non experimental data hypothesis testing von engsted tom (21 Ergebnisse)

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Paperback or Softback. Zustand: New. Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective 0.27. Book.

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Paperback. Zustand: New. Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. Frequentist tests comprise incompatible repeated sampling frameworks that do not obey the Likelihood Principle (LP). For probabilistic inference, methods that are guided by the LP, that do not rely on repeated sampling, and that focus on model comparison instead of testing (e.g., subjectivist Bayesian methods) are better suited for passively observed social science data and are better able to accommodate the huge model uncertainty and highly approximative nature of structural models in the social sciences. In addition to formal probabilistic inference, informal model evaluation along relevant substantive and practical dimensions should play a leading role. The authors sketch the ideas of an alternative paradigm containing these elements.

Paperback. Zustand: New. Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. Frequentist tests comprise incompatible repeated sampling frameworks that do not obey the Likelihood Principle (LP). For probabilistic inference, methods that are guided by the LP, that do not rely on repeated sampling, and that focus on model comparison instead of testing (e.g., subjectivist Bayesian methods) are better suited for passively observed social science data and are better able to accommodate the huge model uncertainty and highly approximative nature of structural models in the social sciences. In addition to formal probabilistic inference, informal model evaluation along relevant substantive and practical dimensions should play a leading role. The authors sketch the ideas of an alternative paradigm containing these elements.

Paperback. Zustand: New. Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. Frequentist tests comprise incompatible repeated sampling frameworks that do not obey the Likelihood Principle (LP). For probabilistic inference, methods that are guided by the LP, that do not rely on repeated sampling, and that focus on model comparison instead of testing (e.g., subjectivist Bayesian methods) are better suited for passively observed social science data and are better able to accommodate the huge model uncertainty and highly approximative nature of structural models in the social sciences. In addition to formal probabilistic inference, informal model evaluation along relevant substantive and practical dimensions should play a leading role. The authors sketch the ideas of an alternative paradigm containing these elements.

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Paperback. Zustand: New. Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. Frequentist tests comprise incompatible repeated sampling frameworks that do not obey the Likelihood Principle (LP). For probabilistic inference, methods that are guided by the LP, that do not rely on repeated sampling, and that focus on model comparison instead of testing (e.g., subjectivist Bayesian methods) are better suited for passively observed social science data and are better able to accommodate the huge model uncertainty and highly approximative nature of structural models in the social sciences. In addition to formal probabilistic inference, informal model evaluation along relevant substantive and practical dimensions should play a leading role. The authors sketch the ideas of an alternative paradigm containing these elements.

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Paperback. Zustand: Brand New. 78 pages. 9.21x6.14x0.16 inches. In Stock.

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Paperback. Zustand: new. Paperback. Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. Frequentist tests comprise incompatible repeated sampling frameworks that do not obey the Likelihood Principle (LP). For probabilistic inference, methods that are guided by the LP, that do not rely on repeated sampling, and that focus on model comparison instead of testing (e.g., subjectivist Bayesian methods) are better suited for passively observed social science data and are better able to accommodate the huge model uncertainty and highly approximative nature of structural models in the social sciences. In addition to formal probabilistic inference, informal model evaluation along relevant substantive and practical dimensions should play a leading role. The authors sketch the ideas of an alternative paradigm containing these elements. Argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. The authors sketch the ideas of an alternative paradigm. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. Frequentist tests comprise incompatible repeated sampling frameworks that do not obey the Likelihood Principle (LP). For probabilistic inference, methods that are guided by the LP, that do not rely on repeated sampling, and that focus on model comparison instead of testing (e.g., subjectivist Bayesian methods) are better suited for passively observed social science data and are better able to accommodate the huge model uncertainty and highly approximative nature of structural models in the social sciences. In addition to formal probabilistic inference, informal model evaluation along relevant substantive and practical dimensions should play a leading role. The authors sketch the ideas of an alternative paradigm containing these elements.

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