Psychology of Addictive Behaviors 2015, Vol. 29, No. 1, 201–210

© 2014 American Psychological Association 0893-164X/15/$12.00 http://dx.doi.org/10.1037/adb0000002

Longitudinal Test of a Reciprocal Model of Smoking Expectancies and Smoking Experience in Youth Leila Guller

Tamika C. B. Zapolski

University of Kentucky

Indiana University Purdue University at Indianapolis

Gregory T. Smith This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

University of Kentucky This article reports on a longitudinal test of a developmental model of early smoking that specifies reciprocal predictive relationships between smoking expectancies and smoking behavior in youth. The model was tested on 1,906 children during the transition from elementary school to middle school across 3 time points: the spring of 5th grade, the fall of 6th grade, and the spring of 6th grade. Key findings were (a) elementary school expectancies for reinforcement from smoking predicted smoking behavior during middle school; (b) smoking experience predicted increased subsequent smoking expectancies; and (c) among children who had never smoked, smoking expectancies predicted subsequent smoking onset. The finding that smoking expectancies and smoking behavior predicted each other reciprocally and positively across time in children this young may prove important in developing and refining early intervention and prevention efforts. Keywords: longitudinal, smoking, youth, expectancies, risk

affect predicts smoking behavior in children this young, as suggested by one study (Chung et al., 2010) and as demonstrated in later adolescence and early adulthood (Heinz et al., 2010; Wetter et al., 1992, 1994). To introduce this test, we briefly review the problematic nature of early smoking behavior, the importance of the transition to middle school, and expectancy theory; we then introduce the specifics of our model test.

This article reports on a test of the relationship between psychosocial learning about the perceived benefits of smoking and smoking behavior very early in life; specifically, across the developmental transition from elementary school to middle school. Building on prior work that has shown longitudinal prediction of smoking behavior from smoking expectancies among college students (Doran et al., 2013; Wetter, Brandon, & Baker, 1992) and midadolescents (Heinz, Kassel, Berbaum, & Mermelstein, 2010), covariation in the growth of smoking expectancies and smoking behavior among early adolescents (Chung, White, Hipwell, Stepp, & Loeber, 2010), and cross-sectional associations between smoking expectancies and smoker status in elementary schoolchildren (Combs, Spillane, Caudill, Stark, & Smith, 2012), we tested whether elementary school smoking expectancies predicted middle school smoking behavior, including the onset of smoking during this period. We further tested whether there was reciprocal prediction, that is, did early smoking experience predict subsequent smoking expectancies. In addition, we examined whether negative

Early Adolescent Smoking and the Transition to Middle School The transition into middle school represents an important contextual change related to the move from childhood to adolescence. Middle schoolchildren encounter larger, more impersonal school contexts (Barber & Olsen, 2004; Eccles et al., 1993) and they experience a new level of independence from their parents (Eccles & Midgley, 1990). Even if they have not experienced pubertal onset themselves, many middle schoolchildren have, which contributes to a setting in which the needs and drives associated with physically mature bodies are manifest. For these reasons, this transition has been described as a potential turning point in development (Graber & Brooks-Gunn, 1996; Rutter, 1994); that is, a period characterized by significant behavioral and developmental change. Not surprisingly, initiation of smoking is common during this developmental period, with some of the highest rates of increases in smoking onset occurring from elementary to middle school ages (Everett et al., 1999). The importance of the transition from elementary school to middle school for smoking behavior is clear. The onset of smoking during this transition is of considerable importance. Any smoking at this age is a useful indicator of the

This article was published Online First September 1, 2014. Leila Guller, Department of Psychology, University of Kentucky; Tamika C. B. Zapolski, Department of Psychology, Indiana University Purdue University at Indianapolis; Gregory T. Smith, Department of Psychology, University of Kentucky. Portions of this research were supported by grants from the National Institute on Alcohol Abuse and Alcoholism (NIAAA), (RO1AA016166) to Gregory T. Smith and the National Institute on Drug Abuse (T32 DA035200) to Craig Rush that supported Leila Guller. Correspondence concerning this article should be addressed to Gregory T. Smith, Department of Psychology, University of Kentucky, 105 Kastle Hall 0044, Lexington, KY 40506-0044. E-mail: [email protected] 201

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presence of smoking-related problems. A small percentage of children have smoked cigarettes before age 12 (Abroms, SimonsMorton, Haynie, & Chen, 2005; Chassin, Presson, Pitts, & Sherman, 2000; Colder et al., 2001; Combs et al., 2012; White, Pandina, & Chen, 2002), and the rates of adolescents who smoke regularly increases substantially across the adolescent years (Chassin, Presson, Sherman, & Edwards, 1990). This early use is important because it means a longer timeframe of exposure to the health damaging effects of nicotine (Wills, Sandy, Yaeger, Cleary, & Shinar, 2001), an increase in the quantity of cigarettes smoked per day during adolescence (Everett et al., 1999), an increased likelihood of tobacco addiction during adolescence and adulthood (Chassin et al., 2000), and, for girls, stunted physical growth (Stice & Martinez, 2005). A great deal of research has investigated the consequences of early adolescent smoking; the intent of this article is to test whether early adolescent smoking can itself be predicted from characteristics of elementary schoolchildren. If it can be, then researchers and prevention specialists might profitably focus on risk processes underway in elementary school. In particular, it is important to determine whether elementary schoolchildren have already learned to expect benefits from smoking and whether those expectations predict subsequent smoking behavior.

Psychosocial Learning Risk: Expectancies for Reinforcement From Smoking Learning Risk: Smoking Expectancies One way to measure psychosocial learning regarding smoking is to assess smoking expectancies. In the expectancy model we use, expectancies are thought to represent summaries of one’s learning history about the outcomes of one’s behavioral choices (Bolles, 1972; Tolman, 1932). Reports of explicit expectancies are understood to provide markers of memory-based associative learning (Goldman, Darkes, & Del Boca, 1999; Goldman, Reich, & Darkes, 2006). For example, high scores on a scale reflecting the expectancy that cigarette smoking reduces negative affect are thought to reflect a strong learned association between smoking and reduction of negative affective states. The association influences the behavior (Bolles, 1972; Tolman, 1932). Considerable support for this model, and for the explicit measurement of smoking expectancies as markers of learned associations, has accrued over several decades (Bolles, 1972; Brandon & Baker, 1991; Lewis-Esquerre, Rodrigue, & Kahler, 2005; Myers, MacPherson, McCarthy, & Brown, 2003; Rotter, 1975; Tolman, 1932). Indeed, a substantial body of research has demonstrated that positive smoking expectancies are associated with smoking status (Chung et al., 2010), quantity and frequency (Heinz et al., 2010), and likelihood of initiation (Bauman & Chenoweth, 1984) among adolescents. Two smoking expectancies in particular have been predictive across midadolescent and young adult samples. The expectancy that smoking facilitates positive social interactions has been shown to be associated with smoking behavior across adolescence (Bauman & Chenoweth, 1984; Brandon & Baker, 1991; Lewis-Esquerre et al., 2005; Myers et al., 2003). The finding that expectancies concerning social facilitation predict behavior is consistent with extensive literature highlighting the importance of

social relationships during these years (Havighurst, 1972; Masten et al., 1995). Another important predictor of smoking behavior is the expectancy that smoking will reduce negative affect (Heinz et al., 2010; Kassel, Stroud, & Paronis, 2003). Negative affect reduction expectancies play an important role in the relationship between reported mood and engagement in smoking (Cohen, McCarthy, Brown, & Myers, 2002) and endorsement of them predicts smoking behavior and nicotine dependence prospectively among midadolescents (Heinz et al., 2010). Among preadolescents, depression correlated with smoking behavior cross-sectionally (Chung et al., 2010). However, it has not previously been determined whether (a) negative affect or (b) negative affect reduction smoking expectancies measured in elementary schoolchildren predict subsequent smoking behavior.

Reciprocal Influence Between Expectancies and Smoking Because positive smoking expectancies are understood to represent learned associations between smoking and reward, positive expectancies at one time point should predict subsequent smoking behavior. In the same way, smoking experience should influence learned associations between smoking and outcomes. To date, research with midadolescents and young adults has shown that smoking expectancies predict subsequent smoking behavior, including nicotine dependence (Doran et al., 2013; Heinz et al., 2010; Wetter et al., 1992, 1994). Concerning prediction from smoking experience to subsequent expectancies, one study with college students found that positive reinforcement expectancies increased after initiation of cigarette smoking (Doran, Schweizer, & Myers, 2011). However, no studies have tested for a reciprocal predictive relationship between smoking expectancies and smoking behavior; nor have any studies investigated this possibility in a sample of youth making the transition from elementary school to middle school.

Early Pubertal Onset It is important to consider early pubertal onset in any risk model concerning the transition into middle school. Early pubertal onset, often defined as occurring before 75% of one’s peers (LynneLandsman, Graber, & Andrews, 2010), predicts early smoking, although these findings tend to be inconsistent (Bratberg, Nilsen, Holmen, & Vatten, 2007; Drapela, Gebelt, & McRee, 2006; van Jaarsveld, Fidler, Simon, & Wardle, 2007; Westling, Andrews, & Peterson, 2012). Its presumed influence is thought to reflect biological, social and contextual factors, and even to represent parental psychopathology (Dick et al., 2007; Ellis, 2004; Ellis & Garber, 2000).

Current Study At present, little is known about whether smoking expectancies can be assessed reliably in elementary school children (Combs et al., 2012, provides a positive finding in this regard), whether distinctions among smoking expectancies are present in these children, whether elementary school smoking expectancies predict subsequent smoking behavior, and whether there is reciprocal

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YOUTH SMOKING EXPECTANCIES AND SMOKING EXPERIENCE

prediction from smoking behavior back to smoking expectancies. To address these questions, we sampled 1,906 children at three time points: spring of 5th grade (the last year of elementary school), fall of 6th grade (the first year of middle school), and spring of 6th grade. We addressed the following questions: (a) Can expectancies for reinforcement from smoking be assessed reliably in elementary schoolchildren? (b) Are expectancies for social facilitation and negative affect reduction from smoking distinct in children this young, and do they have different correlates? (c) Do elementary school smoking expectancies predict middle school smoking behavior? (d) Do elementary school smoking expectancies predict the onset of smoking? If so, to some degree smoking expectancies must be formed via modeling, and not just from direct experience with smoking. (e) Does smoking experience modify subsequent expectancies? (f) If so, does smoking experience lead to reductions in reinforcement expectancies? That is, does early smoking lead to reduced risk? Or, does early smoking experience strengthen expectancies for reinforcement from smoking? (g) Are any such effects present when also modeling the influence of early pubertal onset? (h) Do elementary school levels of negative affect predict subsequent smoking? If they do, is the effect mediated by negative affect reduction expectancies? (i) Do the prospective relationships between smoking expectancies and smoking behavior differ as a function of sex or race?

Method Sample Participants were 1,906 5th graders from urban, rural, and suburban backgrounds recruited from 23 public school systems in the southeastern United States. The sample was equally divided between girls (49.9%) and boys. Most participants were 11 years old (66.8%), 22.8% were 10 years old, 10% were 12 years old, and 0.2% were either 9 or 13 years old. The ethnic breakdown of the sample was as follows: 60.9% European American, 18.7% African American, 8.2% Hispanic, 3% Asian American, and 8.8% other racial/ethnic groups.

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Questionnaire for adults (Brandon & Baker, 1991) and provides assessment of expectancies for social facilitation and for negative affect reduction from smoking. Sample items for social facilitation and negative affect reduction include “Hanging out with friends is more fun if everyone is smoking,” and “Smoking helps calm an angry person down,” respectively. Response options for each item range from 1 ⫽ never, to 5 ⫽ always. In the current sample, estimates of internal consistency for the two scales were as follows. For the social facilitation expectancy, Wave 1 ␣ ⫽ .79, with higher estimates for Waves 2 and 3. For negative affect reduction, Wave 1 ␣ ⫽ .89, again with higher estimates for Waves 2 and 3. As we describe below, we combined the two expectancy scales to measure a global expectancy for reinforcement from smoking. Wave 1 ␣ ⫽ .91 for the combined scale, and internal consistency estimates were higher for Waves 2 and 3. Smoking behavior. We used a single item measure of the frequency of smoking: “Which of these best describes how often you smoke cigarettes?” The answer choices are: “I have never smoked,” “I have smoked cigarettes 1, 2, 3, or 4 times in my life,” “I smoke cigarettes 3 or 4 times a year,” “I smoke about once a month,” “I smoke about once or twice a week,” and “I smoke almost daily or every day.” At Wave 1, the item refers to lifetime smoking. At each subsequent wave, the item is modified to refer to smoking in the preceding 6 months. Because very few children in our young sample endorsed smoking frequently, we dichotomized the item, where never having smoked was assigned a “0” and any other response was assigned a “1.” We refer to the score as reflecting smoker status. Negative affect: Positive and Negative Affective Scale-Child Version (PANAS-C). The PANAS-C (Laurent et al., 1999) measures the dimensions of positive affectivity and negative affectivity in children. It was developed and validated on children in Grades 4 – 8, based on the adult PANAS (Watson et al., 1988). Items were adapted from asking how one feels over “the past few weeks” to how one “generally” feels in relation to several affectrelated words (e.g., “miserable,” “afraid,” “lonely”). Response options for each item range from 1 ⫽ very slightly or not at all, to 5 ⫽ extremely. We used the negative affectivity scale only (␣ ⫽ .90, Wave 1).

Measures The Pubertal Development Scale (PDS). This scale consists of five questions for boys and five questions for girls (Petersen et al., 1988). Sample questions are, for boys, “Do you have facial hair yet?” and, for girls, “Have you begun to have your period?” Individuals respond on a 4-point scale. The scale has acceptable reliability estimates (␣’s ranging from .67 to .76 for 11 year olds), and scores on it correlate highly with physician ratings and other forms of self-report (r values ranging from .61 to .67; BrooksGunn, Warren, Rosso, & Gargiulo, 1987; Coleman & Coleman, 2002). The PDS permits dichotomous classifications as prepubertal or pubertal, with mean scores above 2.5 indicative of pubertal onset. As is common (e.g., Culbert, Burt, McGue, Iacono, & Klump, 2009), dichotomous classification was used in the current study. Adolescent Smoking Consequences Questionnaire (ASCQ). The ASCQ was used to measure outcome expectancies for smoking among adolescents (Lewis-Esquerre, Rodrigue, & Kahler, 2005). This scale was adapted from the Smoking Consequences

Procedure Participants were administered questionnaires at three time points: Spring of 5th grade (Wave 1), fall of 6th grade (Wave 2), and spring of 6th grade (Wave 3). The questionnaires were administered in 23 public elementary schools at Wave 1, then in 15 middle schools at Waves 2 and 3. A passive-consent procedure was used. Each family was sent a letter, through the U.S. Mail, introducing the study. Families were asked to return an enclosed, stamped letter or call a phone number if they did not want their child to participate. Out of 1,988 5th graders in the participating schools, 1,906 participated in the study (95.9%). Reasons for nonparticipation included declination of consent from parents, declination of assent from children, and language or cognitive difficulties. Questionnaires were administered by study staff in the children’s classrooms or in a central location, such as the school cafeteria, during school hours. It was made clear to the students that their responses on the questionnaire were to be kept confiden-

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tial and no one outside of the research team would see them. The research team introduced the federal certificate of confidentiality for the project and emphasized that they were legally bound to keep all responses confidential. After each participant signed the assent form, the researchers then passed out packets of questionnaires. The questionnaires took 60 min or less to complete. This procedure was approved by the University’s IRB and by the participating school systems. Children who left the school system were asked to continue to participate. Those who consented did so either by completing hard copies of questionnaires delivered through the mail or by completing the measures on a secure Web site. They were paid $30 for doing so.

2005). RMSEA values of .06 or lower are thought to indicate a close fit, .08 a fair fit, and .10 a marginal fit (Browne & Cudeck, 1993; Hu & Bentler, 1999), and SRMR values of approximately .09 or lower are thought to indicate good fit (Hu & Bentler, 1999). To determine group differences and significance of bivariate correlations, we used p ⬍ .01 because of the large sample size. Because the SEM test corrected each model variable for the effect of all other variables, we used p ⬍ .05 to determine significance within SEM model tests.

Results Participation Retention

Data Analysis After conducting internal consistency reliability tests and calculating correlations among study variables, we tested our longitudinal hypotheses using structural equation modeling (SEM). We first constructed a baseline model that included autoregressive prediction as well as prediction from early pubertal onset and sex. Smoking expectancies and smoker status at each wave were predicted only by the same variable reported on earlier in time (e.g., smoker status Wave 1 predicts smoker status Wave 2). In addition, Waves 2 and 3 smoker status was predicted by early (Wave 1) pubertal onset and biological sex. Puberty and sex were included in this model because of the potential influences these variables may have on engagement in risky behaviors. Cross-sectional associations among all variables were included in this model. We next tested a second model in which we added negative affect to the first model. We modeled autoregressive prediction from negative affect at each wave to negative affect at subsequent waves, and we tested whether negative affect at Waves 1 and 2 predicted smoking behavior or smoking expectancies at the subsequent wave. We tested this model to allow for the possibility that negative affect predicts smoking behavior, mediated by negative affect reduction expectancies (Heinz et al., 2010). As we describe below, negative affect was not predictive of either expectancies or smoking behavior in any prospective test, so it was not included in our final model. Our final model included all pathways described in Model 1 plus a test of the reciprocal smoking-expectancy transaction. Thus, we added prospective pathways from Wave 1 smoking expectancies to Wave 2 smoking behavior; Wave 2 smoking expectancies to Wave 3 smoking behavior; Wave 1 smoking behavior to Wave 2 smoking expectancies; and Wave 2 smoking behavior to Wave 3 smoking expectancies. We tested this sequence of models using SEM in Mplus (Muthén & Muthén, 2004). We used maximum likelihood parameter estimates and an adjusted ␹2 statistic that is robust to nonnormality (the MLR method). To test mediation, we used the indirect test provided by Mplus (Muthén & Muthén, 2004), which computes the product of the two regression coefficients as described by MacKinnon, Lockwood, Hoffman, West, and Sheets (2002). To measure model fit, we relied on four fit indices: the Comparative Fix Index (CFI), the Nonnormed Fit Index (NNFI), the root mean square error of approximation (RMSEA), and the standardized root-mean-square residual (SRMR). Guidelines for what constitutes good fit vary. CFI and NNFI values above either .90 or .95 are thought to represent very good fit (Hu & Bentler, 1999; Kline,

Of the 1,906 participants, 1,843 first participated at Wave 1 and the remaining 63 first participated at Wave 2 (those 63 were recruited before Wave 1 but were absent from school on each testing day during Wave 1). 94.6% of those who participated in Wave 1 participated in Wave 2, and 98.0% of those who participated in Wave 2 also participated in Wave 3. Of the full sample of 1,906, 96.7% participated in Wave 1, 94.8% participated in Wave 2, and 92.9% participated in Wave 3. In total, 1,730 (90.8%) participated in all three waves. Individuals who participated in all three waves of the study did not differ from those who participated in only one or two waves on any demographic, criterion, or trait variable. Therefore, we inferred that data were missing at random. Missing data were imputed using the expectation maximization (EM) procedure, which has been shown to produce more accurate estimates of population parameters than do other methods, such as deletion of missing cases or mean substitution (Enders, 2006).

Possible Effects Because of School Membership To determine whether there was significant covariance among the study variables because of participants attending the same school, we calculated intraclass coefficients for each variable (using elementary school membership, n ⫽ 23, as the nesting variable). Intraclass coefficients ranged from .03 to .00. Therefore, we concluded that school membership was essentially unrelated to study variables.

Descriptive Statistics At Wave 1, more girls than boys had experienced pubertal onset (27.9% of girls and 21.6% of boys: ␹2(1) ⫽ 10.13, p ⬍ .001). Despite this significant difference, we defined pubertal onset at Wave 1 (spring, 5th grade) as early: rates for both sexes approximated the early quartile for pubertal development within the sample (24.8% of the sample as a whole had experienced pubertal onset). Table 1 includes descriptive statistics for expectancies, smoker status, and negative affect at Waves 1, 2, and 3 of the study. In subsequent analyses, smoker status was dichotomized because of the low rates of endorsement of higher frequencies of smoking in this age group.

Reliability and Distinctiveness of Smoking Expectancy Assessment As noted above, internal consistency estimates supported the reliability of both expectancies for social facilitation and negative

YOUTH SMOKING EXPECTANCIES AND SMOKING EXPERIENCE

Table 1 Mean Levels of Expectancies and Smoker Status at Each Time Point Spring, 5th grade Fall, 6th grade Spring, 6th grade

Expectancies Negative affect

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Nonsmoker Smoked 1–4 times Smoked ⬎4 times

Mean (SD)

Mean (SD)

Mean (SD)

3.02 (1.21) 2.10 (0.75)

3.17 (1.35) 1.82 (0.73)

3.21 (1.48) 1.75 (0.70)

N (%)

N (%)

N (%)

1,800 (94.4%) 85 (4.5%) 21 (1.1%)

1,783 (93.5%) 101 (5.3%) 22 (1.2%)

1,755 (92.1%) 103 (5.4%) 48 (2.5%)

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associated with smoker status across all three waves for each variable. Expectancies were most strongly associated with smoker status at the same waves (e.g., expectancies at Wave 1 were more strongly correlated with Wave 1 smoker status than with Waves 2 or 3 smoker status). Negative affect correlated modestly with global smoking expectancies but not meaningfully with smoking behavior.

Model Tests

Note. Smoking frequencies assessed in the Spring of 5th grade refer to lifetime use; frequencies assessed at each subsequent wave refer to the previous 6 months. N ⫽ 1,906.

affect reduction from smoking. Correlations between the two expectancy scales were quite high: for Waves 1, 2, and 3, respectively, r ⫽ .69, .74, and .78. We then tested whether the correlations between the two expectancies and other study variables (smoking behavior, negative affect, early pubertal onset, and sex) differed significantly. For example, did the correlation between social facilitation expectancies and smoking differ from the correlation between negative affect reduction expectancies and smoking at a given assessment? None did. Thus, there was no reason to measure the two expectancies separately, so we transformed each scale to z scores and summed them within year. As noted above, the combined expectancy for reinforcement from smoking scale was highly internally consistent each year.

Bivariate Correlations Table 2 presents bivariate correlations among Wave 1 pubertal status, biological sex, and Wave 1, 2, and 3 assessments of smoking expectancies, negative affect, and smoker status. As anticipated, expectancies, negative affect, and smoker status measured at three time points correlated with themselves across time (e.g., Wave 1 expectancies with Wave 2 and Wave 3 expectancies). Pubertal status and smoking expectancies were both significantly

The first model we tested specified cross-sectional associations among smoking expectancies, smoker status, early pubertal status, and sex. Longitudinally, this model specified autoregressions: (a) Wave 1 expectancies predicted Wave 2 and Wave 3 expectancies and Wave 2 expectancies predicted Wave 3 expectancies; (b) Wave 1 smoking predicted Wave 2 and Wave 3 smoking and Wave 2 smoking predicted Wave 3 smoking. The model also included prospective prediction from early pubertal onset and sex to Wave 2 and Wave 3 smoking expectancies and smoking behavior. Cross-sectional associations at each wave were also modeled. Fit index values were: ␹2(6) ⫽ 24.86 (p ⬍ .001); CFI ⫽ .96; TLI ⫽ .94; RMSEA ⫽ .04 (90% confidence interval [CI]: .03 to .06), SRMR ⫽ .03. This model explained 38% of the variance in Wave 3 smoker status, and 28% of the variance for Wave 3 expectancies. As noted above, we next added negative affect to the first model. We modeled negative affect at each wave, autoregressive predictions of negative affect across time, cross-sectional associations between negative affect and the other variables, and prospective prediction from negative affect at a given wave to smoker status and smoking expectancies the following wave. This model fit the data well: ␹2(18) ⫽ 55.83 (p ⬍ .001); CFI ⫽ .98; TLI ⫽ .96; RMSEA ⫽ .03 (90% CI: .02 to .04), SRMR ⫽ .03. Although negative affect at a given wave predicted subsequent smoking expectancies (Wave 1 to Wave 2: b ⫽ .05, p ⬍ .05; Wave 2 to Wave 3: b ⫽ .07, p ⬍ .01), negative affect did not predict smoker status prospectively over either longitudinal window. The final model added in reciprocal prediction between smoking expectancies and smoker status. In this model, we did not include negative affect because it did not predict smoker status. Using the first model as a base, we added four new pathways: Wave 1

Table 2 Bivariate Correlations Among Study Variables

Pub1 Sex Neg1 Neg2 Neg3 Ex1 Ex2 Ex3 Sm1 Sm2 Sm3

Pub1

Sex

Neg1

Neg2

— .05ⴱ .03 .04 .07ⴱⴱ .10ⴱⴱ .10ⴱⴱ .11ⴱⴱ .18ⴱⴱ .11ⴱⴱ .12ⴱⴱ

— .10ⴱⴱ .06ⴱ .09ⴱⴱ ⫺.02 ⫺.03 ⫺.01 ⫺.03 .03 .02

— .47ⴱⴱ .39ⴱⴱ .14ⴱⴱ .10ⴱⴱ .12ⴱⴱ .06ⴱⴱ .03 .03

— .55ⴱⴱ .13ⴱⴱ .14ⴱⴱ .16ⴱⴱ .03 .05 .06ⴱ

Neg3

Ex1

Ex2

Ex3

Sm1

Sm2

Sm3

— .40ⴱⴱ .38ⴱⴱ .26ⴱⴱ .20ⴱⴱ .18ⴱⴱ

— .50ⴱⴱ .09ⴱⴱ .16ⴱⴱ .14ⴱⴱ

— .12ⴱⴱ .17ⴱⴱ .27ⴱⴱ

— .46ⴱⴱ .41ⴱⴱ

— .60ⴱⴱ

—

— .13ⴱⴱ .19ⴱⴱ .01 .03 .04

Note. Pub ⫽ early pubertal onset; Neg ⫽ negative affec; Ex ⫽ expectancies; Sm ⫽ smoking; 1 ⫽ Wave 1; 2 ⫽ Wave 2; 3 ⫽ Wave 3; sex: 0 ⫽ male, 1 ⫽ female. N ⫽ 1,906. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.

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expectancies to Wave 2 smoker status; Wave 2 smoker status to Wave 3 expectancies; Wave 1 smoker status to Wave 2 expectancies; and Wave 2 expectancies to Wave 3 smoker status. Of those four new pathways, three were significantly greater than zero. The only nonsignificant pathway was from Wave 1 smoker status to Wave 2 expectancies. Values for all four pathways are provided in Figure 1. We also found support for the following mediational pathway: Expectancies at Wave 1 predicted Wave 2 smoker status, which in turn predicted Wave 3 expectancies (b ⫽ .01, z ⫽ 2.08, p ⬍ .02). Using the scaled ␹2 test of model change (necessary for nonnormal distributions: Muthén & Muthén, 2004), this model fit significantly better than the first model (change in ␹2(4) ⫽ 23.58. p ⬍ .001). Fit indices for this model were: ␹2(2) ⫽ .42, not significantly different from zero; CFI ⫽ 1.0; TLI ⫽ 1.0; RMSEA ⫽ .001 (90% CI: .00 to .03), SRMR ⫽ .002. This model explained 39% of the variance in Wave 3 smoker status, and 29% of the variance in Wave 3 expectancies. One possible reason Wave 1 smoker status did not predict Wave 2 smoking expectancies is the low base rate of smokers at Wave 1. To investigate this possibility, we identified 106 Wave 1 nonsmokers, matched to the smokers on sex, pubertal status, and race, and compared that group to the 106 smokers on Wave 2 expectancies. We did so to test whether a relationship is present when the two groups are of equal size. As anticipated, Wave 1 smokers had significantly higher Wave 2 smoking expectancies, t(210) ⫽ 1.96, p ⬍ .05. To understand the effect sizes of each predictor of Wave 3 smoker status in the final model, we calculated odds ratios (OR) for each predictor: Wave 1 smoker status OR ⫽ 5.62, p ⬍ .001 (95% CI: 3.08 to 10.26); Wave 2 smoker status OR ⫽ 32.27, p ⬍ .001 (95% CI: 19.63 to 53.07); and Wave 2 smoking expectancy OR ⫽ 1.14, p ⬍ .01 (95% CI: 1.03 to 1.26). It was of course true that prior smoker status predicted current smoker status at Wave 3. Perhaps interestingly, smoker status at each wave predicted Wave

Figure 1. A depiction of the full model tested for smoker status. For ease of presentation, cross-sectional associations and disturbance terms are not depicted. All arrows reflect pathways hypothesized in the model; solid arrows were significantly greater than zero with standardized weights; dotted arrows were nonsignificant. Ex ⫽ expectancies; Sm ⫽ smoking; W1 ⫽ Wave 1; W2 ⫽ Wave 2; W3 ⫽ Wave 3; Pub ⫽ early pubertal onset. ⴱ p ⬍ .01. ⴱⴱ p ⬍ .001. N ⫽ 1,906.

3 smoker status independently. Controlling for those effects, each 1 unit increase in Wave 2 smoking expectancy was associated with being 14% more likely to be positive for smoker status at Wave 3. To look at potential differences in outcomes for smoking initiation (as opposed the results presented for smoker status above), analyses were repeated for participants who reported no history of smoking at wave 1 (n ⫽ 1800). Analyses of smoking onset yielded similar findings as both the baseline model (autoregressions plus early pubertal onset and sex) and the reciprocal model. Specifically, values for the baseline model were ␹2(11) ⫽ 37.80 (p ⬍ .001); CFI ⫽ .97; TLI ⫽ .95; RMSEA ⫽ .04 (90% CI: .02 to .05), SRMR ⫽ .03, and values for the reciprocal model were ␹2(9) ⫽ 18.96 (p ⬍ .001); CFI ⫽ .99; TLI ⫽ .98; RMSEA ⫽ .03 (90% CI: .01 to .04), SRMR ⫽ .02. Using the scaled ␹2 test of model change, the final, reciprocal model fit significantly better than the baseline model (change in ␹2(2) ⫽ 14.46, p ⬍ .001). The final model explained 25% of the variance in Wave 3 smoking and 29% of the variance in Wave 3 smoking expectancies. For 5th graders who had not smoked, their expectancies for reinforcement from smoking predicted the onset of smoking 6 months later (fall of 6th grade): b ⫽ .08, p ⬍ .01. The other reciprocal relationships between expectancies and smoking were also present: Wave 2 expectancies to Wave 3 smoking: b ⫽ .07, p ⬍ .01; Wave 2 smoking behavior to Wave 3 expectancies: b ⫽ .07, p ⬍ .01.

Model Invariance Across Sex and Race To determine whether the model was invariant across sex, using the final structural model and all participants, we considered two additional models. The first specified the same structure for both boys and girls, treating the sexes as two different groups. Fit indices for that model were: ␹2(130) ⫽ 441.44; CFI ⫽ .96; TLI ⫽ .93; RMSEA ⫽ .05 (90% CI: .04 to .05), SRMR ⫽ .06. This model did not produce a decrement in fit; applying the same model to both sexes did not reduce the fit of the structural model. The second model added the additional constraint that all prospective pathways were required to be equal for boys and girls. Fit indices for this model were: ␹2(150) ⫽ 437.94; CFI ⫽ .96, TLI ⫽ .94, RMSEA ⫽ .04 (90% CI: .04 to .06), SRMR ⫽ .06. There was no drop in fit from specifying the same magnitude of prospective influence across sex for each variable in the model. We concluded that the model was invariant across sex. For race, we restricted the test to include only African Americans and European Americans, because of the relatively low numbers of other ethnicities in the sample. We conducted the same model comparisons. The first specified the same structure for both European Americans and African Americans, defined as two different groups. Fit indices for that model were: ␹2(150) ⫽ 465.01; CFI ⫽ .95; TLI ⫽ .91; RMSEA ⫽ .05 (90% CI: .05 to .06), SRMR ⫽ .06. This model did not produce a decrement in fit. The second model added the additional constraint that all prospective pathways were required to be equal for the two races. Fit indices for this model were: ␹2(170) ⫽ 451.56; CFI ⫽ .95, TLI ⫽ .93, RMSEA ⫽ .05 (90% CI: .04 to .05), SRMR ⫽ .06. There was no drop in fit from specifying the same magnitude of prospective influence across race for each variable in the model. We concluded that the model was invariant across race.

YOUTH SMOKING EXPECTANCIES AND SMOKING EXPERIENCE

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Discussion Early adolescent smoking is associated with a host of negative outcomes, including longer exposure to damaging health effects, increased risk of nicotine dependence later in life, and stunted physical growth in girls (Chassin et al., 2000; Stice & Martinez, 2005; Wills et al., 2001). Thus, understanding the precursors of early adolescent smoking is crucial for targeted prevention and early intervention efforts. This report describes a successful test of a reciprocal learning-behavior risk model to predict smoker status and smoking onset during the transition from elementary school to middle school. Four findings of this study may be particularly important. First, we found that smoking expectancies can be assessed reliably in elementary schoolchildren. Second, the two expectancies we studied, for social facilitation and for negative affect reduction from smoking, were highly correlated and had the same pattern of correlations with other variables. Thus, we found no evidence of separate functions for different forms of smoking reinforcement expectancies. Third and most importantly, we found that expectancies for reinforcement from smoking, held by elementary schoolchildren, predicted subsequent smoking behavior, including the onset of smoking. The finding that smoking expectancies predict smoking onset suggests that expectancy development is influenced by factors other than direct smoking experience, such as modeling. In addition to modeling by significant others, there is evidence that media-based modeling, such as behavior in movies, influences this process (Lochbuehler, Sargent, Scholte, Pieters, & Engels, 2012). These findings highlight the potential value of preventive intervention during the elementary school years. Fourth, we found reciprocal, positive prediction from smoking experience to subsequent smoking expectancies. That is, early smoking led to increased endorsement of expectancies for reinforcement from smoking. There was no evidence of a corrective process, in which early smoking led to reduced expectancy-based risk; the opposite pattern was present. These findings are consistent with what has been observed in older youth, including midadolescents and emerging adults (Doran et al., 2011; Heinz et al., 2010; Wetter et al., 1992, 1994). Psychosocial learning processes appear to increase risk for smoking not just among adolescents and young adults, but also in elementary school and beginning middle schoolchildren. In adult smoking samples, challenging smoking expectancies has been explored as a way to change motivations to smoke cigarettes (Copeland & Brandon, 2000). Future research aimed at developing similar strategies for challenging smoking expectancies among youth may be prove a useful prevention strategy in the future. The need for interventions of this kind is particularly striking in light of the finding that some youth hold high-risk smoking expectancies before their first smoking experience. The support for this risk model was invariant across sex. The model was also invariant across race; there were no significant differences between African American and European American children on any hypothesized pathway. For children in the relatively limited time window capturing the transition from elementary school to middle school, we found no evidence of sex or race differences in the risk process. Of course, because the sample size allowed only for comparisons between European Americans and

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African Americans, examination of the model in other racial groups could not be conducted and is certainly warranted. The knowledge gained from this study can be integrated with information on other predictors of very early smoking to develop more comprehensive models of risk. For example, low parental monitoring and responsiveness (Dick et al., 2007), parental smoking (Gilman et al., 2009), children’s engagement in externalizing behaviors (Brook et al., 2008; Kendler, Myers, & Keyes, 2011), and children’s engagement in internalizing behaviors (Brown, Lewinsohn, Seeley, & Wagner, 1996) all predict early smoking. In the current study, we selected one set of risk factors and tested a model of their reciprocal nature in the risk process. Ultimately, models such as the one we tested need to be integrated with other risk processes to represent the risk process more fully. It is likely that risk reflects transactions among all of these variables, and others, both within and across time. It is also likely that the nature and importance of risk factors vary across development. For example, although negative affect has proven important at older ages, it did not predict smoker status in these young children. The finding that it did predict smoking expectancies is consistent with its emergence as a risk factor later in development. It is important to note the study’s limitations. First, although we tested prospective relationships consistent with an underlying causal model, this study did not provide a direct test of causality. Second, data were collected by self-report and not by interview; thus, there was no opportunity to answer questions or clarify meanings. Third, we did not include assessment of the context of smoking; further understanding of context (Alexander, Piazza, Mekos, & Valente, 2001; Ennett & Bauman, 1993) is of course crucial. Fourth, some children had already begun smoking by the end of 5th grade. This study did not include earlier time points and, therefore, did not account for the very earliest stages of early adolescent smoking. Fifth, smoker status was measured by a single variable; improved measurement in future studies would be beneficial. Sixth, because of low endorsement of higher-frequency smoking his sample, smoking was dichotomized into a single smoker status variable; therefore, the potential importance of variability within the smoking group could not be assessed in this study. Seventh, personality factors, including traits that predispose adolescents to risky behaviors, were not included in the current model. Studying personality within a smoking expectancy-behavior model would provide a more comprehensive picture about developmental processes that increase risk for smoking behaviors in adolescence, a crucial period involved in the trajectory of smoking behavior throughout adulthood (Chassin et al., 2000). Possibilities such as the acquired preparedness model of risk (Combs et al., 2012; Doran et al., 2013; Gunn & Smith, 2010; Pearson, Combs, & Smith, 2010; Pearson, Combs, Zapolski, & Smith, 2012), which specifies a process by which personality increases risk because of its influence on the learning process, could provide greater insight into the development of smoking during adolescence, thus facilitating the development of prevention and intervention strategies aimed at reducing smoking risk during this developmental period in the future. Thus far, only two studies have investigated the acquired preparedness model of smoking. The first was on 5th grade children: Combs et al. (2012) found that the trait of urgency, the tendency to act rashly when emotional, concurrently predicted smoking expectancies, and statistical tests were consistent with the

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hypothesis that urgency’s prediction of smoker status was mediated by smoking expectancies. The cross-sectional nature of the study is an important limitation of a test of mediation, and it also prevented a test of the reciprocal model of smoking expectancies and behaviors. The second study, conducted by Doran and colleagues (2013), investigated college freshmen. They did utilize a longitudinal design and found that urgency and sensation seeking had predictive influences on subsequent smoking that appeared to be mediated by smoking expectancies. That study did not address potential reciprocal relationships between smoking and expectancies. Integration of both reciprocal and acquired preparedness models would be crucial in understanding smoking behaviors in this age group. Future analyses of youth, spanning a greater longitudinal period, may be useful in furthering understanding of the transactional, predictive relationships among personality, smoking expectancies, and smoking behavior. Finally, the group level analyses conducted in this study do not allow for identification of different developmental pathways that might be taken by different children. Inclusion of later time points and utilization of person-centered analytic approaches may therefore be useful in identifying (a) different risk processes for different children, (b) the long-term implications of different risk pathways, and (c) moderators of such risk processes. In summary, the current study adds to what is known about developmental processes that predict the important problem of very early smoking in children. Learning and behavioral factors, including those related to smoking expectancies and engagement in smoking behaviors, should be integrated with other knowledge to develop more comprehensive models of the risk process.

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Received February 6, 2014 Revision received March 19, 2014 Accepted March 26, 2014 䡲