Policy & Practice | Winter 2023

finding of a RAND study which found a similar variation. 2 By noting differences in the same context, for example by adding or removing an alternative poverty measure from the model, the study con cludes that use of a poverty measure is a choice dependent on policy factors. No single alternative poverty measures have consistent findings that meet or exceed the magnitude of the NSLP eligibility measure when analyzing student outcomes. Yet, our conclusions differ from the RAND study. The lack of consistency of the alternative poverty measures to meet or exceed NSLP eli gibility values leads to the conclusion that decisions about use of alterna tive poverty measures depend on the various constructs, policy or otherwise, of the poverty measures. An example of a construct is the value added when analyzing student neighborhoods by geolocating school or student addresses to derive an income estimate. By taking a granular approach, we can more readily identify differences and account of insufficiencies present in the NSLP eligibility data.

each poverty measure, we found that the NSLP eligibility data explained, to a greater degree, student outcome measures when compared with the alternative poverty measures. Direct certification matched the magnitude of eligibility more reliably than par ticipation and the other alternative poverty measures. The measures based on the geolocation of school or student address had magnitudes that were higher than NSLP. The SAIPE and longevity proved to explain little of the variation in student outcome or institutional variables. In a model, we analyzed the degree to which variation in satis factory attendance is predicted by student outcome measures while controlled by a poverty measure. This allows us to see differences between poverty measures when swapping out one poverty measure for another. The magnitude of the regression coefficients is similar between the alternative poverty measures compared with the magni tude of the NSLP eligibility and the naïve condition. This confirms the

The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R372A200011 to the Montana Office of Public Instruction. The opinions expressed are those of the author and do not represent views of the Institute, the U.S. Department of Education, or the Montana Office of Public Instruction. Robin Clausen is a Research Liaison for the Montana Office of Public Instruction. supplemental poverty indicator for school neighborhoods (NCES 2017-039) . U.S. Department of Education. Washington, DC: National Center for Education Statistics. https://nces.ed.gov/programs/ edge/docs/2017039.pdf 2. Doan, S., Diliberti, M., Grant, D. (2022). Measuring school poverty matters, but how should we measure it: Comparing results of survey analyses conducted using various measures of school poverty. Rand Corporation: Santa Monica, CA. https://www.rand.org/pubs/working_ papers/WRA168-1.html Reference Notes 1. Geverdt, D., & Nixon, L. (2018). Sidestepping the box: Designing a

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Winter 2023 Policy & Practice 33