A huge pain, but doable with undergrad participants essentially for free (or at least historically so).

I’m not saying what is right… I’m saying what is driving behavior

But where does 1a say anything about personhood? I’m reading it and it seems clear that you can’t restrict speech—and that would obviously apply to one or more people organized under a LLC. IMO the bigger question is “when is money free speech, vs when is it corruption”

45. Academia doesn't reward building useful tools nearly as much as it should

14. We mostly evaluate latent variable models with the equivalent of Rorschach tests

5. You should use a precision-recall curve for a binary classifier, not an ROC curve

… set of paths consistently supported by the data. Even getting that down is a trick. And making sense of it is fraught and doesn’t get you much further than one would get from regression. But at least then we would have some confidence we understand the correlational relationships!

… the model is correct and then gives you what the path would be under that specification. There’s nothing different when we go to SEM other than your ability to p-hack goes up exponentially. IMO this would be a great place to use machine learning approaches to train / tune models to find …

… all those hypotheses together (in the same way that ANOVA contest many multiple comparisons at once). There’s nothing different between this and running a bunch of regressions and claiming the results support the way you specified those models. In reality, it’s the reverse. Regression assumes …

Yes and see this a lot in social too. Proper use of SEM implies a particular philosophy of hypothesis testing in regression contexts. An omitted path is hypothesizing that path is exactly 0. A non-omitted path hypothesizing it is non-zero. Model fit is effectively the joint set of …

Yikes!

… SEM for causal discovery. However, if you have a good read on the causal process, it can be great for estimating parameters such as factor, loadings or paths with latent variables

This is probably not anything you don’t already know …. But I did a lot of SEM work and will repeat it anyway. The model assumes you know the causal structure. Fit indices will confirm that the model is a fit to the data, but many incorrect models can fit the data. So I would not use …

Counterpoint: the ability to chat with an article or literature and find patterns in our own work that perhaps we missed I think has a lot of potential to augment our scientific work