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Augäpfel, Murmeltiere und Bayes: Zur Auswertung stochastischer Daten aus Vollerhebungen

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  • Broscheid, Andreas
  • Gschwend, Thomas

Abstract

In diesem Papier diskutieren wir theoretisch-methodologische Grundlagen zur Analyse so genannter Vollerhebungen, also Datensätze, die Beobachtungen aller Elemente einer Population enthalten. Solche Datensätze spielen vor allem in quantitativen Makro-Analysen politischer und sozialer Systeme eine Rolle, und ihre inhärenten Probleme führen oft zu methodischer Verwirrung, die wir mit dem vorliegenden Essay verringern wollen. Da Vollerhebungen nicht das Resultat einer Zufallsstichprobe sind, ist die Anwendung frequentistischer Wahrscheinlichkeitskonzeptionen zur Begründung inferentieller statistischer Methoden nicht gegeben; außerdem kann die statistische Unabhängigkeit der Beobachtungen voneinander nicht ohne weiteres angenommen werden. Dennoch werden Vollerhebungsdaten durch stochastische Komponenten oder 'Fehler' beeinflusst. Wir argumentieren, dass die Stochastizität der Daten in die Analyse einbezogen werden muss, etwa in Form von Parameter-Varianzen, Signifikanztests, oder Konfidenzintervallen. Wir diskutieren verschiedene theoretische Strategien, mit denen Analysen der Stochastizität begründet werden können, wobei wir vor allem für die Annahme von Superpopulationen oder die Anwendung bayesianischer Ansätze plädieren. -- This paper discusses the theoretical and methodological foundation for the analysis of apparent populations, i.e., data sets that include observations of all elements of a population. Such data sets are commonly used in quantitative macro studies of political or social systems. Our essay tries to reduce methodological problems resulting from the fact that apparent population data are not the result of random sampling designs. First, the lack of random sampling prevents the use of the frequentist interpretation of probability commonly employed to justify inferential statistical methods. Second, with apparent populations, we cannot assume that observations are statistically independent. We argue that apparent population data are subject to a variety of stochastic processes, or 'errors,' that have to be part of the analysis, for example through the investigation of parameter variances, significance tests, or confidence intervals. We discuss several theoretical strategies to justify the analysis of stochastic components of apparent populations, emphasizing in particular the concept of superpopulations and the usefulness of Bayesian approaches.

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Bibliographic Info

Paper provided by Max Planck Institute for the Study of Societies in its series MPIfG Working Paper with number 03/7.

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Date of creation: 2003
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Handle: RePEc:zbw:mpifgw:037

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  1. Broscheid, Andreas & Teske, Paul E, 2003. " Public Members on Medical Licensing Boards and the Choice of Entry Barriers," Public Choice, Springer, vol. 114(3-4), pages 445-59, March.
  2. A. P. Thirlwall, 1983. "Introduction," Journal of Post Keynesian Economics, M.E. Sharpe, Inc., vol. 5(3), pages 341-344, April.
  3. A. Meltzer & Peter Ordeshook & Thomas Romer, 1983. "Introduction," Public Choice, Springer, vol. 41(1), pages 1-5, January.
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