ScholarGate
Asystent

Porównaj metody

Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.

Variationist Sociolinguistics×Regresja logistyczna×
DziedzinaJęzykoznawstwoStatystyka w badaniach
RodzinaProcess / pipelineProcess / pipeline
Rok powstania19721958
TwórcaWilliam LabovDavid Roxbee Cox
TypQuantitative field study of socially conditioned linguistic variationMethod
Źródło pierwotneLabov, W. (1972). Sociolinguistic Patterns. University of Pennsylvania Press. ISBN: 9780812210521Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗
Inne nazwyVariationist Analysis, Labovian Sociolinguistics, Quantitative Sociolinguisticslogit model, binomial logistic regression, LR
Pokrewne43
PodsumowanieVariationist sociolinguistics is the quantitative study of how linguistic variation is structured by social and linguistic factors. Pioneered by William Labov in the 1960s and 1970s, it treats alternative ways of saying the same thing — the 'linguistic variable' — as systematically conditioned by speaker characteristics (class, age, sex, ethnicity), stylistic context, and the surrounding linguistic environment, and it uses statistical modeling of natural speech to reveal the orderly heterogeneity beneath apparent randomness.Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.
ScholarGateZbiór danych
  1. v1
  2. 3 Źródła
  3. PUBLISHED
  1. v1
  2. 2 Źródła
  3. PUBLISHED

Przejdź do wyszukiwania Pobierz slajdy

ScholarGatePorównaj metody: Variationist Sociolinguistics · Logistic Regression. Pobrano 2026-06-24 z https://scholargate.app/pl/compare