A Closer Look at the Measurement of Well-being: Methodological and Statistical Issues

David Disabato

Major Professor: Jerome Short, PhD, Department of Psychology

Committee Members: Todd Kashdan, Leah Adams

David King Hall, #2064
July 24, 2018, 10:00 AM to 12:00 PM


Well-being is arguably one of the most important outcomes in clinical psychology. Moving beyond the medical model of symptom remission, the goal of psychological interventions is to facilitate well-being after mental disorder. However, researchers have focused on the prediction and change of well-being without sufficient attention to measurement. This dissertation looked at two aspects of well-being measurement: different types of well-being and capturing people very high on well-being. Some researchers have claimed aspects of well-being clump into three types: emotional, psychological, and social. Although popular among positive psychologists, no study to date has tested how robust these well-being types are to different data analytic decisions. From analyzing the factor structure of well-being 180 different ways, I concluded the data support models with one, two, or three types of well-being depending on six methodological and statistical factors. Whether from a single or multiple type well-being measurement model, I then tested whether some of the most common self-report measures of well-being were able to quantify individuals across the entire well-being spectrum. While well-being measures were excellent at separating out the most miserable, they failed to separate out the most well. As many as one-fourth of individuals reported the maximum possible score on these measures, conflating together those high, very high, and extremely high on well-being. Such a ceiling effect prevents researchers from adequately determining the change trajectories of individuals from naturalistic life event and structured psychological interventions. Recommendations for revised well-being measurement approaches are presented and discussed.