Sentences with phrase «multilevel modeling with»

«Multilevel modeling with SEM,» in Introduction to Multilevel Modeling Techniques, eds S. L. Thomas and R. H. Heck (Mahwah, NJ: Lawrence Erlbaum Associates, Inc.), 89 — 127.

Multilevel modeling with dyadic data from 142 couples was used to identify the characteristics associated with men who have had UAI with both their main partner and a casual MSM partner within the same timeframe.

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

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

In all analyses, we fitted multilevel models with a random effects term for course and for outcomes corresponding to individual child data and a random effects term for family.

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.

Table 2 presents results from the multilevel models with functional limitations, disability, and self - rated health as outcomes.

Not exact matches

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.

His methodological research interests include item writing, reliability theory and multilevel item response models, with a substantive interest in Latino youth development.

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.

«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).

Comparisons with the multilevel model, however, indicate that the spatial patterns are robust.

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.

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).

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.

Multilevel Models Predicting Parents With a Child Looked After Intensively by a Grandparent (10 Countries)

Multilevel models are ideal because families with missing data are not removed from the analyses, and the models are not affected by unbalanced data (i.e., in which some families include more member reports than others).

Multilevel models versus single - level models with sparse data

Multilevel modeling was also conducted on each outcome, with condition, time, and the condition × time interaction included in the model; random intercepts and slopes were estimated for each participant.

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

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.

Two multilevel models were estimated, one without baseline functioning and one including baseline outcome variables when they were collected with the first multilevel model similar to the analysis conducted in Sure Start.

Multilevel Models Predicting Patient and Spouse Depressive Symptoms With Own and Cross-Partner Health

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).

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 (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.

In each multilevel model, we will allow the outcome to vary by geographic area (random intercept) and, in the all children model, the Aboriginal to non-Aboriginal ratio to vary (random slope), enabling us to identify areas that are performing better or worse in terms of early childhood developmental outcomes, and areas with greater or lesser inequality in these outcomes.

The data was analyzed using generalized linear models and generalized estimating equations, which are specifically used to address the multilevel design of data in which schools with participating schoolchildren were randomized (rather than individual participants).

The accuracy of multilevel structural equation modeling with pseudobalanced groups and small samples

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).

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).

[book] Zaidman - Zait, A / 2005 / Multilevel (HLM) models for modeling change with Incomplete Data: Demonstrating the effects of Missing data and Level - 1 Model Mis - specification / Paper presented at the Hierarchical Linear Modeling (SIG) of themodeling change with Incomplete Data: Demonstrating the effects of Missing data and Level - 1 Model Mis - specification / Paper presented at the Hierarchical Linear Modeling (SIG) of theModeling (SIG) of the America

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

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).

In a different vein, if the data set is sufficiently large, one can also consider multilevel TAR models with more than two states, reflecting different regulatory mechanisms.

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.

Thus, a posterior distribution was obtained for this difference, and the 95 % credible interval of this difference was then used as a decision criterion: When 0 was included in the credible interval, there was no evidence that there are two different mean inertias, so we selected the multilevel AR model; when 0 was not included in the credible interval of the mean difference, this was taken as evidence that there are two distinct states with different mean inertias, so we selected the multilevel TAR model.

Meta - analysis with standardized effect sizes from multilevel and latent growth models.

Use a multilevel EMDR attachment - focused model with parents with insecure states of mind and their children.

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.

In addition, this workshop presents an innovative multilevel EMDR attachment - focused model to work with parents with insecure states of mind and their children.

Special thanks to Deborah A. Kashy for her invaluable assistance with the multilevel modeling analyses.

Van den Noortgate and Onghena (2003) compared multilevel meta - analysis with traditional meta - analytic methods and concluded that maximum likelihood multilevel approach is in general superior to the fixed - effects approaches and that the results of the multilevel approach are not substantially different from the results of the traditional random - effects approaches for intercept only models.

A multilevel mixed - effects linear regression model with an unstructured covariance matrix was used to test whether different patterns of financial difficulty were associated with subsequent changes in ADHD symptoms.

However, it is important to interpret these with caution given the considerable debate in the field regarding the calculation of variance accounted for in multilevel models (Singer & Willett, 2003).

Diagram of multilevel confirmatory factor analysis model with standardized factor loadings and latent covariances.