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| Author | : Rex B. Kline |
| Publisher | : American Psychological Association (APA) |
| Total Pages | : 363 |
| Release | : 2015-05-09 |
| Genre | : Electronic books |
| ISBN | : 9781433812798 |
Rev ed. of: Beyond significance testing: reforming data analysis methods in behavioral research. c2004.
| Author | : Rex B. Kline |
| Publisher | : Amer Psychological Assn |
| Total Pages | : 325 |
| Release | : 2004-01-01 |
| Genre | : Psychology |
| ISBN | : 9781591471189 |
Annotation "The book is intended for applied researchers and students who may not have quantitative backgrounds. Readers will learn how to measure effect size on continuous or dichotomous outcomes in comparative studies with independent or dependent samples. They will also learn how to calculate and correctly interpret confidence intervals for effect sizes. Numerous research examples from a wide range of areas illustrate how to apply these principles and how to estimate substantive significance instead of just statistical significance. Additional alternatives to statistical tests are described, including meta-analysis, resampling techniques like bootstrapping, and Bayesian estimation."--BOOK JACKET.Title Summary field provided by Blackwell North America, Inc. All Rights Reserved.
| Author | : Rex B. Kline |
| Publisher | : Amer Psychological Assn |
| Total Pages | : 349 |
| Release | : 2013 |
| Genre | : Psychology |
| ISBN | : 9781433812781 |
Traditional education in statistics that emphasises significance testing leaves researchers and students ill prepared to understand what their results really mean. Specifically, most researchers and students who do not have strong quantitative backgrounds have difficulty understanding outcomes of statistical tests. As more and more people become aware of this problem, the emphasis on statistical significance in the reporting of results is declining. Increasingly, researchers are expected to describe the magnitudes and precisions of their findings and also their practical, theoretical, or clinical significance. This accessibly written book reviews the controversy about significance testing, which has now crossed various disciplines as diverse as psychology, ecology, commerce, education, and biology, among others. It also introduces readers to alternative methods, especially effect size estimation (at both the group and case levels) and interval estimation (confidence intervals) in comparative studies. Basics of bootstrapping and Bayesian estimation are also considered. Research examples from substance abuse, education, learning, and other areas illustrate how to apply these methods. A companion website promotes learning by providing chapter exercises and sample answers, downloadable raw data files for many research examples, and links to other useful websites. New to this edition is coverage of robust statistical methods for parameter estimation, effect size estimation, and interval estimation. A new chapter covers the logic and illogic of significance testing. This edition also addresses recent developments such as the new requirements of some journals for the reporting of effect sizes.
| Author | : Cram101 Textbook Reviews |
| Publisher | : Cram101 |
| Total Pages | : 82 |
| Release | : 2013-08 |
| Genre | : |
| ISBN | : 9781490245775 |
Never HIGHLIGHT a Book Again! Includes all testable terms, concepts, persons, places, and events. Cram101 Just the FACTS101 studyguides gives all of the outlines, highlights, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanies: 9781433812781. This item is printed on demand.
| Author | : Deborah G. Mayo |
| Publisher | : Cambridge University Press |
| Total Pages | : 503 |
| Release | : 2018-09-20 |
| Genre | : Mathematics |
| ISBN | : 1108563309 |
Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.
| Author | : Jacob Cohen |
| Publisher | : Routledge |
| Total Pages | : 625 |
| Release | : 2013-05-13 |
| Genre | : Psychology |
| ISBN | : 1134742770 |
Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; * expanded power and sample size tables for multiple regression/correlation.
| Author | : Geoff Cumming |
| Publisher | : Routledge |
| Total Pages | : 549 |
| Release | : 2013-06-19 |
| Genre | : Psychology |
| ISBN | : 1136659188 |
This is the first book to introduce the new statistics - effect sizes, confidence intervals, and meta-analysis - in an accessible way. It is chock full of practical examples and tips on how to analyze and report research results using these techniques. The book is invaluable to readers interested in meeting the new APA Publication Manual guidelines by adopting the new statistics - which are more informative than null hypothesis significance testing, and becoming widely used in many disciplines. Accompanying the book is the Exploratory Software for Confidence Intervals (ESCI) package, free software that runs under Excel and is accessible at www.thenewstatistics.com. The book’s exercises use ESCI's simulations, which are highly visual and interactive, to engage users and encourage exploration. Working with the simulations strengthens understanding of key statistical ideas. There are also many examples, and detailed guidance to show readers how to analyze their own data using the new statistics, and practical strategies for interpreting the results. A particular strength of the book is its explanation of meta-analysis, using simple diagrams and examples. Understanding meta-analysis is increasingly important, even at undergraduate levels, because medicine, psychology and many other disciplines now use meta-analysis to assemble the evidence needed for evidence-based practice. The book’s pedagogical program, built on cognitive science principles, reinforces learning: Boxes provide "evidence-based" advice on the most effective statistical techniques. Numerous examples reinforce learning, and show that many disciplines are using the new statistics. Graphs are tied in with ESCI to make important concepts vividly clear and memorable. Opening overviews and end of chapter take-home messages summarize key points. Exercises encourage exploration, deep understanding, and practical applications. This highly accessible book is intended as the core text for any course that emphasizes the new statistics, or as a supplementary text for graduate and/or advanced undergraduate courses in statistics and research methods in departments of psychology, education, human development , nursing, and natural, social, and life sciences. Researchers and practitioners interested in understanding the new statistics, and future published research, will also appreciate this book. A basic familiarity with introductory statistics is assumed.
| Author | : Stephen Thomas Ziliak |
| Publisher | : University of Michigan Press |
| Total Pages | : 349 |
| Release | : 2008-02-19 |
| Genre | : Business & Economics |
| ISBN | : 0472050079 |
How the most important statistical method used in many of the sciences doesn't pass the test for basic common sense
| Author | : Lisa L. Harlow |
| Publisher | : Routledge |
| Total Pages | : 436 |
| Release | : 2016-03-02 |
| Genre | : Psychology |
| ISBN | : 131724284X |
The classic edition of What If There Were No Significance Tests? highlights current statistical inference practices. Four areas are featured as essential for making inferences: sound judgment, meaningful research questions, relevant design, and assessing fit in multiple ways. Other options (data visualization, replication or meta-analysis), other features (mediation, moderation, multiple levels or classes), and other approaches (Bayesian analysis, simulation, data mining, qualitative inquiry) are also suggested. The Classic Edition’s new Introduction demonstrates the ongoing relevance of the topic and the charge to move away from an exclusive focus on NHST, along with new methods to help make significance testing more accessible to a wider body of researchers to improve our ability to make more accurate statistical inferences. Part 1 presents an overview of significance testing issues. The next part discusses the debate in which significance testing should be rejected or retained. The third part outlines various methods that may supplement significance testing procedures. Part 4 discusses Bayesian approaches and methods and the use of confidence intervals versus significance tests. The book concludes with philosophy of science perspectives. Rather than providing definitive prescriptions, the chapters are largely suggestive of general issues, concerns, and application guidelines. The editors allow readers to choose the best way to conduct hypothesis testing in their respective fields. For anyone doing research in the social sciences, this book is bound to become "must" reading. Ideal for use as a supplement for graduate courses in statistics or quantitative analysis taught in psychology, education, business, nursing, medicine, and the social sciences, the book also benefits independent researchers in the behavioral and social sciences and those who teach statistics.
| Author | : Michel Wedel |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023 |
| Genre | : |
| ISBN | : |
A crisis of confidence in research findings in consumer psychology and other academic disciplines has led to various proposals to abandon, replace, strengthen, or supplement the null hypothesis significance testing paradigm. The proliferation of such proposals, and their often- conflicting recommendations, can increase confusion among researchers. We aim to bring some clarity by proposing five simple principles for the new era of data analysis and reporting of research in consumer psychology. We avoid adding to researchers' confusion and proposing more onerous or rigid standards. Our goal is to offer straightforward practical principles that are easy for researchers to keep in mind while analyzing their data and reporting their findings. These principles involve 1) interpreting p-values as continuous measures of the strength of evidence, 2) being aware of assumptions that determine whether one can rely on p-values, 3) using theory to establish the applicability of findings to new settings, 4) employing multiple measures of evidence and various processes to obtain them, but assigning special privilege to none, 5) reporting procedures and findings transparently and completely. We hope that these principles provide researchers with some guidance and help to strengthen the reliability of the conclusions derived from their data, analyses, and findings.
