Should Students Trust their Instructors in Statistics? Differences in PLS Path Modelling while using WarpPLS and R

Elena Druică, Zizi Goschin
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Abstract

Abstract A common problem with using different statistical packages for the same data and method is the risk of getting dissimilar results. While the reasons behind this outcome are often known and accepted, the negative consequences might be significant. In a teaching environment, usually involving toy models, with no practical implications, only a reputation risk is at stake. Nevertheless, students should be aware of such incongruities, their causes and possible solutions. Starting from these considerations, our paper addresses the differences that arise between R and WarpPLS while applying the Partial Least Squares Path Modelling (PLS-PM) method. To this end we estimate a PLS-PM model for analysing health-positioning data, compare the results and explain how the two statistical packages differ and complement each other in an attempt to derive the best fit for the data.
学生应该相信他们的统计老师吗?在使用WarpPLS和R时PLS路径建模的差异
对于相同的数据和方法,使用不同的统计软件包的一个常见问题是有可能得到不同的结果。虽然这种结果背后的原因通常是已知和接受的,但其负面后果可能是显著的。在教学环境中,通常涉及玩具模型,没有实际意义,只有声誉风险是危险的。然而,学生们应该意识到这些不协调,它们的原因和可能的解决方案。从这些考虑出发,我们的论文在应用偏最小二乘路径建模(PLS-PM)方法时解决了R和WarpPLS之间出现的差异。为此,我们估计了用于分析健康定位数据的PLS-PM模型,比较了结果并解释了两个统计包之间的差异和相互补充,试图得出最适合数据的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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