnnnnnnnnnnnnnn

nnnnnnnnnnnnnnnnnnnnnnn

nnnnnnnnnnnnnnnnnnnnnnn

nnnnnnnnnnnnnnnnnnnnnnn

n Home-Delivered Meals* Congregate Meals

Number of Meals Served

Exhibit 1. Older Americans Act Title III Nutrition Services: 

Number of Home-Delivered Meals Served by Fiscal Year
22

Continuing Education/ Community Training: training provided by UCEDD faculty/staff 

to enhance knowledge of a variety of community members (individuals with developmental and other disabilities, their families, professionals, paraprofessionals, 

policy makers, students or others in the community).

Exhibit 2. Participants in Continuing Education/Community Training: FYs 2014–2018

UCEDD FYs 2014-2018

800

700

600

500

400

300

200

100

0

2014 2015 2016 2017 2018

513,700

299,229

571,564

618,873

794,691

THOUSAND
23

Summary

The collection, management, and use of high-quality data are critical to ACL and its 

mission to ensure older adults and people with disabilities are able to live independently and fully participate in their communities. ACL, like other federal agencies, is required to collect data to assess the performance of its federally funded 

programs and grants. Findings from these data collection activities are used to 

measure and communicate progress toward programmatic goals and priorities. 

Data quality is a process, and ACL is continuously seeking to improve the quality 

of our data through the development of formatting and interoperability standards, 

data transmission protocols, cleansing processes, and advisory groups such as the 

ACL Data Council. These evolving activities will, in time, build upon the recommendations made within this document. 
24

Term Definition Source

Data A value or set of values representing a specific 

concept or concepts. Data become information 

when analyzed and possibly combined with other 

data in order to extract meaning and to provide 

context. The meaning of data can vary depending 

on its context.

The Data Reference Model: 

Version 2.0 

https://obamawhitehouse.

archives.gov/sites/default/

files/omb/assets/egov_

docs/DRM_2_0_Final.pdf

Data Dictionary A data dictionary is used to catalog and communicate 

the structure and content of data and provides meaningful descriptions for individually named data objects.

U.S. Geological Survey 

https://www.usgs.gov/

Data Extraction 

Tool

Data extraction tools allow a user to select a data basket full of variables and then recode those variables 

into a form that the user desires. The user can then 

develop customized displays of any selected data.

www.data.gov

Data Set Datasets (or data sets) are a group of data files—

usually numeric or encoded—along with the documentation files (such as a codebook, technical or 

methodology report, data dictionary) which explain 

their production or use. 

Data Information Specialists 

Committee-UK, 2007

http://www.disc-uk.org/

qanda.html

Metadata Metadata describe a number of characteristics or 

attributes of data; that is, data that describe data. 

For any particular datum, the metadata may describe 

how the datum is represented, ranges of acceptable 

values, its label, and its relationship to other data.

Adapted from The Data 

Reference Model: Version 2.0

https://obamawhitehouse.

archives.gov/sites/default/

files/omb/assets/egov_

docs/DRM_2_0_Final.pdf

Glossary of Terms
25

Data Quality Resources

ACL, along with many other federal organizations, is actively engaged in improving 

the quality of data. While additional internal guidance is in development by ACL’s 

Data Council, the external resources below can supplement the recommendations 

presented in this guide. 

Federal Government Data Maturity Model

https://www.dlt.com/blog/2018/10/08/federal-government-data-maturity-model-2019-datamanagement

Federal Data Strategy 2020 Action Plan

https://strategy.data.gov/assets/docs/2020-federal-data-strategy-action-plan.pdf

H.R.150 - Grant Reporting Efficiency and Agreements Transparency Act of 2019

https://www.congress.gov/bill/116th-congress/house-bill/150/text

H.R.2142 - GPRA Modernization Act of 2010 

https://www.congress.gov/bill/111th-congress/house-bill/2142/text

A Playbook in Support of the Federal Data Strategy

https://resources.data.gov/assets/documents/fds-data-governance-playbook.pdf

The Federal Data Strategy

https://strategy.data.gov/overview/

Leveraging Data as a Strategic Asset

https://www.performance.gov/CAP/leveragingdata/

A Guide to the Paperwork Reduction Act (PRA)

https://pra.digital.gov/about/
26

Citations 

Benson, C. & Bishaw, A. (2019). Poverty: 2017 and 2018: American Community Survey briefs. Retrieved 

from https://www.census.gov/content/dam/Census/library/publications/2019/acs/acsbr18-02.pdf

Data Information Specialists Committee-UK (2007). Datashare project, questions and answers.

Retrieved from http://www.disc-uk.org/qanda.html

Faculty Development and Instructional Center at Northern Illinois University. (2005). Responsible 

conduct in data management: Data collection. Retrieved from https://ori.hhs.gov/education/

products/n_illinois_u/datamanagement/dctopic.html

General Services Administration & the Office of Management and Budget. (2019). CAP goal overview: 

Leveraging data as a strategic asset. Retrieved from https://www.performance.gov/CAP/

leveragingdata/

Government Accounting Office (2000). Assessing the quality of program performance data. Retrieved 

from https://www.gao.gov/products/GAO/GGD-00-140R

IBM. (1993). IBM dictionary of computing. New York, NY: McGraw-Hill, Inc. ISBN: 978-0-07 031488-7. 

Retrieved from https://dl.acm.org/doi/book/10.5555/541721

IGI Global. (n.d.). What is data relevance? Retrieved from https://www.igi-global.com/dictionary/dataquality-assessment/6800

Informatica. (2020). What is data quality? Redwood City, CA: Author. Retrieved from https://www.

informatica.com/services-and-training/glossary-of-terms/data-quality-definition.html

Iowa State University, University Library. (n.d.). Data management plan (DMP) guide. Retrieved from 

https://instr.iastate.libguides.com/dmp

Kang, H. (2013). The prevention and handling of the missing data. Korean Journal of Anesthesiology, 

64(5), 402–406. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3668100/

Lotame. (2019). Why is data quality important? Retrieved from https://www.lotame.com/why-is-dataquality-important/

McLeod, S. (2019). What’s the difference between qualitative and quantitative research? Retrieved 

from https://www.simplypsychology.org/qualitative-quantitative.html

Oak Ridge National Laboratory, Distributed Active Archive Center for Biochemical Dynamics. (n.d.). 

Data quality review checklist. Retrieved from https://daac.ornl.gov/submit/qachecklist/

Office of Management and Budget. (2001). Guidelines for ensuring and maximizing the quality, 

objectivity, utility, and integrity of information disseminated by federal agencies. Retrieved from 

https://obamawhitehouse.archives.gov/omb/fedreg_final_information_quality_guidelines/
27

Prell, M., Chapman, C., Adeshiyan, S., Fixler, D., Garin, T., Mirel, L., & Phipps, P. (2019). Transparent 

reporting for integrated data quality: Practices of seven federal statistical agencies. FCSM 19-01. 

Federal Committee on Statistical Methodology. Retrieved from https://nces.ed.gov/FCSM/pdf/

Transparent_Reporting_FCSM_19.01.pdf

Statistical Modeling, Causal Inference, and Social Science: Sometimes the Raw Numbers Are Better 

Than a Percentage. (2010). Retrieved from https://statmodeling.stat.columbia.edu/2010/06/24/

sometimes_the_r/

Statistics How To. (2020). Variance: Simple definition, step by step examples. Retrieved from https://

www.statisticshowto.com/probability-and-statistics/variance/

University of Virginia Library Research Data Services + Sciences. (2020). Data types & file formats. 

Retrieved from https://data.library.virginia.edu/data-management/plan/format-types/

U.S. Census Bureau. (n.d.). American Community Survey (ACS). Retrieved from https://www.census.

gov/programs-surveys/acs

U.S. Geological Service (n.d.). Data management: Data dictionaries. Retrieved from https://www.usgs.

gov/products/data-and-tools/data-management/data-dictionaries

U.S. Office of Management and Budget (2005). The data reference model: Version 2.0. Retrieved from 

https://obamawhitehouse.archives.gov/sites/default/files/omb/assets/egov_docs/DRM_2_0_

Final.pdf

U.S. Office of Personnel Management. (2011). Paperwork Reduction Act (PRA) guide: Version 2.0. 

Retrieved from https://www.opm.gov/about-us/open-government/digital-government-strategy/

fitara/paperwork-reduction-act-guide.pdf

U.S. Office of Research Integrity. (n.d.). Data analysis. https://ori.hhs.gov/education/products/n_

illinois_u/datamanagement/datopic.html

Wassen, O. (2019). 11 benefits of achieving high data quality (including confidence, scalability & 

consistency). Retrieved from https://www.nodegraph.se/11-benefits-of-achieving-high-dataquality/

Developed by Jennifer Tillery, ACL Sr. Social Science Analyst

in partnership with New Editions Consulting, Inc.

For additional information contact:

Office of Performance and Evaluation

Center for Policy and Evaluation

Administration for Community Living

330 C St SW

Washington, DC 20201

www.acl.gov/contact
Administration for Community Living

Office of Performance Evaluation