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Reducing Bias in Estimates of Linear Models by Remeasurement of a Random Subsample
Oleh:
Allison, Paul D.
;
Hauser, Robert M.
Jenis:
Article from Journal - ilmiah internasional
Dalam koleksi:
Sociological Methods & Research (SMR) vol. 19 no. 04 (May 1991)
,
page 466-492.
Topik:
Random.
;
Bias
;
Linear models
Fulltext:
ALLISON-466-92; Bernard.pdf
(1.97MB)
Ketersediaan
Perpustakaan PKPM
Nomor Panggil:
S28
Non-tandon:
1 (dapat dipinjam: 0)
Tandon:
tidak ada
Lihat Detail Induk
Isi artikel
Measurement error in independent variables produces biased estimates of the coefficients in linear models. These biases can be reduced by obtaining repeated measurements of the variables and then estimating structural equation models with multiple indicators of latent variables. Remeasurement is usually costly, however, raising the question of whether the same benefits can be obtained by remeasuring only a fraction of the sample. Although this strategy has been tried previously, there were no appropriate statistical methods for combining the data in the remeasured subsample and the single measurement subsample. We demonstrate here how recently developed methods for incomplete data provide an attractive solution to this estimation problem. The methodology is illustrated by a reanalysis of Bielby, Hauser, and Featherman's (l977a) study of the OCG-II data.
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