| Title |
Assessing statistical differences between parameters estimates in Partial Least Squares path modeling
|
|---|---|
| Published in |
Quality & Quantity, August 2016
|
| DOI | 10.1007/s11135-016-0400-8 |
| Pubmed ID | |
| Authors |
Macario Rodríguez-Entrena, Florian Schuberth, Carsten Gelhard |
| Abstract |
Structural equation modeling using partial least squares (PLS-SEM) has become a main-stream modeling approach in various disciplines. Nevertheless, prior literature still lacks a practical guidance on how to properly test for differences between parameter estimates. Whereas existing techniques such as parametric and non-parametric approaches in PLS multi-group analysis solely allow to assess differences between parameters that are estimated for different subpopulations, the study at hand introduces a technique that allows to also assess whether two parameter estimates that are derived from the same sample are statistically different. To illustrate this advancement to PLS-SEM, we particularly refer to a reduced version of the well-established technology acceptance model. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 399 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Malaysia | 2 | <1% |
| Unknown | 397 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 103 | 26% |
| Student > Master | 33 | 8% |
| Student > Doctoral Student | 28 | 7% |
| Researcher | 26 | 7% |
| Professor | 26 | 7% |
| Other | 99 | 25% |
| Unknown | 84 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Business, Management and Accounting | 140 | 35% |
| Social Sciences | 45 | 11% |
| Economics, Econometrics and Finance | 26 | 7% |
| Computer Science | 18 | 5% |
| Arts and Humanities | 13 | 3% |
| Other | 55 | 14% |
| Unknown | 102 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 February 2018.
All research outputs
#13,784,610
of 22,884,315 outputs
Outputs from Quality & Quantity
#316
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Outputs of similar age
#186,301
of 338,387 outputs
Outputs of similar age from Quality & Quantity
#3
of 11 outputs
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