LISREL, AMOS or PLS does not deliver what you need for your Ph.D. studies?
Discover how researchers explore nonlinearities and
interactions in their cause-effect models
and innovate their research fields with exciting new discoveries.
Why you should read this article?
How will you feel when you discover
something really exciting in your research, unlike thousands of other researchers before you?
Won’t you be elated? Think of how you’ll feel when your (doctoral) supervisor is astounded
by your latest breakthrough cause-effect insight discovered using NEUSREL! You realize that such discoveries were
simply impossible before and how fortunate you are to have access to such a powerful and easy to use
causal modeling suite.
As you read this article, you’ll learn how to make your thesis/dissertation stand out impressively from “the crowd.”
Why causal analysis methods like LISREL or PLS are now of limited help
Structural Equation Modeling (SEM) methods represent a
major improvement over classical statistical methods for multivariate
analysis. They are designed for testing theoretically supported linear
and additive causal models. For related applications they deliver
excellent results. SEM methods will undoubtedly play an increasingly important role in
the social sciences into the future.
however, when their application assumptions are not met.
In practice, it is usually the exception rather than the rule that the existing
theory indicates clearly to us which paths in the model we should select and keep.An open secret these days is that, using such tools as LISREL and PLS,
causal models of one’s data are often constructed a posteriori
following fairly extensive exploratory analyses. This type of “pruning process”
is the result of an extensive and selective literature search. Finally, your results
are represented in the form of a priori hypotheses about your data.
This confirmatory veneer underlying many LISREL/PLS/AMOS studies conducted
over the past few decades has resulted in a loss of trust and confidence in
quantitative research using linear SEM methods.
methods not only assume that all relationships in the data are linear,
but that they are also independent of one another (i.e. that no
moderating effects exist). Even considering more elaborate experimental methods,
the main challenge remains: if one cannot
describe the relationships among variables in an a priori manner,
then a new method is sorely needed to help researchers
learn more about the causal relationships in their data.
Professor Hennig-Thurau and Dr. Buckler took a deeper look
at four arbitrarily chosen datasets from articles published in two distinguished
scientific journals: The Journal of Marketing Research
and The Journal of Marketing.
In each study we found clear indications of the existence of previously
unknown nonlinear data relationships and interactions.
If the world’s leading marketing researchers are not currently deploying
state-of-the-art causal analysis methods (NEUSREL), how can we expect
doctoral students to do so?
If problems are so obvious, why has nobody developed a solution?
The short answer is: The solution is not obvious because …
- Linear SEM methods are simply unsuitable for solving exploratory problems.
- The SEM research community is dominated by a confirmatory research approach.
Many researchers do not accept methods that draw structural
conclusions from their data.
- While SEM was fully developed in the 1960’s and 1970’s,
modern multivariate and exploratory methods such as Artificial Neural Networks (ANN) experienced
major developments in recent years.
- ANN’s however
are also, generally speaking, not suitable for causal modeling as they suffer from the Black Box Problem: they
increase predictive performance but are unable to explain
or convey the reasons for the models derived from them.
… that your research field of
Customer Confusion is heavily influenced
by a number of nominal scale variables.
You apply an advanced causal analysis and retrieve the model shown in
the figure just below. All paths that terminate with a dot represent
interacting influences in your data. For every interaction,
your analysis shows plots like the second one below. It indicates that the
effect of education on customer confusion is only valid for medium
income respondents. The higher the education, the lower the confusion,
but only for medium incomes. All others have probably developed simpler
decision heuristics: the poor spend the least possible for them, the wealthy spend the most.
Have you (or your supervisor) ever seen such findings using a linear SEM analysis?
Using NEUSREL it’s more likely that your supervisor will recommend your graduation with honors!
… that your research field is service quality within
service centers. You build a theoretical model, and estimate it using AMOS
– but you are neither satisfied with the fit nor with some
contradictory results. You apply an alternative method and obtain the results
shown in the figure just below. You discover that many paths are
digressive (NLd). This makes perfect sense to you,
since a number of variables are known from the literature as
“hygienic factors” –
a specific level is needed but not more than that.
Other paths turn out to be progressive (NLp)
indicating that they may be viewed as “satisfaction factors.”
Most interestingly you find new, meaningful paths.
Linear SEM modeling of your data indicates that satisfaction influences the factors
Word-Of-Mouth, Cross-Buying and Willingness-To-Pay, directly and
independently. You discover that willingness is required for Word-Of-Mouth
in order to foster Cross-Buying, and that you need Cross-Buying-Willingness in order to enable
Re-analyzing your data in AMOS, you learn that indeed your fit
has increased when introducing this new structure.
Despite having used an unsystematic and ad-hoc trial and error search,
you nevertheless achieved your results using a
systematic approach that only considers theoretically supportable
paths. Imagine how achieving these results will make you stand out relative to other
researchers who are only using linear SEM methods.
… a retail store chain
is sponsoring your research survey. With the collected data you build an advanced
causal model. You conclude that perceived relationship investment is a
major prerequisite for repeat purchases. With your analysis you
demonstrate that …
… excellent interpersonal communications
with customers is the major ingredient of success. Expensive “tangible
rewards” (especially free gifts such as shoe polish) are just one
alternative but a less effective tool. Simply by eliminating this expense item,
you reduce overall costs by 1.5%, which in turn boosts profits by almost 30%.
Your client firm will now likely offer you an attractive employment contract
or at the least, renewed research support!
Which methodology can do all this?
The answer: Universal Structural Modeling (USM). The
foundation for USM was developed during a five year research project
conducted in cooperation with Harun Gebhardt. Together we developed a
stock forecasting system based on neural networks. In 1999 we launched
Profit-Station.com, which has been successfully used
since then by dozens of individuals and companies.
In that same year, Dr. Buckler began his doctoral studies with
the ambitious goal of reinventing Structural Equation Modeling – the crown
jewel of the social sciences. In 2001, he published the book entitled “NEUSREL.” This
introduced a new causal analysis method based on the same neural
networks that made Profit-Station successful. Since then,
NEUSREL has been continually refined and used in a large number of research and consulting
projects. Furthermore, it has matured as a result of extensive scientific discussions
with several world class researchers. Important improvements were
stimulated as a result of an ongoing dialog with Professor Hennig-Thurau.
As a result of all this, the methodological group “USM for NEUSREL” was
How does USM work? Cause-effect networks are constructed in two steps:
- At the measurement level, survey data is compressed into latent variables,
- At the structural level, cause-effect relations among latent variables are analyzed.
At the measurement level, USM uses principal components
analysis to compute and determine the latent variables.
At the structural level a specific neural network we developed
is trained for every dependent latent variable, thereby
determining in turn the influence of all the other latent variables.
The neural network that we use ensures that irrelevant effect paths
are eliminated. The
black box problem is solved mainly through a methodology introduced by
Plate in 1998. This allows for the visualization of each causal effect.
If you’d like to learn more about NEUSREL,
please read Dr. Buckler’s latest
scientific article published in “Marketing – Journal of Research and Management”
co-authored with Professor Hennig-Thurau.
Please request the article (.pdf) with an email message to
Please provide your name, phone number, and insitution or employer.
A number of site visitors have asked us how USM can assist them in their
research projects. A good way for you to begin your project would be to use
our very fast, efficient, and cost-effective analysis service. We provide you
an Excel spreadsheet template to insert your data and option settings. You then
email your spreadsheet to us. We then run the calculations and email the results back to you. For
frequent users of USM-NEUSREL we provide you with a software license.
What the experts say about USM:
- “I had the chance to read the book NEUSREL in 2001 as an early draft.
Within the scientific tradition of data-mining, I believe that both
NEUSREL and USM add a powerful instrument to uncover hidden, more complex,
and perhaps meaningful relationships among variables.” -Prof. Dr. Rene Weber,
University of California at Santa Barbara, USA.
- “I use USM whenever I am working on a problem that
falls within its capabilities, for example, to estimate structural
equation models with many nominal variables such as gender. In the
field of customer confusion we found that confusion is particularly
prevalent among medium-income consumers, whereas low- and high-income
consumers employ buying heuristics that shield them from confusion. A
simple finding, however one we would have never found without
-Prof. Dr. Gianfranco Walsh,
Strathclyde Business School, University ofGlasgow and University of Jena.
- “We are planning to apply USM for communication
control and planning in the advertising-intensive food industry. We
estimate we will save companies a considerable part of their communication
-Prof. Dr. Holger Buxel,
University of Applied Science, Muenster.
- “In contrast to classical methods of linear structural modeling NEUSREL
offers three advantages: Exploration capabilities, nonlinear relations
and arbitrary interactions between constructs are considered and allowed.
Models are more realistic as a result of these factors.
Results from classical covariance-based methods might possibly be better, but only
if the data perfectly conform to all the (quite restrictive) assumptions of the method.”
-Prof. Dr. Volker Trommsdorff, Technical University of Berlin.
- “USM allows for exploratory modeling of structural equation models. With this
quasi-confirmatory method, new paths, unknown nonlinearities and
interactions may be discovered, described and quantified.”
-Professor Dr. Rolf Weiber, University of Trier.
- “With NEUSREL Dr. Buckler introduces us to an outstanding
contribution to marketing research that has the potential to close a
major research gap.”
-Professor Dr. Klaus-Peter Wiedmann, University of Hanover.
- “Best wishes as you expand the influence of this exciting software.”
-Christopher P. Blocker, Ph.D., Assistant Professor, Hankamer School
- “[The inventor of PLS]
Wold talked about a dialog between the researcher and the data,
facilitated by the method. … I think a tool like NEUSREL brings PLS
closer to Wold’s original intent for PLS.”
-Prof. Edward E. Rigdon, Department of Marketing,
very much enjoyed the MJRM article about NEUSREL and I am particularly
intrigued by the nonlinear/interaction capabilities.”
-Prof. Dr. Claes Fornell,
University of Michigan.
NEUSREL user testimonials:
“We are convinced about NEUSRELs capabilities.”
-Mag. DI Ryffel GFK Swiss.
“… congratulations on creating a wonderful product. I am going to be
recommending it at places that I already have connections with.”
-John Steele, MS, ABD, Kansas State University and
Army Research Institute(ARI).
“… with the aid of NEUSREL we were able to uncover important
nonlinear effects in the field of psychological brand impact.”
-Gregor Waller, lic. phil., Head of Research,
“The program provides very interesting diagnostics which give me a lot
of clues to dive into more insightful investigation of the data.”
-Jae Cha, Chief Research Scientist,
CFI Claes Fornell International.
“I have applied the NEUSREL software designed by Dr. Buckler to
customer satisfaction and loyalty data, and found that it provides some
very desirable features. I have been happy about its ease of use,
functionality, and new and desirable features such as the ability to
identify nonlinear and interaction effects in the model.”
-Kunal Gupta, Ph.D., Vice President, Burke, Inc.
“We used NEUSREL for exploring product adoption drivers. Thanks to
NEUSREL’s capability to include all kinds of variables (e.g. moderators
or categorial variables such as gender) into our analysis, we were finally
able to avoid spurious findings and to derive some meaningful
recommendations concerning our proposition design and go-to-market
-Daniel Klein, Senior Manager, T-Mobile, Inc.
Let’s summarize what USM-NEUSREL provide to users:
Today’s causal analysis methods are designed to test
existing theories, rather than to explore new paths, or previously unknown/unanticipated
nonlinearities and moderating effects. These three items however are exactly
what is required in order for causal modeling to be maximally successful
in practical applications.
Prior to the development of USM, the scientific community generally speaking,
did not address the abovementioned practical causal modeling issues.
Clearly, it became necessary to pursue a new approach. The
foundation of this new approach has been developed over the last 25 years or so – a foundation
that enabled us to bring forth USM-NEUSREL.
USM is a new type of causal
analysis that uses artificial neural network technology and has the
following key features …
- Exploratory analysis: USM requires less a priori information-knowledge.
- Nonlinearity: USM allows the user to explore (even unknown) nonlinear relationships.
- Interactions: USM reveals, displays and quantifies interactions among causes.
- Universality: USM makes use of arbitrarily
distributed variables, in particular nominally scaled variables such as gender,
profession, brand name, etc. Furthermore, USM enables one to
model circular causal networks – eliminating the need to distinguish between
endogenous and exogenous variables.
- Quantification: USM allows quantification of
every significant property in one’s data, including path strength,
linear path coefficient, interaction strength, and significance level.
- Simplicity – USM is very easy to use –
no need for elaborate option settings.
We have received numerous success stories indicating
the tremendous value that USM delivers to both researchers and the business community.
With the aid of our analysis service and a trial usage of
the software license, you have the opportunity to experience the
vast potential of USM to reveal previously undiscovered cause-effect relationships
in your own data.
Perhaps you are analyzing broadband usage patterns,
or consumer preference. Whatever
type of data you are exploring, USM-NEUSREL could be your next great step
toward amazing scientific discoveries!
Please contact us to explore together how USM-NEUSREL may
significantly benefit your research and/or corporate projects.
Contact Dr. Buckler (email): Buckler at neusrel.de
Within the “NEUSREL Ph.D.
a limited number of doctoral students will be afforded the opportunity
to use NEUSREL at no cost.