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- W2133132483 abstract "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." @default.
- W2133132483 created "2016-06-24" @default.
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- W2133132483 date "2003-01-01" @default.
- W2133132483 modified "2023-09-27" @default.
- W2133132483 title "Augäpfel, Murmeltiere und Bayes: zur Auswertung stochastischer Daten aus Vollerhebungen" @default.
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