Theory-driven multi-group analyses via latent growth with PLS-SEM: A two-stage anchor-factorial approach

The article below shows how one can conduct theory-driven multi-group analyses via latent growth, in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2025). Theory-driven multi-group analyses via latent growth with PLS-SEM: A two-stage anchor-factorial approach. Data Analysis Perspectives Journal, 6(4), 1-8.

Link to full-text file for this and other DAPJ articles:

https://scriptwarp.com/dapj/#Published_Articles

Abstract:

Classic multi-group analyses (MGAs) are plagued by two primary flaws: they require splitting samples into smaller subsamples, which complicates parameter comparisons due to reduced power and varying subsample characteristics; and they are typically exploratory rather than theory-driven. This paper addresses these issues by proposing a theory-driven MGA via latent growth method, using a two-stage anchor-factorial approach. The methodology is implemented within the context of structural equation modeling with partial least squares (PLS-SEM) using the WarpPLS software. The first stage involves creating and analyzing a target SEM model, and then converting a categorical grouping variable (e.g., Country) into a numeric latent growth variable (LGV) using an anchor-factorial conversion with variation sharing. The LGV is anchored on the latent variables involved in the hypothesized effects. The second stage involves inspecting the LGV's scores, related path coefficients, and 3D graphs to confirm the theory-driven effects. This approach offers a robust and theoretically superior alternative to classic MGA for assessing how multi-group influences affect model parameters.

Video demonstrating the techniques employed in the article:

Best regards to all!

Using conditional probabilistic queries for NCA and variants in PLS-SEM

The article below shows how one can use conditional probabilistic queries for NCA and variants in PLS-SEM. It focuses on necessary and sufficient conditions analyses (Jan Dul’s original NCA and variants) employing latent variables.

Kock, N. (2025). Using conditional probabilistic queries for NCA and variants in PLS-SEM. Data Analysis Perspectives Journal, 6(3), 1-6.

Link to full-text file for this and other DAPJ articles:

https://scriptwarp.com/dapj/#Published_Articles

Abstract:

We discuss the use of conditional probabilistic queries for necessary and sufficient conditions analyses (Jan Dul’s original NCA and variants) employing latent variables within the partial least squares structural equation modeling (PLS-SEM) context. While traditional PLS-SEM path coefficients establish linear causal links, they do not directly estimate the conditional probabilities central to NCA. These probabilities are crucial for both researchers and practitioners seeking a deeper understanding of necessary and sufficient conditions. This article demonstrates how to conduct bivariate and multivariate conditions analyses to systematically assess such relationships. Using an illustrative model analyzed with WarpPLS, we show how to identify specific levels of latent variables (e.g., job satisfaction and organizational commitment) that are necessary or sufficient to achieve a target outcome (e.g., above-average job performance). This methodology offers a powerful complement to classic SEM, allowing for the identification of essential prerequisites for desired outcomes.

Video demonstrating the techniques employed in the article:

Best regards to all!

ICIS 2023: Why I love India so much!

In a few hours I’ll be returning to the Great State of Texas from India, where I’ve been attending the ICIS 2023 Conference. I had the opportunity to meet with WarpPLS users, which I always enjoy very much, and with methodological researchers doing PLS-related work.

Talking about people doing PLS-related work, it was a special treat to be able to meet and talk with Nicholas Danks. The man is a true scholar and a genius. I hope to collaborate with him in the future, and (perhaps, if I am lucky) get some of that talent through osmosis.

Another highlight was talking again with the incomparable Dr. Boo. I was busy distracting her with nonsense when her name was mentioned at the awards ceremony. For those of you who don’t know, she is one of the forerunners of the field of Information Systems, a field that she begun influencing at the young age of 13 (according to my calculations).

This was my first time in India. I loved it so much! This was such a nice experience in no small measure due to Glory George, who was kind enough to show me some of Hyderabad. The people of India are so smart and hard working. Take for example the person on the photo below; he solved a 100-year-old numeric computing problem while riding on the back of a bike in heavy traffic!

Okay, just my imagination. But he was indeed doing what seemed to be some coding, using his friend’s constantly moving upper back as a table. By the way, if you think that traffic in India is chaotic, think again. Those who pay close attention will notice that there is method to what looks like disorderly flow. More than method actually, it is a form of art. Just don’t try driving if you are a beginner; it will be like challenging Ma Long to a “ping pong” match.

Should you want to see and hear the person who is writing this, in keeping with media naturalness theory, check this video. More views and likes will help make my dear friends Steve Harmon and Rolando Santos happy about their masterful video creation and editing work.

Best regards to all!

WarpPLS 8.0 upgraded to stable: Logistic regression, full latent growth graphs, HTMT2 ratios, and more!

Dear colleagues:

Version 8.0 of WarpPLS is now available as a stable version. You can download and install it for a free trial from:

https://warppls.com

Each new version of the software incorporates features that aim at achieving an important end goal: to allow users to employ SEM to conduct any of the major statistical tests; from relatively simple tests such as comparisons of means, to more sophisticated ones such as nonlinear SEM tests employing logistic regression. Among the community of users of this software, there are very sophisticated SEM experts that constantly challenge us to implement new data analysis features, as well as to make the existing features as easy to use as possible. Because of the constant input from our users, including those who are very knowledgeable about SEM, the software now arguably provides the most extensive set of features of any SEM software. We hope to continue in this path as the SEM field evolves. Below we outline new features added to the current version of the software.

Logistic regression variables. The menu option “Explore logistic regression” now allows you to create a logistic regression variable as a new indicator that has both unstandardized and standardized values. Logistic regression is normally used to convert an endogenous variable on a non-ratio scale (e.g., dichotomous) into a variable reflecting probabilities. You need to choose the variable to be converted, which should be an endogenous variable, and its predictors. The new logistic regression variable is meant to be used as a replacement for the endogenous variable on which it is based. Two algorithms are available: probit and logit. The former is recommended for dichotomous variables; the latter for non-ratio variables where the number of different values (a.k.a. “distinct observations”) is greater than 2 but still significantly smaller than the sample size; e.g., 10 different values over a sample size of 100. The unstandardized values of a logistic regression variable are probabilities; going from 0 to 1. Since a logistic regression variable can be severely collinear with its predictors, you can set a local full collinearity VIF cap for the logistic regression variable. Predictor-criterion collinearity, or lateral collinearity (Kock & Lynn, 2012), is rarely assessed or controlled in classic logistic regression algorithms.

Absolute and relative variation measures. You can now view the number of different values (a.k.a. “distinct observations”) for all indicators and latent variables, as well as the ratio between the number of different values and sample size. The first is an absolute and the second a relative variation measure. These are available under the menu options “View or save correlations and descriptive statistics for indicators” and “View latent variable coefficients”, respectively. These measures can help inform decisions about whether to use logistic regression, particularly in connection with endogenous latent variables. If the number of different values is significantly smaller than the sample size (e.g., 10 different values over a sample size of 100) for an endogenous latent variable, that means that a new logistic regression variable could be created and used as a replacement for the endogenous variable. If several predictors are available, the new logistic regression variable will incorporate more variation than the endogenous variable on which it is based, which will typically be reflected in larger coefficients of association (e.g., path coefficients) when the logistic regression variable is used in the model.

Graphs for full latent growth coefficients. You can now view several graphs for each of the full latent growth coefficients provided under the menu option “Explore full latent growth”. Full latent growth coefficients have a number of applications, such as: moderating effects analyses, nonlinearity tests, multi-group and measurement invariance tests, and the assessment of moderated mediation effects. Each of the graphs is made up of several plots, which refer to changes in the coefficients selected (e.g., path coefficients) for the relationship between the variables shown in the X and Y axes, as the latent growth variable goes from low to high. The following graph menu options are available: “Full sample splits (megaphones)”, “Partial sub-samples splits (megaphones)”, “Full sample splits (bars)”, “Partial sub-samples splits (bars)”, “Full sample splits (lines)”, and “Partial sub-samples splits (lines)”.

HTMT2 ratios. The sub-option “'Discriminant validity coefficients (extended set)”, under the menu option “Explore additional coefficients and indices”, now allows you to inspect the newest version of the set of heterotrait-monotrait (HTMT) ratios calculated by the software. These have been dubbed HTMT2 ratios. The HTMT and HTMT2 ratios have been proposed for discriminant validity assessment, particularly in the context of composite-based SEM via classic PLS algorithms; as opposed to factor-based SEM via modern algorithms that estimate factors (which have been available from this software for quite some time now). Discriminant validity is a measure of the quality of a measurement instrument; the instrument itself is typically a set of question-statements. A measurement instrument has good discriminant validity if the question-statements (or other measures) associated with each latent variable are not confused by the respondents, in terms of their meaning, with the question-statements associated with other latent variables.

Incremental interface improvement. This is conducted in each new version of the software. At several points the code has been modified so that the user interface experiences are improved. This has led in several cases to what appears to be a smoother flow through the several steps and procedures guided by the user interface. Several elements of the graphical user interface, such as screens and warning messages, have been optimized so that users can perform SEM analysis tasks with only a few clicks – and in a straightforward fashion. Nevertheless, care is always taken to ensure that the user interfaces do not change too much, otherwise users would have to re-learn how to use the interface whenever a new version is released.

Incremental code optimization. This is also conducted in each new version of the software. At several points the code has been optimized for speed, stability, and coefficient estimation precision. In some cases, the optimization has led to lesser propagation of sampling error, making the software reach accurate results at lower sample sizes – that is, increasing the statistical efficiency of the software. These incremental code optimization changes have led to incremental gains in speed even as new features have been added. More often than not, new features require additional computational steps and often complex calculations, mostly to generate internal checks and coefficients that were not available before.

Take a look at the following videos, which have been created using this new version. They illustrate the new features outlined above.

Explore Logistic Regression in WarpPLS

View the Number of Different Values for Variables in WarpPLS

View Full Latent Growth Graphs in WarpPLS

Holistic Measurement Model Assessment in SEM with WarpPLS

Reduce Common Structural Variation with WarpPLS

Assessing Multiple Reciprocal Relationships in SEM with WarpPLS

Choose the Correlation Signs for Anchor Variables in a Multilevel Analysis with WarpPLS

Using Logistic Regression in PLS-SEM with Composites and Factors

Conducting a What-if Analysis in PLS-SEM with Analytic Composites

Reference:

Kock, N., & Lynn, G. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546-580.

Best regards to all!

WarpPLS 8.0 beta now available: Logistic regression, full latent growth graphs, HTMT2 ratios, and more!

Dear colleagues:

Version 8.0 of WarpPLS is now available, as a beta version. You can download and install it for a free trial from:

https://warppls.com

Each new version of the software incorporates features that aim at achieving an important end goal: to allow users to employ SEM to conduct any of the major statistical tests; from relatively simple tests such as comparisons of means, to more sophisticated ones such as nonlinear SEM tests employing logistic regression. Among the community of users of this software, there are very sophisticated SEM experts that constantly challenge us to implement new data analysis features, as well as to make the existing features as easy to use as possible. Because of the constant input from our users, including those who are very knowledgeable about SEM, the software now arguably provides the most extensive set of features of any SEM software. We hope to continue in this path as the SEM field evolves. Below we outline new features added to the current version of the software.

Logistic regression variables. The menu option “Explore logistic regression” now allows you to create a logistic regression variable as a new indicator that has both unstandardized and standardized values. Logistic regression is normally used to convert an endogenous variable on a non-ratio scale (e.g., dichotomous) into a variable reflecting probabilities. You need to choose the variable to be converted, which should be an endogenous variable, and its predictors. The new logistic regression variable is meant to be used as a replacement for the endogenous variable on which it is based. Two algorithms are available: probit and logit. The former is recommended for dichotomous variables; the latter for non-ratio variables where the number of different values (a.k.a. “distinct observations”) is greater than 2 but still significantly smaller than the sample size; e.g., 10 different values over a sample size of 100. The unstandardized values of a logistic regression variable are probabilities; going from 0 to 1. Since a logistic regression variable can be severely collinear with its predictors, you can set a local full collinearity VIF cap for the logistic regression variable. Predictor-criterion collinearity, or lateral collinearity (Kock & Lynn, 2012), is rarely assessed or controlled in classic logistic regression algorithms.

Absolute and relative variation measures. You can now view the number of different values (a.k.a. “distinct observations”) for all indicators and latent variables, as well as the ratio between the number of different values and sample size. The first is an absolute and the second a relative variation measure. These are available under the menu options “View or save correlations and descriptive statistics for indicators” and “View latent variable coefficients”, respectively. These measures can help inform decisions about whether to use logistic regression, particularly in connection with endogenous latent variables. If the number of different values is significantly smaller than the sample size (e.g., 10 different values over a sample size of 100) for an endogenous latent variable, that means that a new logistic regression variable could be created and used as a replacement for the endogenous variable. If several predictors are available, the new logistic regression variable will incorporate more variation than the endogenous variable on which it is based, which will typically be reflected in larger coefficients of association (e.g., path coefficients) when the logistic regression variable is used in the model.

Graphs for full latent growth coefficients. You can now view several graphs for each of the full latent growth coefficients provided under the menu option “Explore full latent growth”. Full latent growth coefficients have a number of applications, such as: moderating effects analyses, nonlinearity tests, multi-group and measurement invariance tests, and the assessment of moderated mediation effects. Each of the graphs is made up of several plots, which refer to changes in the coefficients selected (e.g., path coefficients) for the relationship between the variables shown in the X and Y axes, as the latent growth variable goes from low to high. The following graph menu options are available: “Full sample splits (megaphones)”, “Partial sub-samples splits (megaphones)”, “Full sample splits (bars)”, “Partial sub-samples splits (bars)”, “Full sample splits (lines)”, and “Partial sub-samples splits (lines)”.

HTMT2 ratios. The sub-option “'Discriminant validity coefficients (extended set)”, under the menu option “Explore additional coefficients and indices”, now allows you to inspect the newest version of the set of heterotrait-monotrait (HTMT) ratios calculated by the software. These have been dubbed HTMT2 ratios. The HTMT and HTMT2 ratios have been proposed for discriminant validity assessment, particularly in the context of composite-based SEM via classic PLS algorithms; as opposed to factor-based SEM via modern algorithms that estimate factors (which have been available from this software for quite some time now). Discriminant validity is a measure of the quality of a measurement instrument; the instrument itself is typically a set of question-statements. A measurement instrument has good discriminant validity if the question-statements (or other measures) associated with each latent variable are not confused by the respondents, in terms of their meaning, with the question-statements associated with other latent variables.

Incremental interface improvement. This is conducted in each new version of the software. At several points the code has been modified so that the user interface experiences are improved. This has led in several cases to what appears to be a smoother flow through the several steps and procedures guided by the user interface. Several elements of the graphical user interface, such as screens and warning messages, have been optimized so that users can perform SEM analysis tasks with only a few clicks – and in a straightforward fashion. Nevertheless, care is always taken to ensure that the user interfaces do not change too much, otherwise users would have to re-learn how to use the interface whenever a new version is released.

Incremental code optimization. This is also conducted in each new version of the software. At several points the code has been optimized for speed, stability, and coefficient estimation precision. In some cases, the optimization has led to lesser propagation of sampling error, making the software reach accurate results at lower sample sizes – that is, increasing the statistical efficiency of the software. These incremental code optimization changes have led to incremental gains in speed even as new features have been added. More often than not, new features require additional computational steps and often complex calculations, mostly to generate internal checks and coefficients that were not available before.

Take a look at the following videos, which have been created using this new version. They illustrate the new features outlined above.

Explore Logistic Regression in WarpPLS

View the Number of Different Values for Variables in WarpPLS

View Full Latent Growth Graphs in WarpPLS

Holistic Measurement Model Assessment in SEM with WarpPLS

Reduce Common Structural Variation with WarpPLS

Assessing Multiple Reciprocal Relationships in SEM with WarpPLS

Choose the Correlation Signs for Anchor Variables in a Multilevel Analysis with WarpPLS

Using Logistic Regression in PLS-SEM with Composites and Factors

Conducting a What-if Analysis in PLS-SEM with Analytic Composites

Reference:

Kock, N., & Lynn, G. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546-580.

Best regards to all!

Robustness tests in PLS-SEM: Video for the Qatar Chapter of the Decision Sciences Institute

The so-called "robustness" tests that some refer to, in connection with PLS-SEM, are generally tests of: nonlinearity, common method bias, and endogeneity. In WarpPLS, nonlinearity can be tested via full latent growth, commom method bias via FCVIFs, and endogeneity via instrumental variables. The video linked below covers most of these issues.

https://youtu.be/I3YYdpdXhII

This video is of a presentation to faculty and students at the Qatar Chapter of the Decision Sciences Institute on the topics of factor-based PLS-SEM (PLSF-SEM), endogeneity, and common method bias. (SEM = structural equation modeling.) Addressing these issues helps with publishing in top-tier information systems and decision sciences journals, among others.

Best regards to all!

Multilevel analyses in PLS-SEM: Video, article, and sample dataset

The video linked below provides an overview on how to conduct a multilevel analysis, in the context of structural equation modeling via partial least squares (PLS-SEM).

https://youtu.be/pNXI1Cz-Qkk

The article below explains how one can conduct a multilevel analysis in PLS-SEM. It employs a dataset that is very similar to the one used in the video above.

Kock, N. (2020). Multilevel analyses in PLS-SEM: An anchor-factorial with variation diffusion approach. Data Analysis Perspectives Journal, 1(2), 1-6.

A link to a PDF file is available (from the "Publications" area of WarpPLS.com):

https://scriptwarp.com/warppls/#Publications

Finally, the site area below (the "Resources" area of WarpPLS.com) provides a sample dataset available to users interested in trying the procedures discussed above: "Job performance in three companies dataset".

https://scriptwarp.com/warppls/#Resources

Enjoy!

Common method bias in PLS-SEM: Video, three articles, and sample dataset

The video linked below provides an overview on how to test for common method bias, in the context of structural equation modeling via partial least squares (PLS-SEM).

https://youtu.be/r5p0zHBqfBs

The articles below explain how one can conduct tests for common method bias in PLS-SEM. The first two articles (particularly the second) discuss the highest full collinearity variance inflation factor (FCVIF) test. The third article discusses Harman’s single factor test.

Kock, N., & Lynn, G.S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546-580.

Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10.

Kock, N. (2021). Harman’s single factor test in PLS-SEM: Checking for common method bias. Data Analysis Perspectives Journal, 2(2), 1-6.

Links to PDF files are available (from the "Publications" area of WarpPLS.com):

https://scriptwarp.com/warppls/#Publications

Finally, the site area below (the "Resources" area of WarpPLS.com) provides a sample dataset available to users interested in trying the tests discussed above: "Dataset with and without common method bias".

https://scriptwarp.com/warppls/#Resources

Enjoy!

Multilevel analyses in PLS-SEM: Article, video, and sample dataset

The article below explains how one can conduct a multilevel analysis, in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock (2020). Multilevel analyses in PLS-SEM: An anchor-factorial with variation diffusion approach. Data Analysis Perspectives Journal, 1(2), 1-6.

A link to a PDF file is available ().

Abstract:

A multilevel analysis, in the context of structural equation modeling via partial least squares (PLS-SEM), can be seen as an analysis in which: (a) data is collected at the individual level from multiple groups, and (b) group membership is expected to influence data analysis results. In this paper we illustrate such an analysis employing WarpPLS, a leading PLS-SEM software tool. The analysis employs an anchor-factorial with variation diffusion approach.

The short video linked below provides an overview on how to conduct multilevel analyses in PLS-SEM.

https://youtu.be/pNXI1Cz-Qkk

Finally, the site below provides a sample dataset: "Job performance in three companies dataset". This dataset is available under the "Resources" area.

https://warppls.com

Enjoy!

Multilevel analyses in PLS-SEM

Dear colleagues:

The short video linked below provides an overview on how to conduct multilevel analyses in PLS-SEM.

https://youtu.be/pNXI1Cz-Qkk

Enjoy!

How time series data can be analyzed with PLS-SEM

Dear colleagues:

The short video linked below, on how to predict the price of bitcoin, is for those who want to understand how time series data can be analyzed with PLS-SEM.

https://youtu.be/8nTLYH-4uWM

Enjoy!

Individual paths can be set as linear or nonlinear in PLS-SEM analyses

In WarpPLS, the “View or change individual inner model analysis algorithm settings” option allows you to set inner model algorithms for individual paths in PLS-SEM analyses; whether these analyses are composite-based or factor-based. That is, for each path a user can select a different algorithm from among the following choices: “Linear”, “Warp2”, “Warp2 Basic”, “Warp3”, and “Warp3 Basic”.

This option is particularly useful in empirical investigations where researchers have solid theoretical reasons to expect certain paths to be associated with nonlinear relationships of particular types. Those researchers may also have solid theoretical reasons to expect certain paths to be associated with linear relationships. Given that one of the main goals of SEM is to test theory, theoretical considerations should be given a very high priority in the selection of algorithms to be used for each path in a model.

There is a short video that illustrates this ().

On a related note - since classic moderating effects analyses already capture nonlinearity, it is usually advisable for users to set moderating paths as linear. They can do this even as they set direct paths as nonlinear. This leads to 2D graphs that are easier to interpret. If users set moderating paths as nonlinear, their interpretation becomes more difficult, as more complex types of moderating relationships are captured.

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. They can also be used in multi-level analyses.

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

The use of data labels to discover moderating effects 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

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

Reciprocal relationships assessment

Instrumental variables, available in WarpPLS starting in version 6.0, can be used to estimate reciprocal (or non-recursive) relationships. For this, you should use the sub-option “Reciprocal stochastic variation sharing”, under the new menu option “Explore analytic composites and instrumental variables”. To illustrate the sub-option “Reciprocal stochastic variation sharing” let us consider a population model with the following links: A > C, B > D, C > D and D > C. To test the reciprocal relationship between C and D you should first control for endogeneity in C and D, due to variation coming from B and A respectively, by creating two instrumental variables iC and iD via the sub-option “Single stochastic variation sharing” and adding these variables to the model. Next you should create two other instrumental variables through the sub-option “Reciprocal stochastic variation sharing”, which we will call here iCrD and iDrC, referring to the conceptual reciprocal links C > D and D > C respectively. (No links between C and D should be included in the model graph, since reciprocal links cannot be directly represented in this version of this software.) The final model, with all the links, will be as follows: A > C, iC > C, B > D, iD > D, iDrC > D and iCrD > C. Here the link iDrC > D represents the conceptual link C > D, and can be used to test this conceptual link; and the link iCrD > C represents the conceptual link D > C, and can similarly be used to test this conceptual link.

Related YouTube video:

Estimate Reciprocal Relationships in WarpPLS

http://youtu.be/jn8VZaOWe90

Analytic composites and instrumental variables

Analytic composites are weighted aggregations of indicators where the relative weights are set by you, usually based on an existing theory. The menu option “Explore analytic composites and instrumental variables”, available in WarpPLS starting in version 6.0, allows you to create analytic composites. This menu option also allows you to create instrumental variables. Instrumental variables are variables that selectively share variation with other variables, and only with those variables. Instrumental variables can be used to test and control for endogeneity.

Related YouTube videos:

Explore Analytic Composites in WarpPLS

http://youtu.be/bxGi0OY8RD4

Test and Control for Endogeneity in WarpPLS

http://youtu.be/qCvvUxR978U