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To derive prototypes of the disorders currently represented in DSM-IV, we collected data from a random national sample of experienced clinicians (N = 530), each of whom described a patient with one of the Axis II disorders (Shedler & Westen, in press; Westen & Shedler, 1999a) (To maximize comprehensiveness, a subgroup of these clinicians described instead a patient with a disorder included in the appendix to DSM-IV or in a prior edition of the manual) This procedure yielded data on 26 to 43 patients with each Axis II disorder We then averaged, or aggregated, the 200-item profiles of patients sharing a diagnosis to derive composite personality descriptions of each disorder An important psychometric benefit of aggregation is that the idiosyncrasies of individual patients and clinicians (ie, error variance) tend to cancel out in adequately sized samples (Horowitz, Inouye, & Siegelman, 1979; Rushton, Brainerd, & Preisley, 1983).



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This method of aggregating descriptions does not assume the reliability of any individual clinician s description of a patient Rather, following Block (1978), we assess the reliability of composite descriptions using coefficient alpha The logic is the same as computing the reliability of a psychometric scale, except that we are interested in the extent to which 200-item description of patients are consistent across observers, rather than the extent to which a set of items is internally consistent Coefficient alpha for all composites was > 80, suggesting that we were, in fact, able to identify reliably shared features of all the DSM-IV PDs Thus, a composite description of patients with a given PD should identify the core psychological features shared by these patients.

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Because the item set of the SWAP-200 includes all the Axis II criteria from DSM-IV, we are able to determine, using this method, whether the criteria in the diagnostic manual provide the best criteria for each disorder or whether some other combination of criteria might provide a more empirically accurate description From these data, we can derive two kinds of composite descriptions of patients with each PD The first most closely approximates the concept of a prototype (ie, the average patient with the disorder) and is derived by taking the average item score for each item for patients who share a diagnosis and displaying the items in descending order of magnitude (ie, of centrality to the construct) Table 132 presents the prototype for borderline PD aggregated in this way (Westen & Shedler, 1999a).

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A second method more closely approximates the concept of an ideal type, that is, a portrait of the disorder that is somewhat idealized, which emphasizes its distinct features (ie, those features that distinguish it from other PDs) Rather than aggregating the raw SWAP item scores, as before, we first standardize the SWAP items across patients, so that all 200 items have a mean of 0 and a standard deviation of 1 We then average item scores (Z-scores) across all patients who share a diagnosis to generate a standardized prototype This second method reduces the centrality of items that are highly descriptive of the average patient with a given disorder but also highly descriptive of the average patient in the sample Table 133 presents the composite standardized description of borderline PD aggregated this way As seen by comparing Tables 132 and 13.

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3, the two approaches yield similar but not identical diagnostic descriptions The advantage of compositing the raw scores is that doing so identifies features of the disorder that might readily be overlooked, such as the desperate pain and despondency of borderline patients This intense psychological pain is not reflected in the Axis II criteria for the disorder but likely plays a causal role in generating many features of the disorder, such as suicide attempts The advantage of compositing Zscores, in contrast, is that doing so provides a more pure description of the central features.

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