Significados de hierarchical regression en inglés

Pearson's correlation and hierarchical regression were used for data analysis.

We used hierarchical regression modeling on the subsample of regular patients at the clinic.

Statistical analyses included analyses of covariance and hierarchical regression.

Analyses of variance and hierarchical regression analyses were conducted.

We used hierarchical regression to test the hypotheses.

Prespecified effect modifiers were tested using multivariable hierarchical regression, adjusting for important prognostic factors and clustering effects.

The norms for change included correction for factors found to significantly affect follow-up performance, using hierarchical regression.

Three separate hierarchical regression analyses were conducted for each outcome namely, inhibition, working memory, and cognitive flexibility.

A variety of measures (obtained at baseline) were evaluated using hierarchical regression techniques to predict improvement following treatment.

Main outcome measure: Survival to hospital discharge by AED use, using multivariable hierarchical regression analyses to adjust for patient factors and hospital site.

In this paper, we describe and illustrate the use of a hierarchical regression-based approach to evaluate multiple factors for a small number of organisms individually.

Methods: Using a hierarchical regression model based on 2003 Medicare administrative data, we calculated RSMRs for ranked and nonranked hospitals in the treatment of AMI.

The predictive effect of demographics, depression related characteristics, and disease specific characteristics on change in depressive symptoms was assessed by means of hierarchical regression analyses.

A series of hierarchical regression analyses were conducted to examine the contribution of the model to PA goal intention, implementation intention, self-reported and objective PA.

Multiple hierarchical regression indicated that the Theory of Planned Behavior variables explained 58% of the variance in nurses' intention to adopt an electronic health record.

Hierarchical regression models were used to identify predictors of out-of-bed mobility.