Jouni Kuha; Irini Moustaki; Chris Skinner;
The problem of measurement error in statistical analysis is both common and serious; common because very many of the quantities of interest in the social sciences are difficult to determine accurately; serious because even moderate amounts of measurement error can cause substantial biases in estimated models of interest. It is possible to reduce these biases by using appropriately modified estimation methods, provided that sufficient information about the measurement error is available in the form of either additional data or realistic assumptions. Different measurement error problems may require rather different solutions, depending on, for example, the type of model (linear, log-linear, logistic etc.), the erroneously measured variables (explanatory or response, continuous or discrete) and the method of estimation (e.g. moment or likelihood based, exact or approximate). The work carried out in this area has focused in particular in problems involving more than one type of inaccurate measurement, such as measurement error of continuous and discrete variables, error in both explanatory and response variables, and measurement error together with missing data.