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Summary: When missing data are produced by a non‐ignorable non‐response mechanism, analysis of the observed data should include a model for the probabilities of responding. We propose such models for non‐response in survey questions which are treated as measures of latent constructs and analysed by using latent variable models. The non‐response models that we describe include additional latent variables (latent response propensities) which determine the response probabilities. We argue that this model should be specified as flexibly as possible, and we propose models where the response propensity is a categorical variable (a latent response class). This can be combined with any latent variable model for the survey items, and an association between the latent variables measured by the items and the latent response propensities then implies a model with non‐ignorable non‐response. We consider in particular such models for the analysis of data from cross‐national surveys, where the non‐response model may also vary across the countries. The models are applied to data on welfare attitudes in 29 countries in the European Social Survey. [ABSTRACT FROM AUTHOR]

Many health surveys conduct an initial household interview to obtain demographic information and then request permission to obtain detailed information on health outcomes from the respondent's health care providers. A ‘complete response’ results when both the demographic information and the detailed health outcome data are obtained. A ‘partial response’ results when the initial interview is complete but, for one reason or another, the detailed health outcome information is not obtained. If ‘complete responders’ differ from ‘partial responders’ and the proportion of partial responders in the sample is at least moderately large, statistics that use only data from complete responders may be severely biased. We refer to bias that is attributable to these differences as ‘partial non-response’ bias. In health surveys it is customary to adjust survey estimates to account for potential differences by employing adjustment cells and weighting to reduce bias from partial response. Before making these adjustments, it is important to ask whether an adjustment is expected to increase or decrease bias from partial non-response. After making these adjustments, an equally important question is ‘How well does the method of adjustment work to reduce partial non-response bias?’. The paper describes methods for answering these questions. Data from the US National Immunization Survey are used to illustrate the methods. [ABSTRACT FROM AUTHOR]