In this, our first volume of Sociological Methodology, we have grouped the articles into four sections into which the papers naturally seemed to fit. The first section is entitled “Statistical Models: Prediction, Model Evaluation, and Missing Data.” Papers in this section build on the long-standing tradition in sociology that relies on statistical models based on observational data, and they focus on important issues in prediction, model evaluation, and missing data. In “On the Assignment of Individuals to Latent Classes,” Leo A. Goodman compares two main competing procedures as to how individuals in a multiway contingency table can be assigned to latent classes in a latent class analysis. Andrew Gelman and Iain Pardoe, in “Average Predictive Comparisons for Models with Nonlinearity, Interactions, and Variance Components,” discuss the problem of predictions from regression models that are nonlinear, multilevel, and interactive. In “Multilevel Covariance Structure Analysis by Fitting Multiple Single-Level Models,” Ke-Hai Yuan and Peter M. Bentler propose methods for estimating and evaluating structural equation models at separate levels for multilevel data. Finally, in “Regression with Missing Ys:AnImproved Strategy for Analyzing Multiply Imputed Data,” Paul T. von Hippel offers concrete and sensible advice when dealing with missing data on the dependent variable in regression analysis.>
