Dolce, Pasquale
(2015)
Component-based Path Modeling
Open Issues and Methodological Contributions.
[Tesi di dottorato]
| Item Type: |
Tesi di dottorato
|
| Resource language: |
English |
| Title: |
Component-based Path Modeling
Open Issues and Methodological Contributions |
| Creators: |
| Creators | Email |
|---|
| Dolce, Pasquale | pasquale.dolce@unina.it |
|
| Date: |
31 March 2015 |
| Number of Pages: |
149 |
| Institution: |
Università degli Studi di Napoli Federico II |
| Department: |
Scienze Economiche e Statistiche |
| Scuola di dottorato: |
Scienze economiche e statistiche |
| Dottorato: |
Statistica |
| Ciclo di dottorato: |
27 |
| Coordinatore del Corso di dottorato: |
| nome | email |
|---|
| Lauro, Carlo Natale | clauro@unina.it |
|
| Tutor: |
| nome | email |
|---|
| Lauro, Carlo Natale | UNSPECIFIED |
|
| Date: |
31 March 2015 |
| Number of Pages: |
149 |
| Keywords: |
PLS-PM, formative measurement model, component-based methods, Non-Symmetrical, quantile regression |
| Settori scientifico-disciplinari del MIUR: |
Area 13 - Scienze economiche e statistiche > SECS-S/01 - Statistica |
[error in script]
[error in script]
| Date Deposited: |
14 Apr 2015 12:31 |
| Last Modified: |
27 Oct 2015 02:00 |
| URI: |
http://www.fedoa.unina.it/id/eprint/10468 |
| DOI: |
10.6092/UNINA/FEDOA/10468 |
Collection description
In this work we discussed some issues in PLS-PM and proposed methodological
contributions to enhance PLS-PM potentialities.
PLS-PM is a component-based method for SEM. Instead of severing every tie
between component-based methods and factor-based methods we think that researchers
should commit themselves in finding out which approach works best in
which circumstances, and a continuous dialogue between the two communities of
researchers is highly recommended for progress in this area of research.
We compared PLS-PM and ML-SEM in the framework of the same simulation
design, investigating the effects of measurement model misspecification and the
implications of formative MVs on both methods.
A common impression found in the literature is that only PLS-PM allows the
estimation of SEM including formative blocks. The implication of formative MVs
in Covariance-Based framework is a rather difficult task. However, if certain model
specification conditions are satisfied the model is identified, and it is possible to
estimate a Covariance-Based SEM with formative blocks (Bollen and Davis, 2009;
Williams et al., 2003).
Measurement model misspecification has the potential for poor parameter estimates
and misleading conclusions (see Dolce and Lauro, 2014; Jarvis et al., 2003;
MacKenzie et al., 2005, among others). Its effects extend also to the estimates of
the path coefficients connected to the misspecified block. Our simulation results
showed that the effect of measurement model misspecification is much larger on
the ML-SEM parameter estimates.
Besides considering PLS-PM as an alternative method for SEM, PLS-PM is a descriptive
and prediction oriented method, deserving a prominent place in research applications when the aims of the analysis is prediction (Becker et al., 2013). For
this reasons, further studies on the predictive ability of PLS-PM are needed.
The PLS-PM evaluation criteria should include the predictive ability and further
criteria and evaluation techniques for PLS-PM are needed (Sarstedt et al., 2014).
Based on the proposed criteria, further extensions and modifications should be
made on the basic PLS-PM algorithm in order to improve the predictive capabilities
of the model estimation. The non-symmetrical approach for component-based
path modelling (NSC-PM) presented in the third chapter of this dissertation is an
example of work in this direction.
NSC-PM is a non-symmetrical approach that aims at maximizing the explained
variance of the MVs of the endogenous and bridge blocks ( i.e. an approach based
on the optimization of a redundancy-related criterion in a multi-block framework).
Unlike PLS-PM, NSC-PM respects the direction of the relationship specified in
the Path diagram (i.e. the path directions), since the directions of the links in the
inner model play a role in the algorithm. Compared to the other component-based
methods, NSC-PM seems to be a good compromise between favoring stability (high
explained variance) in the blocks and correlation between components.
In the last chapter of the thesis we presented the Quantile Composite-based Path
Modelling (QC-PM). QC-PM exploits both Quantile regression (QR) (Koenker
and Basset, 1978) and quantile correlation (QC) (Li et al., 2014), which allow respectively
the estimation of a set of conditional quantile functions and a correlation
measure to examine the linear linear relationships between any two variables for
different quantiles, providing a more complete picture of the relationships between
varia
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