Sentences with phrase «multilevel models of»

Parameter estimates and t - values for multilevel models of treatment outcome predicted by measurement occasion

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.

comorbidities [2], it is not surprising that they are at high Results Multilevel modeling of data from 158 couples risk for experiencing psychological

Peer Pressure and Family Smoking Habits Influence Smoking Uptake in Teenage Boys Attending School: Multilevel Modeling of Survey Data

In support of these results, multilevel modeling of the outcomes revealed the predicted time × condition interaction for the primary outcome of clinician - rated PTSD symptom severity (t37.5 = − 3.09; P =.004) and for patient - reported relationship satisfaction (t68.5 = 2.00; P =.049).

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).

Multilevel modeling of data from 158 couples revealed that baseline spouses» reports of caregiving - related health problems were significantly associated with 3 - month (p < 0.001) and 6 - month (p = 0.01) follow - up distress in both patients and spouses even when controlling for baseline distress and dyadic adjustment.

Multilevel modeling of direct effects and interactions of peers, parents, school, and community influences on adolescent substance use

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.

The former built a seat and vote share prediction model based on huge quantities of fieldwork (7000 interviews per week) plus the now - famous Multilevel Regression and Post-stratification (or MRP) that converted that data into seat - by - seat estimates.

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

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.

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.

BioEmergences proposes collaborative services for the reconstruction of multilevel dynamics from the in vivo observation of developing model organisms.

This study investigates the relationships of perceived plausibility and comprehension of multiple articles related to a social science topic (the PISA study) and effects of recipients» reading goal with multilevel models (items nested within recipients) on a trial - by - trial basis.

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.

Ms. Bai is a dual - title Ph.D. candidate in Educational Theory & Policy and Comparative International Education at the Pennsylvania State University specializing in a variety of statistical methods, including multilevel modeling, structural equation modeling, and propensity score matching.

Examples of his contributions include improved effect size estimates, multilevel mediation models, and Bayesian approaches to mediation analysis.

A comparison of hierarchical linear and multilevel structural equation growth models and their application in school effectiveness research

The Impact of School Climate and School Identification on Academic Achievement: Multilevel Modeling with Student and Teacher Data

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.

Combining longitudinal data, multilevel modeling and state - of - the - art measurement scales from The Lexile ® Framework for Reading and The Quantile ® Framework for Mathematics, Williamson (2016) premiered incremental velocity norms for average reading growth and average mathematics growth.

In my first study, I will employ multilevel models to understand the quality of teacher - child interactions experienced by DLL and non-DLL children and their associations with children's school readiness outcomes.

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).

Changing patterns of diversity projected from the multilevel model are very similar to the patterns of diversity projected from Maxent.

Finally, we describe the results of our multilevel probit models, which considered each brief's raw readability score without regard to the opposing brief's readability.

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

The results of the multilevel modeling revealed mixed support for our predictions.

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

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

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

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.

Multilevel regression models do not provide a direct estimate of first - level variance (parents in our model); for logistic models, the variance at the first level is fixed as the variance of the standard logistic distribution, that is at π 2 / 3, or about 3.29 (Goldstein, Browne, & Rasbash, 2002; Snijders & Bosker, 1999).

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.

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

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).

In contrast, the Triple P multilevel system of parenting support is based on a population - based public health model which seeks to shift prevalence rates across the community.

Combining all of our explanatory indicators, Table 4 shows the results of five multilevel models.

A Multilevel Analysis of Classroom Goal Structures» Effects on Intrinsic Motivation and Peer Modeling: Teachers» Promoting Interaction as a Classroom Level Mediator

Finally, the estimates from both sets of multilevel models suggest that CfC had the effect of reducing the number of jobless households for those in low - income and not low - income households.

Multilevel model estimates with and without the baseline as a control suggested that CfC had a positive effect on involvement in community service activity and reduced the rate of household joblessness for households with low education mothers.

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.

Table 3 describes the estimated effects of the CfC initiative on the 19 outcome variables from multilevel models with demographic variables and multilevel models with demographic variables and the baseline as a control.

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).

The multilevel models that did not control for baseline functioning suggest that children in low - income and those not in low - income households had significantly lower levels of physical functioning than children in CfC sites than in comparison sites.

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).

The multilevel models estimated take into account the clustering of the data in the calculation of standard errors.

Although some research has begun to collect and analyze data at the level of the dyad (Lyons et al., 2007; Pruchno, Wilson - Genderson, & Cartwright, 2008; Wilson - Genderson, Pruchno, & Cartwright, 2008), there remains much to be learned, yet it is clear that advances made regarding multilevel modeling strengthen our ability to conduct such research.