The traits simulated under each model, standardized to zero mean and unit variance, were then “administered” two simulated tests comprising 20 binary items. Item parameters were chosen to reflect two different extreme measurement models. Raw test scores were generated by summing the 0/1 item responses across items. Both tests assumed normal ogive item characteristic curves for each item. The items of the first test were assumed to have unit thresholds (item difficulties) and sensitivities (discriminating powers). Item difficulties of the second test were assumed to be distributed uniformly (ranging from −2 to 2) with discrimination parameters distributed uniformly (ranging from 0.5 to 1.5). Thus, the first test generated symptom counts with a J-shaped distribution characteristic of those often encountered in psychiatric assessment. The second test, with item difficulties distributed uniformly across most of the range of simulated trait values generated scores more symmetrically distributed around an intermediate mode. Table 4 shows the specific item parameters simulated for the second test. In addition, the raw trait values and test scores were dichotomized to generate outcome (“disease” phenotypes) at thresholds giving the
