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October 7, 2025Open Access

A step-by-step guide to generalized estimating equations using SPSS in the field of dentistry

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Generalized estimating equations effectively analyze correlated outcomes from longitudinal data in dental studies.
The study introduces covariance matrix types and working correlation matrix variations for better model fitting.
Quasi-likelihood information criterion and its approximation serve to select optimal working matrices in analysis.
Highlights the importance of accounting for multiple outcomes in dentistry, where patient data is inherently correlated.

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This guide demonstrates the use of generalized estimating equations to analyze correlated outcomes in dentistry, highlighting matrix selection.

Lim et al. (2025) studied this question.

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This guide demonstrates the use of generalized estimating equations to analyze correlated outcomes in dentistry, highlighting matrix selection.