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Choices of variables and hypothesis to be tested in the adaptation model include variables such as: use of climate change resilient variety forage stock, crop diversity, crop intensity, change planting date, irrigation, water harvesting, soil and water conservation, fertilizer use and others.
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However, a combined model including variables from both theoretical approaches accounted for the most variance in help-seeking behavior.
Our specification of the model includes variables typically employed in the literature (Dang et al. 2014; Cruces et al. 2011), plus statistically significant variables at the regional level.
A multivariable logistic regression model included variables whose associations with vaccination decisions were statistically significant, defined as p<0.05.
aThe model included variables with statistical significance only.
The imputation model included variables of the logistic regression models and auxiliary variables in the database.
The final model included variables with a p value < 0.10 on univariate analysis.
The final model included variables that were significant at p < 0.05.
The regression model included variables related to the MLHFQ score with P<0.10 calculated in univariate analysis.
The final multivariate model included variables retaining significance after a forward likelihood-ratio stepwise elimination procedure.
a The multivariate model includes variables for which p < 0.10 by univariate analysis.
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CEO of Professional Science Editing for Scientists @ prosciediting.com