This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Impact of Small Group Size on Neighborhood Influences in Multilevel Models

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Theall, Katherine P.
Scribner, Richard
Lynch , Sara
Simonsen, Neal
Schonlau, Matthias
Carlin, Bradley
Cohen, Deborah
Abstract

Objective: Although there is a growing body of literature on sample size in multilevel or hierarchical modeling, few studies have examined the impact of group size < 5. Design: We examined the impact of a group size less than five on both a continuous and dichotomous outcome in a simple two-level multilevel model utilizing data from two studies. Setting: Models with balanced and unbalanced data of group sizes 2 to 5 were compared to models with complete data. Impact on both fixed and random components were examined. Results: Random components, particularly group-level variance estimates, were more affected by small group size than were fixed components. Both fixed and random standard error estimates were inflated with small group size. Datasets where there are a large number of groups yet all the groups are of very small size may fail to find or even consider a group-level effect when one may exist and also may be under-powered to detect fixed effects. Conclusions: Researchers working with multilevel study designs should be aware of the potential impact of small group size when a large proportion of groups has very small (< 5) sample sizes.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://mpra.ub.uni-muenchen.de/11648/
File Format:
File Function:
Download Restriction: no

Publisher Info
Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 11648.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: 17 Jul 2008
Date of revision:
Handle: RePEc:pra:mprapa:11648

Contact details of provider:
Postal: Schackstr. 4, D-80539 Munich, Germany
Phone: +49-(0)89-2180-2219
Fax: +49-(0)89-2180-3900
Web page: http://mpra.ub.uni-muenchen.de
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (Ekkehart Schlicht).

Related research
Keywords: Multilevel; Neighborhood; Body Weight; Obesity; Sample Size;

Find related papers by JEL classification:
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General
I12 - Health, Education, and Welfare - - Health - - - Health Production
I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Cora J. M. Maas & Joop J. Hox, 2004. "Robustness issues in multilevel regression analysis," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(2), pages 127-137. [Downloadable!] (restricted)
Full references

Statistics
Access and download statistics

Did you know? Want to help out with this project? Look for volunteer opportunities.

This page was last updated on 2009-12-3.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.