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Appendices
A.4 DATA VERIFICATION AND VALIDATION
Grantees and partners, such as states, collect most data for ACF programs with collection schedules written into statutes and regulations. ACF uses considerable resources to verify and validate program data through automatic edit checks, manual reviews or audits, and other forms of quality control and assurance.
Specific data issues are discussed in the individual performance goal sections. ACF has developed a number of different strategies to deal with these issues. There are a number of broad data-related challenges affecting ACF's performance plan. Resolving these challenges (listed below) and other data issues is necessary, time-consuming, difficult, and costly.
- Quantitative and qualitative measurement of outcomes in social programs are experimental and still being validated;
- States, Tribes, and non-profit grantees vary in their ability to collect, produce and report reliable data
- Data validation and verification are highly complex and costly;
- Particularly for our numerous new or changed programs, baseline data are frequently unavailable and must be developed before progress can be measured;
- Data collection systems fully geared to state flexibility are still being implemented; and
- Investments in the design, development and implementation of data collection systems are costly and must be balanced against other priorities at all levels – Federal, state, and local.
Many ACF grantees receive programmatic funds that the legislation either designates or permits to be used for data collection. Discretionary, formula, and entitlement grant awards generally carry reporting requirements directed at facilitating oversight and measuring performance. However, block grants and devolution of program authority to states have resulted in limitations on ACF's collection of data. ACF has worked with its partners to collect a reasonable amount of data from which to determine performance and assure program integrity.
For a number of major programs, ACF is largely dependent upon state administrative systems for collecting performance data, e.g., Temporary Assistance to Needy Families, Developmental Disabilities, Refugee Resettlement, Child Welfare, Child Support Enforcement, Child Care, and Low Income Home Energy Assistance Program. For these programs, performance results are measured and validated through the administrative data.
Currently, ACF has the following major data system infrastructures in place: the National Directory of New Hires (Child Support and TANF), the Unemployment Insurance Wage data (UI), the TANF Data Reporting System, the TANF SSP-MOE Data Reporting System; and the Tribal TANF Data Reporting System; the Child Support Survey; the Residential Energy Consumption Survey; March Current Population Survey (CPS) Supplement (Census Bureau); the Refugee Resettlement Survey; Head Start Family and Child Experiences (FACES) Survey; and the National Child Welfare Longitudinal Study.
Other ACF programs, e.g., Head Start, Youth programs, CSBG, and Family Violence, rely on local community data systems. Native Americans programs use two internal data tracking systems (Project Information and Evaluation System and the Grant Award Tracking and Evaluation System). The Head Start information is collected at local grantee sites through Program Information Reports and the Family and Child Experiences Survey (FACES) which has rigorously defined collection procedures. Several programs use survey information to supplement the data.
As a result of many of the challenges listed above, there is some delay in the availability of administrative data. These delays limit knowledge of current program activity and hinder policy-making and program planning. Some delays are inherent in the goals and measures of the program, e.g., job retention and earnings gain in TANF. ACF reviewed the data reporting time frames for the performance measures in this plan. A chart summarizing the timetables for ACF programs using state and grantee administrative data is included in Appendix A-11.
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