Periodic changes in occupational classifications make it difficult to obtain consistent measures of social class over time, potentially jeopardizing research on class-based trends. The severity of this problem depends, in part, on the measurement strategies used to address those changes. The authors propose that when a sample has been coded partly…

This study shows that residual variation can cause problems related to scaling in exponential random graph models (ERGM). Residual variation is likely to exist when there are unmeasured variables in a model--even those uncorrelated with other predictors--or when the logistic form of the model is inappropriate. As a consequence, coefficients cannot…

Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model…

This study evaluated three types of bias--total, measurement, and selection bias (SB)--in three sequential mixed-mode designs of the Dutch Crime Victimization Survey: telephone, mail, and web, where nonrespondents were followed up face-to-face (F2F). In the absence of true scores, all biases were estimated as mode effects against two different…

We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model…