STATS-DC

9–A The Value and Impact of Geocoding on SLDS Administrative Data

Kris Stevens, Kentucky Center for Statistics
Scott Secamiglio, Kentucky Center for Statistics

Analysis at the school district or county level inherently means that different administrative areas or institutions can have wildly differing populations. Geocoding educational administrative data to the Census block level allows analysts to circumvent this problem during analysis and visualization of SLDS, SEA or LEA data sets. This session will discuss the technical process involved in incorporating geocoding into SLDS data, analytic use cases, and examples of using geocoded data in our current reporting and analysis pipeline. Finally, it will detail how geocoding allows education sector data analysts to incorporate third-party data to help fill in the missing puzzle pieces.

Complexity: Intermediate Level

9–B SEA and LEA Student Data Privacy: Locally Addressed, State-Level Supported

Jessica Kallin, Utah State Board of Education
Peter Drescher, Vermont Agency of Education
Peter Tamayo, Oregon Department of Education

While student privacy issues are best addressed at their school source levels, many state agencies have been increasingly supporting their schools through projects, partnerships, and effective practice sharing. This session will highlight LEA/SEA partnerships that are possible to support everyone's data steward roles for learners.

Complexity: Intermediate Level

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• SEA and LEA Student Data Privacy: Locally Addressed, State-Level Supported (433 KB)

9–C Applying Longitudinal Data Analysis Methods to Examine Poverty as a Predictor of Wage Trajectories

Bess Rose, Maryland Longitudinal Data System Center

One method researchers can use to analyze longitudinal cross-sector data, like those in the Maryland Longitudinal Data System (MLDS), is repeated measure or growth curve modeling. This method enables researchers to estimate individuals' initial outcomes at a set point in time, their subsequent growth for each increment of time, and the impact of individual events or policy changes 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 relationship of student and school poverty with long-term outcomes.

Complexity: Intermediate Level

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• Applying Longitudinal Data Analysis Methods to Examine Poverty as a Predictor of Wage Trajectories (1.43 MB)

9–D Development of a School Climate Survey and Index as a School Performance Measure in Maryland: a REL-MSDE Research Partnership

Amir Francois, Maryland State Department of Education
Tim Kautz, Mathematica Policy Research
Christine Ross, Mathematica Policy Research
Charles Tilley, Mathematica Policy Research

The presentation will discuss the REL Mid-Atlantic research partnership with the Maryland State Department of Education (MSDE) to develop and validate a climate survey of students and educators, and use the results to create a summary measure for use in the state's school accountability system. We will discuss how the survey was developed based on existing surveys with documented psychometric properties; how district representatives were involved in survey development; and experiences from a field test including psychometric analyses, and development of the school climate index.

Complexity: Intermediate Level

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• Development of a School Climate Survey and Index as a School Performance Measure in Maryland: a REL-MSDE Research Partnership (651 KB)

9–E Supporting States in Improving Local Data Quality

Heather Reynolds, IDEA Data Center, Westat
Lindsay Wise, IDEA Data Center, Westat
Joanna LaGuardia, California Department of Education

To improve data quality, states must effectively support LEAs in developing and implementing local high-quality data practices. The IDEA Data Center partnered with California to create an LEA toolkit the state can use with its LEAs to help achieve the state's goal of increasing LEA capacity to assess and improve local data quality processes, IT data systems, and data use. Presenters will discuss what they learned about helping states develop tools for LEA use and supporting LEAs in use of the tools and development of processes, as well as possible implications for future work in building and sustaining LEA capacity.

Complexity: Intermediate Level

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• Supporting States in Improving Local Data Quality, Part 1 (1.49 MB)
• Supporting States in Improving Local Data Quality, Part 2 (122 KB)

9–F EDFacts Data Quality Recap and Future Look

Julia Redmon, AEM Corporation
Elizabeth Fening, National Center for Education Statistics

The EDFacts team supports the U.S. Department of Education EDFacts data stewards and other internal data users to assess the quality of EDFacts data. Come join us for a session summarizing improvements in data quality from the last cycle, changes made to ED processes and resources over the past year, and a summary of what's to come. This session will include an overview of the CCD and EDFacts data quality review schedules, a walk-through of the EDFacts Business Rules Single Inventory (BRSI) resource--which contains all submission and post submission rules run on EDFacts data--and lessons learned that will inform improvements in the next review cycle. The team will also highlight how ED uses the state responses to data quality (for data quality reviews, informing internal ED analyses and program office monitoring, and providing context in public file documentation).

Complexity: Intermediate Level

9–G Formative Learning for Program Improvement in Louisiana

Nicholas Cheng, UPD Consulting
Nicole Bono, Louisiana Department of Education

The Louisiana DOE and UPD Consulting team will share the process, inquiry questions, metrics, and findings from their formative learning process designed to enable the LDOE to understand the levels of implementation success of the Content Leader Guidebooks program and the local and programmatic conditions that impacted first year instructional outcomes. The team will discuss the various data sources used to triangulate a complete picture of implementation in multiple communities across the state and will provide insights about what they learned through engaging in this process.

Complexity: Entry Level

9–H Protecting Privacy While Supporting Students Who Change Schools

Elizabeth Laird, Center for Democracy & Technology
Nicole Lee-Mwandha, Office of the State Superintendent of Education
Gwen Rubinstein, Office of the State Superintendent of Education

Changing schools for reasons other than grade progression, especially during the school year, can negatively affect educational achievement. As policymakers and practitioners seek to address these inequities, they are looking to data portability, sometimes referred to as a data backpack, as a possible solution to close these gaps. At the same time, they have to consider the potential harms that could come to the student from porting that data. This session will provide recommendations on striking the right balance to leverage the benefits of data portability while mitigating the potential privacy harms. It will feature the work of Washington, DC to support students experiencing homelessness, who change schools more frequently than their peers, while protecting their privacy.

Complexity: Entry Level

Download Zipped Adobe Presentation:

• Protecting Privacy While Supporting Students Who Change Schools (994 KB)

9–J Reporting Educational Outcomes for Hawaii's English Learners

Meera Garud, Hawaii P-20 Partnerships for Education

Hawaii is one of the most ethnically and linguistically diverse states. Under an Asian American and Pacific Islander (AAPI) data disaggregation grant, Hawaii has developed new resources that help educators explore the linguistic diversity of Hawaii's public schools and examine academic outcomes for English Learners (ELs). The presenter will describe how being an embedded analyst in a workgroup of EL subject matter experts helped her better understand the group's information needs. Participants will leave with a deeper understanding of Hawaii's EL population and ideas for how to report on diverse populations.

Complexity: Entry Level

9–K Creating Impact with NAEP:  Using NAEP's API for Reporting Results and Contextualizing NAEP Results with Policy Relevant Visuals

Brian Cramer, Optimal Solutions Group
Sadaf Asrar, Optimal Solutions Group
Sarah Guile, Optimal Solutions Group
Rahul Rathi, Optional Solutions Group

This presentation will focus on two related topics: Using NAEP's API for reporting results and contextualizing NAEP results with policy relevant visuals. A publicly accessible API for analyzing NAEP data is now available on the Nation's Report Card (NRC). While the API allows users to quickly obtain large amounts of NAEP data in JSON format for data exploration and analysis, the structure and format of the data extract is difficult to navigate and may not be very user friendly to the general public. This presentation will demonstrate how to turn the data queried through the NAEP API into user friendly tables and develop static and dynamic reports to answer critical policy questions relevant to a range of NAEP stakeholders.

Contextualizing NAEP student achievement is important for helping the public understand the results. NCES' efforts in this area have expanded in recent years with greater reporting of results from NAEP's survey questionnaires and the Common Core of Data through reports and tools on the Nation's Report Card (NRC). This presentation will show how a broader set of data sources can be linked to create visualizations for contextualizing state-or district-level NAEP student achievement. Some of the data presented to contextualize NAEP student achievement will focus on teacher pay, certification, and professional development; school and student expenditures; pre-K programs; curriculum standards; and course requirements. The visualizations allow NAEP results to be filtered through the lens of the contextual data to provide users a better understanding of the variation in NAEP student achievement for states or districts.

Complexity: Entry Level