Speaker
Description
Measurement is fundamental to accurately investigate constructs in psychological science, influencing the estimation of effects, the validity of study results, and ultimately replicability. Illness has a dramatic psychological impact on patients and (informal) caregivers, often termed “hidden patients”.
However, crucial constructs like uncertainty in illness (UI) and caregiver burden were overlooked from both psychometric and clinical perspectives. Moreover, existing assessment tools in health psychology often lack validation or are inadequately tailored for patients and caregivers.
This research aimed to enhance assessment methodologies in health psychology by developing and validating tools specific to illness-related constructs through original studies involving diverse patient and caregiver samples.
The first part details the development and psychometric validation of the Uncertainty in Illness Questionnaire (UIQ).
In Study 1, Exploratory Graph Analysis (EGA) identified four UI stable dimensions: uncertainty about symptoms, treatments, future change, and relationships.
In Study 2, Confirmatory Factor Analysis (CFA) supported a hierarchical second-order model, also demonstrating measurement invariance between patients and caregivers.
In Study 3, a Structural Equation Model (SEM) established the discriminant validity between UIQ and the Intolerance of Uncertainty Scale-Revised (IUS-R) confirming they measure distinct constructs.
The second part presents a short form of a widely used measure for caregiver burden, not previously validated. In Study 4, EGA detected three stable burden dimensions: impairment, emotions, uncertainty. In Study 5 a subsequent hierarchical second-order CFA model provided good fit.
The third part focuses on constructs relations, Study 6 tested a theoretically driven sequential mediation SEM, from IU, through worry and UI to anxiety, revealing good fit and a complete mediation effect.
Concluding, psychometrics can enhance research and societal benefits by accurately assessing health conditions, reducing costs, and ultimately improving individuals' psychological well-being. This research emphasizes the necessity of valid measures and robust statistics in quantitative psychology to improve research quality and replicability.
