Sentences with phrase «between multilevel models»

A comparison between multilevel models demonstrated that sleep was a better predictor of physical activity than morning ratings of pain intensity or mood.

Not exact matches

Individual growth curve models were developed for multilevel analysis and specifically designed for exploring longitudinal data on individual changes over time.23 Using this approach, we applied the MIXED procedure in SAS (SAS Institute) to account for the random effects of repeated measurements.24 To specify the correct model for our individual growth curves, we compared a series of MIXED models by evaluating the difference in deviance between nested models.23 Both fixed quadratic and cubic MIXED models fit our data well, but we selected the fixed quadratic MIXED model because the addition of a cubic time term was not statistically significant based on a log - likelihood ratio test.

Multilevel logistic regression was used to estimate the odds ratios (ORs) for conversion to laparotomy, CRM +, intraoperative complications, and postoperative complications between treatment groups, adjusting for the stratification factors, where operating surgeon was modeled as a random effect.

To estimate the proportion of each racial disparity attributable to within - plan differences and to determine the correlation between the outcome measure results and racial disparities in the results, we fitted multilevel linear regression models predicting the result of each HEDIS indicator.

Design, Setting, and Patients We assessed the relationship between quality and racial disparity using multilevel multivariable regression models.

Our multilevel probit model, however, did not show a significant correlation between moving party repeat player status and summary judgment success.

To address the limited empirical research on the putative educational impact of such policies, this study used multilevel structural equation models to investigate the longitudinal associations between teacher evaluation and reward policies, and student mathematics achievement and dropout with a national sample of students (n = 7,779) attending one of 431 public high schools.

Effects of Differential Item Discriminations between Individual - Level and Cluster - Level under the Multilevel Item Response Theory Model

Unlike logistic models with only one random error capturing all the variance in the outcome that is unexplained by the model, multilevel models divide the residual variance into three levels, allowing us to capture variation between (i) different parents with the same grandparents; (ii) different grandparent households within the same country, and (iii) different countries.

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.

Thus, available data at each assessment for the entire sample were used in the multilevel models conducted using SAS software, version 9.2.29 The primary outcome was the least - squares mean difference in clinician - rated PTSD symptoms, derived from these models (see below), from pretreatment to posttreatment compared between the CBCT and wait - list groups.

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.

First, multilevel modelling was used to estimate the impact of CfC by comparing the difference between CfC and comparison sites in the outcome measures at wave 3 after taking account of demographic variables (see table 2).

We will use two - level multilevel linear and logistic regression models (mothers and babies nested within areas) to compare outcomes between individuals living in an AMIHS area compared with individuals who live in a propensity - matched comparison area, using an intention - to - treat approach.

In the dynamic relationship between research and intervention underlined in this paper, it should be emphasized that interventions should not work with models that explain development and change from a lineal or even interactive perspective, since empirical evidence shows data in favor of transactional models that involve much more complex multilevel dynamic systems (Sameroff, 2010).

Because the children are nested within families, we have used multilevel modeling, which takes into account the absence of independence between siblings within families and allows for one than one positive case at the family level.

In conclusion, these findings illustrate the usefulness of the new multilevel TAR model, as well as the importance of considering the dependence between spouses» characteristics in dyadic research.

Suls et al. (1998) found a positive relationship between neuroticism and inertia, obtained with the multilevel AR model.

Multilevel modeling of data from 158 couples revealed that, at baseline, dyadic adjustment moderated the association between blame and distress for patients but not spouses (p < 0.05).

The multilevel TAR model allows the inertia parameter to be state - dependent, such that the strength of regulation could differ between (more) positive and (more) negative behavior.

Multilevel autoregressive model with restricted maximum likelihood estimator was utilized in order to explore cross-lagged associations between schema modes and personality psychopathology scores over subsequent measurements at baseline, 6, 12, 18, 24 and 36 months.

Estimating between and within individual variation in cortisol levels using multilevel modeling

The moderating effect of emotion differentiation on the relation between the specific emotions and intrinsic motivation was tested by means of a series of two - level multilevel regression models using the lme4 package in R.

First we conducted an additional analysis in the multilevel models that included a four - category couple drinking variable and gender as well as the interaction between gender and couple drinking categories as the predictors.

Multilevel modeling was employed to test the hypothesis that the association between maternal acculturation and adolescents» conduct problems could be explained by differences in mothers» reliance on monitoring and harsh discipline.

We employed structural equation modeling and supplementary multilevel modeling, finding consistent evidence that the association between delinquency and the parent — child relationship is at least partially shared environmental in origin.

Multivariate multilevel modeling was used to identify factors associated with differences between and within couples about their attitudes towards using CVCT.

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

The most appropriate statistical technique for nested data is multilevel modeling, which is useful in analyzing longitudinal data, as it effectively handles missing data, serial dependence among observations, and varying time periods between observations (Raudenbush & Bryk, 2002; Singer & Willett, 2003).

Thus, we controlled for three level 1 variables (age, pubertal status, and treatment delivery method), two level 2 variables (baseline social status and baseline BMI), and the interaction between age and BMI in cross-sectional multilevel models.

Multilevel models revealed that child internalizing symptoms increased from ages 4 to 10 years old, but only in females, and especially between ages 7 and 10.