From 4cea1e9a3e2dc8e3edbf9f4325c7bd7c31fea853 Mon Sep 17 00:00:00 2001 From: paxtonfitzpatrick Date: Mon, 19 Feb 2024 21:54:29 -0500 Subject: [PATCH 1/5] add BateEtal15 --- cdl.bib | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/cdl.bib b/cdl.bib index 29d9001..cfc64a0 100644 --- a/cdl.bib +++ b/cdl.bib @@ -1,6 +1,17 @@ -@article{GordEtal16, +@Article{BateEtal15, + title = {Fitting Linear Mixed-Effects Models Using {lme4}}, + author = {Douglas Bates and Martin M{\"a}chler and Ben Bolker and Steve Walker}, + journal = {Journal of Statistical Software}, + year = {2015}, + volume = {67}, + number = {1}, + pages = {1--48}, + doi = {10.18637/jss.v067.i01}, + } + + @article{GordEtal16, author = {E M Gordon and T O Laumann and B Adeyemo and J F Huckins and W M Kelley and S E Petersen}, journal = {Cerebral Cortex}, pages = {288--303}, From 8b4dae4fa9c454e638270d133b24941c622bddbb Mon Sep 17 00:00:00 2001 From: paxtonfitzpatrick Date: Mon, 19 Feb 2024 22:27:38 -0500 Subject: [PATCH 2/5] add BateEtal15b, change BateEtal15 -> BateEtal15a --- cdl.bib | 33 +++++++++++++++++++++------------ 1 file changed, 21 insertions(+), 12 deletions(-) diff --git a/cdl.bib b/cdl.bib index cfc64a0..7ff171b 100644 --- a/cdl.bib +++ b/cdl.bib @@ -1,17 +1,26 @@ -@Article{BateEtal15, - title = {Fitting Linear Mixed-Effects Models Using {lme4}}, - author = {Douglas Bates and Martin M{\"a}chler and Ben Bolker and Steve Walker}, - journal = {Journal of Statistical Software}, - year = {2015}, - volume = {67}, - number = {1}, - pages = {1--48}, - doi = {10.18637/jss.v067.i01}, - } - - @article{GordEtal16, +@article{BateEtal15b, + author = {Douglas Bates and Reinhold Kliegl and Shravan Vasishth and Harald Baayen}, + date-added = {2024-02-19 22:18:43 -0500}, + date-modified = {2024-02-19 22:26:28 -0500}, + journal = {{arXiv}}, + title = {Parsimonious Mixed Models}, + volume = {1506.04967}, + year = {2015}} + +@article{BateEtal15a, + author = {Douglas Bates and Martin M{\"a}chler and Ben Bolker and Steve Walker}, + date-modified = {2024-02-19 22:26:37 -0500}, + doi = {10.18637/jss.v067.i01}, + journal = {Journal of Statistical Software}, + number = {1}, + pages = {1--48}, + title = {Fitting Linear Mixed-Effects Models Using {lme4}}, + volume = {67}, + year = {2015}} + +@article{GordEtal16, author = {E M Gordon and T O Laumann and B Adeyemo and J F Huckins and W M Kelley and S E Petersen}, journal = {Cerebral Cortex}, pages = {288--303}, From 1ac832c3b0eba231611b28b59964352e25770b4b Mon Sep 17 00:00:00 2001 From: paxtonfitzpatrick Date: Tue, 20 Feb 2024 10:19:09 -0500 Subject: [PATCH 3/5] add BarrEtal13 --- cdl.bib | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/cdl.bib b/cdl.bib index 7ff171b..1836094 100644 --- a/cdl.bib +++ b/cdl.bib @@ -1,5 +1,30 @@ +@article{BarrEtal13, + abstract = {Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F1 and F2 tests, and in many cases, even worse than F1 alone. Maximal LMEMs should be the `gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.}, + author = {Dale J. Barr and Roger Levy and Christoph Scheepers and Harry J. Tily}, + date-added = {2024-02-20 10:18:31 -0500}, + date-modified = {2024-02-20 10:18:52 -0500}, + doi = {https://doi.org/10.1016/j.jml.2012.11.001}, + journal = {Journal of Memory and Language}, + number = {3}, + pages = {255-278}, + title = {Random effects structure for confirmatory hypothesis testing: Keep it maximal}, + volume = {68}, + year = {2013}} + +@article{MatuEtal17, + abstract = {Linear mixed-effects models have increasingly replaced mixed-model analyses of variance for statistical inference in factorial psycholinguistic experiments. Although LMMs have many advantages over ANOVA, like ANOVAs, setting them up for data analysis also requires some care. One simple option, when numerically possible, is to fit the full variance-covariance structure of random effects (the maximal model; Barr, Levy, Scheepers & Tily, 2013), presumably to keep Type I error down to the nominal α in the presence of random effects. Although it is true that fitting a model with only random intercepts may lead to higher Type I error, fitting a maximal model also has a cost: it can lead to a significant loss of power. We demonstrate this with simulations and suggest that for typical psychological and psycholinguistic data, higher power is achieved without inflating Type I error rate if a model selection criterion is used to select a random effect structure that is supported by the data.}, + author = {Hannes Matuschek and Reinhold Kliegl and Shravan Vasishth and Harald Baayen and Douglas Bates}, + date-added = {2024-02-19 23:26:49 -0500}, + date-modified = {2024-02-19 23:27:20 -0500}, + doi = {https://doi.org/10.1016/j.jml.2017.01.001}, + journal = {Journal of Memory and Language}, + pages = {305-315}, + title = {Balancing Type I error and power in linear mixed models}, + volume = {94}, + year = {2017}} + @article{BateEtal15b, author = {Douglas Bates and Reinhold Kliegl and Shravan Vasishth and Harald Baayen}, date-added = {2024-02-19 22:18:43 -0500}, From ddf200f8d40252e4352d661741e09e27fbbaa5e0 Mon Sep 17 00:00:00 2001 From: "Jeremy R. Manning" Date: Wed, 5 Nov 2025 10:53:38 -0500 Subject: [PATCH 4/5] Refactor author names and format pages in cdl.bib Updated author names to abbreviated forms and adjusted page formatting. --- cdl.bib | 32 ++++++-------------------------- 1 file changed, 6 insertions(+), 26 deletions(-) diff --git a/cdl.bib b/cdl.bib index 09e622d..3190941 100644 --- a/cdl.bib +++ b/cdl.bib @@ -1,43 +1,33 @@ @article{BarrEtal13, - abstract = {Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F1 and F2 tests, and in many cases, even worse than F1 alone. Maximal LMEMs should be the `gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.}, - author = {Dale J. Barr and Roger Levy and Christoph Scheepers and Harry J. Tily}, - date-added = {2024-02-20 10:18:31 -0500}, - date-modified = {2024-02-20 10:18:52 -0500}, + author = {DJ Barr and Rr Levy and C Scheepers and HJ Tily}, doi = {https://doi.org/10.1016/j.jml.2012.11.001}, journal = {Journal of Memory and Language}, number = {3}, - pages = {255-278}, + pages = {255--278}, title = {Random effects structure for confirmatory hypothesis testing: Keep it maximal}, volume = {68}, year = {2013}} @article{MatuEtal17, - abstract = {Linear mixed-effects models have increasingly replaced mixed-model analyses of variance for statistical inference in factorial psycholinguistic experiments. Although LMMs have many advantages over ANOVA, like ANOVAs, setting them up for data analysis also requires some care. One simple option, when numerically possible, is to fit the full variance-covariance structure of random effects (the maximal model; Barr, Levy, Scheepers & Tily, 2013), presumably to keep Type I error down to the nominal α in the presence of random effects. Although it is true that fitting a model with only random intercepts may lead to higher Type I error, fitting a maximal model also has a cost: it can lead to a significant loss of power. We demonstrate this with simulations and suggest that for typical psychological and psycholinguistic data, higher power is achieved without inflating Type I error rate if a model selection criterion is used to select a random effect structure that is supported by the data.}, - author = {Hannes Matuschek and Reinhold Kliegl and Shravan Vasishth and Harald Baayen and Douglas Bates}, - date-added = {2024-02-19 23:26:49 -0500}, - date-modified = {2024-02-19 23:27:20 -0500}, + author = {H Matuschek and R Kliegl and S Vasishth and H Baayen and D Bates}, doi = {https://doi.org/10.1016/j.jml.2017.01.001}, journal = {Journal of Memory and Language}, - pages = {305-315}, + pages = {305--315}, title = {Balancing Type I error and power in linear mixed models}, volume = {94}, year = {2017}} @article{BateEtal15b, - author = {Douglas Bates and Reinhold Kliegl and Shravan Vasishth and Harald Baayen}, - date-added = {2024-02-19 22:18:43 -0500}, - date-modified = {2024-02-19 22:26:28 -0500}, + author = {D Bates and R Kliegl and S Vasishth and H Baayen}, journal = {{arXiv}}, title = {Parsimonious Mixed Models}, volume = {1506.04967}, year = {2015}} @article{BateEtal15a, - author = {Douglas Bates and Martin M{\"a}chler and Ben Bolker and Steve Walker}, - date-modified = {2024-02-19 22:26:37 -0500}, - doi = {10.18637/jss.v067.i01}, + author = {D Bates and M M{\"a}chler and B Bolker and S Walker}, journal = {Journal of Statistical Software}, number = {1}, pages = {1--48}, @@ -45,16 +35,6 @@ @article{BateEtal15a volume = {67}, year = {2015}} -@article{MartEtal18, - author = {J L Martinez-Rodrigues and I Lopez-Arevalo and A B Rios-Alvarado}, - date-added = {2025-08-02 00:51:30 -0400}, - date-modified = {2025-08-02 00:51:30 -0400}, - journal = {Expert Systems with Applications}, - pages = {339--355}, - title = {{OpenIE}-based approach for knowledge graph construction from text}, - volume = {113}, - year = {2018}} - @article{ZhanEtal18b, author = {Y Zhang and H Dai and Z Kozereva and A Smola and L Song}, date-added = {2025-08-02 00:50:52 -0400}, From 693675465014015d809e8a0240cd7e819105bdef Mon Sep 17 00:00:00 2001 From: "Jeremy R. Manning" Date: Wed, 5 Nov 2025 10:57:21 -0500 Subject: [PATCH 5/5] Clean up BibTeX entries by removing date fields Removed date-added and date-modified fields from multiple articles. --- cdl.bib | 16 ---------------- 1 file changed, 16 deletions(-) diff --git a/cdl.bib b/cdl.bib index 3190941..38b61f9 100644 --- a/cdl.bib +++ b/cdl.bib @@ -1,5 +1,3 @@ - - @article{BarrEtal13, author = {DJ Barr and Rr Levy and C Scheepers and HJ Tily}, doi = {https://doi.org/10.1016/j.jml.2012.11.001}, @@ -37,8 +35,6 @@ @article{BateEtal15a @article{ZhanEtal18b, author = {Y Zhang and H Dai and Z Kozereva and A Smola and L Song}, - date-added = {2025-08-02 00:50:52 -0400}, - date-modified = {2025-08-02 00:50:52 -0400}, journal = {Proceedings of the {AAAI} Conference on Artificial Intelligence}, number = {1}, pages = {doi.org/10.1609/aaai.v32i1.12057}, @@ -48,8 +44,6 @@ @article{ZhanEtal18b @article{GuoEtal20, author = {Q Guo and F Zhuang and C Qin and H Zhu and X Xie and H Xiong and Q He}, - date-added = {2025-08-02 00:50:39 -0400}, - date-modified = {2025-08-02 00:50:39 -0400}, journal = {{IEEE} {Xplore}}, number = {8}, pages = {3549--3568}, @@ -59,8 +53,6 @@ @article{GuoEtal20 @article{XionEtal17, author = {C Xiong and R Power and J Callan}, - date-added = {2025-08-02 00:50:29 -0400}, - date-modified = {2025-08-02 00:50:29 -0400}, journal = {{WWW} '17: Proceedings of the 26th International Conference on World Wide Web}, pages = {1271--1279}, title = {Explicit semantic ranking for academic search via knowledge graph embedding}, @@ -68,8 +60,6 @@ @article{XionEtal17 @article{WeisEtal16, author = {K Weiss and T M Khoshgoftaar and D D Wang}, - date-added = {2025-08-02 00:50:10 -0400}, - date-modified = {2025-08-02 00:50:10 -0400}, journal = {Journal of Big Data}, number = {9}, pages = {doi.org/10.1186/s40537-016-0043-6}, @@ -79,8 +69,6 @@ @article{WeisEtal16 @article{ClanEtal19, author = {R Clancy and I F Ilyas and J Lin}, - date-added = {2025-08-02 00:49:40 -0400}, - date-modified = {2025-08-02 00:49:40 -0400}, journal = {Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)}, pages = {39--46}, title = {Scalable knowledge graph construction from text collections}, @@ -88,8 +76,6 @@ @article{ClanEtal19 @article{MartiEtal18, author = {J L Martinez-Rodrigues and I Lopez-Arevalo and A B Rios-Alvarado}, - date-added = {2025-08-02 00:49:20 -0400}, - date-modified = {2025-08-02 00:49:20 -0400}, journal = {Expert Systems with Applications}, pages = {339--355}, title = {{OpenIE}-based approach for knowledge graph construction from text}, @@ -98,8 +84,6 @@ @article{MartiEtal18 @article{AgraEtal22, author = {G Agrawal and Y Deng and J Park and H Liu and Y-C Chen}, - date-added = {2025-08-02 00:49:05 -0400}, - date-modified = {2025-08-02 00:49:05 -0400}, journal = {Information}, number = {11}, pages = {doi.org/10.3390/info13110526},