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

SOCIAL NETWORKS IN A RURAL SETTING
7.2. Regression Analysis: Building a Model
Using linear regression analyses, we will build a model that adds first age,
then sex, then the interaction between age and sex into the equation. After
that, the relevant social variables will be added in order to test their
significance as predictors of language use.
The two results to take note of are:
1) The P-value (probability of the variation occurring by chance), the lower
this figure the more significant the result. The minimum accepted for most
studies is 0.05 (or 5% possibility of the variation occurring by chance).
2) The (adjusted) r-squared value, a measure of how much of the variation in
the dependent variable is accounted for by the independent variable(s).
First we will test LIFMOD, the index score for degree of mental urbanisation,
as a predictor of PHOVAR, the first of the linguistic scores, which was
achieved by means of the picture list. First, the variable AGE will be entered,
and the output examined. Next, the variable SEX will be entered, then the
variable AGE x SEX, and finally, the predictor variable LIFMOD. This has
the effect of ‘smoothing out’ the effect of the first three, and testing the last as
a predictor of dialect maintenance. Simply finding that the oldest people in the
community speak the dialect the most, or that men use the dialect more than
women is not surprising. The research question requires that we find out
which, if any, of the other social variables predict language maintenance.
7.3. LIFMOD as a Predictor of PHOVAR
The regression equation is
PHOVAR = 18.2 + 0.569 AGE
Predictor Coef StDev T P
Constant 18.168 2.625 6.92 0.000
AGE 0.56890 0.06742 8.44 0.000
S = 11.69 R-Sq = 53.9% R-Sq(adj) = 53.1%
The P value is highly significant, and the adjusted r-squared value is
reasonable. This confirms that AGE is a strong predictor of dialect use. Next
we add the variable SEX:
165