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Introduce concepts and develop understanding in readiness for GCSE with clear explanations of all of the up-to-date terminology.

generalized linear models, latent trait model, EM algorithm, scoring methods,

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We discuss the use of latent variable models with observed covariates for computing response propensities for sample respondents. A response propensity score is often used to weight item and unit responders to account for item and unit non-response and to obtain adjusted means and proportions. In the context of attitude scaling, we discuss computing response propensity scores by using latent variable models for binary or nominal polytomous manifest items with covariates. Our models allow the response propensity scores to be found for several different items without re®tting. They allow any pattern of missing responses for the items. If one prefers, it is possible to estimate population proportions directly from the latent variable models, so avoiding the use of propensity scores. Arti®cial data sets and a real data set extracted from the 1996 British Social Attitudes Survey are used to compare the various methods proposed.