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| Language Attitude Survey× | Variationist Sociolinguistics× | |
|---|---|---|
| Tudományterület | Nyelvtudomány | Nyelvtudomány |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1992 | 1972 |
| Megalkotó≠ | Survey methodologists and attitude researchers (e.g., A. N. Oppenheim; Colin Baker; Peter Garrett) | William Labov |
| Típus≠ | Direct self-report survey measure of language attitudes | Quantitative field study of socially conditioned linguistic variation |
| Alapmű≠ | Garrett, P. (2010). Attitudes to Language. Cambridge University Press. ISBN: 9780521759175 | Labov, W. (1972). Sociolinguistic Patterns. University of Pennsylvania Press. ISBN: 9780812210521 |
| Alternatív nevek | Language Attitude Questionnaire, Direct Attitude Measurement, Language Attitudes Survey | Variationist Analysis, Labovian Sociolinguistics, Quantitative Sociolinguistics |
| Kapcsolódó | 4 | 4 |
| Összefoglaló≠ | A direct language attitude survey measures what people think and feel about languages, dialects, and varieties by asking them explicitly. Using questionnaires built from Likert scales, semantic-differential items, and open-ended questions, the direct approach gathers respondents' self-reported evaluations of varieties — their prestige, beauty, usefulness, or appropriateness — and analyses these responses for reliability, underlying structure, and differences between social groups. It is the self-report counterpart to indirect techniques such as the matched-guise test, trading some protection against socially desirable answers for transparency, scale, and ease of administration. | 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. |
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