Addressing Data Sparseness in Contextual Population Research: Using Cluster Analysis to Create Synthetic Neighborhoods  

Oleh:

Clarke, Philippa ; Wheaton, Blair

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Sociological Methods & Research (SMR) vol. 35 no. 03 (Feb. 2007), page 311-351.
Topik: Multilevel Models; Data Sparseness; Cluster Analysis; Monte Carlo Simulations; Survey Research;
Fulltext: SMR vol.35 no.3 p.311 Feb 2007_win.pdf (239.0KB)

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Perpustakaan PKPM Nomor Panggil: S28 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The use of multilevel modeling with data from population-based surveys is often limited by the small number of cases per Level 2 unit, prompting a recent trend in the neighborhood literature to apply cluster techniques to address the problem of data sparseness. In this study, the authors use Monte Carlo simulations to investigate the effects of marginal group sizes on multilevel model performance, bias, and efficiency. They then employ cluster analysis techniques to minimize data sparseness and examine the consequences in the simulations. They find that estimates of the fixed effects are robust at the extremes of data sparseness, while cluster analysis is an effective strategy to increase group size and prevent the overestimation of variance components. However, researchers should be cautious about the degree to which they use such clustering techniques due to the introduction of artificial within-group heterogeneity.

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