Factor structure and psychometric properties of the german version chronic uncertainty scale (CU-20)
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Abstract
Background The experience of uncertainty is ubiquitous and universal across the globe. Many available tools
measuring uncertainty are focused on one aspect of uncertainty, e.g., patients with life-threatening illnesses, hence
a measure considering (chronic) uncertainty as an integral experience reflect ongoing uncertainties from a socio cultural perspective is missing. Additionally, current tools do not account for an extended timeframe to measure
chronic forms of uncertainty. The objective of this study is to validate a translated German version of the 20 item
Chronic Uncertainty Scale (CU-20).
Methods The full sample comprised N=462 participants. Most of the participants were young German citizens and
the sex distribution was relatively balanced (60% females; age in average: M=24.56; SD=4.78). Using equally split
samples, an exploratory factor analysis (EFA) evaluated the CU-20 factor structure, followed by a confirmatory factor
analysis (CFA) to test the established factor structure. Measurement invariance between male and female groups
was evaluated. Internal consistency of the six-factor model was shown and scale discrimination was shown against
chronic stress.
Results The EFA results showed decent model fit for the five-factor structure, however based on the CFA results, the
theoretically established six-factor model fits the data significantly better. Measurement invariance between male
and female groups was shown to be clearly scalar invariant. Cronbach’s alpha, omega and lambda all support internal
consistency and reliability of CU-20.
Conclusions The CU-20 is a valid and reliable measure of one’s state of chronic uncertainty reflecting the individuals’
experiences of macrosocial forms of uncertainty, compared to the existing ones. This scale is especially useful in
the context of migration, refugees or during global crises. Further psychometric testing is required in more diverse
samples and a deeper look into measurement invariance is recommended.