2015
DOI: 10.1002/ajp.22405
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Taking the aggravation out of data aggregation: A conceptual guide to dealing with statistical issues related to the pooling of individual‐level observational data

Abstract: Field data often include multiple observations taken from the same individual. In order to avoid pseudoreplication, it is commonplace to aggregate data, generating a mean score per individual, and then using these aggregated data in subsequent analyses. Aggregation, however, can generate problems of its own. Not only does it lead to a loss of information, it can also leave analyses vulnerable to the "ecological fallacy": the drawing of false inferences about individual behavior on the basis of population level… Show more

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Cited by 59 publications
(45 citation statements)
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References 99 publications
(127 reference statements)
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“…This structure was used to explore individual and contextual ethnic level factors associated with the outcome variables. Recognising this clustering within a multilevel model distinguishes between the individual and higher-level variation avoiding incorrect inferences based on ecological fallacies 54 . However, although there is no consensus on sample size for multilevel analysis, it is generally recommended that it should be over 15 to avoid standard errors being underestimated 43 .…”
Section: B) Statistical Analysismentioning
confidence: 99%
“…This structure was used to explore individual and contextual ethnic level factors associated with the outcome variables. Recognising this clustering within a multilevel model distinguishes between the individual and higher-level variation avoiding incorrect inferences based on ecological fallacies 54 . However, although there is no consensus on sample size for multilevel analysis, it is generally recommended that it should be over 15 to avoid standard errors being underestimated 43 .…”
Section: B) Statistical Analysismentioning
confidence: 99%
“…This applies both across populations (such that the United States may be markedly different from the United Kingdom), but also within populations (such as the distinction between rural and urban areas). A failure to account for such heterogeneity may lead to false inferences (Mace 2008; Pollet et al 2015). For example, a recent study found that fewer children were born in wealthier urban areas of Mongolia than in poorer rural areas; yet, within both urban and rural areas, there was a positive association between resources and fertility (Alvergne and Lummaa 2014).…”
Section: The Use Of Secondary Data In Studying Fertility Behavior In mentioning
confidence: 99%
“…We should reiterate that our paper does not deal with issues regarding to pooling across trials (e.g., Kievit et al, 2013;Pollet et al, 2015). When averaging across multiple trials, it is possible that a small number of trials are driving the effect.…”
Section: Discussionmentioning
confidence: 97%
“…Also, this paper is not intended as an introduction into statistical simulations (e.g., Stulp & Barrett, 2013). Finally, the purpose of this paper is explicitly not to discuss the potential problems of pooling data across trials, which have been discussed extensively elsewhere (e.g., Kievit et al, 2013;Pollet et al, 2015). Instead, the purpose of this short methodological note is to derive the 'true' probability for experiments varying in sample size and number of trials.…”
Section: Introductionmentioning
confidence: 99%