Author's Keywords: multilevel modelling, data sparseness, contextual effects, survey data	Convention All Academic Convention can solve the abstract management needs for any association's annual meeting. Submission - Custom fields, multiple submission types, tracks, audio visual, multiple upload formats, automatic conversion to pdf. Review - Peer Review, Bulk reviewer assignment, bulk emails, ranking, z-score statistics, and multiple worksheets! Reports - Many standard and custom reports generated while you wait. Print programs with participant indexes, event grids, and more! Scheduling - Flexible and convenient grid scheduling within rooms and buildings. Conflict checking and advanced filtering. Communication - Bulk email tools to help your administrators send reminders and responses. Use form letters, a message center, and much more! Management - Search tools, duplicate people management, editing tools, submission transfers, many tools to manage a variety of conference management headaches! Click here for more information.
Association: Name: American Sociological Association URL: http://www.asanet.org
Citation: URL: http://www.allacademic.com/meta/p106942_index.html Direct Link: HTML Code:

Clarke, Philippa. and Wheaton, Blair. "Addressing Data Sparseness in Contextual Population Research: Using Cluster Analysis to Create Synthetic Neighborhoods" Paper presented at the annual meeting of the American Sociological Association, Atlanta Hilton Hotel, Atlanta, GA, Aug 16, 2003 <Not Available>. 2009-05-26 <http://www.allacademic.com/meta/p106942_index.html>

Clarke, P. J. and Wheaton, B. , 2003-08-16 "Addressing Data Sparseness in Contextual Population Research: Using Cluster Analysis to Create Synthetic Neighborhoods" Paper presented at the annual meeting of the American Sociological Association, Atlanta Hilton Hotel, Atlanta, GA Online <.PDF>. 2009-05-26 from http://www.allacademic.com/meta/p106942_index.html

Publication Type: Conference Paper/Unpublished Manuscript Review Method: Peer Reviewed Abstract: The use of multilevel modeling with data from population-based surveys is often limited by the small number of cases per macro level group, due to the fact that the data were not specifically collected hierarchically. This data sparseness problem can render multilevel modeling inappropriate because of insufficient within-group variability, and can also lead to convergence problems in more complex multilevel models. As a result, many studies that examine contextual effects with population-based survey data are forced to ignore the hierarchical structure of the data altogether by resorting to OLS regression techniques. We used a simple strategy to reduce data sparseness in a hierarchical cross-classified model using a cluster analysis procedure to group single observation neighborhoods into larger “synthetic” neighborhoods with similar characteristics. In this paper we present the details of this technique and outline its effect on our results and the relative performance of the models with and without these “synthetic” neighborhoods.

Clustering Effect Within Families and the Role of Power in Family Communication: A Revised Analysis on Sillars et al.’s (2005) Article

Contextual Effects on Students’ Safety: Modeling Differential Effects of Neighborhood and School Social Organization

Hierarchical Linear Modeling (HLM) Analysis of Student Growth in Secondary Education Mathematics: A School-Based Application of Growth Modeling