15th19th and 22nd-25th April 2024 (9 days, approx 35 hours in total)
This course, based primarily on Bills' 2016 book, teaches you how to use path analysis and structural equations modelling to test causal hypotheses using observational data that is typical of research in ecology and evolution. It is taught in half-day sessions so that you can practice individually after each half-day session. You will learn how to conduct these tests, why (and when) they are justified, and how to interpret the results. The first few lectures will primarily present the theory but practical sessions will become more prominent later in the course. The practical work will be based on R and RStudio. Students will receive R script, datasets, and a list of R packages to install. It is highly recommended that each student have a copy of my 2016 book for the course, but not essential.
By the end of the course, participants should be able to:
Understand the logical relationships between d-separation, data, and causal hypotheses.
Know when to use piecewise SEM, when to use covariance- based SEM, and the advantages/disadvantages and assumptions of each
Be able to construct, test, and interpret measurement models involving latent variables
Be able to construct and identify equivalent models
Be able to incorporate nested or mixed models, multigroup models, and non-normal distributions into SEM
Participants are encouraged to bring their own data, as there will be opportunities throughout the course to plan, analyze, and receive feedback on structural equation models.
Please email oliverhooker@prstatistics.com with any questions
ONLINE COURSE – Path Analysis, Structural Equations and Causal Inference for Biologists (PSCB02)
https://www.prstatistics.com/course/path-analysis-structural-equations-and-causal-inference-for-biologists-pscb02/
15th19th and 22nd-25th April 2024 (9 days, approx 35 hours in total)
This course, based primarily on Bills' 2016 book, teaches you how to use path analysis and structural equations modelling to test causal hypotheses using observational data that is typical of research in ecology and evolution. It is taught in half-day sessions so that you can practice individually after each half-day session. You will learn how to conduct these tests, why (and when) they are justified, and how to interpret the results. The first few lectures will primarily present the theory but practical sessions will become more prominent later in the course. The practical work will be based on R and RStudio. Students will receive R script, datasets, and a list of R packages to install. It is highly recommended that each student have a copy of my 2016 book for the course, but not essential.
By the end of the course, participants should be able to:
Understand the logical relationships between d-separation, data, and causal hypotheses.
Know when to use piecewise SEM, when to use covariance- based SEM, and the advantages/disadvantages and assumptions of each
Be able to construct, test, and interpret measurement models involving latent variables
Be able to construct and identify equivalent models
Be able to incorporate nested or mixed models, multigroup models, and non-normal distributions into SEM
Participants are encouraged to bring their own data, as there will be opportunities throughout the course to plan, analyze, and receive feedback on structural equation models.
Please email oliverhooker@prstatistics.com with any questions