Information for Participants

Our week-long Summer Institute on Longitudinal Data Analysis is designed to meet the needs of 50 participants each year, welcoming individuals from diverse career stages and backgrounds. We aim to support graduate students, post-docs, and faculty, all of whom are eager to enhance their knowledge in longitudinal data analysis.  

The summer institute is ideal for individuals with a foundational understanding of statistics who seek to learn and apply longitudinal methods in their work. We specifically encourage applicants who are not already experts in longitudinal data analysis but who see the potential for these skills to enhance their research or professional contributions. 

Learning Outcome Goals

1. Expand interest and comfort applying longitudinal data to health and social science questions 

1. Increase understanding of mastery of longitudinal data and models, including developing skills to make justified measurement and modeling decisions 

1. Provide tools for data visualization for broad application of longitudinal analysis and dissemination of findings to interdisciplinary audiences 

Featured Faculty 

Dr. Kristine Marceau (MCAP Co-Director) 
Dr. Marceau is an Associate Professor of Human Development and Family Science who specializes in longitudinal methods, including SEM, multilevel modeling, and growth curve analysis. She frequently uses family-based designs and large datasets to explore developmental and behavioral trajectories. Dr. Marceau regularly teaches multilevel modeling and longitudinal data analysis to graduate students. 

Dr. Trenton D. Mize (MCAP Co-Director) 
Dr. Mize is the Dean's Associate Professor of Sociology and Statistics (by courtesy) and a quantitative methodologist with expertise in categorical data analysis, latent variable modeling, and data visualization. His research develops and applies innovative methods for analyzing complex social data, and he regularly teaches graduate courses on categorical data and experimental design.  

Dr. James A. McCann (MCAP Co-Director) 
Dr. McCann is a Professor of Political Science with expertise in longitudinal survey analysis and latent variable modeling. He has led multiple large-N longitudinal studies on political behavior and representation and regularly applies advanced econometric and multilevel techniques in his research. Dr. McCann teaches graduate seminars on research design and quantitative analysis, focusing on panel data and survey methodologies. 

Dr. Sharon Christ  (MCAP Co-Director) 
Dr. Christ is an Associate Professor of Human Development and Family Science specializing in emergent statistical models, particularly structural equation modeling (SEM) and complex sample designs. Her expertise in multilevel modeling, SEM, and growth models has been applied across numerous large-scale cohort studies. She has taught graduate-level courses on sample design, inferential statistics, and SEM. 

Dr. Brian C. Kelly 
Dr. Kelly is a Professor of Sociology at Indiana University with extensive experience using large national datasets and contextual indicators to examine health behaviors, particularly substance use. His NIH-funded research frequently integrates complex statistical models to assess the social contexts influencing health outcomes.  

Dr. Robert Duncan 
Dr. Duncan is an Assistant Professor of Human Development and Family Science at Colorado State University with expertise in advanced longitudinal data analysis, including multilevel modeling, structural equation modeling (SEM), and growth curve modeling. His work focuses on children’s development within multilevel contexts like classrooms.  

Dr. Dongjuan Xu 
Dr. Xu, an Associate Professor in the School of Nursing, specializes in longitudinal cohort studies that evaluate the quality of care and outcomes for older adults. Her expertise spans applied biostatistics, epidemiological methods, and outcome evaluation. She regularly teaches graduate courses in these areas, incorporating advanced quantitative techniques into her instruction, such as weighting methods and sampling designs. 

Dr. Robbee Wedow 
Dr. Wedow is an Assistant Professor of Sociology and Data Science at Purdue University, with expertise in statistical genetics and sociogenomics. His research applies advanced statistical methods, including gene-environment interaction models, to large-scale genetic datasets to investigate social and health outcomes. 

How to Apply

We prioritize individuals based on their need for longitudinal methods training, and those with extensive experience in using large-scale longitudinal data may be rated as a lower priority for enrollment. However, all applicants will be reviewed carefully to ensure they will benefit from the course content. We are especially committed to supporting participants from underrepresented backgrounds.  

Stay tuned for more details about the application process.