Bootstrap scree tests: a Monte Carlo simulation and applications to published data.
A non-parametric procedure for Cattell's scree test is proposed, using the bootstrap method. Bentler and Yuan developed parametric tests for the linear trend of scree eigenvalues in principal component analysis. The proposed method is for cases where parametric assumptions are not realistic. We define the break in the scree trend in several ways, based on linear slopes defined with two or three consecutive eigenvalues, or all eigenvalues after the k largest. The resulting scree test statistics are evaluated under various data conditions, among which Gorsuch and Nelson's bootstrap CNG performs best and is reasonably consistent and efficient under leptokurtic and skewed conditions. We also examine the bias-corrected and accelerated bootstrap method for these statistics, and the bias correction is found to be too unstable to be useful. Using seven published data sets which Bentler and Yuan analysed, we compare the bootstrap approach to the scree test with the parametric linear trend test.
Hong
Sungjin
S
University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA. hongsj@uiuc.edu
Mitchell
Stephen K
SK
Harshman
Richard A
RA
D006801:Humans; D008960:Models, Psychological; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011584:Psychology / Q000706:statistics & numerical data*; D011643:Publishing / Q000706:statistics & numerical data*