This study aims to explore the usefulness and characteristics of data from the Divorce Predictors Scale (DPS), based on Gottman couples therapy, in predicting and understanding marital stability. The data used in this study is sourced from a previous Turkish research paper that employed the DPS questionnaire. The participants consisted of 84 (49%) divorced and 86 (51%) married couples. In addition to completing the DPS, participants also provided personal information.
The current study utilizes a different approach by applying structural equation modelling (SEM) and statistical analyses with varying granularity to the existing data. The main objectives are to assess the predictive power of the DPS and identify the key features/items within the scale that significantly influence divorce outcomes.
Furthermore, this study incorporates the Bayesian prediction of categories modelling technique to enhance the predictive accuracy of the DPS. By employing Bayesian methods, the study aims to capture the uncertainty and variability within the data, providing more robust predictions of divorce outcomes.
Additionally, the study explores the data mining properties of the DPS dataset through clustering analysis. The goal is to identify distinct patterns or clusters within the data that may reveal underlying subgroups or characteristics related to marital stability.

Authors:
Frank H, NPA Rockville, United States

About the Presenter(s)
Dr Frank (D-C) H is a University Postdoctoral Fellow or Instructor at NPA in United States

See this presentation on the full schedule – Friday Schedule

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