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<p>This tutorial and case study provides a comprehensive, step-by-step guide to conducting multiple and logistic regression analyses using SmartPLS 4. Although SmartPLS is primarily known for partial least squares structural equation modeling (PLS-SEM), its latest version includes features that enable researchers to perform and visualize regression analyses effectively. The tutorial introduces the theoretical foundations of both standard multiple and logistic regression, outlines the main stages of model specification and estimation, and demonstrates how to implement these analyses within the SmartPLS environment. Emphasis is placed on key analytical decisions, such as model design, assumption testing, interpretation of coefficients, and goodness-of-fit measures. Practical examples and graphical outputs are included to illustrate the implementation process and to enhance understanding of the results.</p>

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Multiple linear and logistic regression analysis: a SmartPLS 4 software tutorial

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月2日

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  • Vasilica-Maria Margalina,
  • Charlotte Kreienbaum,
  • Joseph F. Hair,
  • Jan-Michael Becker,
  • Christian M. Ringle

摘要

This tutorial and case study provides a comprehensive, step-by-step guide to conducting multiple and logistic regression analyses using SmartPLS 4. Although SmartPLS is primarily known for partial least squares structural equation modeling (PLS-SEM), its latest version includes features that enable researchers to perform and visualize regression analyses effectively. The tutorial introduces the theoretical foundations of both standard multiple and logistic regression, outlines the main stages of model specification and estimation, and demonstrates how to implement these analyses within the SmartPLS environment. Emphasis is placed on key analytical decisions, such as model design, assumption testing, interpretation of coefficients, and goodness-of-fit measures. Practical examples and graphical outputs are included to illustrate the implementation process and to enhance understanding of the results.