Common source bias has been the focus of much attention. To minimize the problem, researchers have sometimes been advised to take measurements of predictors from one observer and measurements of outcomes from another observer or to use separate occasions of measurement. We propose that these efforts to eliminate biases due to common source…

Incomplete or missing data is a common problem in almost all areas of empirical research. It is well known that simple and ad hoc methods such as complete case analysis or mean imputation can lead to biased and/or inefficient estimates. The method of maximum likelihood works well; however, when the missing data mechanism is not one of missing…

In studies of relatives, conventional multiple regression may not be appropriate because observations are not independent. Obtaining estimates of regression coefficients and correct standard errors from these populations through a structural equation modeling framework is discussed and illustrated with data from twins. (SLD)

Procedures for determining the number of clusters in a data set are explored. A proposed stopping rule, the GRAPH criterion, is compared to four stopping rules currently in use. The GRAPH criterion's mathematically attractive properties and utility in solving the number-of-clusters problem are demonstrated. (SLD)

The analysis of discrete dyadic sequential behavior and, in particular, the problem of forecasting future behavior from current and past behavior in such data is the main theme of the present article. We propose to use multivariate multinomial logit models and the potential of which will be demonstrated with data on Imagery play therapy. In such a…