Longitudinal Analysis and Multi-level Modelling of Population Health Data

Code: PHDA05
Apply this course towards: Population Health Data Analysis

Course description

This course will provide you with an introduction to—and hands-on experience 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.

Learning objectives

  • 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 requirements

In order to register for this course, as part of the certificate program or for professional development, you must be admitted to the Population Health Data Analysis program. Please note: in addition to other course fees, new students will be charged a one-time $100 secure ID token fee when registering for their first course. (Please unclick this box if you have already paid this fee.)

Prerequisites

    • Admission to the PSC in Population Health Data Analysis or permission of the Faculty Advisor.
    • Be comfortable with basic SAS programming (e.g., DATA step and procedures). Familiarity with Mplus will also be helpful.
    • Be comfortable with linear regression models. Familiarity with generalized linear models such as logistic regression will also be helpful. 

 

Stand-alone

If space is available, you may take PHDA05 as a stand-alone course for professional development. Non-certificate students must meet the same prerequisites as certificate students.

Minimum registration requirement

This course may be cancelled if the minimum enrollment requirement is not met by the registration deadline.

Our students say...

To see what other students are saying about this course, please visit https://www.popdata.bc.ca/etu/testimonials.

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