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Due to previously reported mixed findings, there is a need for further empirical research on the factorial structure of the commonly used Geriatric Anxiety Inventory (GAI). Therefore, the psychometric properties of the GAI and its short form version (GAI-SF) were evaluated in a psychogeriatric mixed in-and-out patient sample (n = 543).
Methods:
Unidimensionality was tested using a bifactor analysis. Rasch modeling was used to assess scale properties. Sex, cognitive functioning and depressive symptoms were tested for differential item functioning (DIF).
Results:
The bifactor analysis identified an essential unidimensional (general) factor structure but also specific local factors. The general factor comprises all the 20 items as one factor, and the results showed that the variance in the general and specific factors (subscale) scores is best explained by the single general factor. These findings were demonstrated for both versions of the GAI. Furthermore, the Rasch models identified extensive item overlap, indicating redundant items in the full version of the GAI. The GAI-SF also seems to extract much of the same information as the full form. Test scores and items have the same meaning for older adults across different demographic status.
Conclusion:
The findings support the use of a total sum score for both GAI and GAI-SF. Notably, when using the GAI-SF, no information is lost, in comparison with the full scale, thus, supporting the option of choosing the short form (version) when considered most appropriate in demanding clinical contexts.
There is considerable debate surrounding the effective measurement of DSM-IV symptoms used to assess manic disorders in epidemiological samples.
Method
Using two nationally representative datasets, the National Epidemiological Survey of Alcohol and Related Conditions (NESARC, n=43 093 at wave 1, n=34 653 at 3-year follow-up) and the National Comorbidity Survey – Replication (NCS-R, n=9282), we examined the psychometric properties of symptoms used to assess DSM-IV mania. The predictive utility of the mania factor score was tested using the 3-year follow-up data in NESARC.
Results
Criterion B symptoms were unidimensional (single factor) in both samples. The symptoms assessing flight of ideas, distractibility and increased goal-directed activities had high factor loadings (0.70–0.93) with moderate rates of endorsement, thus providing good discrimination between individuals with and without mania. The symptom assessing grandiosity performed less well in both samples. The quantitative mania factor score was a good predictor of more severe disorders at the 3-year follow-up in the NESARC sample, even after controlling for a past history of DSM-IV diagnosis of manic disorder.
Conclusions
These analyses suggest that questions based on some DSM symptoms effectively discriminate between individuals at high and low liability to mania, but others do not. A quantitative mania factor score may aid in predicting recurrence for patients with a history of mania. Methods for assessing mania using structured interviews in the absence of clinical assessment require further refinement.
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