Objectives. We used a validated copy test method to examine the effectiveness of 8 types of antismoking advertisements representing health, counterindustry, and industry approaches. We tested the hypothesis that health ads about tobacco victims can lower most adolescents’ intent to smoke if the ads elicit disgust and anti-industry feelings rather than fear. We hypothesized null effects for adolescents with conduct disorder because of their abnormally low empathy.

Methods. Ninth-grade students from 8 California public schools (n=1725) were randomly assigned to view 1 of 9 videotapes containing a TV show with ads that included either a set of antismoking ads or a set of control ads. Participants completed baseline measures assessing personality traits and postexposure measures assessing smoking intent, feelings, beliefs, and ad evaluations.

Results. Ads focusing on young victims suffering from serious tobacco-related diseases elicited disgust, enhanced anti-industry motivation, and reduced intent to smoke among all but conduct-disordered adolescents. Counterindustry and industry ads did not significantly lower smoking intention.

Conclusions. Sponsors of tobacco use prevention ad campaigns should consider using ads showing tobacco-related disease and suffering, not just counterindustry ads. Ads should be copy tested before airing.

Twenty-four US states have initiated tobacco use prevention advertising campaigns.1 Different message themes and styles of execution are used,2,3 and there is controversy over which approaches work best.1,48 Past studies have asked adolescents their opinions of anti-smoking ads, and adolescents have generally preferred health-themed ads evoking strong negative emotions.915 We conducted a randomized controlled trial or “copy test” to examine how exposure to different ad types affects adolescents’ intention to smoke relative to a control (no antismoking ad exposure) condition.

In a copy test, subjects are shown an ad and then asked to answer questions about their product-related feelings, beliefs, and intentions. These responses are statistically compared either with the same subjects’ baseline (preexposure) responses or with the responses of similar people who were randomly assigned to a no-exposure control condition. We used the latter approach. We tested 8 ad types representing common health, counter–tobacco industry, and tobacco industry approaches. We paid particular attention to health ads, which are often referred to as “fear appeals” and are especially controversial.16,17

Fear appeals have at least 2 potential limitations. First, evoking fear among adolescents who feel unable to cope may lead to maladaptive responses such as denial of the problem.18,19 Further, highlighting risks among adolescents who feel invincible may serve to increase the attractiveness of smoking as “forbidden fruit.”17,20,21 Health appeals need not evoke fear, though; they may evoke disgust.22,23 Research indicates that associating smoking with disgust is perhaps the single most effective way to make smoking socially unacceptable and encourage antismoking activism.24,25 Disgust is what people feel in response to an immoral act,26,27 and it motivates action.28 Whereas fear is associated with a desire to escape or hide, disgust is associated with a desire to expel or obliterate.29,30 Research also suggests how disgust-provoking ads can be created: by showing innocent victims suffering, empathy and moral indignation are elicited.22,23,31,32 Hence, our first hypothesis was this: among adolescents, antismoking ads that focus on victims suffering from smoking’s serious health effects will elicit more disgust than other ad types and will increase anti-industry motivation and lower intention to smoke relative to the control (no antismoking ad exposure) condition.

Three types of ads fit this description: ads focusing on disease and suffering, ads focusing on a dying parent, and ads focusing on environmental tobacco smoke (Table 1). Counterindustry ads focus on the victimizers, not on the victims; they directly attack and ridicule industry executives. We could find no research regarding whether ads that directly attack tobacco executives evoke empathy for victims or disgust for tobacco executives. However, researchers have compared ads that explicitly state conclusions versus ads that do not.33 The findings indicate that, if people are interested in and knowledgeable about an issue, it is preferable to give them the facts and let them draw their own conclusions. The implication is that indirect attacks on the tobacco industry, focusing on tobacco victims, might be preferable to direct attacks if youths are knowledgeable about and interested in the issue.

Research also indicates that adolescents’ reactions to ads may be moderated by their personality traits. Past studies have focused on sensation seeking or the need for varied, novel, and complex experiences.34 This trait both predicts drug use3537 and moderates response to antidrug ads.3841 Conduct disorder, “a repetitive and persistent pattern of behavior in which the basic rights of others and major age-appropriate social norms or rules are violated,”42(p1469) is even more strongly associated with adolescent smoking.43,44 Conduct-disordered youths are up to 4 times as likely to smoke as youths without conduct disorder.45,46 Further, conduct-disordered youths may be less responsive than others to ads that focus on victims, inasmuch as people with conduct disorder have abnormally low empathy.4749 Thus, our second hypothesis was that these antismoking ads would be expected to have null effects on conduct-disordered adolescents. However, we considered adolescents without conduct disorder, who constituted 81% of our sample, to be a meaningful target for antismoking interventions; for instance, 39% of them had tried smoking.

We obtained 150 English-language anti-smoking TV ads that had aired from 1997 to 2001 in campaigns focused on youths or on a general audience. Most were from Massachusetts (24%), the American Legacy Foundation (23%), Florida (16%), California (10%), or Philip Morris (7%). We identified 3 common message themes (social, health, and counterindustry) and 8 ad types, or unique combinations of theme(s), subtheme, and execution (speaker and tone; Table 1). The social message theme was the least common, and it was primarily used by the tobacco industry; for example, Philip Morris used ads showing social acceptance of nonsmokers11,50 and Lorillard used ads focusing on the unattractive cosmetic effects of smoking.1

We conducted an ad screening study to select 3 similar ads of each type for the copy test. We created 14 videotapes, each containing 10 or 11 ads. Each videotape was viewed by about 35 ninth-grade students (aged 14–15 years, total n = 466) from 2 schools in the area where the copy test would be conducted. After seeing an ad twice, the students were asked to appraise it. They were instructed to put a check mark next to each theme (social, health, counterindustry) and subtheme that the ad contained. They were also asked to judge the speaker’s age (youth or adult), and the ad’s emotional tone (they were asked, “Did the antismoking advertising make you feel angry? Sad? Disgusted? Fearful? Amused? Happy? Upbeat? Like laughing?”). For each ad type, we identified 3 ads that were judged by the majority of students to have the characteristic (intended) theme(s) and subtheme and no others and that did not differ significantly from each other in terms of emotional tone or speaker’s age. None of the ads chosen for the copy test were fear inducing, according to the vast majority of students who viewed them in the ad screening study. Sponsor identifications were removed.

Participants in the copy test were 1725 male and female ninth-grade students (aged 14–15 years; 42% White, 46% Hispanic, 12% Asian) at 8 public high schools in middle- to lower-middle-class neighborhoods in southern California. Participation was voluntary but exceeded 90%. About 191 individuals viewed each videotape. All videotapes were shown at every school.

The copy test was conducted in spring 2002. During each class period, about 40 students were released from classes and randomly assigned to 1 of 2 empty rooms. The participants completed a baseline questionnaire asking about personality traits, smoking behavior, and demographics. (Intent was not assessed at baseline because a pilot test showed that asking about intent at baseline contaminated the posttest intent measure.) They were then shown a 10-minute videotape of the TV show The Price is Right. Embedded in the commercial breaks were either 3 anti-smoking ads of a particular type or 3 control ads that were non–tobacco-related public service announcements. Each ad appeared twice, in 2 separate commercial breaks, providing 6 total exposures so cumulative ad effects could be assessed.51,52 The participants also saw several non–tobacco-related commercials that had aired on The Price is Right.53 After watching the videotape, participants completed the outcome and ad measures.

The design was a 2-factor experiment. The first factor was ad type with 9 levels, which we manipulated by randomly assigning participants to view 1 of 9 randomly selected videotapes. The second factor was conduct disorder (present vs absent). Comparisons were made between groups, that is, participants who saw antismoking ads were compared with those who saw control ads. Because participants were randomly assigned to groups and there were no demographic differences between groups, covariates did not affect the results and were dropped.54

At baseline, in addition to measuring smoking behavior, we measured the personality traits that research indicates are most highly associated with youth smoking.43,45,46,55 We used Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition56 scales for aggression,37,57 attention deficit–hyperactivity disorder,58,59 and conduct disorder57,60,61; the Children’s Manifest Anxiety Scale, Revised55,6265; the Center for Epidemiological Studies Depression Scale57,63,64,6668; Rosenberg’s Self-Esteem Scale55,69; and the Brief Sensation Seeking Scale.35,38 Standard diagnostic criteria were used.

The outcome measures consisted of intent to smoke and smoking-related beliefs. After participants viewed the videotape, we asked them to indicate their smoking intent20,70,71 by agreeing or disagreeing with the following statements: “In the future, I might smoke one puff or more of a cigarette,” “I might try out cigarette smoking for a while” and “If one of my best friends were to offer me a cigarette, I would smoke it.” (A scale of 1 [strongly disagree] to 5 [strongly agree] was used throughout unless otherwise indicated). We calculated the mean intent-to-smoke score, which was 1.66. We then classified each participant whose responses were above the mean as having a smoking intent and computed the percentage of participants who had a smoking intent; however, the mean score was more precise because it used the full response range.

We also measured beliefs that previous research suggested might be affected by anti-smoking ads and might correspondingly affect smoking intent.20,72 A key measure was anti-industry motivation: “I think we can stop cigarette companies from trying to get people to smoke,” “If we all try to stop cigarette companies, we can make a difference,” and “If I try to stop cigarette advertising, fewer people would smoke.”

The antismoking ad measures consisted of ad recall, recall of the number of ad spots seen, message theme and subtheme, ad tone, speaker age, and judged ad efficacy and ad sensation value. Participants were asked, “In the commercial breaks of the TV show you just saw, did you see any antismoking ad(s)?” and “How many antismoking ads did you see?” If participants reported seeing antismoking ads, they were asked, “In your opinion, was this advertising effective at stopping young people from smoking?” (0 = not effective, 1 = moderately effective, 2 = highly effective)?10,20 Regarding sensation value, they were asked, “Was the advertising emotional? Unusual or unique? Exciting? Dramatic in that it tells a strong story? Powerful, forceful, or impactful? Intense or extreme?” (Reliability, or consistency of participants’ answers to 2 or more similar questions, was measured with the statistic α; for sensation value, α = .82.3840) Regarding emotional tone (negative or positive), they were asked, “Did the antismoking advertising make you feel angry? Sad? Disgusted? Amused? Happy? Upbeat? Like laughing?”73 (for negative tone [first 3 items], α = .76; for positive tone, α = .79). They were also queried about theme, sub-theme, and speaker age.

Finally, all participants were asked what they thought the study was about (responses were coded by 2 judges with 90% agreement). Nine percent guessed the study might be about antismoking ads, but removing these participants’ responses from the analyses did not affect the results, so they were retained.

For the statistical analyses, we used SPSS (SPSS Inc, Chicago, Ill), setting type I error to 5%. To analyze outcome measures, we used 2-factor (ad type, conduct disorder) analyses of variance (ANOVAs). Then we conducted 1-factor (ad type) ANOVAs within each conduct-disorder subgroup. Next we conducted pairwise comparisons of each ad type versus the control, using the Dunn-Sidak critical t value (8 comparisons, 2-tailed).74 For anti-smoking ad measures (e.g., judged ad efficacy), we compared each antismoking ad type with all others, using the Tukey-Kramer critical t value,74 and ranked ad types from highest (1) to lowest. If 2 ad types showed an identical pattern of similarity/dissimilarity to all others, they received the same rank. (This approach is similar to using unique superscripts to designate dissimilar means.) To examine whether an ad type had a relatively negative or positive tone, we used repeated measures. To compare proportions, we used χ2. To compute odds ratios for intent, we used binary logistic regression with ad category as a categorical covariate (simple contrasts vs control). We used multivariate linear regression to relate beliefs and personality traits to smoking intent, and univariate linear regression to compare participants’ ad efficacy judgments with the ads’ actual effectiveness at changing smoking intent (change in intent = control mean –ad type mean).

About 93% of the participants exposed to antismoking advertising recalled seeing such ads. They recalled seeing, on average, 3.4 spots, slightly more than the 3 they actually saw, probably because of ad repetition. (Conduct-disordered participants and current smokers were less likely to report having seen the antismoking ads. When we dropped participants who failed to report seeing the antismoking ads, the ad effects discussed below became stronger. We have reported the findings for the full sample because they are more generalizable.) Table 2 shows the percentages of participants who recalled seeing each message theme. The vast majority of participants said they saw the message theme(s) each ad type was intended to convey; significantly fewer thought another message was conveyed.

The beliefs we measured were associated with smoking intent, but not always in the expected negative direction (Table 3). Beliefs about the severity of the health risks of smoking and vulnerability to social and marketing pressures were positively associated with intent. Most of the personality traits we measured were associated with smoking intent, but conduct disorder was more strongly associated with intent than were the other traits. Further analyses showed that conduct-disordered participants were substantially more likely than participants without conduct disorder to have smoked in their lifetime (68% vs 39%; χ2 = 89.16, P < .01) and in the previous month (36% vs 13%; χ2 = 93.59, P < .01).

The 2-factor ANOVA on smoking intent revealed an ad type main effect (F[8,1707] = 2.16, P < .05), a conduct disorder main effect (F[1,1707] = 154.41, P < .01), and an ad type × conduct disorder 2-way interaction (F[8,1707] = 2.28, P < .05). Among all participants, no antismoking ad type lowered smoking intent (vs control condition). Among participants without conduct disorder, ads portraying disease and suffering significantly lowered mean smoking intent (vs control condition) and also reduced the proportion of participants who indicated intent to smoke by 42% (from 38% to 22%); no other ad type did so (Table 4). Among conduct-disordered participants, ad type did not significantly affect intent. In post hoc analyses, we verified that no other personality trait interacted with ad type to influence intent or any other outcome.

A 2-factor ANOVA on disgust revealed an antismoking ad main effect (F[7,1418] = 4.72, P < .01), a conduct disorder main effect (F[1,1418] = 6.93, P < .01), and a 2-way interaction (F[7,1418] = 3.39, P < .01). Among participants without conduct disorder, ad type influenced disgust (F[7,1163] = 18.10, P < .01), and ads depicting disease and suffering induced more disgust than any other ad type. Among conduct-disordered participants, ad type did not affect disgust (F[7255] = 1.10, P = .36).

A 2-factor ANOVA on anti-industry motivation showed main effects for ad type (F[8,1674]=3.27, P<.01) and for conduct disorder (F[1,1674]=47.86, P<.01) but no interaction (F[8,1674]=1.42, P=.18). However, follow-up analyses showed that ads portraying disease and suffering (vs control ads) significantly enhanced anti-industry motivation only among participants without conduct disorder (Table 4). Ad type did not affect other beliefs.

To summarize, for participants without conduct disorder, ads depicting disease and suffering engendered disgust and anti-industry motivation, lowering smoking intent. Thus, we conducted standard regression-based tests75 to verify that disgust and anti-industry motivation mediated the ad effects on intent. Disgust was predictive of anti-industry motivation (B=.13, SE=.02, t[1136]=6.30, P<.01, adjusted R2=.03) and anti-industry motivation was predictive of intent (B=–.12, SE=.02, t[1365]=–5.23, P<.01, adjusted R2=.02). The ad type effect on anti-industry motivation (B=–.04, SE=.01, t[1202]=–3.10, P=.002, adjusted R2=.01) became nonsignificant (B=–.02, SE=.01, t[1135]=–1.86, P=.06) when disgust was included as a covariate (B=.12, SE=.02, t[1135]=5.85, P<.001, adjusted R2=.04), indicating that disgust was a prime cause of anti-industry motivation. The effect of ad type on intent (B=.02, SE=.01, t[1229]=2.23, P=.03, adjusted R2=.003) became nonsignificant (B=.01, SE=.01, t[1194]=1.46, P=.15) when anti-industry motivation was included as a covariate (B= –.11, SE=.02, t[1194]=–4.56, P<.001, adjusted R2=.02), indicating that anti-industry motivation was a prime cause of lowered intent.

Judged ad efficacy was influenced by ad type (F[7,1404]=6.41, P<.01) and conduct disorder (F[1,1404]=11.35, P<.01); the interaction was nonsignificant (F[7,1404]=1.66, P=.11). In the total sample, ads depicting disease and suffering had a significantly higher mean efficacy rating than any other ad type. The percentages who judged disease-and-suffering ads to be at least moderately effective were as follows: total sample, 89%; participants without conduct disorder, 90%; conduct-disordered participants, 84%. Judged ad sensation value was also influenced by ad type (F[7,1410]=4.61, P<.05) and conduct disorder (F[1,1410]=11.27, P<.01); there was no interaction (F[7,1410]=1.60, P=.13). Ads showing a dying parent consistently received the highest sensation value rating.

The higher the judged efficacy of the ads, the more the ads lowered mean smoking intent (vs the control condition) in the total sample (r = 0.71, B = .47, SE = .19, t[6] = 2.48, P < .05) and among those without conduct disorder (r = 0.92, B = .68, SE = .12, t[6] = 5.80, P < .01), but not among those with conduct disorder (r = 0.13, B = .18, SE = .54, t[6] = 0.33, P = .75). Ad sensation value did not predict actual ad efficacy at lowering intent.

Overall, our findings suggest that it is difficult to create effective antismoking ads for adolescents. Seven of the 8 ad types failed to significantly lower adolescents’ intent to smoke (vs the control condition). The 1 ad type that significantly lowered most youths’ intent to smoke, the disease-and-suffering ad type, focused on young victims suffering from devastating tobacco-related diseases. However, even this ad type did not lower smoking intent among adolescents with conduct disorder, who constituted 19% of the sample.

One of the effective disease-and-suffering ads featured a young woman with severe emphysema who showed all the pills she must take to stay alive. Her doctor displayed a diseased lung and stated that emphysema is incurable. Another effective ad depicted a young man, a smoker, with a bad cough and the onset of heart disease. The ad demonstrated the dangerous fatty deposits accumulating in his arteries and stated, “Every cigarette is doing you damage.” Although these ads were clearly heath-related, they did not affect health risk beliefs or elicit fear about health risks. Instead, most youths apparently empathized with the victims and felt disgust and anti-industry motivation, which lowered their smoking intent (vs the control condition). Other research likewise indicates that showing innocent victims is an effective way to elicit empathy31,32 and disgust,22,23 and that disgust, not fear, motivates societal prohibitions and social activism.2427

We expected dying-parent and environmental-tobacco-smoke ads to perform similarly to disease-and-suffering ads. They did not. These 2 ad types elicited less disgust than disease-and-suffering ads, and they did not significantly increase anti-industry motivation or decrease smoking intent (vs the control condition). These ads emphasized that parents who smoke may harm their children by dying prematurely or by filling the air with toxins. Among adolescents, the parent-oriented messages may have lacked relevance. A previous copy test study20 indicated that environmental tobacco smoke ads can lower adolescents’ smoking intent if the ads convey that adolescent smokers risk peer disapproval. The social-message ads we tested—the type of ad often used by the tobacco industry—did not significantly lower smoking intent, perhaps because they did not credibly portray peer disapproval.20

The counter-industry ads elicited less disgust than the disease-and-suffering ads, and they did not significantly enhance anti-industry motivation or reduce smoking intent (vs the control condition). Previous studies indicate that counter-industry ads can, however, increase adolescents’ knowledge about the tobacco industry’s manipulative and deceptive tactics.20,76 Hence, the counterindustry ads may have set the stage for the disease-and-suffering ads. The disease-and-suffering ads increased anti-industry motivation without even mentioning the tobacco industry. It seems that our California participants already knew whom to blame for the tobacco victims’ suffering because of the state’s counterindustry campaign.

Of the 24 US states conducting tobacco use prevention media campaigns, 15 (63%) employ counterindustry ads.1 The decision to employ such ads may have been based on the reported successes of the Florida and American Legacy Foundation “truth” campaigns.50,7680 However, earlier studies examined the “truth” campaign while it was still novel. Our participants had seen counter-industry ads since 1990.2,81 Consistent with our own findings, Thrasher et al.82 found that the national “truth” campaign had no effects in California, Massachusetts, or Florida, where well-funded counterindustry campaigns had already aired. Those researchers concluded that “anti-industry ad campaigns may have diminishing returns” and that “other prevention strategies may be needed.”82(p287) Our findings support this conclusion and suggest that disease-and-suffering ads may be useful as a supplemental approach. Massachusetts supplemented its counterindustry ads with disease-and-suffering ads, apparently with much success.2,83 From 1996 through 1999, adolescent smoking declined significantly more in Massachusetts than regionally or nationally.84


We did not study the ads’ effects on smoking behavior. When major marketing firms conduct this type of copy test, they generally assess behavioral effects by offering participants free product samples immediately after ad exposure and seeing which products are chosen.85 This simulated choice behavior has been shown to predict in-market sales.8689 For ethical and other reasons, though, we could not offer adolescents cigarettes. Thus, our outcome measure was smoking intent. However, prospective studies have found that adolescents who do express intent to smoke are approximately 3 times as likely as those who do not to start smoking.70,9094

Another limitation is that when we classified ads into types, we considered only 2 executional factors: emotional tone and spokesperson age. Recent research indicates that testimonials may be especially effective,13 and we did not consider this factor. Thus, there is unexplained heterogeneity in the ad stimuli that likely affected the results. In other words, the results are partially a function of the specific ad exemplars used. There is no guarantee that other disease-and-suffering ads will work among adolescents, or that other ad types will necessarily fail. Health messages that elicit fear may do more harm than good among youths who feel unable to cope18,19 or who feel invincible and view smoking as forbidden fruit.17,20,21

Recommendations to Practitioners

We make the following recommendations regarding tobacco control mass media campaigns for adolescents. (1) Consider using health ads that depict young adult victims suffering from devastating tobacco-related diseases; (2) try to evoke empathy for the victims, disgust, and anti-industry motivation in executing these ads rather than evoking fear; (3) copy test each ad before airing95; (4) consider excluding highly troubled youths with conduct disorders, because their responses may be atypical, when screening copy test participants; (5) do not use an ad if it fails to produce statistically significant effects relative to the control or baseline condition, or produces adverse effects—try to improve it and then retest it.

In this study, participants’ judgments of ad efficacy were significantly correlated with the ads’ actual effectiveness at reducing smoking intent. However, research indicates that copy testing is the most reliable and valid method of ascertaining an ad’s behavioral effects before airing.86,87 Copy testing is widely used by both marketing academics and practitioners, including the US government.8689,95 Copy testing is more costly than focus group ad testing, primarily because larger samples are required95; however, the costs are low compared with the costs of airing weak or even counterproductive ads.

TABLE 1— Antismoking Ad Types
TABLE 1— Antismoking Ad Types
Ad TypeMessage ThemeSpeakerToneMessage SubthemeExemplary Ad (Title, Advertiser, Year)
Disease and sufferingHealthAdultNegativeYounger smokers suffer from horrors of living with tobacco-related diseasesYoung mother is gravely ill with emphysema; says her life is full of pills (“Pills,” Massachusetts, 1998)
Dying parentHealthYouthNegativeSmokers die prematurely, leaving behind grieving children and familyBoy talks tearfully about learning his father was dying from smoking (“Backyard,” California, 1999)
Environmental tobacco smokeHealthAdultNegativeEnvironmental tobacco smoke endangers the health of family members and othersChildren are shown while statistics scroll across the screen on the smoke children inhale from parents’ cigarettes (“Baby Smokers,” California, 1997)
Selling disease and deathCounterindustry and healthAdultNegativeTobacco industry uses manipulation and deception to sell a lethal productTobacco executives thank dying smoker, eye daughter as substitute (“Thanking Customer,” Florida, 2000)
Counterindustry activismCounterindustryYouthNegative and positiveYouths resent manipulative tobacco marketing tactics, engage in protestsTeens confront, humiliate liquor store owner about his tobacco ads (“Bodega,” American Legacy, 2000)
Marketing tacticsCounterindustryYouthNegativeTobacco industry targets youths and others with manipulative ads, promotionsA youth reveals that a tobacco sales representative admitted to targeting kids (“Aaron,” Minnesota, 2000)
Acceptance of nonsmokersSocialYouthPositiveMany youths who do not smoke are attractive, popular, and admiredCool teenagers at beach are nonsmokers; they don’t need cigarettes (“Beach,” Philip Morris, 2000)
Cosmetic effectsSocialYouthNegativeSmokers have smelly breath and clothes, yellow teeth and nailsPretty female turns unattractive and teeth turn yellow upon lighting up (“Pretty Disgusting,” New Jersey, 1998)
TABLE 2— Percentages of Participants Who Recalled Seeing the Antismoking Ads and Message Themes, Mean Number of Ads Recalled, and Mean Ratings of Ad Tone
TABLE 2— Percentages of Participants Who Recalled Seeing the Antismoking Ads and Message Themes, Mean Number of Ads Recalled, and Mean Ratings of Ad Tone
Ad TypeSaw Ads, % (SE)No. of Ad Spots Seen, Mean (SE)Saw Health Message, % (SE)Saw Counterindustry Message, % (SE)Saw Social Message, % (SE)Negative Ad Tone, Mean Rating (SE)Direction of Difference Between Negative and Positive Tone Means, P < .05Positive Ad Tone, Mean Rating (SE)
Disease and suffering0.95 (0.02)3.25 (0.06)0.881 (0.03)0.432 (0.04)0.387 (0.04)2.842 (0.08)>1.784 (0.07)
Dying parent0.93 (0.02)3.43 (0.06)0.921 (0.02)0.352 (0.03)0.633 (0.04)3.111 (0.08)>1.506 (0.07)
Environmental tobacco smoke0.90 (0.02)3.40 (0.06)0.821 (0.03)0.402 (0.04)0.564 (0.04)2.753 (0.08)>1.625 (0.07)
Selling disease and death0.96 (0.01)3.04 (0.06)0.801 (0.03)0.851 (0.03)0.426 (0.04)2.733 (0.08)>1.933 (0.07)
Counterindustry activism0.86 (0.03)3.45 (0.06)0.552 (0.04)0.821 (0.03)0.574 (0.04)2.474 (0.08)=2.431 (0.07)
Marketing tactics0.94 (0.02)3.33 (0.06)0.542 (0.04)0.861 (0.03)0.515 (0.04)2.345 (0.08)>1.884 (0.07)
Acceptance of nonsmokers0.96 (0.01)3.54 (0.06)0.522 (0.04)0.432 (0.04)0.742 (0.03)1.896 (0.08)<2.112 (0.07)
Cosmetic effects0.93 (0.02)3.56 (0.06)0.452 (0.04)0.342 (0.04)0.851 (0.03)2.315 (0.08)>1.894 (0.07)

Note. Subscripts indicate differences in ad rank within column, P < .05. Standard interval scales (1–5) were used.

TABLE 3— Beliefs and Personality Traits as Predictors of Smoking Intent
TABLE 3— Beliefs and Personality Traits as Predictors of Smoking Intent
 Reliability, αMeana% Met CriterionbBSEt
    Severity of health risks.724.00. . ..16.035.37**
    Vulnerability to health risks.824.18. . .–0.09.03–2.87**
    Severity of social risks (of smoking).823.30. . .–0.21.02–9.66**
    Vulnerability to social risks.782.67. . ..09.024.46**
    Severity of tobacco marketing.914.35. . .–0.13.03–4.31**
    Vulnerability to tobacco marketing.692.34. . ..19.029.97**
    Anti-industry motivation.743.40. . .–0.10.02–4.23**
Personality traits
    Aggression.85. . .**
    Anxiety.89. . .*
    Attention deficit–hyperactivity disorder.90. . .
    Conduct disorder.85. . .**
    Depression.88. . .*
    Low self-esteem.80. . .*
    Sensation seeking.72. . .**

Note. Multiple regression results for beliefs showed an adjusted R2 = .17, P < .01 (df for t tests = 1625). Multiple regression results for personality traits showed an adjusted R2 = .12, P < .01 (df for t tests = 1561).

aStandard interval scales (1–5) were used. For intent: α = .87, mean = 1.66.

bDiagnostic criteria for trait cutoffs were as follows: aggression, 4 of 8 symptoms; anxiety, 7 of 28 symptoms; attention-deficit/hyperactivity disorder, 6 of 18 symptoms; conduct disorder, 3 of 7 symptoms; depression, weighted score exceeding 23; low self-esteem, median split of 10 items; sensation seeking, median split of 4 items.

* P < .05; **P < .01.

TABLE 4— Antismoking Ad Effects on Outcome and Ad Measures
TABLE 4— Antismoking Ad Effects on Outcome and Ad Measures
Ad TypeSmoking Intent, Mean (SE)Smoking Intent, % Above Mean (SE)Smoking Odds Ratio vs Control (95% Confidence Interval)Disgust Evoked by Ads, Mean (SE)Anti-Industry Motivation, Mean (SE)Judged Ad Efficacy, Mean (SE)Judged Ad Sensation Value, Mean (SE)
Overall (n = 1725)
Disease and suffering1.55 (0.07)31.6 (0.03)0.68 (0.45, 1.02)3.501 (0.11)3.65* (0.08)1.171 (0.04)3.092 (0.07)
Dying parent1.62 (0.07)34.2 (0.03)0.76 (0.51, 1.14)2.952 (0.10)3.42 (0.08)1.042 (0.04)3.231 (0.07)
Environmental tobacco smoke1.62 (0.07)37.4 (0.04)0.87 (0.58, 1.31)2.882 (0.11)3.29 (0.08)0.912 (0.05)2.884 (0.07)
Selling disease and death1.73 (0.07)38.1 (0.03)0.90 (0.60, 1.35)2.932 (0.11)3.26 (0.08)0.893 (0.04)2.953 (0.07)
Counterindustry activism1.64 (0.07)34.6 (0.03)0.77 (0.51, 1.16)2.623 (0.11)3.52 (0.08)0.932 (0.05)2.835 (0.07)
Marketing tactics1.64 (0.08)35.3 (0.03)0.80 (0.53, 1.20)2.523 (0.11)3.47 (0.08)0.92= (0.04)2.806 (0.07)
Acceptance of nonsmokers1.61 (0.08)31.7 (0.03)0.68 (0.45, 1.03)2.084 (0.10)3.41 (0.08)0.883 (0.04)2.308 (0.07)
Cosmetic effects1.78 (0.07)37.5 (0.03)0.88 (0.59, 1.32)2.942 (0.11)3.29 (0.08)0.833 (0.04)2.587 (0.07)
Control1.78 (0.07)40.6 (0.03)NANA3.33 (0.08)NANA
Participants without conduct disorder (n = 1404)
Disease and suffering1.34** (0.07)22.0** (0.03)0.46** (0.28, 0.75)3.671 (0.12)3.74* (0.09)1.19 (0.05)3.212 (0.08)
Dying parent1.50 (0.07)31.1 (0.04)0.73 (0.47, 1.15)2.993 (0.11)3.49 (0.08)1.02 (0.05)3.241 (0.07)
Environmental tobacco smoke1.52 (0.07)32.5 (0.04)0.78 (0.49, 1.23)2.953 (0.12)3.37 (0.09)0.92 (0.05)2.863 (0.08)
Selling disease and death1.55 (0.07)32.7 (0.04)0.79 (0.50, 1.24)2.983 (0.11)3.36 (0.08)0.90 (0.05)2.973 (0.07)
Counterindustry activism1.49 (0.07)28.1 (0.04)0.63 (0.39, 1.02)2.564 (0.12)3.65 (0.09)0.98 (0.05)2.873 (0.08)
Marketing tactics1.56 (0.07)30.8 (0.04)0.72 (0.46, 1.14)2.465 (0.11)3.46 (0.09)0.94 (0.05)2.784 (0.07)
Acceptance of nonsmokers1.52 (0.07)30.3 (0.04)0.70 (0.44, 1.12)2.066 (0.11)3.50 (0.09)0.92 (0.05)2.356 (0.07)
Cosmetic effects1.49 (0.07)29.3 (0.04)0.67 (0.42, 1.07)3.172 (0.12)3.49 (0.09)0.91 (0.05)2.605 (0.08)
Control1.69 (0.07)38.2 (0.04)NANA3.40 (0.08)NANA
Participants with conduct disorder (n = 321)
Disease and suffering2.31 (0.21)65.1 (0.07)1.52 (0.58, 3.98)2.83 (0.25)3.35 (0.18)1.11 (0.10)2.672 (0.15)
Dying parent2.28 (0.24)50.0 (0.09)0.81 (0.30, 2.23)2.75 (0.26)3.07 (0.20)1.13 (0.11)3.191 (0.17)
Environmental tobacco smoke2.06 (0.23)58.3 (0.08)1.14 (0.42, 3.05)2.58 (0.26)2.99 (0.19)0.85 (0.11)2.972 (0.16)
Selling disease and death2.63 (0.24)65.6 (0.08)1.55 (0.55, 4.36)2.67 (0.29)2.71 (0.21)0.83 (0.12)2.852 (0.18)
Counterindustry activism2.13 (0.20)55.6 (0.07)1.02 (0.40, 2.60)2.82 (0.24)3.12 (0.17)0.74 (0.11)2.722 (0.15)
Marketing tactics2.02 (0.24)58.1 (0.09)1.12 (0.40, 3.13)2.77 (0.27)3.49 (0.21)0.84 (0.11)2.902 (0.17)
Acceptance of nonsmokers2.07 (0.26)39.3 (0.09)0.53 (0.18, 1.51)2.20 (0.30)2.93 (0.22)0.62 (0.13)2.023 (0.19)
Cosmetic effects2.73 (0.20)64.4 (0.07)1.47 (0.57, 3.82)2.17 (0.23)2.63 (0.17)0.58 (0.10)2.512 (0.15)
Control2.32 (0.25)55.2 (0.09)NANA2.92 (0.21)NANA

Note. NA = not applicable. Subscripts indicate differences in ad rank within column for each panel (overall, participants without conduct disorder, participants with conduct disorder), P < .05. Standard interval scales (1–5) were used except for judged ad efficacy (0–2).

* P < .05; **P < .01 in comparisons between indicated antismoking ad type and control.

This research was funded by the California Tobacco-Related Disease Research Program.

The authors thank the participating schools, UCI Health Education, Linda Levine, Carol Whalen, and Guangzhi (Terry) Zhao for their assistance with the research, and Craig Andrews, Stanton Glantz, Pamela Ling, Michael Slater, Laura Solomon, and the Journal reviewers and Editor for their very helpful comments.


1. Farrelly MC, Niederdeppe J, Yarsevich J. Youth tobacco prevention mass media campaigns: past, present, and future directions. Tob Control. 2003;12(suppl 1): i35–i47. Crossref, MedlineGoogle Scholar
2. Pechmann C, Reibling ET. Anti-smoking advertising campaigns targeting youth: case studies from USA and Canada. Tob Control. 2000;9(suppl 2):ii18–ii31. Crossref, MedlineGoogle Scholar
3. Beaudoin CD. Exploring antismoking ads: appeals, themes and consequences. J Health Commun. 2002;7: 123–137. Crossref, MedlineGoogle Scholar
4. Wakefield M, Flay BR, Nichter M, Giovino GA. Role of the media in influencing trajectories of youth smoking. Addiction. 2003;98:79–103. Crossref, MedlineGoogle Scholar
5. Goldman LK, Glantz SA. Antismoking advertising campaigns for youth. JAMA. 1998;280:324. Crossref, MedlineGoogle Scholar
6. Goldman LK, Glantz SA. Evaluation of antismoking advertising campaigns. JAMA. 1998;279:772–777. Crossref, MedlineGoogle Scholar
7. Worden JK, Flynn BS, Secker-Walker RH. Anti-smoking advertising campaigns for youth. JAMA. 1998;280:323–324. Crossref, MedlineGoogle Scholar
8. Balch GI, Rudman G. Antismoking advertising campaigns for youth. JAMA. 1998;280:323–324. Crossref, MedlineGoogle Scholar
9. White V, Tan N, Wakefield M, Hill D. Do adult-focused anti-smoking campaigns have an impact on adolescents? The case of the Australian National Tobacco Campaign. Tob Control. 2003;12(suppl 2):ii23–ii29. MedlineGoogle Scholar
10. Biener L. Adult and youth response to the Massachusetts anti-tobacco television campaign. J Public Health Manag Pract. 2000;6:40–44. Crossref, MedlineGoogle Scholar
11. Biener L. Anti-tobacco advertisements by Massachusetts and Philip Morris: what teenagers think. Tob Control. 2002;11(suppl 2):ii43–ii46. Crossref, MedlineGoogle Scholar
12. Counter-Tobacco Advertising Exploratory Summary Report January–March 1999. Northbrook, Ill: Teenage Research Unlimited; 1999. Google Scholar
13. Wakefield M, Durrant R, Terry-McElrath Y, et al. Appraisal of anti-smoking advertising by youth at risk for regular smoking: a comparative study in the United States, Australia, and Britain. Tob Control. 2003;12 (suppl 2):ii82–ii86. MedlineGoogle Scholar
14. Biener L, Ji M, Gilpin EA, Albers AB. The impact of emotional tone, message, and broadcast parameters in youth anti-smoking advertisements. J Health Commun. 2004;9:259–274. Crossref, MedlineGoogle Scholar
15. Montazeri A, McEwen J. Effective communication: perception of two anti-smoking advertisements. Patient Educ Couns. 1997;30:29–35. Crossref, MedlineGoogle Scholar
16. Biener L, Taylor TM. The continuing importance of emotion in tobacco control media campaigns: a response to Hastings and MacFadyen. Tob Control. 2002;11:75–77. Crossref, MedlineGoogle Scholar
17. Hastings G, MacFadyen L. The limitations of fear messages. Tob Control. 2002;11:73–75. Crossref, MedlineGoogle Scholar
18. Rippetoe PA, Rogers RW. Effects of components of protection-motivation theory on adaptive and mal-adaptive coping with a health threat. J Pers Soc Psychol. 1987;52:596–604. Crossref, MedlineGoogle Scholar
19. Witte K. Fear control and danger control: a test of the extended parallel process model (EPPM). Commun Monogr. 1994;61:113–134. CrossrefGoogle Scholar
20. Pechmann C, Zhao G, Goldberg ME, Reibling ET. What to convey in antismoking advertisements for adolescents? The use of protection motivation theory to identify effective message themes. J Marketing. 2003;67:1–18. CrossrefGoogle Scholar
21. Turbin MS, Jessor R, Costa FM. Adolescent cigarette smoking: health-related behavior or normative transgression? Prev Sci. 2000;1:115–124. Crossref, MedlineGoogle Scholar
22. Harvey T, Troop NA, Treasure JL, Murphy T. Fear, disgust, and abnormal eating attitudes: a preliminary study. Int J Eat Disord. 2002;32:213–218. Crossref, MedlineGoogle Scholar
23. Stark R, Schienle A, Walter B, et al. Hemodynamic responses to fear and disgust-inducing pictures: an f MRI study. Int J Psychophysiol. 2003;50:225–234. Crossref, MedlineGoogle Scholar
24. Marzillier SL, Davey GCL. The emotional profiling of disgust-eliciting stimuli: evidence for primary and complex disgusts. Cogn Emotion. 2004;18:313–336. CrossrefGoogle Scholar
25. Rozin P, Singh L. The moralization of cigarette smoking in the United States. J Consumer Psychol. 1999;8:321. CrossrefGoogle Scholar
26. Rozin P, Haidt J, McCauley C. Disgust. In: Lewis M, Haviland-Jones JM, eds. Handbook of Emotions. New York, NY: Guildford Press; 2000:637–653. Google Scholar
27. Rozin P, Haidt J, McCauley C. Disgust: the body and soul emotion. In: Dalgleish T, Power MJ, eds. Handbook of Cognition and Emotion. New York, NY: John Wiley & Sons Inc; 1999:429–445. Google Scholar
28. Frijda NH, Kuipers P, ter Schure E. Relations among emotion, appraisal, and emotional action readiness. J Pers Soc Psychol. 1989;57:212–228. CrossrefGoogle Scholar
29. Roseman IJ, Wiest C, Swartz TS. Phenomenology, behaviors, and goals differentiate discrete emotions. J Pers Soc Psychol. 1994;67:206–221. CrossrefGoogle Scholar
30. Woody SR, Teachman BA. Intersection of disgust and fear: normative and pathological views. Clin Psychol Sci Pract. 2000;7:291–311. CrossrefGoogle Scholar
31. Bagozzi RP, Moore DJ. Public service advertisements: emotions and empathy guide prosocial behavior. J Marketing. 1994;58:56–70. CrossrefGoogle Scholar
32. Pizarro D. Nothing more than feelings? The role of emotions in moral judgment. J Theor Soc Behav. 2000;30:355–375. CrossrefGoogle Scholar
33. Kardes FR. Spontaneous inference processes in advertising: The effects of conclusion omission and involvement on persuasion. J Consumer Res. 1988;15:225–233. CrossrefGoogle Scholar
34. Zuckerman M. Sensation Seeking: Beyond the Optimal Level of Arousal. Hillsdale, NJ: Lawrence Erlbaum Associates Inc; 1979. Google Scholar
35. Hoyle RH, Stephenson MT, Philip P, Lorch EP, Donohew RL. Reliability and validity of a brief measure of sensation seeking. Pers Individual Differences. 2002;32:401–414. CrossrefGoogle Scholar
36. Crawford AM, Pentz MA, Chou C-P, Li C, Dwyer JH. Parallel developmental trajectories of sensation seeking and regular substance use in adolescents. Psychol Addict Behav. 2003;17:179–192. Crossref, MedlineGoogle Scholar
37. Arnett JJ. Sensation seeking, aggressiveness, and adolescent reckless behavior. Pers Individual Differences. 1996;20:693–702. CrossrefGoogle Scholar
38. Palmgreen P, Donohew L, Lorch EP, Hoyle RH, Stephenson MT. Television campaigns and adolescent marijuana use: Tests of sensation seeking targeting. Am J Public Health. 2001;91:292–296. LinkGoogle Scholar
39. Donohew L, Lorch EP, Palmgreen P. Sensation seeking and targeting of televised anti-drug PSAs. In: Donohew L, Sypher HE, Bukoski WJ, eds. Persuasive Communication and Drug Abuse Prevention. Hillsdale, NJ: Lawrence Erlbaum Associates; 1991:209–226. Google Scholar
40. Everett MW, Palmgreen P. Influences of sensation seeking, message sensation value and program context on effectiveness of anticocaine public service announcements. Health Commun. 1995;7:225–248. CrossrefGoogle Scholar
41. Palmgreen P, Donohew L, Lorch EP, Rogus M, Helm D, Grant N. Sensation seeking, message sensation value, and drug use as mediators of PSA effectiveness. Health Commun. 1991;3:217–227. CrossrefGoogle Scholar
42. Loeber R, Burke JD, Lahey BB, Winters A, Zera M. Oppositional defiant and conduct disorder: a review of the past 10 years, part 1. J Am Acad Child Adolesc Psychiatry. 2000;39:1468–1484. Crossref, MedlineGoogle Scholar
43. Brown RA, Lewinsohn PM, Seeley JR, Wagner EF. Cigarette smoking, major depression, and other psychiatric disorders among adolescents. J Am Acad Child Adolesc Psychiatry. 1996;35:1602–1610. Crossref, MedlineGoogle Scholar
44. Greene K, Krcmar M, Walters LH, Rubin DL, Hale JL. Targeting adolescent risk-taking behaviors: the contribution of egocentrism and sensation-seeking. J Adolesc. 2000;23:439–461. Crossref, MedlineGoogle Scholar
45. Clark DB, Cornelius J. Childhood psychopathology and adolescent cigarette smoking: a prospective survival analysis in children at high risk for substance use disorders. Addict Behav. 2004;29:837–841. Crossref, MedlineGoogle Scholar
46. Lewinsohn PM, Brown RA, Seeley JR, Ramsey SE. Psychosocial correlates of cigarette smoking abstinence, experimentation, persistence and frequency during adolescence. Nicotine Tob Res. 2000;2:121–131. Crossref, MedlineGoogle Scholar
47. Cohen D, Strayer J. Empathy in conduct-disordered and comparison youth. Dev Psychol. 1996;32:988–998. CrossrefGoogle Scholar
48. Kaplan PJ, Arbuthnot J. Affective empathy and cognitive role-taking in delinquent and nondelinquent youth. Adolescence. 1985;20:323–333. MedlineGoogle Scholar
49. Miller PA, Eisenberg N. The relation of empathy to aggressive and externalizing/antisocial behavior. Psychol Bull. 1988;103:324–344. Crossref, MedlineGoogle Scholar
50. Farrelly MC, Healton CG, David KC, Messeri P, Hersey JC, Haviland ML. Getting to the truth: evaluating national tobacco countermarketing campaigns. Am J Public Health. 2002;92:901–907. LinkGoogle Scholar
51. Pechmann C, Stewart DW. Advertising repetition: a critical review of wearin and wearout. In: Leigh JH, Martin CR, eds. Current Issues and Research in Advertising. Ann Arbor: University of Michigan Press; 1988:285–330. Google Scholar
52. Tellis GJ. Effective frequency: one exposure or three factors? J Advertising Res. 1997;37:75–80. Google Scholar
53. Harrington NG, Lane DR, Donohew L, et al. Persuasive strategies for effective anti-drug messages. Commun Monogr. 2003;70:16–38. CrossrefGoogle Scholar
54. Cook TD, Campbell DT. Quasi-Experimentation: Design and Analysis Issues for Field Settings. Boston, Mass: Houghton Mifflin; 1979. Google Scholar
55. Tyas SL, Pederson LL. Psychosocial factors related to adolescent smoking: a critical review of the literature. Tob Control. 1998;7:409–420. Crossref, MedlineGoogle Scholar
56. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Washington DC: American Psychiatric Association; 2000. Google Scholar
57. Whalen CK, Jamner LD, Henker B, Delfino RJ. Smoking and moods in adolescents with depressive and aggressive dispositions: evidence from surveys and electronic diaries. Health Psychol. 2001;20:99–111. Crossref, MedlineGoogle Scholar
58. Tercyak KP, Lerman C, Audrain J. Association of attention-deficit/hyperactivity disorder symptoms with levels of cigarette smoking in a community sample of adolescents. J Am Acad Child Adolesc Psychiatry. 2002; 41:799–805. Crossref, MedlineGoogle Scholar
59. Whalen CK, Jamner LD, Henker B, Delfino RJ, Lozano JM. The ADHD spectrum and everyday life: experience sampling of adolescent moods, activities, smoking, and drinking. Child Dev. 2002;73:209–227. Crossref, MedlineGoogle Scholar
60. Lloyd-Richardson EE, Papandonatos G, Kazura A, Stanton C, Niaura R. Differentiating stages of smoking intensity among adolescents: stage-specific psychological and social influences. J Consult Clin Psychol. 2002; 70:998–1009. Crossref, MedlineGoogle Scholar
61. Miller-Johnson S, Lochman JE, Coie JD, Terry R, Hyman C. Comorbidity of conduct and depressive problems at sixth grade: substance use outcomes across adolescence. J Abnorm Child Psychol. 1998;26:221–232. Crossref, MedlineGoogle Scholar
62. Comeau N, Stewart SH, Loba P. The relations of trait anxiety, anxiety sensitivity and sensation seeking to adolescents’ motivations for alcohol, cigarette and marijuana use. Addict Behav. 2001;26:803–825. Crossref, MedlineGoogle Scholar
63. Patton GC, Carlin JB, Coffey C, Wolfe R, Hibbert M, Bowes G. Depression, anxiety, and smoking initiation: a prospective study over 3 years. Am J Public Health. 1998;88:1518–1522. LinkGoogle Scholar
64. Patton GC, Hibbert M, Rosier MJ, Carlin JB, Caust J, Bowers G. Is smoking associated with depression and anxiety in teenagers? Am J Public Health. 1996;86: 225–230. LinkGoogle Scholar
65. Reynolds CR, Richmond BO. What I think and feel: a revised measure of children’s manifest anxiety. J Abnorm Child Psychol. 1997;25:15–20. Crossref, MedlineGoogle Scholar
66. Radloff LS. The use of the Center for Epidemiologic Studies Depression Scale in adolescents and young adults. J Youth Adolesc. 1991;20:149–166. Crossref, MedlineGoogle Scholar
67. Albers AB, Biener L. The role of smoking and rebelliousness in the development of depressive symptoms among a cohort of Massachusetts adolescents. Prev Med. 2002;34:625–631. Crossref, MedlineGoogle Scholar
68. Windle M, Windle RC. Depressive symptoms and cigarette smoking among middle adolescents: prospective associations and intrapersonal and interpersonal influences. J Consult Clin Psychol. 2001;69:215–226. Crossref, MedlineGoogle Scholar
69. Rosenberg M. Society and Adolescent Self-Image. Princeton, NJ: Princeton University Press; 1965. Google Scholar
70. Pierce JP, Choi WS, Gilpin EA, Farkas AJ, Merritt RK. Validation of susceptibility as a predictor of which adolescents take up smoking in the United States. Health Psychol. 1996;15:355–361. Crossref, MedlineGoogle Scholar
71. Pierce JP, Farkas AJ, Evans N, Gilpin EA. An improved surveillance measure for adolescent smoking. Tob Control. 1995;4:S37–S56. CrossrefGoogle Scholar
72. Rogers RW, Newborn CR. Fear appeals and attitude change: effects of a threat’s noxiousness, probability of occurrence, and the efficacy of coping responses. J Pers Soc Psychol. 1976;34:54–61. Crossref, MedlineGoogle Scholar
73. Gross JJ, Levenson RW. Emotion elicitation using films. Cogn Emotion. 1995;9:87–108. CrossrefGoogle Scholar
74. Kirk RE. Experimental Design: Procedures for the Behavioral Sciences. Pacific Grove, Calif: Brooks/Cole; 1982. Google Scholar
75. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51:1173–1182. Crossref, MedlineGoogle Scholar
76. Sly DF, Trapido E, Ray S. Evidence of the dose effects of an antitobacco counteradvertising campaign. Prev Med. 2002;35:511–518. Crossref, MedlineGoogle Scholar
77. Bauer UE, Johnson TM, Hopkins RS, Brooks RG. Changes in youth cigarette use and intentions following implementation of a tobacco control program: findings from the Florida Youth Tobacco Survey, 1998–2000. JAMA. 2000;284:723–728. Crossref, MedlineGoogle Scholar
78. Sly DF, Heald GR, Ray S. The Florida “truth” anti-tobacco media evaluations: design, first year results, and implications for planning future state media evaluations. Tob Control. 2001;10:9–15. Crossref, MedlineGoogle Scholar
79. Sly DF, Hopkins RS, Trapido E, Ray S. Influence of a counteradvertising media campaign on initiation of smoking: the Florida “truth” campaign. Am J Public Health. 2001;91:233–238. LinkGoogle Scholar
80. Farrelly MC, Davis KC, Haviland ML, Messeri P, Healton CG. Evidence of a dose–response relationship between “truth” antismoking ads and youth smoking prevalence. Am J Public Health. 2005;95:425–31. LinkGoogle Scholar
81. Stevens C. Designing an effective counteradvertising campaign—California. Cancer. 1998;83:2736–2741. Crossref, MedlineGoogle Scholar
82. Thrasher JF, Niederdeppe J, Farrelly MC, Davis KC, Ribisl KM, Haviland ML. The impact of anti-tobacco industry prevention messages in tobacco producing regions: evidence from the US truth campaign. Tob Control. 2004;13:283–288. Crossref, MedlineGoogle Scholar
83. DeJong W, Hoffman KD. A content analysis of television advertising for the Massachusetts Tobacco Control Program media campaign, 1993–1996. J Public Health Manag Pract. 2000;6:27–39. Crossref, MedlineGoogle Scholar
84. Soldz S, Clark TW, Stewart E, Celebucki C, Walker DK. Decreased youth tobacco use in Massachusetts 1996 to 1999: evidence of tobacco control effectiveness. Tob Control. 2002;11(suppl 2):ii14–ii19. Crossref, MedlineGoogle Scholar
85. Shiffman S, Burton SL, Pillitteri JL, Gitchell JG, DiMarino ME. Test of “light” cigarette counter-advertising using a standard test of advertising effectiveness. Tob Control. 2001;10(suppl 1):i33–i40. Crossref, MedlineGoogle Scholar
86. Haley RI, Baldinger AL. The ARF copy research validity project. J Advertising Res. 1991;2:11–32. Google Scholar
87. Rossiter JR, Eagleson G. Conclusions from the ARF’s copy research validity project. J Advertising Res. 1994;34:19–32. Google Scholar
88. Adams AJ, Blair MH. Persuasive advertising and sales accountability: past experience and forward validation. J Advertising Res. 1992;32:20–25. Google Scholar
89. Blair MH, Rabuck MJ. Advertising wearin and wearout: ten years later—more empirical evidence and successful practice. J Advertising Res. 1998;38:7–18. Google Scholar
90. Wakefield M, Kloska DD, O’Malley PM, et al. The role of smoking intentions in predicting future smoking among youth: findings from Monitoring the Future data. Addiction. 2004;99:914–922. Crossref, MedlineGoogle Scholar
91. Gritz ER, Prokhorov AV, Hudmon KS, et al. Predictors of susceptibility to smoking and ever smoking: a longitudinal study in a triethnic sample of adolescents. Nicotine Tob Res. 2003;5:493–506. Crossref, MedlineGoogle Scholar
92. Flay BR, Hu FB, Richardson J. Psychosocial predictors of different stages of cigarette smoking among high school students. Prev Med. 1998;27(5 Pt 3):A9–18. Crossref, MedlineGoogle Scholar
93. Choi WS, Pierce JP, Gilpin EA, Farkas AJ, Berry CC. Which adolescent experimenters progress to established smoking in the United States. Am J Prev Med. 1997;13:385–391. Crossref, MedlineGoogle Scholar
94. Jackson C. Cognitive susceptibility to smoking and initiation of smoking during childhood: a longitudinal study. Prev Med. 1998;27:129–134. Crossref, MedlineGoogle Scholar
95. Foley D, Pechmann C. The national youth anti-drug media campaign copy test system. Soc Marketing Q. 2004;10:34–42. CrossrefGoogle Scholar


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Cornelia Pechmann, PhD, MS, MBA, and Ellen T. Reibling, PhD, MACornelia Pechmann is with the Paul Merage School of Business, University of California, Irvine. Ellen T. Reibling is with the Department of Health Education, University of California, Irvine. “Antismoking Advertisements for Youths: An Independent Evaluation of Health, Counter-Industry, and Industry Approaches”, American Journal of Public Health 96, no. 5 (May 1, 2006): pp. 906-913.


PMID: 16571709