Reducing bias in ecological studies: an evaluation of different methodologies
Statistical methods of ecological analysis that attempt to reduce ecological bias are empirically evaluated to determine in which circumstances each method might be practicable. The method that is most successful at reducing ecological bias is stratified ecological regression. It allows individual l...
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| Veröffentlicht in: | Journal of the Royal Statistical Society. Series A, Statistics in society Statistics in society, 2006-10, Vol.169 (4), p.681-700 |
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| container_end_page | 700 |
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| container_issue | 4 |
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| container_title | Journal of the Royal Statistical Society. Series A, Statistics in society |
| container_volume | 169 |
| creator | Lancaster, Gillian A. Green, Mick Lane, Steven |
| description | Statistical methods of ecological analysis that attempt to reduce ecological bias are empirically evaluated to determine in which circumstances each method might be practicable. The method that is most successful at reducing ecological bias is stratified ecological regression. It allows individual level covariate information to be incorporated into a stratified ecological analysis, as well as the combination of disease and risk factor information from two separate data sources, e.g. outcomes from a cancer registry and risk factor information from the census sample of anonymized records data set. The aggregated individual level model compares favourably with this model but has convergence problems. In addition, it is shown that the large areas that are covered by local authority districts seem to reduce between-area variability and may therefore not be as informative as conducting a ward level analysis. This has policy implications because access to ward level data is restricted. |
| doi_str_mv | 10.1111/j.1467-985X.2006.00418.x |
| format | Article |
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Series A, Statistics in society</title><description>Statistical methods of ecological analysis that attempt to reduce ecological bias are empirically evaluated to determine in which circumstances each method might be practicable. The method that is most successful at reducing ecological bias is stratified ecological regression. It allows individual level covariate information to be incorporated into a stratified ecological analysis, as well as the combination of disease and risk factor information from two separate data sources, e.g. outcomes from a cancer registry and risk factor information from the census sample of anonymized records data set. The aggregated individual level model compares favourably with this model but has convergence problems. In addition, it is shown that the large areas that are covered by local authority districts seem to reduce between-area variability and may therefore not be as informative as conducting a ward level analysis. 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| fulltext | fulltext |
| identifier | ISSN: 0964-1998 |
| ispartof | Journal of the Royal Statistical Society. Series A, Statistics in society, 2006-10, Vol.169 (4), p.681-700 |
| issn | 0964-1998 1467-985X |
| language | eng |
| recordid | cdi_proquest_miscellaneous_36432248 |
| source | Jstor Complete Legacy; Oxford University Press Journals All Titles (1996-Current); RePEc; Wiley Online Library Journals Frontfile Complete; JSTOR Mathematics & Statistics; EBSCOhost Business Source Complete |
| subjects | Aggregate analysis Aggregated compound multinomial model Aggregated individual level model Applications Bias Binomial model Biology, psychology, social sciences Data analysis Disease models Diseases Ecological analysis Ecological modeling Ecological regression Ecology Empirical research Epidemiologic bias Exact sciences and technology Insurance, economics, finance Mathematical foundations Mathematics Methodology Modeling Parametric models Predisposing factors Probability and statistics Regression analysis Risk Sampling theory, sample surveys Sciences and techniques of general use Social classes Statistical methods Statistics Stratified ecological analysis Wetland ecology |
| title | Reducing bias in ecological studies: an evaluation of different methodologies |
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