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Abstract Details
Activity Number:
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529
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Social Statistics Section
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Abstract - #302888 |
Title:
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Data Sparseness and Multilevel Models: The Impact of Small Cluster Size on Point and Interval Estimates in Two-Level Logistic Models
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Author(s):
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Bethany Ann Bell*+ and Jason Schoeneberger and Jeffrey Kromrey and John Ferron
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Companies:
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University of South Carolina and University of South Carolina and University of South Florida and University of South Florida
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Address:
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College of Education, WD 133, Columbia, SC, 29208,
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Keywords:
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multilevel logistic models ;
sample size ;
binary predictors ;
survey research
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Abstract:
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Methodological research continues to address the important issue of sample size at each level when estimating multilevel models. However, to date, these investigations have primarily focused on multilevel linear models, with normally distributed continuous outcomes. Fewer studies have examined the impact that sample sizes have on the statistical properties of multilevel models with binary outcomes. Moreover, even less attention has been given to the consequences of estimating multilevel logistic models with sparse level-2 units. To help address this gap in the literature, this Monte Carlo study focuses on the consequences of level-2 sparseness on the estimation of fixed and random effects in terms of point and interval estimates as a function of the level-1 sample size, number of level-2 units, proportion of singletons (level-2 units with one observation), collinearity, outcome prevalence, intraclass correlation, and model complexity. SAS IML was used to simulate 1000 data sets across 5760 conditions. Results are presented in terms of statistical bias, confidence interval coverage, and statistical power.
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