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@book{aikenMultipleRegressionTesting1991,
title = {Multiple Regression: {{Testing}} and Interpreting Interactions},
shorttitle = {Multiple {{Regression}}},
author = {Aiken, Leona S. and West, Stephen G.},
date = {1991},
eprint = {LcWLUyXcmnkC},
eprinttype = {googlebooks},
publisher = {{SAGE}},
abstract = {This successful book, now available in paperback, provides academics and researchers with a clear set of prescriptions for estimating, testing and probing interactions in regression models. Including the latest research in the area, such as Fuller's work on the corrected/constrained estimator, the book is appropriate for anyone who uses multiple regression to estimate models, or for those enrolled in courses on multivariate statistics.},
isbn = {978-0-7619-0712-1},
langid = {english},
pagetotal = {228},
keywords = {Business & Economics / Statistics,Mathematics / Probability & Statistics / General,Mathematics / Study & Teaching,Social Science / Research,Social Science / Statistics}
}
@article{baronModeratorMediatorVariable1986,
title = {The Moderator\textendash Mediator Variable Distinction in Social Psychological Research: {{Conceptual}}, Strategic, and Statistical Considerations},
shorttitle = {The Moderator\textendash Mediator Variable Distinction in Social Psychological Research},
author = {Baron, Reuben M. and Kenny, David A.},
date = {1986},
journaltitle = {Journal of Personality and Social Psychology},
volume = {51},
number = {6},
pages = {1173--1182},
publisher = {{American Psychological Association}},
location = {{US}},
issn = {1939-1315(Electronic),0022-3514(Print)},
doi = {10.1037/0022-3514.51.6.1173},
abstract = {In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators. (46 ref) (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
keywords = {Experimentation,Independent Variables,Social Psychology,Statistical Analysis},
file = {/Users/solomonkurz/Zotero/storage/6BJI6J7Z/Baron and Kenny - 1986 - The moderator–mediator variable distinction in soc.pdf;/Users/solomonkurz/Zotero/storage/IRBXZAKN/1987-13085-001.html}
}
@article{bauerProbingInteractionsFixed2005,
title = {Probing Interactions in Fixed and Multilevel Regression: Inferential and Graphical Techniques},
shorttitle = {Probing {{Interactions}} in {{Fixed}} and {{Multilevel Regression}}},
author = {Bauer, Daniel J. and Curran, Patrick J.},
date = {2005-07-01},
journaltitle = {Multivariate Behavioral Research},
volume = {40},
number = {3},
eprint = {26794689},
eprinttype = {pmid},
pages = {373--400},
publisher = {{Routledge}},
issn = {0027-3171},
doi = {10.1207/s15327906mbr4003_5},
url = {https://www.tandfonline.com/doi/pdf/10.1207/s15327906mbr4003_5?needAccess=true},
urldate = {2020-12-09},
abstract = {Many important research hypotheses concern conditional relations in which the effect of one predictor varies with the value of another. Such relations are commonly evaluated as multiplicative interactions and can be tested in both fixed- and random-effects regression. Often, these interactive effects must be further probed to fully explicate the nature of the conditional relation. The most common method for probing interactions is to test simple slopes at specific levels of the predictors. A more general method is the Johnson-Neyman (J-N) technique. This technique is not widely used, however, because it is currently limited to categorical by continuous interactions in fixed-effects regression and has yet to be extended to the broader class of random-effects regression models. The goal of our article is to generalize the J-N technique to allow for tests of a variety of interactions that arise in both fixed- and random-effects regression. We review existing methods for probing interactions, explicate the analytic expressions needed to expand these tests to a wider set of conditions, and demonstrate the advantages of the J-N technique relative to simple slopes with three empirical examples.},
annotation = {\_eprint: https://doi.org/10.1207/s15327906mbr4003\_5},
file = {/Users/solomonkurz/Zotero/storage/VAXIAYKZ/Bauer and Curran - 2005 - Probing Interactions in Fixed and Multilevel Regre.pdf;/Users/solomonkurz/Zotero/storage/XNALDDEM/s15327906mbr4003_5.html}
}
@incollection{bemWritingEmpiricalJournal1987,
title = {Writing the Empirical Journal Article},
booktitle = {The Complete Academic: {{A}} Practical Guide for the Beginning Social Scientist},
author = {Bem, Daryl J},
editor = {Zanna, Mark P. and Darley, John M.},
date = {1987},
pages = {171--201},
publisher = {{Lawrence Erlbaum Associates}},
langid = {english},
file = {/Users/solomonkurz/Zotero/storage/Z5MQB8NC/Bem - Writing the Empirical Journal Article.pdf}
}
@online{BetterBibTeXZotero2020,
title = {Better {{BibTeX}} for Zotero},
author = {Heyns, Emiliano},
date = {2020},
url = {https://retorque.re/zotero-better-bibtex/},
urldate = {2020-05-19}
}
@online{BibTeX2020,
title = {{{BibTeX}}},
date = {2020},
url = {http://www.bibtex.org/},
urldate = {2020-05-19},
file = {/Users/solomonkurz/Zotero/storage/PMDJYC3M/www.bibtex.org.html}
}
@article{blavaan2021,
title = {Efficient {{Bayesian}} Structural Equation Modeling in {{Stan}}},
author = {Merkle, Edgar C. and Fitzsimmons, Ellen and Uanhoro, James and Goodrich, Ben},
date = {2021},
journaltitle = {Journal of Statistical Software},
volume = {100},
number = {6},
pages = {1--22},
doi = {10.18637/jss.v100.i06}
}
@manual{bmlm2017,
type = {manual},
title = {{{bmlm}}: {{Bayesian}} Multilevel Mediation},
author = {Vuorre, Matti},
date = {2017},
url = {https://cran.r-project.org/package=bmlm}
}
@article{bolgerCausalProcessesPsychology2019,
title = {Causal Processes in Psychology Are Heterogeneous},
author = {Bolger, Niall and Zee, Katherine S. and Rossignac-Milon, Maya and Hassin, Ran R.},
date = {2019},
journaltitle = {Journal of Experimental Psychology: General},
volume = {148},
number = {4},
pages = {601--618},
publisher = {{American Psychological Association}},
location = {{US}},
issn = {1939-2222(Electronic),0096-3445(Print)},
doi = {10.1037/xge0000558},
url = {https://www.researchgate.net/profile/Niall_Bolger/publication/332358948_Causal_processes_in_psychology_are_heterogeneous/links/5cd9b471a6fdccc9ddaa7879/Causal-processes-in-psychology-are-heterogeneous.pdf},
abstract = {All experimenters know that human and animal subjects do not respond uniformly to experimental treatments. Yet theories and findings in experimental psychology either ignore this causal effect heterogeneity or treat it as uninteresting error. This is the case even when data are available to examine effect heterogeneity directly, in within-subjects designs where experimental effects can be examined subject by subject. Using data from four repeated-measures experiments, we show that effect heterogeneity can be modeled readily, that its discovery presents exciting opportunities for theory and methods, and that allowing for it in study designs is good research practice. This evidence suggests that experimenters should work from the assumption that causal effects are heterogeneous. Such a working assumption will be of particular benefit, given the increasing diversity of subject populations in psychology. (PsycINFO Database Record (c) 2019 APA, all rights reserved)},
keywords = {Experimental Methods,Experimental Psychology,Experimenters,Homogeneity of Variance,Models,Repeated Measures,Theory Formulation},
file = {/Users/solomonkurz/Zotero/storage/CAWSDRIT/2019-19962-002.html}
}
@book{brms2020RM,
title = {{{brms}} Reference Manual, {{Version}} 2.14.4},
author = {B\"urkner, Paul-Christian},
date = {2020},
url = {https://CRAN.R-project.org/package=brms/brms.pdf}
}
@book{brms2021RM,
title = {{{brms}} Reference Manual, {{Version}} 2.15.0},
author = {B\"urkner, Paul-Christian},
date = {2021},
url = {https://CRAN.R-project.org/package=brms/brms.pdf}
}
@book{brms2022RM,
title = {{{brms}} Reference Manual, {{Version}} 2.18.0},
author = {B\"urkner, Paul-Christian},
date = {2022},
url = {https://CRAN.R-project.org/package=brms/brms.pdf}
}
@article{Bürkner2022Distributional,
title = {Estimating Distributional Models with Brms},
author = {B\"urkner, Paul-Christian},
date = {2022-04},
url = {https://CRAN.R-project.org/package=brms/vignettes/brms_distreg.html}
}
@article{Bürkner2022HandleMissingValues,
title = {Handle Missing Values with Brms},
author = {B\"urkner, Paul-Christian},
date = {2022-09-19},
url = {https://CRAN.R-project.org/package=brms/vignettes/brms_missings.html},
urldate = {2022-09-26}
}
@article{Bürkner2022Multivariate,
title = {Estimating Multivariate Models with Brms},
author = {B\"urkner, Paul-Christian},
date = {2022-09-19},
url = {https://CRAN.R-project.org/package=brms/vignettes/brms_multivariate.html},
urldate = {2022-09-25}
}
@article{Bürkner2022Parameterization,
title = {Parameterization of Response Distributions in Brms},
author = {B\"urkner, Paul-Christian},
date = {2022-09-19},
url = {https://CRAN.R-project.org/package=brms/vignettes/brms_families.html},
urldate = {2022-09-26}
}
@article{burknerAdvancedBayesianMultilevel2018,
title = {Advanced {{Bayesian}} Multilevel Modeling with the {{R}} Package Brms},
author = {B\"urkner, Paul-Christian},
date = {2018},
journaltitle = {The R Journal},
volume = {10},
number = {1},
pages = {395--411},
doi = {10.32614/RJ-2018-017}
}
@article{burknerBrmsPackageBayesian2017,
title = {{{brms}}: {{An R}} Package for {{Bayesian}} Multilevel Models Using {{Stan}}},
author = {B\"urkner, Paul-Christian},
date = {2017},
journaltitle = {Journal of Statistical Software},
volume = {80},
number = {1},
pages = {1--28},
doi = {10.18637/jss.v080.i01}
}
@article{chapmanClimateChangeDisasters2016,
title = {Climate Change and Disasters: {{How}} Framing Affects Justifications for Giving or Withholding Aid to Disaster Victims},
shorttitle = {Climate {{Change}} and {{Disasters}}},
author = {Chapman, Daniel A. and Lickel, Brian},
date = {2016-01-01},
journaltitle = {Social Psychological and Personality Science},
shortjournal = {Social Psychological and Personality Science},
volume = {7},
number = {1},
pages = {13--20},
publisher = {{SAGE Publications Inc}},
issn = {1948-5506},
doi = {10.1177/1948550615590448},
url = {https://doi.org/10.1177/1948550615590448},
urldate = {2020-12-09},
abstract = {This research examined whether framing a natural disaster as the product of climate change impacts attitudes toward disaster victims and humanitarian relief. Participants (n = 211) read an article about a famine caused by severe droughts, with one condition attributing the droughts to climate change and the other condition made no mention of climate change. All participants then responded to measures of justifications for or against providing aid, attitudes toward the possibility of donating, and climate change beliefs. As predicted, those high in climate change skepticism reported greater justifications for not helping the victims when the disaster was attributed to climate change. Additional moderated mediation analyses showed there was an indirect effect of climate change framing on attitudes toward donating through donation justifications.},
langid = {english},
keywords = {climate change,decision making,disaster relief,motivated reasoning,prosocial behavior}
}
@article{cheongInvestigationMediationalProcesses2003,
title = {Investigation of Mediational Processes Using Parallel Process Latent Growth Curve Modeling},
author = {Cheong, JeeWon and MacKinnon, David P. and Khoo, Siek Toon},
date = {2003-04-01},
journaltitle = {Structural equation modeling : a multidisciplinary journal},
shortjournal = {Struct Equ Modeling},
volume = {10},
number = {2},
eprint = {20157639},
eprinttype = {pmid},
pages = {238},
issn = {1070-5511},
doi = {10.1207/S15328007SEM1002_5},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2821108/},
urldate = {2020-12-15},
abstract = {This study investigated a method to evaluate mediational processes using latent growth curve modeling. The mediator and the outcome measured across multiple time points were viewed as 2 separate parallel processes. The mediational process was defined as the independent variable influencing the growth of the mediator, which, in turn, affected the growth of the outcome. To illustrate modeling procedures, empirical data from a longitudinal drug prevention program, Adolescents Training and Learning to Avoid Steroids, were used. The program effects on the growth of the mediator and the growth of the outcome were examined first in a 2-group structural equation model. The mediational process was then modeled and tested in a parallel process latent growth curve model by relating the prevention program condition, the growth rate factor of the mediator, and the growth rate factor of the outcome.},
pmcid = {PMC2821108},
file = {/Users/solomonkurz/Zotero/storage/MC5II9KU/Cheong et al. - 2003 - Investigation of Mediational Processes Using Paral.pdf}
}
@book{cohenAppliedMultipleRegression2003,
title = {Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences},
author = {Cohen, Jacob and Cohen, Patricia and West, Stephen G. and Aiken, Leona S.},
date = {2003},
edition = {3rd edition},
publisher = {{Lawrence Erlbaum Associates}},
doi = {10.4324/9780203774441},
abstract = {This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual},
isbn = {978-0-203-77444-1},
langid = {english},
file = {/Users/solomonkurz/Zotero/storage/4L78KZC3/9780203774441.html}
}
@article{cohenMultipleRegressionGeneral1968,
title = {Multiple Regression as a General Data-Analytic System},
author = {Cohen, Jacob},
date = {1968},
journaltitle = {Psychological Bulletin},
volume = {70},
pages = {426--443},
publisher = {{American Psychological Association}},
location = {{US}},
issn = {1939-1455(Electronic),0033-2909(Print)},
doi = {10.1037/h0026714},
abstract = {Presents techniques for using multiple regression (MR) as a general variance-accounting procedure of great flexibility, power, and fidelity to research aims in both manipulative and observational psychological research. The identity of MR and fixed-model analysis of variance/covariance (AV/ACV) is sketched. This requires an exposition of means of expressing nominal scale (qualitative) data as independent variables in MR. Attention is given to methods for handling interactions, curvilinearity, missing data, and covariates, for either uncorrelated or correlated independent variables in MR. The relative roles of AV/ACV and MR in data analysis are described, and the practical advantages of the latter are set forth. (16 ref.) (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
issue = {6, Pt.1},
keywords = {Statistical Analysis},
file = {/Users/solomonkurz/Zotero/storage/WTMR4TW6/Cohen - 1968 - Multiple regression as a general data-analytic sys.pdf;/Users/solomonkurz/Zotero/storage/CW6GYJ94/1969-06106-001.html}
}
@article{coleTestingMediationalModels2003,
title = {Testing Mediational Models with Longitudinal Data: {{Questions}} and Tips in the Use of Structural Equation Modeling},
shorttitle = {Testing {{Mediational Models With Longitudinal Data}}},
author = {Cole, David A. and Maxwell, Scott E.},
date = {2003},
journaltitle = {Journal of Abnormal Psychology},
volume = {112},
number = {4},
pages = {558--577},
publisher = {{American Psychological Association}},
location = {{US}},
issn = {1939-1846(Electronic),0021-843X(Print)},
doi = {10.1037/0021-843X.112.4.558},
abstract = {R. M. Baron and D. A. Kenny (1986; see record 1987-13085-001) provided clarion conceptual and methodological guidelines for testing mediational models with cross-sectional data. Graduating from cross-sectional to longitudinal designs enables researchers to make more rigorous inferences about the causal relations implied by such models. In this transition, misconceptions and erroneous assumptions are the norm. First, we describe some of the questions that arise (and misconceptions that sometimes emerge) in longitudinal tests of mediational models. We also provide a collection of tips for structural equation modeling (SEM) of mediational processes. Finally, we suggest a series of 5 steps when using SEM to test mediational processes in longitudinal designs: testing the measurement model, testing for added components, testing for omitted paths, testing the stationarity assumption, and estimating the mediational effects. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
keywords = {Methodology,Models,Statistical Measurement,Structural Equation Modeling},
file = {/Users/solomonkurz/Zotero/storage/J2CCV9C3/Cole and Maxwell - 2003 - Testing Mediational Models With Longitudinal Data.pdf;/Users/solomonkurz/Zotero/storage/8HG6GXSJ/2003-10098-004.html}
}
@article{cummingNewStatisticsWhy2014,
title = {The New Statistics: {{Why}} and How},
shorttitle = {The {{New Statistics}}},
author = {Cumming, Geoff},
date = {2014-01-01},
journaltitle = {Psychological Science},
shortjournal = {Psychol Sci},
volume = {25},
number = {1},
pages = {7--29},
publisher = {{SAGE Publications Inc}},
issn = {0956-7976},
doi = {10.1177/0956797613504966},
url = {https://journals.sagepub.com/doi/pdf/10.1177/0956797613504966},
urldate = {2020-05-21},
abstract = {We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data-analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.},
file = {/Users/solomonkurz/Zotero/storage/UJMRBZGC/Cumming - 2014 - The New Statistics Why and How.pdf}
}
@book{darlingtonRegressionAnalysisLinear2017,
title = {Regression Analysis and Linear Models: Concepts, Applications, and Implementation},
shorttitle = {Regression {{Analysis}} and {{Linear Models}}},
author = {Darlington, Richard B. and Hayes, Andrew F.},
date = {2017},
publisher = {{Guilford Press}},
url = {https://www.guilford.com/books/Regression-Analysis-and-Linear-Models/Darlington-Hayes/9781462521135/reviews},
urldate = {2020-12-09},
abstract = {Emphasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics.},
isbn = {978-1-4625-2113-5},
langid = {american},
file = {/Users/solomonkurz/Zotero/storage/ELHV37CA/reviews.html}
}
@article{dawsonModerationManagementResearch2014,
title = {Moderation in Management Research: {{What}}, Why, When, and How},
shorttitle = {Moderation in {{Management Research}}},
author = {Dawson, Jeremy F.},
date = {2014-03-01},
journaltitle = {Journal of Business and Psychology},
shortjournal = {J Bus Psychol},
volume = {29},
number = {1},
pages = {1--19},
issn = {1573-353X},
doi = {10.1007/s10869-013-9308-7},
url = {https://doi.org/10.1007/s10869-013-9308-7},
urldate = {2020-12-10},
abstract = {Many theories in management, psychology, and other disciplines rely on moderating variables: those which affect the strength or nature of the relationship between two other variables. Despite the near-ubiquitous nature of such effects, the methods for testing and interpreting them are not always well understood. This article introduces the concept of moderation and describes how moderator effects are tested and interpreted for a series of model types, beginning with straightforward two-way interactions with Normal outcomes, moving to three-way and curvilinear interactions, and then to models with non-Normal outcomes including binary logistic regression and Poisson regression. In particular, methods of interpreting and probing these latter model types, such as simple slope analysis and slope difference tests, are described. It then gives answers to twelve frequently asked questions about testing and interpreting moderator effects.},
langid = {english},
file = {/Users/solomonkurz/Zotero/storage/LB7WXR6V/Dawson - 2014 - Moderation in Management Research What, Why, When.pdf}
}
@article{dawsonProbingThreewayInteractions2006,
title = {Probing Three-Way Interactions in Moderated Multiple Regression: {{Development}} and Application of a Slope Difference Test},
shorttitle = {Probing Three-Way Interactions in Moderated Multiple Regression},
author = {Dawson, Jeremy F. and Richter, Andreas W.},
date = {2006},
journaltitle = {Journal of Applied Psychology},
volume = {91},
number = {4},
pages = {917--926},
publisher = {{American Psychological Association}},
location = {{US}},
issn = {1939-1854(Electronic),0021-9010(Print)},
doi = {10.1037/0021-9010.91.4.917},
abstract = {Researchers often use 3-way interactions in moderated multiple regression analysis to test the joint effect of 3 independent variables on a dependent variable. However, further probing of significant interaction terms varies considerably and is sometimes error prone. The authors developed a significance test for slope differences in 3-way interactions and illustrate its importance for testing psychological hypotheses. Monte Carlo simulations revealed that sample size, magnitude of the slope difference, and data reliability affected test power. Application of the test to published data yielded detection of some slope differences that were undetected by alternative probing techniques and led to changes of results and conclusions. The authors conclude by discussing the test's applicability for psychological research. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
keywords = {Interpersonal Interaction,Multiple Regression},
file = {/Users/solomonkurz/Zotero/storage/II9CIEQI/2006-08435-013.html}
}
@article{earpReplicationFalsificationCrisis2015,
title = {Replication, Falsification, and the Crisis of Confidence in Social Psychology},
author = {Earp, Brian D. and Trafimow, David},
date = {2015},
journaltitle = {Frontiers in Psychology},
shortjournal = {Front. Psychol.},
volume = {6},
publisher = {{Frontiers}},
issn = {1664-1078},
doi = {10.3389/fpsyg.2015.00621},
url = {https://www.frontiersin.org/articles/10.3389/fpsyg.2015.00621/full},
urldate = {2020-12-15},
abstract = {The (latest) ``crisis in confidence'' in social psychology has generated much heated discussion about the importance of replication, including how such replication should be carried out as well as interpreted by scholars in the field. What does it mean if a replication attempt ``fails''\textemdash does it mean that the original results, or the theory that predicted them, have been falsified? And how should ``failed'' replications affect our belief in the validity of the original research? In this paper, we consider the ``replication'' debate from a historical and philosophical perspective, and provide a conceptual analysis of both replication and falsification as they pertain to this important discussion. Along the way, we introduce a Bayesian framework for assessing ``failed'' replications in terms of how they should affect our confidence in purported findings.},
langid = {english},
keywords = {crisis of replicability,Falsification,Philosophy of science,Psychology,Replication,Social},
file = {/Users/solomonkurz/Zotero/storage/WDR3FQKG/Earp and Trafimow - 2015 - Replication, falsification, and the crisis of conf.pdf}
}
@book{enders2022applied,
title = {Applied Missing Data Analysis},
author = {Enders, Craig K},
date = {2022},
edition = {Second Edition},
publisher = {{Guilford Press}},
url = {http://www.appliedmissingdata.com/},
isbn = {978-1-4625-4986-3}
}
@article{gabry2019visualization,
title = {Visualization in {{Bayesian}} Workflow},
author = {Gabry, Jonah and Simpson, Daniel and Vehtari, Aki and Betancourt, Michael and Gelman, Andrew},
date = {2019},
journaltitle = {Journal of the Royal Statistical Society: Series A (Statistics in Society)},
volume = {182},
number = {2},
pages = {389--402},
publisher = {{Wiley Online Library}},
doi = {10.1111/rssa.12378},
url = {https://arxiv.org/abs/1709.01449}
}
@online{gabryGraphicalPosteriorPredictive2019,
title = {Graphical Posterior Predictive Checks Using the Bayesplot Package},
author = {Gabry, Jonah},
date = {2019-11-29},
url = {https://CRAN.R-project.org/package=bayesplot/vignettes/graphical-ppcs.html}
}
@article{gabryVisualMCMC2022,
title = {Visual {{MCMC}} Diagnostics Using the {{bayesplot}} Package},
author = {Gabry, Jonah and Modr\'ak, Martin},
date = {2022-11-16},
url = {https://cran.r-project.org/web/packages/bayesplot/vignettes/visual-mcmc-diagnostics.html}
}
@article{galakCorrectingFailuresReplicate2012,
title = {Correcting the Past: {{Failures}} to Replicate Psi},
shorttitle = {Correcting the Past},
author = {Galak, Jeff and LeBoeuf, Robyn A. and Nelson, Leif D. and Simmons, Joseph P.},
date = {2012},
journaltitle = {Journal of Personality and Social Psychology},
volume = {103},
number = {6},
pages = {933--948},
publisher = {{American Psychological Association}},
location = {{US}},
issn = {1939-1315(Electronic),0022-3514(Print)},
doi = {10.1037/a0029709},
abstract = {Across 7 experiments (N = 3,289), we replicate the procedure of Experiments 8 and 9 from Bem (2011), which had originally demonstrated retroactive facilitation of recall. We failed to replicate that finding. We further conduct a meta-analysis of all replication attempts of these experiments and find that the average effect size (d = 0.04) is no different from 0. We discuss some reasons for differences between the results in this article and those presented in Bem (2011). (PsycInfo Database Record (c) 2020 APA, all rights reserved)},
keywords = {Effect Size (Statistical),Experimentation,Experimenter Bias,Extrasensory Perception,Parapsychological Phenomena,Precognition,Sensation Seeking},
file = {/Users/solomonkurz/Zotero/storage/MS5AAIGF/Galak et al. - 2012 - Correcting the past Failures to replicate psi.pdf;/Users/solomonkurz/Zotero/storage/WPSDCWL9/2012-23134-001.html}
}
@article{gelman2006difference,
title = {The Difference between ``Significant'' and ``Not Significant'' Is Not Itself Statistically Significant},
author = {Gelman, Andrew and Stern, Hal},
date = {2006},
journaltitle = {The American Statistician},
volume = {60},
number = {4},
pages = {328--331},
publisher = {{Taylor \& Francis}},
doi = {10.1198/000313006X152649},
url = {https://www.tandfonline.com/doi/pdf/10.1198/000313006X152649?needAccess=true}
}
@article{gelmanConnectionVaryingTreatment2015,
title = {The Connection between Varying Treatment Effects and the Crisis of Unreplicable Research: {{A Bayesian}} Perspective},
shorttitle = {The {{Connection Between Varying Treatment Effects}} and the {{Crisis}} of {{Unreplicable Research}}},
author = {Gelman, Andrew},
date = {2015-02-01},
journaltitle = {Journal of Management},
shortjournal = {Journal of Management},
volume = {41},
number = {2},
pages = {632--643},
publisher = {{SAGE Publications Inc}},
issn = {0149-2063},
doi = {10.1177/0149206314525208},
url = {https://doi.org/10.1177/0149206314525208},
urldate = {2020-12-09},
abstract = {We connect the replication crisis in social science to the default model of constant effects coupled with the flawed statistical approach of null hypothesis significance testing and the related problems arising from the default model of constant treatment effects. We argue that Bayesian modeling of interactions could lead to a general improvement in the communication and understanding of research results. Moving to Bayesian methods (or, more generally, multilevel approaches that incorporate external information) offers an opportunity for introspection into how social scientists interact with social phenomena to produce knowledge.},
langid = {english},
keywords = {hierarchical modeling,multilevel modeling,null hypothesis significance testing,p values,regression analysis,variation},
file = {/Users/solomonkurz/Zotero/storage/B6KDSRN2/Gelman - 2015 - The Connection Between Varying Treatment Effects a.pdf}
}
@book{gelmanDataAnalysisUsing2006,
title = {Data Analysis Using Regression and Multilevel/Hierarchical Models},
author = {Gelman, Andrew and Hill, Jennifer},
date = {2006},
series = {Analytical {{Methods}} for {{Social Research}}},
publisher = {{Cambridge University Press}},
location = {{Cambridge}},
doi = {10.1017/CBO9780511790942},
url = {https://www.cambridge.org/core/books/data-analysis-using-regression-and-multilevelhierarchical-models/32A29531C7FD730C3A68951A17C9D983},
urldate = {2020-09-17},
abstract = {Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.},
isbn = {978-0-521-86706-1},
file = {/Users/solomonkurz/Zotero/storage/KFY9IC96/32A29531C7FD730C3A68951A17C9D983.html}
}
@article{gelmanPowerCalculationsAssessing2014,
title = {Beyond Power Calculations: {{Assessing}} Type {{S}} (Sign) and Type {{M}} (Magnitude) Errors},
shorttitle = {Beyond {{Power Calculations}}},
author = {Gelman, Andrew and Carlin, John},
date = {2014-11-01},
journaltitle = {Perspectives on Psychological Science},
shortjournal = {Perspect Psychol Sci},
volume = {9},
number = {6},
pages = {641--651},
publisher = {{SAGE Publications Inc}},
issn = {1745-6916},
doi = {10.1177/1745691614551642},
url = {https://journals.sagepub.com/doi/pdf/10.1177/1745691614551642},
urldate = {2020-12-08},
abstract = {Statistical power analysis provides the conventional approach to assess error rates when designing a research study. However, power analysis is flawed in that a narrow emphasis on statistical significance is placed as the primary focus of study design. In noisy, small-sample settings, statistically significant results can often be misleading. To help researchers address this problem in the context of their own studies, we recommend design calculations in which (a) the probability of an estimate being in the wrong direction (Type S [sign] error) and (b) the factor by which the magnitude of an effect might be overestimated (Type M [magnitude] error or exaggeration ratio) are estimated. We illustrate with examples from recent published research and discuss the largest challenge in a design calculation: coming up with reasonable estimates of plausible effect sizes based on external information.},
file = {/Users/solomonkurz/Zotero/storage/TI8P2XDM/Gelman and Carlin - 2014 - Beyond Power Calculations Assessing Type S (Sign).pdf}
}
@article{gelmanRsquaredBayesianRegression2019,
title = {R-Squared for {{Bayesian}} Regression Models},
author = {Gelman, Andrew and Goodrich, Ben and Gabry, Jonah and Vehtari, Aki},
date = {2019-07-03},
journaltitle = {The American Statistician},
shortjournal = {The American Statistician},
volume = {73},
number = {3},
pages = {307--309},
issn = {0003-1305, 1537-2731},
doi = {10.1080/00031305.2018.1549100},
url = {https://www.tandfonline.com/doi/full/10.1080/00031305.2018.1549100},
urldate = {2020-05-16},
langid = {english}
}
@book{grolemundDataScience2017,
title = {R for Data Science},
author = {Grolemund, Garrett and Wickham, Hadley},
date = {2017},
publisher = {{O'Reilly}},
url = {https://r4ds.had.co.nz}
}
@incollection{hamakerWhyResearchersShould2012,
title = {Why Researchers Should Think "within-Person": {{A}} Paradigmatic Rationale},
shorttitle = {Why Researchers Should Think "within-Person"},
booktitle = {Handbook of Research Methods for Studying Daily Life},
author = {Hamaker, Ellen L.},
date = {2012},
pages = {43--61},
publisher = {{The Guilford Press}},
location = {{New York, NY, US}},
url = {https://www.guilford.com/books/Handbook-of-Research-Methods-for-Studying-Daily-Life/Mehl-Conner/9781462513055},
abstract = {This chapter presents reasoning for taking an alternative research approach to the study of processes that unfold within individuals over time as part of their daily lives. To this end I focus on three issues. First, I present a brief historical account that shows the large-sample approach is not necessarily the only appropriate research approach in psychology. Second, I discuss the limitations of this approach, specifically, if our interest is in studying psychological processes that take place within individuals. Finally, I discuss several alternatives to the standard large-sample approach that allow us to take a closer and more detailed look at the processes as they are occurring in daily life. (PsycINFO Database Record (c) 2019 APA, all rights reserved)},
isbn = {978-1-60918-747-7 978-1-60918-749-1},
keywords = {Cognitive Processes,Experiences (Events),Experimental Psychologists,Experimentation,History,Methodology,Personality Processes},
file = {/Users/solomonkurz/Zotero/storage/7IAKF3TS/2012-05165-003.html}
}
@article{hayesIndexTestLinear2015,
title = {An Index and Test of Linear Moderated Mediation},
author = {Hayes, Andrew F.},
date = {2015-01-02},
journaltitle = {Multivariate Behavioral Research},
volume = {50},
number = {1},
eprint = {26609740},
eprinttype = {pmid},
pages = {1--22},
publisher = {{Routledge}},
issn = {0027-3171},
doi = {10.1080/00273171.2014.962683},
url = {https://doi.org/10.1080/00273171.2014.962683},
urldate = {2020-12-08},
abstract = {I describe a test of linear moderated mediation in path analysis based on an interval estimate of the parameter of a function linking the indirect effect to values of a moderator\textemdash a parameter that I call the index of moderated mediation. This test can be used for models that integrate moderation and mediation in which the relationship between the indirect effect and the moderator is estimated as linear, including many of the models described by Edwards and Lambert (2007) and Preacher, Rucker, and Hayes (2007) as well as extensions of these models to processes involving multiple mediators operating in parallel or in serial. Generalization of the method to latent variable models is straightforward. Three empirical examples describe the computation of the index and the test, and its implementation is illustrated using Mplus and the PROCESS macro for SPSS and SAS.},
annotation = {\_eprint: https://doi.org/10.1080/00273171.2014.962683},
file = {/Users/solomonkurz/Zotero/storage/NIBZDRUR/00273171.2014.html}
}
@book{hayesIntroductionMediationModeration2018,
title = {Introduction to Mediation, Moderation, and Conditional Process Analysis: {{A}} Regression-Based Approach},
shorttitle = {Introduction to {{Mediation}}, {{Moderation}}, and {{Conditional Process Analysis}}},
author = {Hayes, Andrew F},
date = {2018},
edition = {Second edition},
publisher = {{The Guilford Press}},
url = {https://www.guilford.com/books/Introduction-to-Mediation-Moderation-and-Conditional-Process-Analysis/Andrew-Hayes/9781462534654},
urldate = {2020-11-27},
isbn = {978-1-4625-3465-4},
langid = {american}
}
@book{hayesSAGESourcebookAdvanced2008,
title = {The {{SAGE}} Sourcebook of Advanced Data Analysis Methods for Communication Research},
author = {Hayes, Andrew F. and Slater, Michael D. and Snyder, Leslie B.},
date = {2008},
url = {https://us.sagepub.com/en-us/nam/the-sage-sourcebook-of-advanced-data-analysis-methods-for-communication-research/book228339},
urldate = {2020-12-15},
isbn = {978-1-4129-2790-1},
langid = {english},
file = {/Users/solomonkurz/Zotero/storage/G48E36RK/book228339.html}
}
@book{hayesStatisticalMethodsCommunication2005,
title = {Statistical Methods for Communication Science},
author = {Hayes, Andrew F.},
date = {2005},
publisher = {{Routledge}},
doi = {10.4324/9781410613707},
url = {https://www.taylorfrancis.com/books/9781410613707},
urldate = {2020-12-09},
abstract = {Statistical Methods for Communication Science is the only statistical methods volume currently available that focuses exclusively on statistics in communication},
isbn = {978-1-4106-1370-7},
langid = {english},
file = {/Users/solomonkurz/Zotero/storage/TJJR5HKK/9781410613707.html}
}
@book{healyDataVisualization2018,
title = {Data Visualization: {{A}} Practical Introduction},
author = {Healy, Kieran},
date = {2018},
publisher = {{Princeton University Press}},
url = {https://socviz.co/},
isbn = {978-0-691-18161-5}
}
@online{HomeTwitterb,
title = {Home / {{Twitter}}},
url = {https://twitter.com/home},
urldate = {2020-12-13},
langid = {english},
organization = {{Twitter}}
}
@software{ibmcorporationIBMSPSSStatistics2020,
title = {{{IBM SPSS Statistics}} for {{Windows}}},
author = {{IBM Corporation}},
date = {2020},
url = {https://www.ibm.com/products/spss-statistics},
version = {27.0}
}
@article{imaiGeneralApproachCausal2010,
title = {A General Approach to Causal Mediation Analysis},
author = {Imai, Kosuke and Keele, Luke and Tingley, Dustin},
date = {2010},
journaltitle = {Psychological Methods},
volume = {15},
number = {4},
pages = {309--334},
publisher = {{American Psychological Association}},
location = {{US}},
issn = {1939-1463(Electronic),1082-989X(Print)},
doi = {10.1037/a0020761},
abstract = {Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the inability to specify the key identification assumption, and the difficulty of extending the framework to nonlinear models. In this article, we propose an alternative approach that overcomes these limitations. Our approach is general because it offers the definition, identification, estimation, and sensitivity analysis of causal mediation effects without reference to any specific statistical model. Further, our approach explicitly links these 4 elements closely together within a single framework. As a result, the proposed framework can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and various types of outcome variables. The general definition and identification result also allow us to develop sensitivity analysis in the context of commonly used models, which enables applied researchers to formally assess the robustness of their empirical conclusions to violations of the key assumption. We illustrate our approach by applying it to the Job Search Intervention Study. We also offer easy-to-use software that implements all our proposed methods. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
keywords = {Causal Analysis,Statistical Measurement,Structural Equation Modeling},
file = {/Users/solomonkurz/Zotero/storage/HFD8KHU5/Imai et al. - 2010 - A general approach to causal mediation analysis.pdf;/Users/solomonkurz/Zotero/storage/MLP9FTTB/2010-21388-001.html}
}
@book{jaccardInteractionEffectsMultiple2003,
title = {Interaction Effects in Multiple Regression},
author = {Jaccard, James and Turrisi, Robert},
date = {2003},
edition = {2nd edition},
eprint = {n0pIZTQqvmIC},
eprinttype = {googlebooks},
publisher = {{Sage Publications}},
url = {https://dx.doi.org/10.4135/9781412984522},
abstract = {Interaction Effects in Multiple Regression has provided students and researchers with a readable and practical introduction to conducting analyses of interaction effects in the context of multiple regression. The new addition will expand the coverage on the analysis of three way interactions in multiple regression analysis.Learn more about "The Little Green Book" - QASS Series! Click Here},
isbn = {978-0-7619-2742-6},
langid = {english},
pagetotal = {108},
keywords = {Mathematics / Probability & Statistics / Regression Analysis,Psychology / Statistics,Social Science / Research,Social Science / Statistics}
}
@misc{kallioinen2021DetectingAndDiagnosing,
title = {Detecting and Diagnosing Prior and Likelihood Sensitivity with Power-Scaling},
author = {Kallioinen, Noa and Paananen, Topi and B\"urkner, Paul-Christian and Vehtari, Aki},
date = {2021},
publisher = {{arXiv}},
doi = {10.48550/ARXIV.2107.14054},
url = {https://arxiv.org/abs/2107.14054},
copyright = {arXiv.org perpetual, non-exclusive license},
keywords = {FOS: Computer and information sciences,Methodology (stat.ME)}
}
@misc{kayExtractingVisualizingTidy2021,
title = {Extracting and Visualizing Tidy Draws from Brms Models},
author = {Kay, Matthew},
date = {2021-12-30},
url = {https://mjskay.github.io/tidybayes/articles/tidy-brms.html},
urldate = {2022-09-25}
}
@article{kelley2012effect,
title = {On Effect Size},
author = {Kelley, Ken and Preacher, Kristopher J},
date = {2012},
journaltitle = {Psychological methods},
volume = {17},
number = {2},
pages = {137},
publisher = {{American Psychological Association}},
doi = {10.1037/a0028086},
url = {https://www3.nd.edu/~kkelley/publications/articles/Kelley_and_Preacher_Psychological_Methods_2012.pdf}
}
@report{kleinManyLabsFailure2019,
title = {Many {{Labs}} 4: {{Failure}} to Replicate Mortality Salience Effect with and without Original Author Involvement},
shorttitle = {Many {{Labs}} 4},
author = {Klein, Richard A. and Cook, Corey L. and Ebersole, Charles R. and Vitiello, Christine and Nosek, Brian A. and Chartier, Christopher R. and Christopherson, Cody D. and Clay, Samuel and Collisson, Brian and Crawford, Jarret and Cromar, Ryan and Vidamuerte, DeVere and Gardiner, Gwendolyn and Gosnell, Courtney and Grahe, Jon and Hall, Calvin and Joy-Gaba, Jennifer and Legg, Angela M. and Levitan, Carmel and Mancini, Anthony and Manfredi, Dylan and Miller, Jason Michael and Nave, Gideon and Redford, Liz and Schlitz, Ilaria and Schmidt, Kathleen and Skorinko, Jeanine and Storage, Daniel and Swanson, Trevor and van Swol, Lyn and Vaughn, Leigh Ann and Ratliff, Kate},
date = {2019-12-11T18:08:31},
institution = {{PsyArXiv}},
doi = {10.31234/osf.io/vef2c},
url = {https://psyarxiv.com/vef2c/},
urldate = {2020-12-15},
abstract = {Interpreting a failure to replicate is complicated by the fact that the failure could be due to the original finding being a false positive, unrecognized moderating influences between the original and replication procedures, or faulty implementation of the procedures in the replication. One strategy to maximize replication quality is involving the original authors in study design. We (N = 21 Labs and N = 2,220 participants) experimentally tested whether original author involvement improved replicability of a classic finding from Terror Management Theory (Greenberg et al., 1994). Our results were non-diagnostic of whether original author involvement improves replicability because we were unable to replicate the finding under any conditions. This suggests that the original finding was either a false positive or the conditions necessary to obtain it are not yet understood or no longer exist. Data, materials, analysis code, preregistration, and supplementary documents can be found on the OSF page: https://osf.io/8ccnw/},
keywords = {many labs,Meta-science,metascience,mortality salience,psychology,replication,Social and Behavioral Sciences,Social and Personality Psychology,terror management theory},
file = {/Users/solomonkurz/Zotero/storage/MJULFQKA/Klein et al. - 2019 - Many Labs 4 Failure to Replicate Mortality Salien.pdf}
}
@article{kruschkeBayesianNewStatistics2018,
title = {{{The Bayesian New Statistics}}: {{Hypothesis}} Testing, Estimation, Meta-Analysis, and Power Analysis from a {{Bayesian}} Perspective},
shorttitle = {The {{Bayesian New Statistics}}},
author = {Kruschke, John K. and Liddell, Torrin M.},
date = {2018-02-01},
journaltitle = {Psychonomic Bulletin \& Review},
shortjournal = {Psychon Bull Rev},
volume = {25},
number = {1},
pages = {178--206},
issn = {1531-5320},
doi = {10.3758/s13423-016-1221-4},
url = {https://link.springer.com/content/pdf/10.3758/s13423-016-1221-4.pdf},
urldate = {2020-05-18},
abstract = {In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed ``the New Statistics'' (Cumming 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.},
langid = {english},
file = {/Users/solomonkurz/Zotero/storage/SRKQT967/Kruschke and Liddell - 2018 - The Bayesian New Statistics Hypothesis testing, e.pdf}
}
@book{kruschkeDoingBayesianData2015,
title = {Doing {{Bayesian}} Data Analysis: {{A}} Tutorial with {{R}}, {{JAGS}}, and {{Stan}}},
author = {Kruschke, John K.},
date = {2015},
publisher = {{Academic Press}},
url = {https://sites.google.com/site/doingbayesiandataanalysis/}
}
@book{kurzDoingBayesianDataAnalysis2023,
title = {Doing {{Bayesian}} Data Analysis in Brms and the Tidyverse},
author = {Kurz, A. Solomon},
date = {2023-01},
edition = {Version 1.1.0},
url = {https://bookdown.org/content/3686/}
}
@book{kurzStatisticalRethinkingBrms2023,
title = {Statistical {{Rethinking}} with {{brms}}, {{ggplot2}}, and the {{tidyverse}}},
author = {Kurz, A. Solomon},
date = {2023-01},
edition = {version 1.3.0},
url = {https://bookdown.org/content/3890/}
}
@book{kurzStatisticalRethinkingSecondEd2023,
title = {Statistical {{Rethinking}} with Brms, {{ggplot2}}, and the Tidyverse: {{Second Edition}}},
author = {Kurz, A. Solomon},
date = {2023-01},
edition = {version 0.4.0},
url = {https://bookdown.org/content/4857/}
}
@book{leglerBroadeningYourStatistical2019,
title = {Broadening Your Statistical Horizons: {{Generalized}} Linear Models and Multilevel Models},
author = {Legler, Julie and Roback, Paul},
date = {2019},
url = {https://bookdown.org/roback/bookdown-bysh/}
}
@article{little2007new,
title = {New Developments in Latent Variable Panel Analyses of Longitudinal Data},
author = {Little, Todd D and Preacher, Kristopher J and Selig, James P and Card, Noel A},
date = {2007},
journaltitle = {International journal of behavioral development},
volume = {31},
number = {4},
pages = {357--365},
publisher = {{Sage Publications Sage UK: London, England}},
doi = {10.1177/0165025407077757}
}
@book{loo2022RM,
title = {{{loo}} Reference Manual, {{Version}} 2.5.1},
author = {Gabry, Jonah},
date = {2022-03-23},
url = {https://CRAN.R-project.org/package=loo/loo.pdf}
}
@article{maxwellBiasCrosssectionalAnalyses2007,
title = {Bias in Cross-Sectional Analyses of Longitudinal Mediation.},
author = {Maxwell, Scott E. and Cole, David A.},
date = {2007},
journaltitle = {Psychological Methods},
shortjournal = {Psychological Methods},
volume = {12},
number = {1},
pages = {23--44},
issn = {1939-1463, 1082-989X},
doi = {10.1037/1082-989X.12.1.23},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/1082-989X.12.1.23},
urldate = {2020-12-15},
abstract = {Most empirical tests of mediation utilize cross-sectional data despite the fact that mediation consists of causal processes that unfold over time. The authors considered the possibility that longitudinal mediation might occur under either of two different models of change: (a) an autoregressive model or (b) a random effects model. For both models, the authors demonstrated that cross-sectional approaches to mediation typically generate substantially biased estimates of longitudinal parameters even under the ideal conditions when mediation is complete. In longitudinal models where variable M completely mediates the effect of X on Y, cross-sectional estimates of the direct effect of X on Y, the indirect effect of X on Y through M, and the proportion of the total effect mediated by M are often highly misleading.},
langid = {english},
file = {/Users/solomonkurz/Zotero/storage/U4SBETCY/Maxwell and Cole - 2007 - Bias in cross-sectional analyses of longitudinal m.pdf}
}
@article{maxwellBiasCrosssectionalAnalyses2011,
title = {Bias in Cross-Sectional Analyses of Longitudinal Mediation: {{Partial}} and Complete Mediation under an Autoregressive Model},
shorttitle = {Bias in {{Cross-Sectional Analyses}} of {{Longitudinal Mediation}}},
author = {Maxwell, Scott E. and Cole, David A. and Mitchell, Melissa A.},
date = {2011-09-30},
journaltitle = {Multivariate Behavioral Research},
volume = {46},
number = {5},
eprint = {26736047},
eprinttype = {pmid},
pages = {816--841},
publisher = {{Routledge}},
issn = {0027-3171},
doi = {10.1080/00273171.2011.606716},
url = {https://doi.org/10.1080/00273171.2011.606716},
urldate = {2020-12-15},
abstract = {Maxwell and Cole (2007) showed that cross-sectional approaches to mediation typically generate substantially biased estimates of longitudinal parameters in the special case of complete mediation. However, their results did not apply to the more typical case of partial mediation. We extend their previous work by showing that substantial bias can also occur with partial mediation. In particular, cross-sectional analyses can imply the existence of a substantial indirect effect even when the true longitudinal indirect effect is zero. Thus, a variable that is found to be a strong mediator in a cross-sectional analysis may not be a mediator at all in a longitudinal analysis. In addition, we show that very different combinations of longitudinal parameter values can lead to essentially identical cross-sectional correlations, raising serious questions about the interpretability of cross-sectional mediation data. More generally, researchers are encouraged to consider a wide variety of possible mediation models beyond simple cross-sectional models, including but not restricted to autoregressive models of change.},
annotation = {\_eprint: https://doi.org/10.1080/00273171.2011.606716},
file = {/Users/solomonkurz/Zotero/storage/HLADTNFH/Maxwell et al. - 2011 - Bias in Cross-Sectional Analyses of Longitudinal M.pdf;/Users/solomonkurz/Zotero/storage/BSPA9EFJ/00273171.2011.html}
}
@article{mccabe2018improving,
title = {Improving Present Practices in the Visual Display of Interactions},
author = {McCabe, Connor J and Kim, Dale S and King, Kevin M},
date = {2018},
journaltitle = {Advances in Methods and Practices in Psychological Science},
volume = {1},
number = {2},
pages = {147--165},
publisher = {{Sage Publications Sage CA: Los Angeles, CA}},
doi = {10.1177/2515245917746792}
}
@book{mcelreathStatisticalRethinkingBayesian2015,
title = {Statistical Rethinking: {{A Bayesian}} Course with Examples in {{R}} and {{Stan}}},
author = {McElreath, Richard},
date = {2015},
publisher = {{CRC press}},
url = {https://xcelab.net/rm/statistical-rethinking/}
}
@book{mcelreathStatisticalRethinkingBayesian2020,
title = {Statistical Rethinking: {{A Bayesian}} Course with Examples in {{R}} and {{Stan}}},
shorttitle = {Statistical {{Rethinking}}},
author = {McElreath, Richard},
date = {2020-03-13},
edition = {Second Edition},
publisher = {{CRC Press}},
url = {https://xcelab.net/rm/statistical-rethinking/},
abstract = {Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work. The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding. The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses. Features Integrates working code into the main text Illustrates concepts through worked data analysis examples Emphasizes understanding assumptions and how assumptions are reflected in code Offers more detailed explanations of the mathematics in optional sections Presents examples of using the dagitty R package to analyze causal graphs Provides the rethinking R package on the author's website and on GitHub},
isbn = {978-0-429-63914-2},
langid = {english},
pagetotal = {575},
keywords = {Mathematics / Probability & Statistics / General}
}
@article{Merkle2018blavaan,
title = {{{blavaan}}: {{Bayesian}} Structural Equation Models via Parameter Expansion},
author = {Merkle, Edgar C. and Rosseel, Yves},
date = {2018},
journaltitle = {Journal of Statistical Software},
volume = {85},
number = {4},
pages = {1--30},
doi = {10.18637/jss.v085.i04}
}
@article{miocevicPowerBayesianMediation2017,
title = {Power in {{Bayesian}} Mediation Analysis for Small Sample Research},
author = {Mio\v{c}evi\'c, Milica and MacKinnon, David P. and Levy, Roy},
date = {2017-09-03},
journaltitle = {Structural Equation Modeling: A Multidisciplinary Journal},
volume = {24},
number = {5},
eprint = {29662296},
eprinttype = {pmid},
pages = {666--683},
publisher = {{Routledge}},
issn = {1070-5511},
doi = {10.1080/10705511.2017.1312407},
url = {https://doi.org/10.1080/10705511.2017.1312407},
urldate = {2020-12-08},
abstract = {Bayesian methods have the potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, \& Sin, 2015; Yuan \& MacKinnon, 2009). This article compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N {$\leq$} 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N {$\geq$} 100 and the effects were small, N {$<$} 60 and the effects were large, and N {$<$} 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results.},
keywords = {Bayesian statistics,power,single mediator model,small sample sizes},
annotation = {\_eprint: https://doi.org/10.1080/10705511.2017.1312407},
file = {/Users/solomonkurz/Zotero/storage/LSMUP4A4/Miočević et al. - 2017 - Power in Bayesian Mediation Analysis for Small Sam.pdf;/Users/solomonkurz/Zotero/storage/D5U2U4AI/10705511.2017.html}
}
@article{mitchellComparisonCrosssectionalSequential2013,
title = {A Comparison of the Cross-Sectional and Sequential Designs When Assessing Longitudinal Mediation},
author = {Mitchell, Melissa A. and Maxwell, Scott E.},
date = {2013-05-01},
journaltitle = {Multivariate Behavioral Research},
volume = {48},
number = {3},
eprint = {26741846},
eprinttype = {pmid},
pages = {301--339},
publisher = {{Routledge}},
issn = {0027-3171},
doi = {10.1080/00273171.2013.784696},
url = {https://doi.org/10.1080/00273171.2013.784696},
urldate = {2020-12-15},
abstract = {Mediational studies are often of interest in psychology because they explore the underlying relationship between 2 constructs. Previous research has shown that cross-sectional designs are prone to biased estimates of longitudinal mediation parameters. The sequential design has become a popular alternative to the cross-sectional design for assessing mediation. This design is a compromise between the cross-sectional and longitudinal designs because it incorporates time in the model but has only 1 measurement each of X, M, and Y. As such, this design follows the recommendation of the MacArthur group approach, which stresses the importance of multiple waves of data for studying mediation. These 2 designs were compared to see whether the sequential design assesses longitudinal mediation more accurately than the cross-sectional design. Specifically, analytic expressions are derived for the bias of estimated direct and indirect effects as calculated from the sequential design when the actual mediational process follows a longitudinal autoregressive model. It was found that, in general, the sequential design does not assess longitudinal mediation more accurately than the cross-sectional design. As a result, neither design can be depended on to assess longitudinal mediation accurately.},
annotation = {\_eprint: https://doi.org/10.1080/00273171.2013.784696},
file = {/Users/solomonkurz/Zotero/storage/S7K26BLR/00273171.2013.html}
}
@article{muthenCausalEffectsMediation2015,
title = {Causal Effects in Mediation Modeling: {{An}} Introduction with Applications to Latent Variables},
shorttitle = {Causal {{Effects}} in {{Mediation Modeling}}},
author = {Muth\'en, Bengt and Asparouhov, Tihomir},
date = {2015-01-02},
journaltitle = {Structural Equation Modeling: A Multidisciplinary Journal},
volume = {22},
number = {1},
pages = {12--23},
publisher = {{Routledge}},
issn = {1070-5511},
doi = {10.1080/10705511.2014.935843},
url = {https://doi.org/10.1080/10705511.2014.935843},
urldate = {2020-12-15},
abstract = {Causal inference in mediation analysis offers counterfactually based causal definitions of direct and indirect effects, drawing on research by Robins, Greenland, Pearl, VanderWeele, Vansteelandt, Imai, and others. This type of mediation effect estimation is little known and seldom used among analysts using structural equation modeling (SEM). The aim of this article is to describe the new analysis opportunities in a way that is accessible to SEM analysts and show examples of how to perform the analyses. An application is presented with an extension to a latent mediator measured with multiple indicators.},
keywords = {counterfactuals,randomization,structural equation modeling},
annotation = {\_eprint: https://doi.org/10.1080/10705511.2014.935843},
file = {/Users/solomonkurz/Zotero/storage/6A8GXCSJ/Muthén and Asparouhov - 2015 - Causal Effects in Mediation Modeling An Introduct.pdf;/Users/solomonkurz/Zotero/storage/JW964RNK/10705511.2014.html}
}
@book{navarroLearningStatistics2019,
title = {Learning Statistics with {{R}}},
author = {Navarro, Danielle},
date = {2019},
url = {https://learningstatisticswithr.com},
langid = {english}
}
@article{navarroPaths2020,
title = {Paths in Strange Spaces: {{A}} Comment on Preregistration},
author = {Navarro, Danielle},
date = {2020-09-23},
doi = {10.31234/osf.io/wxn58}
}
@article{nelsonPsychologyRenaissance2018,
title = {Psychology's Renaissance},
author = {Nelson, Leif D. and Simmons, Joseph and Simonsohn, Uri},
date = {2018-01-04},
journaltitle = {Annual Review of Psychology},
shortjournal = {Annu. Rev. Psychol.},
volume = {69},
number = {1},
pages = {511--534},
issn = {0066-4308, 1545-2085},
doi = {10.1146/annurev-psych-122216-011836},
url = {http://www.annualreviews.org/doi/10.1146/annurev-psych-122216-011836},
urldate = {2020-12-15},
abstract = {In 2010\textendash 2012, a few largely coincidental events led experimental psychologists to realize that their approach to collecting, analyzing, and reporting data made it too easy to publish false-positive findings. This sparked a period of methodological reflection that we review here and call Psychology's Renaissance. We begin by describing how psychologists' concerns with publication bias shifted from worrying about file-drawered studies to worrying about p-hacked analyses. We then review the methodological changes that psychologists have proposed and, in some cases, embraced. In describing how the renaissance has unfolded, we attempt to describe different points of view fairly but not neutrally, so as to identify the most promising paths forward. In so doing, we champion disclosure and preregistration, express skepticism about most statistical solutions to publication bias, take positions on the analysis and interpretation of replication failures, and contend that meta-analytical thinking increases the prevalence of false positives. Our general thesis is that the scientific practices of experimental psychologists have improved dramatically.},
langid = {english},
file = {/Users/solomonkurz/Zotero/storage/W3DDP4JG/Nelson et al. - 2018 - Psychology's Renaissance.pdf}
}
@article{pashlerEditorsIntroductionSpecial2012,
title = {Editors' Introduction to the Special Section on Replicability in Psychological Science: {{A}} Crisis of Confidence?},
shorttitle = {Editors' {{Introduction}} to the {{Special Section}} on {{Replicability}} in {{Psychological Science}}},
author = {Pashler, Harold and Wagenmakers, Eric\textendash Jan},
date = {2012-11-01},
journaltitle = {Perspectives on Psychological Science},
shortjournal = {Perspect Psychol Sci},
volume = {7},
number = {6},
pages = {528--530},
publisher = {{SAGE Publications Inc}},
issn = {1745-6916},
doi = {10.1177/1745691612465253},
url = {https://doi.org/10.1177/1745691612465253},
urldate = {2020-12-15},
langid = {english},
file = {/Users/solomonkurz/Zotero/storage/S443T3NY/Pashler and Wagenmakers - 2012 - Editors’ Introduction to the Special Section on Re.pdf}
}
@book{pengMasteringSoftwareDevelopment2020,
title = {Mastering Software Development in {{R}}},
author = {Peng, Roger D. and Kross, Sean and Anderson, Brooke},
date = {2020-12-20},
url = {https://github.com/rdpeng/RProgDA},
urldate = {2023-01-24}
}
@book{pengProgrammingDataScience2022,
title = {R Programming for Data Science},
author = {Peng, Roger D.},
date = {2022-05-31},
url = {https://bookdown.org/rdpeng/rprogdatascience/}
}
@article{preacherAddressingModeratedMediation2007,
title = {Addressing Moderated Mediation Hypotheses: {{Theory}}, Methods, and Prescriptions},
shorttitle = {Addressing {{Moderated Mediation Hypotheses}}},
author = {Preacher, Kristopher J. and Rucker, Derek D. and Hayes, Andrew F.},
date = {2007-06-29},
journaltitle = {Multivariate Behavioral Research},
volume = {42},
number = {1},
eprint = {26821081},
eprinttype = {pmid},
pages = {185--227},
publisher = {{Routledge}},
issn = {0027-3171},
doi = {10.1080/00273170701341316},
url = {https://doi.org/10.1080/00273170701341316},
urldate = {2020-12-14},