Being Both Too Liberal and Too Conservative : The Perils of Treating Grouped Data as Though They Were Independent  

Oleh:

Hanges, Paul J. ; Bliese, Paul D.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Organizational Research Methods vol. 7 no. 4 (Oct. 2004), page 400-417.
Topik: multilevel; multilevel; power; error; applied
Fulltext: 400.pdf (150.44KB)

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Perpustakaan Pusat (Semanggi) Nomor Panggil: OO3.6 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Organizational data are inherently nested : consequently, lower level data are typically influenced by higher level grouping factors. Stated another way, almost all lower level organizational data have some degree of non independence due to work group, geographic membership, and so on. Unaccounted for non independence can be problematic because it affects standard error estimates used to determine statisitcal significance. Currently, researchers interested in modeling higher level variables routinely use multilevel modeling techniques to avoid well - known problems with type I error rates. In this article, however, the authors examine how non independence affects statistical inferences in cases in which researchers are interested only in relationships among lower level variables. They show that ignoring non independence when modeling only lower level variables reduces power (increases type II errors) and through simulations the authors show where this loss of power is most pronounced.

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