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!

PLS Applications Symposium; 15-17 April 2026; Laredo, Texas

PLS Applications Symposium; 15-17 April 2026; Laredo, Texas
(Abstract submissions accepted until 6 February 2026)

*** Attendance (face-to-face or online) ***

The Symposium will be conducted as part of the multidisciplinary Annual Western Hemispheric Trade Conference, organized by the Center for the Study of Western Hemispheric Trade. Our workshop in PLS-SEM will be conducted entirely online. Our expectation is that participants will be allowed to attend Conference sessions either face-to-face or online.

When indicating the type of their submission, participants should indicate whether they intend to attend face-to-face or online. This should be done within parentheses after indicating the submission type. For example - "Type of submission: Presentation (online)".

*** 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:

https://plsas.net

*** Workshop on PLS-SEM ***

On 15 April 2026 a full-day workshop on PLS-SEM will be conducted online 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.

-----------------------------------------------------------
Ned Kock
Symposium Chair
https://plsas.net

Statistical significance and effect size tests in SEM: Common method bias and strong theorizing

The article below provides evidence in support of a few very important methodological propositions: (a) we should not do away with classic statistical significance tests, but should combine them with effect size tests, and tests of common method bias; (b) high quality theorizing is very important if we are to profitably use a combination of classic statistical significance, effect size, and common method bias tests; and (c) the full collinearity VIF threshold in common method bias assessment for factor-based PLSF-SEM should be 10, as opposed to the 3.3 number used with classic composite-based PLS algorithms.

Kock, N., & Dow, K. E. (2025). Statistical significance and effect size tests in SEM: Common method bias and strong theorizing. Advances in Management Accounting, 37(1), 95–105.

Link to full-text file for this article:

Statistical significance and effect size tests in SEM: Common method bias and strong theorizing.

Abstract:

We generally acknowledge the problematic nature of classic statistical significance tests based on P-values or confidence intervals. In fact, we demonstrate based on an illustrative model for which we created simulated data, that with low and high statistical power, path coefficients in structural equation modeling whose true values are zero, routinely end up being found to be significantly different from zero at the P < .05 level. However, we argue that we should not do away with classic statistical significance tests, and that these tests can be useful but should be complemented by other methodological tools, including effect size tests, and tests of common method bias. We also argue that high quality theorizing is very important if we are to profitably use a combination of classic statistical significance, effect size, and common method bias tests.

Important note for PLSF-SEM users (repeated below for emphasis):

The full collinearity VIF threshold in common method bias assessment for factor-based PLSF-SEM should be 10, as opposed to the 3.3 number used with classic composite-based PLS algorithms.

Best regards to all!

Do employees care about diversity and inclusion? Why academic research should not be politically biased

The article below provides a good example of a robust path analysis employing WarpPLS. It suggests, among other things, that the degree to which a company promotes diversity and inclusion has a negligible effect on how an employee rates the company (which reflects job satisfaction). This is in fact not the main theme of the article, but it is something that received plenty of pushback from reviewers. The analysis was conducted a while ago, when research results that did not strongly support diversity and inclusion were typically viewed rather unfavorably by review panels in many academic journals. We thank the prestigious journal Personnel Review for their academic integrity.

Kock, N., Haddoud, M.Y., Onjewu, A.-K., & Yang, S. (2025). Unveiling workplace dynamics: Insights from voluntary disclosures on business outlook and CEO approval. Personnel Review, 54(2), 474–497.

Links to full-text versions of the article:

https://scriptwarp.com/pubs/Kock_etal_2025_PR_WorkplaceDynamics.pdf

https://pure-oai.bham.ac.uk/ws/portalfiles/portal/253297641/KockN2025Unveiling_AAM.pdf

https://www.emerald.com/insight/content/doi/10.1108/pr-03-2024-0251/full/html

Abstract:

Purpose: This inquiry extends the discourse on job satisfaction and employee referral. It aims to examine the moderating effects of perceived business outlook and CEO approval in the dynamics of job satisfaction and employee referral. A model predicting job satisfaction and employee referral through the lens of Herzberg’s two-factor theory is developed and tested. Design/methodology/approach: To remedy the overreliance on self-reported surveys, impeding generalization and representativeness, this study uses large evidence from 14,840 voluntary disclosures of US employees. A structural equation modeling technique is adopted to test the hypotheses. Findings: The inherent robust path analysis revealed intriguing findings highlighting culture and values as exerting the most substantial positive impact on job satisfaction, while diversity and inclusion played a relatively trivial role. Moreover, employees’ view of the firms’ outlook and their approval of the incumbent CEO were found to strengthen the job satisfaction–referral nexus. Originality/value: The study revisits the relationship between job satisfaction and employee referral by capturing the moderating effects of perceived business outlook and CEO approval. We believe that this investigation is one of the first to capture the impact of these two pivotal factors.

The figure below summarizes the results of the study. The overall rating variable reflects satisfaction with one’s job at a particular company, which predictably influences the probability that a person will recommend the company to a friend as a potential employer. If we had relied only on statistical significance tests, the effect of diversity and inclusion on job satisfaction would actually be negative and statistically significant. But based on the small effect size, we felt that it would be more scholarly to report the effect in question as indistinguishable from zero. With large samples, the likelihood of type I errors (false positives) increases dramatically in statistical significance tests, whether P values or confidence intervals are used.

Shiyu got us the awesome Glassdoor dataset, while Yacine and Adah-Kole did most of the theory development and later discussion work (thank you, my talented co-authors). The curious thing is that I did the analyses for this article, using WarpPLS and double-checking with other analysis tools, and was not only surprised but rather displeased with the results. But why was I displeased with the results? Well, as an academic, I work in a very diverse environment, and find that diversity stimulating. In particular, I am very interested about countries and regions (domestically and abroad), their cultures, and histories. Furthermore, as someone with a diverse background, I have lived in Brazil and New Zealand, before settling in the US. While in the US, Belgium was like a second home for several years, as I travelled there often to consult for the European Commission.

Yet, regardless of personal background, and for the sake of societal credibility, academics must report research results as they are, to the best of their ability. Furthermore, they must report research results independently from political orientation and how they personally feel about those results. Finally, they have to resign themselves to the fact that all empirical studies provide incomplete views of the world, and usually call for more research using different approaches and epistemologies.

Best regards to all!

PS: I thank Nadya Larumbe for her comments on a previous version of this post.

Will PLS have to become factor-based to survive and thrive?

The article below provides an overview of various SEM approaches. It argues that minimization of type I and II errors, or false positives and negatives respectively in hypothesis testing, can only happen if latent variables are implemented as factors (and not as composites). It is argued that this requires the use of modern, factor-based PLS methods (known as PLSF methods), which have some advantages not only over classic PLS implementations, but also over covariance-based SEM approaches. We discussed a PLSF type in the article; namely type CFM3.

Kock, N. (2024). Will PLS have to become factor-based to survive and thrive? European Journal of Information Systems, 33(6), 882-902.

Link to full-text file for this article:

Will PLS have to become factor-based to survive and thrive?

Abstract:

Structural equation modelling (SEM) is a general method that aims at estimating models with latent variables (LVs), where the LVs are measured indirectly and with some imprecision via questionnaires. This is done usually employing question-statements answered on Likert-type scales. In this paper we discuss various forms of SEM, and demonstrate that composite-based models, common in classic partial least squares (PLS) implementations, are poorly aligned with the very idea of SEM. We argue that minimisation of type I and II errors, or false positives and negatives respectively in hypothesis testing, can only happen if LVs are implemented as factors (and not as composites). This requires the use of modern, factor-based PLS methods, which have some advantages not only over classic PLS implementations, but also over covariance-based SEM approaches. Our main goal with this paper is to stimulate debate, whether pro or against our views. If we are generally correct in our thinking, the impact on how quantitative research is conducted in the field of information systems, as well as many other fields, could be quite dramatic. The reason for this is the widespread use of SEM in information systems, business, and the behavioural sciences.

Note: Some readers of this blog have brought to our attention that a critique of the article above is already out, and with a number of mistakes and incorrect statements, such as that: they (the critics) used CFM1 because this is the only PLSF type documented in the WarpPLS User Manual (untrue and very easy to check); and that the algorithm that they analyzed (PLSF-CFM1) is a slow version of Dijkstra’s PLSc technique (CFM1 does not use PLSc at all); among other easy-to-avoid mistakes and incorrect statements. We are aware of this critique.

Best regards to all!

Conducting a difference-in-differences analysis with PLS-SEM: The classic 2x2 approach

The article below shows how one can conduct a difference-in-differences analysis employing the classic 2x2 approach for this type of analysis, using structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2024). Conducting a difference-in-differences analysis with PLS-SEM: The classic 2x2 approach. Data Analysis Perspectives Journal, 5(5), 1-8.

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

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

Abstract:

Difference-in-differences analyses often employ a classic 2x2 scenario, which involves two conditions, control and treatment; and two points in time, before and after an intervention that may be tied to one of the conditions. In our analysis, we assess the impact on labor productivity of being in a more technology-intensive US state, instead of a more manufacturing-intensive one. Consistently with the difference-in-differences analysis scenario, we also assess the full latent growth effect of a government-driven age discrimination crackdown, in the technology-intensive state, on the previous effect – of being in a technology-intensive state on labor productivity. We do this by employing a model analyzed in the context of structural equation modeling via partial least squares (PLS-SEM). We also discuss advantages of using PLS-SEM in this scenario; which include assessments of causality, common method bias, and endogeneity.

Best regards to all!

Combining composites and factors in PLS-SEM models: A multi-algorithm technique

The article below presents a multi-algorithm technique for combining latent variables estimated as composites or factors into a single model, in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2024). Combining composites and factors in PLS-SEM models: A multi-algorithm technique. Data Analysis Perspectives Journal, 5(4), 1-8.

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

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

Abstract:

A multi-algorithm technique is presented for combining latent variables estimated as composites or factors into a single model, in the context of structural equation modeling via partial least squares. The multi-algorithm technique consists of three key steps: selecting composite-based or factor-based outer model analysis algorithms to be used for latent variable estimation; adding the latent variables estimated with the chosen composite-based or factor-based algorithms as new standardized variables; and creating and estimating a final model with the new variables added as single indicators of latent variables.

Best regards to all!

A comparison of data analyses with WarpPLS and Stata: A study of trust and its role regarding internet use and subjective well-being

The article below provides a comparative assessment of analyses using the software packages WarpPLS and Stata, in the context of structural equation modeling via partial least squares (PLS-SEM), based on an illustrative study of trust and its role regarding internet use and subjective well-being.

Samak, A., Islam, M. R., & Hanke, D. (2024). A comparison of data analyses with WarpPLS and Stata: A study of trust and its role regarding internet use and subjective well-being. Data Analysis Perspectives Journal, 5(3), 1-6.

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

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

Abstract:

This study investigates the mediating roles of social and institutional trust in the relationship between internet use and subjective well-being, using partial least squares (PLS)-based structural equation modeling (SEM). We compare WarpPLS 8.0 and Stata’s PLS-SEM package, utilizing data from the European Social Survey (ESS), round 8. Our results show consistent model fit and path coefficients across both tools, confirming the significant mediating effects of trust. WarpPLS stands out for its advanced model diagnostics, while Stata’s PLS-SEM excels in integrating with Stata’s broader data management and statistical analysis tools. This comparative analysis contributes to the SEM methodological literature.

Best regards to all!

A comparison of multiple regression analyses in Stata and WarpPLS

The article below provides a comparative assessment of multiple regression analyses using the software packages WarpPLS and Stata.

Tarkom, A., & Gopal, P. (2024). A comparison of multiple regression analyses in Stata and WarpPLS. Data Analysis Perspectives Journal, 5(2), 1-8.

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

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

Abstract:

This paper illustrates a comparative analysis of multiple regression analysis using two different software. The software packages used are WarpPLS 8.0 and Stata 17. Multiple regression analyses performed with both software produce the same results. WarpPLS 8.0 has the added advantage over Stata owing to its graphic user interface that aids in model specification and visualization. Furthermore, it provides users with additional tools to visualize moderating effects. Both software have equal accuracy in terms of the results but differences in terms of what they offer users.

Best regards to all!

PLS Applications Symposium; 9-11 April 2025; Laredo, Texas

PLS Applications Symposium; 9-11 April 2025; Laredo, Texas
(Abstract submissions accepted until 14 February 2025)

*** Attendance (face-to-face or online) ***

The Symposium will be conducted as part of the multidisciplinary Annual Western Hemispheric Trade Conference, organized by the Center for the Study of Western Hemispheric Trade. Our workshop in PLS-SEM will be conducted entirely online. Our expectation is that participants will be allowed to attend Conference sessions either face-to-face or online.

When indicating the type of their submission, participants should indicate whether they intend to attend face-to-face or online. This should be done within parentheses after indicating the submission type. For example - "Type of submission: Presentation (online)".

*** 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:

https://plsas.net

*** Workshop on PLS-SEM ***

On 9 April 2025 a full-day workshop on PLS-SEM will be conducted only 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.

-----------------------------------------------------------
Ned Kock
Symposium Chair
https://plsas.net

PLS Applications Symposium; 10-12 April 2024; Laredo, Texas

PLS Applications Symposium; 10-12 April 2024; Laredo, Texas
(Abstract submissions accepted until 16 February 2024)

*** Attendance (face-to-face or online) ***

The Symposium will be conducted as part of the multidisciplinary Annual Western Hemispheric Trade Conference, organized by the Center for the Study of Western Hemispheric Trade. Our workshop in PLS-SEM will be conducted entirely online. Our expectation is that participants will be allowed to attend Conference sessions either face-to-face or online.

When indicating the type of their submission, participants should indicate whether they intend to attend face-to-face or online. This should be done within parentheses after indicating the submission type. For example - "Type of submission: Presentation (online)".

*** 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:

https://plsas.net

*** Workshop on PLS-SEM ***

On 10 April 2024 a full-day workshop on PLS-SEM will be conducted only 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.

-----------------------------------------------------------
Ned Kock
Symposium Chair
https://plsas.net

Combining sub-samples for improved statistical power in PLS-SEM: A constrained latent growth approach

The article below discusses how a researcher can combine sub-samples for improved statistical power in multigroup analyses, employing a constrained latent growth approach, in the context of structural equation modeling via partial least squares (PLS-SEM).

Cox, J. (2024). Combining sub-samples for improved statistical power in PLS-SEM: A constrained latent growth approach. Data Analysis Perspectives Journal, 5(1), 1-5.

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

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

Abstract:

Often researchers gather data that contain or can be segmented into subsamples. Therefore, sometimes a question arises as to whether the data can be treated as one sample or as several distinct samples. In this paper, I discuss how to conduct a multigroup analysis in a structural equation model with partial least squares (PLS-SEM) and demonstrate how empirical data from two different countries can be treated as one sample when using WarpPLS 8.0 to achieve higher statistical power.

Best regards to all!

Methods showcase - Using PLSF-SEM in business communication research

The article below discusses how one can employ PLSF-SEM in business communication research. The discussion is generic enough to guide the use of the method in other areas of research. PLSF-SEM builds on partial least squares (PLS) algorithms to generate correlation-preserving factors; the F refers to it being factor-based, as opposed to composite-based. A primer on the use of PLSF-SEM in business communication research is provided, based on an illustrative model inspired by motivating language theory, and where simulated data was analyzed with the software WarpPLS.

Kock, N. (2024). Methods showcase - Using PLSF-SEM in business communication research. International Journal of Business Communication (forthcoming: 23294884241233281).

Link to full-text file for this article:

Methods showcase - Using PLSF-SEM in business communication research.

Abstract:

Structural equation modeling (SEM) is a data analysis method that is widely used in business communication research, as well as research in many other fields, when scholars need to test complex models with multiple outcomes, interactions, or operations across different situations. To date, however, researchers have had to choose between using covariance-based SEM, and dealing with convergence problems; or composite-based SEM, and facing serious methodological issues. This article describes a way to combine strong aspects of both SEM types through PLSF-SEM. By utilizing this novel method, empirical researchers can employ several of the same tests traditionally used in covariance-based SEM, as well as new tests that rely on latent variable estimates, in a succinct and scholarly way. PLSF-SEM builds on partial least squares (PLS) algorithms to generate correlation-preserving factors; the F refers to it being factor-based, as opposed to composite-based. A primer on the use of PLSF-SEM in business communication research is provided, based on an illustrative model inspired by motivating language theory, and where simulated data was analyzed with the software WarpPLS.

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: A bit of history

The YouTube video linked below (scroll down to the end of the news article) provides a bit of history in connection with the development of WarpPLS. A big thank you to Rolando Santos for his professional video creation work!

https://www.tamiu.edu/newsinfo/2023/10/topworldresearcher10262023.shtml

Best regards to all!

Using logistic regression in PLS-SEM: Dichotomous endogenous variables

The article below discusses how one can use logistic regression with the probit approach, to avoid the problems associated with having dichotomous endogenous variables, in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2023). Using logistic regression in PLS-SEM: Dichotomous endogenous variables. Data Analysis Perspectives Journal, 4(4), 1-6.

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

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

Abstract:

A dichotomous endogenous variable would be impossible to occur at the population level, which an empirical sample is assumed to represent, because the structural error term associated with the endogenous variable is expected to be a random variable with many distinct values. Consequently, the endogenous variable is also expected to have many distinct values. This paper discusses how to address this problem, using logistic regression with the probit approach, in the context of structural equation modeling via partial least squares (PLS-SEM). Our discussion is based on an illustrative model analyzed with the software WarpPLS.

Best regards to all!

Contributing to the success of PLS in SEM: An action research perspective

The article below discusses how I employed an action research approach, by working closely with WarpPLS users, to contribute to the success of PLS in SEM. A big thank you to WarpPLS users!

Kock, N. (2023). Contributing to the success of PLS in SEM: An action research perspective. Communications of the Association for Information Systems, 52(1), 730-734.

Link to full-text file for this article:

Contributing to the success of PLS in SEM: An action research perspective

Abstract:

I share with Evermann and Rönkkö (2022) the belief that classic composite-based partial least squares path modeling (PLS-PM) presents shortcomings when used to conduct structural equation modeling (SEM) analyses. The shortcomings can be traced back to one fundamental problem, which is that latent variables (LVs) are approximated in PLS-PM as exact linear combinations of their corresponding indicators. In SEM, each LV is in fact a factor; i.e., a linear combination of the indicators and a measurement residual. My approach to addressing the shortcomings of PLS-PM is rather unique among researchers concerned with quantitative methods. I have employed an action research approach, helping investigators employ SEM in their empirical studies. This has led to my development of a widely used software tool for SEM analyses. I illustrate my action research orientation by discussing three recent methodological developments with which I have been closely involved.

Best regards to all!

Assessing multiple reciprocal relationships in PLS-SEM

The article below discusses how one can assess multiple reciprocal relationships, in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2023). Assessing multiple reciprocal relationships in PLS-SEM. Data Analysis Perspectives Journal, 4(3), 1-8.

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

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

Abstract:

Two latent variables may influence each other in both directions, in what characterizes a reciprocal relationship. This paper discusses how one can assess multiple reciprocal relationships in the context of structural equation modeling via partial least squares (PLS-SEM). We discuss the assessment of multiple reciprocal relationships, through an illustrative model analyzed with the software WarpPLS.

Best regards to all!

On the validity assessment of formative measurement models in PLS-SEM

The article below discusses how one can conduct a validity assessment of formative measurement models, in the context of structural equation modeling via partial least squares (PLS-SEM).

Amora, J. T. (2023). On the validity assessment of formative measurement models in PLS-SEM. Data Analysis Perspectives Journal, 4(2), 1-7.

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

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

Abstract:

Structural equation modeling via partial least squares (PLS-SEM) is the preferred approach when a research model includes formative measurement models. In this paper, the validity assessment of first-order and higher-order measurement models is illustrated using real data employing the WarpPLS, a prominent software tool for PLS-SEM.

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