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J. MARSHALL PHOVAR = 21.1 + 0.573 AGE - 5.97 SEX Predictor Coef StDev T P Constant 21.067 2.912 7.23 0.000 AGE 0.57295 0.06569 8.72 0.000 SEX1 -5.965 2.869 -2.08 0.042 S = 11.38 R-Sq = 57.0% R-Sq(adj) = 55.5% The P value for sex is significant at the 0.05 level, showing that SEX is a reasonable predictor variable. The r-squared value has risen slightly, showing that the addition of SEX hasn’t improved the model much. Next we add the variable AGE x SEX. PHOVAR = 24.6 + 0.460 AGE - 13.2 SEX + 0.225 AGE x SEX Predictor Coef StDev T P Constant 24.639 3.520 7.00 0.000 AGE 0.45984 0.09151 5.03 0.000 SEX -13.231 5.029 -2.63 0.011 AGE x SE 0.2255 0.1292 1.75 0.086 S = 11.19 R-Sq = 59.1% R-Sq(adj) = 57.0% This variable is not significant at the 0.05 level, and raises the adjusted r- squared value only marginally. There is no significant interaction between AGE and SEX. Next we add the first social variable that we want to test as a predictor of PHOVAR. LIFMOD is a test of the degree of mental urbanisation of a speaker, and the hypothesis is that a low degree of mental urbanisation will predict a high degree of dialect maintenance: PHOVAR = 7.74 + 0.233 AGE - 11.5 SEX + 0.224 AGE x SEX + 1.02 LIFMOD Predictor Coef StDev T P Constant 7.736 3.584 2.16 0.035 AGE 0.23321 0.07570 3.08 0.003 SEX -11.502 3.760 -3.06 0.003 AGE x SEX 0.22410 0.09638 2.33 0.024 LIFMOD 1.0218 0.1475 6.93 0.000 S = 8.348 R-Sq = 77.6% R-Sq(adj) = 76.1% LIFMOD is shown to be very highly significant, and its addition has greatly improved the r-squared value of the model. A low degree of mental urbanisation reliably predicts dialect maintenance. The earlier indication from the correlations and plots that this was so have been solidly confirmed by the multiple linear regression analysis. This is similar to the tests for the other two 166
SOCIAL NETWORKS IN A RURAL SETTING linguistic scores. 7.4. LIFMOD as a Predictor of SSSCOR The second linguistic index score is SSSCOR, which, the reader will recall, was achieved by means of non-linguistic tasks, where the speaker was concentrating more on the task, than on his or her language use. SSSCOR = 0.225 + 0.0100 AGE Predictor Coef StDev T P Constant 0.22530 0.05703 3.95 0.000 AGE 0.010024 0.001465 6.84 0.000 S = 0.2539 R-Sq = 43.4% R-Sq(adj) = 42.5% The p-value is highly significant, and the r-squared value is reasonable. AGE is again a reliable predictor of dialect maintenance. Next we add the variable SEX. SSSCOR = 0.292 + 0.0101 AGE - 0.138 SEX Predictor Coef StDev T P Constant 0.29218 0.06298 4.64 0.000 AGE 0.010118 0.001420 7.12 0.000 SEX1 -0.13759 0.06204 -2.22 0.030 S = 0.2461 R-Sq = 47.7% R-Sq(adj) = 46.0% The p-value for SEX is significant at the 0.05 level, and the r-squared value has risen slightly. Again, SEX is a reasonable predictor of dialect maintenance. Next we add the variable AGE x SEX: SSSCOR = 0.351 + 0.00826 AGE - 0.257 SEX + 0.00370 AGE x SEX Predictor Coef StDev T P Constant 0.35075 0.07695 4.56 0.000 AGE 0.008263 0.002000 4.13 0.000 SEX -0.2567 0.1099 -2.34 0.023 AGE x SEX 0.003697 0.002824 1.31 0.196 S = 0.2446 R-Sq = 49.2% R-Sq(adj) = 46.6% 167
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K ALJENAIE testing ground. To what
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R. INGHAM cognitive deficits. They
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S. KULKARNI 100 mean % std. score 9
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Page 124 and 125: S. KULKARNI Bapuji, B.R. (1994).
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