Search results for ""Author Wolfgang Wiedermann""

>STATISTICS AND CAUSALITY A one-of-a-kind guide to identifying and dealing with modern statistical developments in causality Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Research focuses on the most up-to-date developments in statistical methods in respect to causality. Illustrating the properties of statistical methods to theories of causality, the book features a summary of the latest developments in methods for statistical analysis of causality hypotheses. The book is divided into five accessible and independent parts. The first part introduces the foundations of causal structures and discusses issues associated with standard mechanistic and difference-making theories of causality. The second part features novel generalizations of methods designed to make statements concerning the direction of effects. The third part illustrates advances in Granger-causality testing and related issues. The fourth part focuses on counterfactual approaches and propensity score analysis. Finally, the fifth part presents designs for causal inference with an overview of the research designs commonly used in epidemiology. Statistics and Causality: Methods for Applied Empirical Research also includes: New statistical methodologies and approaches to causal analysis in the context of the continuing development of philosophical theories End-of-chapter bibliographies that provide references for further discussions and additional research topics Discussions on the use and applicability of software when appropriate Statistics and Causality: Methods for Applied Empirical Research is an ideal reference for practicing statisticians, applied mathematicians, psychologists, sociologists, logicians, medical professionals, epidemiologists, and educators who want to learn more about new methodologies in causal analysis. The book is also an excellent textbook for graduate-level courses in causality and qualitative logic.

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Covers the latest developments in direction dependence research Direction Dependence in Statistical Modeling: Methods of Analysis incorporates the latest research for the statistical analysis of hypotheses that are compatible with the causal direction of dependence of variable relations. Having particular application in the fields of neuroscience, clinical psychology, developmental psychology, educational psychology, and epidemiology, direction dependence methods have attracted growing attention due to their potential to help decide which of two competing statistical models is more likely to reflect the correct causal flow. The book covers several topics in-depth, including: A demonstration of the importance of methods for the analysis of direction dependence hypotheses A presentation of the development of methods for direction dependence analysis together with recent novel, unpublished software implementations A review of methods of direction dependence following the copula-based tradition of Sungur and Kim A presentation of extensions of direction dependence methods to the domain of categorical data An overview of algorithms for causal structure learning The book's fourteen chapters include a discussion of the use of custom dialogs and macros in SPSS to make direction dependence analysis accessible to empirical researchers.

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Dieser Band stellt umfassend die Methoden der Konfigurationsfrequenzanalyse (KFA) vor, eines von G.A. Lienert erstmals eingebrachten Verfahrens zur Testung von Hypothesen in Bezug auf Häufigkeiten in individuellen Zellen oder Gruppen einer Kreuzklassifikation. Die Autoren, die die Methode weiterentwickelt haben, bieten eine umfassende Darstellung der Grundlagen, Modelle und konkreten Anwendungsfälle in der psychologischen und sozialwissenschaftlichen, personen-orientierten Forschung. Dabei werden die Anfänge der KFA und ihr Bezug zur Chi-Quadrat Analyse ebenso beschrieben wie die Entwicklungen, die auf log-linearen Modellen basieren. Für jedes Modell und für jede Fragestellung, die mit der KFA untersucht werden können, werden empirische Datenbeispiele präsentiert. Neue Ergebnisse werden durch Monte-Carlo Simulationen untermauert sowie neue Modelle entwickelt und vorgestellt.Das Buch richtet sich zum einen an Leser*innen, die über grundlegendes Hintergrundwissen in der angewandten Statistik aus einführenden Kursen und Kursen über log-lineare Modelle verfügen. Aber auch Leserinnen und Leser ohne diese Kenntnisse können von diesem Buch profitieren, weil alle nötigen technischen Elemente eigens eingeführt und erklärt werden. Computerprogramme werden vorgestellt und in Beispielen angewendet. Insgesamt stellt sich die KFA als statistische Methode dar, mit der für kategoriale Daten wichtige und interessante Fragen bearbeitet werden können, die im Kontext der Anwendung von Routinemethoden der Statistik nicht zugänglich sind.

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