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We fitted general linear mixed models (GLMM) using MlwiN 2.02, with two hierarchical levels, female ID nested in block, to control for our experimental design.
We controlled for variation across blocks by including the block as a random effect in our analysis, and included the 'pair code' as a random effect nested within the block to control for the genetic pedigree of the beetles.
Go trials comprised separate random selections from all correct go trials and were matched in number to correct stop or shift trials in that block to control for the same number of trials.
Hierarchical logistic regression was used with school in the first block to control for clustering of students within schools, with the exception of analysis of regular smokers where sample sizes in individual schools were insufficient.
Only the predictor variables that were statistically significant in the bivariate analyses (p <.05) were entered into the multivariate logistic regression models all in a single block to control for possible confounding between these variables.
The PF dams were restricted to the caloric intake of their respective weight-matched ethanol dam within each block to control for potential nutritional deficits that might arise due to the ET dam voluntarily consuming less diet.
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The glasshouse was divided into two blocks to control for natural gradients of sunlight, temperature and humidity.
We allocated treatment using a randomized block design, with capture day as a blocking factor to control for potential seasonal effects.
Messenger RNA and protein expression levels were analyzed by analysis of variance (ANOVA) using experiment as a blocking factor to control for experiment-to-experiment variability.
Two replicates of each feature block are employed to control for the reproducibility of the measurement.
Age was entered in the first block in order to control for the effects of this variable.
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