مدل سازی ترکیبی عاملی پرسشنامه نگرانی دولت پن : شواهد برای طبقه مجزای نگرانی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|49941||2016||8 صفحه PDF||سفارش دهید||7425 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Anxiety Disorders, Volume 37, January 2016, Pages 40–47
Worry, the anticipation of future threat, is a common feature of anxiety and mood psychopathology. Considerable research has examined the latent structure of worry to determine whether this construct reflects a dimensional or taxonic structure. Recent taxometric investigations have provided support for a unidimensional structure of worry; however, the results of these studies are limited in that taxometric approaches are unable to assess for the presence of more than two classes of a given construct. Given the complex nature of worry, it is possible that worry may actually reflect a latent structure comprised of multiple classes that cannot be assessed through taxometric approaches. Thus, it is important to utilize newer statistical techniques, such as factor-mixture modeling (FMM), which allow for a more nuanced assessment of the latent structure of a given psychological construct. The aim of the current study was to examine the latent structure of worry using FMM. It was predicted that worry would reflect a three-class structure comprised of (1) a class of low, normative levels of worry, (2) a class of moderate, subclinical worry, and (3) a class of high, pervasive worry. The latent class structure of worry was assessed using FMM in a sample of 1337 participants recruited from the community through a research clinic. Results revealed a three-class structure of the PSWQ comprising low, moderate-high, and high classes of worry. We also provided convergent and discriminant validity of the worry classes by demonstrating that the high worry class was most associated with GAD and that the low worry class was the least associated with GAD. The clinical utility of the worry classes, including the creation of empirically based cut-scores, and the implications for future research are discussed.