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Thursday, May 22, 2014

Dichotomous variables


Models with dichotomous variables can be tested with WarpPLS. Based on preliminary Monte Carlo simulations, the following combination of algorithm and P value calculation method seems to be the most advisable: PLS regression and stable.

A model with a dichotomous dependent variable can also be tested with WarpPLS; another technique that can be used is logistic regression, which is a variation of ordinary multiple regression.

Below is a model with a dichotomous dependent variable - Effe. The variable assumes two values, 0 or 1, to reflect low or high levels of "effectiveness".



The graph below shows the expected values of Effe given Effi. The latter is one of the LVs that point at Effe in the model. The values of Effe and Effi are unstandardized.



Arguably a model with a dichotomous dependent variable cannot be viably tested with ordinary multiple regression because the dependent variable is not normally distributed (as it assumes only two values).

The graph below shows a histogram with the distribution of values of Effe. This variable's skewness is -0.423 and excess kurtosis is -1.821.



This is not a problem for WarpPLS because P values are calculated via nonparametric techniques that do not assume in their underlying design that any variables in the model meet parametric expectations; such as the expectations of univariate and multivariate unimodality and normality.

If a dependent variable refers to a probability, and is expected to be associated with a predictor according to a logistic function, you should use the Warp3 or Warp3 basic inner model algorithms to relate the two variables.

5 comments:

Sayema Sultana said...

I have an independent variable in my model which is measured by a nominal scale with only two categories. Now, I just wanna know that how I can analyze this variable in warp pls?

Ned Kock said...

Hi Sayema. These two posts should be quite useful in the context of your question:

http://warppls.blogspot.com/2010/02/how-do-i-control-for-effects-of-one-or.html

http://warppls.blogspot.com/2011/08/using-warppls-in-e-collaboration.html

Btw, are you attending our whole-day workshop in the PLS Appls. Symposium?

http://plsas.net

Sayema Sultana said...

Dear Prof.
Many thanks for your prompt reply.
I would love to attend the workshop, but, I am not residing in USA.
I am Ph. D student and in my last colloquium, the accessors were asking, why I have used Warp PLS not, Smart PLS. Can you please point out some of the advantages of using Warp PLS over Smart PLS?

Thanks again
Best regards

Sayema Sultana said...

Dear Prof.
Many thanks for your prompt reply.
I would love to attend the workshop, but, I am not residing USA.
I am Ph. D student and in my colloquium, the accessors were asking, why I have used Warp PLS not, Smart PLS. Can you please point out some of the advantages of using Warp PLS over Smart PLS?

Thanks again
Best regards

Ned Kock said...

The text below can be used to justify the use of WarpPLS. Some of its features, such as the ability to conduct a factor-based SEM and model nonlinear relationships, are, to the best of my knowledge, unique among SEM software.

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WarpPLS has been used to study a number of topics in a variety of disciplines and fields, including: accounting, anthropology, clinical psychology, ecology, economics, education, global environmental change, epidemiology, evolutionary psychology, exercise physiology, information systems, international business, finance, management, marketing, medicine, nursing, organizational psychology, and sociology. Articles presenting empirical studies employing WarpPLS as the main data analysis tool have been published in journals that are highly ranked in their respective fields, such as: Global Environmental Change, Journal of Advanced Nursing, Journal of Management Information Systems, International Business Review, Journal of the Association for Information Systems, Decision Support Systems, and Management Information Systems Quarterly.[3]

Main features
Among the main features of WarpPLS is its ability to identify and model non-linearity among variables in path models, whether these variables are measured as latent variables or not, yielding parameters that take the corresponding underlying heterogeneity into consideration.[4][5] This and other notable features are summarized through the list below.

Guides SEM analysis flow via a step-by-step user interface guide.[6]
Implements classic (composite-based) as well as factor-based PLS algorithms.
Identifies nonlinear relationships, and estimates path coefficients accordingly.
Also models linear relationships, using classic and factor-based PLS algorithms.
Models reflective and formative variables, as well as moderating effects.
Calculates P values, model fit and quality indices, and full collinearity coefficients.
Calculates effect sizes and Q-squared predictive validity coefficients.
Calculates indirect effects for paths with 2, 3 etc. segments; as well as total effects.
Calculates several causality assessment coefficients.
Provides zoomed 2D graphs and 3D graphs.

References
Kock, N., & Mayfield, M. (2015). PLS-based SEM algorithms: The good neighbor assumption, collinearity, and nonlinearity. Information Management and Business Review, 7(2), 113-130.
Kock, N. (2015). A note on how to conduct a factor-based PLS-SEM analysis. International Journal of e-Collaboration, 11(3), 1-9.
Google Scholar list of links to academic publications using or discussing WarpPLS
Gountas, S., & Gountas, J. (2016). How the ‘warped’ relationships between nurses' emotions, attitudes, social support and perceived organizational conditions impact customer orientation. Journal of Advanced Nursing, 72(2), 283-293.
Guo, K.H., Yuan, Y., Archer, N.P., & Connelly, C.E. (2011). Understanding nonmalicious security violations in the workplace: A composite behavior model. Journal of Management Information Systems, 28(2), 203-236.
Short YouTube video illustrating step-by-step user interface guide