Sentences with phrase «multivariate logistic»

Bivariate and multivariate logistic regression analyses were performed to test the associations between stressful prenatal life events and infectious diseases.

Multivariate logistic regression was used to assess whether attachment relationships were independently associated with posttraumatic stress, depression and anxiety symptoms.

Multivariate logistic regression analyses indicated that past adolescent conduct disorder, being younger and male, symptoms of Akathisia (movement disorder, most often develops as a side effect of antipsychotic medications), and particularly drug abuse increase the risk for CJS involvement.

Second, we conducted multivariate logistic regression analyses in which the association between attachment and mental health symptoms including depression, anxiety and posttraumatic stress symptoms was examined, adjusting for age, gender, orphan status and adverse childhood experiences.

Multivariate logistic regression analyses examined parity progression by birth order, while multinomial logistic regression was used to identify associations between sex composition and use of permanent, temporary and traditional contraceptive methods.

Couple - level multivariate logistic regression models, weighted to account for the complex sampling design, were used in the analysis.

Multivariate logistic regression analysis was used to evaluate the relationship between parenting style and overweight in first grade, controlling for gender, race, maternal education, income / needs ratio, marital status, and child behavior problems.

Multivariate logistic regression analyses * assessing impact of unawareness of the health consequences of tobacco use on attitudes and behaviour towards tobacco control programmes among school personnel from 29 African countries, 2006 — 2011 (n = 17 929)

Multivariate logistic regression models were used to estimate odds ratios (ORs) while adjusting for factors associated with obesity risk using the SAS PROC LOGISTIC procedure (SAS Institute, Inc, Cary, North Carolina).

Finally, we examined the association between sociodemographic variables (child age, sex, race / ethnicity, maternal obesity, maternal education, poverty) and prevalence of having a chronic condition during any part of the 6 - year study period in multivariate logistic regression models that included all participants.

Weighted bivariate and multivariate logistic analyses were used to assess the relationship between maternal depressive symptoms (trichotomized to depression at both time points, at 1 time point, and at neither time point) and parental prevention practices, while controlling for a wide variety of sociodemographic variables.

Factors associated with nicotine dependence among regular smokers according to the multivariate logistic regression analysis (ORs with corresponding 95 % CI)

Multivariate logistic regression models were used to determine the association between IPV and childhood obesity.

Finally, multivariate logistic regression analyses and multivariate regression analyses were conducted to identify the factors associated with the scores and rates on the CBCL (i.e., scores for internalizing, externalizing, and total problems).

After examining the univariate characteristics of the independent variables, we ran 3 multivariate logistic regression models.

Methods We used multivariate logistic regression, using data from the National Longitudinal Survey of Youth, to adjust for multiple confounding factors.

In bivariate and multivariate logistic regression models, 8 social risk factors were tested as independent predictors of 4 parent - reported child health outcomes: global health status, dental health, socioemotional health, and overweight.

We used multivariate logistic regression, using data from the National Longitudinal Survey of Youth, to adjust for multiple confounding factors.

We therefore selected these three tests for inclusion into a final multivariate logistic regression model (Table S9) that used program outcome from only the observed (nonimputed) dataset as the dependent variable.

Using multivariate logistic regression, BDI data will be statistically correlated with OR rate and PFST following CHOP and rescue chemotherapy.

Some online dating sites offering compatibility matching methods use the word similarity as: «a proprietary Dyadic Adjustment Scale», others mean: «a proprietary multivariate linear regression equation», some say a mix of similarity and complementarity meaning: «a proprietary multivariate logistic regression equation», still others mix similarity and complementarity meaning: «a proprietary equation to calculate «compatibility» between prospective mates!»

Results for the final multivariate logistic regression model that included maternal BMI (n = 394 with complete data) are summarized in Table 2.

• In another Australian study, in multivariate logistic regression analyses «feeling close to the unborn baby» and a «high level of knowledge about the effects of passive smoking on baby» were associated with early quit attempts by fathers Moffatt & Stanton (2005).

Not exact matches

Multivariate stepwise logistic regression was used to investigate the potential risk role of intended place of birth.

Multivariate analyses were performed with logistic regression for outcome variables with paternal depression and other covariates as predictors.

In multivariate analysis, stepwise logistic regression analysis demonstrated that FAB (odds ratio [OR] = 0.79, 95 % confidence interval [CI] = 0.66 — 0.94) and the use of CCB (OR = 2.72, 95 % CI = 1.09 — 6.77) were significantly associated with UI at 1 year.

Performed advanced statistical analysis (univariate and multivariate analysis of variance, cluster and path analysis, principle component and factor analysis, analysis of covariance, survival & longitudinal analysis, logistic and linear regression modeling), created customized reports and presentation quality data summary tables and figures.

Topics Include Exploratory Data Analysis, Multiple Regression, Logistic Regression, Correlation, Multivariate Analysis Of Variance (manova), Factorial Analysis Of Variance (anova), Factor Analysis And Principal Components, Discriminant Analysis, Structural Equation Modeling, And Emerging Data Analysis Techniques.

She has technical expertise in a wide range of statistical techniques used in the social sciences, including structural equation modeling, confirmatory factor analysis and MIMIC approaches to measurement, path modeling, regression analysis (e.g., linear, logistic, Poisson), latent class analysis, hierarchical linear models (including growth curve modeling), latent transition analysis, mixture modeling, item response theory, as well as more commonly used techniques drawing from classical test theory (e.g., reliability analysis through Cronbach's alpha, exploratory factor analysis, uni - and multivariate regression, correlation, ANOVA, etc).

Multivariate hierarchical logistic regression was used to evaluate the determinants of being in the optimal versus less optimal feeders group.

To adjust for potential confounding variables and to derive maximal likelihood estimates of combined relative odds with 95 % confidence intervals, multivariate, unconditional, logistic regression analyses were performed for each of the 6 dependent variables.

For multivariate analyses, logistic regression analyses were used to assess the independent associations between CIS scores ≥ 16 and the aforementioned child and parental characteristics.