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Title: Relative Distribution Methods
Resulting in 1 citation.
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Handcock, Mark S. Morris, Martina |
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Relative Distribution Methods Sociological Methodology 28 (1998): 53-97. Also: http://depts.washington.edu/socmeth2/2abst98.htm Cohort(s): NLS General Publisher: American Sociological Association Keyword(s): Data Analysis; Income Level; Methods/Methodology; Statistics; Variables, Independent - Covariate; Wage Levels Permission to reprint the abstract has not been received from the publisher. Presents an outline of relative distribution methods, with an application to recent changes in the US wage distribution, using data from the 1966 & 1979 panels of the National Longitudinal Survey. Relative distribution methods are a nonparametric statistical framework for analyzing data in a fully distributional context. The framework combines the graphical tools of exploratory data analysis with statistical summaries, decomposition, & inference. The relative distribution is similar to a density ratio, & technically defined as the random variable obtained by transforming a variable from a comparison group by the cumulative distribution function (CDF) of that variable for a reference group. This transformation produces a set of observations, the relative data, that represent the rank of the original comparison value in terms of the reference group's CDF. The density & CDF of the relative data can be used to fully represent & analyze distributional differences, allowing analysis to move beyond comparisons of means & variances. The analytic framework is general & flexible, as the relative density is decomposable into the effect of location & shape differences, & into effects that represent both compositional changes in covariates & changes in the covariate-outcome variable relationship. 5 Tables, 6 Figures, 2 Appendixes, 67 References. Adapted from the source document |
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Bibliography Citation
Handcock, Mark S. and Martina Morris. "Relative Distribution Methods." Sociological Methodology 28 (1998): 53-97.
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