Regression analyses
REGRESSIONS, REGRESSION
Table Regression models explaining the three outcome variablesColumns in the table show estimates from different regressions. Coefficient estimates for reference categories are zero. Model 1 included only the dummy variables for conditions. Model 2 added other control variables. Ordinary least squares was used to estimate regressions in columns (1)-(4). Linear probability model was used to estimate regressions in columns (5)-(6). Condition 1: Brand logos + text, Condition 2: Brand logos + old text + old pictures, Condition 3: Brand logos + new text + new pictures, Condition 4: PP + old text + old pictures, Condition 5: PP + new text + new pictures. Negative affect was the average of scores in eight questions (1 to 5). Avoidant responses was the average of scores in five questions (1 to 5). Intentions to quit was a dummy variable that was 1 if the respondent intended to quit within six months or sooner, and 0 otherwise. The categories of ‘Past 30-day consumption’, ‘Smoking is addictive’, and ‘Smoking but not harming self’ were condensed to present the results more concisely. Since a large majority of the responses to harm perception question was ‘absolutely harmful’, a new variable ‘Cigarettes absolutely harmful’ was created as a dummy variable equal to 1 if ‘absolutely harmful’ was chosen, 0 if one of the other three responses was chosen. *** The first important result was that the coefficient estimates of the condition dummies were very similar in the two models. Estimates in Column [The estimates in Column [Column [