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Despite controversy regarding the classification and diagnostic status of hoarding disorder, there remains a paucity of research on the nosology of hoarding that is likely to inform the classification debate. The present investigation examined the latent structure of hoarding in three, large independent samples. Data for three well-validated measures of hoarding were subjected to taxometric procedures, including MAXimum EIGenvalue, Mean Above Minus Below A Cut, and Latent-Mode factor. Two symptom measures, one of which closely mirrors the proposed diagnostic criteria for hoarding disorder, and a measure of hoarding beliefs were analyzed. Sample 1 (n = 2,501) was representative of the general German population, while Samples 2 (n = 1,149) and 3 (n = 500) consisted of unselected undergraduate students. Findings across all three samples and taxometric procedures provided converging evidence that hoarding is best conceptualized as a dimensional construct, present in varying degrees in all individuals. Results have implications across research and treatment domains, particularly with respect to assessment approaches, treatment response determination, and policy decisions. These findings underscore the need for further investigations on the nosology of hoarding, to help validate this construct as we move forward with respect to our research and treatment efforts, as well as the potential inclusion of hoarding disorder in the "Diagnostic and Statistical Manual of Mental Disorders" (5th ed.; "DSM-5"; American Psychiatric Association, 2012). (Contains 2 tables and 3 figures.)

Background: Published validation studies have reported different factor structures for the Self-Compassion Scale (SCS). The objective of this study was to assess the factor structure of the SCS in a large general population sample representative of the German population. Methods: A German population sample completed the SCS and other self-report measures. Confirmatory factor analysis (CFA) in MPlus was used to test six models previously found in factor analytic studies (unifactorial model, two-factor model, three-factor model, six-factor model, a hierarchical (second order) model with six first-order factors and two second-order factors, and a model with arbitrarily assigned items to six factors). In addition, three bifactor models were also tested: bifactor model #1 with two group factors (SCS positive items, called SCS positive) and SCS negative items, called SCS negative) and one general factor (overall SCS); bifactor model #2, which is a two-tier model with six group factors, three (SCS positive subscales) corresponding to one general dimension (SCS positive) and three (SCS negative subscales) corresponding to the second general dimension (SCS negative); bifactor model #3 with six group factors (six SCS subscales) and one general factor (overall SCS). Results: The two-factor model, the six-factor model, and the hierarchical model showed less than ideal, but acceptable fit. The model fit indices for these models were comparable, with no apparent advantage of the six-factor model over the two-factor model. The one-factor model, the three-factor model, and bifactor model #3 showed poor fit. The other two bifactor models showed strong support for two factors: SCS positive and SCS negative. Conclusion: The main results of this study are that, among the German general population, six SCS factors and two SCS factors fit the data reasonably well. While six factors can be modelled, the three negative factors and the three positive factors, respectively, did not reflect reliable or meaningful variance beyond the two summative positive and negative item factors. As such, we recommend the use of two subscale scores to capture a positive factor and a negative factor when administering the German SCS to general population samples and we strongly advise against the use of a total score across all SCS items. [ABSTRACT FROM AUTHOR]