Sentences with phrase «multilevel models examining»

We explored this issue further by estimating additional multilevel models examining the difference between G1 and G2 reports (G1 − G2) as predictors of target (G2) reports and offspring (G3) reports.

Not exact matches

Group differences in cortisol and DHEA - S were examined by using multilevel modeling.

Jennifer A. Theiss, Denise Haunani Solomon; Coupling Longitudinal Data and Multilevel Modeling to Examine the Antecedents and Consequences of Jealousy Experiences in Romantic Relationships: A Test of the Relational Turbulence Model, Human Communication Research, Volume 32, Issue 4, 1 October 2006, Pages 469 — 503, https://doi.org/10.1111/j.1468-2958.2006.00284.x

We used longitudinal data and multilevel modeling to examine how intimacy, relational uncertainty, and failed attempts at interdependence influence emotional, cognitive, and communicative responses to romantic jealousy, and how those experiences shape subsequent relationship characteristics.

The authors employed multilevel and instrumental variables models to examine class size effects on fourth graders» reading achievement in Greece.

Next, we used multilevel models to examine whether relationship qualities are transmitted from the grandmother and grandfather relationships with middle - aged target (G1 — G2) to the target — offspring relationship (G2 - G3).

We used multilevel models to examine associations between intensive grandparental childcare and contextual - structural and cultural factors, after controlling for grandparent, parent, and child characteristics using nationally representative data from the Survey of Health, Ageing and Retirement in Europe.

Multilevel modeling was used to examine which actor — partner effects of these factors were predictive of individuals and their partners having had UAI within and outside the relationship.

Using a sample of 526 third - to sixth - grade students and 69 teachers, multilevel modelling was conducted to examine teachers» reports of students» externalizing, internalizing, and prosocial behaviours as factors affecting TSE with respect to individual students in various domains (instructional strategies, behaviour management, student engagement, and emotional support).

This paper illustrates a method for operationalizing affect dynamics using a multilevel stochastic differential equation (SDE) model, and examines how those dynamics differ with age and trait - level tendencies to deploy emotion regulation strategies (reappraisal and suppression).

Next, we used multilevel modeling to examine the longitudinal or lagged relations between predictor variables and metabolic control.

Drawing on longitudinal data from the Toledo Adolescent Relationships Study (TARS)(N = 1242) and multilevel modeling, analyses examine direct and indirect ways that traditional parenting practices, as well as parental histories of problematic behavior influence trajectories of offspring antisocial behavior.

First, we used multilevel modeling to examine the concurrent relations of the psychosocial variables to metabolic control across the four waves of assessment.

This general approach — to first quantify the intradyad relationships and then examine interdyad differences in the intradyad relationships — is the basis for most contemporary dyadic data analysis techniques, including sequential and state space grid analyses, coupled dynamic systems, and multilevel modeling (Bakeman & Gottman, 1997; Bakeman & Quera, 2011; Boker & Laurenceau, 2007; Gonzalez & Griffin, 2012; Gottman, Murray, Swanson, Tyson, & Swanson, 2002; Hollenstein, 2013; Laurenceau & Bolger, 2005; Ram & Pedersen, 2008).

Finally, and most importantly, multilevel modeling allows one to examine individual variability in rates of change.