دانلود رایگان مقاله مسائل مربوط به ارزیابی با PLS و CBSEM: جایی که بایاس نادرست است!

abstract

Discussions concerning different structural equation modeling methods draw on an increasing array of concepts and related terminology. As a consequence, misconceptions about the meaning of terms such as reflective measurement and common factor models as well as formative measurement and composite models have emerged. By distinguishing conceptual variables and their measurement model operationalization from the estimation perspective, we disentangle the confusion between the terminologies and develop a unifying framework. Results from a simulation study substantiate our conceptual considerations, highlighting the biases that occur when using (1) composite-based partial least squares path modeling to estimate common factor models, and (2) common factor-based covariance-based structural equation modeling to estimate composite models. The results show that the use of PLS is preferable, particularly when it is unknown whether the data's nature is common factor- or composite-based.

6. Conclusion

“Professional statisticians tend to know little about factor analysis and seldom practice it. Indeed, statisticians mostly have a cool negative attitude towards the subject. They hardly ever write about it. […] I can see nothing advantageous in factor analytic methods. Factor analysis is technically under-developed and at times appears almost cretinous. Its practitioners seem to be largely unaware of the technical and methodological problems, which they let themselves in for.” This text, which is more than fifty years old and taken from Ehrenberg's (1962, p. 191 and p. 206) article “Some Questions About Factor Analysis”, appears.