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Path model analyzed with ordinary least squares multiple regression versus LISREL.

T J Kline¹, J D Klammer

¹Department of Psychology, The University of Calgary, Alberta, Canada. babbitt@ucalgary.ca

The Journal of Psychology

|June 14, 2001

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This study compared two statistical methods for analyzing organizational behavior data. LISREL analysis proved more effective than ordinary least squares regression for understanding voice, support, being heard, and justice.

Area of Science:

Organizational Psychology
Quantitative Research Methods

Background:

Understanding employee voice and perceptions of fairness is crucial in organizational settings.
Structural equation modeling (SEM) is a powerful tool for testing complex relationships.

Purpose of the Study:

To compare the efficacy of two SEM techniques: Ordinary Least Squares (OLS) regression and LISREL.
To evaluate these methods using data on voice, perceived organizational support, being heard, and procedural justice.

Main Methods:

Path model analysis was conducted using two distinct structural equation modeling techniques.
Ordinary Least Squares (OLS) regression and LISREL were applied to the same dataset.
Results from both analytical approaches were compared.

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Main Results:

LISREL analysis demonstrated superior theoretical and statistical performance compared to OLS regression.
Differences in results between the two methods were identified and discussed.
The study highlights the strengths of LISREL for complex organizational research.

Conclusions:

LISREL is a more robust method for analyzing path models in organizational research.
Researchers should carefully consider the choice of SEM technique for optimal data interpretation.
The findings provide guidance on selecting appropriate statistical tools for organizational behavior studies.