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Demographic Diversity and Effective Message Design: How Behavioral Theory Can Help
Unformatted Document Text:  Diversity effects 15 demographic segments would necessarily be based on rather small samples. Thus, we subjected the overall effectiveness ratings to an analysis of variance with ethnicity, gender and age as independent variables. We included dummy variables representing the nine experimental conditions as covariates to control for differences between the conditions. This analysis showed a main effect of gender, F(1, 661) = 6.61, p < .01. In contrast to previous research on perceived effectiveness of anti-drug ads (Fishbein et al., 2000), female adolescents had a somewhat more positive evaluation of the ads’ effectiveness (M = 3.69) than male adolescents (M = 3.50). No other main or interaction effects of ethnicity, gender and age were found. Effects on actual effectiveness of anti-drug ads. We used analyses of variance with condition (control, experimental), ethnicity, gender and age as independent variables, and outcome and normative beliefs, attitude, perceived norms, self-efficacy and intention as dependent variables. We were particularly interested in interactions that involve condition, because these interactions suggest that anti-drug ads have different effects on demographic diverse segments. Consistent with our analyses of diversity risk effects for which only data from the control condition were used, there were significant and substantial age effects. However, there were no interactions of any kind, including interactions between condition and ethnicity, gender and/or age. Finally, we tested the more general assumption that perceived message effectiveness is related to actual message effectiveness. To this end, we correlated the overall perceived effectiveness scores with each of the determinants of regular marijuana use. These correlations were small to moderate, ranging between |.15| and |.29|. Note, however, that this analysis is not optimal. To have more confidence in analyses of perceived effectiveness as a predictor of actual effectiveness one would need to correlate perceived effectiveness with changes in determinants

Authors: Fishbein, Martin., Cappella, Joseph. and Yzer, Marcus.
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Diversity effects 15
demographic segments would necessarily be based on rather small samples. Thus, we subjected
the overall effectiveness ratings to an analysis of variance with ethnicity, gender and age as
independent variables. We included dummy variables representing the nine experimental
conditions as covariates to control for differences between the conditions. This analysis showed a
main effect of gender, F(1, 661) = 6.61, p < .01. In contrast to previous research on perceived
effectiveness of anti-drug ads (Fishbein et al., 2000), female adolescents had a somewhat more
positive evaluation of the ads’ effectiveness (M = 3.69) than male adolescents (M = 3.50). No
other main or interaction effects of ethnicity, gender and age were found.
Effects on actual effectiveness of anti-drug ads. We used analyses of variance with
condition (control, experimental), ethnicity, gender and age as independent variables, and
outcome and normative beliefs, attitude, perceived norms, self-efficacy and intention as
dependent variables. We were particularly interested in interactions that involve condition,
because these interactions suggest that anti-drug ads have different effects on demographic
diverse segments. Consistent with our analyses of diversity risk effects for which only data from
the control condition were used, there were significant and substantial age effects. However,
there were no interactions of any kind, including interactions between condition and ethnicity,
gender and/or age.
Finally, we tested the more general assumption that perceived message effectiveness is
related to actual message effectiveness. To this end, we correlated the overall perceived
effectiveness scores with each of the determinants of regular marijuana use. These correlations
were small to moderate, ranging between |.15| and |.29|. Note, however, that this analysis is not
optimal. To have more confidence in analyses of perceived effectiveness as a predictor of actual
effectiveness one would need to correlate perceived effectiveness with changes in determinants


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