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Our interest with this paper is in making inferences at a cultural level, relevant to understanding how ideas and values circulate in society and are consumed by individuals.
Table 2 also shows that raters were most likely to be making inferences at T1, when they were forming their first impressions.
Finally, although we have attempted to account for variations in important individual-level factors, we must be cautious in making inferences at the regional level as there is substantial diversity in socio-cultural practices and beliefs within regions.
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We progress through a series of models with increasing complexity that incorporate an increasing number of features that seem desirable in a (meta community model, especially: (1) the ability to make inferences at multiple levels (metacommunity, local community, individual species) and (2) correction for measurement error.
Finally, in order to make inferences at the population level, individual contrast images were incorporated into a random effects model [34], [35].
Here, we present a hierarchical Bayes state-space framework that allows us to combine tracking data from a collection of animals and make inferences at both individual and broader levels.
Additionally, while we aggregate all states together to make inferences at the national level, this approach could overlook effects at the state level.
A weighted analysis may provide better estimation when using aggregated data from random samples of a population to make inferences at the individual level.
The summary data for each individual were incorporated into the 2nd-level analysis using a random effect model, in order to make inferences at a population level.
For the imaging findings to be clinically useful, one must be able to make inferences at the individual rather than the group level.
Record linkage is widely used in population-based studies to make inferences about specific outcomes and in cohort studies to make inferences at the level of the individual (3, 7– 10).
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