Reading Working Papers in Linguistics 4 (2000) - The University of ...

More documents

Recommendations

Info

J. MARSHALL 7.6. SOCNET as a Predictor of PHOVAR The penultimate output from the PHOVAR model above was: 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% Now we add SOCNET, in order to test its value as a predictor of dialect maintenance: PHOVAR = 29.5 + 0.469 AGE - 13.1 SEX + 0.227 AGE x SEX - 0.829 SOCNET Predictor Coef StDev T P Constant 29.504 6.717 4.39 0.000 AGE 0.46920 0.09238 5.08 0.000 SEX -13.106 5.043 -2.60 0.012 AGE x SE 0.2274 0.1295 1.76 0.084 SOCNET -0.8288 0.9738 -0.85 0.398 S = 11.22 R-Sq = 59.6% R-Sq(adj) = 56.8% SOCNET is not a significant predictor of PHOVAR, even at the 0.05 level. The r-squared value of the model has been lowered by the addition of SOCNET. Next we consider SOCNET as a predictor of SSSCOR, the linguistic index score achieved during the non-linguistic tasks. Again, the model will be re-used from above, with SOCNET added at the end: 7.7. SOCNET as a Predictor of SSSCOR 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% 170
SOCIAL NETWORKS IN A RURAL SETTING Here we add SOCNET to the model, to test it as a predictor of dialect maintenance: SSSCOR = 0.446 + 0.00845 AGE - 0.254 SEX + 0.00374 AGE x SEX - 0.0163 SOCNET Predictor Coef StDev T P Constant 0.4462 0.1470 3.03 0.004 AGE 0.008447 0.002022 4.18 0.000 SEX -0.2543 0.1104 -2.30 0.025 AGE x SE 0.003736 0.002835 1.32 0.193 SOCNET -0.01626 0.02132 -0.76 0.449 S = 0.2455 R-Sq = 49.7% R-Sq(adj) = 46.2% SOCNET is not a significant predictor of SSSCOR, even at the 0.05 level. The r-squared value of the model is again lowered by the addition of this variable. Lastly we test SOCNET as a predictor of LEXREC, the linguistic score achieved during a test of knowledge of fifty lexical items. 7.8. SOCNET as a Predictor of LEXREC The model from above was: LEXREC = 26.9 + 0.349 AGE - 0.38 SEX + 0.0083 AGE x SEX Predictor Coef StDev T P Constant 26.879 2.117 12.69 0.000 AGE 0.34908 0.05504 6.34 0.000 SEX -0.377 3.025 -0.12 0.901 AGE x SEX 0.00828 0.07771 0.11 0.916 S = 6.731 R-Sq = 58.4% R-Sq(adj) = 56.2% Now we add SOCNET in order to test its value as a predictor of lexical maintenance: LEXREC = 24.6 + 0.345 AGE - 0.44 SEX + 0.0074 AGE x SEX + 0.387 SOCNET Predictor Coef StDev T P Constant 24.606 4.050 6.08 0.000 AGE 0.34471 0.05570 6.19 0.000 SEX -0.435 3.041 -0.14 0.887 AGE x SE 0.00736 0.07810 0.09 0.925 SOCNET 0.3873 0.5872 0.66 0.512 S = 6.764 R-Sq = 58.7% R-Sq(adj) = 55.8% 171
Page 1 and 2:
Reading Working Papers in Linguisti
Page 3:
Reading Working Papers in Linguisti
Page 6 and 7:
K ALJENAIE testing ground. To what
Page 8 and 9:
K ALJENAIE causative form is basica
Page 10 and 11:
K ALJENAIE person diagram, the two
Page 12 and 13:
K ALJENAIE 3.2 Data Analysis The sa
Page 14 and 15:
K ALJENAIE Table 4 Osama’s longit
Page 16 and 17:
K ALJENAIE The asterisk indicates t
Page 18 and 19:
K ALJENAIE which is the transitive
Page 20 and 21:
K ALJENAIE confirming Hyams (1986)
Page 22 and 23:
K ALJENAIE Table 12 Sara’s realiz
Page 24 and 25:
K ALJENAIE Table 15 Number of Infle
Page 26 and 27:
K ALJENAIE masculine forms are cons
Page 28 and 29:
K ALJENAIE Motar, A. (1969). Khasai
Page 30 and 31:
P. COUTSOUGERA Plural Plural Singul
Page 32 and 33:
P. COUTSOUGERA e.g. [peδja'] *[pe
Page 34 and 35:
P. COUTSOUGERA Tableau 1: Opacity i
Page 36 and 37:
P. COUTSOUGERA As can be seen, in r
Page 38 and 39:
P. COUTSOUGERA 6.1 Selection of the
Page 40 and 41:
P. COUTSOUGERA /alevr-I-a/ Tableau
Page 42 and 43:
P. COUTSOUGERA different areas of t
Page 44 and 45:
P. COUTSOUGERA ruled out as it viol
Page 46 and 47:
P. COUTSOUGERA operation McCarthy p
Page 48 and 49:
P. COUTSOUGERA Tableau 6: Applicati
Page 50 and 51:
P. COUTSOUGERA References Anderson,
Page 52 and 53:
R. INGHAM cognitive deficits. They
Page 54 and 55:
R. INGHAM 2. Subjects and hypothese
Page 56 and 57:
R. INGHAM good as their CA matches
Page 58 and 59:
R. INGHAM 100 90 80 70 60 50 40 30
Page 60 and 61:
R. INGHAM Did the [+LD] group perfo
Page 62 and 63:
R. INGHAM using the Wilcoxon ranked
Page 64 and 65:
R. INGHAM representations but also
Page 66 and 67:
R. INGHAM Van der Lely, H., & L. St
Page 68 and 69:
P. KERSWILL & A. WILLIAMS some vene
Page 70 and 71:
P. KERSWILL & A. WILLIAMS far soone
Page 72 and 73:
P. KERSWILL & A. WILLIAMS outside l
Page 74 and 75:
P. KERSWILL & A. WILLIAMS 3. Intern
Page 76 and 77:
P. KERSWILL & A. WILLIAMS • Varia
Page 78 and 79:
P. KERSWILL & A. WILLIAMS Despite s
Page 80 and 81:
P. KERSWILL & A. WILLIAMS naturalne
Page 82 and 83:
P. KERSWILL & A. WILLIAMS apparent
Page 84 and 85:
P. KERSWILL & A. WILLIAMS Summary f
Page 86 and 87:
P. KERSWILL & A. WILLIAMS Table 2 T
Page 88 and 89:
P. KERSWILL & A. WILLIAMS regard it
Page 90 and 91:
P. KERSWILL & A. WILLIAMS The ‘gr
Page 92 and 93:
P. KERSWILL & A. WILLIAMS Table 4 (
Page 94 and 95:
P. KERSWILL & A. WILLIAMS sociodemo
Page 96 and 97:
P. KERSWILL & A. WILLIAMS Bardovi-H
Page 98 and 99:
P. KERSWILL & A. WILLIAMS språkfö
Page 100 and 101:
S. KULKARNI in the Indian caste hie
Page 102 and 103:
S. KULKARNI ideologies, each emphas
Page 104 and 105:
S. KULKARNI century. The Sabha was,
Page 106 and 107:
S. KULKARNI The main study includes
Page 108 and 109:
S. KULKARNI 4. Quantitative Analyse
Page 110 and 111:
S. KULKARNI comparatively lower (in
Page 112 and 113:
S. KULKARNI Table 3: Percentage use
Page 114 and 115:
S. KULKARNI showing an almost compl
Page 116 and 117:
S. KULKARNI 50 mean % std. score 40
Page 118 and 119:
S. KULKARNI 100 mean % std. score 9
Page 120 and 121:
S. KULKARNI Marathi the kin term /m
Page 122 and 123:
S. KULKARNI not by the less-privile
Page 124 and 125: S. KULKARNI Bapuji, B.R. (1994).
Page 126 and 127: S. KULKARNI Sastry, J.V. (1993). Re
Page 128 and 129: J. MARSHALL 2. Change versus Resist
Page 130 and 131: J. MARSHALL have become more expose
Page 132 and 133: J. MARSHALL anthropologists. As man
Page 134 and 135: J. MARSHALL for data such as Milroy
Page 136 and 137: J. MARSHALL To some extent, [networ
Page 138 and 139: J. MARSHALL these close-knit ties a
Page 140 and 141: J. MARSHALL Life mode 1. Life modes
Page 142 and 143: J. MARSHALL There are, however, som
Page 144 and 145: J. MARSHALL can choose a high level
Page 146 and 147: J. MARSHALL Pedersen therefore, lik
Page 148 and 149: J. MARSHALL case that these people
Page 150 and 151: J. MARSHALL 6. Research Question an
Page 152 and 153: J. MARSHALL ‘are high in frequenc
Page 154 and 155: J. MARSHALL 6.5. Morphological Vari
Page 156 and 157: J. MARSHALL 4 Do you work/go to sch
Page 158 and 159: J. MARSHALL 8) I never eat brose or
Page 160 and 161: J. MARSHALL mentioned, a ‘real ti
Page 162 and 163: J. MARSHALL 3. LEXREC. This is an i
Page 164 and 165: J. MARSHALL which comes nearest to
Page 166 and 167: J. MARSHALL LIFMODLEXREC 60 50 40 L
Page 168 and 169: J. MARSHALL the different tests wer
Page 170 and 171: J. MARSHALL PHOVAR = 21.1 + 0.573 A
Page 172 and 173: J. MARSHALL There is no significant
Page 176 and 177: J. MARSHALL SOCNET is again not sig
Page 178 and 179: J. MARSHALL integrated sociolinguis
Page 180 and 181: I. SINKA, M. GARMAN AND C. SCHELLET
show all

Reading Working Papers in Linguistics 4 (2000) - The University of ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?