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Using Monte-Carlo-algorithms for the estimation of ß-error: alternative inference strategies for multidimensional contingency tables
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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 te... view more
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).... view less
Keywords
electronic data processing; statistical analysis; sample; data; statistical method; software; statistics
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Method
development of methods; basic research
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