The exit poll in 2010 was almost exactly correct, but what is it, and how does it actually work?

At 10pm on election day, the ballots will close, and the counting will begin. It may be many hours before it becomes clear which party has the most seats in the new parliament, and many days until we know which parties will form the next government. However, shortly after the 10 o’clock deadline, broadcasters will release a forecast based on the exit poll, which will give the first insight into how voters have actually voted in the general election. In this post, Jouni Kuha explains the detail behind the exit poll forecast, and indicates some of the difficulties in putting together a forecast under considerable time pressures

Explaining the Exit Poll

At 10pm on election day, the ballots will close, and the counting will begin. It may be many hours before it becomes clear which party has the most seats in the new parliament, and many days until we know which parties will form the next government. However, shortly after the 10 o’clock deadline, broadcasters will release a forecast based on the exit poll, which will give the first insight into how voters have actually voted in the general election. In this post, Jouni Kuha explains the detail behind the exit poll forecast, and indicates some of the difficulties in putting together a forecast under considerable time pressures

Non-equivalence of measurement in latent variable modeling of multigroup data: a sensitivity analysis

In studies of multiple groups of respondents, such as cross-national surveys and cross-cultural assessments in psychological or educational testing, an important methodological consideration is the comparability or \equivalence" of measurement across the groups. Ideally full equivalence would hold, but very often it does not. If non-equivalence of measurement is ignored when it is present, substantively interesting comparisons between the groups may become distorted. We consider this question in multigroup latent variable modeling of multiple-item scales, specifically latent trait models for categorical items. We use numerical sensitivity analyses to examine the nature and magnitude of the distortions in different circumstances, and the factors which affect them. The results suggest that estimates of multigroup latent variable models can be sensitive to assumptions about measurement, in that non-equivalence of measurement does not need to be extreme before ignoring it may substantially affect cross-group comparisons. We also discuss the implications of such findings on the analysis of large comparative studies

On group comparisons with logistic regression models

It is widely believed that regression models for binary responses are problematic if we want to compare estimated coeffcients from models for different groups or with different explanatory variables. This concern has two forms. The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response, and the second when models are compared between groups which have different distributions of other causes of the binary response. We argue that these concerns are usually misplaced. The first of them is only relevant if the unobserved continuous response is really the subject of substantive interest. If it is, the problem should be addressed through better measurement of this response. The second concern refers to a situation which is unavoidable but unproblematic, in that causal effects and descriptive associations are inherently group-dependent and can be compared as long as they are correctly estimated

Patrick Sturgis & Jouni Kuha: over 5 million people in Britain think they have been infected with COVID-19

How many people in Britain have already been infected with the coronavirus? The truth is, we don’t really know. Yet, this is a very important number because, without it, it is difficult to properly assess the severity of the virus, its likely trajectory, and the consequent burden on health and social care systems. Using a random sample survey and self-reported data on infection and symptoms, Patrick Sturgis and Jouni Kuha (LSE) find a symptomatic infection rate in the general population of Great Britain of approximately 10 per cent, equating to around 5 million people, by the end of April

Two-step estimation of latent trait models

We consider two-step estimation of latent variable models, in which just the measurement model is estimated in the first step and the measurement parameters are then fixed at their estimated values in the second step where the structural model is estimated. We show how this approach can be implemented for latent trait models (item response theory models) where the latent variables are continuous and their measurement indicators are categorical variables. The properties of two-step estimators are examined using simulation studies and applied examples. They perform well, and have attractive practical and conceptual properties compared to the alternative one-step and three-step approaches. These results are in line with previous findings for other families of latent variable models. This provides strong evidence that two-step estimation is a flexible and useful general method of estimation for different types of latent variable models.Comment: 39 pages, 2 figures, 17 table

New poll suggests…: how to tell when public opinion has really changed

Every day we are being presented with new opinion polls on various social and political issues, but do these really represent public attitudes? Using a statistical method called bootstrapping to estimate sampling variance, Patrick Sturgis and Jouni Kuha explain how such an approach can improve the quality of debate about UK public opinion

Estimating how many Britons have already had COVID-19 using self-reported data

Using a random sample survey and self-reported data on infection and symptoms, Patrick Sturgis and Jouni Kuha find a symptomatic infection rate in the general population of Great Britain of approximately 10% by the end of April

Relating latent class membership to external variables: an overview

In this article we provide an overview of existing approaches for relating latent class membership to external variables of interest. We extend on the work of Nylund-Gibson et al. (Structural Equation Modeling: A Multidisciplinary Journal, 2019, 26, 967), who summarize models with distal outcomes by providing an overview of most recommended modeling options for models with covariates and larger models with multiple latent variables as well. We exemplify the modeling approaches using data from the General Social Survey for a model with a distal outcome where underlying model assumptions are violated, and a model with multiple latent variables. We discuss software availability and provide example syntax for the real data examples in Latent GOLD