scholarly journals Longitudinal Data Analyses Using Linear Mixed Models in SPSS: Concepts, Procedures and Illustrations

2011 ◽  
Vol 11 ◽  
pp. 42-76 ◽  
Author(s):  
Daniel T. L. Shek ◽  
Cecilia M. S. Ma
Keyword(s):  
Longitudinal Data ◽  
Generalized Linear Models ◽  
Mixed Models ◽  
Linear Models ◽  
Linear Mixed Models ◽  
Hierarchical Linear Models ◽  
Data Analyses ◽  
Basic Concepts ◽  
User Friendly ◽  
Over Time

Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented.

PERFORMANCE OF MIXED EFFECTS FOR CLUSTERED COUNTING DATA MODELS

2020 ◽  
Vol 3 (2) ◽  
pp. 34-41
Author(s):  
Intesar N. El-Saeiti ◽  
Khalil Mostafa ALsawi
Keyword(s):  
Generalized Linear Models ◽  
Standard Error ◽  
Mixed Models ◽  
Linear Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Likelihood Estimation ◽  
Real Data ◽  
Estimation Technique ◽  
Counting Data

This article is concerned with hierarchical generalized linear models. It includes generalized linear models and generalized linear mixed models, which are related to linear models. In generalized linear mixed models, the dependent variable and the standard error follow any distribution from the exponential family, e.g. normal, Poisson, binomial, gamma, etc. We studied counting data, and then use the Poisson-gamma model,where the dependentvariable follows the Poisson distribution and the standard error follow the gamma distribution. Several estimation techniques can be used for generalized linear mixed model. In this paperthe hierarchical likelihood estimation technique was used to prove the performance of H-likelihood methodwhen thecounting data were balanced or unbalanced. Real data were used to test the performance of Poisson-gamma H-likelihood estimation method in case of balanced and unbalanced counting data.When real data used in the past research for another problem, it was noticed that the performance of the hierarchical likelihood estimation technique gave a close approximations in the event of balanced and unbalanced counting data, and the output of the technique was approximately equivalent in both instances.

l2-Penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time

2021 ◽  
pp. 096228022110175
Author(s):  
Jan P Burgard ◽  
Joscha Krause ◽  
Ralf Münnich ◽  
Domingo Morales
Keyword(s):  
Parameter Estimation ◽  
Medical Treatment ◽  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Obesity Prevalence ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Public Health Reporting ◽  
Over Time

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.

A semiparametric Bayesian approach to generalized partial linear mixed models for longitudinal data

2012 ◽  
Vol 56 (12) ◽  
pp. 4348-4365 ◽  
Author(s):  
Nian-Sheng Tang ◽  
Xing-De Duan
Keyword(s):  
Longitudinal Data ◽  
Bayesian Approach ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
Partial Linear

Generalized linear models for beta correlated binary longitudinal data

1997 ◽  
Vol 26 (3) ◽  
pp. 617-635 ◽  
Author(s):  
Brajendra C. Sutradhar ◽  
Kalyan Das
Keyword(s):  
Longitudinal Data ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Binary Longitudinal Data

Weighted empirical likelihood for generalized linear models with longitudinal data

2010 ◽  
Vol 140 (11) ◽  
pp. 3446-3456 ◽  
Author(s):  
Yang Bai ◽  
Wing Kam Fung ◽  
Zhongyi Zhu
Keyword(s):  
Longitudinal Data ◽  
Empirical Likelihood ◽  
Generalized Linear Models ◽  
Linear Models
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