Sentences with phrase «as hierarchical regression»

Not exact matches

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.

Two hierarchical multiple regressions were calculated, both of which included estimated METs per week, mean choice reaction time, age, and gender as predictors at the first level and the five NEO-FFI scores at the second - level.

As shown in the results of the Pearson's correlations and the hierarchical regression analysis, social support had a significant negative association with PTSD symptoms, and this finding is consistent with other researches.9 36 51 52 The level of PTSD symptoms was significantly and negatively correlated with the healthcare workers» scores for objective support and utilisation of support.

Hierarchical multiple regression analyses indicated that commonly investigated psychosocial factors such as affectivity, coping, and social support moderated the relationship between perceived stress and one illness behavior (report of illness without visits to the doctor).

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.

Means and Standard Deviations of General Liking and Romantic Attraction as a Function of Availability, Commitment, and Sociosexuality Predicting the Target s General Likability: Hierarchical Regression Analyses Predicting Romantic Attraction toward a Target: Hierarchical Regression Analyses.

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).

Drawing on three waves of data collected from an ethnically diverse sample of middle school girls (n = 912), hierarchical multiple regression analyses revealed that more advanced development at the start of middle school predicted peer - and teacher - reported popularity as well as increased risk of being targeted for rumors.

Summary of hierarchical regression analyses testing peer - and teacher - reported popularity among boys as a mediator of the link between pubertal timing and rumors

Hierarchical regression analyses were conducted to examine the effects of friendship quality on the adolescents» well - being as a function of country and hearing status.

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

Therefore, given that only these four parameters were significantly associated with CU traits and ODD problems (teacher rate), we further conducted four separate multiple hierarchical regression analyses, one for each of these parameters, in order to examine the contributions of CU traits, anxiety, ODD - related problems and their interactions on attentional processing of emotional faces as indexed by these parameters.

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.

To test our hypothesis that DO would moderate the association between social support and crying proneness, we followed generally established procedures (Aiken & West, 1991) and conducted two hierarchical linear regressions (one for family and friend support, the other for the social provisions variable) with crying proneness as the outcome variable.

As seen in Step 2 of the hierarchical linear regression predicting crying proneness from destructive overdependence, family and friend support, and their interactions (Table 2), being female, in a relationship, and highly stressed predicted a greater tendency to cry.

Here, we conducted two hierarchical regression analyses with demographics entered into block one, the four mindfulness subscales entered into block two, and conflict separation and conflict avoidance as the two criterion variables.

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

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.

In the hierarchical regression for predicting T2 Conduct Problems, for example, T1 Conduct Problems was entered together with T1 Direct Aggression as independent variables at step 2, and the PANIBI measures of Direct Aggression was found to contribute to the prediction of T2 Conduct Problems even when T1 Conduct Problems was controlled for (see Table 8); this result can only be due to the non-overlapping parts of these measures.

A three - step, hierarchical regression analysis was performed to predict change in generalized anxiety from cognitive vulnerabilities, sub-dimensions of psychological well - being, and their interaction (as well as T1 generalized anxiety).

Results of a hierarchical multiple regression analyses showed that wives» perceptions of husbands» rejection predicted children's perceptions of maternal rejection, as well as — but to a significantly lesser extent — children's perceptions of paternal rejection.

Summary of hierarchical regression analyses for children's birth status and children's sustained selective attention performance predicting children's problem behavior, as reported by mothers and teachers

Hierarchical multiple regressions were performed for nonemergency services, ER visits, ear infections, and acute respiratory illnesses, as they were continuous outcome variables.

As can be seen in Table II, these effects were maintained in the final step of the hierarchical regression, which included interaction terms.

Results of hierarchical multiple regressions indicated that sibling attachment uniquely influenced conflict and cooperation in the sibling relationship even after controlling for the effects of attachment to mothers, fathers and peers, as well as the reported warmth between siblings.

To explore main and moderating effects, we conducted a hierarchical regression analysis, to test for linear associations between exposure to bullying behaviors and symptoms of anxiety, as well as the interactive effects of exposure to bullying and the ability to defend, with regard to anxiety.

At a preliminary stage, before testing hypotheses with Hierarchical Regression Analyses, Confirmatory Factor Analyses (CFAs) as implemented by AMOS [50].