This report describes the impacts of text message reminders on couples’ attendance at UF’s ELEVATE group workshop sessions. To estimate impacts, we randomly assigned 1,700 each couple to receive a different type of text message reminder or to a control group that did not receive reminders.
Predicting incoming enrollment is an ongoing concern for the School District of Philadelphia (SDP) and similar districts with school choice systems, substantial student mobility, or both.
Education policymakers view transparency and accountability as critical to the success of schools.
The authors discuss the advantages and limitations of applying multilevel regression with poststratification to data collected through SMS surveys.
We examine whether financial inclusion may help mitigate the effects of the COVID-19 pandemic on households’ economic behavior and well-being in three Sub-Saharan African countries: Kenya, Nigeria, and Uganda.
This report presents findings from a multi-state evaluation examining how managed long-term services and supports (MLTSS) beneficiaries compare to those receiving long-term services and supports (LTSS) in fee-for-service (FFS) on spending, service use, quality of care, access to care, and beneficiary...
Comparison group selection is paramount for health policy evaluations, where randomization is seldom practicable. Rolling enrolment is common in these evaluations, introducing challenges for comparison group selection and inference.
Pittsburgh Public Schools (PPS), the Propel Schools charter network, and the Allegheny County Department of Human Services (DHS) want to better identify students at risk for academic problems in the near term.
The study team collected and linked five academic years of student-level administrative data from Pittsburgh Public Schools (PPS), Propel Schools, and the Allegheny County Department of Human Services (DHS).
This report provides information for administrators, researchers, and student support staff in local education agencies who are interested in identifying students who are likely to have near-term academic problems such as absenteeism, suspensions, poor grades, and low performance on state tests.
The authors introduce a novel method of principal component analysis for data with varying domain lengths for each functional observation.
Mathematica and Casey Family Programs have published the final report from a project linking child welfare and Medicaid data to conduct analyses to understand types of high service use and to identify factors predictive of high service use among children in foster care.
This interim evaluation examines how utilization of specific services by MLTSS enrollees compares to that of fee for service (FFS) beneficiaries using LTSS.