کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5109853 | 1377720 | 2016 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Estimation issues with PLS and CBSEM: Where the bias lies!
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کلمات کلیدی
موضوعات مرتبط
علوم انسانی و اجتماعی
مدیریت، کسب و کار و حسابداری
کسب و کار و مدیریت بین المللی
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Business Research - Volume 69, Issue 10, October 2016, Pages 3998-4010
Journal: Journal of Business Research - Volume 69, Issue 10, October 2016, Pages 3998-4010
نویسندگان
Marko Sarstedt, Joseph F. Hair, Christian M. Ringle, Kai O. Thiele, Siegfried P. Gudergan,