About MDRC
Porter, deputy director of MDRC Center for Data Insights, is a statistician with expertise in predictive analytics, causal inference, and statistical computing. As a quantitative methodologist, she develops methods and tools for applied researchers and for practitioners who work with data. As a data scientist, she helps social service providers harness data to improve their programs and the lives of the low-income people they serve. For more than 20 years Porter has contributed to quantitative research in the areas of K-12 education, work and income security, criminal justice, and public health. She has a PhD in biostatistics from the University of California, Berkeley, and a master’s degree in public policy from the University of Chicago.
-
MDRC Publications
MethodologyJune, 2022Multiple testing procedures reduce the likelihood of false positive findings, but can also reduce the probability of detecting true effects. This post introduces two open-source software tools from the Power Under Multiplicity Project that can help researchers plan analyses for randomized controlled trials using multiple testing procedures.
BriefEvidence from Child First
May, 2022This brief presents results from a proof-of-concept exercise that examined the potential benefits of using predictive analytics to improve service delivery by Child First, a program that provides therapeutic support to families with young children. The information may be useful for other organizations interested in implementing these cutting-edge tools.
BriefMay, 2020Pretrial release and detention decisions for defendants are increasingly guided by risk assessments guided by data, which are intended to counteract biases but have the potential to introduce new biases and perpetuate racial disparities. This research brief describes the approach taken by MDRC to understand, assess, and address these biases.
MethodologyMarch, 2019Schools use individual screening tests to identify students at risk of falling behind in their reading levels. Could predictive analytics, incorporating multiple composite and subsection scores from a series of tests over time, do a better job of identifying at-risk students? Reflections on Methodology gives an example of this approach.
MethodologyNovember, 2017Assessing an intervention’s effects on multiple outcomes increases the risk of false positives. Procedures that make adjustments to address this risk can reduce power, or the probability of detecting effects that do exist. MDRC’s Reflections on Methodology discusses how to estimate power when making adjustments as well as alternative definitions of power.
Issue FocusSeptember, 2017As organizations increase their use of sophisticated screening and risk assessments in their decision making, the results have the potential to fundamentally change practice, organizational culture, and the structure of work. Implementation researchers can inform the use of predictive analytic tools both before and after their adoption.
MethodologySeptember, 2017Machine learning algorithms, when combined with the contextual knowledge of researchers and practitioners, offer service providers nuanced estimates of risk and opportunities to refine their efforts. The first post of a new series, Reflections on Methodology, discusses how MDRC helps organizations make the most of predictive modeling tools.
BriefApril, 2017Many low-income young people are not reaching important milestones, but the social-service organizations and schools that serve them often struggle to identify who is at more or less risk. Predictive analytics use schools’ and programs’ existing data to help them identify risk earlier and more accurately.
BriefResults from a Partnership Between New Visions for Public Schools and MDRC
November, 2016A custom-designed intervention aimed to improve New York City high school students’ attendance by using text messaging to send parents daily absence updates and weekly attendance summaries. The rapid-turnaround randomized evaluation found that the short-term intervention did not improve attendance rates during the second semester of the 2015-2016 school year.
MethodologyA Primer for Researchers Working with Education Data
November, 2016Predictive modeling estimates individuals’ probabilities of future outcomes by building and testing a model using data on similar individuals whose outcomes are already known. The method offers benefits for continuous improvement efforts and efficient allocation of resources. This paper explains MDRC’s framework for using predictive modeling in education.
MethodologyA Guide for Researchers
July, 2016Conducting multiple statistical hypothesis tests can lead to spurious findings of effects. Multiple testing procedures (MTPs) counteract this problem but can substantially change statistical power. This paper presents methods for estimating multiple definitions of power and presents empirical findings on how power is affected by the use of MTPs.
MethodologyLessons from a Simulation Study
November, 2014This paper makes valuable contributions to the literature on multiple-rating regression discontinuity designs (MRRDDs). It makes concrete recommendations for choosing among existing MRRDD estimation methods, for implementing any chosen method using local linear regression, and for providing accurate statistical inferences.
ReportElementary Student Achievement and the Bay Area School Reform Collaborative’s Focal Strategy
December, 2006The Bay Area School Reform Collaborative’s focal strategy, a system-wide reform that coaches district and school leaders, supports evidence-based decision-making, and promotes networking within and among schools, has no strong association with changes in elementary student achievement.
ReportElementary Student Achievement and the Bay Area School Reform Collaborative’s Focal Strategy
February, 2006The Bay Area School Reform Collaborative’s strategy seeks to raise student achievement in six elementary school districts in the San Francisco Bay Area by coaching supervisors, principals, and teachers, instituting evidence-based decision making, and promoting sharing of experiences among schools. During the first two years of implementation, MDRC found no strong, pervasive association with student achievement.
ReportFinal Report on the Center for Employment Training Replication Sites
September, 2005The Center for Employment Training (CET) in San Jose, California, produced large, positive employment and earnings effects for out-of-school youth in the late 1980s. However, in this replication study, even the highest-fidelity sites did not increase employment or earnings for youth over the 54-month follow-up period, despite short-term positive effects for women.
Working PaperEvidence from a Sample of Recent CET Applicants
September, 2005This working paper examines employment and earnings over a four-year period for a group of disadvantaged out-of-school youth who entered the Evaluation of the Center for Employment Training (CET) Replication Sites between 1995 and 1999. It assesses the importance of three key factors as barriers to employment: lack of a high school diploma, having children, and having an arrest record.
ReportA Study of Adult Student Persistence in Library Literacy Programs
January, 2005Library-based literacy programs face serious challenges to improving adult students’ participation. This study suggests programs should be prepared to accommodate intermittent participation by adult students and to connect students to social services and other supports.
ReportThirty-Month Findings from the Evaluation of the Center for Employment Training Replication Sites
June, 2003Efforts to replicate the experience of the Center for Employment Training in San Jose, California — a uniquely successful program that helped at-risk youth develop skills needed to compete in today’s labor market — showed mixed results.
ReportResponding to the Challenges of Adult Student Persistence in Library Literacy Programs
April, 2003Based on a study of nine adult literacy programs in public libraries, this report examines student characteristics, participation patterns, and new strategies to raise student persistence.
-
Other Publications
-
Projects
Kristin Porter, Bret Barden, Melanie Skemer, Chloe Anderson Golub, Brit Henderson, Viktoriya Syrov, Lily Freedman, Emily Brennan, Sara Ellis, Olivia Williams, Sarah PicardJurisdictions across the United States are taking steps to reduce the number of people who are detained in jail unnecessarily while awaiting trial. In particular, they are seeking to reduce the use of cash bail as a mechanism to ensure court appearance. Many jurisdictions have introduced risk-assessment tools to inform release decisions and pretrial supervision...
Richard Hendra, Barbara S. Goldman, Stephanie Rubino, Kristin Porter, Erika Lundquist, Stephen Freedman, David Navarro, Melissa Wavelet, Alexandra Pennington, Caroline Morris, Audrey Yu, Edith Yang, Caitlin AnzeloneTANF Data Innovation ( TDI ) is a new national initiative to support Temporary Assistance for Needy Families ( TANF ) agencies as they harness the full potential of using administrative records data to improve program administration, payment integrity, and outcomes for low-income families. Launched in 2017, TDI is sponsored by the Office of Planning, Research, and...
Richard Hendra, Kristin Porter, Stephanie Rubino, Colleen Sommo, Mark van Dok, Zarni Htet, Camille Préel-Dumas, Jonathan BigelowAcross the social sector, government agencies, educational institutions, and nonprofit organizations are all benefiting from greater access both to more detailed and frequent data and to a variety of options for increased computing power. With data-science tools and guidance in applying them, practitioners can harness multiple sources of data to gain new insights about the individuals they serve, the contexts in which they operate, their staff members, and their program features. When such tools are incorporated into daily operations in a responsible way, they can help practitioners improve their programs and the lives of those they serve.
A research-practice partnership between MDRC and New Visions for Public Schools (a nonprofit organization supporting district-run high schools in New York City) operated in 2015 and 2016 to tackle challenges related to high school graduation.
Current practice for ensuring that impact evaluations in education have adequate statistical power does not take the use of multiplicity adjustments into account. Multiplicity adjustments to p-values protect against spurious statistically significant findings when there are multiple statistical tests (for example, due to multiple outcomes, subgroups, or time points),...
Virginia Knox, Charles Michalopoulos, JoAnn Hsueh, Helen Lee, Desiree Principe Alderson, Dina A. R. Israel, Erika Lundquist, Kristin Porter, Electra Small, Rebecca Behrmann, Anne Warren, Samantha Xia, Kelly Saunders, Ilana Blum, Jessica Kopsic, Noemi Altman, Caroline Mage, Sharon RowserAdverse birth outcomes result in significant emotional and economic costs for families and communities. One promising avenue for helping expectant women is home visiting programs, which work with parents to promote prenatal care and improve infant health. The Mother and Infant Home Visiting Program Evaluation-Strong Start ( MIHOPE -Strong Start) will evaluate the...
Virginia Knox, Charles Michalopoulos, JoAnn Hsueh, Helen Lee, Desiree Principe Alderson, Dina A. R. Israel, Erika Lundquist, Kristin Porter, Electra Small, Carolyn Hill, Rebecca Behrmann, Ximena Portilla, Anne Warren, Samantha Xia, Kelly Saunders, Ilana Blum, Rachael Metz, Mallory Undestad, Emily Davies, Cullen MacDowell, Marissa Strassberger, Sharon Rowser, Livia MartinezHome visiting programs operate around the country to prevent child maltreatment, improve maternal and child health outcomes, and increase school readiness. The Patient Protection and Affordable Care Act of 2010 authorized the creation of the Maternal, Infant, and Early Childhood Home Visiting ( MIECHV ) program, expanding federal funding of home visiting programs....