The use of sample weights in multivariate multilevel models with an application to income data collected by using a rotating panel survey

2013 ◽  
Vol 63 (1) ◽  
pp. 65-84 ◽  
Author(s):  
Alinne Veiga ◽  
Peter W. F. Smith ◽  
James J. Brown
Keyword(s):  
Multilevel Models ◽  
Panel Survey ◽  
Sample Weights ◽  
Income Data ◽  
Multivariate Multilevel Models

scholarly journals Application of Multilevel Models to Morphometric Data. Part 2. Correlations

2003 ◽  
Vol 25 (4) ◽  
pp. 187-191 ◽  
Author(s):  
O. Tsybrovskyy ◽  
A. Berghold
Keyword(s):  
Multilevel Modeling ◽  
Multilevel Models ◽  
Correlation Coefficients ◽  
Morphometric Data ◽  
Cell Level ◽  
Units Of Analysis ◽  
Major Interest ◽  
Level Statistics ◽  
Indispensable Tool ◽  
Multivariate Multilevel Models

Multilevel organization of morphometric data (cells are “nested” within patients) requires special methods for studying correlations between karyometric features. The most distinct feature of these methods is that separate correlation (covariance) matrices are produced for every level in the hierarchy. In karyometric research, the cell‐level (i.e., within‐tumor) correlations seem to be of major interest. Beside their biological importance, these correlation coefficients (CC) are compulsory when dimensionality reduction is required. Using MLwiN, a dedicated program for multilevel modeling, we show how to use multivariate multilevel models (MMM) to obtain and interpret CC in each of the levels. A comparison with two usual, “single‐level” statistics shows that MMM represent the only way to obtain correct cell‐level correlation coefficients. The summary statistics method (take average values across each patient) produces patient‐level CC only, and the “pooling” method (merge all cells together and ignore patients as units of analysis) yields incorrect CC at all. We conclude that multilevel modeling is an indispensable tool for studying correlations between morphometric variables.

scholarly journals Mind the step: A more insightful and robust analysis of the sentencing process in England and Wales under the new sentencing guidelines

2018 ◽  
Vol 20 (3) ◽  
pp. 268-301 ◽  
Author(s):  
Jose Pina-Sánchez ◽  
Ian Brunton-Smith ◽  
Guangquan Li
Keyword(s):  
Multilevel Models ◽  
Sentencing Guidelines ◽  
Robust Analysis ◽  
England And Wales ◽  
One Stage ◽  
Relevant Case ◽  
Case Characteristics ◽  
Multivariate Multilevel Models

The ‘England and Wales Sentencing Guidelines’ aim to promote consistency by organizing the sentencing process as a sequence of steps, with initial judicial assessments subsequently adjusted to reflect relevant case characteristics. Yet, existing evaluations of the guidelines have failed to incorporate this structure adequately, instead concentrating solely on sentence outcomes. We use multivariate multilevel models to offer new insights into the decisions made throughout the sentencing process. Focusing on cases of assault sentenced at the Crown Court we show that the level of compliance with the guidelines is high. However, we also show that some case characteristics are being unduly considered at more than one stage of the sentencing process, meaning existing studies may be underestimating their true influence.

The Role of Sex and Age on Pre-drinking: An Exploratory International Comparison of 27 Countries

2019 ◽  
Vol 54 (4) ◽  
pp. 378-385 ◽  
Author(s):  
Jason Ferris ◽  
Cheneal Puljević ◽  
Florian Labhart ◽  
Adam Winstock ◽  
Emmanuel Kuntsche
Keyword(s):  
Multilevel Models ◽  
The United States ◽  
Drinking Behaviour ◽  
Single Model ◽  
Policy Makers ◽  
Worldwide Phenomenon ◽  
Using Data ◽  
The Impact ◽  
Multivariate Multilevel Models

Abstract Aims This exploratory study aims to model the impact of sex and age on the percentage of pre-drinking in 27 countries, presenting a single model of pre-drinking behaviour for all countries and then comparing the role of sex and age on pre-drinking behaviour between countries. Methods Using data from the Global Drug Survey, the percentages of pre-drinkers were estimated for 27 countries from 64,485 respondents. Bivariate and multivariate multilevel models were used to investigate and compare the percentage of pre-drinking by sex (male and female) and age (16–35 years) between countries. Results The estimated percentage of pre-drinkers per country ranged from 17.8% (Greece) to 85.6% (Ireland). The influence of sex and age on pre-drinking showed large variation between the 27 countries. With the exception of Canada and Denmark, higher percentages of males engaged in pre-drinking compared to females, at all ages. While we noted a decline in pre-drinking probability among respondents in all countries after 21 years of age, after the age of 30 this probability remained constant in some countries, or even increased in Brazil, Canada, England, Ireland, New Zealand and the United States. Conclusions Pre-drinking is a worldwide phenomenon, but varies substantially by sex and age between countries. These variations suggest that policy-makers would benefit from increased understanding of the particularities of pre-drinking in their own country to efficiently target harmful pre-drinking behaviours.

Catching Up on Multilevel Modeling

2022 ◽  
Vol 73 (1) ◽  
pp. 659-689
Author(s):  
Lesa Hoffman ◽  
Ryan W. Walters
Keyword(s):  
Best Practices ◽  
Structural Equation ◽  
Multilevel Models ◽  
Structural Equation Models ◽  
Catching Up ◽  
Scale Models ◽  
Fixed And Random Effects ◽  
Error Terms ◽  
Multilevel Structural Equation ◽  
Multivariate Multilevel Models

This review focuses on the use of multilevel models in psychology and other social sciences. We target readers who are catching up on current best practices and sources of controversy in the specification of multilevel models. We first describe common use cases for clustered, longitudinal, and cross-classified designs, as well as their combinations. Using examples from both clustered and longitudinal designs, we then address issues of centering for observed predictor variables: its use in creating interpretable fixed and random effects of predictors, its relationship to endogeneity problems (correlations between predictors and model error terms), and its translation into multivariate multilevel models (using latent-centering within multilevel structural equation models). Finally, we describe novel extensions—mixed-effects location–scale models—designed for predicting differential amounts of variability.

scholarly journals Analyzing multiple outcomes in clinical research using multivariate multilevel models.

2014 ◽  
Vol 82 (5) ◽  
pp. 920-930 ◽  
Author(s):  
Scott A. Baldwin ◽  
Zac E. Imel ◽  
Scott R. Braithwaite ◽  
David C. Atkins
Keyword(s):  
Clinical Research ◽  
Multilevel Models ◽  
Multiple Outcomes ◽  
Multivariate Multilevel Models

scholarly journals On the Interpretation of Parameters in Multivariate Multilevel Models Across Different Combinations of Model Specification and Estimation

2019 ◽  
Vol 2 (3) ◽  
pp. 288-311 ◽  
Author(s):  
Lesa Hoffman
Keyword(s):  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Models ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Recent Change ◽  
Model Specification ◽  
Predictor Variables ◽  
Random Slopes ◽  
Multivariate Multilevel Models

The increasing availability of software with which to estimate multivariate multilevel models (also called multilevel structural equation models) makes it easier than ever before to leverage these powerful techniques to answer research questions at multiple levels of analysis simultaneously. However, interpretation can be tricky given that different choices for centering model predictors can lead to different versions of what appear to be the same parameters; this is especially the case when the predictors are latent variables created through model-estimated variance components. A further complication is a recent change to Mplus (Version 8.1), a popular software program for estimating multivariate multilevel models, in which the selection of Bayesian estimation instead of maximum likelihood results in different lower-level predictors when random slopes are requested. This article provides a detailed explication of how the parameters of multilevel models differ as a function of the analyst’s decisions regarding centering and the form of lower-level predictors (i.e., observed or latent), the method of estimation, and the variant of program syntax used. After explaining how different methods of centering lower-level observed predictor variables result in different higher-level effects within univariate multilevel models, this article uses simulated data to demonstrate how these same concepts apply in specifying multivariate multilevel models with latent lower-level predictor variables. Complete data, input, and output files for all of the example models have been made available online to further aid readers in accurately translating these central tenets of multivariate multilevel modeling into practice.

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