Confidence Intervals

Author: Michael Smithson
Publisher: SAGE
ISBN: 9780761924999
Format: PDF
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Using lots of easy to understand examples from different disciplines, the author introduces the basis of the confidence interval framework and provides the criteria for `best' confidence intervals, along with the trade-offs between confidence and precision. The book covers such pertinent topics as: the transformation principle whereby a confidence interval for a parameter may be used to construct an interval for any monotonic transformation of that parameter; confidence intervals on distributions whose shape changes with the value of the parameter being estimated; and, the relationship between confidence interval and significance testing frameworks, particularly regarding power.

Bootstrapping

Author: Christopher Z. Mooney
Publisher: SAGE
ISBN: 9780803953819
Format: PDF
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Bootstrapping, a computational nonparametric technique for "re-sampling," enables researchers to draw a conclusion about the characteristics of a population strictly from the existing sample rather than by making parametric assumptions about the estimator. Using real data examples from per capita personal income to median preference differences between legislative committee members and the entire legislature, Mooney and Duval discuss how to apply bootstrapping when the underlying sampling distribution of the statistics cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, they show the advantages and limitations of four bootstrap confidence interval methods: normal approximation, percenti

Quantile Regression

Author: Lingxin Hao
Publisher: SAGE
ISBN: 9781412926287
Format: PDF, Kindle
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Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao and Naiman show, in their application of quantile regression to empirical research, how this model yields a more complete understanding of inequality. Inequality is a perennial concern in the social sciences, and recently there has been much research in health inequality as well. Major software packages have also gradually implemented quantile regression. Quantile Regression will be of interest not only to the traditional social science market but other markets such as the health and public health related disciplines. Key Features: Establishes a natural link between quantile regression and inequality studies in the social sciences Contains clearly defined terms, simplified empirical equations, illustrative graphs, empirical tables and graphs from examples Includes computational codes using statistical software popular among social scientists Oriented to empirical research

The Logic of Causal Order

Author: James A. Davis
Publisher: SAGE
ISBN: 9780803925533
Format: PDF
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This monograph is not statistical. It looks instead at pre-statistical assumptions about dependent variables and causal order. Professor Davis spells out the logical principles that underlie our ideas of causality and explains how to discover causal direction, irrespective of the statistical technique used. He stresses throughout that knowledge of the "real world" is important and repeatedly challenges the myth that causal problems can be solved by statistical calculations alone.

Measures of Association

Author: Albert M. Liebetrau
Publisher: SAGE
ISBN: 9780803919747
Format: PDF, ePub, Mobi
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Clearly reviews the properties of important contemporary measures of association and correlation. Liebetrau devotes full chapters to measures for nominal, ordinal, and continuous (interval) data, paying special attention to the sampling distributions needed to determine levels of significance and confidence intervals. Valuable discussions also focus on the relationships between various measures, the sampling properties of their estimators and the comparative advantages and disadvantages of different approaches.

Bayesian Statistical Inference

Author: Gudmund R. Iversen
Publisher: SAGE
ISBN: 9780803923287
Format: PDF, ePub, Docs
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Empirical researchers, for whom Iversen's volume provides an introduction, have generally lacked a grounding in the methodology of Bayesian inference. As a result, applications are few. After outlining the limitations of classical statistical inference, the author proceeds through a simple example to explain Bayes' theorem and how it may overcome these limitations. Typical Bayesian applications are shown, together with the strengths and weaknesses of the Bayesian approach. This monograph thus serves as a companion volume for Henkel's Tests of Significance (QASS vol 4).

Statistics with Confidence

Author: Douglas Altman
Publisher: John Wiley & Sons
ISBN: 1118702506
Format: PDF, ePub
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This highly popular introduction to confidence intervals has been thoroughly updated and expanded. It includes methods for using confidence intervals, with illustrative worked examples and extensive guidelines and checklists to help the novice.

The SAGE Encyclopedia of Social Science Research Methods

Author: Michael Lewis-Beck
Publisher: SAGE
ISBN: 9780761923633
Format: PDF, ePub
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"The first encyclopedia to cover inclusively both quantitative and qualitative research approaches, this set provides clear explanations of 1,000 methodologies, avoiding mathematical equations when possible with liberal cross-referencing and bibliographies. Each volume includes a list of works cited, and the third contains a comprehensive index and lists of person names, organizations, books, tests, software, major concepts, surveys, and methodologies."--"Reference that rocks," American Libraries, May 2005.

Nonparametric Measures of Association

Author: Jean D. Gibbons
Publisher: SAGE
ISBN: 9780803946644
Format: PDF, ePub, Mobi
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When analyzing data, how should the relationship between two or more sets of observations be described, that is, values of two or more variables, when the variables are ordinal and not bivariate normal? Aimed at helping the researcher select the most appropriate measure of association for two or more variables, the author clearly describes such techniques as Spearman's rho, Kendall's tau, Goodman and Kruskals' gamma and Somer's d and carefully explains the calculation procedures as well as the substantive meaning of each measure. In addition, each technique is illustrated by one or more examples from recent social or behavioural science studies. Finally, Gibbons provides information on the strengths and weaknesses of leading statisti

Understanding The New Statistics

Author: Geoff Cumming
Publisher: Routledge
ISBN: 1136659188
Format: PDF, ePub
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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.