Drew Bailey

So hard! When there is no cross-lagged effect in this data generating model, RI-CLPM estimates one, but when there *is* one effect, the ARTS model doesn't! Not sure the paper bears much on whether cross-lagged effects are rare, but def on our ability to use these models without external info.

Interested in models used to estimate lagged effects in panel data? We (@rebiweidmann.bsky.social, Hyewon Yang) have a new paper looking at patterns of stability and their implications for bias and model choice: osf.io/preprints/ps... [1/x]

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I really like this paper dealing with the problem of “mischievous” responding in longitudinal panel data, by @joecimpian.bsky.social

journals.sagepub.com/doi/full/10....

Dag makhani: Causal inference and Indian cuisine

Collider effect in the real world!

Like, the effect of dropping a bouncing ball on the velocity of the ball over time is a weird oscillating function?

About 2/3 of the posts on this platform linking to the recent NYT article on null findings from Baby’s First Years have this reaction. You can search the headline and verify yourself!

www.nytimes.com/2025/07/28/u...

I used Paige’s first book in a class students with a wide range of previous exposure to and attitudes about behavior genetics, and they all seem to find it very interesting. Will probably try this one too!

Random Intercepts and Slopes in Longitudinal Models: When Are They "Good" and "Bad" Controls?

or

Illusory Traits 2: Revenge of the Slopes

Led by Siling Guo, with Nicolas Hübner, Steffen Zitzmann, Martin Hecht, and Kou Murayama.

Comments welcome!

osf.io/preprints/ps...

Reviewer notes: So you’re interested in “lagged effects.” In some fields, researchers who end up with time series of two variables of interest (X and Y) like to analyze (reciprocal) lagged effects between them. Does X affect Y at a later point in time, and d...

New blog post! Let's say you've measured two variables repeatedly and want to investigate how one affects the other over time. Here are some recommendations for how to do that well.

www.the100.ci/2025/06/25/r...

Although field-specific authorship norms probably mostly just reflect the values of people in the field, I also think they can affect those values too. This seems like a good example! (I have some guesses about unintended consequences of tiny authorship teams too, btw.)

6) LCGAs never replicate across datasets or in the same dataset. They usually just produce the salsa pattern (Hi/med/low) or the cats cradle (Hi/low/increasing/decreasing).

This has misled entire fields (see all of George Bonnano's work on resilience, for example).

psycnet.apa.org/fulltext/201...

Treemap showing measurement fragmentation across subfields in psychology. Hill-Shannon Diversity 𝐷=1626.05

How often measures in the APA PsycTESTS database are (re)used according to the APA PsycInfo database: rarely, the majority are never reused.

Our fragmentation index (Hill-Shannon diversity) over time across subdisciplines shows fragmentation rising.

Our paper "A fragmented field" has just been accepted at AMPPS. We find it's not just you, psychology is really getting more confusing (construct and measure fragmentation is rising).
We updated the preprint with the (substantial) revision, please check it out.
osf.io/preprints/ps...

But I really hope we get 10 more years of strong studies now on the effects of large increases in access on outcomes for "always takers" and especially for elite students. There are lots of good reasons to expect these effects should differ. (2/2)

I have seen lots of higher ed talks and papers in the last 10 years convincingly demonstrating that just making some cutoff (getting into a more selective college or major, not taking remedial classes) helps the marginal student. Great to see an emerging consensus. (1/2)

For every cause, x, there is some group of people (often disproportionately people who study x) who think the effects of x are way bigger than they are. Therefore, I think we are doomed to read (or worse, make) "Yeah, but the effect of x is small" takes forever.

Mix of Figures 2 and 4 from the paper

I investigated how often papers' significant (p < .05) results are fragile (.01 ≤ p < .05) p-values. An excess of such p-values suggests low odds of replicability.

From 2004-2024, the rates of fragile p-values have gone down precipitously across every psychology discipline (!)

Hope to see at least one of these in each APS policy brief from now on!

In Memory of Lynn Fuchs Email from CEC Division for Research May 12, 2025 In Memory of Lynn Fuchs The field of special education lost a visionary and beloved leader with the passing of Lynn Fuchs on May 7, 2025. Her absenc

IN MEMORY OF LYNN FUCHS

The field of special education lost a visionary and beloved leader with the passing of Lynn Fuchs on May 7, 2025. Her absence leaves a profound void—not only in our scholarly community, but in the hearts of all who had the privilege of knowing her.

[ click reading below ]

Really like it!

Thanks to everybody who chimed in!

I arrived at the conclusion that (1) there's a lot of interesting stuff about interactions and (2) the figure I was looking for does not exist.

So, I made it myself! Here's a simple illustration of how to control for confounding in interactions:>

(Not saying the public is right necessarily; you can get programs that pass a cost-benefit test with much smaller effects on test scores than laypeople want. But it is a problem for policymakers that the public wants them policy to deliver unrealistically sized effects.)

If you ask people what kinds of effects they’d need to decide to implement something new, they’re much bigger than realistically sized effects in ed policy. We’ve decided collectively to pretend this isn’t a problem and then get surprised at the backlash when it comes.

Is there a name for the fallacy that, because things are different from each other, one cannot compare them? (If not, I propose the “apples and oranges fallacy”)

@stefanschubert.bsky.social

Starting to feel like "don't look at the coefficients, just calculate whatever metric is relevant to your research question" is a highly underappreciated stats hack and also I may have to get myself a marginaleffects T-shirt.

And you can think of the RI-CLPM as doing something like this too, using repeated measures of the same x over time.

Triangulating on Developmental Models with a Combination of Experimental and Non-Experimental Estimates Plausible competing developmental models show similar or identical structural equation modeling (SEM) model fit indices, despite making very different causal predictions. One way to help address this ...

Not eloquently. But in the appendix of this paper, we show that a "multivariate intercept" model that does this (constraining all loadings to equality) reproduces patterns of causal impacts of some RCTs better than OLS (see Table S4 + Fig S1):
pmc.ncbi.nlm.nih.gov/articles/PMC...

Do one for when people realize the extracted factor might be more useful as a *control* for estimating the effects of interest than as the key predictor of interest.

You like good music and are in North Carolina: are you into Wednesday?