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Survey-based research remains a cornerstone in disciplines such as management, information systems, and social sciences. In this field, structural equation models (SEM) allow for rigorous examination of theoretical relationships between latent constructs. However, a common risk is that significance results may depend on a specific model specification. To address this, robustness analyses have evolved from simple regressions to hybrid approaches incorporating machine learning. This article explores a framework that connects SEM, ordinary least squares (OLS) regression, and Double Machine Learning (DML), offering a practical perspective for researchers and businesses.
The core idea involves using SEM to refine the measurement structure and establish a baseline model that maintains the entire system of structural paths. Then, OLS is applied to the obtained factor scores, providing a transparent reference. Finally, the DML phase introduces residualization using algorithms such as Random Forest, Gradient Boosting, or SVM, allowing for the evaluation of the stability of each focal relationship after flexibly adjusting for covariates. This process not only increases the credibility of the findings but also reveals which links require cautious interpretation.
The practical implementation of these workflows demands a robust technological infrastructure. This is where companies like Q2BSTUDIO provide real value. Their expertise in artificial intelligence for businesses and AI agent development enables the automation of comparisons between multiple learners, while their solutions in AWS and Azure cloud services ensure the secure scaling of large volumes of survey data. Additionally, integration with Power BI and other business intelligence services facilitates the visualization of robustness results for non-technical teams.
From a business perspective, having custom applications to manage this type of analysis avoids reliance on generic tools and allows for the incorporation of domain-specific logic. Organizations that adopt this approach not only improve the validity of their market studies but also strengthen evidence-based decision-making. Cybersecurity also plays a key role, as survey data often contains sensitive customer information; therefore, Q2BSTUDIO integrates protection protocols from the design stage.
Ultimately, the combination of SEM with OLS and Double Machine Learning represents a significant advancement for robustness in survey research. And when supported by modern technological platforms, developed with custom software and backed by artificial intelligence experts, it becomes an undeniable competitive advantage for any organization seeking to deeply understand the voice of its customers or collaborators.
