Thesis: Coherent Bayesian Regularization Methods in Measurement Invariance Evaluation

Marco studies latent variable model evaluation and its application in scale assessment.

Thesis: Empirical Methods to Remedy Confound Indeterminacy in Confirmatory Factor Analysis Models

Atiyah studies measurement validity and scoring methods used for educational assessment.

Thesis: Applying Multilevel Structural Equation Models to Control for Measurement and Sampling Error in Intensive Longitudinal Data

Sarah studies methodological and applied measurement issues in education and clinical psychology, focusing on multilevel modeling, structural equation modeling, and intra-individual analysis. She is also interested in instrument construction and scoring thresholds.

Thesis: Machine Learning-Based Estimation and Goodness-of-Fit for Large-Scale Confirmatory Item Factor Analysis

Chris seeks to integrate deep learning with traditional psychometric methods to address computational issues and allow for greater modeling flexibility relative to traditional methods.

Thesis: Evaluating latent variable interactions with structural equation mixture models

Dissertation: Person level analysis in latent growth curve models

Current Position: Data Scientist in the SAS Advanced Analytics Lab at SAS Institute

Dr. Baldasaro identifies data analysis needs, explores client data, builds analytic models, develops analysis reports, and presents reports to customers. Her projects have focused on identifying health insurance fraud, waste and abuse and detecting fraud for state and local governments.

Thesis: Literary theory within a cross-classified multilevel framework: Personality similarity between writers and readers predicts reader inspiration (Completed at William & Mary)

Thesis: A Regularization Approach for Testing Differential Item Functioning Between Two Groups (Completed at UNC-Chapel Hill)

Dissertation: Using Regularization to Evaluate Differential Item Functioning Among Multiple Covariates: A Penalized Expectation-Maximization Algorithm via Coordinate Descent and Soft-Thresholding

Current Position: Assessment Scientist and Psychometrician at Duolingo

Will specializes in psychometrics, item response theory, and computational statistics. He is currently working at Duolingo as an Assessment Scientist specializing in psychometrics, using statistical methods to test and correct for possible measurement bias on the Duolingo English Test (DET).

Thesis: Modeling complex longitudinal data from heterogeneous samples using longitudinal latent profile analysis

Dissertation: Adapting Mixture Models to Take into Account Measurement Non-Invariance

Fellowship: Post-doctoral Researcher at the Center for Developmental Science at the University of North Carolina at Chapel Hill (2017-2018)

Fellowship: CCHD Post-doctoral Fellow at the Center for Developmental Science at the University of North Carolina at Chapel Hill (2018-2019)

Current Position: Assistant Professor in the Department of Psychology, Wake Forest University

Veronica works to develop and apply latent variable methods in a developmental psychopathology context, including the identification of risk and protective factors for the development of negative health behaviors in adolescence and early adulthood.

Thesis: A discrete-time multiple event process survival mixture MEPSUM model for investigating the order and timing of multiple non-repeatable events

Dissertation: Utilizing multilevel event history analysis to model temporal characteristics of friendships unfolding in discrete-time social networks

Current Position: Technical Director of Machine Learning at iRobot

Danielle Dean, PhD is the Technical Director of Machine Learning at iRobot where she is helping lead the intelligence revolution for robots. She leads a team that leverages machine learning, reinforcement learning, and software engineering to build algorithms that will result in massive improvements in our robots. Before iRobot, Danielle was a Principal Data Scientist Lead at Microsoft Corp. in AzureCAT Engineering within the Cloud AI Platform division.

Thesis: An empirical evaluation of the disaggregated effects of educational diversity in a national sample of law schools (Adviser: Abigail Panter)

Dissertation: Evaluating shared parameter mixture models for analyzing change in the presence of non-randomly missing data

Current Position: Research Public Health Analyst in the Substance Use Prevention, Evaluation, and Research Program at RTI International.

Dr. Gottfredson applies and develops statistical models used for understanding processes that unfold within individuals over time. She specializes in modeling nested data structures (e.g., multilevel and structural equation models), mixture models and psychometric measurement. Her current applied research examines self-regulatory methods used during addiction recovery and unpacks the nature of co-morbidity across substance use disorders and eating disorders.

Thesis: The ordinal count factor model: an improved latent variable model for ordinal count items

Current Position: Nathan is now pursuing a career in medicine.

Thesis: Joint trajectories of internalizing and externalizing problems in early childhood : testing theories of symptom covariation (Adviser: Mitch Prinstein)

Dissertation: Recovery of predictor relationships via semiparametric and parametric growth models under misspecification

Current Position: Professor and Director of the Quantitative Methods Program in the Psychology and Human Development Department at Vanderbilt University

Dr. Sterba conducts research on latent variable models for longitudinal and cross-sectional data, mixture models, and multilevel models, with a focus on advancing developmental psychopathology research.

Fellowship: Center for Developmental Science at the University of North Carolina at Chapel Hill (2008-2010)

Current Position: Associate Professor in Human Development & Family Studies and the Department of Statistics

Dr. Christ’s research concerns quantitative methodologies in the social and behavioral sciences, including structural equation modeling, multilevel (hierarchical, random effects, mixed effects) models, longitudinal modeling, and analysis of complex sample data, with a substantive focus on the developmental consequences of adolescent maltreatment (abuse and neglect)

Fellowship: Center for Developmental Science at the University of North Carolina at Chapel Hill (2014-2015)

Current Position: Research and Evaluation Coordinator for the Colorado Department of Education.

Dr. Schaper’s research interests focus on quantitative research methodologies in educational and behavioral sciences including multilevel modeling, longitudinal analysis and psychometric measurement, with a substantive focus on educational policy research, implementation and improvement science.