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Partial Least Squares Modeling in Research on Educational Achievement

Partial Least Squares Modeling in Research on Educational Achievement

This paper contains a discussion of partial least squares (PLS) path modeling with latent
+ T6 c8 n" A1 P: X. Y/ N# Kspsschina.cnconstructs as a general method for research on educational achievement. To the extent that
; w* @, y" `9 V6 Q/ Lsuch research requires the analysis of comparatively large and complex models under mildspss论坛|spss下载|spss视频|Amos|SEM|SAS|Matlab|Eviews+ G3 h* m3 W( K$ L
supplementary assumptions, PLS is an extremely flexible and powerful tool for statisticalSPSS,spss下载,spss 下载,spss 教程,spss软件,spss中文版下载,spss免费下载,数据分析师,数据分析论坛,数据分析软件,spss13.0下载,SPSS教程,Spss视频amos ,sem analysis,spss function,spss net,spss software,spss statistical,数据分析师,数据分析论坛,数据分析软件,PLS,DEA,4 ^, m2 H  C. U2 y9 o
model building. The formal specification, estimation, and evaluation of PLS models is
. D+ M3 P! m( z% J% W7 {; a9 udescribed with special emphasis on the features that distinguish PLS from other methods for5 _+ T: r, N* y9 Q, K" j) F+ p
path analysis. This specifically concerns distribution-free least squares estimation and, S' K1 S1 q$ D9 K
distribution-free model evaluation using jackknife techniques.

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