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Table 4 Regressions with objective (ratings from the masked cigarette) and expected ratings predicting ratings of experienced enjoyment, quality and harshness when the brand name was known

From: Influence of premium vs masked cigarette brand names on the experienced taste of a cigarette after tobacco plain packaging in Australia: an experimental study

  Perceived Enjoyment of the branded cigarette (n = 70)a
  Unadjusted Model Adjusted ModelA, B
Predictor variables b [95% CI] β b [95% CI] β
Objective Enjoyment (ratings of the masked cigarette) 0.20 [0.01, 0.39] .24* 0.23 [0.05, 0.41] .28*
Expected Enjoyment 0.32 [0.11, 0.52] .34** 0.31 [0.11, 0.51] .34**
Cigarette order     −9.29 [−17.59, − 1.00] −.24*
Difference in number of puffs between conditions     7.58 [−.19, 15.36] .20
  Perceived Quality of the branded cigarette (n = 71)b
  Unadjusted Model Adjusted Model
Predictor variables b [95% CI] β b [95% CI] β
Objective Quality (ratings of the masked cigarette) 0.06 [−0.17, 0.28] .06 0.08 [−0.13, 0.30] .09
Expected Quality 0.40 [0.13, 0.66] .35** 0.46 [0.21, 0.72] .42**
Cigarette order     −7.64 [−16.40, 1.12] −.19
Difference in number of puffs between conditions     10.82 [2.43, 19.20] .28*
  Perceived Harshness of the branded cigarette (n = 71)c
  Unadjusted Model Adjusted ModelC, D, E, F
Predictor variables b [95% CI] β b [95% CI] β
Objective Harshness (ratings of the masked cigarette) 0.08 [−0.18, 0.33] .07 0.11 [−0.14, 0.36] .10
Expected Harshness 0.18 [−0.05, 0.40] .19 0.19 [−0.02, 0.41] .21
Cigarette order     12.68 [.80, 24.55] .25*
Difference in number of puffs between conditions     −1.62 [− 12.60, 9.37] −.03
  1. Note: There was no indication of multicollinearity in any model, with correlations between objective measures being low: r = .24 (p = .043) for enjoyment, r = .35 (p = .003) for quality, and r = .09 (p = .454) for harshness. VIF values from the adjusted regression models ranged from 1.02 to 1.18, further suggesting that multicollinearity was not a concern
  2. aUnadjusted model: R2 = .22, F(2,67) = 9.19, p < .001); Adj. model: R2 = .31, F(4,65) = 7.17, p < .001)
  3. bUnadjusted model: R2 = .14, F(2,68) = 5.70, p = .005); Adj. model: R2 = .24, F(4,66) = 5.32, p = .001)
  4. cUnadjusted model: R2 = .04, F(2,68) = 1.54, p = .223); Adj. model: R2 = .11, F(4,66) = 1.94, p = .115)
  5. ASensitivity 1: Objective enjoyment no longer predicted perceived enjoyment when the brand variant name was known (Adj. model: β = .18, t(52) = 1.38, p = .174)
  6. BSensitivity 4: Objective enjoyment no longer predicted perceived enjoyment when the brand variant name was known (Adj. model: β = .17, t(59) = 1.43, p = .157)
  7. CSensitivity 1: Expected harshness no longer tended to predict perceived harshness when the brand variant name was known (Adj. model: β = .19, t(53) = 1.41, p = .164)
  8. DSensitivity 2: Objective harshness tended to predict perceived harshness when the brand variant name was known (Adj. model: β = .22, t(56) = 1.73, p = .089). Expected harshness no longer tended to predict perceived harshness when the brand variant name was known (Adj. model: β = .14, t(56) = 1.11, p = .272)
  9. ESensitivity 3: Expected harshness no longer tended to predict perceived harshness when the brand variant name was known (Adj. model: β = .18, t(61) = 1.53, p = .132)
  10. FSensitivity 4: Expected harshness no longer tended to predict perceived harshness when the brand variant name was known (Adj. model: β = .19, t(60) = 1.56, p = .123)
  11. ** p < .01. * p < .05, † p < .10