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Expanding the Reach of Health Campaigns: Can Community Organizations Serve as Viable Channels of Health Information?
Unformatted Document Text:  Expanding the Reach 18 issues. The second step of the regression equation revealed that effects of age on knowledge were both linear ( β = .38, p < .0001) and quadratic ( β = -.31, p < .0001). Examination of the means revealed that the relation between age and knowledge was positive till about the age of 60, after which it became negative. As might be expected, education was positively correlated with knowledge ( β = .45, p < .0001). Males were found to be less knowledgeable than females ( β = .09, p < .0001). Overall, demographic measures explained 23.4 percent of the variance in knowledge. In the third step, we added two media use variables – newspaper reading and television viewing – into the regression model. These variables accounted for an additional .7 percent of the variance. Whereas newspaper reading was positively associated with knowledge ( β = .05, p < .0001), television viewing was negatively associated with knowledge ( β = -.07, p < .0001). In the fourth step, we added health information seeking into the model. This variable explained an additional 3.17 percent of the variance ( β = .20, p < .0001). Furthermore, it subsumed the effects of newspaper reading – when it was added in the model, the effect of newspaper reading was rendered insignificant. The final step of the regression equation represents the test of our second hypothesis. In it, we introduced organizational membership as a predictor, which accounted for an additional .59 percent of the variance ( β = .08, p < .0001). Although the variance accounted for by this variable was small, it was noteworthy not only because of its statistical significance, but also because it was added after most of the other predictors were already introduced in the model. Hence, our second hypothesis was supported.

Authors: Stephens, Keri., Rimal, Rajiv. and Flora, June.
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Expanding the Reach 18
issues. The second step of the regression equation revealed that effects of age on knowledge
were both linear (
β
= .38, p < .0001) and quadratic (
β
= -.31, p < .0001). Examination of the
means revealed that the relation between age and knowledge was positive till about the age of
60, after which it became negative. As might be expected, education was positively correlated
with knowledge (
β
= .45, p < .0001). Males were found to be less knowledgeable than females
(
β
= .09, p < .0001). Overall, demographic measures explained 23.4 percent of the variance in
knowledge.
In the third step, we added two media use variables – newspaper reading and television
viewing – into the regression model. These variables accounted for an additional .7 percent of
the variance. Whereas newspaper reading was positively associated with knowledge (
β
= .05, p
< .0001), television viewing was negatively associated with knowledge (
β
= -.07, p < .0001).
In the fourth step, we added health information seeking into the model. This variable
explained an additional 3.17 percent of the variance (
β
= .20, p < .0001). Furthermore, it
subsumed the effects of newspaper reading – when it was added in the model, the effect of
newspaper reading was rendered insignificant.
The final step of the regression equation represents the test of our second hypothesis. In
it, we introduced organizational membership as a predictor, which accounted for an additional
.59 percent of the variance (
β
= .08, p < .0001). Although the variance accounted for by this
variable was small, it was noteworthy not only because of its statistical significance, but also
because it was added after most of the other predictors were already introduced in the model.
Hence, our second hypothesis was supported.


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