Compară metode

	Ecological Fallacy Diagnostics	Spatial Scan Statistic
Domeniu	Social Epidemiology	Spatial Epidemiology
Familie	Process / pipeline	Process / pipeline
Anul apariției	1950	1997
Autorul original	William S. Robinson (ecological correlation); Sander Greenland & Hal Morgenstern (ecological bias theory)	Martin Kulldorff (with Neville Nagarwalla)
Tip	Diagnostic and design pipeline for detecting and avoiding cross-level inferential bias	Likelihood-ratio scanning procedure for detecting and testing geographic disease clusters
Sursa seminală	Robinson, W. S. (1950). Ecological Correlations and the Behavior of Individuals. American Sociological Review, 15(3), 351-357.	Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics - Theory and Methods, 26(6), 1481-1496.
Denumiri alternative	Cross-Level Bias Diagnostics, Ecological Bias Assessment, Aggregation Bias Diagnostics, Ecological Inference Bias Checks	Kulldorff Scan Statistic, SaTScan Cluster Detection, Circular Scan Statistic, Spatial Likelihood-Ratio Scan
Înrudite	4	4
Rezumat	Ecological fallacy diagnostics are the design and analysis tools used to detect, quantify, and avoid the bias that arises when associations measured on groups are mistakenly taken to hold for individuals. The problem was crystallized by W. S. Robinson (1950), who showed that the correlation between, say, immigrant share and illiteracy across U.S. states bore no resemblance to the correlation between being an immigrant and being illiterate among individuals, sometimes even reversing sign. Greenland and Morgenstern (1989) gave the modern account, decomposing ecological bias into within-group confounding, effect modification, and model misspecification, and clarifying that the ecological fallacy is not a single artifact but a family of cross-level biases. As a pipeline, the diagnostics contrast ecological and individual associations, attribute any discrepancy to its sources, model the within-group covariate distribution that aggregate analyses ignore, place bounds on the individual-level quantity, and where possible move to hybrid or multilevel designs that recover individual effects.	The spatial scan statistic is a likelihood-ratio method for detecting localized clusters of disease without pre-specifying where they are. Introduced by Martin Kulldorff and Neville Nagarwalla (1995) and generalized by Kulldorff (1997), it slides a circular window of varying size and position across the study region, and for each candidate window compares the observed-to-expected case ratio inside the window against outside it using a likelihood ratio under a Poisson or Bernoulli model. The window that maximizes the likelihood ratio is the most likely cluster, and its statistical significance is obtained by Monte Carlo simulation under the null of no clustering, which correctly accounts for the enormous multiplicity of windows examined. Implemented in the widely used SaTScan software, the method has become the standard tool for screening surveillance data for spatial and space-time disease clusters.