Sample size issues in multilevel logistic regression models


Autoři: Amjad Ali aff001;  Sabz Ali aff001;  Sajjad Ahmad Khan aff001;  Dost Muhammad Khan aff002;  Kamran Abbas aff003;  Alamgir Khalil aff004;  Sadaf Manzoor aff001;  Umair Khalil aff002
Působiště autorů: Department of Statistics Islamia College, Peshawar, Pakistan aff001;  Department of Statistics, Abdul Wali Khan University Mardan, Pakistan aff002;  Department of Statistics, University of Azad Jammu & Kashmir, Muzaffarabad, Pakistan aff003;  Department of Statistics, University of Peshawar, Pakistan aff004
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225427

Souhrn

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.

Klíčová slova:

Analysis of variance – Generalized linear model – Normal distribution – Psychological and psychosocial issues – Psychologists – Simulation and modeling – Statistical models – Social epidemiology


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Článek vyšel v časopise

PLOS One


2019 Číslo 11