Sentences with phrase «using hierarchical regression»

Moderation was tested separately for all four family functioning variables by using hierarchical regression with metabolic control as the dependent variable.

Methods: Main - and mediation effects were investigated using hierarchical regression analysis.

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

Not exact matches

Hierarchical multiple regression analyses were used to adjust for the four confounding factors shown in Table 1.

Logistic regression using a hierarchical stepwise method indicated that none of the demographic factors, which were entered first, were found to be predictive of the infants» ability to achieve developmental milestones.

Three analyses were conducted with the cross-sectional data using teachers» WSS ratings of student achievement and students» WJ - R standard scores: a) correlations comparing the students» standard scores on the various subtests of the WJ - R and the WSS checklist and summary report ratings of student achievement within the corresponding WSS domains, b) four - step hierarchical regressions examining the different factors that accounted for the variance in students» spring WJ - R scores, and c) Receiver - Operating - Characteristic (ROC) curves, which make possible a determination of whether a random pair of average and below - average scores on the WJ - R would be ranked correctly in terms of performance on the WSS.

Hierarchical multiple regressions were used to test whether maternal feeding practices could predict changes in child eating behaviours over time.

Several hierarchical regressions were used to determine the relationship between children's social and emotional development, during their preschool years and their academic success.

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).

The researchers used hierarchical linear regression to predict the relation between pain and alcohol - seeking cognitions.

Hierarchical regression analyses using the «Enter» procedure were therefore computed.

To test the hypothesis, Muise conducted an online survey with 308 respondents, age 17 to 24, and used hierarchical multiple regression analysis, controlling for individual, personality and relationship factors (to tease out what's Facebook's contribution to jealousy).

Data analyzed using zero - order correlation and hierarchical regression analysis showed positive correlations of POS and job satisfaction with work performance, and also showed independent and joint positive associations of POS and job satisfaction with OCB and each of its four dimensions.

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

Independent sample t - test was used to compare the level of self - esteem, family function score and social support score between the two groups with and without grandparenting experience; Pearson correlation was calculated to explore how levels of self - esteem and family functions as well as perceived social support were related; Hierarchical regression analysis was applied to examine the moderating effect of social support on the relationship between family function and self - esteem.

Using the first year women's panel data of Korea Women's Development Institute, a series of analyses including Hierarchical Multiple Regression were taken to examine the comparative influence of socio - demographic factors, factors of the interaction with spouse and factors of the interaction with family of origin, Findings of hierarchical multiple regression identified that the influence of interaction with spouse was high, and factors of interaction with family of origin had a significant influence on marital satisfaction even after controlling the influences of otHierarchical Multiple Regression were taken to examine the comparative influence of socio - demographic factors, factors of the interaction with spouse and factors of the interaction with family of origin, Findings of hierarchical multiple regression identified that the influence of interaction with spouse was high, and factors of interaction with family of origin had a significant influence on marital satisfaction even after controlling the influences of otheRegression were taken to examine the comparative influence of socio - demographic factors, factors of the interaction with spouse and factors of the interaction with family of origin, Findings of hierarchical multiple regression identified that the influence of interaction with spouse was high, and factors of interaction with family of origin had a significant influence on marital satisfaction even after controlling the influences of othierarchical multiple regression identified that the influence of interaction with spouse was high, and factors of interaction with family of origin had a significant influence on marital satisfaction even after controlling the influences of otheregression identified that the influence of interaction with spouse was high, and factors of interaction with family of origin had a significant influence on marital satisfaction even after controlling the influences of other factors.

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.

Hierarchical regression analyses were used to examine relations between contingent responsiveness and child compliance, after accounting for the quality of parent directives and parent negativity.

Hierarchical regression analyses were conducted to examine the effect of pc use with a friend, country, hearing status, school setting and age on the online, mixed and offline friendship quality.

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

By using... hierarchical regression analyses potential moderating influences of self - reported trait - mindfulness and trait values of general psychological stress reactivity on stress protective effects of a three months Taiji training were examined.

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

Hierarchical multiple regressions, controlling for age and gender, were used to predict prosocial behaviors and emotional symptoms, and test the moderating role of individual protective factors.

We used hierarchical linear regression analyses to test for program effects on parenting stress, parenting behaviors, mental health, satisfaction with social support, and social support need.

A caveat is that the present study made use of ten hierarchical regressions.

Pearson correlations were used to examine the association among the study variables and hierarchical regression analyses were used to predict body satisfaction at age 16.

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.

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.

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 regression procedures were used to determine interaction effects, that is, whether parental depression, intelligence, or the demographic covariates differed in the relationship with adaptive behavior.

Hierarchical regression analyses were used to test this study's ecological and transactional hypothesis.

Hierarchical multiple regressions were used for the main analyses.

The prediction of children's teacher - rated social skills at 8 y of age from their attachment security at 42 mo of age and the moderating influence of EEG activity was examined for the institutionalized groups (CAUG and FCG) using hierarchical linear regression analysis (see SI Text for further details).