Results for other, more complex, genetic architectures (Models 2–5, Tables 6, 7, 8, 9) only get worse. In every case, fitting the full regression model to the simulated latent trait values with normal errors (N) yields unbiased estimates and most conclusions are qualitatively correct when models are fitted to scores on the second test with items spanning a wide range of difficulty. However, dichotomizing the trait or test scores, even with these large samples, leads to such marked loss of information that recovery of the true genetic architecture may be difficult or impossible given the range of possibilities a priori. In virtually every case, scores based on counts of relatively infrequent symptoms yield spurious results of remarkable complexity. The problem is not generally resolved by simple transformation.
