mixed model

(redirected from Multilevel model)
Also found in: Wikipedia.

mixed model

[′mikst ‚mäd·əl]
(statistics)
A model having both determinate and stochastic elements in its equations.
A model having both difference and differential equations.
A model containing both endogenous and exogenous elements.
In analysis of variance for a two-way layout, the combined rows and columns.
Mentioned in ?
References in periodicals archive ?
The results reported in the text focus on the multilevel model with individual random effects, as results from the censored tobit regression were provided as a type of sensitivity analysis.
KEY WORDS: shell shape, linear morphometric methods, contour detection, environmental effect, linear multilevel model, bivalve, Atlantic Ocean, Venerupis philippinarum
Last, variables included in the final multivariate model for participant-based factors and those included in the multilevel model for farm-based factors were combined in a multivariate multilevel analysis to identify the independent risk determinants for seropositivity.
They move on to aggregating research findings through meta-analysis, namely conducting meta-analysis in outcome research and correlation coefficients, and identifying and analyzing change in psychotherapy, including moderators, mediators, and mechanisms of change; multilevel models and a three-level growth multilevel model approach for the analysis of longitudinal data and nested data; the analysis of group and rolling group data; aggregated time-series analysis; assessing change; and decision rules for the evaluation of progress and the application of feedback tools.
The next step in developing the multilevel model is to consider the moderating influence of norms.
This conception of the data suggests a multilevel model as the method of analysis.
We used the multilevel model for 150 countries with at least one survey data point.
The results were tested not only using the Cox regression model but also using a multilevel model, which may be better suited for clustered data.
The multilevel model has an advantage since the number of observations and temporal spacing between measurements can vary among subjects; all available data can thus be incorporated into the analysis.
Before running the multilevel model, we diagnosed for multicollinearity among key independent variables and covariates with a variance inflation factor (VIF) test.
This study developed an ex post facto investigation, done by the multilevel model methodological approach with four analysis levels: student, classroom, school and country.
It should be noted that the interaction between risk regulators and the individual-level risk factors can be modeled statistically as a cross-level interaction in a multilevel model (e.

Full browser ?