So sánh phương pháp

	Multidimensional Register Analysis	Variationist Sociolinguistics
Lĩnh vực	Ngôn ngữ học	Ngôn ngữ học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1988	1972
Người khởi xướng	Douglas Biber	William Labov
Loại	Factor-analytic analysis of co-occurring linguistic features across registers	Quantitative field study of socially conditioned linguistic variation
Công trình gốc	Biber, D. (1988). Variation across Speech and Writing. Cambridge University Press. ISBN: 9780521425568	Labov, W. (1972). Sociolinguistic Patterns. University of Pennsylvania Press. ISBN: 9780812210521
Tên gọi khác	Multidimensional Analysis (MD/MDA), Biber's Multidimensional Analysis, Dimensions of Register Variation	Variationist Analysis, Labovian Sociolinguistics, Quantitative Sociolinguistics
Liên quan	4	4
Tóm tắt	Multidimensional (MD) analysis is a corpus-linguistic method, developed by Douglas Biber in the 1980s, for describing how language varies across registers — speech versus writing, conversation versus academic prose, and so on. Its central idea is that many individual linguistic features (pronouns, passives, nominalizations, modals, and dozens more) systematically co-occur, and that these co-occurrence patterns define underlying dimensions of variation. Biber tags and counts a large set of features in every text of a balanced corpus, then uses factor analysis to extract the dimensions, interprets each functionally (Biber's Dimension 1 contrasts 'involved' interactive production with 'informational' production), and scores every text and register along them. The result is a quantitative, multifaceted map of register variation that replaces single rankings (such as a simple formality scale) with several independent dimensions.	Variationist 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.