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Multilevel Modeling: When and Why [PDF]

open access: yes, 1998
Multilevel models have become popular for the analysis of a variety of problems. This chapter gives a summary of the reasons for using multilevel models, and provides examples why these reasons are indeed valid. Next, recent (simulation) research is reviewed on the robustness and power of the usual estimation procedures with varying sample sizes.
openaire   +5 more sources

Fixed or random? On the reliability of mixed‐effects models for a small number of levels in grouping variables

open access: yesEcology and Evolution, 2022
Biological data are often intrinsically hierarchical (e.g., species from different genera, plants within different mountain regions), which made mixed‐effects models a common analysis tool in ecology and evolution because they can account for the non ...
Johannes Oberpriller   +2 more
doaj   +1 more source

Performance of likelihood-based estimation methods for multilevel binary regression models. [PDF]

open access: yes
By means of a fractional factorial simulation experiment, we. compare the performance of penalised quasi-likelihood (PQL), non-adaptive Gaussian quadrature and adaptive Gaussian quadrature in estimating parameters for multilevel logistic regression ...
Croux, Christophe, Callens, M
core   +3 more sources

What Does It Take for Immigrants to Join Political Parties?

open access: yesPolitics and Governance
Political parties are crucial agents in democratic representation and political integration of persons of immigrant origin, a growing category of citizens in the European Union.
Monika Bozhinoska Lazarova   +2 more
doaj   +1 more source

Influences of School Climate and Teacher’s Behavior on Student’s Competencies in Mathematics and the Territorial Gap between Italian Macro-areas in PISA 2012

open access: yesJournal of Educational, Cultural and Psychological Studies, 2016
In this study the effects of school and classroom climate and teacher’s behavior on Italian students’ mathematical achievement score in PISA 2012 were investigated. Simple and scale indices provided by the PISA database, constructed by responses from the
Giuseppe Bove   +2 more
doaj   +1 more source

Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships

open access: yesMathematics, 2021
Our article explores an underused mathematical analytical methodology in the social sciences. In addition to describing the method and its advantages, we extend a previously reported application of mixed models in a well-known database about corruption ...
Luiz Paulo Fávero   +4 more
doaj   +1 more source

Nonlinear mediation in clustered data : a nonlinear multilevel mediation model [PDF]

open access: yes, 2013
textMediational analysis quantifies proposed causal mechanisms through which treatments act on outcomes. In the presence of clustered data, conventional multiple regression mediational methods break down, requiring the use of hierarchical linear modeling
Lockhart, Lester Leland
core   +1 more source

Mean exit times and the multilevel Monte Carlo method [PDF]

open access: yes, 2013
Numerical methods for stochastic differential equations are relatively inefficient when used to approximate mean exit times. In particular, although the basic Euler–Maruyama method has weak order equal to one for approximating the expected value of the ...
Roj, Mikolaj   +4 more
core   +1 more source

Homework and Academic Achievement in Latin America: A Multilevel Approach

open access: yesFrontiers in Psychology, 2019
The relationship between homework and academic results has been widely researched. Most of that research has used English-speaking, European or Asian samples, and to date there have been no detailed studies into that relationship in Latin America and the
Rubén Fernández-Alonso   +7 more
doaj   +1 more source

Fitting Multilevel Factor Models

open access: yesSIAM Journal on Matrix Analysis and Applications
We examine a special case of the multilevel factor model, with covariance given by multilevel low rank (MLR) matrix~\cite{parshakova2023factor}. We develop a novel, fast implementation of the expectation-maximization algorithm, tailored for multilevel factor models, to maximize the likelihood of the observed data.
Tetiana Parshakova   +2 more
openaire   +2 more sources

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