Sentences with phrase «multivariate analysis modelled»

Not exact matches

In this study, the association of maternal alcohol drinking in the 3 months before or during pregnancy was of borderline significance on univariate analysis but was not significant when prenatal smoking and case - versus - control status were in the model.39 However, this study had limited power for multivariate analysis because of its small sample size.

The research applied Multivariate Regression Analysis combined with a Logit Model to the real data to identify statistically significant factors that have influenced voting preference simultaneously as well as the odds ratio in favour of Leave.

Summary estimates were calculated using a general variance - based method (random - effects model) with 95 % CIs.19 Because the potential confounders considered in multivariate analyses vary across studies, we used the parameter estimates in the most complex model, which typically include demographic, lifestyle, and dietary factors.

This is why, in our modeling efforts, we do massive multivariate, longitudinal analyses in order to exploit the covariance structure of student data over grades and subjects to dampen the errors of measurement in individual student test scores.

The Tennessee Value - Added Assessment System (TVAAS) has been designed to use statistical mixed - model methodologies to conduct multivariate, longitudinal analyses of student achievement to make

«Estimating teacher productivity using a multivariate multilevel model for value - added analysis.»

The analysis is carried out using a risk sharing and self insurance framework and econometric modeling is carried out using binary outcomes and multivariate probit estimation through GHK (Geweke - Hajivassiliou - Keane) estimator.

I believe I have an unusual perspective to cast on these two arguments, as I have extensive experience with principal components analysis (PCA) as used by Mann et al in the paleo papers, and also multivariate modelling.

Doing multivariate analysis with underspecified models, small samples and metrics with large error terms is questionable at the best of times.

Aires, F., and W.B. Rossow, 2003: Inferring instantaneous, multivariate and nonlinear sensitivities for the analysis of feedback processes in a dynamical system: The Lorenz model case study.

I have experience as a statistical modeler and analyst developing risk models using multivariate techniques, marketing segmentation using clustering, process analysis using decision tree machine learning techniques, and time series analysis for...

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.

Executed inferential statistical analysis through constructing regression models such as multivariate linear

Tags for this Online Resume: Statistics / Multivariate analysis, Marketing Analytics, Predictive modeling, Data mining, CRM, Leadership / communication skills, Project management, Strategic planning, Management, Business Intelligence

A covariate was included in the multivariate analyses if theoretical or empirical evidence supported its role as a risk factor for obesity, if it was a significant predictor of obesity in univariate regression models, or if including it in the full multivariate model led to a 5 % or greater change in the OR.48 Model 1 includes maternal IPV exposure, race / ethnicity (black, white, Hispanic, other / unknown), child sex (male, female), maternal age (20 - 25, 26 - 28, 29 - 33, 34 - 50 years), maternal education (less than high school, high school graduation, beyond high school), maternal nativity (US born, yes or no), child age in months, relationship with father (yes or no), maternal smoking during pregnancy (yes or no), maternal depression (as measured by a CIDI - SF cutoff score ≥ 0.5), maternal BMI (normal / underweight, overweight, obese), low birth weight (< 2500 g, ≥ 2500 g), whether the child takes a bottle to bed at age 3 years (yes or no), and average hours of child television viewing per day at age 3 years (< 2 h / d, ≥ 2 h model led to a 5 % or greater change in the OR.48 Model 1 includes maternal IPV exposure, race / ethnicity (black, white, Hispanic, other / unknown), child sex (male, female), maternal age (20 - 25, 26 - 28, 29 - 33, 34 - 50 years), maternal education (less than high school, high school graduation, beyond high school), maternal nativity (US born, yes or no), child age in months, relationship with father (yes or no), maternal smoking during pregnancy (yes or no), maternal depression (as measured by a CIDI - SF cutoff score ≥ 0.5), maternal BMI (normal / underweight, overweight, obese), low birth weight (< 2500 g, ≥ 2500 g), whether the child takes a bottle to bed at age 3 years (yes or no), and average hours of child television viewing per day at age 3 years (< 2 h / d, ≥ 2 h Model 1 includes maternal IPV exposure, race / ethnicity (black, white, Hispanic, other / unknown), child sex (male, female), maternal age (20 - 25, 26 - 28, 29 - 33, 34 - 50 years), maternal education (less than high school, high school graduation, beyond high school), maternal nativity (US born, yes or no), child age in months, relationship with father (yes or no), maternal smoking during pregnancy (yes or no), maternal depression (as measured by a CIDI - SF cutoff score ≥ 0.5), maternal BMI (normal / underweight, overweight, obese), low birth weight (< 2500 g, ≥ 2500 g), whether the child takes a bottle to bed at age 3 years (yes or no), and average hours of child television viewing per day at age 3 years (< 2 h / d, ≥ 2 h / d).

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.

All of the factors included in the model were associated with exposure to movie smoking (Table 2) and were therefore included in the multivariate analysis.

Sex - difference analyses, multivariate structural equation modeling, and graphic productions were performed with R (version 3.1.2) and AMOS (version 22).

Table 2 reports the results of the analysis aimed at identifying risk factors that distinguish the relatively small group of children (13.9 %) in the high - aggression trajectory group from the other 2 groups in the context of a multivariate model.

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).

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

The FACES 2000 dataset provided a unique opportunity to conduct more complex modeling and multivariate analyses than had been done to date, analyses that capitalized on the longitudinal nature of the data and the multiple domains and repeated measures used.

Moreover, conducting moderator analyses could result in artifactual findings if study characteristics that are tested as moderators would have been used as control variables in the multivariate models of primary studies from which effect sizes are included in the meta - analysis.

We use the expression univariate latent growth curve models to distinguish these analyses from the following multivariate latent growth curve model that simultaneously includes both PA and RA.

None of the genotypes showed significant associations with delinquency in univariate analyses, whereas most interaction terms, as well as several gene main effects, were significant in the multivariate models and validated by complementary statistical analyses.

The effect sizes derived from multivariate models with different control variables are therefore not comparable with each other and also not comparable with effect sizes derived from bivariate analyses.

[jounal] Loken, E. / 2004 / Using latent class analysis to model temperament types / Multivariate Behavioral Research 39 (4): 625 ~ 652