Rao and Wu's re-scaling bootstrap modified to achieve extended coverages

Pal, Sanghamitra

doi:10.1016/j.jspi.2009.04.006

Cited by 3 publications

(1 citation statement)

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“…However, the resampling procedures available in PLS-PM software assume that the data are collected via simple random sampling, such that observations are both independent and identically distributed (e.g., Kock, 2013; Ringle et al, 2005; Sanchez & Trinchera, 2013). When complex survey data are involved, the resampling strategy must mimic the original sampling scheme (Aidara, 2013; Antal & Tillé, 2011; Pal, 2009; Preston, 2009), and PLS resampling routines should be modified accordingly. Similarly, the new permutation approach to PLS-PM inference (cf.…”

Section: Does Pls-pm Provide Valid Inference On Path Coefficients?mentioning

confidence: 99%

Reflections on Partial Least Squares Path Modeling

McIntosh

Edwards

Antonakis

2014

Organizational Research Methods

182

125

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The purpose of the present article is to take stock of a recent exchange in Organizational Research Methods between critics (Rönkkö & Evermann, 2013) and proponents (Henseler et al., 2014) of partial least squares path modeling (PLS-PM). The two target articles were centered around six principal issues, namely whether PLS-PM: (1) can be truly characterized as a technique for structural equation modeling (SEM); (2) is able to correct for measurement error; (3) can be used to validate measurement models; (4) accommodates small sample sizes; (5) is able to provide null hypothesis tests for path coefficients; and (6) can be employed in an exploratory, model-building fashion. We summarize and elaborate further on the key arguments underlying the exchange, drawing from the broader methodological and statistical literature in order to offer additional thoughts concerning the utility of PLS-PM and ways in which the technique might be improved. We conclude with recommendations as to whether and how PLS-PM serves as a viable contender to SEM approaches for estimating and evaluating theoretical models.

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