Developing and Evaluating Methods for Using American Community Survey Data to Support the School Meals Programs Interim Report (2010) / Chapter Skim
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6 Evaluation of Estimates
6 Evaluation of Estimates
Pages 87-99
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From page 87... ...
by exploring sources of possible discrepancies between the estimated claiming percentages and the concept behind the authorizing legislation and regulations of the school meals programs, (2) by evaluating the quality of the estimates in the context of the error associated with existing practices and provisions, and (3)
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From page 88... ...
Finally, when estimates for small populations, such as small school districts or individual schools, are needed, the estimation method will almost certainly involve some form of statistical model that specifies a structure to approximate -- with error -- the observed relationships in the population. While this list may seem extensive, the current procedures for certification and meal counting in the school meals program, for example, are subject to their own errors associated with administrative processes that involve parents, students, lunch room staff, and office staff.
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From page 89... ...
From the national perspective, accurate and predictable reimbursements pro vide incentives for schools to foster program access and meet the nutri tional needs of children while providing adequate oversight to control the program's overall budget. Local schools desire accurate and predictable reimbursements to protect local resources from being inadvertently diverted from other educational priorities to the school meals programs.
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From page 90... ...
These methods rely on observational data from districts that are not from a nationally representative sample, which may affect the accuracy of reimbursement based on the estimated claiming percentages. The panel will consider these issues as it carries out its analysis.
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From page 91... ...
Although the required annual verification of a sample of applications might reduce errors in the completion and review of applications, substantial certification errors still remain, as discussed below. Once a student is approved for a specific eligibility status (free, reduced price, or full price)
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From page 92... ...
It also demonstrates that the net effect of certification error tends to result in overreimbursement to districts, as illustrated next. To quantify the effect that certification errors can have on the distribution of students by eligibility status when using the traditional method, the APEC study compared the distribution of students based on the categories for which they had been approved with the distribution based 5 Overcertification occurs when a student is certified for more benefits than those to which she or he is entitled.
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From page 93... ...
If we assume that the participation rate is based solely on the approval status and if eligibility has been correctly assessed, the blended rate for students going though the certification process and taking participation into account would be $2.45 per meal. Based on the approval status determined by the certification process, the blended rate for students going through the certification process and taking participation into account is $2.55.7 The APEC study also evaluated noncertification errors, classified as cashier error or aggregation error.
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From page 94... ...
In general, the sources of error in reimbursements noted for the tra ditional method will also apply to Provision 2. Given that the claiming percentages remain fixed over a 3-year period, claims for reimbursement under Provision 2 will not reflect changes in income or demographics that would be reflected in the traditional method, which creates an addi tional source of error.
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From page 95... ...
However, any change in participation when enrollment stays steady would result in erroneous payments. POTENTIAL ERRORS IN PROPOSED DATA SOuRCES AND ESTIMATION METHODS The primary data source for estimating eligibility percentages under Provision 4 is the ACS.
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From page 96... ...
For this study, it is likely that the reliability of direct estimates of the fraction of students eligible in each school meals category for individual schools, and even some entire school districts, will not be acceptable. In this situation, it is common, as discussed in Chap ter 5, to use a model-based or model-assisted estimation approach that uses auxiliary data and modeled relationships to improve the precision of estimates for the small domains.
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From page 97... ...
There are many potential sources of measurement error, depending on the type of question. For example, a respondent may have difficulty understanding or be inattentive to the correct meaning of the question; have trouble recalling past events or estimating income in accordance with the question's definition; or provide erroneous answers due to social desirability pressures, perceived stigma, or privacy concerns in answering sensitive questions, such as questions about income and program participation.
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From page 98... ...
For example, we will examine and measure the uncertainties introduced at the school district and school attendance-area levels by boundary mapping and translate these into potential uncertainties regarding estimates. We will also use data from the schools in the case study school districts to assess the potential magnitude of discrepancies between reimburse ments based on ACS-based claiming percentages under Provision 4 and
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From page 99... ...
We will encourage school district representatives to discuss potential errors and costs associated with both the current approaches and Provision 4. Case study district representa tives will be key participants in the workshop.
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