search

lter / ssecr

Synthesis Skills for Early Career Researchers Course
https://lter.github.io/ssecr/
2 stars 0 forks source link

Draft Module 11: 'Analysis & Modeling' #9

Open njlyon0 opened 4 months ago

njlyon0 commented 4 months ago

Summary

Need to create a strong draft of this module so that we can revise and eventually offer this section of the course.

Sub-Tasks

Resources

njlyon0 commented 1 month ago

Progress!

Fleshed out a pretty solid mixed effects model case study to showcase some of the relevant concepts. Included a nice simulated dataset with the prerequisite nested structure which also set up the architecture (tracked by Git and ignored in advanced) for other modules that benefit for example datasets

njlyon0 commented 1 month ago

Mixed-Effects Fully Drafted

I want to revisit the case study but at least a draft topic covering random intercepts, slopes, and nested random effects is now live in the module

njlyon0 commented 1 month ago

Frequentist vs. Multi-Model Inference Aside Drafted

As I was starting the MMI bit I realized it would just be simpler to address the philosophical difference between the two at the start of the module rather than trying to define MMI in opposition to mixed-effects models after a third of the module's content is already done

njlyon0 commented 1 month ago

MMI Fully Drafted

I may need to revisit this to refine it / add detail but the current draft of the multi-model inference module is not unreasonable.

njlyon0 commented 1 week ago

Meta-Analysis Work Ongoing

Continuing to read into meta-analysis to flesh out that topic of this module. Somewhat slow going as many resources focus more on the use of meta-analysis functions and less on how users need to structure their data going into that process. That feels like the more important bit for our tutorial of meta-analysis so I'm focusing there (at least for now).

The 'meta' R package is promising but their vignette often does not specify arguments so it is difficult to figure out what are hard requirements of the input data file and what are convenience changes to make the vignette functions simpler

scelmendorf commented 1 week ago

@njlyon0 I added a few resources to the task list above if helpful. I think that the structure of data going in will vary depend on what effect size you intend to calculate. But once effect sizes are calculated the syntax should be similarish from there on out. Maybe give 1-2 examples and then refer people to escalc documentation?

njlyon0 commented 1 week ago

Please do attach any resources you have to this issue and I'll integrate them into the module page!

I'm thinking along the same lines as you module structure-wise, just having a hard time finding any solid documentation of what data format(s) are accepted by the effect size calculation functions.

njlyon0 commented 4 days ago

Meta-Analysis Drafted

Drafted meta-analysis section and added Sarah's resources (see below). That section is a little short but we're pressed for time if we do need to do a deep dive on the preceding statistical methods so maybe that's fine. We also need to decide whether to cover this module at all if we're leaning towards a flipped approach (or schedule two--likely consecutive--class sessions to split the load)

New Resources