เปรียบเทียบวิธี

	Multidimensional Register Analysis	Variationist Sociolinguistics
สาขาวิชา	ภาษาศาสตร์	ภาษาศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1988	1972
ผู้ริเริ่ม	Douglas Biber	William Labov
ประเภท	Factor-analytic analysis of co-occurring linguistic features across registers	Quantitative field study of socially conditioned linguistic variation
แหล่งต้นตำรับ	Biber, D. (1988). Variation across Speech and Writing. Cambridge University Press. ISBN: 9780521425568	Labov, W. (1972). Sociolinguistic Patterns. University of Pennsylvania Press. ISBN: 9780812210521
ชื่อเรียกอื่น	Multidimensional Analysis (MD/MDA), Biber's Multidimensional Analysis, Dimensions of Register Variation	Variationist Analysis, Labovian Sociolinguistics, Quantitative Sociolinguistics
ที่เกี่ยวข้อง	4	4
สรุป	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.