Principal speaker
Daniel Phipps
This workshop follows on from "SEM for Basic Pathway Models". After running a simple pathway analysis using SEM, we provide practical advice on how to detect inadequate model performance via diagnostics, and then suggest some remedies. This includes examining the possibility of extending an SEM to include moderation.
This workshop has 4 parts:
How to Address Poor Model Fit (Theory and Computing);
Running an SEM with Moderating Variables in R;
Interpreting SEMs (Part 2);
Discussing SEM and how it applies to your research.
Recommended Prerequisites:
1. Some knowledge of SEM. Absolute beginners should attend the first workshop to clarify whether SEM is relevant to them, and help judge whether the workshops will be useful for them.
2. As each workshop builds on previous ones, it is not recommended that you skip workshops or attend out of order without first notifying the teaching team.
3. Familiarity with concepts of probability (e.g. in a histogram) as well as regression and inferential statistics (e.g. interpreting p values, confidence or credible intervals of effects).
4. Some basic knowledge of R is strongly recommended, e.g. Induction in R and Excellence in Graphics in R (RED), R programming (Software Carpentry) or free R courses by Data Camp.
5. Mathematical knowledge assumed is addition, multiplication, and interpretation of a scatter plot. It could be helpful to understand the equation for a straight line.
6. Participants will be required to bring a laptop with R installed.
There will be a lunchbreak and short stretch breaks throughout the workshop.