Sentences with phrase «multilevel model for»

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

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.

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

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.

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.

My goal is to build a Bayesian multilevel model that can provide useful information for specific writers.

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).

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

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

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.

Multilevel structural equation modeling was applied in order to account for individual, class - average, and teacher effects.

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.

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.

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 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.

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.

Children residing in CfC sites had significantly lower reported physical functioning than children in comparison sites even in the multilevel model that controlled for baseline functioning.

Second, because baseline data were available for many outcomes, a second multilevel model was run that included baseline functioning.

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.

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).

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

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

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).

Continuous and dichotomous outcomes for the five AEDI domains and the aggregate AEDI measure will be modelled separately using multilevel linear and logistic regression, respectively.

Separate multilevel models will be developed for each of our outcomes, using an iterative process.

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

[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

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 sum, given the results from our simulation study and the empirical applications, we conclude that the multilevel TAR model is a valuable addition to the available techniques for analyzing intensive longitudinal data.

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).

Bayesian estimation is used here because classical approaches are problematic for the multilevel TAR 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).

For this reason, the autoregressive coefficient in the multilevel AR model is also called the inertia, and has been interpreted as a measure of regulatory weakness.

This multilevel AR model enables researchers to estimate the average inertia in the population and to use observed person - level variables as predictors for the inertias, to see which person characteristics are related to regulatory weakness.

The BUGS input files for the multilevel AR and TAR models are given in Appendix 1.

The multilevel TAR model that we develop in this study is suitable for testing this hypothesis, and can be used in a broader context for investigating various proposed mechanisms of state - dependent regulation.

Importantly, using the multilevel TAR model, researchers can use person - level variables as predictors both for the inertias, representing the state - dependent regulatory weakness, and for the threshold representing a person's equilibrium.

Next, we describe the existing AR and TAR models and we present the basic multilevel TAR model for state - dependent regulation.

A possible explanation for these null findings may be that the variability in inertia and in inertia difference in a multilevel TAR model may be smaller than the variability in inertia in a multilevel AR model.

In standard multilevel software, it is not even possible to specify the multilevel TAR model unless plugin values are used for the unknown thresholds.

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.

In conclusion, we note that the TAR model was clearly preferable to an AR model for these data, since affect regulation was state - dependent for most of the individuals in the sample and a multilevel AR model would misrepresent the underlying regulatory process.

Based on the results of our simulations, we can conclude that Bayesian estimation of the multilevel TAR model is feasible for the sample sizes under consideration, and yields accurate estimates of the average inertias and threshold.

Madhyastha et al. (2011) analyzed these data using single - person TAR and AR models for each spouse, rather than a multilevel model.

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.