Sample size issues in multilevel logistic regression models.
Educational researchers, psychologists, social, epidemiological and medical scientists are often dealing with multilevel data. Sometimes, the response variable in multilevel data is categorical in nature and needs to be analyzed through Multilevel Logistic Regression Models. The main theme of this p...
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Online Access: | https://doi.org/10.1371/journal.pone.0225427 |
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doaj-d7a364f6eec6404cadd004e49bf803df2021-03-03T21:15:22ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-011411e022542710.1371/journal.pone.0225427Sample size issues in multilevel logistic regression models.Amjad AliSabz AliSajjad Ahmad KhanDost Muhammad KhanKamran AbbasAlamgir KhalilSadaf ManzoorUmair KhalilEducational researchers, psychologists, social, epidemiological and medical scientists are often dealing with multilevel data. Sometimes, the response variable in multilevel data is categorical in nature and needs to be analyzed through Multilevel Logistic Regression Models. The main theme of this paper is to provide guidelines for the analysts to select an appropriate sample size while fitting multilevel logistic regression models for different threshold parameters and different estimation methods. Simulation studies have been performed to obtain optimum sample size for Penalized Quasi-likelihood (PQL) and Maximum Likelihood (ML) Methods of estimation. Our results suggest that Maximum Likelihood Method performs better than Penalized Quasi-likelihood Method and requires relatively small sample under chosen conditions. To achieve sufficient accuracy of fixed and random effects under ML method, we established ''50/50" and ''120/50" rule respectively. On the basis our findings, a ''50/60" and ''120/70" rules under PQL method of estimation have also been recommended.https://doi.org/10.1371/journal.pone.0225427 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Amjad Ali Sabz Ali Sajjad Ahmad Khan Dost Muhammad Khan Kamran Abbas Alamgir Khalil Sadaf Manzoor Umair Khalil |
spellingShingle |
Amjad Ali Sabz Ali Sajjad Ahmad Khan Dost Muhammad Khan Kamran Abbas Alamgir Khalil Sadaf Manzoor Umair Khalil Sample size issues in multilevel logistic regression models. PLoS ONE |
author_facet |
Amjad Ali Sabz Ali Sajjad Ahmad Khan Dost Muhammad Khan Kamran Abbas Alamgir Khalil Sadaf Manzoor Umair Khalil |
author_sort |
Amjad Ali |
title |
Sample size issues in multilevel logistic regression models. |
title_short |
Sample size issues in multilevel logistic regression models. |
title_full |
Sample size issues in multilevel logistic regression models. |
title_fullStr |
Sample size issues in multilevel logistic regression models. |
title_full_unstemmed |
Sample size issues in multilevel logistic regression models. |
title_sort |
sample size issues in multilevel logistic regression models. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2019-01-01 |
description |
Educational researchers, psychologists, social, epidemiological and medical scientists are often dealing with multilevel data. Sometimes, the response variable in multilevel data is categorical in nature and needs to be analyzed through Multilevel Logistic Regression Models. The main theme of this paper is to provide guidelines for the analysts to select an appropriate sample size while fitting multilevel logistic regression models for different threshold parameters and different estimation methods. Simulation studies have been performed to obtain optimum sample size for Penalized Quasi-likelihood (PQL) and Maximum Likelihood (ML) Methods of estimation. Our results suggest that Maximum Likelihood Method performs better than Penalized Quasi-likelihood Method and requires relatively small sample under chosen conditions. To achieve sufficient accuracy of fixed and random effects under ML method, we established ''50/50" and ''120/50" rule respectively. On the basis our findings, a ''50/60" and ''120/70" rules under PQL method of estimation have also been recommended. |
url |
https://doi.org/10.1371/journal.pone.0225427 |
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