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@book{mackinnon2008intro,
title={Introduction to statistical mediation analysis},
author={MacKinnon, David Peter},
year={2008},
publisher={Routledge}
}
@book{intelbook1999,
title={Psychology of Intelligence Analysis},
author={Heuer Jr., Richard J.},
year={1999},
publisher={Center for the Study of Intelligence}
}
@book{Iacobucci2008book,
title={Mediation Analysis},
author={Iacobucci, Dawn},
year={2008},
publisher={SAGE [Kindle Version]}
}
@book{Hayes2013book,
title={Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach},
author={Hayes, Andrew F.},
year={2013},
publisher={The Guilford Press}
}
@article{Goodman2016a,
author = {Goodman, Steven N},
doi = {10.1126/science.aaf5406},
volume = {352},
number = {6290},
journal = {Science},
pages = {1180-1181},
title = {{Aligning statistical and scientific reasoning}},
year = {2016}
}
@article{Serang2017,
author = {Serang, S and Jacobucci, R and Brimhall, Kim C. and Grimm, Kevin J.},
doi = {10.1080/10705511.2017.1311775},
journal = {Structural Equation Modeling: A Multidisciplinary Journal},
issn = {1070-5511},
pages = {1-12},
title = {{Exploratory Mediation Analysis via Regularization}},
year = {2017}
}
@article{Deykin1986,
author = {Deykin, E. Y. and Levy, J. C. and Wells, V.},
journal = {American Journal of Public Health},
pages = {178-182},
title = {{Adolescent Depression, Alcohol and Drug Abuse}},
year = {1986},
volume = {77},
number = {2}
}
@article{Manasse2009,
author = {Manasse, M. E. and Ganem, N. M.},
journal = {Journal of Criminal Justice},
pages = {371-378},
title = {{Victimization as a cause of delinquency: The role of depression and gender}},
year = {2009},
volume = {37}
}
@article{Hoeppner2017,
author = {Hoeppner, Bettina B. and Hoeppner, Susanne S. and Abroms, Lorien C.},
doi = {10.1111/add.13685},
journal = {Addiction},
pages = {673-682},
issn = {13600443},
pmid = {27943511},
title = {{How do text-messaging smoking cessation interventions confer benefit? A multiple mediation analysis of Text2Quit}},
year = {2017}
}
@manual{Stata14,
author = {StataCorp},
title = {{Stata Statistical Software: Release 14}},
institution = {StataCorp LLC},
year = {2015}
}
@article{Montreuil2005,
author = {Montreuil, B and Bendavid, Y and Brophy, J},
journal = {J Can Chir},
volume = {48},
number = {5},
pages = {400-408},
title = {{What is so odd about odds?}},
year = {2005}
}
@article{Efron1996,
author = {Efron, B. and Tibshirani, R.},
journal = {Statistical Science},
pages = {54-77},
title = {{Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy}},
year = {1996},
volume = {1},
number = {1}
}
@article{Prinz2011,
author = {Prinz, F. and Schlange, T. and Asadullah, K.},
journal = {Nature Review Drug Discovery},
pages = {712},
title = {{Believe it or not: how much can we rely on published data on potential drug targets?}},
year = {2011},
volume = {10},
doi = {10.1038/nrd3439-c1}
}
@article{Sobel1982,
author = {Sobel, Michael E.},
journal = {Sociological Methodology},
pages = {290–312},
title = {{Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models}},
year = {1982},
volume = {13}
}
@article{Loken2017,
abstract = {Measurement error adds noise to predictions, increases uncertainty in parameter estimates, and makes it more difficult to discover new phenomena or to distinguish among competing theories. A common view is that any study finding an effect under noisy conditions provides evidence that the underlying effect is particularly strong and robust. Yet, statistical significance conveys very little information when measurements are noisy. In noisy research settings, poor measurement can contribute to exaggerated estimates of effect size. This problem and related misunderstandings are key components in a feedback loop that perpetuates the replication crisis in science.},
author = {Loken, Eric and Gelman, Andrew},
doi = {10.1126/science.aal3618},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/Loken{\_}MeasureError{\_}Rep{\_}2017.pdf:pdf},
isbn = {0036-8075},
issn = {0036-8075},
journal = {Science},
number = {6325},
pages = {584--585},
pmid = {28183939},
title = {{Measurement error and the replication crisis}},
url = {http://www.sciencemag.org/lookup/doi/10.1126/science.aal3618},
volume = {355},
year = {2017}
}
@article{Preacher2011,
author = {Preacher, Kristopher J and Kelley, Ken},
doi = {10.1037/a0022658},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/Preacher{\_}EffectSize{\_}Mediation{\_}2011.pdf:pdf},
keywords = {10,1037,a0022658,consider the case in,doi,dx,effect size,established that,explains some of the,http,indirect effect,mediation,or,org,some regressor,supp,supplemental materials,variance in a criterion,which a researcher has,x},
number = {2},
pages = {93--115},
title = {{Effect Size Measures for Mediation Models : Quantitative Strategies for Communicating Indirect Effects}},
volume = {16},
year = {2011}
}
@article{Muthen1984,
author = {Muth{\'{e}}n, Bengt},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/Muthen{\_}SEMcategorical{\_}1984.pdf:pdf},
journal = {Psychometrika},
number = {1},
pages = {115--132},
title = {{A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indictors}},
volume = {49},
year = {1984}
}
@article{Williamson2013,
abstract = {BACKGROUND: Relative risk is a summary metric that is commonly used in epidemiological investigations. Increasingly, epidemiologists are using log-binomial models to study the impact of a set of predictor variables on a single binary outcome, as they naturally offer relative risks. However, standard statistical software may report failed convergence when attempting to fit log-binomial models in certain settings. The methods that have been proposed in the literature for dealing with failed convergence use approximate solutions to avoid the issue. This research looks directly at the log-likelihood function for the simplest log-binomial model where failed convergence has been observed, a model with a single linear predictor with three levels. The possible causes of failed convergence are explored and potential solutions are presented for some cases. RESULTS: Among the principal causes is a failure of the fitting algorithm to converge despite the log-likelihood function having a single finite maximum. Despite these limitations, log-binomial models are a viable option for epidemiologists wishing to describe the relationship between a set of predictors and a binary outcome where relative risk is the desired summary measure. CONCLUSIONS: Epidemiologists are encouraged to continue to use log-binomial models and advocate for improvements to the fitting algorithms to promote the widespread use of log-binomial models.},
author = {Williamson, T and Eliasziw, M and Fick, G},
doi = {10.1186/1742-7622-10-14},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/AME/Williamson{\_}LogBin{\_}Logistic{\_}2010.pdf:pdf},
issn = {1742-7622},
journal = {Emerging themes in epidemiology},
keywords = {failed convergence,likelihood estimation,log relative risk,log-binomial,logistic regression alternatives,maximum likelihood estimates,method of maximum likelihood,non-convergence,relative risk},
number = {1},
pages = {14},
pmid = {24330636},
title = {{Log-binomial models: exploring failed convergence.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3909339{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {10},
year = {2013}
}
@article{Liu2011,
abstract = {Many diseases commonly associated with aging are now thought to have social and physiologic antecedents in early life. Understanding how the timing of exposure to early life risk factors influences later-life health may illuminate mechanisms driving adult health inequalities and identify possible points for effective interventions. Recognizing chronic diseases as developing across the lifecourse also has implications for the conduct of research on adult risk factors for disease. We review alternative conceptual models that describe how the timing of risk factor exposure relates to the development of disease. We propose some expansions of lifecourse models to improve their relevance for research on adult chronic disease, using the relationship between education and adult cognitive decline and dementia as an example. We discuss the important implications each of the lifecourse conceptual models has on study design, analysis, and interpretation of research on aging and chronic diseases. We summarize several research considerations implied by the lifecourse framework, including: advantages of analyzing change in function rather than onset of impairment; the pervasive challenge of survivor bias; the importance of controlling for possible confounding by early life conditions; and the likely heterogeneity in responses of adults to treatment.},
archivePrefix = {arXiv},
arxivId = {1003.3921v1},
author = {Liu, Sze and Jones, Richard N. and Glymour, M. Maria},
doi = {10.1037/a0031034.Mediation},
eprint = {1003.3921v1},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/Valeri{\_}mediatorInteractions{\_}2013.pdf:pdf},
isbn = {1351-0711},
issn = {03010422},
journal = {Public Health Reviews},
keywords = {Aging,Chronic disease,Dementia,Lifecourse epidemiology,Models},
number = {2},
pages = {489--511},
pmid = {24639598},
title = {{Implications of lifecourse epidemiology for research on determinants of adult disease}},
volume = {33},
year = {2011}
}
@article{Podsakoff2011,
abstract = {Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms ?method? and ?method bias? and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.},
author = {Podsakoff, P.M. and MacKenzie, S.B. and Podsakoff, N.P.},
doi = {10.1146/annurev-psych-120710-100452},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/Podsakoff{\_}Biases{\_}2012.pdf:pdf},
isbn = {0066-4308},
issn = {0066-4308},
journal = {Annual Review of Psychology},
keywords = {common method variance,instrumental variable technique,marker variable,response style biases,technique,unmeasured latent},
number = {1},
pages = {539--569},
pmid = {21838546},
title = {{Sources of method bias in social science research and recommendations on how to control it}},
url = {http://dx.doi.org/10.1146/annurev-psych-120710-100452},
volume = {63},
year = {2011}
}
@article{Bentler2011,
abstract = {This paper summarizes some of the literature on causal effects in mediation analysis. It presents causally-defined direct and indirect effects for continuous, binary, ordinal, nominal, and count variables. The expansion to non-continuous mediators and outcomes offers a broader array of causal mediation analyses than previously considered in structural equation modeling practice. A new result is the ability to handle mediation by a nominal variable. Examples with a binary outcome and a binary, ordinal or nominal mediator are given using Mplus to compute the effects. The causal effects require strong assumptions even in randomized designs, especially sequential ignorability, which is presumably often violated to some extent due to mediator-outcome confounding. To study the effects of violating this assumption, it is shown how a sensitivity analysis can be carried out. This can be used both in planning a new study and in evaluating the results of an existing study.},
author = {Bentler, P. M.},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/Muthen{\_}Med{\_}SEM{\_}2011.pdf:pdf},
journal = {Psychological bulletin},
number = {2},
pages = {238--246},
title = {{Applications of Causally Defined Directd and Indirected effects in mediation analysis using SEM in Mplus}},
volume = {107},
year = {2011}
}
@article{MacKinnon2007a,
abstract = {This article describes a program, PRODCLIN (distribution of the PRODuct Confidence Limits for INdirect effects), written for SAS, SPSS, and R, that computes confidence limits for the product of two normal random variables. The program is important because it can be used to obtain more accurate confidence limits for the indirect effect, as demonstrated in several recent articles (MacKinnon, Lockwood, {\&} Williams, 2004; Pituch, Whittaker, {\&} Stapleton, 2005). Tests of the significance of and confidence limits for indirect effects based on the distribution of the product method have more accurate Type I error rates and more power than other, more commonly used tests. Values for the two paths involved in the indirect effect and their standard errors are entered in the PRODCLIN program, and distribution of the product confidence limits are computed. Several examples are used to illustrate the PRODCLIN program. The PRODCLIN programs in rich text format may be downloaded from www.psychonomic.org/archive.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {MacKinnon, David Peter and Fritz, Matthew S and Williams, Jason and Lockwood, Chondra M},
doi = {10.3758/BF03193007},
eprint = {NIHMS150003},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/McKinnon{\_}distribtionindirect{\_}2007.pdf:pdf},
isbn = {1554-3528(Electronic);1554-351X(Print)},
issn = {1554-351X},
journal = {Behavior Research Methods},
number = {3},
pages = {384--389},
pmid = {17958149},
title = {{Distribution of the product confidence limits for the indirect effect: Program PRODCLIN}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17958149{\%}5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2819369{\%}5Cnhttp://www.springerlink.com/index/10.3758/BF03193007},
volume = {39},
year = {2007}
}
@article{Baron1986a,
abstract = {Baron, R. M., {\&} Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173.},
archivePrefix = {arXiv},
arxivId = {4},
author = {Baron, Reuben M. and Kenny, David a.},
doi = {10.1037/0022-3514.51.6.1173},
eprint = {4},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/Baron-Kenny1986{\_}The moderator-mediator variable distinction.pdf:pdf},
isbn = {0022-3514$\backslash$r1939-1315},
issn = {0022-3514},
journal = {Journal of Personality and Social Psychology},
number = {6},
pages = {1173--1182},
pmid = {3806354},
title = {{The Moderator-Mediator Variable Distinction in Social The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations}},
volume = {51},
year = {1986}
}
@article{Eid2016,
abstract = {This article presents an overview of recent psychometric developments in the area of multimethod measurement, in which we argue that different types of research designs require different types of models. In particular, two types of measurement models for method effects can be distinguished. First, models with a general factor, in which method effects are defined as deviations from a common trait, and second, models for contrasting methods, in which method effects are defined relative to another method but not to a general trait. We argue that the first type of models require a two-level research design (interchangeable methods) whereas the second type of models can be applied to a one-level research design (structurally different methods). Current directions in the uses of these approaches for longitudinal research and multiple-rater studies are described. Keywords},
author = {Eid, M. and Geiser, C. and Koch, T.},
doi = {10.1177/0963721416649624},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/Eid{\_}MethodEffects{\_}20.pdf:pdf},
issn = {0963-7214},
journal = {Current Directions in Psychological Science},
keywords = {1,bi-factor model,convergent validity,ctc,it is one of,m,model,multimethod research,multiple methods in their,multitrait-multimethod analysis,rules in psychology,that researchers should use,the most widely accepted},
number = {4},
pages = {275--280},
title = {{Measuring Method Effects: From Traditional to Design-Oriented Approaches}},
url = {http://cdp.sagepub.com/lookup/doi/10.1177/0963721416649624},
volume = {25},
year = {2016}
}
@article{Wasserstein2016,
abstract = {Additional reading: http://www.nature.com/news/statisticians-issue-warning-over-misuse-of-p-values-1.19503},
archivePrefix = {arXiv},
arxivId = {1011.1669},
author = {Wasserstein, Ronald L. and Lazar, Nicole A.},
doi = {10.1080/00031305.2016.1154108},
eprint = {1011.1669},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/Wasserstein{\_}StatementPValues{\_}2016.pdf:pdf},
isbn = {0003-1305 1537-2731},
issn = {0003-1305},
journal = {The American Statistician},
number = {2},
pages = {129--133},
pmid = {25246403},
publisher = {Taylor {\&} Francis},
title = {{The ASA's Statement on p -Values: Context, Process, and Purpose}},
url = {http://www.tandfonline.com/doi/full/10.1080/00031305.2016.1154108},
volume = {70},
year = {2016}
}
@article{Sackrowitz1999,
abstract = {ISSN: 0003-1305 (Print) 1537-2731 (Online) Journal homepage: http://amstat.tandfonline.com/loi/utas20 p values are extensively reported in practical hypothesis testing situations. Although carefully studied by Dempster and Schatzoff, the stochastic aspect of p values is often neglected. In this expository note we borrow from Demp-ster and Schatzoff to rekindle interest in-and explore the potential usefulness of-understanding the stochastic be-havior of p values. We relate the expected p value (EPV) under the alternative to the more familiar concepts of sig-nificance level and power. We then go on to argue that in cases where it is difficult to evaluate the power function, the EPV can be used as a measure of the performance of a test. EPV's are always easily evaluated or simulated. Different test statistics for the same hypotheses can also be com-pared by means of EPV's. We carry out such a comparison between the two-sample, one-sided Kolmogorov-Smirnov, Mann-Whitney, and t tests, for a variety of underlying dis-tributions. The EPV can also be a valuable tool in sample size determination and in the interpretation of observed p values. We hope to convince practitioners of the usefulness of EPV's.},
author = {Sackrowitz, Harold and Samuel-Cahn, Ester and Sackr{\~{}}witz, Harold},
doi = {10.1080/00031305.1999.10474484doi.org/10.1080/00031305.1999.10474484},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/Sackrowitz{\_}PValuesRandomVariablesExpected{\_}1999.pdf:pdf},
issn = {0003-1305},
journal = {The American Statistician},
keywords = {Comparison of tests,Expected p value,Power,Random p value},
number = {April},
pages = {326--331},
pmid = {12510683},
title = {{P Values as Random Variables—Expected P Values}},
url = {http://amstat.tandfonline.com/action/journalInformation?journalCode=utas20},
volume = {534},
year = {1999}
}
@article{Murdoch2017,
author = {Murdoch, Duncan J and Tsai, Yu-ling and Adcock, James and Urdoch, Duncan J M and Sai, Yu-ling T and Dcock, James A},
doi = {10.1198/000313008X332421},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/Murdoch{\_}PValuesRandomVars{\_}2011.pdf:pdf},
keywords = {ecdf,empirical cumulative distribution function,histograms,hypothesis testing,teaching statistics},
number = {April},
title = {{P-Values are Random Variables P -Values are Random Variables}},
volume = {1305},
year = {2017}
}
@article{MacKinnon2012,
author = {MacKinnon, David Peter and Cox, Matthew C.},
doi = {10.1016/j.jcps.2012.03.009.Commentary},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/McKinnon{\_}commentary{\_}2012.pdf:pdf},
journal = {Journal of Consumer Psychology},
number = {4},
pages = {600--602},
title = {{Commentary on "Mediation Analysis and Categorical Variables: The Final Frontier" by Dawn Iacobucci}},
volume = {22},
year = {2012}
}
@article{MacKinnon2012a,
abstract = {Business theories often specify the mediating mechanisms by which a predictor variable affects an outcome variable. In the last 30 years, investigations of mediating processes have become more widespread with corresponding developments in statistical methods to conduct these tests. The purpose of this article is to provide guidelines for mediation studies by focusing on decisions made prior to the research study that affect the clarity of conclusions from a mediation study, the statistical models for mediation analysis, and methods to improve interpretation of mediation results after the research study. Throughout this article, the importance of a program of experimental and observational research for investigating mediating mechanisms is emphasized.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {MacKinnon, David Peter and Coxe, Stefany and Baraldi, Amanda N.},
doi = {10.1007/s10869-011-9248-z},
eprint = {NIHMS150003},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/MacKinnon{\_}MediationBusiness{\_}2012.pdf:pdf},
isbn = {0889-3268$\backslash$r1573-353X},
issn = {08893268},
journal = {Journal of Business and Psychology},
keywords = {Causal inference,Confidence intervals,Indirect effects,Longitudinal models,Mediation,Moderation,Significance testing},
number = {1},
pages = {1--14},
pmid = {25237213},
title = {{Guidelines for the Investigation of Mediating Variables in Business Research}},
volume = {27},
year = {2012}
}
@article{Iacobucci2012,
abstract = {Many scholars are interested in understanding the process by which an independent variable affects a dependent variable, perhaps in part directly and perhaps in part indirectly, occurring through the activation of a mediator. Researchers are facile at testing for mediation when all the variables are continuous, but a definitive answer had been lacking heretofore as to how to analyze the data when the mediator or dependent variable is categorical. This paper describes the problems that arise as well as the potential solutions. In the end, a solution is recommended that is both optimal in its statistical qualities as well as practical and easily implemented: compute z Mediation. ?? 2012 Society for Consumer Psychology.},
author = {Iacobucci, Dawn},
doi = {10.1016/j.jcps.2012.03.006},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/JCP-mediat-categ.pdf:pdf},
isbn = {1532-7663$\backslash$r1057-7408},
issn = {10577408},
journal = {Journal of Consumer Psychology},
keywords = {Binary variables,Categorical variables,Mediation analysis},
number = {4},
pages = {582--594},
title = {{Mediation analysis and categorical variables: The final frontier}},
volume = {22},
year = {2012}
}
@article{Imai2010a,
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.},
author = {Imai, Kosuke and Keele, Luke and Tingley, Dustin},
doi = {10.1037/a0020761},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/Imai{\_}GenApproach{\_}Mediation{\_}2010.pdf:pdf},
isbn = {1082-989X$\backslash$n1939-1463},
issn = {1939-1463},
journal = {Psychological methods},
keywords = {Algorithms,Causality,Data Interpretation,Linear Models,Models,Sensitivity and Specificity,Social Sciences,Social Sciences: methods,Statistical,causal inference,causal inference is a,causal mechanisms,central goal of social,direct and indirect effects,in,linear structural equation,models,randomized experiments are typically,science research,seen as a gold,sensitivity analysis,this context},
number = {4},
pages = {309--34},
pmid = {20954780},
title = {{A general approach to causal mediation analysis.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20954780},
volume = {15},
year = {2010}
}
@article{Iacobucci2012a,
abstract = {Many scholars are interested in understanding the process by which an independent variable affects a dependent variable, perhaps in part directly and perhaps in part indirectly, occurring through the activation of a mediator. Researchers are facile at testing for mediation when all the variables are continuous, but a definitive answer had been lacking heretofore as to how to analyze the data when the mediator or dependent variable is categorical. This paper describes the problems that arise as well as the potential solutions. In the end, a solution is recommended that is both optimal in its statistical qualities as well as practical and easily implemented: compute z Mediation. ?? 2012 Society for Consumer Psychology.},
author = {Iacobucci, Dawn},
doi = {10.1016/j.jcps.2012.03.006},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/Iacabucci{\_}mediationCategorical{\_}.pdf:pdf},
isbn = {1532-7663$\backslash$r1057-7408},
issn = {10577408},
journal = {Journal of Consumer Psychology},
keywords = {Binary variables,Categorical variables,Mediation analysis},
number = {4},
pages = {582--594},
publisher = {Society for Consumer Psychology},
title = {{Mediation analysis and categorical variables: The final frontier}},
url = {http://dx.doi.org/10.1016/j.jcps.2012.03.006},
volume = {22},
year = {2012}
}
@article{Gonzalez2016,
abstract = {Statistical mediation analysis allows researchers to identify the most important med- iating constructs in the causal process studied. Identifying specific mediators is espe- cially relevant when the hypothesized mediating construct consists of multiple related facets. The general definition of the construct and its facets might relate differently to an outcome. However, current methods do not allow researchers to study the rela- tionships between general and specific aspects of a construct to an outcome simulta- neously. This study proposes a bifactor measurement model for the mediatingconstruct as a way to parse variance and represent the general aspect and specific facets of a construct simultaneously. Monte Carlo simulation results are presented to help determine the properties of mediated effect estimation when the mediator has a bifactor structure and a specific facet of a construct is the true mediator. This study also investigates the conditions when researchers can detect the mediated effect when the multidimensionality of the mediator is ignored and treated as unidimen- sional. Simulation results indicated that the mediation model with a bifactor mediator measurement model had unbiased and adequate power to detect the mediated effect with a sample size greater than 500 and medium a- and b-paths. Also, results indicate that parameter bias and detection of the mediated effect in both the data-generating model and the misspecified model varies as a function of the amount of facet variance represented in the mediation model. This study contributes to the largely unexplored area of measurement issues in statistical mediation analysis. Keywords},
author = {Gonzalez, O. and MacKinnon, David Peter},
doi = {10.1177/0013164416673689},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/Gonzalez{\_}BifactorMediation{\_}2016.pdf:pdf},
issn = {0013-1644},
journal = {Educational and Psychological Measurement},
pages = {1--27},
title = {{A Bifactor Approach to Model Multifaceted Constructs in Statistical Mediation Analysis}},
url = {http://epm.sagepub.com/cgi/doi/10.1177/0013164416673689},
year = {2016}
}
@article{Button2013,
author = {Button, Katherine S and Ioannidis, John P A and Mokrysz, Claire and Nosek, Brian A and Flint, Jonathan and Robinson, Emma S J and Munaf{\`{o}}, Marcus R},
doi = {10.1038/nrn3475},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/Button{\_}PowerFailure{\_}2013.pdf:pdf},
journal = {Nature Reviews: Neuroscience},
number = {May},
pages = {365--376},
publisher = {Nature Publishing Group},
title = {{Power failure: why small sample size undermines the reliability of neuroscience}},
url = {http://dx.doi.org/10.1038/nrn3475},
volume = {14},
year = {2013}
}
@article{Frazier2004,
abstract = {On page 134, line 8, right column, under the heading Checklist for Evaluating Mediation Analyses Using Multiple Regression, the question incorrectly asks, “Was the relation between the predictor and the outcome (Path b) greater than or equal to the relation between the predictor and the mediator (Path a)?” The correct question is “Was the relation between the mediator and the outcome (Path b) greater than or equal to the relation between the predictor and the mediator (Path a)?”},
author = {Frazier, Patricia A. and Tix, Andrew P. and Barron, Kenneth E.},
doi = {10.1037/0022-0167.51.1.115},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/Frazier{\_}moderatorMediator{\_}2004.pdf:pdf},
isbn = {1939-2168},
issn = {1939-2168},
journal = {Journal of Counseling Psychology},
number = {1},
pages = {2004},
pmid = {8306},
title = {{Testing Moderator and Mediator Effects in Counseling Psychology Research}},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/0022-0167.51.1.115},
volume = {51},
year = {2004}
}
@article{Imai2010b,
abstract = {Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines. The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles of intermediate variables that lie in the causal paths between the treatment and outcome variables. In this paper we first prove that under a particular version of sequential ignorability assumption, the average causal mediation effect (ACME) is nonparametrically identified. We compare our identification assumption with those proposed in the literature. Some practical implications of our identification result are also discussed. In particular, the popular estimator based on the linear structural equation model (LSEM) can be interpreted as an ACME estimator once additional parametric assumptions are made. We show that these assumptions can easily be relaxed within and outside of the LSEM framework and propose simple nonparametric estimation strategies. Second, and perhaps most importantly, we propose a new sensitivity analysis that can be easily implemented by applied researchers within the LSEM framework. Like the existing identifying assumptions, the proposed sequential ignorability assumption may be too strong in many applied settings. Thus, sensitivity analysis is essential in order to examine the robustness of empirical findings to the possible existence of an unmeasured confounder. Finally, we apply the proposed methods to a randomized experiment from political psychology. We also make easy-to-use software available to implement the proposed methods.},
archivePrefix = {arXiv},
arxivId = {1011.1079},
author = {Imai, Kosuke and Keele, Luke and Yamamoto, Teppei},
doi = {10.1214/10-STS321},
eprint = {1011.1079},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/Imai{\_}CausalMediation{\_}2010.pdf:pdf},
isbn = {0883-4237},
issn = {0883-4237},
journal = {Statistical Science},
keywords = {Causal inference, causal mediation analysis, direc,and indirect effects,and phrases,causal inference,causal mediation analysis,direct,linear structural equation models,sequential ignorability,unmeasured confounders},
number = {1},
pages = {51--71},
pmid = {24077092},
title = {{Identification, Inference and Sensitivity Analysis for Causal Mediation Effects}},
url = {http://arxiv.org/abs/1011.1079},
volume = {25},
year = {2010}
}
@article{Chang2015a,
abstract = {We attempt to replicate 67 papers published in 13 well-regarded economics journals using author-provided replication files that include both data and code. Some journals in our sample require data and code replication files, and other journals do not require such files. Aside from 6 papers that use confidential data, we obtain data and code replication files for 29 of 35 papers (83{\%}) that are required to provide such files as a condition of publication, compared to 11 of 26 papers (42{\%}) that are not required to provide data and code replication files. We successfully replicate the key qualitative result of 22 of 67 papers (33{\%}) without contacting the authors. Excluding the 6 papers that use confidential data and the 2 papers that use software we do not possess, we replicate 29 of 59 papers (49{\%}) with assistance from the authors. Because we are able to replicate less than half of the papers in our sample even with help from the authors, we assert that economics research is usually not replicable. We conclude with recommendations on improving replication of economics research.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Chang, Andrew C and Li, Phillip},
doi = {10.17016/FEDS.2015.083},
eprint = {arXiv:1011.1669v3},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/Chang{\_}ReproducibilityEconomics{\_}2015.pdf:pdf},
isbn = {9788578110796},
issn = {19362854},
journal = {Finance and Economics Discussion Series},
keywords = {Data and Code Archives,GDP,Gross Domestic Product,Journals,Macroeconomics,National Income and Product Accounts,Publication,Replication,Research},
pages = {1--26},
pmid = {25246403},
title = {{Is Economics Research Replicable? Sixty Published Papers from Thirteen Journals Say "Usually Not"}},
volume = {083},
year = {2015}
}
@article{Boos2011,
abstract = {P-values are useful statistical measures of evidence against a null hypothesis. In contrast to other statistical estimates, however, their sample-to-sample variability is usually not considered or estimated, and therefore not fully appreciated. Via a systematic study of log-scale p-value standard errors, bootstrap prediction bounds, and reproducibility probabilities for future replicate p-values, we show that p-values exhibit surprisingly large variability in typical data situations. In addition to providing context to discussions about the failure of statistical results to replicate, our findings shed light on the relative value of exact p-values vis-a-vis approximate p-values, and indicate that the use of *, **, and *** to denote levels 0.05, 0.01, and 0.001 of statistical significance in subject-matter journals is about the right level of precision for reporting p-values when judged by widely accepted rules for rounding statistical estimates.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Boos, Dennis D. and Stefanski, Leonard A.},
doi = {10.1198/tas.2011.10129},
eprint = {NIHMS150003},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/Boos{\_}PValueReproducibility{\_}2011.pdf:pdf},
isbn = {0940926389998},
issn = {0003-1305},
journal = {The American Statistician},
keywords = {2010,highlighted science,in odds are,interval,it,log p -value,measure of evidence,p -values and ar-,prediction under the litmus,reproducibility probability,s love affair with,s wrong,science fails to face,siegfried,test of replication,the shortcomings of statistics},
number = {4},
pages = {213--221},
pmid = {22690019},
title = {{P-Value Precision and Reproducibility}},
url = {http://dx.doi.org/10.1198/tas.2011.10129{\%}5Cnhttp://www.tandfonline.com/doi/abs/10.1198/tas.2011.10129?src=recsys{\#}.VQyaj-nd9cY{\%}5Cnhttp://www.tandfonline.com/doi/pdf/10.1198/tas.2011.10129},
volume = {65},
year = {2011}
}
@article{Feinberg2012,
abstract = {Iacobucci (2012) provides a conceptually appealing, readily implemented measure to assess mediation for a far wider range of data type combinations than traditional OLS-based analyses permit. Here, we consider potential applications and extensions along several lines, particularly in terms of random utility models, simulation-based estimation, and potential nonlinearities, as well as some methodological and cultural impediments. {\textcopyright} 2012 Society for Consumer Psychology.},
author = {Feinberg, Fred M.},
doi = {10.1016/j.jcps.2012.03.007},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Categorical Mediation/Feinberg{\_}medcat{\_}2012.pdf:pdf},
isbn = {1057-7408},
issn = {10577408},
journal = {Journal of Consumer Psychology},
keywords = {Bayesian statistics,Categorical data analysis,Consumer behavior,Marketing,Mediation analysis,Regression},
number = {4},
pages = {595--598},
publisher = {Society for Consumer Psychology},
title = {{Mediation analysis and categorical variables: Some further frontiers}},
url = {http://dx.doi.org/10.1016/j.jcps.2012.03.007},
volume = {22},
year = {2012}
}
@article{Berger2004,
abstract = {Bayarri, M. J., {\&} Berger, J. O. (2004). The interplay of Bayesian and frequentist analysis. Statistical Science, 58-80.},
author = {Berger, J. O. and Bayarri, M. J.},
doi = {10.1214/088342304000000116},
file = {:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/Berger, Bayarri - 2004 - The Interplay of Bayesian and Frequentist Analysis.pdf:pdf;:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/Berger, Bayarri - 2004 - The Interplay of Bayesian and Frequentist Analysis(2).pdf:pdf},
isbn = {0883-4237},
issn = {0883-4237},
journal = {Statistical Science},
keywords = {admissibility,and phrases,bayesian model checking,chical models,condi-,confidence intervals,consistency,coverage,design,hierar-,nonparametric bayes,objective bayesian methods,p -values,reference priors,testing,tional frequentist},
number = {1},
pages = {58--80},
title = {{The Interplay of Bayesian and Frequentist Analysis}},
volume = {19},
year = {2004}
}
@article{Tukey1980,
author = {Tukey, John W.},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/ElasticNet/Med and EN Articles/Tukey{\_}ExploratoryConfirmatory{\_}1980.pdf:pdf},
journal = {The American Statistician},
number = {1},
pages = {23--25},
title = {{We Need Both Exploratory and Confirmatory Author}},
url = {http://www.jstor.org/stable/2682991},
volume = {34},
year = {1980}
}
@article{Borcard2008,
abstract = {Instructional Book for using R for programming},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Borcard, Daniel and Gillet, Fran{\c{c}}ois and Legendre, Pierre},
doi = {10.1007/978-0-387-78171-6},
eprint = {arXiv:1011.1669v3},
file = {:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/Borcard, Gillet, Legendre - 2008 - Exploratory Data Analysis.pdf:pdf},
isbn = {978-0-387-78170-9},
issn = {9780387938363},
journal = {Applied Spatial Data Analysis with R},
number = {1999},
pages = {21--54},
pmid = {22057480},
title = {{Exploratory Data Analysis}},
url = {http://www.springerlink.com/index/10.1007/978-1-4419-7976-6},
volume = {2},
year = {2008}
}
@article{Vanderweele2012,
abstract = {In this commentary, structural equation models (SEMs) are discussed as a tool for epidemiologic analysis. Such models are related to and compared with other analytic approaches often used in epidemiology, including regression analysis, causal diagrams, causal mediation analysis, and marginal structural models. Several of these other approaches in fact developed out of the SEM literature. However, SEMs themselves tend to make much stronger assumptions than these other techniques. SEMs estimate more types of effects than do these other techniques, but this comes at the price of additional assumptions. Many of these assumptions have often been ignored and not carefully evaluated when SEMs have been used in practice. In light of the strong assumptions employed by SEMs, the author argues that they should be used principally for the purposes of exploratory analysis and hypothesis generation when a broad range of effects are potentially of interest.},
author = {Vanderweele, Tyler J.},
doi = {10.1093/aje/kws213},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/ElasticNet/Med and EN Articles/VanderWeele{\_}SEM{\_}Epi{\_}2012.pdf:pdf},
isbn = {1476-6256 (Electronic) 0002-9262 (Linking)},
issn = {00029262},
journal = {American Journal of Epidemiology},
keywords = {causal inference,causal modeling,causality,confounding factors (epidemiology),epidemiologic methods,regression analysis,structural equation model},
number = {7},
pages = {608--612},
pmid = {22956513},
title = {{Invited commentary: Structural equation models and epidemiologic analysis}},
volume = {176},
year = {2012}
}
@article{Selig2009,
abstract = {Mediation models are used to describe the mechanism(s) by which one variable influences another. These models can be useful in developmental research to expli- cate the relationship between variables, developmental processes, or combinations of variables and processes. In this article we describe aspects of mediation effects specific to developmental research. We focus on three central issues in longitudinal mediation models: the theory of change for variables in the model, the role of time in the model, and the types of indirect effects in the model. We use these themes as we describe three different models for examining mediation in longitudinal data.},
author = {Selig, James P. and Preacher, Kristopher J.},
doi = {10.1080/15427600902911247},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/ElasticNet/Med and EN Articles/selig{\_}preacher{\_}2009.pdf:pdf},
isbn = {1542760090291},
issn = {1542-7609},
journal = {Research in Human Development},
keywords = {latent growth modeling,mediation},
number = {2-3},
pages = {144--164},
title = {{Mediation Models for Longitudinal Data in Developmental Research}},
url = {http://www.tandfonline.com/doi/abs/10.1080/15427600902911247},
volume = {6},
year = {2009}
}
@article{Hayes2016,
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/ElasticNet/Med and EN Articles/Hayes, 2013 Chapter 1 - Conceptual introduction.pdf:pdf},
number = {October},
title = {{Hayes, Andrew F.. Methodology In The Social Sciences : Introduction to Mediation, Moderation, and Conditional Process Analysis : A Regression-Based Approach. New York, US: The Guilford Press, 2013. ProQuest ebrary. Web. 14 October 2016. Copyright {\textcopyright} 2013. }},
year = {2016}
}
@article{Yarkoni2013,
abstract = {applicability for this approach.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Yarkoni, Tal and Westfall, Jacob},
doi = {10.1017/CBO9781107415324.004},
eprint = {arXiv:1011.1669v3},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/Yarkoni{\_}Westfall{\_}choosing{\_}prediction.pdf:pdf},
isbn = {9788578110796},
issn = {1098-6596},
journal = {Journal of Chemical Information and Modeling},
keywords = {icle},
number = {9},
pages = {1689--1699},
pmid = {25246403},
title = {{Choosing prediction over explanation in psychology: Lessons from machine learning}},
volume = {53},
year = {2013}
}
@article{Lockhart2017,
author = {Lockhart, Ginger and Phillips, Samantha and Bolland, Anneliese and Delgado, Melissa and Tietjen, Juliet and Bolland, John},
doi = {10.3389/fpsyg.2017.00033},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/ElasticNet/Med and EN Articles/Lockhart{\_}ProspectiveMediation{\_}2017.pdf:pdf;:Users/tysonbarrett/Dropbox/1 Dissertation/ElasticNet/Med and EN Articles/Lockhart{\_}ProspectiveMediation{\_}2017.pdf:pdf},
isbn = {1664-1078},
issn = {1664-1078},
journal = {Frontiers in Psychology},
keywords = {adolescent,attachment,poverty,self-worth,subst,substance use,violence},
number = {January},
pages = {1--10},
title = {{Prospective Relations among Low-Income African American Adolescents' Maternal Attachment Security, Self-Worth, and Risk Behaviors}},
url = {http://journal.frontiersin.org/article/10.3389/fpsyg.2017.00033/full},
volume = {8},
year = {2017}
}
@article{Fidler2016,
abstract = {The designing, collecting, analyzing, and reporting of psychological studies entail many choices that are often arbitrary. The opportunistic use of these so-called researcher degrees of freedom aimed at obtaining statistically significant results is problematic because it enhances the chances of false positive results and may inflate effect size estimates. In this review article, we present an extensive list of 34 degrees of freedom that researchers have in formulating hypotheses, and in designing, running, analyzing, and reporting of psychological research. The list can be used in research methods education, and as a checklist to assess the quality of preregistrations and to determine the potential for bias due to (arbitrary) choices in unregistered studies.},
author = {Fidler, Fiona and Hoekstra, Rink and Cumming, Geoff and Wicherts, Jelte M and Veldkamp, Coosje L S and Augusteijn, Hilde E M and Bakker, Marjan and {Van Aert}, Robbie C M and {Van Assen}, Marcel A L M},
doi = {10.3389/fpsyg.2016.01832},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/Wicherts{\_}DF{\_}2016.pdf:pdf},
isbn = {1664-1078},
issn = {1664-1078},
journal = {Frontiers in Psychology},
keywords = {bias,experimental design,p-hacking,questionable research practices,research methods education,significance chasing,significance testing},
number = {November},
pages = {1--12},
pmid = {27933012},
title = {{Degrees of freedom in planning, running, analyzing, and reporting psychological studies: A checklist to avoid p-hacking}},
volume = {7},
year = {2016}
}
@article{Lindsay2015,
abstract = {In 2014, then Editor of Psychological Science Erich Eich put in place several initiatives meant to strengthen the science reported in the journal. In this Editorial, Interim Editor D. Stephen Lindsay commits to continuing and extending these efforts. He notes that he and his editors are on the lookout for articles displaying a "troubling trio" that suggest poor replicability -- a mix of low statistical power, a p value only slightly less than .05, and a surprising result. Editors will also be on the lookout for evidence of p-hacking, the misinterpretation of nonsignificant results, and the way researchers interpret correlations, especially from small samples of data. Through these and similar efforts, the editors of Psychological Science are confident they can reduce the number of studies with Type 1 errors published in the journal while still featuring the exciting and relevant findings for which the journal is known.},
author = {Lindsay, Stephen},
doi = {10.1177/0956797615616374},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/PsychScience{\_}Replicate{\_}2015.pdf:pdf},
isbn = {0956797615616},
issn = {0956-7976},
journal = {Psychological Science},
number = {12},
pages = {1827--1832},
pmid = {26553013},
title = {{Replication in Psychological Science}},
volume = {26},
year = {2015}
}
@article{Ioannidis2005,
abstract = {There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.},
archivePrefix = {arXiv},
arxivId = {gr-qc/0208024},
author = {Ioannidis, John P A},
doi = {10.1371/journal.pmed.0020124},
eprint = {0208024},
file = {:Users/tysonbarrett/Downloads/Ioannidis{\_}Reproducibility{\_}2005.PDF:PDF;:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/Ioannidis{\_}2005.PDF:PDF},
isbn = {3540239081},
issn = {15491277},
journal = {PLoS Medicine},
number = {8},
pages = {0696--0701},
pmid = {16060722},
primaryClass = {gr-qc},
title = {{Why most published research findings are false}},
volume = {2},
year = {2005}
}
@article{MacKinnon2007,
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 Peter and Fairchild, Amanda J. and Fritz, MS},
doi = {10.1146/annurev.psych.58.110405.085542.Mediation},
file = {:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/MacKinnon, Fairchild, Fritz - 2007 - Mediation Analysis.pdf:pdf;:Users/tysonbarrett/Dropbox/1 Dissertation/ElasticNet/Med and EN Articles/intro{\_}mediation{\_}2007.pdf:pdf},
isbn = {0066-4308 (Print)$\backslash$n0066-4308 (Linking)},
issn = {0066-4308},
journal = {Annual review of Psychology},
keywords = {indirect effect,intervening variable,mediator,third variable},
number = {Hebb 1966},
pages = {593--602},
pmid = {16968208},
title = {{Mediation Analysis}},
volume = {58},
year = {2007}
}
@article{Paxton2001,
abstract = {The use of Monte Carlo simulations for the empirical assessment of statistical estimators is becoming more common in structural equation modeling research. Yet, there is little guidance for the researcher interested in using the technique. In this article we illustrate both the design and implementation of Monte Carlo simulations. We present 9 steps in planning and performing a Monte Carlo analysis: (1) developing a theoretically derived research question of interest, (2) creating a valid model, (3) designing specific experimental conditions, (4) choosing values of population parameters, (5) choosing an appropriate software package, (6) executing the simulations, (7) file storage, (8) troubleshooting and verification, and (9) summarizing results. Throughout the article, we use as a running example a Monte Carlo simulation that we performed to illustrate many of the relevant points with concrete information and detail.},
author = {Paxton, Pamela and Curran, Patrick J. and Bollen, Kenneth a. and Kirby, Jim and Chen, Feinian},
doi = {10.1207/S15328007SEM0802_7},
file = {:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/Paxton et al. - 2001 - Monte Carlo Experiments Design and Implementation.pdf:pdf;:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/Paxton{\_}MC{\_}Simulation{\_}2001.pdf:pdf},
isbn = {10705511},
issn = {1070-5511},
journal = {Structural Equation Modeling: A Multidisciplinary Journal},
number = {2},
pages = {287--312},
pmid = {21149643},
title = {{Monte Carlo Experiments: Design and Implementation}},
volume = {8},
year = {2001}
}
@article{Simmons2011,
abstract = {In this article, we accomplish two things. First, we show that despite empirical psychologists' nominal endorsement of a low rate of false-positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. We present computer simulations and a pair of actual experiments that demonstrate how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis. Second, we suggest a simple, low-cost, and straightforwardly effective disclosure-based solution to this problem. The solution involves six concrete requirements for authors and four guidelines for reviewers, all of which impose a minimal burden on the publication process.},
archivePrefix = {arXiv},
arxivId = {2021},
author = {Simmons, Joseph P. and Nelson, Leif D. and Simonsohn, Uri},
doi = {10.1177/0956797611417632},
eprint = {2021},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/FalsePositive{\_}Psych{\_}.pdf:pdf;:Users/tysonbarrett/Downloads/SimmonsNelsonSimonsohn{\_}2011.pdf:pdf},
isbn = {1467-9280 (Electronic)$\backslash$n0956-7976 (Linking)},
issn = {0956-7976},
journal = {Psychological Science},
keywords = {11,17,23,about the world,disclosure,is to discover truths,methodology,motivated reasoning,our job as scientists,publication,received 3,revision accepted 5,we},
number = {11},
pages = {1359--1366},
pmid = {22006061},
title = {{False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant}},
volume = {22},
year = {2011}
}
@article{Patil2016,
abstract = {A recent study of the replicability of key psychological findings is a major contribution toward understanding the human side of the scientific process. Despite the careful and nuanced analysis reported, the simple narrative disseminated by the mass, social, and scientific media was that in only 36{\%} of the studies were the original results replicated. In the current study, however, we showed that 77{\%} of the replication effect sizes reported were within a 95{\%} prediction interval calculated using the original effect size. Our analysis suggests two critical issues in understanding replication of psychological studies. First, researchers' intuitive expectations for what a replication should show do not always match with statistical estimates of replication. Second, when the results of original studies are very imprecise, they create wide prediction intervals—and a broad range of replication effects that are consistent with the original estimates. This may lead to effects that replicate successfully, in that replication results are consistent with statistical expectations, but do not provide much information about the size (or existence) of the true effect. In this light, the results of the Reproducibility Project: Psychology can be viewed as statistically consistent with what one might expect when performing a large-scale replication experiment.},
author = {Patil, Prasad and Peng, Roger D. and Leek, Jeffrey T.},
doi = {10.1177/1745691616646366},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/Patil{\_}ReplicationExpect{\_}2016.pdf:pdf;:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/Patil, Peng, Leek - 2016 - What Should Researchers Expect When They Replicate Studies A Statistical View of Replicability in Psychologic.pdf:pdf;:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/Patil{\_}ReplicationPredInterval{\_}2016.pdf:pdf},
isbn = {1745-6924 (Electronic) 1745-6916 (Linking)},
issn = {1745-6916},
journal = {Perspectives on Psychological Science},
number = {4},
pages = {539--544},
pmid = {27474140},
title = {{What Should Researchers Expect When They Replicate Studies? A Statistical View of Replicability in Psychological Science}},
volume = {11},
year = {2016}
}
@article{Tam2008,
abstract = {Aim: Although unplanned admissions to the intensive care unit (ICU) are associated with poorer prognoses, there is no published prognostic tool available for predicting this risk in an individual patient. We developed a nomogram for calculating the individualised absolute risk of unplanned ICU admission during a hospital stay. Method: Hospital administrative data from a large district hospital of consecutive admissions from 1 January 2000 to 31 December 2006 of aged over 14 years was used. Patient data was extracted from 94,482 hospital admissions consisted of demographic and clinical variables, including diagnostic categories, types of admission and time and day of admission. Multivariate logistic regression coefficients were used to develop a predictive nomogram of individual risk to patients admitted to the study hospital of unplanned ICU admission. Results: A total of 672 incident unplanned ICU admissions were identified over this period. Independent predictors of unplanned ICU admissions included being male, older age, emergency department (ED) admissions, after-hour admissions, weekend admissions and six principal diagnosis groups: fractured femur, acute pancreatitis, liver disease, chronic airway disease, pneumonia and heart failure. The area under the receiver operating characteristic curve was 0.81. Conclusion: The use of a nomogram to accurately identify at-risk patients using information that is readily available to clinicians has the potential to be a useful tool in reducing unplanned ICU admissions, which in turn may contribute to the reduction of adverse events of patients in the general wards. ?? 2008 Elsevier Ireland Ltd. All rights reserved.},
author = {Tam, Victor and Frost, Steven A. and Hillman, Ken M. and Salamonson, Yenna},
doi = {10.1016/j.resuscitation.2008.06.023},
isbn = {0300-9572 (Print)$\backslash$n0300-9572 (Linking)},
issn = {03009572},
journal = {Resuscitation},
keywords = {Intensive care,Nomogram,Outcome prediction,Unplanned ICU admissions},
number = {2},
pages = {241--248},
pmid = {18691801},
title = {{Using administrative data to develop a nomogram for individualising risk of unplanned admission to intensive care}},
volume = {79},
year = {2008}
}
@article{Ngamake2016,
abstract = {Stigma and discrimination related to sexual minority status are unique stressors associated with mental and physical health concerns among sexual minority individuals. Although some theoretical models propose that the strategies sexual minorities apply to cope with these particular stressors can mediate and/or moderate their impact on mental health outcomes, only a few studies have examined this proposition empirically, typically using measures of broad coping strategies that are not discrimination- specific. A nonprobability sample of 354 self-identified lesbian, gay, and bisexual (LGB) individuals completed self-report measures of perceived discrimination experiences, psychological distress, and discrimination-specific coping strategies used, including education/advocacy, internalization, drug and alcohol use, resistance, and detachment strategies. Perceived discrimination explained levels of depres- sion, anxiety, and stress after controlling for income, education, and race. Drug and alcohol use mediated the relationship between discrimination and depression, anxiety, and stress symptoms. Internalization mediated the relationship between discrimination and anxiety symptoms and moderated the relationship between discrimination and depression and anxiety. The education/advocacy, resistance, and detachment strategies played no clear mediator or moderator roles in the relationship between discrimination and psychological distress. Psychological interventions that assist sexual minority clients in the development of effective coping skills, such as avoidance of drug and alcohol use for coping and internalization may reduce the harm associated with the experience of stigma and discrimination.},
author = {Ngamake, Sakkaphat T and Walch, Susan E and Raveepatarakul, Jirapattara},
doi = {10.1037/sgd0000163},
file = {:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/Ngamake, Walch, Raveepatarakul - 2016 - Discrimination and Sexual Minority Mental Health Mediation and Moderation Effects of Coping.pdf:pdf;:Users/tysonbarrett/Box Sync/Stigma Meta/Articles Coded Twice/Ngamake{\_}16{\_}Discrimination and Sexual Minority Mental{\_}BF{\_}KL.pdf:pdf},
isbn = {2329-0382},
issn = {2329-0382},
journal = {Psychology of Sexual Orientation and Gender Diversity},
keywords = {anxiety,coping,depression,gay/lesbian/bisexual,perceived discrimination},
number = {2},
pages = {1--14},
title = {{Discrimination and Sexual Minority Mental Health: Mediation and Moderation Effects of Coping}},
url = {http://dx.doi.org/10.1037/ sgd0000163},
volume = {Advance on},
year = {2016}
}
@article{Delgado2013,
abstract = {BACKGROUND: Emergency department (ED) ward admissions subsequently transferred to the intensive care unit (ICU) within 24 hours have higher mortality than direct ICU admissions. DESIGN, SETTING, PATIENTS: Describe risk factors for unplanned ICU transfer within 24 hours of ward arrival from the ED. METHODS: Evaluation of 178,315 ED non-ICU admissions to 13 US community hospitals. We tabulated the outcome of unplanned ICU transfer by patient characteristics and hospital volume. We present factors associated with unplanned ICU transfer after adjusting for patient and hospital differences in a hierarchical logistic regression. RESULTS: There were 4,252 (2.4{\%}) non-ICU admissions transferred to the ICU within 24 hours. Admitting diagnoses most associated with unplanned transfer, listed by descending prevalence were: pneumonia (odds ratio [OR] 1.5; 95{\%} confidence interval [CI] 1.2-1.9), myocardial infarction (MI) (OR 1.5; 95{\%} CI 1.2-2.0), chronic obstructive pulmonary disease (COPD) (OR 1.4; 95{\%} CI 1.1-1.9), sepsis (OR 2.5; 95{\%} CI 1.9-3.3), and catastrophic conditions (OR 2.3; 95{\%} CI 1.7-3.0). Other significant predictors included: male sex, Comorbidity Points Score {\textgreater}145, Laboratory Acute Physiology Score ≥7, arriving on the ward between 11 PM and 7 AM. Decreased risk was found with admission to monitored transitional care units (OR 0.83; 95{\%} CI 0.77-0.90) and to higher volume hospitals (OR 0.94 per 1,000 additional annual ED inpatient admissions; 95{\%} CI 0.91-0.98). CONCLUSIONS: ED patients admitted with respiratory conditions, MI, or sepsis are at modestly increased risk for unplanned ICU transfer and may benefit from better triage from the ED, earlier intervention, or closer monitoring to prevent acute decompensation. More research is needed to determine how intermediate care units, hospital volume, time of day, and sex affect unplanned ICU transfer. Journal of Hospital Medicine 2013. {\textcopyright} 2012 Society of Hospital Medicine.},
author = {Delgado, M. Kit and Liu, Vincent and Pines, Jesse M. and Kipnis, Patricia and Gardner, Marla N. and Escobar, Gabriel J.},
doi = {10.1002/jhm.1979},
isbn = {1553-5606},
issn = {15535592},
journal = {Journal of Hospital Medicine},
number = {1},
pages = {13--19},
pmid = {23024040},
title = {{Risk factors for unplanned transfer to intensive care within 24 hours of admission from the emergency department in an integrated healthcare system}},
volume = {8},
year = {2013}
}
@techreport{nhis,
address = {Hyattsville, MD},
booktitle = {Medicine},
doi = {10.1097/00005768-199706001-00029},
file = {:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/Unknown - 1997 - National Health Interview Survey.pdf:pdf},
institution = {Centers for Disease Control and Prevention, National Center for Health Statistics},
number = {Supplement},
pages = {190--195},
title = {{National Health Interview Survey}},
volume = {29},
year = {1997}
}
@article{Cumming2014,
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.},
author = {Cumming},
doi = {10.1177/0956797613504966},
isbn = {1467-9280 (Electronic)$\backslash$r0956-7976 (Linking)},
issn = {1467-9280},
journal = {Psychological Science},
keywords = {13,20,8,estimation,meta-analysis,received 7,replication,research integrity,research methods,revision accepted 8,statistical analysis,that most current,the new statistics,there is increasing concern},
number = {1},
pages = {7-29},
pmid = {24220629},
title = {{The new statistics: Why and how}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24220629{\%}5Cnhttp://pss.sagepub.com/lookup/doi/10.1177/0956797613504966},
volume = {25},
year = {2014}
}
@article{Gershengorn2015a,
author = {Gershengorn, Hayley B. and Garland, Allan and Gong, Michelle N.},
doi = {10.1513/AnnalsATS.201506-366BC},
file = {:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/Gershengorn, Garland, Gong - 2015 - Patterns of daily costs differ for medical and surgical intensive care unit patients.pdf:pdf},
isbn = {0000000273602},
issn = {23256621},
journal = {Annals of the American Thoracic Society},
keywords = {Costs and cost analysis,Hospital costs,Intensive care},
number = {12},
pages = {1831--1836},
title = {{Patterns of daily costs differ for medical and surgical intensive care unit patients}},
volume = {12},
year = {2015}
}
@article{MacKinnon2010,
abstract = {Mediating variables continue to play an important role in psychological theory and research. A mediating variable transmits the effect of an antecedent variable on to a dependent variable, thereby providing more detailed understanding of relations among variables. Methods to assess mediation have been an active area of research for the last two decades. This paper describes the current state of methods to investigate mediating variables. Keywords},
author = {MacKinnon, David Peter and Fairchild, Amanda J.},
doi = {10.1111/j.1467-8721.2009.01598.x.Current},
file = {:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/MacKinnon, Fairchild - 2010 - Current Directions in Mediation Analysis.pdf:pdf;:Users/tysonbarrett/Dropbox/1 Dissertation/ElasticNet/Med and EN Articles/Current{\_}mediation{\_}2009.pdf:pdf},
isbn = {0963-7214; 0963-7214},
issn = {0963-7214},
journal = {Current Directions in Psychological Science},
keywords = {indirect effect,mediation,statistical methods},
number = {1},
pages = {16--20},
pmid = {20157637},
title = {{Current Directions in Mediation Analysis}},
volume = {18},
year = {2010}
}
@article{Boulet2009,
abstract = {OBJECTIVE: To present nationally representative estimates of health-related limitations, needs, and service use among US children with and without developmental disabilities (DDs). DESIGN: Retrospective analysis of data from a sample of US households from the 1997-2005 National Health Interview Surveys. PARTICIPANTS: Children aged 3 to 17 years (n = 95 132). MAIN OUTCOME MEASURES: Parents or other knowledgeable adults reported on their children's DDs, health needs, and use of health and education services. Developmental disabilities included attention-deficit/hyperactivity disorder, autism, blindness, cerebral palsy, deaf/a lot of trouble hearing, learning disability, mental retardation, seizures, stuttering/stammering, and other developmental delay. RESULTS: Among children with 1 or more DDs, prevalence estimates for limitations in movement (6.1{\%}), needed help with personal care (3.2{\%}), needed special equipment (3.5{\%}), received home health care (1.4{\%}), and regularly took prescription medication(s) (37.5{\%}) were 4 to 32 times higher than for children without DDs. Children with DDs were 2 to 8 times as likely to have had more than 9 health care visits (14.9{\%}), received special education (38.8{\%}), had a surgical or medical procedure (7.5{\%}), and recently visited a medical specialist (23.9{\%}), mental health professional (26.6{\%}), therapist/allied health professional (19.6{\%}), and/or emergency department (10.3{\%}). Effects were generally stable during the study interval and independent of age, race, sex, and family income. Cerebral palsy, autism, mental retardation, blindness, and deafness/a lot of trouble hearing were associated with the highest levels of health and functional impact indicators. CONCLUSIONS: Developmental disabilities profoundly affect children's health and functioning. These data can inform evidence-based targeted prevention strategies for minimizing functional limitations and lifetime disability. Additional study of unmet needs and access to care is needed.},
author = {Boulet, Sheree L and Boyle, Coleen A and Schieve, Laura A},
doi = {10.1001/archpediatrics.2008.506},
isbn = {1538-3628},
issn = {1538-3628},
journal = {Archives of pediatrics {\&} adolescent medicine},
keywords = {Adolescent,Age Factors,Attention Deficit Disorder with Hyperactivity,Attention Deficit Disorder with Hyperactivity: dia,Attention Deficit Disorder with Hyperactivity: epi,Attention Deficit Disorder with Hyperactivity: the,Autistic Disorder,Autistic Disorder: diagnosis,Autistic Disorder: epidemiology,Autistic Disorder: therapy,Cerebral Palsy,Cerebral Palsy: diagnosis,Cerebral Palsy: epidemiology,Cerebral Palsy: therapy,Child,Child Welfare,Databases,Delivery of Health Care,Delivery of Health Care: utilization,Developmental Disabilities,Developmental Disabilities: diagnosis,Developmental Disabilities: epidemiology,Developmental Disabilities: therapy,Factual,Female,Health Status,Humans,Intellectual Disability,Intellectual Disability: diagnosis,Intellectual Disability: epidemiology,Intellectual Disability: therapy,Learning Disorders,Learning Disorders: diagnosis,Learning Disorders: epidemiology,Learning Disorders: therapy,Male,Mental Health,Preschool,Retrospective Studies,Risk Assessment,Sex Factors,Socioeconomic Factors,Treatment Outcome,United States,United States: epidemiology},
number = {1},
pages = {19--26},
pmid = {19124699},
title = {{Health care use and health and functional impact of developmental disabilities among US children, 1997-2005.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19124699},
volume = {163},
year = {2009}
}
@article{Barber2004a,
abstract = {OBJECTIVE: Choosing an appropriate method for regression analyses of cost data is problematic because it must focus on population means while taking into account the typically skewed distribution of the data. In this paper we illustrate the use of generalised linear models for regression analysis of cost data.$\backslash$n$\backslash$nMETHODS: We consider generalised linear models with either an identity link function (providing additive covariate effects) or log link function (providing multiplicative effects), and with gaussian (normal), overdispersed poisson, gamma, or inverse gaussian distributions. These are applied to estimate the treatment effects in two randomised trials adjusted for baseline covariates. Criteria for choosing an appropriate model are presented.$\backslash$n$\backslash$nRESULTS: In both examples considered, the gaussian model fits poorly and other distributions are to be preferred. When there are variables of prognostic importance in the model, using different distributions can materially affect the estimates obtained; it may also be possible to discriminate between additive and multiplicative covariate effects.$\backslash$n$\backslash$nCONCLUSIONS: Generalised linear models are attractive for the regression of cost data because they provide parametric methods of analysis where a variety of non-normal distributions can be specified and the way covariates act can be altered. Unlike the use of data transformation in ordinary least-squares regression, generalised linear models make inferences about the mean cost directly.},
author = {Barber, Julie and Thompson, Simon},
doi = {10.1258/1355819042250249},
file = {:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/Barber, Thompson - 2004 - Multiple regression of cost data use of generalised linear models.pdf:pdf},
issn = {1355-8196},
journal = {Journal of Health Services Research {\&} Policy},
keywords = {Costs and Cost Analysis,Costs and Cost Analysis: methods,Great Britain,Health Services Research,Linear Models,Normal Distribution},
month = {oct},
number = {4},
pages = {197--204},
pmid = {15509405},
title = {{Multiple regression of cost data: use of generalised linear models.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15509405 http://hsr.sagepub.com/lookup/doi/10.1258/1355819042250249},
volume = {9},
year = {2004}
}
@article{Hanmer2013,
abstract = {Models designed for limited dependent variables are increasingly common in political science. Researchers estimating such models often give little attention to the coefficient estimates and instead focus on marginal effects, predicted probabilities, predicted counts, etc. Since the models are nonlinear, the estimated effects are sensitive to how one generates the predictions. The most common approach involves estimating the effect for the “average case.” But this approach creates a weaker connection between the results and the larger goals of the research enterprise and is thus less preferable than the observed-value approach. That is, rather than seeking to understand the effect for the average case, the goal is to obtain an estimate of the average effect in the population. In addition to the theoretical argument in favor of the observed-value approach, we illustrate via an empirical example and Monte Carlo simulations that the two approaches can produce substantively different results.},
author = {Hanmer, Michael J. and {Ozan Kalkan}, Kerem},
doi = {10.1111/j.1540-5907.2012.00602.x},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/AME/Hanmer{\_}AverageMargin2013.pdf:pdf},
isbn = {1540-5907},
issn = {00925853},
journal = {American Journal of Political Science},
number = {1},
pages = {263--277},
title = {{Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable Models}},
volume = {57},
year = {2013}
}
@article{Pinquart2011,
abstract = {To examine the risk of emotional and behavioral problems among children with a chronic physical illness. Random-effects meta-analysis was computed to integrate the results of 569 studies that used the Child Behavior Checklist, Youth Self Report, and the Teacher Report Form. Young people with a chronic physical illness have higher levels of internalizing (g = .47 standard mean difference), externalizing (g = .22) and total behavior problems (g = .42) than healthy peers. The largest differences were found in parental ratings and the weakest differences in adolescent self-ratings. Strongest elevations of internalizing problems were found for chronic fatigue syndrome and strongest elevations of externalizing problems were observed for epilepsy and migraine/tension-type headache. Effects also varied by country and, in part, by age, gender, year of publication, and study design. The results call for regular screens for psychological distress and referrals for mental health services, when needed.},
author = {Pinquart, M and Shen, Y},
doi = {http://dx.doi.org/10.1093/jpepsy/jsq104},
file = {:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/Pinquart, Shen - 2011 - Behavior problems in children and adolescents with chronic physical illness A meta-analysis.pdf:pdf},
isbn = {1465-735X},
journal = {Journal of pediatric psychology},
keywords = {adolescent,article,child,chronic disease,depression/di [Diagnosis],female,human,male,mental health,meta analysis,psychological aspect},
number = {4},
pages = {375--384},
pmid = {21088072},
title = {{Behavior problems in children and adolescents with chronic physical illness: A meta-analysis}},
url = {http://ovidsp.ovid.com/ovidweb.cgi?T=JS{\&}CSC=Y{\&}NEWS=N{\&}PAGE=fulltext{\&}D=emed10{\&}AN=21088072{\%}5Cnhttp://oxfordsfx.hosted.exlibrisgroup.com/oxford?sid=OVID:embase{\&}id=pmid:21088072{\&}id=doi:10.1093/jpepsy/jsq104{\&}issn=1465-735X{\&}isbn={\&}volume=36{\&}issue=4{\&}spage=375{\&}page},
volume = {36},
year = {2011}
}
@article{Shargorodsky2010,
abstract = {Context Hearing loss is common and, in young persons, can compromise social development, communication skills, and educational achievement. Objective To examine the current prevalence of hearing loss in US adolescents and determine whether it has changed over time. Design Cross-sectional analyses of US representative demographic and audiometric data from the 1988 through 1994 and 2005 through 2006 time periods. Setting The Third National Health and Nutrition Examination Survey (NHANES III), 1988-1994, and NHANES 2005-2006. Participants NHANES III examined 2928 participants and NHANES 2005-2006 examined 1771 participants, aged 12 to 19 years. Main Outcome Measures We calculated the prevalence of hearing loss in participants aged 12 to 19 years after accounting for the complex survey design. Audiometrically determined hearing loss was categorized as either unilateral or bilateral for low frequency (0.5, 1, and 2 kHz) or high frequency (3, 4, 6, and 8 kHz), and as slight loss ({\textgreater}15 to {\textless}25 dB) or mild or greater loss (≥25 dB) according to hearing sensitivity in the worse ear. The prevalence of hearing loss from NHANES 2005-2006 was compared with the prevalence from NHANES III (1988-1994). We also examined the cross-sectional relations between several potential risk factors and hearing loss. Logistic regression was used to calculate multivariate adjusted odds ratios (ORs) and 95{\%} confidence intervals (CIs). Results The prevalence of any hearing loss increased significantly from 14.9{\%} (95{\%} CI, 13.0{\%}-16.9{\%}) in 1988-1994 to 19.5{\%} (95{\%} CI, 15.2{\%}-23.8{\%}) in 2005-2006 (P = .02). In 2005-2006, hearing loss was more commonly unilateral (prevalence, 14.0{\%}; 95{\%} CI, 10.4{\%}-17.6{\%}, vs 11.1{\%}; 95{\%} CI, 9.5{\%}-12.8{\%} in 1988-1994; P = .005) and involved the high frequencies (prevalence, 16.4{\%}; 95{\%} CI, 13.2{\%}-19.7{\%}, vs 12.8{\%}; 95{\%} CI, 11.1{\%}-14.5{\%} in 1988-1994; P = .02). Individuals from families below the federal poverty threshold (prevalence, 23.6{\%}; 95{\%} CI, 18.5{\%}-28.7{\%}) had significantly higher odds of hearing loss (multivariate adjusted OR, 1.60; 95{\%} CI, 1.10-2.32) than those above the threshold (prevalence, 18.4{\%}; 95{\%} CI, 13.6{\%}-23.2{\%}). Conclusion The prevalence of hearing loss among a sample of US adolescents aged 12 to 19 years was greater in 2005-2006 compared with 1988-1994.},
author = {Shargorodsky, Josef and Curhan, Sharon G and Curhan, Gary C. and Eavey, Roland and Curan, Sharen G. and Curhan, Gary C. and Eavey, Roland},
doi = {10.1016/j.yped.2010.12.008},
file = {:Users/tysonbarrett/Downloads/JAMA{\_}hearing{\_}loss{\_}NHANES.pdf:pdf},
isbn = {0098-7484},
issn = {00843954},
journal = {JAMA},
number = {7},
pages = {772--778},
pmid = {20716740},
title = {{Change in Prevalence of Hearing Loss in US Adolescents}},
volume = {304},
year = {2010}
}
@article{Tibshirani2011,
abstract = {Summary. In the paper I give a brief review of the basic idea and some history and then dis- cuss some developments since the original paper on regression shrinkage and selection via the lasso},
archivePrefix = {arXiv},
arxivId = {1369–7412/11/73273},
author = {Tibshirani, Robert},
doi = {10.1111/j.1467-9868.2011.00771.x},
eprint = {11/73273},
file = {:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/Tibshirani - 1996 - Regression Shrinkage and Selection Via the Lasso.pdf:pdf;:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/Tibshirani - 2011 - Regression Shrinkage and Selection via the Lasso.pdf:pdf},
isbn = {0035-9246},
issn = {13697412},
journal = {Journal of the Royal Statistical Society},
keywords = {1л -penalty,Subset selection,penalization,regularization},
number = {3},
pages = {267--288},
pmid = {16272381},
primaryClass = {1369–7412},
title = {{Regression Shrinkage and Selection via the Lasso}},
volume = {73},
year = {2011}
}
@article{Munafo2017,
abstract = {Improving the reliability and efficiency of scientific research will increase the credibility of the published scientific literature and accelerate discovery. Here we argue for the adoption of measures to optimize key elements of the scientific process: methods, reporting and dissemination, reproducibility, evaluation and incentives. There is some evidence from both simulations and empirical studies supporting the likely effectiveness of these measures, but their broad adoption by researchers, institutions, funders and journals will require iterative evaluation and improvement. We discuss the goals of these measures, and how they can be implemented, in the hope that this will facilitate action toward improving the transparency, reproducibility and efficiency of scientific research.},
author = {Munaf{\`{o}}, Marcus R and Nosek, Brian A and Bishop, Dorothy V M and Button, Katherine S and Chambers, Christopher D and {Percie du Sert}, Nathalie and Simonsohn, Uri and Wagenmakers, Eric-jan and Ware, Jennifer J. and Ioannidis, John P A and Percie, Nathalie and Simonsohn, Uri and Wagenmakers, Eric-jan},
doi = {10.1038/s41562-016-0021},
file = {:Users/tysonbarrett/Library/Application Support/Mendeley Desktop/Downloaded/Munaf{\`{o}} et al. - 2017 - A manifesto for reproducible science.pdf:pdf;:Users/tysonbarrett/Dropbox/1 Dissertation/Reproducibility/ManifestoReproducibility{\_}2017.pdf:pdf},
isbn = {4156201600},
issn = {2397-3374},
journal = {Nature Human Behaviour},
number = {January},
pages = {1--9},
publisher = {Macmillan Publishers Limited},
title = {{A manifesto for reproducible science}},
url = {http://dx.doi.org/10.1038/s41562-016-0021},
volume = {1},
year = {2017}
}
@article{Friedman2010a,
abstract = {We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ?1 (the lasso), ?2 (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.},
archivePrefix = {arXiv},
arxivId = {arXiv:0908.3817v2},
author = {Friedman, Jerome and Hastie, Trevor and Tibshirani, Rob},
doi = {10.18637/jss.v069.i12},
eprint = {arXiv:0908.3817v2},
file = {:Users/tysonbarrett/Dropbox/1 Dissertation/ElasticNet/Med and EN Articles/glmnet{\_}algorithms.pdf:pdf;:Users/tysonbarrett/Dropbox/1 Dissertation/ElasticNet/Med and EN Articles/glmnet{\_}algorithm{\_}paper.pdf:pdf},
isbn = {9780387981406},
issn = {1548-7660},
journal = {Journal of Statistical Software},
keywords = {elastic net,lasso,logistic regression},
number = {1},
pages = {1--22},
pmid = {20808728},
title = {{Regularization Paths for Generalized Linear Models via Coordinate Descent}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2929880{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {33},
year = {2010}
}
@techreport{CentersforDiseaseControlandPrevention,
address = {Hyattsville, MD},
author = {{Centers for Disease Control and Prevention} and {National Center for Health Statistics}},
institution = {U.S. Department of Health and Human Survices, Centers for Disease Control and Prevention},
title = {{National Health and Nutrition Examination Survey Data}},
url = {http://www.cdc.gov/nchs/nhanes/},
year = {2016}
}