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Applying Generalisability Theory to examine the distinction between state and trait in the State and Trait Anxiety Inventory (STAI)

Accurate distinction between state and trait anxiety is necessary for monitoring of individual anxiety levels over time and developing effective interventions to reduce anxiety, which is especially important in the current COVID-19 pandemic situation increasing anxiety of the world population. The widely used State and Trait Anxiety Inventory (STAI) with 78,600 Google scholar citations to date, was specifically designed to measure both state and trait anxiety. However, ability of the STAI to accurately distinguish between the two and the overall reliability and generalisability of its assessment scores were not rigorously investigated using appropriate methodology. Generalisability theory (G-theory) is increasingly used as the most robust method to distinguish between state and trait and establish the overall reliability while accounting for specific error sources in the assessment of psychological conditions. G-theory was applied to the 40-item STAI completed by 139 participants on three occasions separated by two-week intervals. Both subscales of the STAI demonstrated excellent reliability in measuring trait anxiety with high generalizability of scores across occasions (G=0.84-0.92) but fail to distinguish state from trait. This means that the state subscale of the STAI is not suitable to detect changes over time and reliably measure state anxiety. A minor amount of error variance identified in the STAI subscales were mainly attributed to interaction between person and occasion, which reflected state anxiety, and interaction between person, item and occasion. Dynamic aspects of anxiety were identified in both subscales including feelings of satisfaction, nervousness, feeling pleasant, restlessness, perceived failure, lack of calmness, feeling insecure, feeling inadequate and sensitivity to disappointments. This study derived a sensitive state anxiety scale using G-theory that includes items the most sensitive to state changes. State anxiety can be measured with higher accuracy by using the proposed short state scale without modifications of the original STAI format. Dynamic aspects of anxiety identified using G-theory, are more amendable, and proposed as the primary target of interventions aiming at effectively reducing anxiety. Further enhancement of state anxiety measurement informed by G- theory is warranted. Overall, this study contributed to enhanced assessment of state and trait anxiety and informs psychological interventions aiming at more effective reduction of anxiety.
Type of thesis
Forrest, S. J. (2020). Applying Generalisability Theory to examine the distinction between state and trait in the State and Trait Anxiety Inventory (STAI) (Thesis, Master of Social Sciences (MSocSc)). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/14013
The University of Waikato
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