BACKGROUND: W S Robinson made a seminal contribution by demonstrating that correlations for the same two variables can be different at the individual and ecologic level. This study reanalyzes and historically situates Robinson's influential study that laid the foundation for the primacy of analyzing data at only the individual level. METHODS: We applied a binomial multilevel logistic model to analyse variation in illiteracy as enumerated by the 1930 US. Census (the same data as used by Robinson). The outcome was log odds of being illiterate, while predictors were race/nativity ('native whites', 'foreign-born whites' and 'negroes') at the individual-level, and presence of Jim Crow segregation laws for education at the state-level. We conducted historical research to identify the social and scientific context within which Robinson's study was produced and favourably received. RESULTS: Empirically, the substantial state variations in illiteracy could not be accounted by the states' race/nativity composition. Different approaches to modelling state-effects yielded considerably attenuated associations at the individual-level between illiteracy and race/nativity. Furthermore, state variation in illiteracy was different across the race/nativity groups, with state variation being largest for whites and least for foreign-born whites. Strong effects of Jim Crow education laws on illiteracy were observed with the effect being strongest for blacks. Historically, Robinson's study was consonant with the post-World War II ascendancy of methodological individualism. CONCLUSION: Applying a historically informed multilevel perspective to Robinson's profoundly influential study, we demonstrate that meaningful analysis of individual-level relationships requires attention to substantial heterogeneity in state characteristics. The implication is that perils are posed by not only ecological fallacy but also individualistic fallacy. Multilevel thinking, grounded in historical and spatiotemporal context, is thus a necessity, not an option.
BACKGROUND: W S Robinson made a seminal contribution by demonstrating that correlations for the same two variables can be different at the individual and ecologic level. This study reanalyzes and historically situates Robinson's influential study that laid the foundation for the primacy of analyzing data at only the individual level. METHODS: We applied a binomial multilevel logistic model to analyse variation in illiteracy as enumerated by the 1930 US. Census (the same data as used by Robinson). The outcome was log odds of being illiterate, while predictors were race/nativity ('native whites', 'foreign-born whites' and 'negroes') at the individual-level, and presence of Jim Crow segregation laws for education at the state-level. We conducted historical research to identify the social and scientific context within which Robinson's study was produced and favourably received. RESULTS: Empirically, the substantial state variations in illiteracy could not be accounted by the states' race/nativity composition. Different approaches to modelling state-effects yielded considerably attenuated associations at the individual-level between illiteracy and race/nativity. Furthermore, state variation in illiteracy was different across the race/nativity groups, with state variation being largest for whites and least for foreign-born whites. Strong effects of Jim Crow education laws on illiteracy were observed with the effect being strongest for blacks. Historically, Robinson's study was consonant with the post-World War II ascendancy of methodological individualism. CONCLUSION: Applying a historically informed multilevel perspective to Robinson's profoundly influential study, we demonstrate that meaningful analysis of individual-level relationships requires attention to substantial heterogeneity in state characteristics. The implication is that perils are posed by not only ecological fallacy but also individualistic fallacy. Multilevel thinking, grounded in historical and spatiotemporal context, is thus a necessity, not an option.
Authors: S V Subramanian; Jarvis T Chen; David H Rehkopf; Pamela D Waterman; Nancy Krieger Journal: Am J Public Health Date: 2005-02 Impact factor: 9.308
Authors: Lori M Hunter; Raphael Nawrotzki; Stefan Leyk; Galen J Mac Laurin; Wayne Twine; Mark Collinson; Barend Erasmus Journal: Popul Space Place Date: 2014-07-01
Authors: Stephanie B Wheeler; Tzy-Mey Kuo; Ravi K Goyal; Anne-Marie Meyer; Kristen Hassmiller Lich; Emily M Gillen; Seth Tyree; Carmen L Lewis; Trisha M Crutchfield; Christa E Martens; Florence Tangka; Lisa C Richardson; Michael P Pignone Journal: Health Place Date: 2014-07-24 Impact factor: 4.078