Sentences with phrase «using multilevel modeling»

Data from 216 students, nested in 48 groups were analyzed using a multilevel modeling approach.

Using multilevel modeling, our study found that hypothesized relationships were moderated by participant sex.

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

[book] Kenny, D. A. / 2009 / Dyadic data analysis using multilevel modeling, In The handbook of multilevel analysis / Taylor Francis

Longitudinal data from 315 older couples in which one partner had end - stage renal disease were analyzed using multilevel modeling.

Using multilevel modeling, we regressed wives» reports of satisfaction at each assessment onto the dummy code indicating whether wives used HCs at each assessment.

Sociodemographic characteristics of the neighborhood and depressive symptoms in older adults: using multilevel modeling in geriatric psychiatry

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

This paper attempts to evaluate these factors using multilevel modeling methods where the traits of individual research group participants (e.g. gender, ethnicity, discipline area) are modeled within group - level factors (e.g. number of meetings, group size, group composition) as determinants of Working Group - related journal article production.

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.

Next, because the data included multiple reports from individuals within the same family, we used multilevel modeling to address the issue of dependencies in the data (SAS PROC MIXED; Singer, 1998).

Together with site - level intraclass correlation coefficient, treatment effects and 95 % CI will be derived using multilevel modelling.

The study uses a multilevel modeling approach to test the effects of such variables as supervisor leadership style, emotional intelligence, empathy, implicit person theory, trust, and feedback environment on employees» perceptions of the coaching relationships they share with their supervisors.

Data Analytic Strategy For these analyses, we used multilevel modeling and the HLM 7.01 software (Raudenbush, Bryk, Fai, Congdon, & du Toit, 2011).

Where appropriate controls were not used, we will request individual participant data and re-analyse the data using multilevel models that control for clustering.

Data were analysed using multilevel modelling.

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.

We will use multilevel models where the child» behaviors in pre-intervention will be considered as a covariate.

To be able to incorporate all available data in the analyses, data were analyzed using multilevel modelling.

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

Given the nested nature of our data, we used a multilevel modeling approach.

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

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.

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

Multilevel modeling techniques were used with a sample of 643 students enrolled in 37 secondary school classrooms to predict future student achievement (controlling for baseline achievement) from

Repeated measures of both teachers and students are planned over a three - year period, with annual analysis making use of latent variable measurement models and accounting for the multilevel and longitudinal structure of the data.

The multilevel modelling of student achievement, both in terms of educational quality and equality, using data from large - scale international assessments best characterizes his current research emphasis and trajectory.

«Estimating teacher productivity using a multivariate multilevel model for value - added analysis.»

Perhaps the most enabling resource for the educational research community was Singer's (1998) article demonstrating how to implement multilevel (including growth) models using one of the most widely available general - purpose statistical packages.

Two longitudinal analytic strategies, latent class analyses and multilevel modeling, are used to test these hypotheses.

«The estimates are derived from a statistical model using multilevel regression with post-stratification (MRP) on a large national survey dataset (n > 18,000), along with demographic and geographic population characteristics.

Rather, I recommend that they --- you --- become aware (to the best of your technical ability) of how these methods work, so you can use them in cases where they are most appropriate (these situations would include forecasting, multilevel modeling, inference for complex models with many parameters, and settings with weak data).

We used multilevel probit models based on this dichotomous variable.

We use probit models in these regressions because we can run these as multilevel models, which we describe briefly below.

All statistical analyses were conducted using SAS software V. 9.4, estimating the logistic multilevel models with the GLIMMIX procedure.

Multilevel modeling (MLM) of complex survey data is an approach increasingly being used in public health research.

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.

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.

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.

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).

Building on these ideas, we used rich data on selection into and out of neighborhoods to formulate a cross-classified multilevel model designed to estimate causal effects when contextual treatments, outcomes, and confounders all potentially vary over time (32, 33, 48).

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

Because of the nested nature of our data, with supervisors providing performance ratings for multiple employees, we tested our research model with multilevel path analysis using MPlus 6.11 (Muthén and Muthén 2010).

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.

Multilevel modeling was used to test for the effects of the intervention on grades.

In the report, before - and after - marriage data from an average of nine waves and multilevel modeling were used to prospectively estimate how premarital characteristics are related to marital quality.

Using publicly available community - level AEDI data, 62, 63 we ran a two - level multilevel logistic regression model for one aggregate developmental outcome measure (ie, risk of developmental vulnerability; figure 3A) and an example simulation (figure 3B) using a total sample of 181 500, with the proportion of Aboriginal children in each LGA derived from ABS estimates.64, 65 Binomial outcome data were simulated assuming a baseline risk of being vulnerable of 21 % and a community - level random effect based on the actual variation in the published data (figureUsing publicly available community - level AEDI data, 62, 63 we ran a two - level multilevel logistic regression model for one aggregate developmental outcome measure (ie, risk of developmental vulnerability; figure 3A) and an example simulation (figure 3B) using a total sample of 181 500, with the proportion of Aboriginal children in each LGA derived from ABS estimates.64, 65 Binomial outcome data were simulated assuming a baseline risk of being vulnerable of 21 % and a community - level random effect based on the actual variation in the published data (figureusing a total sample of 181 500, with the proportion of Aboriginal children in each LGA derived from ABS estimates.64, 65 Binomial outcome data were simulated assuming a baseline risk of being vulnerable of 21 % and a community - level random effect based on the actual variation in the published data (figure 3A).

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.