Sentence examples for multinomial logistic analyses from inspiring English sources

Finally, we modelled multinomial logistic analyses to evaluate the combined contribution of variables.

In our multinomial logistic analyses, we examined the relationship between access to health care and failure to detect diabetes in the population who self-reported not having diabetes (Table 3).

In addition to the aforementioned statistical limitations arising from the reliance upon imputation for 47% of children, which were overcome by supplementing linear analyses with multinomial logistic regression analyses, a clear limitation is the difficulty in firmly attributing change to legislation, due to the absence of a counterfactual.

To examine predictors of smoking and snus use, respectively, in multivariate analyses, multi-level multinomial logistic regression analyses were conducted in which students (level 1) were nested within schools (level 2).

* p < .05; * p < .05; Odds ratios (OR) and 95% confidence intervals (CI) from single multinomial logistic regression analyses, variables standardized in regression analyses; n = 1,065 (T1 sociometrics) and 1,007 (T2 sociometrics).

* p < .05; Odds ratios (OR) and 95% confidence intervals (CI) from single multinomial logistic regression analyses (T1, T2 and T3 predictors) and single binary logistic regression analyses (T4 predictors); CBCL = Child Behavior Checklist (parent report); YSR = Youth Self Report; ASR = Adult Self Report; CBCL, YSR and ASR were assessed on a 0 2 scale, but were standardized for analyses.

Analyses of covariance were used for continuous outcomes and logistic or multinomial logistic regression analyses were used for categorical outcomes.

The basic analyses were for "asthma symptoms" and "cough or phlegm", but we also conducted multinomial logistic regression analyses comparing: i) those with asthma symptoms without cough or phlegm; ii) those with cough or phlegm without asthma symptoms; and iii) those with the combination of the two conditions, with the remaining subjects, negative for both symptoms.

Multinomial logistic regression analyses were conducted to identify the sociodemographic predictors of cluster membership and cluster transition.

Multinomial logistic regression analyses were performed on the data using a p value of < 0.05 to determine statistical significance.

Separate multinomial logistic regression analyses were conducted to examine which variables distinguished the violence and victimization groups from those reporting no violence or victimization.