From d2a563b5b2f263e5dc52a43c6d6d5f49c060780a Mon Sep 17 00:00:00 2001
From: alexpron <45215023+alexpron@users.noreply.github.com>
Date: Wed, 24 Jul 2024 13:16:21 +0000
Subject: [PATCH] content: import publications from Bibtex

---
 .../publication/soskic-garden-nodate/cite.bib | 13 +++++++
 .../publication/soskic-garden-nodate/index.md | 38 +++++++++++++++++++
 2 files changed, 51 insertions(+)
 create mode 100644 content/publication/soskic-garden-nodate/cite.bib
 create mode 100644 content/publication/soskic-garden-nodate/index.md

diff --git a/content/publication/soskic-garden-nodate/cite.bib b/content/publication/soskic-garden-nodate/cite.bib
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+++ b/content/publication/soskic-garden-nodate/cite.bib
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+@article{soskic_garden_nodate,
+ abstract = {Abstract This study tackles the Garden of Forking Paths, as a challenge for replicability and reproducibility of ERP studies. Here, we applied a multiverse analysis to a sample ERP N400 dataset, donated by an independent research team. We analyzed this dataset using 14 pipelines selected to showcase the full range of methodological variability found in the N400 literature using systematic review approach. The selected pipelines were compared in depth by looking into statistical test outcomes, descriptive statistics, effect size, data quality, and statistical power. In this way we provide a worked example of how analytic flexibility can impact results in research fields with high dimensionality such as ERP, when analyzed using standard null-hypothesis significance testing. Out of the methodological decisions that were varied, high-pass filter cut-off, artifact removal method, baseline duration, reference, measurement latency and locations, and amplitude measure (peak vs. mean) were all shown to affect at least some of the study outcome measures. Low-pass filtering was the only step which did not notably influence any of these measures. This study shows that even some of the seemingly minor procedural deviations can influence the conclusions of an ERP study. We demonstrate the power of multiverse analysis in both identifying the most reliable effects in a given study, and for providing insights into consequences of methodological decisions.},
+ author = {Šoškić, Anđela and Styles, Suzy J. and Kappenman, Emily S. and Ković, Vanja},
+ doi = {https://doi.org/10.1111/psyp.14628},
+ journal = {Psychophysiology},
+ keywords = {to read},
+ note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/psyp.14628},
+ number = {n/a},
+ pages = {e14628},
+ title = {Garden of forking paths in ERP research: Effects of varying pre-processing and analysis steps in an N400 experiment},
+ url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/psyp.14628},
+ volume = {n/a}
+}
diff --git a/content/publication/soskic-garden-nodate/index.md b/content/publication/soskic-garden-nodate/index.md
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+---
+title: 'Garden of forking paths in ERP research: Effects of varying pre-processing
+  and analysis steps in an N400 experiment'
+authors:
+- Anđela Šoškić
+- Suzy J. Styles
+- Emily S. Kappenman
+- Vanja Ković
+date: -01-01
+publishDate: '2024-07-24T13:16:21.318117Z'
+publication_types:
+- article-journal
+publication: '*Psychophysiology*'
+doi: https://doi.org/10.1111/psyp.14628
+abstract: Abstract This study tackles the Garden of Forking Paths, as a challenge
+  for replicability and reproducibility of ERP studies. Here, we applied a multiverse
+  analysis to a sample ERP N400 dataset, donated by an independent research team.
+  We analyzed this dataset using 14 pipelines selected to showcase the full range
+  of methodological variability found in the N400 literature using systematic review
+  approach. The selected pipelines were compared in depth by looking into statistical
+  test outcomes, descriptive statistics, effect size, data quality, and statistical
+  power. In this way we provide a worked example of how analytic flexibility can impact
+  results in research fields with high dimensionality such as ERP, when analyzed using
+  standard null-hypothesis significance testing. Out of the methodological decisions
+  that were varied, high-pass filter cut-off, artifact removal method, baseline duration,
+  reference, measurement latency and locations, and amplitude measure (peak vs. mean)
+  were all shown to affect at least some of the study outcome measures. Low-pass filtering
+  was the only step which did not notably influence any of these measures. This study
+  shows that even some of the seemingly minor procedural deviations can influence
+  the conclusions of an ERP study. We demonstrate the power of multiverse analysis
+  in both identifying the most reliable effects in a given study, and for providing
+  insights into consequences of methodological decisions.
+tags:
+- to read
+links:
+- name: URL
+  url: https://onlinelibrary.wiley.com/doi/abs/10.1111/psyp.14628
+---