Data Quality 101 • Have you checked your data for quality issues? 

• Has anyone questioned the quality of your data? 

• Have you worried about including your data in a presentation? 

• Have you recently changed the way your data are collected 

 (e.g., new system/contractor/data elements)?

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For questions, 

contact ACL’s Office of 

Performance and Evaluation.

Identify

Potential 

Issues

Inaccuracy Inconsistency Missing Data Bias Outliers

Ask Yourself

the Following

Are the data values the correct 

values, and are the data 

represented in a consistent 

and unambiguous form?

Are there inconsistencies or 

contradictions in data across 

variables and/or time (e.g., 

duplicate data or discrepancies 

in data definitions between 

data sources)?

Are all available data elements or data points included 

in the data set? How many 

data are missing? Missing 

data can occur because of 

nonresponse or attrition.

Are the data systematically 

different from the population 

parameter being estimated? 

Do respondents have different 

characteristics from nonrespondents?

Are there data points with characteristics 

that are considerably different from most 

of the other data points in the data set? 

Outliers could indicate “interesting” 

cases or errors in the data.

ACL 

Examples

Information about OAA Title III 

services are usually overreported or underreported. For 

example, a state would report 

100,000 services for several 

years and then increase to 

500,000 the next year, or 

vice versa.

For OAA Title VI data, a tribe was 

erroneously listed under another state. Though it was recently 

corrected on AGID, because it 

was listed erroneously in other 

places it has to be listed that 

way on AGID for those 

past years.

Missing data in data sets such 

as the SPR and CIL PPR leads 

to underreporting and gaps 

in data. (Missing data is not 

posted on AGID). 

NSOAAP suffers from nonresponse bias, so the respondents 

who answered the survey may 

not be representative of the target population. Among surveys 

targeting the aging population, 

the individual may no longer 

receive the service or may have 

moved into assisted living or a 

nursing home.

For variables with a small overall total, 

outliers can have a large impact. One state 

or grantee’s number can impact the entire 

region or U.S. total and can skew a data 

set. ACL is not able to force corrections 

even if the error is an obvious typo. In one 

case the reported number spiked from 3 to 

11,000 and back down to 3.

What to 

Do Next

• Use trend data to verify 

if data are inaccurate 

(over- or underreported).

• Follow up with data

provider.

• Verify with stakeholder 

as appropriate.

• Use historical data to verify 

changes, possibly reviewing 

data from before changes 

were made (e.g., PPR 

updates, new grant cycles).

• Verify with stakeholder as 

appropriate.

• Use filtering to identify 

missing data points; calculate and report percentage 

of data missing.

• Identify columns that have 

no data reported.

• Verify with stakeholder 

as appropriate.

• Intentionally explore alternative explanations for the data.

• Conduct subgroup analysis 

where possible.

• Verify with stakeholder as 

appropriate.

• Identify outliers by analysis 

(e.g., at-a-glance, filtering, Min/Max 

functions in Excel).

• Are there extenuating circumstances 

that cause outliers (e.g., change in 

program delivery, COVID-19 reporting, 

change in services)?

• Verify with stakeholder as appropriate.