Wardhaugh – Chapter 6 – LING VARIATION

1. Wardhaugh – Chapter 6 – LING VARIATION Regional Variation • Diachronic or historical linguistics • Variation based on region or space • Dialect geography • Create linguistic atlases • Identify isoglosses - bundles of isoglosses define dialect regions • Relic or remnant dialects • Look at maps on page 140 • Transition area - talk about Northern Texas - where the West meets the South - low back merger but still southern features like ay-monophthongization • Linguistic Atlas Projects of US • NORMS = non-mobile older rural men were the ideal subjects - also roughly estimate social class (discussed further) • Dialect mixture and free variation

2. Wardhaugh – Chapter 6 – LING VARIATION Regional Variation - http://www.ling.upenn.edu/phono_atlas/NationalMap/NatFig1.GIF http://www.ling.upenn.edu/phono_atlas/NationalMap/NationalMap.html

3. Wardhaugh – Chapter 6 – LING VARIATION The Linguistic Variable • “a linguistic item which has identifiable variants” p. 145 • Shown in ( )s = variable. The variants can be sounds [ ] or other forms (grammatical elements like 3rd Person Sing [s], etc) • One of the variant forms can be zero or Ø • Counting each variant for a variable gives you a quantitative representation of a speaker’s frequency for that variable (performance) which needs to be somehow related to their competence of language! Joe is 70% r-less • Not all variables are created equal (some conscious, some not, etc.)

4. Wardhaugh – Chapter 6 – LING VARIATION The Linguistic Variable • Labov’s terms (p. 148 in Wardhaugh) • Indicator = ling variable to which little or no social import is attached • caught/cot merger - untested hypothesis!!! • Marker = ling variable that carries social significance • NYC r-lessness, (ING) • Stereotype = ling variable that is a popular and, therefore conscious characterization of the speech of a particular group (not necessarily reality) • Boston r-lessness (park the car in Harvard yard), toidy-toid NYC

5. Wardhaugh – Chapter 6 – LING VARIATION Linguistic and Social Variation • Variation in the blender is broken / the blender is broke gives us an idea of social information of the person who would choose the 2nd over the first • Age, gender (typically sex of speakers) important social factors • Social class - usually devised from an index of occupation, education and residence value to give someone a category like lower, middle, upper middle class or working class (LWC, MMC, etc) • Blue collar versus white collar • Eckert’s study of Detroit HS - jocks and burnouts = kids create own social structure • Many problems with assigning social class - women, kids, Portland, etc • Sociolects vs. idiolects • Milroys used social networks rather than social class to explore variation in Northern Ireland

6. Wardhaugh – Chapter 6 – LING VARIATION Linguistic and Social Variation • Social Class - Labov’s study of Philadelphia (2001) - study conducted in 1970s

7. Wardhaugh – Chapter 6 – LING VARIATION Data Collection and Analysis • Identify social and linguistic variables to control for and investigate • Avoid “observer’s paradox” • Questionnaire • Develop formal methods - reading passage, word list, minimal pairs, semantic differentials, elicited data (i.e., say the days of the week) • Interview style and casual speech through narratives (what Labov talked about) – how identify narrative? Ling and non-ling factors • Subjective reaction test = how subjects react to the variables in question (will talk more about this when talk about Philadelphia study) • Sampling the community - random versus judgment sample vs. stratified sample (subjects selected to fit certain social factor cells - age, sex, class, etc)

8. Wardhaugh – Chapter 6 – LING VARIATION Data Collection and Analysis • Correlational - correlate linguistic variation (frequency of variable or use of variable) with social factors • Ling variable is dependent variable - social variables are independent variable and use statistics to explore degree of correlation • This is quantitative sociolinguistics! • Statistically significant = the variation explained by the statistical model only has less than a 5% possibility that it is due to chance = p < .05 level of significance

9. Wardhaugh – Chapter 6 – LING VARIATION Data Collection and Analysis • This model with age, occupation and generation can account for 46% of the variation (r2 = 0.46) of F1 (ay0) in the data, with age as a significant predictor at p < .0001 • Data show change in apparent time 650 700 Predicted F1 (ay0) 750 800 14-29 30-39 40-49 50-59 60+ age groups