Partial least squares path modeling using ordinal categorical indicators
Partial least squares path modeling using ordinal categorical indicators
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DOI：
10.1007/s11135-016-0401-7
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发表时间：
2018-01-01
影响因子：
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通讯作者：
Dijkstra, Theo K.
中科院分区：
文献类型：
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作者：
Schuberth, Florian;Henseler, Jorg;Dijkstra, Theo K.
本文介绍了一种新的一致性方差估计，称为有序一致偏最小二乘（OrdPLSc）。OrdPLSc完成了由PLS、PLSc和OrdPLS组成的基于方差的估计器系列，并且如果某些或所有指标在有序分类尺度上测量，则允许估计复合材料和公共因子的结构方程模型。使用不同总体模型的Monte Carlo模拟（N）表明，OrdPLSc提供了几乎无偏的估计。如果所有结构都建模为公共因子，则OrdPLSc产生的估计值接近其基于协方差的对应物WLSMV，但效率较低。如果一些结构被建模为复合材料，OrdPLSc几乎没有竞争对手。
This article introduces a new consistent variance-based estimator called ordinal consistent partial least squares (OrdPLSc). OrdPLSc completes the family of variance-based estimators consisting of PLS, PLSc, and OrdPLS and permits to estimate structural equation models of composites and common factors if some or all indicators are measured on an ordinal categorical scale. A Monte Carlo simulation (N ) with different population models shows that OrdPLSc provides almost unbiased estimates. If all constructs are modeled as common factors, OrdPLSc yields estimates close to those of its covariance-based counterpart, WLSMV, but is less efficient. If some constructs are modeled as composites, OrdPLSc is virtually without competition.