Links to specific topics

Saturday, April 14, 2018

PLS Applications Symposium; 11 - 13 April 2018; Laredo, Texas


PLS Applications Symposium; 11 - 13 April 2018; Laredo, Texas
(Abstract submissions accepted until 15 February 2018)

*** Only abstracts are needed for the submissions ***

The partial least squares (PLS) method has increasingly been used in a variety of fields of research and practice, particularly in the context of PLS-based structural equation modeling (SEM). The focus of this Symposium is on the application of PLS-based methods, from a multidisciplinary perspective. For types of submissions, deadlines, and other details, please visit the Symposium’s web site:

http://plsas.net

*** Workshop on PLS-SEM ***

On 11 April 2018 a full-day workshop on PLS-SEM will be conducted by Dr. Ned Kock and Dr. Geoffrey Hubona, using the software WarpPLS. Dr. Kock is the original developer of this software, which is one of the leading PLS-SEM tools today; used by thousands of researchers from a wide variety of disciplines, and from many different countries. Dr. Hubona has extensive experience conducting research and teaching topics related to PLS-SEM, using WarpPLS and a variety of other tools. This workshop will be hands-on and interactive, and will have two parts: (a) basic PLS-SEM issues, conducted in the morning (9 am - 12 noon) by Dr. Hubona; and (b) intermediate and advanced PLS-SEM issues, conducted in the afternoon (2 pm - 5 pm) by Dr. Kock. Participants may attend either one, or both of the two parts.

The following topics, among others, will be covered - Running a Full PLS-SEM Analysis - Conducting a Moderating Effects Analysis - Viewing Moderating Effects via 3D and 2D Graphs - Creating and Using Second Order Latent Variables - Viewing Indirect and Total Effects - Viewing Skewness and Kurtosis of Manifest and Latent Variables - Viewing Nonlinear Relationships - Solving Collinearity Problems - Conducting a Factor-Based PLS-SEM Analysis - Using Consistent PLS Factor-Based Algorithms - Exploring Statistical Power and Minimum Sample Sizes - Exploring Conditional Probabilistic Queries - Exploring Full Latent Growth - Conducting Multi-Group Analyses - Assessing Measurement Invariance - Creating Analytic Composites.

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Ned Kock
Symposium Chair
http://plsas.net

Monday, December 4, 2017

Data labels


In WarpPLS data labels can be added through the menu options “Add data labels from clipboard” and “Add data labels from file”. Data labels are text identifiers that are entered by you through these options, one column at a time.

Like the original numeric dataset, the data labels are stored in a table. Each column of this table refers to one data label variable, and each row to the corresponding row of the original numeric dataset.



Data labels can be shown on graphs (as illustrated above), either next to each data point that they refer to, or as part of the legend for a graph. The short video linked below illustrates this.

https://youtu.be/i5-_WIMXVl4

Once they have been added, data labels can be viewed or saved using the “View or save data labels” option.

Data labels can also be used to discover moderating effects, as discussed in the blog post linked below.

http://warppls.blogspot.com/2014/02/using-data-labels-to-discover.html

This can be done in conjunction with the “Explore full latent growth” option, which provides a powerful alternative for the identification of moderating effects:

https://warppls.blogspot.com/2017/10/full-latent-growth.html


Thursday, October 5, 2017

True composite and factor reliabilities


The menu option “Explore additional coefficients and indices”, available in WarpPLS starting in version 6.0,  allows you to obtain an extended set of reliabilities. The extended set of reliabilities includes the classic reliability coefficients already available in the previous version of this software, plus the following, for each latent variable in your model: Dijkstra's PLSc reliability (also available via the new menu option “Explore Dijkstra's consistent PLS outputs”), true composite reliability, and factor reliability. When factor-based PLS algorithms are used in analyses, the true composite reliability and the factor reliability are produced as estimates of the reliabilities of the true composites and factors. They are calculated in the same way as the classic composite reliabilities available from the previous version of this software, but with different loadings. When classic composite-based (i.e., non-factor-based) algorithms are used, both true composites and factors coincide, and are approximated by the composites generated by the software. As such, true composite and factor reliabilities equal the corresponding composite reliabilities whenever composite-based algorithms are used.

Related YouTube video:

Explore True Composite and Factor Reliabilities in WarpPLS

http://youtu.be/DwslOCEvOd4

Fit indices comparing indicator correlation matrices


The new menu option “Explore additional coefficients and indices”, available in WarpPLS starting in version 6.0, allows you to obtain an extended set of model fit and quality indices. The extended set of model fit and quality indices includes the classic indices already available in the previous version of this software, as well as new indices that allow investigators to assess the fit between the model-implied and empirical indicator correlation matrices. These new indices are the standardized root mean squared residual (SRMR), standardized mean absolute residual (SMAR), standardized chi-squared (SChS), standardized threshold difference count ratio (STDCR), and standardized threshold difference sum ratio (STDSR). As with the classic model fit and quality indices, the interpretation of these new indices depends on the goal of the SEM analysis. Since these indices refer to the fit between the model-implied and empirical indicator correlation matrices, they become more meaningful when the goal is to find out whether one model has a better fit with the original data than another, particularly when used in conjunction with the classic indices. When assessing the model fit with the data, several criteria are recommended. These criteria are discussed in the WarpPLS User Manual.

Related YouTube video:

Explore Indicator Correlation Matrix Fit Indices in WarpPLS

http://youtu.be/YutkhEPW-CE

Dijkstra's consistent PLS outputs


The menu option “Explore Dijkstra's consistent PLS outputs”, available in WarpPLS starting in version 6.0,  allows you to obtain key outputs generated based on Dijkstra's consistent PLS (a.k.a. PLSc) technique. These outputs include PLSc reliabilities for each latent variable, also referred to as Dijkstra's rho_a's, which appear to be, in many contexts, better approximations of the true reliabilities than the measures usually reported in PLS-based SEM contexts – the composite reliability and Cronbach’s alpha coefficients. Also included in the outputs generated via this menu option are PLSc loadings; along with the corresponding standard errors, one-tailed and two-tailed P values, T ratios, and confidence intervals.

Related YouTube video:

Explore Dijkstra's Consistent PLS Outputs in WarpPLS

http://youtu.be/WdKogy29OVg

Categorical-to-numeric conversion


The menu option “Explore categorical-numeric-categorical conversion”, available in WarpPLS starting in version 6.0, allows you to perform categorical-to-numeric conversions. In a categorical-to-numeric conversion a user can convert a categorical variable, stored as a data label variable, into a numeric variable that is added to the dataset as a new standardized indicator. This new variable can then be used as a new indicator of an existing latent variable, or as a new latent variable with only one-indicator. Three categorical-to-numeric conversion modes, to be used under different circumstances, are available: anchor-factorial with fixed variation, anchor-factorial with variation diffusion, and anchor-factorial with variation sharing.

Related YouTube video:

Explore Categorical-to-Numeric Conversion in WarpPLS

http://youtu.be/XsytZqX7DBc

Numeric-to-categorical conversion


The menu option “Explore categorical-numeric-categorical conversion”, available in WarpPLS starting in version 6.0, allows you to perform numeric-to-categorical conversions. In a numeric-to-categorical conversion one or more of the following are converted into a single data label variable: latent variable, standardized indicator, or unstandardized indicator. This option is useful in multi-group analyses where the investigator wants to employ more than one numeric field for grouping. For example, let us assume that the following two unstandardized indicators are available: C, with the values 1 and 0 referring to individuals from the countries of Brazil and New Zealand; and G, with the values 1 and 0 referring to females and males. By using a numeric-to-categorical conversion a researcher could create a new data label variable to conduct a multi-group analysis based on four groups: “C=1G=1”, “C=1G=0”, “C=0G=1” and “C=0G=0”.

Related YouTube video:

Explore Numeric-to-Categorical Conversion in WarpPLS

http://youtu.be/TWTC-5pqKx8