Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 2681815

Choosing and using a microcomputer for tropical epidemiology. II. Study implementation.

P Byass
View original source ↗Export metadata ↓

Abstract

Continuing from the previous paper (Byass 1989), practical considerations in setting up a computer system to assist in the collection of data from an epidemiological project are discussed. Organizing field work so as to facilitate computer processing is considered, together with establishing data entry and checking procedures. The use of the hypothetical example in the first paper is continued here.

Explore related subjects

Data CollectionDatabase Management SystemsHumansMicrocomputersSoftwareSoftware ValidationSurveys and QuestionnairesTropical MedicineJournal ArticleReview

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

P Byass. 1989. Choosing and using a microcomputer for tropical epidemiology. II. Study implementation.. https://pubmed.ncbi.nlm.nih.gov/2681815/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

Probability estimation when some observations are grouped.

This paper considers the use of additional questions for decreasing survey non-response rates and an approach for estimating a probability based on the results obtained. In a survey, the respondents are asked to answer an original question and follow-up questions, where the answers for the follow-up questions are grouped answers for the original question. For example, respondents are asked to provide an exact number of incidents, but in cases of 'Do not know' or 'Refuse' responses, they are subsequently asked to pick an answer from a less specific categorical scale. The new estimator obtains smaller variance asymptotically and does not depend on a distribution family. This method is applied to income questions in a survey regarding injury prevention and behaviours. Another application is survey data on intimate partner violence, where some amendments were applied for incorporating post-stratification weights and for using non-random grouping. For additional illustration, an example of parameter estimation on artificially generated data is presented.

Data Collection↗