Siwei Liu
@siweiliu.bsky.social
120 followers 80 following 16 posts
Professor & Director of the Intensive Longitudinal Methods Lab at UC Davis. https://siweiliu.weebly.com/
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Reposted by Siwei Liu
karolinehuth.bsky.social
Happy to share that our large-scale network analysis is now out in @nathumbehav.nature.com

We show that networks are often supported by too little evidence from the data for results to be reported with confidence, not meaning that results are flawed but rather suggests caution in interpretation.
Reposted by Siwei Liu
whitneyringwald.bsky.social
Great study! A general implication is that when we infer effects of retrospectively measure variables on outcomes, we’re largely just seeing the effects of how people are currently feeling.
ophastings.bsky.social
The GSS asked the same people about their childhood income rank three different times. 56% changed their answer, even though what was trying to be measured couldn’t change! We dig into this in a new article at @socialindicators.bsky.social. 



doi.org/10.1007/s112...

🧵👇 (1/5)
Growing up Different(ly than Last Time We Asked): Social Status and Changing Reports of Childhood Income Rank - Social Indicators Research
How we remember our past can be shaped by the realities of our present. This study examines how changes to present circumstances influence retrospective reports of family income rank at age 16. While retrospective survey data can be used to assess the long-term effects of childhood conditions, present-day circumstances may “anchor” memories, causing shifts in how individuals recall and report past experiences. Using panel data from the 2006–2014 General Social Surveys (8,602 observations from 2,883 individuals in the United States), we analyze how changes in objective and subjective indicators of current social status—income, financial satisfaction, and perceived income relative to others—are associated with changes in reports of childhood income rank, and how this varies by sex and race/ethnicity. Fixed-effects models reveal no significant association between changes in income and in childhood income rank. However, changes in subjective measures of social status show contrasting effects, as increases in current financial satisfaction are associated with decreases in childhood income rank, but increases in current perceived relative income are associated with increases in childhood income rank. We argue these opposing effects follow from theories of anchoring in recall bias. We further find these effects are stronger among males but are consistent across racial/ethnic groups. This demographic heterogeneity suggests that recall bias is not evenly distributed across the population and has important implications for how different groups perceive their own pasts. Our findings further highlight the malleability of retrospective perceptions and their sensitivity to current social conditions, offering methodological insights into survey reliability and recall bias.
doi.org
Reposted by Siwei Liu
dingdingpeng.the100.ci
New paper out with @boryslaw.bsky.social 🥳 In which we sketch out how to rethink measurement invariance causally for applied researchers. And provide a causal definition of measurement invariance!

www.sciencedirect.com/science/arti...
Rethinking measurement invariance causally

Highlights:
It is preferable to work with a causal definition of measurement invariance
A violation of measurement invariance is a potentially substantively interesting observation
Standard tests for measurement invariance rely on strong assumptions
Group differences can be thought of as descriptive results Conceptual graph illustration the central points of the manuscript. A group variable is potentiall connected to a construct of interest which affects items. Measurement invariance is violated if the group variable directly affects the items, for example by modifying the loadings from the construct to the items, or by directly affecting an item To make this less abstract, consider a scenario where students take an exam, R, meant to capture some ability, T, and then are admitted to a program, V, depending on their exam results: R → V. This is sufficient to result in a violation of the statistical definition of measurement invariance. Exam results and admission are not independent given ability because exam results have a direct effect on admission. Even if we know somebody’s ability (e.g., we know it’s very high), learning about their admission status (e.g., they were not admitted) can tell us something about their exam result (e.g., it may have been worse than expected). According to the causal definition, this in itself does not constitute measurement bias, which seems a sensible conclusion here. After all, the scenario does not involve any reason to believe that the measurement process varied systematically by admission status. Admission happens after the exams took place, it cannot retroactively influence the measurement process (and, for example, lead to unfair treatment depending on admission status).
Reposted by Siwei Liu
Reposted by Siwei Liu
psychscience.bsky.social
AMPPS Call for Papers: Replicability and Reproducibility in Methodological Research. Proposals due September 15. @jkflake.bsky.social 

www.psychologicalscience.org
siweiliu.bsky.social
Great post! I just read this paper by @drewhalbailey.bsky.social and colleagues that shows the RI-CLPM also performs better than CLPM when there are unmeasured time-varying confounders:

psycnet.apa.org/record/2025-...
siweiliu.bsky.social
This work was officially accepted for publication today!
siweiliu.bsky.social
New work by my lab members Sebastian Castro-Alvarez and Di Jody Zhou, in collaboration with @eikofried.bsky.social @bringmannlaura.bsky.social and others.
eikofried.bsky.social
1/2

Our new preprint shows how to estimate internal consistency reliability in EMA data:

➡️n~1150, 3 months data, 4 scales
➡️6 nomothetic & idiographic methods
➡️2 timescales (4/day & 1/week)
➡️2 languages (ENG vs NL)
➡️separation of between & within person reliability.

#psychscisky #stats
siweiliu.bsky.social
This is a required reading in my Intro to Research Methods class.
siweiliu.bsky.social
Love it!
dingdingpeng.the100.ci
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:>
Reposted by Siwei Liu
jama.com
JAMA @jama.com · Feb 13
🧵 US states that implemented abortion bans saw higher than expected infant mortality rates, with larger increases among Black infants and those in southern states, according to this analysis of US national vital statistics data from 2012–2023.

ja.ma/4aVchPn

#MedSky
Figure 1.  Trends in Biannual US Infant Mortality Rates, 2012-2023
Reposted by Siwei Liu
karolinehuth.bsky.social
Are psychometric networks sufficiently supported by data such that one can be confident when interpreting its results? We analysed 294 psychometric networks from 126 papers with the Bayesian approach to address this question @jmbh.bsky.social Sara Ruth van Holst @maartenmarsman.bsky.social 🧵
psyarxivbot.bsky.social
Statistical Evidence in Psychological Networks: A Bayesian Analysis of 294 Networks from 126 Studies: http://osf.io/62ydg/
siweiliu.bsky.social
I don’t know if it’s an addition, but drinking too much milk tea (aka bubble tea, which is not just milk and tea but usually high in sugar) is a common problem in Chinese teenagers and a legitimate public health concern. It’s the same problem that Americans have with soda.
siweiliu.bsky.social
It’s fascinating to watch the exchanges between “TikTok refugees” and Chinese netizens on Rednote (Xiaohongshu)! Never imagined the “wall” would start to collapse in this way. Now I just worry that Rednote will get banned here if it becomes too popular😕
siweiliu.bsky.social
@dmcneish.bsky.social has a recent paper on this topic in which he uses a data set on binge eating behavior. It’s published in Psychological Methods.
Reposted by Siwei Liu
madhupai.bsky.social
Non-native English speakers need 50% more time to write a paper

When they do, they face a 2.5 times higher chance of being rejected because of language

journals.plos.org/plosbiology/...
Reposted by Siwei Liu
aidangcw.bsky.social
Wow. I’ve expressed concerns about Frontiers for a long time and won’t submit or review for them, but this suggests things are really bad.
pyhatanja.bsky.social
Big news from Finnish publication forum. Almost all MDPI and Frontiers journals will be downgraded to level 0 and thus are not considered as properly peer reviewed trustworthy scientific journals.
julkaisufoorumi.fi/en/news/chan...
Changes to the classification
julkaisufoorumi.fi
Reposted by Siwei Liu
juliafstrand.bsky.social
Hi all! I'm looking for a short, accessible reading for 1st year UGs that covers:
1) contributors to the replication crisis(e.g., p-hacking, publication bias, researcher degrees of freedom)
2) initiatives to address it (e.g., preregistration, sharing data and code, registered reports).

Any tips?
Reposted by Siwei Liu
rpsychologist.com
Introducing PowerLMM.js!

A new tool for power analysis of longitudinal linear mixed-effects models (LMMs) – with support for missing data, plus non-inferiority and equivalence tests.

powerlmmjs.rpsychologist.com

Would really appreciate your feedback as I refine this app! Details below 🧵👇