Sentences with phrase «multilevel model estimates»

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

Not exact matches

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.

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.

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

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

«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.

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

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 estimated two multilevel models assessing differences in positive quality and negative quality.

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.

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).

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.

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).

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 (figure 3A).

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).

Univariate multilevel SDE models, estimated in a Bayesian framework, were fit to 21 days of ecological momentary assessments of affect valence and arousal (average 6.93 / day, SD = 1.89) obtained from 150 adults (age 18 — 89 years)-- specifically capturing temporal dynamics of individuals» core affect in terms of attractor point, reactivity to biopsychosocial (BPS) inputs, and attractor strength.

In this framework, all the parameters of the multilevel TAR model can be estimated simultaneously, and the model specifications are straightforward.

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.

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.

Three - level multilevel models (MLM) accounts for within - family dependence by incorporating a unique random effect for each family and adult child, and this variability in random effects is taken into account when estimating SEs.

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

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

Research questions were addressed using APIMs (Kenny et al., 2006) and estimated with multilevel modeling (SAS PROC MIXED).

Research questions were addressed using Actor - Partner Interdependence Models (APIMs; Kenny, Kashy, & Cook, 2006) and estimated with multilevel modeling (SAS PROC MIXED).