Explaining the fundamentals of mediation and moderation analysis, this engaging book also shows how to integrate the two using an innovative strategy known as conditional process analysis. Procedures are described for testing hypotheses about the mechanisms by which causal effects operate, the conditions under which they occur, and the moderation of mechanisms. Relying on the principles of ordinary least squares regression, Andrew Hayes carefully explains the estimation and interpretation of direct and indirect effects, probing and visualization of interactions, and testing of questions about moderated mediation. Examples using data from published studies illustrate how to conduct and report the analyses described in the book. Of special value, the book introduces and documents PROCESS, a macro for SPSS and SAS that does all the computations described in the book. The companion website (www.afhayes.com) offers free downloads of PROCESS plus data files for the book's examples. Unique features include: *Compelling examples (presumed media influence, sex discrimination in the workplace, and more) with real data; boxes with SAS, SPSS, and PROCESS code; and loads of tips, including how to report mediation, moderation and conditional process analyses. *Appendix that presents documentation on use and features of PROCESS. *Online supplement providing data, code, and syntax for the book's examples.
Acclaimed for its thorough presentation of mediation, moderation, and conditional process analysis, this book has been updated to reflect the latest developments in PROCESS for SPSS, SAS, and, new to this edition, R. Using the principles of ordinary least squares regression, Andrew F. Hayes illustrates each step in an analysis using diverse examples from published studies, and displays SPSS, SAS, and R code for each example. Procedures are outlined for estimating and interpreting direct, indirect, and conditional effects; probing and visualizing interactions; testing hypotheses about the moderation of mechanisms; and reporting different types of analyses. Readers gain an understanding of the link between statistics and causality, as well as what the data are telling them. The companion website (www.afhayes.com) provides data for all the examples, plus the free PROCESS download. New to This Edition *Rewritten Appendix A, which provides the only documentation of PROCESS, including a discussion of the syntax structure of PROCESS for R compared to SPSS and SAS. *Expanded discussion of effect scaling and the difference between unstandardized, completely standardized, and partially standardized effects. *Discussion of the meaning of and how to generate the correlation between mediator residuals in a multiple-mediator model, using a new PROCESS option. *Discussion of a method for comparing the strength of two specific indirect effects that are different in sign. *Introduction of a bootstrap-based Johnson–Neyman-like approach for probing moderation of mediation in a conditional process model. *Discussion of testing for interaction between a causal antecedent variable [ital]X[/ital] and a mediator [ital]M[/ital] in a mediation analysis, and how to test this assumption in a new PROCESS feature.
Statistical Methods for Communication Science is the only statistical methods volume currently available that focuses exclusively on statistics in communication research. Writing in a straightforward, personal style, author Andrew F. Hayes offers this accessible and thorough introduction to statistical methods, starting with the fundamentals of measurement and moving on to discuss such key topics as sampling procedures, probability, reliability, hypothesis testing, simple correlation and regression, and analyses of variance and covariance. Hayes takes readers through each topic with clear explanations and illustrations. He provides a multitude of examples, all set in the context of communication research, thus engaging readers directly and helping them to see the relevance and importance of statistics to the field of communication. Highlights of this text include: *thorough and balanced coverage of topics; *integration of classical methods with modern "resampling" approaches to inference; *consideration of practical, "real world" issues; *numerous examples and applications, all drawn from communication research; *up-to-date information, with examples justifying use of various techniques; and *downloadable resources with macros, data sets, figures, and additional materials. This unique book can be used as a stand-alone classroom text, a supplement to traditional research methods texts, or a useful reference manual. It will be invaluable to students, faculty, researchers, and practitioners in communication, and it will serve to advance the understanding and use of statistical methods throughout the discipline.
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. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable’s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.
Excavations on the site of this remarkable fort in northern Bulgaria (1996–2005) formed part of a long-term program of excavation and intensive field survey, aimed at tracing the economic as well as physical changes which mark the transition from the Roman Empire to the Middle Ages, a program that commenced with the excavation and full publication of the early Byzantine fortress/city of Nicopolis ad Istrum. The analysis of well-dated finds and their full publication provides a unique database for the late Roman period in the Balkans; they include metal-work, pottery (local and imported fine ware), glass, copper alloy finds, inscriptions and dipinti (on amphorae), as well as quantified environmental reports on animal, birds, and fish with specialist reports on the archaeobotanical material, glass analysis, and querns. The report also details the results of site-specific intensive survey, a new method developed for use in the rich farmland of the central Balkans. In addition, there is a detailed report on a most remarkable and well-preserved aqueduct, which employed the largest siphon ever discovered in the Roman Empire. This publication will provide a substantial database of material and environmental finds, an invaluable resource for the region and for the Roman Empire: material invaluable for studies, which seeks to place the late Roman urban and military identity within its regional and extra-regional economic setting.
The real collusion in the 2016 election was not between the Trump campaign and the Kremlin. It was between the Clinton campaign and the Obama administration. The media–Democrat “collusion narrative,” which paints Donald Trump as cat’s paw of Russia, is a studiously crafted illusion. Despite Clinton’s commanding lead in the polls, hyper-partisan intelligence officials decided they needed an “insurance policy” against a Trump presidency. Thus was born the collusion narrative, built on an anonymously sourced “dossier,” secretly underwritten by the Clinton campaign and compiled by a former British spy. Though acknowledged to be “salacious and unverified” at the FBI’s highest level, the dossier was used to build a counterintelligence investigation against Trump’s campaign. Miraculously, Trump won anyway. But his political opponents refused to accept the voters’ decision. Their collusion narrative was now peddled relentlessly by political operatives, intelligence agents, Justice Department officials, and media ideologues—the vanguard of the “Trump Resistance.” Through secret surveillance, high-level intelligence leaking, and tireless news coverage, the public was led to believe that Trump conspired with Russia to steal the election. Not one to sit passively through an onslaught, President Trump fought back in his tumultuous way. Matters came to a head when he fired his FBI director, who had given explosive House testimony suggesting the president was a criminal suspect, despite privately assuring Trump otherwise. The resulting firestorm of partisan protest cowed the Justice Department to appoint a special counsel, whose seemingly limitless investigation bedeviled the administration for two years. Yet as months passed, concrete evidence of collusion failed to materialize. Was the collusion narrative an elaborate fraud? And if so, choreographed by whom? Against media–Democrat caterwauling, a doughty group of lawmakers forced a shift in the spotlight from Trump to his investigators and accusers. This has exposed the depth of politicization within American law-enforcement and intelligence agencies. It is now clear that the institutions on which our nation depends for objective policing and clear-eyed analysis injected themselves scandalously into the divisive politics of the 2016 election. They failed to forge a new Clinton administration. Will they succeed in bringing down President Trump?
Statistical Methods for Communication Science is the only statistical methods volume currently available that focuses exclusively on statistics in communication research. Writing in a straightforward, personal style, author Andrew F. Hayes offers this accessible and thorough introduction to statistical methods, starting with the fundamentals of measurement and moving on to discuss such key topics as sampling procedures, probability, reliability, hypothesis testing, simple correlation and regression, and analyses of variance and covariance. Hayes takes readers through each topic with clear explanations and illustrations. He provides a multitude of examples, all set in the context of communication research, thus engaging readers directly and helping them to see the relevance and importance of statistics to the field of communication. Highlights of this text include: *thorough and balanced coverage of topics; *integration of classical methods with modern "resampling" approaches to inference; *consideration of practical, "real world" issues; *numerous examples and applications, all drawn from communication research; *up-to-date information, with examples justifying use of various techniques; and *downloadable resources with macros, data sets, figures, and additional materials. This unique book can be used as a stand-alone classroom text, a supplement to traditional research methods texts, or a useful reference manual. It will be invaluable to students, faculty, researchers, and practitioners in communication, and it will serve to advance the understanding and use of statistical methods throughout the discipline.
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