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Using Monte-Carlo-algorithms for the estimation of ß-error : alternative inference strategies for multidimensional contingency tables

[conference paper]

Kelle, Udo; Prein, Gerald

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Abstract Small samples and sparse cell frequencies cause major problems for statistical modelling with categorical data: Sampling zeros or small expected frequencies can lead to situations where asymptotic approximations of test statistics will be inadequate. In such cases one resorts to the use of exact tests or Monte-Carlo-simulations. But also in this ease, inference can yield problematie results, as the power of tests is often extremely low and will therefore lead to the rejection of theoretically plausible hypotheses on the base of poor empirical material. In this paper an alternative modeHing strategy for small samples using Monte-Carlo-algorithms is presented. This strategy is extending the asymptotic power approximations presented by Cohen (1977) or Agresti (1990).
Keywords statistics; statistical analysis; statistical method; electronic data processing; software; sample; data
Classification Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Method basic research; development of methods
Free Keywords statistical modelling; Monte-Carlo-Algorithms
Collection Title Softstat '93: advances in statistical software 4
Editor Faulbaum, Frank
Document language English
Publication Year 1994
Publisher G. Fischer
City Stuttgart u.a.
Page/Pages p. 559-566
ISBN 3-437-40324-9
Status Published Version; reviewed
Licence Creative Commons - Attribution-Noncommercial-No Derivative Works