Foreword

Are heritability estimates generalizable? Lack of evidence from cross-sample correlations

The majority of research indicates that estimates of heritability are not generalizable beyond the specific sample on which they have been calculated. This research has been limited, however, in its scope. The present set of investigations center upon an examination of previously reported heritability estimates for the scales of the Minnesota Multiphasic Personality Inventory (MMPI). The first investigation correlates the estimates for eight samples derived from a variety of published studies. The second investigated correlates the DZ twin pair and MZ twin pair intraclass correlations from four of the first investigation's samples. The third investigation correlates the estimates from one study in which the same sample was tested twice over a period of less than five years. It is concluded that no evidence is available for the generalizability of these estimates of MMPI scale heritability either across samples or even across time with the same sample.

Alternative common factor models for multivariate biometric analyses

In prior research we have shown how linear structural equation models and computer programs (e.g., LISREL) may be simply and directly used to provide alternatives for the traditional biometric twin design. We use structural equations and path models to define biometric group differences, we write traditional common-factor models in the same way, and then we take a detailed look at some alternative multivariate and biometric models. We contrast the biometric-factors covariance structure approach used by Loehlin and Vandenberg (1968), Martin and Eaves (1977), and others with the psychometric-factors approach used by McArdle et al. (1980) and others. We use the multivariate primary mental abilities data on monozygotic (MZ) and dizygotic (DZ) twins from Loehlin and Vandenberg (1968) to detail fundamental differences in model specification and results. We extend both multivariate biometric approaches using exploratory and confirmatory multiple-factor models. These comparisons show that each alternative multivariate methodology has useful features for empirical applications.