Sentences with phrase «hierarchical regression models»

Final Model Results for Hierarchical Regression Models Predicting Child MVPA and Screen Time From Parenting Styles and Support for PA

We estimated separate hierarchical regression models for men and women, using the person weights of each profile (those of men and women, respectively) as predictors of global marital satisfaction.

An examination of collinearity was undertaken comparing changes in the standard errors and magnitude and sign (positive or negative) of the bivariate analyses results with the standard bivariate regression models for each sex and the full hierarchical regression models.

RESULTS: The hierarchical regression model for job stress explained 26.8 % of... the variance among those with a low monthly income (β = − 0.151, p = 0.021), an irregular diet (β = 0.165, p = 0.014), and high daily work hours (β = 0.380, p = 0.000), showing that these respondents were more likely to report high job stress levels.

RESULTS: The hierarchical regression model for job stress explained 26.8 % of

Not exact matches

Specific statistical areas of expertise include factor and cluster analysis, basic bivariate analyses, repeated measures analyses, linear and hierarchical / mixed models, structural equation modeling, and nonparametric analyses including logistic regression techniques.

In order to estimate the contribution of student SES (calculated as the percentage of students in a school eligible for free or reduced lunch) to relationships described in the path model between the three teacher variables and student achievement, we computed three hierarchical regressions.

Multivariable hierarchical regression permits grouping of related variables (as described earlier in the «Instruments» section), which are then added successively to the model.

In hierarchical regression analysis of SA, social support was present in models 2 and 3, but disappeared after adjusting for substance use and depressive symptoms in model 4 (table 3).

METHODS: Data had a hierarchical structure and were analyzed using multilevel logistic regression models.

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).

Models with dysfunctional emotion regulation as a mediation variable were tested via hierarchical multiple regression analyses and bootstrapping procedure.

A model with cognitive efficiency as a mediator variable was tested using hierarchical multiple regression analysis, with a bootstrapping procedure to examine indirect effects.

In hierarchical regression analyses with the various ENRICH factor scores as dependent variables and job satisfaction as the independent variable in the first block, the two SSQ factors in the second block, and the WOC factors in the third block, between 24 and 38 % of the variance in seven of the nine ENRICH factors (not significant model for «Family & Friends» and «Marriage & Children») could be explained by the variation in all the independent variables with varying weight of the several independent variables (Table 3).

To test both questions, hierarchical regression analyses were performed with demographics entered in block one of each model, the mindfulness subscales entered into block two, and one conflict style entered as the criterion variable for each model.

Latent growth modeling and hierarchical logistic regression models were used to adjust for variations at baseline.

Hierarchical Multiple Regressions Predicting 4 - Month Parenting Stress (β's Denoted for Each Step in Model)

Finally, we used hierarchical regression analyses to test the proposed mediation model (Baron and Kenny 1986).

The hierarchical regressions were significant for nonemergency visits [final adj. R2 =.15, F (12, 237) = 4.66, p <.01], ER visits [adj. R2 =.15, F (12, 237) = 4.72, p <.01], ear infections [adj. R2 =.05, F (12, 237) = 2.07, p <.01], and acute respiratory illnesses [adj. R2 =.08, F (12, 237) = 2.84, p <.01], such that these full regression models accounted for a significant proportion of the variance in children's health care usage.

Model 1 of each hierarchical regression analysis contained a block of demographic variables including parent and child gender, parent's childhood SES, age at parenthood, current family SES, and neighborhood risk.

Because they presented bimodal distributions, the hospitalization and injury outcomes variables were analyzed as dichotomous variables with logistic regressions using the same hierarchical model design.

Moreover, although integrative models were tested by using structural - equation modelling or hierarchical regressions to demonstrate the predictive effect of positive youth development on problem behaviour (Jessor et al. 2003; Lent et al. 2005), these cross-sectional studies did not examine the reverse predictive effect of problem behaviour on positive youth development.

Hierarchical Multiple Regression Predicting 4 - Month Maternal Ratings of Infant Distress to Limitations (β's Denoted for Each Step in Model)

Each hierarchical regression analysis included four successive cumulative models.