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Table 4 Multivariate association between select characteristics and usual consumption levels (number of drinks consumed on a typical day) for Australian participants a

From: The association between exposure to social media alcohol marketing and youth alcohol use behaviors in India and Australia

  Facebook n = 246
B (s.e.) p
YouTube n = 151
B (s.e.) p
Twitter n = 65
B (s.e.) p
Model Statistics F(9, 237) 7.205 p < .001, AR2 .185 F(9, 143) 3.597 p < .001, AR2 .133 F(7, 63) 7.731 p < .001, AR2 .402
Model variables
 Age −.096 (.097) .328 −.200 (.137) .146 −.128 (.180) .478
 Gender −1.76 (.387) <.001* −1.821 (.478) < .001* −2.957 (.672) < .001*
 Education .769 (.374) .041 1.220 (.512) .019 1.173 (.677) .092
 Alcohol brand logos on merchandise −.308 (.376) .919 .312 (.486) .522 .439 (.686) .824
 Attended alcohol events advertised on SNS .482 (.370) .194 .110 (.466) .814 −.237 (.677) .728
 Sharing own alcohol-related content on SNS 2.448 (.573) < .001* 1.453 (.854) .091 4.484 (1.320) .001*
 Suggestions on SNS to like/follow alcohol-related content −.297 (.147) .044 −.356 (.206) .086
 Perceived increasing trends in alcohol advertising −.133 (.388) .732 −.808 (.437) .069
 Friends sharing alcohol-related information on SNS −.263 (.207) .205 −.231 (.265) .384
 Noticing alcohol-related content on SNS −.032 (.192) .869
  1. AR2 adjusted r square, B unstandardized coefficient, s.e. standard error
  2. a non-drinkers coded as 0 drinks
  3. *p ≤ .008