Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06) 2006
DOI: 10.1109/hicss.2006.381
|View full text |Cite
|
Sign up to set email alerts
|

PLS, Small Sample Size, and Statistical Power in MIS Research

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
128
1
3

Year Published

2014
2014
2021
2021

Publication Types

Select...
6
3
1

Relationship

0
10

Authors

Journals

citations
Cited by 187 publications
(133 citation statements)
references
References 11 publications
1
128
1
3
Order By: Relevance
“…Moreover, PLSR has greater reliability compared to other techniques (single multiple regression or combination of multiple regression with other multivariate methods) when identifying relevant variables and their magnitudes of influence, independently of the sample size used in the analysis [10]. On the other hand, probably there are limitations with regard to the applicability of the suggested model to other areas or different plant species [31]. Because of its advantages, PLSR method could also be ideal for the prediction of important management goals involving soil bio (chemical) parameters such as those involved in C and N cycles [30,32].…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, PLSR has greater reliability compared to other techniques (single multiple regression or combination of multiple regression with other multivariate methods) when identifying relevant variables and their magnitudes of influence, independently of the sample size used in the analysis [10]. On the other hand, probably there are limitations with regard to the applicability of the suggested model to other areas or different plant species [31]. Because of its advantages, PLSR method could also be ideal for the prediction of important management goals involving soil bio (chemical) parameters such as those involved in C and N cycles [30,32].…”
Section: Discussionmentioning
confidence: 99%
“…Based on the partial nature of the PLS algorithm, PLS requires a relatively small sample size (Goodhue et al 2006;Marcoulides et al 2009 Barclay et al (1995) and Chin (1998) is to consider the number of structural paths and dependent variables. Specifically, Barclay et al (1995) suggested ten times the largest number of structural paths directed at a particular construct in the inner path model.…”
Section: Discussionmentioning
confidence: 99%
“…PLS is a latent variable modeling technique which incorporates the multiple dependent constructs and recognizes measurement error (Karim, 2009). It is more useful, because PLS has the ability to identify path relationships of statistical significance (Goodhue, Lewis, & Thompson, 2006). Other powerful features of PLS path modeling are to examine the model fit in a straightforward manner, test the proposed hypotheses (Luo, Li, Zhang, & Shim, 2010), and provide a more accurate estimation of the mediating effects by looking into the free measurement errors (Chin, Marcolin, & Newsted, 2003).…”
Section: Resultsmentioning
confidence: 99%