PHDA 05 - Longitudinal Analysis and Multi-level Modeling of Population Health Data
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"For my interests, I found the longitudinal data analysis course (PHDA 05) one of the most helpful. This is because, with increasing use of multiple drugs, it is important to be able to establish cohorts of people who use these substances and then follow them over time determining predictors of change."
Lucinda Burns, Associate Professor, Faculty of Medicine, University of New South Wales
"The PHDA 05 in Longitudinal Analysis and Multilevel Modeling helped me gain skills in Mplus software and get an introduction to modeling. This work was very helpful in preparing me for a more advanced longitudinal modeling credit course which I took at UVic last year."
Jennifer McConnell, MHHS Doctoral Candidate, Social Dimensions of Health, University of Victoria
Next course delivery: May to July 2021
This course is now available to individuals residing outside of Canada. Please contact Program Coordinator, Ash Moosavi at email@example.com or 250-721-8779 for more information.
This fully online course will provide you with an introduction to—and hands-on experience including the use of a Secure Remote Training Lab — specifying multi-level modeling and longitudinal analysis. You will gain an understanding of different types of approaches including:
- time varying and invariant predictors
- multivariate and multi-population models with different outcomes
- missing data, errors in measurement and measurement misclassification
This course is designed to serve the needs of researchers who will analyze and model longitudinal data in population health research.
- Define the methodological features of longitudinal data analysis.
- Describe fundamental concepts and issues in multi-level modeling.
- Identify different analytical approaches to longitudinal data analysis and specify their strengths and limitations.
- Use Mplus statistical modeling program to perform longitudinal data analyses in population health research.
- Develop and practice longitudinal model specification, estimation, evaluation, and modification skills.
- Interpret and evaluate findings in longitudinal population health research.
Admission to the PSC in Population Health Data Analysis or permission of the Faculty Advisor.
Knowledge of regression analysis is a requirement for this course.
Although not required, some familiarity with SAS and Mplus software would be helpful.
Instructor: Laura Holder
Laura Holder is a Senior Analyst with the Analytics and Special Projects team at the Western Office of the Canadian Institute for Health Information (CIHI). She completed her M.Sc. in Epidemiology and Biostatistics in the Public Health Sciences Department at Queen’s University, Ontario. Her graduate research focused on how best to employ statistical methodologies to handle missing data in complex survey settings and the impact these methodological choices have on the conclusions drawn in studies. Prior to her role at CIHI, Laura worked as an analyst with the Institute for Clinical and Evaluative Sciences (ICES), where she used administrative health data to conduct the analyses for a broad range of studies. These included international comparisons of childhood mortality, usage of the emergency department for mental health issues, validation of liver disease case definitions, and maternal outcomes for incarcerated women. In her current role at CIHI, Laura and her team work to support the analytic capacity of Canada’s western jurisdictions.
This course is offered in partnership with the Division of Continuing Studies and Department of Geography at the University of Victoria.