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tested matrices with convergent correlations ranging from a low of 20 to a high of 90 Similarly, all of the cross-factor correlations (eg, between measures of Pleasantness and Unpleasantness; we will refer to these as discriminant correlations) were restricted to be the same; we tested matrices with discriminant correlations ranging from 10 to 80 Finally, we ran parallel series of matrices that included two, three, or four indicators of each construct These analyses (which are summarized in Table 143) yielded two noteworthy ndings First, the number of indicators had no effect whatsoever on the estimated correlations; in other words, we obtained identical results regardless of whether we used two, three, or four indicators of each construct Accordingly, Table 143 presents a single matrix of correlations collapsed across this parameter.

This nding is important because it indicates that using a large number of indicators does not necessarily enhance one s ability to model measurement error; we return to this issue later Second, as we suggested earlier, the estimated factor intercorrelation can be computed quite simply by dividing the mean discriminant correlation by the average convergent correlation For example, if the discriminant correlations all are 20 and the convergent correlations all are 40 then the estimated factor intercorrelation is 50 Again, this is conceptually analogous to computing the traditional correction for attenuation, using the multiple indicators of each construct as parallel forms to estimate reliability Viewed in this light, it is easy to see why mood researchers actually have failed to nd instances in which raw correlations of 25 are transformed into latent correlations of 84; indeed, if the average discriminant correlation was only .

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Percentages are commonly used in food service because the majority of food products that we serve to our guests are a percentage of the original item we purchased. Percentages are easiest to use if they are converted to a decimal. The conversion of a percentage to a decimal is easy. The general rule is to divide the number by 100. 87% 87 100 87 100 .87

25, one would need mean convergent correlations of approximately 296 to achieve this result This was hardly the case in the data reported by Green et al (1993) In fact, although they had one raw correlation of 25 in their initial analysis, the average uncorrected correlation was much.

psychopathy as a distinct clinical entity is clearly categorical Exclusionary criteria are appropriate to categorical diagnosis, which does not require a homogeneous scale The latter is more appropriate to a continuous variable The measurement model of the PCL-R is ambiguous and reflects ambivalence about whether psychopathy should be construed as a discrete entity or a continuous dimension, an issue on which Hare (1970, 1998) declares himself agnostic The total score reflects a homogeneous dimension (Hare, 1991), but it is often interpreted as the extent to which an individual meets the prototype of a psychopath and is used to create a dichotomy This is not unreasonable for much research, but cutoff scores distinguishing psychopaths from nonpsychopaths can create the illusion that a discrete category has been identified In fact, the common cutoff for dichotomizing the PCLR is relatively arbitrary (Salekin et al, 1996).

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The PCL-R measurement model more clearly meets the requirement of a quantitative dimension specified earlier by Hare (1970, p 12) than that of a discrete category In this dimensional concept, a single score on the PCL-R represents a person s degree of psychopathy, and psychopaths exist only as a convenient fiction representing an extreme degree Implicitly, a dimensional conception acknowledges the absurdity of assuming that traits such as egocentricity, unreliability, or deceitfulness are limited to only a small section of humanity However, Harris, Rice, and Quinsey (1994) argue that psychopathy is a discrete entity and not merely the end of a continuum Applying taxometric methods to measures of psychopathy and childhood variables reflecting antisocial conduct, they found evidence for a taxon However, chronic antisocial behavior beginning in childhood was the central feature rather than the affective and personality characteristics associated with F1.

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