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

Overview of attention for article published in Frontiers in Psychology, August 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
1 blog
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53 X users
wikipedia
1 Wikipedia page
q&a
3 Q&A threads

Citations

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598 Dimensions

Readers on

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935 Mendeley
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Title
To transform or not to transform: using generalized linear mixed models to analyse reaction time data
Published in
Frontiers in Psychology, August 2015
DOI 10.3389/fpsyg.2015.01171
Pubmed ID
Authors

Steson Lo, Sally Andrews

Abstract

Linear mixed-effect models (LMMs) are being increasingly widely used in psychology to analyse multi-level research designs. This feature allows LMMs to address some of the problems identified by Speelman and McGann (2013) about the use of mean data, because they do not average across individual responses. However, recent guidelines for using LMM to analyse skewed reaction time (RT) data collected in many cognitive psychological studies recommend the application of non-linear transformations to satisfy assumptions of normality. Uncritical adoption of this recommendation has important theoretical implications which can yield misleading conclusions. For example, Balota et al. (2013) showed that analyses of raw RT produced additive effects of word frequency and stimulus quality on word identification, which conflicted with the interactive effects observed in analyses of transformed RT. Generalized linear mixed-effect models (GLMM) provide a solution to this problem by satisfying normality assumptions without the need for transformation. This allows differences between individuals to be properly assessed, using the metric most appropriate to the researcher's theoretical context. We outline the major theoretical decisions involved in specifying a GLMM, and illustrate them by reanalysing Balota et al.'s datasets. We then consider the broader benefits of using GLMM to investigate individual differences.

X Demographics

X Demographics

The data shown below were collected from the profiles of 53 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 935 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 6 <1%
Germany 3 <1%
Netherlands 2 <1%
Belgium 2 <1%
Cuba 1 <1%
Sweden 1 <1%
Ireland 1 <1%
Switzerland 1 <1%
Spain 1 <1%
Other 2 <1%
Unknown 915 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 246 26%
Student > Master 125 13%
Researcher 118 13%
Student > Doctoral Student 75 8%
Student > Bachelor 64 7%
Other 144 15%
Unknown 163 17%
Readers by discipline Count As %
Psychology 278 30%
Neuroscience 96 10%
Linguistics 86 9%
Agricultural and Biological Sciences 80 9%
Social Sciences 25 3%
Other 143 15%
Unknown 227 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 55. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 February 2024.
All research outputs
#761,647
of 25,304,569 outputs
Outputs from Frontiers in Psychology
#1,568
of 34,177 outputs
Outputs of similar age
#9,197
of 270,638 outputs
Outputs of similar age from Frontiers in Psychology
#38
of 541 outputs
Altmetric has tracked 25,304,569 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 34,177 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.2. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 270,638 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 541 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.