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Xinkai Du @xinkaidu.bsky.social · 8d

Day 1 at Stanford and officially started my 4-month US visit in this special time. Amazed by the beautiful campus.

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Xinkai Du @xinkaidu.bsky.social · Aug 23

Adopt Registered Reports at Psychological Methods - Sign the Petition! chng.it/8h2KXXR4jk

Sign the Petition

Adopt Registered Reports at Psychological Methods

chng.it

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Xinkai Du @xinkaidu.bsky.social · Jul 12

Currently visiting Dr. Johnny Zhang in Notre Dame and excited to learn about his approaches combining CS and psychometrics.

Had a wonderful encounter with a deer on the way to campus. :)

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Reposted by Xinkai Du

Marcelo Mattar @marcelomattar.bsky.social · Jul 2

Thrilled to see our TinyRNN paper in @nature! We show how tiny RNNs predict choices of individual subjects accurately while staying fully interpretable. This approach can transform how we model cognitive processes in both healthy and disordered decisions. doi.org/10.1038/s415...

Discovering cognitive strategies with tiny recurrent neural networks - Nature

Modelling biological decision-making with tiny recurrent neural networks enables more accurate predictions of animal choices than classical cognitive models and offers insights into the underlying cog...

doi.org

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Xinkai Du @xinkaidu.bsky.social · Jun 26

@apajournals.bsky.social

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Reposted by Xinkai Du

Sacha Epskamp @sachaepskamp.bsky.social · Jun 26

Happy to share that our article, led by @xinkaidu.bsky.social, on confirmatory network modeling has been published in Psychological Methods!

psycnet.apa.org/record/2026-...

APA PsycNet

psycnet.apa.org

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Xinkai Du @xinkaidu.bsky.social · Jun 26

Thrilled to share that this paper has now been published on Psychological Methods. See 🧵 below for an intro & shinyapp to view the results, as well as non-paywalled version. dx.doi.org/10.1037/met0...

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Reposted by Xinkai Du

Julia M. Rohrer @dingdingpeng.the100.ci · Jun 2

Some papers are really good because they make just one point, but they make it really clearly — such as “Statistical Control Requires Causal Justification”

journals.sagepub.com/doi/10.1177/...

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Xinkai Du @xinkaidu.bsky.social · May 26

The method works both for panel and n=1 data. By enabling researchers to statistically compare networks across groups/individuals, we hope the method opens new avenues for testing genetic influences, developmental theories, treatment mechanisms, and cross-cultural differences.

Xinkai Du @xinkaidu.bsky.social · May 26

I planned to present this method at #SAA2025. Unfortunately I could not make it due to an unforeseen cold. Hope you enjoy the discussion and stay safe and healthy!

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Xinkai Du @xinkaidu.bsky.social · May 26

The paper also comes with a brief tutorial on the usage of the package

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Xinkai Du @xinkaidu.bsky.social · May 26

We have implemented IVPP in an R package under the same name: github.com/xinkaidupsy/...

GitHub - xinkaidupsy/IVPP

Contribute to xinkaidupsy/IVPP development by creating an account on GitHub.

github.com

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Xinkai Du @xinkaidu.bsky.social · May 26

Second, the method allows the comparison of networks when only a few data points (t = 3 or more) are available per person, a situation that is very common in large-scale longitudinal surveys.

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Xinkai Du @xinkaidu.bsky.social · May 26

In contrast, IVPP uncovers edge-level differences through a novel algorithm we present, termed partial pruning, directly constructing the distinct networks of each group/individual. We believe it provides a more meaningful network difference test that reveals the mechanisms underlying heterogeneity.

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Xinkai Du @xinkaidu.bsky.social · May 26

IVPP fills in two essential gaps in the literature: First, previous approaches to comparing dynamical networks unfortunately only report the presence/absence of heterogeneity, and are only viable when intensive measurements are available.

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Xinkai Du @xinkaidu.bsky.social · May 26

The three-month research visit with @sachaepskamp.bsky.social at NUS was a great memory, and even more excited with research output.

Excited to share a novel approach to compare networks models in time-series and panel data, which we term invariance partial pruning (IVPP).
osf.io/vb8dz_v1

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Reposted by Xinkai Du

Hu Chuan-Peng @hcp4715.bsky.social · Feb 14

🥳thrilled that our dockerHDDM tutorial paper, after many years's work was published in my dream journal AMPPS of @psychscience.bsky.social 🤩
👇
doi.org/10.1177/2515....

The image's been downloaded 10K+⏬ docker Hub

Such a pleasure to work w/ Wanke, Ru Yuan, Haiyang & member of HDDM/HSSM team!

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Eiko Fried @eikofried.bsky.social · Feb 13

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Tutorial on exploring ecological momentary assessment data is online at AMPPS, with:
- Accessible ways to visualize data for better understanding
- Models to get some first insights
- Further reading boxes for more advanced topics
- Reproducible pipeline you can run over your own data

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Reposted by Xinkai Du

René Freichel @renefreichel.bsky.social · Feb 7

Check out this important methodological validation study of SEM fit indices for (confirmatory) network modeling. Led by @xinkaidu.bsky.social!

Xinkai Du @xinkaidu.bsky.social · Feb 7

Preprint on using SEM fit indices & conventional cutoffs in confirmatory network analysis updated! Now with a Shiny app for results display.

PsyArXiv: osf.io/preprints/ps...
RG: www.researchgate.net/publication/...
Shiny app: github.com/xinkaidupsy/...

See 🧵 for the summary of results

(PDF) Moving from exploratory to confirmatory network analysis: An evaluation of SEM fit indices and cutoff values in network psychometrics

PDF | Network models are well-suited for phenomena detection, and most empirical network studies have been exploratory so far. Yet, due to the close... | Find, read and cite all the research you need ...

www.researchgate.net

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Xinkai Du @xinkaidu.bsky.social · Feb 7

Thank the collaborators for the continuous support and contribution! @noraskjerdingstad.bsky.social @renefreichel.bsky.social @omidvebrahimi.bsky.social Ria Hoekstra @sachaepskamp.bsky.social

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Xinkai Du @xinkaidu.bsky.social · Feb 7

The Shiny app allows users to view the results interactively, as well as checking the rejection rates of different cutoff values they choose by themselves

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Xinkai Du @xinkaidu.bsky.social · Feb 7

2. Fit indices were sensitive to mis-defined confirmatory network structures and non-stationarity.
3. Conventional cutoffs were convenient assessment criteria and generally performed well, albeit stricter cutoffs might be needed for hypothesis testing and replication studies

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Xinkai Du @xinkaidu.bsky.social · Feb 7

1. Although most network studies are exploratory so far, confirmatory network analysis has been entirely feasible. It is also often neglected that in longitudinal settings, exploratory network models are in-fact semi-confirmatory for the stationarity assumption they rely on.

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Xinkai Du @xinkaidu.bsky.social · Feb 7

Preprint on using SEM fit indices & conventional cutoffs in confirmatory network analysis updated! Now with a Shiny app for results display.

PsyArXiv: osf.io/preprints/ps...
RG: www.researchgate.net/publication/...
Shiny app: github.com/xinkaidupsy/...

See 🧵 for the summary of results

(PDF) Moving from exploratory to confirmatory network analysis: An evaluation of SEM fit indices and cutoff values in network psychometrics

PDF | Network models are well-suited for phenomena detection, and most empirical network studies have been exploratory so far. Yet, due to the close... | Find, read and cite all the research you need ...

www.researchgate.net

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Reposted by Xinkai Du

Charles Driver @charlesdriver.bsky.social · Jan 15

Looking for a longitudinal (ideally intensive) dataset where missing data is expected to be problematic, i.e. MNAR, anyone have pointers to good examples?

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