Petersen estimator, Chapman adjustment, list effects, and heterogeneity

Simon Fraser University Speech Error Database (SFUSED) Cantonese: Methods, design, and usage

The Simon Fraser University Speech Error Database (SFUSED) is a multi-purpose database of speech errors based in audio recordings. The motivation for SFUSED Cantonese, a component of this database, is to create a linguistically rich data set for exploring language production processes in Cantonese, an under-studied language. We describe in detail the methods used to collect, analyze, and explore the database, including details of team workflows, time budgets, data quality, and explicit linguistic and processing assumptions. In addition to showing how to use the database, this account supports future research with a template for investigating additional under-studied languages, and it gives fresh perspective on the benefits and drawbacks of collecting speech error data from spontaneous speech. All of the data and supporting materials are available as open access data sets.

A Review of Capture-recapture Methods and Its Possibilities in Ophthalmology and Vision Sciences

Epidemiological information is expected to be used to develop key aspects of eye care such as to control and minimise the impact of diseases, to allocate resources, to monitor public health actions, to determine the best treatment options and to forecast the consequence of diseases in populations. Epidemiological studies are expected to provide information about the prevalence and/or incidence of eye diseases or conditions. To determine prevalence is necessary to perform a cross-sectional screening of the population at risk to ascertain the number of cases. The aim of this review is to describe and evaluate capture-recapture methods (or models) to ascertaining the number of individuals with a disease (e.g. diabetic retinopathy) or condition (e.g. vision impairment) in the population. The review covers the fundamental aspects of capture-recapture methods that would enable non-experts in epidemiology to use it in ophthalmic studies. The review provides information about theoretical aspects of the method with examples of studies in ophthalmology in which it has been used. We also provide a problem/solution approach for limitations arising from the lists obtained from registers or other reliable sources. We concluded that capture-recapture models can be considered reliable to estimate the total number of cases with eye conditions using incomplete information from registers. Accordingly, the method may be used to maintain updated epidemiological information about eye conditions helping to tackle the lack of surveillance information in many regions of the globe.

Investigating Perceptual Biases, Data Reliability, and Data Discovery in a Methodology for Collecting Speech Errors From Audio Recordings

This work describes a methodology of collecting speech errors from audio recordings and investigates how some of its assumptions affect data quality and composition. Speech errors of all types (sound, lexical, syntactic, etc.) were collected by eight data collectors from audio recordings of unscripted English speech. Analysis of these errors showed that: (i) different listeners find different errors in the same audio recordings, but (ii) the frequencies of error patterns are similar across listeners; (iii) errors collected "online" using on the spot observational techniques are more likely to be affected by perceptual biases than "offline" errors collected from audio recordings; and (iv) datasets built from audio recordings can be explored and extended in a number of ways that traditional corpus studies cannot be.