To Log or Not to Log : Bootstrap as An Alternative to the ParametrIc Estimation of Moderation Effects In the Presence of Skewed Dependent Variables  

Oleh:

Dean, Michelle A. ; Russell, Craig J.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Organizational Research Methods vol. 3 no. 2 (2000), page 166-185.
Topik: variables; bootstrap; parametric estimation; dependent variables
Fulltext: 166[1].pdf (133.0KB)

Ketersediaan

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

Lihat Detail Induk

Isi artikel

When gross deviations from parametric assumptions are observed, conventional data transformations are often applied with little regard for substantive theoretical implications. One such transformation involves using the logarithm of positively skewed dependent variables. Log transformations were shown to severly decrease estimates of true moderator effects using moderated regression procedures in a monte carlo simulation. Estimates of moderator effect sizes were substantially better estimates of the true latent moderator effect (i. e. larger by a multiple of 2,6 to 534) when estimated using a simple percentile bootstrap procedure in the original, positively skewed data. Conclusions with regard to the presence or absence of a true moderator effects using a simple bootstrap procedure were unaffected by the violation of parametric assumptions in the original,posiitvely skewed data. In contrast, moderated regression analysis performed on a log - transformed dependent variavle severely increased type II error. Implications are drawn for applied psychological and management research.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali