Embedded Files
Nature of Research
Nature of Research
Exploratory Quantitative Research
Exploratory Quantitative Research
Research into the unknown.
Predicting, developing, & exploring theory or theories.
Not supported or only partially supported by theory & literature.
What is going on here, how we explain it?
Confirmatory Quantitative Research
Confirmatory Quantitative Research
When we have a pretty good idea about what's going on.
Testing, comparing, & conforming a theory or theories.
Must be supported by strong theory & solid literature.
Is it true what we believe based on the existing theory & literature?
Types of Analysis
Types of Analysis
Source: Hair et al., (2017).
VB-SEM vs CB-SEM
VB-SEM vs CB-SEM
Source: Heir et al. (2013)
The Choice
The Choice
In case of theory is less developed, researchers should consider VB-SEM as an alternative to CB-SEM.
Once VB-SEM is employed, the study is considered exploratory.
Reasons of Using PLS-SEM
Reasons of Using PLS-SEM
Chin (1998), Chin & Newsted (1999), AND Ramayah et al., (2018)
Chin (1998), Chin & Newsted (1999), AND Ramayah et al., (2018)
The goal is to predict key target constructs or to identify key "driver" constructs.
The research is exploratory or an extension of an existing structural theory.
The phenomenon to be investigated is relatively new & measurement models need to be newly developed.
The structural equation model is complex with a large number of latent variables & manifest variables.
Relationships between the indicators and latent variables have to be modeled in different modes (i.e., formative & reflective).
The conditions relating to sample size & normal distribution are not met.
PLS is especially useful for prediction.
In situations where prior theory is strong and further testing and development are the goals, CB-SEM is more appropriate.
However, for application & prediction, when the phenomenon under research is relatively new or changing, or when the theoretical model or measures are not well formed, a PLS-SEM approach is often more suitable (Chin& Newsted, 1999)
Urbach & Ahleman, 2010
Urbach & Ahleman, 2010
PLS makes fewer demands regarding sample size than other methods.
PLS does not require normal-distributed input data.
PLS can be applied to complex structural equation models with a large number of constructs.
PLS is able to handle both reflective and formative constructs.
PLS is better suited for theory development than for theory testing.
PLS is especially useful for prediction.
Misuse of SEM Approach
Misuse of SEM Approach
Solely relying on the data characteristics are NOT SUFFICIENT to decide using PLS-SEM.
Data characteristics are among the most often stated reasons for applying PLS-SEM (Hair et al., 2017; Henseler et al., 2015).
Small sample size is the most often abused argument associated with the use of PLS-SEM (Goodhue et al., 2012; Marcoulides & Saunders, 2006).
Rules of Thumb for Selecting CB-SEM or VB-SEM
Rules of Thumb for Selecting CB-SEM or VB-SEM
Note: With large data sets, CB-SEM and VB-SEM results are similar. VB-SEM results are a good approximation of CB-SEM.
Source: Hair et al. (2011), Ramayah et al., (2018)
Further Readings
Further Readings
Hair Jr, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107-123.
Rigdon, E. E., Sarstedt, M., & Ringle, C. M. (2017). On comparing results from CB-SEM and PLS-SEM: Five perspectives and five recommendations. Marketing Zfp, 39(3), 4-16.
Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 3998-4010.
Astrachan, C. B., Patel, V. K., & Wanzenried, G. (2014). A comparative study of CB-SEM and PLS-SEM for theory development in family firm research. Journal of Family Business Strategy, 5(1), 116-128.
Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. Statistical strategies for small sample research, 1(1), 307-341.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2 ed.). Thousand Oaks, CA: Sage.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice 19(2), 139-152. doi:10.2753/MTP1069-6679190202.
Google Sites
Report abuse