2015
DOI: 10.3389/fpsyg.2015.00555
|View full text |Cite
|
Sign up to set email alerts
|

Striking a balance: analyzing unbalanced event-related potential data

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
35
0

Year Published

2017
2017
2021
2021

Publication Types

Select...
5
1
1

Relationship

1
6

Authors

Journals

citations
Cited by 43 publications
(35 citation statements)
references
References 22 publications
0
35
0
Order By: Relevance
“…The average number of trials included in the ERP analyses for each condition for each subject is given in Table . MLM is robust to unbalanced data, through a procedure called partial pooling (Gelman & Hill, ; Tibon & Levy, ), and therefore differing numbers of trials per condition are less problematic than in traditional rmANOVA.…”
Section: Resultsmentioning
confidence: 99%
See 3 more Smart Citations
“…The average number of trials included in the ERP analyses for each condition for each subject is given in Table . MLM is robust to unbalanced data, through a procedure called partial pooling (Gelman & Hill, ; Tibon & Levy, ), and therefore differing numbers of trials per condition are less problematic than in traditional rmANOVA.…”
Section: Resultsmentioning
confidence: 99%
“…2 To examine change over time in control-related responses, data from this study were analyzed using mixedeffects multilevel modeling (MLM). Recent reports have emphasized the advantages of MLM over traditional repeated measures analysis of variance (rmANOVA) for psychophysiological data (Kristjansson, Kircher, & Webb, 2007;Tibon & Levy, 2015;Tremblay & Newman, 2015;Vossen, Van Breukelen, Hermens, Van Os, & Lousberg, 2011). Of greatest relevance for the current research, MLM allows the modeling of data from individual trials over time (Tibon & Levy, 2015;Vossen et al, 2011).…”
Section: Current Studymentioning
confidence: 99%
See 2 more Smart Citations
“…To analyse ROI data, we used a mixed-models analysis, which accommodates both within-and between-subject variability. This approach is particularly recommended for unbalanced data (an unequal number of trials in each condition, Tibon & Levy, 2015), which we had here due to the post-hoc division of trials into vivid, non-vivid and failure trials (See Tibon et al, 2014;Tibon & Levy, 2014a;Tibon & Levy, 2014b for a similar use of this approach). Importantly, rather than averaged estimates across participant/condition, the mixed-models employed here require estimation of the BOLD response for each trial.…”
Section: Roi Analysesmentioning
confidence: 99%