Tag Archives: fit indices

Three Points of Fantastic Insignificance and One Point of Moderate Meh

FANTASTIC INSIGNIFICANCE:

1. I really like the word “toast.” I also really like toasters. Especially brave ones.

2. Yay, I can still run 10k in under an hour, even after not running since August!

3. I found the perfect job for me. Unfortunately, it’s at Twitter and I don’t know if I could go on living with myself if I worked for Twitter. Google, maybe (ASSIMILATION). Twitter? No.

MODERATE MEH:

Another goal I want to add to my New Year’s Resolution list is this: I want to try and make some progress on a new SEM fit index, one that works better overall than the current popular ones. While I don’t think we’ll ever arrive at an index that is as error free as we’re hoping to find, I think there is currently still a lot of room for improvement.

For example, the CFI works very well for detecting discrepancies between the model and the actual data when the discrepancies are at the latent level (e.g., the researcher’s model proposes two latent variables but the model underlying the actual data in reality has three) but does horribly at properly reflecting the degree of misspecification when there are error covariances omitted from a model (CFI shows excellent fit when the omitted error covariance is low or very high; it shows terrible fit when the omission is moderate in size).

I thought I had this super awesome idea the other day to apply a sort of bootstrapping mechanism to act as a fit index, but that’s already been thought up and either a) doesn’t work very well or b) is very hard to implement, as there are several papers on a bootstrap-like fit index but little documentation of the use of it (I didn’t come across it at all during my lit reviews). So maybe I’ll do some more research into that…perhaps my idea of how bootstrapping should be implemented in assessing fit is different (and probably way more incorrect…but whatever).

There are also transformations to look at, too, which would require examining how the minimum fit function changes as the size of the misspecification (as well as the TYPE of misspecification) changes.

You know what all this means? PARTY TIME WITH R!

I might as well be dating it, it’s not like I’ll ever have a boyfriend again.

But that’s okay. R!

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Party at Claudia’s

Anybody want to come to a party at my house tonight? We’ll be talking about structural equation modeling. Model fit, in particular.

My poor mother will have to undergo three semesters’ worth of math and stats in about half an hour so she can be caught up on my research
to follow my defense presentation enough to be able to ask questions.

The fact that she agreed to do this is one of like five billion reasons why I love her.

Anyway, you all should come. There will be a huge whiteboard and a lot of lambdas.

Be there or be square.

In This Blog: My Data Look like a Napkin Swan

Seriously, standardized root mean square residual? Seriously?

This pattern makes absolutely no sense. It’s pretty, but it makes absolutely no sense.

I feel like this plot should be on Maury or something. “When Bad Patterns Happen to Good Data.”

I’d totally watch that.