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 -

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