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%% This BibTeX bibliography file was created using BibDesk.
%% https://bibdesk.sourceforge.io/
%% Created for Tobias Gerstenberg at 2022-05-17 13:56:04 -0700
%% Saved with string encoding Unicode (UTF-8)
@article{franke2019bayesian,
author = {Franke, Michael and Roettger, Timo B},
date-added = {2022-05-17 13:53:37 -0700},
date-modified = {2022-05-17 13:56:03 -0700},
journal = {PsyArXiv},
title = {Bayesian regression modeling (for factorial designs): A tutorial},
url = {https://psyarxiv.com/cdxv3},
year = {2019}}
@article{winter2012phonetic,
author = {Winter, Bodo and Grawunder, Sven},
date-added = {2022-05-17 13:49:30 -0700},
date-modified = {2022-05-17 13:49:30 -0700},
journal = {Journal of Phonetics},
number = {6},
pages = {808--815},
publisher = {Elsevier},
title = {The phonetic profile of Korean formal and informal speech registers},
volume = {40},
year = {2012}}
@article{kruschke2014doing,
author = {Kruschke, John},
date-added = {2022-05-10 12:01:18 -0700},
date-modified = {2022-05-10 12:01:18 -0700},
publisher = {Academic Press},
title = {Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan},
year = {2014}}
@book{mcelreath2020statistical,
author = {McElreath, Richard},
date-added = {2022-05-10 11:58:58 -0700},
date-modified = {2022-05-10 11:58:58 -0700},
publisher = {Chapman and Hall/CRC},
title = {Statistical rethinking: A Bayesian course with examples in R and Stan},
year = {2020}}
@book{kurz2020statistical,
author = {A. Solomon Kurz},
date-added = {2022-05-10 11:54:53 -0700},
date-modified = {2022-05-10 11:55:05 -0700},
doi = {10.5281/zenodo.4080013},
month = oct,
publisher = {Zenodo},
title = {{ASKurz/Statistical\_Rethinking\_with\_brms\_ggplot2\_an d\_the\_tidyverse: Correct multinomial and Gaussian process models}},
url = {https://doi.org/10.5281/zenodo.4080013},
year = 2020,
bdsk-url-1 = {https://doi.org/10.5281/zenodo.4080013}}
@book{kurz2022doingbayesian,
author = {Kurz, A. Solomon},
date-added = {2022-05-10 11:53:27 -0700},
date-modified = {2022-05-10 11:53:42 -0700},
edition = {Version 1.0.0},
month = {5},
title = {Doing {{Bayesian}} data analysis in brms and the tidyverse},
url = {https://bookdown.org/content/3686/},
year = {2022},
bdsk-url-1 = {https://bookdown.org/content/3686/}}
@book{fox2018r,
author = {Fox, John and Weisberg, Sanford},
date-added = {2021-03-11 11:13:32 -0800},
date-modified = {2021-03-11 11:13:32 -0800},
publisher = {Sage publications},
title = {An R companion to applied regression},
year = {2018}}
@book{xie2015,
address = {Boca Raton, Florida},
author = {Yihui Xie},
date-added = {2020-03-15 11:03:58 -0700},
date-modified = {2020-03-15 11:03:58 -0700},
edition = {2nd},
note = {ISBN 978-1498716963},
publisher = {Chapman and Hall/CRC},
title = {Dynamic Documents with {R} and knitr},
url = {http://yihui.name/knitr/},
year = {2015},
bdsk-url-1 = {http://yihui.name/knitr/}}
@article{liddell2018analyzin,
author = {Liddell, Torrin M. and Kruschke, John K.},
date-added = {2020-03-15 11:03:58 -0700},
date-modified = {2020-03-15 11:03:58 -0700},
doi = {10.1016/j.jesp.2018.08.009},
issn = {0022-1031},
journal = {Journal of Experimental Social Psychology},
month = {Nov},
pages = {328--348},
publisher = {Elsevier BV},
title = {Analyzing ordinal data with metric models: What could possibly go wrong?},
url = {http://dx.doi.org/10.1016/j.jesp.2018.08.009},
volume = {79},
year = {2018},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.jesp.2018.08.009}}
@article{burkner2019ordinal,
abstract = {Ordinal variables, although extremely common in psychology, are almost exclusively analyzed with statistical models that falsely assume them to be metric. This practice can lead to distorted effect-size estimates, inflated error rates, and other problems. We argue for the application of ordinal models that make appropriate assumptions about the variables under study. In this Tutorial, we first explain the three major classes of ordinal models: the cumulative, sequential, and adjacent-category models. We then show how to fit ordinal models in a fully Bayesian framework with the R package brms, using data sets on opinions about stem-cell research and time courses of marriage. The appendices provide detailed mathematical derivations of the models and a discussion of censored ordinal models. Compared with metric models, ordinal models provide better theoretical interpretation and numerical inference from ordinal data, and we recommend their widespread adoption in psychology.},
author = {B{\"u}rkner, Paul-Christian and Vuorre, Matti},
date-added = {2020-03-15 11:03:58 -0700},
date-modified = {2020-03-15 11:03:58 -0700},
file = {/Users/tobi/Dropbox (Personal)/work/papers/B{\"u}rkner and Vuorre - 2019 - Ordinal Regression Models in Psychology A Tutoria.pdf},
journal = {Advances in Methods and Practices in Psychological Science},
language = {en},
pages = {25},
title = {Ordinal Regression Models in Psychology: A Tutorial},
year = {2019}}
@article{wagenmakers2010bayesiana,
abstract = {In the field of cognitive psychology, the p-value hypothesis test has established a stranglehold on statistical reporting. This is unfortunate, as the p-value provides at best a rough estimate of the evidence that the data provide for the presence of an experimental effect. An alternative and arguably more appropriate measure of evidence is conveyed by a Bayesian hypothesis test, which prefers the model with the highest average likelihood. One of the main problems with this Bayesian hypothesis test, however, is that it often requires relatively sophisticated numerical methods for its computation. Here we draw attention to the Savage\textendash{}Dickey density ratio method, a method that can be used to compute the result of a Bayesian hypothesis test for nested models and under certain plausible restrictions on the parameter priors. Practical examples demonstrate the method's validity, generality, and flexibility.},
author = {Wagenmakers, Eric-Jan and Lodewyckx, Tom and Kuriyal, Himanshu and Grasman, Raoul},
date-added = {2020-03-15 11:03:58 -0700},
date-modified = {2020-03-15 11:03:58 -0700},
doi = {10.1016/j.cogpsych.2009.12.001},
file = {/Users/tobi/Dropbox (Personal)/work/papers/Wagenmakers et al. - 2010 - Bayesian hypothesis testing for psychologists A t.pdf},
issn = {00100285},
journal = {Cognitive Psychology},
language = {en},
month = may,
number = {3},
pages = {158--189},
shorttitle = {Bayesian Hypothesis Testing for Psychologists},
title = {Bayesian Hypothesis Testing for Psychologists: {{A}} Tutorial on the {{Savage}}\textendash{{Dickey}} Method},
volume = {60},
year = {2010},
bdsk-url-1 = {https://doi.org/10.1016/j.cogpsych.2009.12.001}}
@article{anscombe1973american,
author = {Anscombe, FJ},
date-added = {2020-03-15 11:03:58 -0700},
date-modified = {2020-03-15 11:03:58 -0700},
journal = {Graphs in Statistical Analysis},
number = {1},
pages = {17--21},
title = {The American Statistician 27},
year = {1973}}
@article{barr2013random-e,
author = {Dale J. Barr and Roger Levy and Christoph Scheepers and Harry J. Tily},
date-added = {2020-03-15 11:03:58 -0700},
date-modified = {2020-03-15 11:03:58 -0700},
doi = {10.1016/j.jml.2012.11.001},
journal = {Journal of Memory and Language},
month = {apr},
number = {3},
pages = {255--278},
publisher = {Elsevier {BV}},
title = {Random effects structure for confirmatory hypothesis testing: Keep it maximal},
url = {https://doi.org/10.1016\%2Fj.jml.2012.11.001},
volume = {68},
year = 2013,
bdsk-url-1 = {https://doi.org/10.1016%5C%2Fj.jml.2012.11.001},
bdsk-url-2 = {https://doi.org/10.1016/j.jml.2012.11.001}}
@article{gelman2000type,
abstract = {In classical statistics, the signi cance of comparisons (e.g., 1 ? 2) is calibrated using the Type 1 error rate, relying on the assumption that the true di erence is zero, which makes no sense in many applications. We set up a more relevant framework in which a true comparison can be positive or negative, and, based on the data, you can state \textbackslash{} 1 {$>$} 2 with con dence," \textbackslash{} 2 {$>$} 1 with con dence," or \textbackslash{}no claim with con dence." We focus on the Type S (for sign) error, which occurs when you claim \textbackslash{} 1 {$>$} 2 with con dence" when 2 {$>$} 1 (or vice-versa). We compute the Type S error rates for classical and Bayesian con dence statements and nd that classical Type S error rates can be extremely high (up to 50\%). Bayesian con dence statements are conservative, in the sense that claims based on 95\% posterior intervals have Type S error rates between 0 and 2.5\%. For multiple comparison situations, the conclusions are similar.},
author = {Gelman, Andrew and Tuerlinckx, Francis},
date-added = {2019-03-20 15:51:49 -0700},
date-modified = {2019-03-20 15:51:49 -0700},
doi = {10.1007/s001800000040},
file = {/Users/tobi/Dropbox (Personal)/work/papers/Gelman and Tuerlinckx - 2000 - Type S error rates for classical and Bayesian sing.pdf},
issn = {09434062},
journal = {Computational Statistics},
language = {en},
month = sep,
number = {3},
pages = {373-390},
title = {Type {{S}} Error Rates for Classical and {{Bayesian}} Single and Multiple Comparison Procedures},
volume = {15},
year = {2000},
bdsk-url-1 = {https://doi.org/10.1007/s001800000040}}
@article{fiedler2011mediation,
author = {Fiedler, Klaus and Schott, Malte and Meiser, Thorsten},
date-added = {2019-03-14 00:09:47 -0700},
date-modified = {2019-03-14 00:09:47 -0700},
journal = {Journal of Experimental Social Psychology},
number = {6},
pages = {1231--1236},
publisher = {Elsevier},
title = {What mediation analysis can (not) do},
volume = {47},
year = {2011}}
@article{baron1986moderator,
author = {Baron, Reuben M and Kenny, David A},
date-added = {2019-03-14 00:09:47 -0700},
date-modified = {2019-03-14 00:09:47 -0700},
journal = {Journal of Personality and Social Psychology},
number = {6},
pages = {1173--1182},
publisher = {American Psychological Association},
title = {The moderator--mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.},
volume = {51},
year = {1986}}
@article{preacher2004spss,
author = {Preacher, Kristopher J and Hayes, Andrew F},
date-added = {2019-03-14 00:09:47 -0700},
date-modified = {2019-03-14 00:09:47 -0700},
journal = {Behavior Research Methods, Instruments \& Computers},
number = {4},
pages = {717--731},
publisher = {Springer},
title = {SPSS and SAS procedures for estimating indirect effects in simple mediation models},
volume = {36},
year = {2004}}
@article{shrout2002mediation,
author = {Shrout, Patrick E and Bolger, Niall},
date-added = {2019-03-14 00:09:47 -0700},
date-modified = {2019-03-14 00:09:47 -0700},
journal = {Psychological Methods},
number = {4},
pages = {422},
publisher = {American Psychological Association},
title = {Mediation in experimental and nonexperimental studies: New procedures and recommendations.},
volume = {7},
year = {2002}}
@article{mackinnon2007mediationa,
abstract = {Mediating variables are prominent in psychological theory and research. A mediating variable transmits the effect of an independent variable on a dependent variable. Differences between mediating variables and confounders, moderators, and covariates are outlined. Statistical methods to assess mediation and modern comprehensive approaches are described. Future directions for mediation analysis are discussed.},
author = {MacKinnon, David P. and Fairchild, Amanda J. and Fritz, Matthew S.},
date-added = {2019-03-14 00:09:47 -0700},
date-modified = {2019-03-14 00:09:47 -0700},
doi = {10.1146/annurev.psych.58.110405.085542},
file = {/Users/tobi/Dropbox (Personal)/work/papers/MacKinnon et al. - 2007 - Mediation Analysis 2.pdf},
issn = {0066-4308, 1545-2085},
journal = {Annual Review of Psychology},
language = {en},
month = jan,
number = {1},
pages = {593-614},
title = {Mediation {{Analysis}}},
volume = {58},
year = {2007},
bdsk-url-1 = {https://doi.org/10.1146/annurev.psych.58.110405.085542}}
@article{cleveland1979robust,
author = {Cleveland, William S},
date-added = {2018-11-19 14:11:56 -0800},
date-modified = {2018-11-19 14:11:56 -0800},
journal = {Journal of the American statistical association},
number = {368},
pages = {829--836},
publisher = {Taylor \& Francis},
title = {Robust locally weighted regression and smoothing scatterplots},
volume = {74},
year = {1979}}
@book{gelman2006data,
author = {Gelman, Andrew and Hill, Jennifer},
date-added = {2018-11-14 10:49:47 -0800},
date-modified = {2018-11-14 10:49:47 -0800},
publisher = {Cambridge university press},
title = {Data analysis using regression and multilevel/hierarchical models},
year = {2006}}
@article{chambers1983graphical,
author = {Chambers, John M and Cleveland, William S and Kleiner, Beat and Tukey, Paul A},
date-added = {2018-11-05 19:55:10 -0800},
date-modified = {2018-11-05 19:55:10 -0800},
journal = {Wadsworth, Belmont, CA},
title = {Graphical methods for data analysis},
year = {1983}}