Front Psychol August 2015 PMID 26300841

To transform or not to transform: using generalized linear mixed models to analyse reaction time data.

Steson Lo, Sally Andrews

Mentions

141
Peak: 100 in Dec 2025
across 4 sources since Jan 2019

Extended reach

13,476
potential audience

Total likes

23

Video views

699

Mentions per month

Jan 2019 – Dec 2025

Where it's discussed

  • Bluesky100 posts
  • Reddit15 posts
  • X / Twitter13 posts
  • YouTube13 videos

AI synopsis

What people are saying

Overall, the group shows strong interest and deep engagement with the article's focus on generalized linear mixed models and reaction time data, exemplified by references to technical papers, specific modeling techniques, and discussions about data transformations. The tone is analytical and collaborative, reflecting a desire to better understand statistical methods, though the impact remains moderate as debates continue rather than reaching definitive conclusions.

AgreementModerate agreement

Most discussions acknowledge the importance of the article and its relevance to the debate on data transformation, showing general support.

InterestHigh interest

Posts demonstrate high curiosity, referencing specific models like GLMMs and discussing their implications.

EngagementHigh engagement

Many comments delve into methodological specifics, sharing articles, papers, and personal opinions, indicating deep involvement.

ImpactModerate impact

The discussions are influential within the community, as users share resources and debate best practices, although the overall consensus impact is moderate.

Abstract in brief

  • Generalized linear mixed models (GLMMs) are gaining popularity in psychology because they can analyze reaction time (RT) data without averaging responses and dealing better with data skewness.
  • The guidelines often recommend transforming RT data to meet normality assumptions, but this can lead to misleading results, such as misinterpreting word recognition effects.
  • Using GLMMs allows researchers to analyze reaction time data more accurately by avoiding transformations, improving the understanding of individual differences and the underlying cognitive processes.]

Top posts

Most liked on X and Bluesky
Pierre Petitet
@PetitetPierre · X

To transform or not to transform your reaction time data? Great discussion here: https://t.co/wVJCl9Onz9

Jan 11, 2019 · 5 likes · 2 shares · 437 followers View post →
Francesco Chiossi
@francescochios3 · X

@SolomonKurz I always refer to this paper: https://t.co/3ZeLYDdQnd

Feb 4, 2024 · 5 likes · 364 followers View post →
Duy Van Vu
@Duyvvu · X

To transform or not to transform: using generalized linear mixed models to analyse reaction time data: https://t.co/7RBkDLlmt5:

Dec 15, 2020 · 2 likes · 2,654 followers View post →
Julian Matthews
@quined_quales · X

@huwbris @AndrewHeathcot8 @3rdincharge This is one I had in my library. We really need to make that (G)LMM mug: https://t.co/64gm9tm58k

Sep 30, 2020 · 2 likes · 821 followers View post →
Jennifer Reed
@DrJenniferReed · X

Curious of #biostatistics thoughts on ➡️approach: https://t.co/OKLDUVVR2l I assess normality of my variables, & then transform if not normally distributed. Sometimes normalization can't be achieved. Are linear mixed-effect models robust enough to handle non-normalized data

May 22, 2021 · 1 like · 1,995 followers View post →
Mixed Model Academy
@MixedModelACAD · X

To transform or not to transform: using generalized linear mixed models to #analyze reaction time data #statistic @FrontPsychol https://t.co/DVoDQugTiG

Jan 24, 2020 · 1 like · 1 share · 157 followers View post →

All mentions

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@nisiro.bsky.social · Bluesky

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Dec 25, 2025 View post →
あしたのおかず
@ashita-no-okz.bsky.social · Bluesky

支え合う黒研とっても良きです

Dec 25, 2025 View post →
András Forgács W
@andraswf.bsky.social · Bluesky

and your problem is...?

Dec 25, 2025 · 1 share View post →
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Bernardo Markus Kampff de Melo
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うぜ
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このアカウントずいぶん前に作ったけど ついったーがちょっと待てぃになったからここにいようかな

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Dec 25, 2025 · 1 share View post →
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@longtail.news · Bluesky

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@fottigraf.bsky.social · Bluesky

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Chris Thorndyke
@sthfan.bsky.social · Bluesky

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Richard
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Merry Christmas

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@derkrieger.bsky.social · Bluesky

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Merry Christmas Violet!

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(隣の市の上映時間が21時30分からだな‥‥‥‥‥)

Dec 25, 2025 View post →
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ゲテモノォ
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Dec 25, 2025 · 1 share View post →
Momakil
@momakil.bsky.social · Bluesky

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Dec 25, 2025 View post →
わたじろ
@rimurimu-uoxou.bsky.social · Bluesky

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Dec 25, 2025 View post →
Huisman_EN
@huismanen.bsky.social · Bluesky

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Dec 25, 2025 · 1 share View post →
湊
@isana3710.bsky.social · Bluesky

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Dec 25, 2025 View post →
斎蔵
@rak3-mo9a3.bsky.social · Bluesky

言い忘れてた。このアカウントは前触れなく4,50cmのトカゲの画像が上がったりはちゅ垢セルフrpしたりするので、苦手な方はお気をつけを〜。さすがに餌は写さないけど、春〜秋には蚕の話も出ます〜。

Dec 25, 2025 View post →
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Francesco Chiossi
@francescochios3 · X

@SolomonKurz I always refer to this paper: https://t.co/3ZeLYDdQnd

Feb 4, 2024 · 5 likes · 364 followers View post →
Jennifer Reed
@DrJenniferReed · X

Curious of #biostatistics thoughts on ➡️approach: https://t.co/OKLDUVVR2l I assess normality of my variables, & then transform if not normally distributed. Sometimes normalization can't be achieved. Are linear mixed-effect models robust enough to handle non-normalized data

May 22, 2021 · 1 like · 1,995 followers View post →
Duy Van Vu
@Duyvvu · X

To transform or not to transform: using generalized linear mixed models to analyse reaction time data: https://t.co/7RBkDLlmt5:

Dec 15, 2020 · 2 likes · 2,654 followers View post →
Julian Matthews
@quined_quales · X

@huwbris @AndrewHeathcot8 @3rdincharge This is one I had in my library. We really need to make that (G)LMM mug: https://t.co/64gm9tm58k

Sep 30, 2020 · 2 likes · 821 followers View post →
Eric Ménétré
@MenetreEric · X

To transform or not to transform ? Here is a nice paper guiding you through the art of data transformation using mixed models, thanks to @DrSallyAndrews https://t.co/cCwfBVi3ol

Jul 7, 2020 · 108 followers View post →
Khaled Al-Shamaa
@arphp · X

RT @mixed_model: To transform or not to transform: using generalized linear mixed models to #analyze reaction time data #statistic @FrontP…

Jan 24, 2020 · 1 share · 2,292 followers View post →
Mixed Model Academy
@MixedModelACAD · X

To transform or not to transform: using generalized linear mixed models to #analyze reaction time data #statistic @FrontPsychol https://t.co/DVoDQugTiG

Jan 24, 2020 · 1 like · 1 share · 157 followers View post →
Qingyuan Gardner
@Qingyuan261 · X

Be very careful log-transforming your RT data for GLMMs.

Dec 13, 2019 · 197 followers View post →
Mark Annuncio
@MarkAnnuncio · X

@jonaslindeloev Found it. First paragraph page 3. May be interested in rest of paper too. https://t.co/R2Uuf23Mw2 There was also that Schramm et al pre-print from this past spring which you may be familiar with and find relevant? “Are RT transformations really beneficial?”

Sep 18, 2019 · 335 followers View post →
Jason Geller
@jgeller_phd · X

@JeffRouder While not advocating for transformations, I think this article presents a nice discussion of the issue: https://t.co/WLkNMzuAtL

Feb 20, 2019 · 1,157 followers View post →
Elisabeth Parés Pujolràs
@elisabethpares · X

RT @PetitetPierre: To transform or not to transform your reaction time data? Great discussion here: https://t.co/wVJCl9Onz9

Jan 11, 2019 · 2 shares · 768 followers View post →
Daniel Borek (@dborek@scholar.social)
@danieltomasz · X

RT @PetitetPierre: To transform or not to transform your reaction time data? Great discussion here: https://t.co/wVJCl9Onz9

Jan 11, 2019 · 2 shares · 1,313 followers View post →
Pierre Petitet
@PetitetPierre · X

To transform or not to transform your reaction time data? Great discussion here: https://t.co/wVJCl9Onz9

Jan 11, 2019 · 5 likes · 2 shares · 437 followers View post →
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