The 7-Minute Causal Analysis

Simple and quick results with the Universal Structure Modeling from NEUSREL

Have you ever experienced this?
When you try a structure equation analysis with Lisrel, Amos or SmartPLS, some requirements are simply not met. Say, the distribution assumptions are not met? Or Non-linear relations might be there, or worse: moderating variables could be interfering, but you don’t know which ones? Which variables are actually affecting which other variables? And in which direction? Question after question that might not be answered by your literature review on proven theories... Shouldn’t ‘real research be different?

The goal should be that you only use the knowledge and theories that you can truly back up. The analysis method should do the rest. These methods should also be flexible enough to show you where, and which, non-linearities and moderation effects -which you were not previously aware of-  are at play.

This method is now finally available. You can test, cost-free, the NEUSREL software. The universal structure modeling (USM) implemented here has already been scientifically published* and is used by leading companies such as T-Mobile, L'Oreal or Procter&Gamble. The best of it for you, as a researcher is: You get fast and powerful results. All results are presented in an Excel file in one view (Video: 7-Minute Causal Analysis) incl. all established fit measures for measurement and structural model. The analysis is not only simpler — it enriches and inspires your research with empirically based hypothesis formation.

Test the „NEUSREL 4.0 – Trial Version“ now for free.
(Here for a list of features)


Request the peer-reviewed journal article on USM:
* Identifying Hidden Structures in Marketing’s Structural Models Through Universal Structure Modeling: An Explorative Neural Network Complement to LISREL and PLS, in: Marketing Journal of Research and Management, Buckler, F./Hennig-Thurau, T., 2/2008


Request the latest white paper:
Causal Direction Detection: A NEUSREL Feature That Reinvents Management Science, Buckler, F. 2011


The 7-minute video on the 7-minute causal analysis
Film starten

Do you have questions for the author?


Features of the NEUSREL 4.0 Trial Version:

  • formative and reflective measurement models
  • non-linear measurement models
  • missing replacement by "K-nearest neighbor"
  • "Causal Direction Discovery" option
  • "Goodness-of-fit" value of model according to Tennenhaus
  • R2 for Neusrel and for linear comparison models
  • Values for measurement models: Factor Scores, Factor weights, Chrombachs Alpha, AEV Average Explained Variance, Composite Reliability
  • Standardized and unstandardized values for each pathway Average Simulated Effect, Significance (Fast Estimate), Degree of Freedom (resp. existence of non-linearity),  Linear Path Coefficients,  Relative Maximum Effect in %, Effect strength (Overall Explained Absolute Deviation)
  • Case values for all latent variables
  • Polynomial equation for non-linear relationships
  • Interactions measurements
  • Graphics of significant paths and for important interactions
  • Restrictions: Max. 30 manifest variables and 1000 cases, license terminates on June 30, 2012

Features only available in the full version:

  • case weighting
  • missing modules, including LEI (Least Entropy Imputation for Structural Missing’s)
  • total effect calculation from the direct effects
  • complete bootstrapping
  • cross-validation and hold-one-out-validation
  • option to overweight rare cases
  • option for log transformation
  • individual settings for model complexity (number of neurons), generalization feature (number in the "committee of networks") and model interactions second-order model module
  • CAA Competitive Advantage Analysis:
  • analysis relative to best competitor
  • time series analysis module (extra license) sign constraints: e.g., negative sign exclusion  
  • Block check:
  • checks dimensionality of measurement models
  • simulation module
  • temporary elimination of outliers and graphical spotting module
  • Individual path coefficients per path using Hierarchical Bayes
  • Segmentation module for handling subgroups of cases
Published in:

Deutsch »
Sitemap »
Imprint »