A Critical Examination of Common Beliefs About PLS-PM
Posted: Thu May 30, 2013 8:16 am
Hello all,
I already read six critical (myth) thinking by Ronkko and Evermann (RE, hereafter) in they article from Organizational Research Methods 2013. This post just for discussion about this issue and for clear all issue. I will begin with show some myth statement from RE compare with Wold original paper (May be some expert PLS will comment this issue future):
a) In Myth 6: PLS Is Most Appropriate for Exploratory or Early Stage Research
RE conclude PLS is not an appropriate choice for early-stage theory development and testing (p. 18). This statement contrast by Noonan and Wold research (see Noonan and Wold 1983). Noonan and Wold conclude PLS is not only a tool for systems analysis; it is also general tool for scientific development. Three ares can be mention: (1) exploratory factor analysis; (2) instrument development; and (3) theory development (p. 328). see also Wold (1975b, p. 350-351).
b) In Myth 5: PLS Has Minimal Requirements on Sample Size
I think this myth clear and consistent by Wold state in original paper (1982 p. 25; 1985b p.231), PLS based on consitency at large with many indicators and sufficient sample size. Current article PLS recommend 10x path coefficient/predictors in model for require sample size (see Barclay et al. 1995, p. 292; Hair et al. 2011, p. 144).
c) Myth 4: PLS Can Be Used for Testing Null Hypotheses About Path Coefficients
RE conclude because NHST (null hypothesis significance testing) requires a test statistic with a known sampling distribution, the PLS path estimates cannot be used in NHST. This statement contrast by Noonan and Wold research (see Noonan and Wold 1983). Noonan and Wold state if the standard errors of the regression coefficients for the inner relations are computed from the diagonal elements of the inverse matrices of the predictor variables, the t-test or F-test can be applied. Again, Wold (1975b, p. 353) also suggest for used confidence interval for hypothesis testing.
d) Myth 3: PLS Can Be Used to Validate Measurement Models
Because of these better alternatives, the measurement model should never be evaluated based on the composite loadings produced by PLS or any statistic derived from these. RE conclude that the idea that PLS results can be used to validate a measurement model is a myth (p. 14). For validate measurement models (Mode A), PLS used PCA and for Mode B PLS used canonical correlation (see Wold 1975, p.122; 1985a, p. 587). Principal Component versus Factor Analysis is typical for the different at issue between PLS and ML approach.
e) Myth 2: PLS Reduces the Effect of Measurement Error
For this issue I not have comment. I need testing again before response.
f) Myth 1: PLS Has Advantages Over Traditional Methods Because It Is an SEM Estimator
I think this myth clear explanation by Marcoulides et al. (2012, p. 721-722).
Please reply another comment if anyone interest this issue.
Regards,
References:
Barclay, D., Higgins, C., and Thompson, R. 1995. “The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration,” Technology Studies (2:2), pp. 285-309.
Marcoulides, G. A., Chin, W. W., and Saunders, C. 2012. "When imprecise statistical statements become problematic: A response to Goodhue, Lewis and Thompson," MIS Quarterly (36:3), pp. 717-728.
Noonan, R., and Wold, H. 1983. “Evaluating School Systems Using Partial Least Squares,” Evaluation in Education, Oxford: Pergamon (7), pp. 219-364.
Wold, H 1975a. “Soft Modeling by Latent Variables: The NIPALS approach,” In Gani, J. (Ed.), Perspectives in probability and statistics: Papers in honour of M.S. Bartlett on the occasion of his sixty fifth birthday pp. 117-142.
Wold, H. 1975b. “Path Models with Latent Variables: The NIPALS Approach,” In Blalock et al. (Eds.) Quantitative Sociology. New York: Academic Press.
Wold, H. 1982. “Soft modeling: the basic design and some extensions,” In: Jöreskog K. G,. and Wold, H (Eds) Systems under indirect observation. Causality, structure, prediction, Vol II. North-Holland, Amsterdam, pp 1-54.
Wold, H. 1985a. “Partial least squares,” In Kotz, S., and Johnson, N.L (Eds.), Encyclopedia of statistical sciences (Vol. 8, pp. 587-599). New York: Wiley.
Wold, H. 1985b. “System Analysis by Partial Least Squares,” In Nijkamp, P., Leitner, G., and Wrigley, N (Eds.), Measuring the Unmeasurable. Martinus Nijhoff Publishers. Dordrecht, Boston, Lancaster, pp. 221-251.
I already read six critical (myth) thinking by Ronkko and Evermann (RE, hereafter) in they article from Organizational Research Methods 2013. This post just for discussion about this issue and for clear all issue. I will begin with show some myth statement from RE compare with Wold original paper (May be some expert PLS will comment this issue future):
a) In Myth 6: PLS Is Most Appropriate for Exploratory or Early Stage Research
RE conclude PLS is not an appropriate choice for early-stage theory development and testing (p. 18). This statement contrast by Noonan and Wold research (see Noonan and Wold 1983). Noonan and Wold conclude PLS is not only a tool for systems analysis; it is also general tool for scientific development. Three ares can be mention: (1) exploratory factor analysis; (2) instrument development; and (3) theory development (p. 328). see also Wold (1975b, p. 350-351).
b) In Myth 5: PLS Has Minimal Requirements on Sample Size
I think this myth clear and consistent by Wold state in original paper (1982 p. 25; 1985b p.231), PLS based on consitency at large with many indicators and sufficient sample size. Current article PLS recommend 10x path coefficient/predictors in model for require sample size (see Barclay et al. 1995, p. 292; Hair et al. 2011, p. 144).
c) Myth 4: PLS Can Be Used for Testing Null Hypotheses About Path Coefficients
RE conclude because NHST (null hypothesis significance testing) requires a test statistic with a known sampling distribution, the PLS path estimates cannot be used in NHST. This statement contrast by Noonan and Wold research (see Noonan and Wold 1983). Noonan and Wold state if the standard errors of the regression coefficients for the inner relations are computed from the diagonal elements of the inverse matrices of the predictor variables, the t-test or F-test can be applied. Again, Wold (1975b, p. 353) also suggest for used confidence interval for hypothesis testing.
d) Myth 3: PLS Can Be Used to Validate Measurement Models
Because of these better alternatives, the measurement model should never be evaluated based on the composite loadings produced by PLS or any statistic derived from these. RE conclude that the idea that PLS results can be used to validate a measurement model is a myth (p. 14). For validate measurement models (Mode A), PLS used PCA and for Mode B PLS used canonical correlation (see Wold 1975, p.122; 1985a, p. 587). Principal Component versus Factor Analysis is typical for the different at issue between PLS and ML approach.
e) Myth 2: PLS Reduces the Effect of Measurement Error
For this issue I not have comment. I need testing again before response.
f) Myth 1: PLS Has Advantages Over Traditional Methods Because It Is an SEM Estimator
I think this myth clear explanation by Marcoulides et al. (2012, p. 721-722).
Please reply another comment if anyone interest this issue.
Regards,
References:
Barclay, D., Higgins, C., and Thompson, R. 1995. “The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration,” Technology Studies (2:2), pp. 285-309.
Marcoulides, G. A., Chin, W. W., and Saunders, C. 2012. "When imprecise statistical statements become problematic: A response to Goodhue, Lewis and Thompson," MIS Quarterly (36:3), pp. 717-728.
Noonan, R., and Wold, H. 1983. “Evaluating School Systems Using Partial Least Squares,” Evaluation in Education, Oxford: Pergamon (7), pp. 219-364.
Wold, H 1975a. “Soft Modeling by Latent Variables: The NIPALS approach,” In Gani, J. (Ed.), Perspectives in probability and statistics: Papers in honour of M.S. Bartlett on the occasion of his sixty fifth birthday pp. 117-142.
Wold, H. 1975b. “Path Models with Latent Variables: The NIPALS Approach,” In Blalock et al. (Eds.) Quantitative Sociology. New York: Academic Press.
Wold, H. 1982. “Soft modeling: the basic design and some extensions,” In: Jöreskog K. G,. and Wold, H (Eds) Systems under indirect observation. Causality, structure, prediction, Vol II. North-Holland, Amsterdam, pp 1-54.
Wold, H. 1985a. “Partial least squares,” In Kotz, S., and Johnson, N.L (Eds.), Encyclopedia of statistical sciences (Vol. 8, pp. 587-599). New York: Wiley.
Wold, H. 1985b. “System Analysis by Partial Least Squares,” In Nijkamp, P., Leitner, G., and Wrigley, N (Eds.), Measuring the Unmeasurable. Martinus Nijhoff Publishers. Dordrecht, Boston, Lancaster, pp. 221-251.