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National Evaluation and Technical Assistance Center for the Education of Children and Youth Who Are Neglected, Delinquent, or At-RiskIntroduction | Recommendations | Findings | Summaries | PDF
Introduction
Obtaining accurate nationwide assessments of program performance and impacts outside of large-scale external evaluations is a challenge for any program. But when the target of the intervention is literally moving-neglected and delinquent students, who pass and cycle through institutions and schools frequently-even the most basic tracking requires substantial commitment and coordination by the agencies involved.
We assessed the level of data currently available on neglected and delinquent students, specifically those children who fall under the umbrella of the U.S. Department of Education's (the Department's) Prevention and Intervention Programs for Children Who Are Neglected, Delinquent, or At-Risk (N/D). The N/D Program is authorized under Title I, Part D, of the Elementary and Secondary Education Act (ESEA), as amended by the Improving America's Schools Act of 1994 and the No Child Left Behind Act of 2001. Subpart 1 of the Act provides federal financial assistance to state-operated educational programs for children and youth in institutions for neglected and delinquent children and for youth in adult correctional facilities. Subpart 2 provides assistance to local education agencies (LEAs) to serve children and youth who are in locally operated correctional faculties or who are at risk of dropping out. In 2000-2001, the N/D program served 124,034 of the nation's most disadvantaged students.
We reviewed the current practices nationwide and within states for collecting data relevant to assessing the progress of educating neglected and delinquent children. Our analysis is organized around four central questions:
- What types of data are available to evaluate the effectiveness of state N/D programs?
- How are N/D data reported across states?
- What are the key issues in determining states' abilities to systematically collect high-quality effectiveness data?
- What successful programs and innovative practices have states used to collect high quality effectiveness data (including software and linkages to other states)?
SUMMARY OF RECOMMENDATIONS
Five general recommendations resulted from this review. The question-by-question discussion section presents more detailed recommendations.
Visit states and attain input from N/D administrators and stakeholders on what measures are important and useful and the feasibility of getting high-quality data on those measures.
The N/D data collected and reported under the federal requirements are not useful according to states and are not consistent or meaningful in an aggregated form according to research studies. Fifty-nine percent of facility coordinators reported that annual program evaluations were not useful measures of program success. The most frequent reason given by these coordinators was that federal evaluation guidelines are not realistic in the corrections environment (LeBlanc & Ratnofsky, 1991). The research also offered suggestion about which indicators are more meaningful. For example, Tashjian’s interviews with N/D staff indicated that measures of student behavior (e.g., attend class regularly) and attitude were considered to be more realistic indicators of program success than test scores.
Inconsistent data across institutions and incomplete or unavailable data are also widespread difficulties. For example, Tashjian’s 2000 study of local education agencies (LEAs) found that although most of the sampled districts were able to identify the number of youth served, data on student achievement were often incomplete. Tashjian specifically recommended keeping a close eye on the usefulness and feasibility of data collection, knowing that good intentions may produce a meaningless paper-pushing process. Anderson and Welsh (2000) noted that fewer than half the state N/D administrators they surveyed reported that local consolidated plans or applications gave them information about key aspects of their programs “very well” or “fairly well.” Problems with incomplete and inconsistent measures were also cited in the 2001–2002 Program Performance Report for N/D Programs. Sinclair and Gutmann (1993, 1994) noted that many coordinators lacked a background in standardized testing and were unable to assess the completeness of test score information. As a result, they submitted incomplete or implausible information.
After the useful and important measures have been determined, N/D sites must be asked about what barriers they face in collecting those data. Although the literature gives some indication about key obstacles—namely, roadblocks to student participation in N/D programs, financial constraints, and lack of or difficulty in obtaining data—targeting technical assistance to the central barriers will be critical to achieving high-quality data.
Partner with offices producing PBDMI, NAAL, and other potential data sources.
The Department’s Performance-Based Data Management Initiative (PBDMI) can offer a core of essential information (e.g., data on student achievement). Using this information will allow the N/D program office to focus on collecting supporting data.
In addition, the U.S. Department of Education’s National Center for Education Statistics (NCES) may be an ideal partner. N/D program leadership should explore the possibility of adding a question or two to the National Assessment of Adult Literacy (NAAL), which NCES produces. The NAAL includes a component that specifically assesses the literacy levels of incarcerated youth (over age 16) and adults and is now in the development phase.
Given that NCES intends to increase the frequency of the NAAL, the assessment may become significantly more useful for the N/D program and its policy. Thus, it would be advantageous to build ties with the NCES staff producing the survey. Other outside groups, such as the Annie E. Casey Foundation, could also be data providing partners.
Develop a small core group of required but useful indicators that are based on the lessons learned from this summary.
Once the N/D program office knows what is meaningful to N/D administrators and stakeholders and what data can be obtained by partnering with other groups, the remaining data needs can be aligned with national data needs. We have incorporated the more than 104 different types of measures in this review into our evaluation model to form a pool from which to select the core measures.
Produce guidance, including a data-definition handbook, even for data that are not part of the core indicators.
Nationally standardized data definitions and other data-quality guidance are greatly needed, even at the most basic levels. Tashjian’s (2000) profile of nine N/D institutions for the Department of Education noted that although basic information, such as demographic characteristics (gender, race) and numbers of youth served, seems to be relatively easily available from N/D institutions, there is no standard definition of which students should be included in those counts. This lack of a standard definition makes federal aggregation, or even comparison from site to site, meaningless. Polk County, Florida, for example, where students are included in totals only if they have resided at a facility for 30 consecutive days, will appear to serve fewer students than will a county that counts every student who enters its facilities.
Develop a national online N/D reporting system verified through site visits or phone calls to a sample of sites.
A web-based online system offers the flexibility and access needed for a multi-state, multi-site reporting tool. More information needs to be gathered, however, on exactly what such a system should look like and how it would work. We strongly suggest that any discussions of the development of a national N/D reporting system include representatives from California’s Alternative Schools Accountability Model (ASAM) system and the Pennsylvania N/D system, who can offer important advice and information about lessons learned.
SUMMARY OF FINDINGS
The summaries of findings for each of the four research questions are presented below. They are followed by a more in-depth discussion of each question.
What types of data are available to evaluate the effectiveness of state N/D programs?
The data available at the federal level address questions regarding the nature of N/D programs across the nation. However, they do not provide sufficient information for evaluating the effectiveness of these programs. Furthermore, and all data carry some quality concerns. Federal data can tell us how many youth are served by N/D institutions, the demographics of students at N/D institutions, the number of institutions that offer N/D programs, the types of institutions that offer N/D programs, and the services offered at N/D programs. But data on student achievement and educational outcomes and details regarding program implementation are lacking.
The three state-level summaries reviewed indicate the availability of some student achievement and outcome indicators, as well as somewhat more detailed information regarding N/D programs, such as assessments and supplemental instructional programs. These kinds of data would be much more useful than simple counts of types of programs and student demographics in evaluating the effectiveness of N/D programs. Given that we have studies from only three states (California, Ohio, and Pennsylvania), it is unclear how difficult it would be to obtain these types of data from the other states.
Data reported across individual studies encompass a much wider range of possible measures useful for evaluating the effectiveness of N/D programs than do the relatively few federal- and state-level studies. However, few studies include long-term student outcomes, such as high school graduation and GED passing rates or post-institutional placement, and none reports on accumulated credits. Although broad measures of program quality are reported (e.g., teacher preparation and professional development), few studies include specific information about such key components as the existence and contents of transition plans; relationships among the program, the community, and community schools; and program dosage (e.g., number of hours of instruction per day, number of days in care). We found almost no data regarding program quality and curriculum content.
How are N/D data reported across states?
The most typical strategy for data collection has data collected by local education agencies and compiled at the state level. These data collection efforts generally involve distributing some form of a survey through mail questionnaires, telephone surveys, or online data collection. However, the types of data that are gathered, the specific methods and frequency by which data are collected, and the mechanisms by which data are reported vary greatly.
Despite this variability, two statewide reporting systems provide promising mechanisms for N/D reporting purposes. The new ASAM system in California and the Pennsylvania N/D reporting tool are both web-based online systems, which are ideal for large states whose constituents are widely dispersed geographically. Both systems are user-friendly with clearly defined indicators, are electronically secure, and are accompanied by adequate user training and technical assistance to minimize error.
What are the key issues in determining states’ abilities to systematically collect high-quality effectiveness data?
We identified four key challenges that prevent many N/D institutions from collecting and reporting to the states high-quality data about the effectiveness of their N/D programs. The first challenge to the systematic collecting and reporting of effectiveness data is the lack of a federal requirement that states do so. Without such a requirement, institutions have little incentive to collect data. A second challenge faced by institutions in collecting high-quality data is financial. N/D institutions often have very limited resources available for evaluation efforts. The third challenge has to do with the low quality of available N/D data. Questionable methodology, a lack of control groups and experimental designs, data management errors, and non-standardized metrics are among the data-quality problems identified by N/D researchers. The fourth challenge is related to the difficult population under study. The mobility of N/D students and their lack of motivation to participate in studies are particularly difficult obstacles to overcome. These challenges as a whole pose difficulties for high-quality data collection.
What successful programs and innovative practices have states used to collect high quality effectiveness data (including software and linkages to other states)?
The tremendous challenges faced by N/D institutions mention above severely attenuate the likelihood of collecting high-quality program data, although some states are demonstrating innovations in collection practices. The California ASAM model is very promising, it is in an early stage of implementation and its success has yet to be determined. The Pennsylvania system offers reasonable data-reporting procedures, but the data that are gathered tend to focus on participants’ characteristics rather than program quality. We were not able to find information about cross-state collaborations to gather high-quality effectiveness data.
Therefore, the need for the development of a uniform evaluation model is great. This model should identify relevant and useful data that are feasible to collect nationally so that data can be obtained from each state, the District of Columbia, the Bureau of Indian Affairs, and Puerto Rico. With this uniform evaluation model in place and with appropriate technical assistance, evaluations can provide local and federal governments with scientifically based evidence on the effectiveness of N/D programs.
STUDY SUMMARIES
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