Regression Models with Employment Status
The results from the regression model that combines employment status
with other key demographic characteristics are presented in Tables A2,
A3, A4 and A5. A full discussion on these findings is articulated in Chapter
Four. The bottom line on employment status reveals that other characteristics,
both complimentary and adjacent, are far more robust in determining the
likelihood of Internet access either at home or elsewhere. While employment
status and type are significant factors in the bivariate analysis of the
level of access to the Internet, when other demographic characteristics
are accounted for, employment status remains for the most part inconclusive.
TABLE A2a
Logistic Regression Table
Internet Access with Key Demographics and Others
| |
In the past three months |
At home |
| exp (B) |
sig. |
exp (B) |
sig. |
| 1999 |
| Language |
0.5049 |
(-) |
0.4988 |
(-) |
| Emplment |
0.9674 |
n.s. |
0.8052 |
n.s. |
| Education |
1.3113 |
(+) |
1.1783 |
(+) |
| Age |
0.64 |
(-) |
0.7904 |
(-) |
| Income |
1.3836 |
(+) |
1.3868 |
(+) |
| Sex |
0.7266 |
(-) |
0.7839 |
(-) |
| Rural |
1.2798 |
(+) |
1.0813 |
n.s. |
| 2000 |
| Language |
0.593 |
(-) |
0.6158 |
(-) |
| Emplment |
0.6924 |
(-) |
0.7724 |
n.s. |
| Education |
1.3222 |
(+) |
1.2168 |
(+) |
| Age |
0.5649 |
(-) |
0.7033 |
(-) |
| Income |
1.3518 |
(+) |
1.3786 |
(+) |
| Sex |
0.7595 |
(-) |
0.6735 |
(-) |
| Rural |
1.4356 |
(+) |
1.227 |
(+) |
note:
Language, employment, sex and rural are categorical variables;
Language: English (0); French (1)
Employment: other (0); unemployed (1)
Sex: men (0); women (1)
Rural: rural (0); urban (1) |
|