The Promise and Limitations of Synthetic Data as a Strategy to Expand Access to State-Level Multi-Agency Longitudinal Data

Americans’ sense of external political efficacy—their belief in their ability to influence government decisions—has declined precipitously in recent decades, eroding the public’s confidence in our system of representative democracy. Scholars have long argued that involvement in civic associations can help ordinary Americans realize their political efficacy, yet a lack of longitudinal data on association members’ attitudes and behaviors has impeded efforts to test this claim. To collect such a dataset, we partnered with a national environmental association to conduct a unique panel study of members of eight state-level organizations. We show that members who get to know their association’s leaders believe that they have greater influence over government decisions. Our findings suggest that civic associations can strengthen their members’ efficacy by cultivating volunteer leadership and fostering relationships between members and leaders.

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Sustaining school improvement in a high-need school

Journal of Educational Administration ◽

10.1108/jea-03-2016-0034 ◽

2017 ◽

Vol 55 (3) ◽

pp. 297-315 ◽

Cited By ~ 3

Author(s):

Nathern Okilwa ◽

Bruce Barnett

Keyword(s):

Academic Performance ◽

School Improvement ◽

Longitudinal Data ◽

Student Performance ◽

Student Mobility ◽

Demographic Data ◽

High Mobility ◽

State Level ◽

Collective Responsibility ◽

Content Type

Purpose The purpose of this paper is to examine how Robbins ES has sustained high academic performance over almost 20 years despite several changes in principals. Design/methodology/approach The paper analyzed longitudinal data based on: state-level academic and demographic data; two earlier studies of the school; and recent interviews with teachers, the principal, and parent leaders. Findings The analyses of these longitudinal data revealed four ongoing factors were responsible for sustained academic performance: high expectations, distributed leadership, collective responsibility for student performance, and data-based decision making. However, challenges that persistently confront Robbins staff include limited resources (e.g. technology and library materials), high mobility rate, and some cases of unsupportive parents. Originality/value This study adds to understanding how high-need urban schools can sustain high academic performance in spite of changes in principals, shifting community demographics, and high student mobility.

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DEFSI: Deep Learning Based Epidemic Forecasting with Synthetic Information

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33019607 ◽

2019 ◽

Vol 33 ◽

pp. 9607-9612 ◽

Cited By ~ 8

Author(s):

Lijing Wang ◽

Jiangzhuo Chen ◽

Madhav Marathe

Keyword(s):

Deep Learning ◽

High Resolution ◽

State Of The Art ◽

Synthetic Data ◽

State Level ◽

The Other ◽

County Level ◽

Short Term ◽

Neural Network Structure ◽

Epidemic Forecasting

Influenza-like illness (ILI) is among the most common diseases worldwide. Producing timely, well-informed, and reliable forecasts for ILI is crucial for preparedness and optimal interventions. In this work, we focus on short-term but highresolution forecasting and propose DEFSI (Deep Learning Based Epidemic Forecasting with Synthetic Information), an epidemic forecasting framework that integrates the strengths of artificial neural networks and causal methods. In DEFSI, we build a two-branch neural network structure to take both within-season observations and between-season observations as features. The model is trained on geographically highresolution synthetic data. It enables detailed forecasting when high-resolution surveillance data is not available. Furthermore, the model is provided with better generalizability and physical consistency. Our method achieves comparable/better performance than state-of-the-art methods for short-term ILI forecasting at the state level. For high-resolution forecasting at the county level, DEFSI significantly outperforms the other methods.

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Bayesian Analysis of State Voter Registration Database Integrity

10.31219/osf.io/vnjbh ◽

2021 ◽

Author(s):

Jian Cao ◽

Seo-young Silvia Kim ◽

R. Michael Alvarez

Keyword(s):

Synthetic Data ◽

State Level ◽

Voter Registration ◽

Multiple Response ◽

Database Integrity ◽

The People ◽

Model Predictions ◽

Registered Voters ◽

Multivariate Multilevel Model ◽

Response Variables

How do we ensure a statewide voter registration database's accuracy and integrity, especially when the database depends on aggregating decentralized, sub-state data with different list maintenance practices? We develop a Bayesian multivariate multilevel model to account for correlated patterns of change over time in multiple response variables, and label statewide anomalies using deviations from model predictions. We apply our model to California's 22 million registered voters, using 25 snapshots from the 2020 presidential election. We estimate countywide change rates for multiple response variables such as changes in voter's partisan affiliation and jointly model these changes. The model outperforms a simple interquartile range (IQR) detection when tested with synthetic data. This is a proof-of-concept that demonstrates the utility of the Bayesian methodology, as despite the heterogeneity in list maintenance practices, a principled, statistical approach is useful. At the county level, the total numbers of anomalies are positively correlated with the average election cost per registered voter between 2017--2019. Given the recent efforts to modernize and secure voter list maintenance procedures in the For the People Act of 2021, we argue that checking whether counties or municipalities are behaving similarly at the state level is also an essential step in ensuring electoral integrity.

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Bayesian Analysis of State Voter Registration Database Integrity

Statistics Politics and Policy ◽

10.1515/spp-2021-0016 ◽

2022 ◽

Vol 0 (0) ◽

Author(s):

Jian Cao ◽

Seo-young Silvia Kim ◽

R. Michael Alvarez

Keyword(s):

Synthetic Data ◽

State Level ◽

Voter Registration ◽

Multiple Response ◽

Database Integrity ◽

The People ◽

Model Predictions ◽

Registered Voters ◽

Multivariate Multilevel Model ◽

Response Variables

Abstract How do we ensure a statewide voter registration database’s accuracy and integrity, especially when the database depends on aggregating decentralized, sub-state data with different list maintenance practices? We develop a Bayesian multivariate multilevel model to account for correlated patterns of change over time in multiple response variables, and label statewide anomalies using deviations from model predictions. We apply our model to California’s 22 million registered voters, using 25 snapshots from the 2020 presidential election. We estimate countywide change rates for multiple response variables such as changes in voter’s partisan affiliation and jointly model these changes. The model outperforms a simple interquartile range (IQR) detection when tested with synthetic data. This is a proof-of-concept that demonstrates the utility of the Bayesian methodology, as despite the heterogeneity in list maintenance practices, a principled, statistical approach is useful. At the county level, the total numbers of anomalies are positively correlated with the average election cost per registered voter between 2017 and 2019. Given the recent efforts to modernize and secure voter list maintenance procedures in the For the People Act of 2021, we argue that checking whether counties or municipalities are behaving similarly at the state level is also an essential step in ensuring electoral integrity.

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Medicaid 101: A Primer for Speech-Language Pathologists in Education Settings

Perspectives on School-Based Issues ◽

10.1044/sbi12.1.3 ◽

2011 ◽

Vol 12 (1) ◽

pp. 3-11

Author(s):

Janet Deppe ◽

Marie Ireland

Keyword(s):

State Level ◽

The State ◽

Speech Language Pathologists ◽

Inspector General ◽

Medical Necessity ◽

School Settings ◽

Speech Language Pathologist ◽

School Based ◽

Emerging Trends

This paper will provide the school-based speech-language pathologist (SLP) with an overview of the federal requirements for Medicaid, including provider qualifications, “under the direction of” rule, medical necessity, and covered services. Billing, documentation, and reimbursement issues at the state level will be examined. A summary of the findings of the Office of Inspector General audits of state Medicaid plans is included as well as what SLPs need to do in order to ensure that services are delivered appropriately. Emerging trends and advocacy tools will complete the primer on Medicaid services in school settings.

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Policy Action on Obesity Mostly at State Level

Internal Medicine News ◽

10.1016/s1097-8690(07)71007-7 ◽

2007 ◽

Vol 40 (16) ◽

pp. 39

Author(s):

MARY ELLEN SCHNEIDER

Keyword(s):

State Level ◽

Policy Action

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Perception of Emotional Expressions in Adults

Journal of Individual Differences ◽

10.1027/1614-0001/a000183 ◽

2016 ◽

Vol 37 (1) ◽

pp. 16-23 ◽

Cited By ~ 2

Author(s):

Chit Yuen Yi ◽

Matthew W. E. Murry ◽

Amy L. Gentzler

Keyword(s):

Facial Expressions ◽

Negative Mood ◽

Emotion Perception ◽

State Level ◽

Past Research ◽

Negative Emotionality ◽

Basic Emotions ◽

Perceived Intensity ◽

Trait Level ◽

Facial Expressions Of Emotion

Abstract. Past research suggests that transient mood influences the perception of facial expressions of emotion, but relatively little is known about how trait-level emotionality (i.e., temperament) may influence emotion perception or interact with mood in this process. Consequently, we extended earlier work by examining how temperamental dimensions of negative emotionality and extraversion were associated with the perception accuracy and perceived intensity of three basic emotions and how the trait-level temperamental effect interacted with state-level self-reported mood in a sample of 88 adults (27 men, 18–51 years of age). The results indicated that higher levels of negative mood were associated with higher perception accuracy of angry and sad facial expressions, and higher levels of perceived intensity of anger. For perceived intensity of sadness, negative mood was associated with lower levels of perceived intensity, whereas negative emotionality was associated with higher levels of perceived intensity of sadness. Overall, our findings added to the limited literature on adult temperament and emotion perception.

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