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ResearchSeries titleMLDSC Research Series
The MLDS Center research series is a forum to bring together researchers, policy makers, and practitioners to discuss MLDS Center research works in progress. Additionally, we invite experts from across the State to present research studies that may inform MLDS Center research projects.

Note: The MLDS Research Series has concluded for the 2018-2019 academic year. The next research series presentation will be in October 2019.

    • Date: 05/02/2019

      Presenters: Laura Stapleton, PhD, Professor and Associate Dean,Research, Innovation and Partnerships

      Topic: An Update on the MLDS Synthetic Data Project

      Presentation Abstract: There is demand among policy-makers for the use of state education longitudinal data systems, yet laws and policies regulating data disclosure limit access to such data, and security concerns and risks remain high. Well-developed synthetic datasets that statistically mimic the relations among the variables in the data from which they were derived, but which contain no records that represent actual persons, present a viable solution to these laws, policies, concerns, and risks. In this presentation, we present our in-progress development of a synthetic data system and highlight potential applications of synthetic data. We begin with an overview of synthetic data, what it is, how it has been utilized thus far, and the potential benefits and concerns in its application to education data systems. We then describe the project, funded by a grant from the State Longitudinal Data Systems Program at the U.S. Department of Education to the Maryland State Department of Education. In this project, we have proposed the steps required to synthesize the data from the Maryland Longitudinal Data System. We review the challenges that we have confronted, and the successes experienced, in the development of our synthetic data system and explain the process going forward for validity testing and data disclosure risk evaluation.

      Presenter Bio: Laura M. Stapleton is Associate Dean for Research, Innovation, and Partnerships. She is also a Professor in Measurement, Statistics and Evaluation (EDMS) in the Department of Human Development and Quantitative Methodology at the University of Maryland and served as the Associate Director of the Research Branch of the Maryland State Longitudinal Data System Center from 2013-2018. She joined the faculty of the college in Fall 2011 after being on the faculty in Psychology at the University of Maryland, Baltimore County and in Educational Psychology at the University of Texas, Austin. She currently serves as Associate Editor of AERA Open and each year teaches as part of the faculty of the National Center for Education Research funded Summer Research Training Institute on Cluster Randomized Trials at Northwestern University. Prior to earning her Ph.D. in Measurement, Statistics and Evaluation, she was an economist at the Bureau of Labor Statistics and, subsequently, conducted educational research at the American Association of State Colleges and Universities and as Associate Director of institutional research at the University of Maryland.

      Presentation Link

    • Date: 04/04/2019

      Presenters: Dr. Bess A. Rose, Statistician, MLDS Center and University of Maryland, School of Social Work

      Topic: Applying Longitudinal Data Analysis Methods to Examine Poverty as a Predictor of Wage Trajectories

      Presentation Abstract: “In life, everything that is truly important is longitudinal.” – John Willett

      Most studies conducted using MLDS data have examined wages as an outcome variable, and estimated the relationship of schooling experiences with total wages. However, we have not yet examined the full picture of how individuals’ wages change over time, and the effect of K12 and postsecondary education experiences on their wage trajectories. This presentation will examine one method researchers could use to examine wage patterns over time in more detail by using repeated measure or growth curve modeling. This method would enable researchers to estimate individuals’ initial outcomes at a set point in time (e.g., in the first quarter after high school graduation), their estimated subsequent growth for each increment of time (e.g., quarter), and the impact of individual events (e.g., enrolling in college, obtaining a college degree) or policy changes (e.g., making two-year college tuition free to all income-eligible individuals) on the shape of these trajectories. This presentation will provide an overview of growth modeling techniques and an applied example using MLDS data from a study of the impact of student and school poverty and race/ethnicity on long-term outcomes. These analyses will further clarify the roles of poverty and race/ethnicity on individuals’ wages over time.

      Presentation Link

    • Date: 03/07/2019

      Presenters: F. Chris Curran, PhD, is an Assistant Professor of Public Policy at the UMBC School of Public Policy where he teaches and advises in the education policy track and the evaluation and analytic methods track. His research focuses on early elementary education, with a specific focus on early science achievement, as well as on issues of school discipline and safety. His research has been published in journals such as Educational Researcher and Educational Evaluation and Policy Analysis and has been featured in outlets such as Education Week and Politico. Previously, Dr. Curran was a middle school science teacher and department chair. More on his work can be found at

      Topic: Early Elementary Science Test Score Gaps: Differences by Race/Ethnicity, Gender, and Language Backgrounds

      Presentation Abstract: Student achievement in science is a pressing goal of educators and policymakers. However, until recently, there has been limited research on the performance of students in science in the earliest grades of elementary school. Recent evidence suggests that the earliest years of elementary school may be critical for setting trajectories of science learning as well as disparities in such achievement between subgroups. This talk draws on several recent studies that examine science achievement in the earliest grades of school (kindergarten to second grade). In particular, it explores how science achievement varies by race/ethnicity and gender and how these disparities compare to early test score gaps in other subject areas. Findings suggest that early elementary test score gaps are often larger in science than in mathematics or reading. For example, while Asian students perform as well or better than their white peers in mathematics and reading, they lag significantly behind in early science test score performance. This work explores some of the predictors of these differences, finding an important role for language and immigration status as well as variability explained by both in and out of school factors. Implications for policy and practice in early STEM are discussed.

      Each semester the MLDS Center invites one external scholar who is engaged in research that can inform current MLDS research initiatives. Dr. Curran will help to inform the MLDS Center’s research efforts on STEM achievement.

      Presentation Link

    • Date: 02/07/2019

      Presenters:Dr. Tracy M. Sweet & Tessa L. Johnson

      Topic: Early Elementary Science Test Score Gaps: Differences by Race/Ethnicity, Gender, and Language Backgrounds

      Presentation Abstract: A social network consists of a group of individuals (or entities) and the relationships (or connections) among them. Examples of social networks outside of Facebook and Twitter include friendship ties among a group of students in a classroom, co-authorship, or other types of collaborative networks, and childcare sharing networks. Due to the structure of these data, social networks have unique methods for analysis. We will present an introduction to social network analysis, a brief introduction to social network models, and discuss how quantitative methods used for network analysis can be used in MLDS research. As an example, we use network methods to explore student mobility across schools within several counties in the state of Maryland.

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    • Date: 12/06/2018

      Presenters: Dr. Dawnsha R. Mushonga, Investigator, MLDS Center and Postdoctoral Fellow, University of Maryland, School of Social Work

      Topic: Using Longitudinal Data to Assess Long-Term Outcomes Associated with Poverty in Maryland Students

      Presentation Abstract: Poverty affects more than 15 million children who are disproportionately racial/ethnic minorities and has been linked to negative outcomes such as poor academic achievement and decreased lifelong earnings. Extant literature has highlighted the profound effects of poverty for students exposed for longer periods of time; however, few studies have focused on disentangling the roles of poverty and race on students’ long-term outcomes. To better understand the multifaceted role of poverty, this study used data from the Maryland Longitudinal Data System (MLDS) to examine the relation between student-level poverty and race and school-level poverty and racial composition to predict students’ long-term educational and career outcomes. This presentation provides an update on findings presented in July to the Commission on Innovation and Excellence in Education. Our findings aid policy makers and practitioners in identifying at-risk students and targeting interventions at the individual and school levels to combat the negative effects of poverty and promote students’ academic and career success.

      Presentation Link

    • Date: 11/01/2018

      Presenters: Dr. Mathew C. Uretsky, Investigator, MLDS Center & Dr. Angela K. Henneberger, Research Director, MLDS Center

      Topic: Remedial Coursework in Maryland Community Colleges: Disentangling Student and High School Level Predictors

      Presentation Abstract: Remedial courses at community colleges are designed to develop the skills of students who are underprepared for the academic rigor of college courses. A significant portion of students in Maryland and nationwide are assessed to need remedial coursework each year. This study used data from the Maryland Longitudinal Data System (MLDS) to examine the individual- and high school-level characteristics that predict the need for remediation in Maryland community colleges. The results can help policy makers and practitioners identify at-risk students before they arrive at college in order to help better prepare them for college-level coursework and reduce the need for remediation among recent high school graduates.

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    • Date: 10/04/2018

      Presenters: Romona C. Carrico, Christopher Wohn, and Amir François

      Topic: Problem, Research, Action: Poverty Measurement Transition in Baltimore City Public Schools

      Presentation Abstract: This presentation will cover the methodology of the longitudinal and historical poverty analysis and subsequent school-level and student subgroup analyses using data from Baltimore City Public Schools. The second part of the presentation will discuss how the Office of Achievement and Accountability (OAA) in Baltimore City Public Schools assessed the impact of the change in the poverty measurement process on school-level poverty rates using a multivariate prediction model.

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    • Date: 05/03/2018

      Presenters: Bess A. Rose, Dawnsha R. Mushonga, and Angela K. Henneberger

      Topic: The Relationship Between Poverty and Long-Term Student Outcomes: Disentangling the Effects of Individual and School Poverty

      Presentation Abstract: The MLDS Center is examining the effects of school-level concentrated poverty and individual student poverty on outcomes such as high school graduation, entry and persistence in post-secondary education, entry into the workforce, and wages earned. From previous research, we know that both individual poverty and school-level poverty are significant barriers to educational success, but the relative impact of these factors and how they interact is unclear. We used statewide longitudinal data to examine the relative impact and interaction of student- and school-level poverty on long-term outcomes. Preliminary findings suggest that defining poverty based on students’ status at a single point in time, rather than considering their history of poverty, may lead to underestimating poverty’s effects. Findings also suggest that while both student poverty and school-level concentrations of poverty have a significant and negative effect on students’ outcomes, the effect of school-level poverty is considerably larger than that of individual poverty alone.

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    • Date: 04/19/2018

      Presenters: Dr. Nolan G. Pope

      Topic: The Multidimensional Impact of Teachers on Students

      Presentation Abstract: For decades, policymakers and researchers have used value-added models that rely solely on student test scores to measure teacher quality. However, since teaching ability is multidimensional, test-score value-added measures of teacher quality may not fully capture the impact of teachers on students. In this talk, Dr. Pope will present research using test-score and non-test-score measures of student achievement and behavior from over a million students in the Los Angeles Unified School District to estimate multiple dimensions of teacher quality. Results indicate that test-score and non- test-score measures of teacher quality are only weakly correlated, and that both measures of teacher quality affect students’ performance in high school. Results from a simulation study removing teachers based on both dimensions of teacher quality show improvement in most long-term student outcomes by over 50 percent compared to removal of teachers using test scores alone. The long-term effects of teachers in later grades are larger than in earlier grades and that performance in core elementary school subjects matters more for long-term outcomes than other subjects.

      Presentation Link

    • Date: 02/01/2018

      Presenters: Heath Witzen, Research Fellow, Maryland Longitudinal Data System Center

      Topic: The Effect of High School Career and Technical Education on Postsecondary Enrollment and Early Career Wages

      Presentation Abstract: Career and Technical Education (CTE) has become a topic of considerable policy-making interest as a way of providing specialized education and expanding the number of career pathways available to high schools students. This research examines the effect of CTE program completion during high school on postsecondary outcomes, including college enrollment and workforce wages. Using propensity score matching, this research uses MLDS data to estimate a causal effect of CTE on postsecondary enrollment and wages up to six years after high school graduation.

      Presentation Link

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