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Personality and substance use correlates of e-cigarette use in college students
Personality and Individual Differences 152 (2020) 109605
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Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Personality and substance use correlates of e-cigarette use in college students☆
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James B. Hittner , Neha Penmetsa, Vincent Bianculli, Rhonda Swickert Department of Psychology, College of Charleston, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: E-cigarettes Vaping Smoking Substance use Personality Forgiveness
Many studies have examined psychological and substance use correlates of e-cigarette use. However, several potentially important personality and substance use variables have yet to be considered. In an effort to remedy this omission, we studied the associations among e-cigarette use, personality, and substance use in a sample of 380 college students. All associations were examined for both weekday and weekend e-cigarette use. The mean age of the sample was 19.1 with a standard deviation of 1.7. Regarding current e-cigarette use, 11.8% of participants reported using e-cigarettes on weekdays and 13.9% reported weekend use. The variables most strongly associated with being an e-cigarette user versus non-user were amount of tobacco cigarette smoking, being male, taking a prescribed stimulant medication for a diagnosed medical condition, and low levels of forgiveness toward others. Two of the variables (taking a prescribed stimulant medication and low forgiveness) are novel predictors that appear to have not been previously examined. Implications of the results for understanding e-cigarette use are discussed and directions for future research are considered.
1. Introduction Electronic nicotine delivery systems, also known as e-cigarettes, are battery-powered devices that deliver nicotine through inhaled vapor. Although e-cigarettes expose users to fewer toxicants and carcinogenic substances than do tobacco cigarettes (Prochaska, 2018), there are many negative consequences associated with e-cigarette use including headaches, trouble breathing, persistent coughing, dizziness, nose bleeds, and heart palpitations (Gostin & Glasner, 2014). In addition, acute nicotine toxicity is a concern. It is estimated that one prefilled liquid nicotine JUUL pod (a popular e-cigarette device) contains as much nicotine as a full pack of cigarettes (Campaign for Tobacco Free Kids, 2018). The prevalence of e-cigarette use (i.e., vaping) among adolescents and emerging adults has increased in recent years. For example, McCabe, West, Veliz, and Boyd (2017) estimated that 9.9% of U.S high school seniors engaged in past-month e-cigarette use. In a study of over 1400 college students from the state of New York, 29.9% reported using e-cigarettes at least once and 14.9% reported being current e-cigarette users (Saddleson et al., 2015). In a large study of U.S. adults, Mirbolouk et al. (2018) found that 9.2% of individuals aged 18 to 24 were current e-cigarette users, and that this age group had the highest prevalence of current e-cigarette use. Similarly, McMillen,
Gottlieb, Shaefer, Winickoff, and Klein (2015) found that adults aged 18–24, compared to other adult age groups, had the highest prevalence of e-cigarette use. To summarize, young adults in general, and college students in particular, have the highest prevalence rates of e-cigarette use (Baeza-Loya et al., 2014; Littlefield, Gottlieb, Cohen, & Trotter, 2015; McMillen et al., 2015; Mirbolouk et al., 2018; Saddleson et al., 2015; Trumbo & Harper, 2013). Given the growing popularity of e-cigarette use on college campuses, a number of recent studies have examined the psychosocial and substance use correlates of e-cigarette use among emerging adults (e.g., Choi & Forster, 2013; Lee, Lin, Seo, & Lohrmann, 2017; Saddleson et al., 2015). These studies consistently find that e-cigarette use is strongly positively associated with being male and being a tobacco cigarette smoker (Anand et al., 2015; Lee et al., 2017; Mirbolouk et al., 2018; Saddleson et al., 2015). Other variables that are positively associated, albeit less consistently, with e-cigarette use among emerging adults include alcohol use (Hefner, Sollazzo, Mullaney, Coker, & Sofuoglu, 2019; Saddleson et al., 2015), marijuana use (Azagba & Wolfson, 2018; Lee et al., 2017; Saddleson et al., 2015), the anticipated positive consequences associated with e-cigarette use (Pineiro et al., 2016), impulsivity (Cohn et al., 2015), and sensation seeking (Hampson, Andrews, Severson, & Barckley, 2015; Hanewinkel & Isensee, 2015).
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We thank Lisa Ross for her helpful comments on an earlier version of this article. Portions of this research were previously presented at the annual meeting of the Southeastern Psychological Association, Charleston, South Carolina, USA (March, 2018). ⁎ Corresponding author at: Department of Psychology, College of Charleston, 66 George Street, Charleston, SC 29424, USA. E-mail address: [email protected] (J.B. Hittner). https://doi.org/10.1016/j.paid.2019.109605 Received 4 June 2019; Received in revised form 22 August 2019; Accepted 3 September 2019 0191-8869/ © 2019 Elsevier Ltd. All rights reserved.
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exploratory predictors, we extrapolate from the cigarette use literature and hypothesize that four of the Big Five factors (i.e., higher extraversion, higher neuroticism, lower conscientiousness, and lower agreeableness) will predict e-cigarette use. We also hypothesize that low forgiveness of others will predict e-cigarette use. For the remaining exploratory predictors (see Method section), we offer no specific a priori hypotheses.
Somewhat surprisingly, it appears that foundational personality characteristics (i.e., the Big Five factors) have not yet been examined in the e-cigarette use literature. The Big Five personality factors consist of openness to experience, conscientiousness, extraversion, agreeableness and neuroticism (Goldberg, 1999). In the tobacco use literature, at least three metaanalyses found associations between the Big Five and cigarette smoking (Hakulinen et al., 2015; Malouff, Thorsteinsson, & Schutte, 2006; Munafo, Zetteler, & Clark, 2007). Although these meta-analyses are not statistically independent (there are overlapping studies), the main findings were that high extraversion, high neuroticism, low conscientiousness, and low agreeableness all predicted a greater likelihood of cigarette smoking. Given the consistent association between cigarette smoking and e-cigarette use (Anand et al., 2015; Mirbolouk et al., 2018; Saddleson et al., 2015), it is perhaps reasonable to surmise that these same Big Five factors might also predict e-cigarette use. In addition to the Big Five, another personality construct that has yet to be examined in relation to e-cigarette use is dispositional forgiveness. The expression of forgiveness has been shown to promote both psychological and physical health (Toussaint, Worthington, & Williams, 2015). In a study by Ross, Hertenstein, and Wrobel (2007), forgiveness of others was negatively associated with personality characteristics from the emotional/erratic cluster, such as impulsivity, unpredictability, and risk-taking. A meta-analysis by Fehr, Gelfand, and Nag (2010) found negative correlations between forgiveness and rumination, negative mood, and anger. One interpretation of these findings is that behaving in an unforgiving manner is associated with heightened negative affect and that the tendency to ruminate serves to perpetuate one's level of distress. Consistent with this proposition, McCullough, Bono, and Root (2007) found that ruminating about interpersonal transgressions induces negative thoughts and aversive emotional states, which then inhibits forgiveness. Given the association between low forgiveness and heightened negative affect (Fehr et al., 2010; McCullough et al., 2007), and considering previous research indicating that nicotine use can reduce negative affect (Kassel et al., 2007), it might be the case that individuals who are interpersonally unforgiving are more inclined to gravitate toward e-cigarettes. Additional data bearing on the link between forgiveness and substance use (e.g., e-cigarette use) come from the drug and alcohol treatment literature. For example, Lin, Mack, Enright, Krahn, and Baskin (2004) found that drug users who underwent forgiveness therapy showed significant reductions in overall vulnerability to drug use. In a study of alcohol dependent adults, Krentzman, Strobbe, Harris, Jester, and Robinson (2017) found favorable effects of treatment in that active Alcoholics Anonymous involvement was associated with greater forgiveness of others. To sum up thus far, a review of the literature suggests that several personality constructs—specifically, the Big Five and forgiveness—might be associated with e-cigarette use. However, given the paucity of research directly linking these predictors to e-cigarette use and the lack of a theoretical framework for unifying these predictors, any inquiry examining the association between these predictors and ecigarette use would be largely exploratory. While it is true that exploratory research is not well positioned to test theory-driven hypotheses, exploratory work can be instrumental for hypothesis generation (Sijtsma, 2016). As Gelman (2016) wrote: “Data-based exploration and hypothesis generation are central to science”. Building on previous research, the purpose of this study was to examine substance use, personality variables, and gender as predictors of e-cigarette use in a young adult sample. By examining a mix of traditional (e.g., gender, tobacco cigarette use) and exploratory (e.g., forgiveness, Big Five) predictors, we hope to contribute new information to the growing literature on e-cigarette use. Regarding the traditional predictors, we hypothesize that male gender status, cigarette smoking, alcohol use, and marijuana use will predict e-cigarette use. These predictions are consistent with previous research. Regarding the
2. Method 2.1. Participants Participants were college undergraduates (N = 380) attending a medium-sized university in the southeastern United States. There were 299 females and 81 males, and participants ranged in age from 17 to 30 years (M = 19.1, SD = 1.7). Regarding race/ethnicity, the breakdown was: 307 White/Caucasian, 36 Black/African American, 7 Hispanic/Latino, 13 Asian, 1 American Indian or Alaskan Native, 2 Native Hawaiian/Pacific Islander, and 14 Other. The breakdown in terms of educational level (i.e., year in college) was: Freshman (68.1%), Sophomore (20.3%), Junior (6.6%), Senior (5%). 2.2. Measures 2.2.1. Traditional predictors Four variables constitute traditional predictors (defined as variables found in previous studies to be consistently, or at least somewhat consistently, associated with e-cigarette use): (1) gender, (2) alcohol use, (3) tobacco cigarette use, and (4) marijuana use. We assessed gender using the following item (meant to minimize sociocultural constructions of gender): “What is your assigned (at birth) biological sex?” Response options were “male” or “female”. Amount of alcohol use was assessed using the following two items: “How much alcohol do you usually drink on a typical weekday?” and “How much alcohol do you usually drink on a typical weekend day?” For both items, the response options were “1 or 2 drinks”, “3 or 4 drinks”, “5 or 6 drinks”, “7 or more drinks”, “I do not drink alcohol on weekdays” (changed to “weekends” for the weekend alcohol item). Similarly, two items assessed tobacco cigarette use: “How many cigarettes do you usually smoke on a typical weekday?” and “How many cigarettes do you usually smoke on a typical weekend day?” The response options were “Between 1 and 10 cigarettes”, “Between 11 and 15 cigarettes”, “Between 16 and 20 cigarettes”, “Between 21 and 40 cigarettes”, “More than 40 cigarettes”, “I do not smoke cigarettes on weekdays” (changed to “weekends” for the weekend cigarette item). Finally, two items assessed amount of marijuana use: “How many times do you usually smoke marijuana on a typical weekday?” and “How many times do you usually smoke marijuana on a typical weekend day?” The response options were “1 or 2 times”, “3 or 4 times”, “5 or 6 times”, “7 or more times”, “I do not use marijuana on weekdays” (changed to “weekends” for the weekend marijuana item). 2.2.2. Exploratory predictors 2.2.2.1. The Mini-IPIP. The Mini-IPIP (Donnellan, Oswald, Baird, & Lucas, 2006) is a 20-item questionnaire measuring the Big Five Personality Factors (Goldberg, 1999). The Big Five consist of openness to experience (labeled “intellect” in the Mini-IPIP), conscientiousness, extraversion, agreeableness and neuroticism. Each personality dimension is measured using four items, and the degree to which each item is self-descriptive is indicated using a 5-point scale ranging from “very inaccurate” to “very accurate”. For all five personality dimensions, higher scores indicate greater levels of the factor being measured. Internal consistency reliability estimates (Cronbach's alphas) for the five scales were: intellect (0.65), conscientiousness (0.72), extraversion (0.79), agreeableness (0.68), neuroticism (0.58). 2
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2.3. Procedure
2.2.2.2. Heartland forgiveness scale. This 18-item measure assesses one's dispositional tendency toward forgiveness (Thompson et al., 2005). Dispositional forgiveness can be defined as the general tendency to be a forgiving person. For each item, participants respond using a 7-point scale ranging from “Almost Always False” to “Almost Always True.” In addition to a total scale score (Cronbach's alpha = 0.87), the Heartland Forgiveness Scale contains three 6-item subscales (alpha coefficients in parentheses): forgiveness of self (0.77), forgiveness of others (0.82), and forgiveness of situations (0.80). Higher scores on all scales indicate greater levels of forgiveness.
This IRB-approved study was conducted during the 2016 and 2017 academic years. Participants were recruited through the university's Psychology Department participant pool. Each participant completed an anonymous online survey that required 20–30 min and all respondents were granted course credit for their participation. No personal identifying information was collected and all responses were maintained on a secure server. 2.4. Data analyses
2.2.2.3. Stimulant use. We assessed the following two variables: (1) use of prescription stimulant medications for recreational purposes, and (2) use of stimulant medication, such as Ritalin, for a prescribed medical condition. For recreational stimulant medication use, participants were presented with two parallel items: one for weekday and one for weekend use. To avoid sounding redundant we present only the weekday use item, which was: “How often do you use stimulantbased prescription medications, such as Ritalin or Adderall, for nonprescription recreational purposes on a typical weekday?” The response options were “1 or 2 times”, “3 or 4 times”, “5 or 6 times”, “7 or more times”, “I do not use stimulant-based medications for recreational purposes on weekdays”. To assess the second variable, we asked the following question: “Do you currently use a stimulant medication, such as Ritalin or Adderall, for a prescribed medical condition?” The response options were “Yes” or “No”.
First, we calculated a Bayes factor for each Traditional and Exploratory predictor variable. A Bayes factor is a ratio of two probabilities: the probability of the data given the alternative model divided by the probability of the data given the null model. In the case of continuously-scaled predictors (e.g., forgiveness scores), the null model would state that mean forgiveness scores are the same for e-cigarette users and non-users. For nominally-scaled categorical predictors (e.g., gender), the null model would state that the number of e-cigarette users is the same for males and females. The larger the Bayes factor, the greater the evidence in support of the alternative model (e.g., that forgiveness scores differ for e-cigarette users and non-users, or that the number of e-cigarette users differs for males and females). All Bayes factors were calculated twice: once for weekday e-cigarette use vs. nonuse, and once for weekend e-cigarette use vs. non-use. Regarding software implementation, the Bayes factors were computed using the JASP computer program (JASP Team, 2017) and were parameterized as twotailed tests using the default prior specification (a Cauchy distribution with a scaling factor of 0.707, see Rouder, Speckman, Sun, Morey, & Iverson, 2009). Unlike frequentist statistical tests (e.g., t-tests), the process of conducting multiple Bayes factors exerts little, if any, influence on the familywise Type I error rate (Rouder et al., 2009; Wagenmakers, Lee, Lodewyckx, & Iverson, 2008). Furthermore, in contrast to performing null hypothesis significance testing (NHST) using frequentist test statistics, Bayes factors directly consider the alternative hypothesis. NHST procedures, on the other hand, do not empirically evaluate the alternative hypothesis. Rather, they are concerned solely with either refuting or not refuting the null hypothesis (Wagenmakers et al., 2008). Second, as a follow up to the Bayes factors, two Bayesian logistic regression analyses were performed, one for weekday and one for weekend e-cigarette use vs. non-use. One advantage of Bayesian (versus frequentist) logistic regression is that Bayesian-generated estimates tend to have lower variance (Gelman et al., 2013). A second advantage is that a Bayesian estimate is accompanied by a credible interval, which can be directly interpreted as a range of values that contain, with a specified degree of probability, the true parameter estimate/true population value (Gelman et al., 2013). This is in contrast to frequentist confidence intervals, which require the assumption of repeated hypothetical sampling in order to make inferences about the true population parameter. For the present analyses, the predictors consisted of those variables that had large Bayes factors (see Results section, and see Lee & Wagenmakers, 2013 for general guidelines). For each predictor, we calculated the mean parameter estimate (regression coefficient) from the posterior distribution by running 10,000 Markov Chain Monte Carlo (MCMC) samples (discarding the first 1000 as burn-in samples). Regarding the prior distribution of regression coefficient parameters, these parameters were given a joint prior distribution specified by a Gaussian/Normal variance mixture (Makalic & Schmidt, 2016). This is a commonly selected default prior distribution for Bayesian regression (Makalic & Schmidt, 2016). The quantities of interest from both regressions (i.e., weekday and weekend e-cigarette use) are the mean regression coefficients and accompanying 95% credible intervals for each predictor, and the importance ranking (rank-ordering) of the predictors. A mean coefficient is interpreted as being substantively
2.2.2.4. Risk-taking behavior. We asked the following two questions: (1) “How frequently do you engage in risky behavior?”, and (2) “How frequently do you intentionally/knowingly approach potentially harmful situations?” The response options were “very frequently”, “somewhat frequently”, “neither frequently nor infrequently”, “somewhat infrequently”, “very infrequently”. For both items, scores were reverse coded such that higher scores indicate greater risk-taking.
2.2.3. Control variable (covariate) 2.2.3.1. Marlowe-Crowne social desirability scale. The 13-item MarloweCrowne Social Desirability Scale (Reynolds, 1982) was used to assess the tendency that participants might have to portray themselves in an overly socially appropriate manner. For each item, participants indicate whether they believe the statement is “true” or “false” of them, and higher scores indicate stronger social desirability response tendencies. The Cronbach's alpha coefficient was 0.68.
2.2.4. Criterion variables The criterion variables (the outcome variables to be predicted) were a pair of binary variables indicating whether participants (1) currently use or do not use e-cigarettes on weekdays, and (2) currently use or do not use e-cigarettes on weekends. The items used to assess weekday and weekend e-cigarette use were: “How many times do you usually smoke e-cigarettes on a typical weekday?” and “How many times do you usually smoke e-cigarettes on a typical weekend day?” The response options were “1 or 2 times”, “3 or 4 times”, “5 or 6 times”, “7 or more times”, and “I do not smoke e-cigarettes on weekdays” (changed to “weekends” for the weekend e-cigarette item). Although there are multiple response options, the number of e-cigarette users (see Results section) was not large enough to separately statistically model the different frequency of use categories. Consequently, for weekday and weekend use separately, we created a binary variable whereby those participants reporting any e-cigarette use were categorized as “current e-cigarette users” and those reporting no vaping at all were categorized as “e-cigarette non-users”. These two binary variables served as our outcome variables of interest. 3
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Of the 380 participants, 45 (11.8%) stated that they currently use ecigarettes on weekdays (29.6% of males, 7% of females). A similar number (53, 13.9%) reported using e-cigarettes on weekends (30.9% of males, 9.4% of females). There were no differences in social desirability response bias (Marlowe-Crowne scores) between e-cigarette users and non-users.
Interval: [0.01, 1.54], t = 1.93). Turning now to weekend use, the following variables, listed in rankorder starting with the strongest predictor, were best able to discriminate e-cigarette use from non-use: higher levels of weekend tobacco cigarette smoking, taking a prescribed stimulant medication for a diagnosed condition, being male, and lower levels of forgiveness toward others. For these predictors, the mean regression coefficient parameter estimates, the 95% credible intervals, and the Bayesian t-test values were as follows: weekend cigarette smoking (M = 1.27, 95% Credible Interval: [0.44, 2.17], t = 2.89), prescribed stimulant medication (M = 1.12, 95% Credible Interval: [0.31, 1.95], t = 2.67), male gender (M = −0.75, 95% Credible Interval: [−1.46, −0.07], t = −2.12), forgiveness of others (M = −0.67, 95% Credible Interval: [−1.37, −0.02], t = −1.97).
3.2. Bayes factors
4. Discussion
For both weekday and weekend e-cigarette use, large Bayes factors (> 12) were obtained for alcohol use, tobacco cigarette use, marijuana use, recreational stimulant medication use, frequency of risky behavior, and the tendency to approach potentially harmful situations. For all of these behaviors, e-cigarette users reported higher levels/greater frequencies than did non-users. The Bayes factors also indicated that ecigarette users were more likely to be male, less likely to be forgiving of others, and more likely to use a prescribed stimulant medication for a diagnosed medical condition (all Bayes factors > 12). Regarding forgiveness of others, the mean score for non-users (29.2) was similar to normative data reported by Thompson et al., 2005 (30.2, averaged across three samples). In contrast, the mean score for e-cigarette users was 26.2. Regarding prescribed stimulant medication use, 49 participants (13% of the sample) reported being current users of a prescribed stimulant medication. Furthermore, 38.8% of those individuals taking a prescribed stimulant, and 7.9% of those not taking a prescribed stimulant, reported being current weekday e-cigarette users. Similarly, 38.8% of those taking a prescribed stimulant, and 10.3% of those not taking a prescribed stimulant, reported being current weekend e-cigarette users. The Bayes factors for all other predictors were small (< 3.5) and thus not clearly supportive of the alternative model (i.e., a model which posits differences between e-cigarette users and nonusers). Because a Bayes factor is a ratio quantifying the relative weight of evidence for the alternative versus null model, it can be conceptualized as an odds-ratio type statistic. A Bayes factor of 12, for example, indicates that the alternative model is 12 times more likely than the null model. For guidelines concerning which Bayes factor magnitudes constitute small, medium and large effects, see Lee and Wagenmakers (2013).
This study looked at personality, substance use, and gender as predictors of e-cigarette use in a young adult sample. Unlike previous studies, we examined both a mix of traditional predictors that have been directly linked to e-cigarette use, and promising exploratory predictors that, based on related streams of research, might be associated with e-cigarette use. Regarding the traditional predictors, we hypothesized that marijuana use, alcohol use, male gender status, and cigarette smoking would predict e-cigarette use. The Bayes factors indicated that marijuana use predicted both weekday and weekend ecigarette use, but this variable did not emerge as a substantive predictor in the Bayesian regressions. Such a pattern (i.e., predictive in one type of Bayesian analysis but not both) suggests that marijuana use is a moderately important predictor. This interpretation is commensurate with past research indicating that marijuana use is an inconsistent predictor of e-cigarette use (Azagba & Wolfson, 2018; Lee et al., 2017; Saddleson et al., 2015). Regarding alcohol use, the Bayes factors indicated that this variable was a substantive predictor of both weekday and weekend e-cigarette use. Furthermore, alcohol use predicted weekday e-cigarette use in the Bayesian regression. This pattern (i.e., a substantive predictor in both Bayes factor analyses and one of the two regressions) suggests that alcohol use is a stronger predictor of e-cigarette use than is marijuana use. Future research is needed to assess the replicability of these alcohol and marijuana use effects. The remaining two traditional predictors—male gender and cigarette smoking—were the strongest of the four predictors in this category. These two variables were substantive predictors in both Bayes factor analyses and both Bayesian regressions. Moreover, these findings are consistent with previous studies highlighting the importance of male gender and cigarette smoking as predictors of e-cigarette use (Anand et al., 2015; Lee et al., 2017; Mirbolouk et al., 2018; Saddleson et al., 2015). Regarding the exploratory predictors, we hypothesized that four of the Big Five factors (i.e., extraversion, neuroticism, conscientiousness, and agreeableness) would predict e-cigarette use. Contrary to our expectations, none of these variables were substantive predictors in any of the analyses. However, it is important to recall that these predictions were based on findings from previous tobacco cigarette use studies, rather than studies on e-cigarette use per se. Thus, one interpretation is that the predictive ability of the Big Five does not generalize from tobacco cigarette use to e-cigarette use. A different, opposing interpretation is that the Big Five can predict e-cigarette use, but that the instrument used in the present study (the Mini-IPIP) was insufficient to capture such associations. One consideration is the length of the MiniIPIP (20-items, 4-items per Big Five factor). Perhaps a longer scale with more items per factor (greater content coverage) would have yielded different results. Another consideration concerns the suboptimal reliability coefficients that we found for agreeableness (α = 0.68) and neuroticism (α = 0.58). These lower reliability values may have attenuated the associations between these two factors and e-cigarette use. This explanation does not apply to extraversion and conscientiousness
important when the 95% credible interval excludes zero. The Bayesian logistic regression analyses were conducted using the Bayesreg package for R (Makalic & Schmidt, 2016). 3. Results 3.1. Prevalence of e-cigarette use
3.3. Bayesian logistic regressions All of the predictors that had large Bayes factors were examined using Bayesian logistic regression. This multivariate procedure pits the predictors against each other to see which variables are most strongly associated with e-cigarette use. Beginning with weekday use, the following variables, listed in rank-order starting with the strongest predictor, were best able to discriminate e-cigarette use from non-use: taking a prescribed stimulant medication for a diagnosed condition, higher levels of weekend tobacco cigarette smoking, being male, lower levels of forgiveness toward others, and greater weekday alcohol use. For these predictors, the mean regression coefficient parameter estimates, the 95% credible intervals, and the Bayesian t-test values were as follows: prescribed stimulant medication (M = 1.52, 95% Credible Interval: [0.64, 2.42], t = 3.37), weekend cigarette smoking (M = 1.37, 95% Credible Interval: [0.41, 2.36], t = 2.76), male gender (M = −1.09, 95% Credible Interval: [−1.89, −0.32], t = −2.75), forgiveness of others (M = −0.83, 95% Credible Interval: [−1.63, −0.11], t = −2.16), weekday alcohol use (M = 0.75, 95% Credible 4
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important in one, but not both, of our Bayesian analyses, we regard them as being of modest import in predicting e-cigarette use. As with most empirical studies, it is important for future research to replicate these findings, especially since many of the predictors were exploratory-based. A number of scholars have argued that exploratory research is under-utilized but is important for hypothesis generation and identifying potentially important associations that may lead to new insights (Gelman, 2016; Sijtsma, 2016). As Gelman (2016) wrote: “Data-based exploration and hypothesis generation are central to science”. Despite several considerations that limit the generalizability of our results (e.g., all participants sampled from a single university, a moderate number of e-cigarette users), there were also several strengths worth emphasizing. First, two novel predictors that we believe warrant further research attention (taking a prescribed stimulant medication for a diagnosed medical condition and low forgiveness of others) were consistently associated with e-cigarette use vs. non-use. Second, our findings are not based on null hypothesis significance testing, the limitations of which are well documented (e.g., Kline, 2004), but, instead, result from a Bayesian model testing approach whereby the alternative model is directly compared against the null model. Third, and related to the second point above, our two-pronged model testing approach using both Bayes factors and Bayesian logistic regression can serve as a data analytic template for other researchers to follow. In conclusion, we hope that our findings will stimulate both replication efforts and further inquiries regarding the predictors and consequences of e-cigarette use.
because the reliability coefficients for these two factors were acceptable (α's of 0.79 and 0.72, respectively). Further research is needed to evaluate the associations between the Big Five factors and e-cigarette use. In addition to the Big Five, another personality factor that might be worth examining is psychoticism (Eysenck & Eysenck, 1977). Individuals scoring high in psychoticism typically are disagreeable, impersonal, unemotional, and antisocial. Such characteristics, either directly or indirectly through compromised relationships, could predispose one toward negative affect which, as discussed previously, could be a risk factor for e-cigarette use. Future research might benefit by examining psychoticism as a predictor of e-cigarette use. In addition to the four Big Five factors, we also hypothesized that low levels of forgiveness toward others would predict e-cigarette use. This hypothesis was supported across all four Bayesian analyses. Being unwilling to forgive others not only heightens negative affect but is also a stressful experience for the unforgiving person (Fehr et al., 2010; McCullough et al., 2007). According to Maltby, Macaskill, and Day (2001), lack of other forgiveness represents an extrapunitive interpersonal style whereby the unforgiving person seeks revenge and blames others for their transgressions. For the relatively unforgiving person who is already at risk for e-cigarette use (i.e., male, tobacco cigarette smoker, stimulant medication user), e-cigarettes might seem particularly attractive given the well-documented link between nicotine use and mood regulation (Kassel et al., 2007). Further research is needed to test this hypothesis. Overall, our findings were similar for weekday and weekend e-cigarette use. Furthermore, the most important predictors of e-cigarette use were: higher levels of cigarette smoking, being male, taking a prescribed stimulant medication for a diagnosed condition, lower levels of forgiveness toward others, and greater weekday alcohol use (the latter being a substantive predictor of weekday e-cigarette use only). As noted in the Results section, all of these predictors had large Bayes factors and they were the top-ranked predictors in the Bayesian regressions. Several of these variables (i.e., tobacco cigarette smoking, male gender, alcohol use) have previously been identified as important predictors of e-cigarette use (Anand et al., 2015; Choi & Forster, 2013; Hefner et al., 2019; Saddleson et al., 2015). The remaining two predictors—low forgiveness of others and taking a prescribed stimulant medication for a diagnosed condition—have not been the focus of earlier research. In fact, to the best of our knowledge, these findings are novel and represent the first report of substantive associations between these two variables and e-cigarette use. The fact that e-cigarette users were substantially more likely to take a prescribed stimulant medication is interesting, but an explanation for this association is not readily apparent. Given the pharmacological similarity between a prescribed stimulant and inhaled nicotine-infused vapor (both are psychostimulants) and considering the reinforcing effects of stimulant use (Advokat, Comaty, & Julien, 2014), perhaps college-aged users of prescribed stimulants are inclined to gravitate toward e-cigarette use, especially if they are males who smoke tobacco cigarettes. Our working assumption is that prescribed stimulant medication use precedes e-cigarette use, but we did not collect data bearing on temporal sequencing so we can't rule out a reverse pattern whereby e-cigarette use precedes stimulant medication use. Additional research is needed to further address this issue. To summarize, the most robust predictors of e-cigarette use (i.e., those variables that had both large Bayes factors and 95% Bayesian logistic regression credible intervals that excluded zero), were amount of tobacco cigarette smoking, taking a prescribed stimulant medication for a diagnosed medical condition, being male, low forgiveness of others, and amount of weekday alcohol use (the latter being important for weekday e-cigarette use only). There were other variables that had large Bayes factors but did not emerge as substantively important in the Bayesian regressions, most notably, marijuana use, recreational stimulant medication use, frequency of risky behavior, and the tendency to approach potentially harmful situations. Because these predictors were
Informed consent Informed consent was obtained from all participants in the study. Declaration of competing interest The authors declare that they have no conflict of interest. References Advokat, C. D., Comaty, J. E., & Julien, R. M. (2014). Primer of drug action (13th ed.). Duffield, United Kingdom: Worth Publishers. Anand, V., McGinty, K. L., O’Brien, K., Guenthner, G., Hahn, E., & Martin, C. A. (2015). Ecigarette use and beliefs among urban public high school students in North Carolina. Journal of Adolescent Health, 57, 46–51. Azagba, S., & Wolfson, M. (2018). E-cigarette use and quantity of cigarette smoking among adolescent cigarette smokers: A finite mixture model analysis. Drug and Alcohol Dependence, 185, 33–39. Baeza-Loya, S., Viswanath, H., Carter, A., Molfese, D. L., Velasquez, K. M., Baldwin, P. R., ... Salas, M. (2014). Perceptions about e-cigarette safety may lead to e-smoking during pregnancy. Bulletin of the Menninger Clinic, 78, 243–253. Campaign for Tobacco Free Kids (2018). JUUL and youth: Rising e-cigarette popularity. Washington, DC: Campaign for tobacco free kidshttps://www.tobaccofreekids.org/ assets/factsheets/0394.pdf. Choi, K., & Forster, J. (2013). Characteristics associated with awareness, perceptions, and use of electronic nicotine delivery systems among young U.S. Midwestern adults. American Journal of Public Health, 103, 556–561. Cohn, A., Villanti, A., Richardson, A., Rath, J. M., Williams, V., Stanton, C., & Mermelstein, R. (2015). The association between alcohol, marijuana use, and new and emerging tobacco products in a young adult population. Addictive Behaviors, 48, 79–88. Donnellan, M. B., Oswald, F. L., Baird, B. M., & Lucas, R. E. (2006). The mini-IPIP scales: Tiny- yet-effective measures of the big five factors of personality. Psychological Assessment, 18, 192–203. Eysenck, H. J., & Eysenck, S. B. (1977). Psychoticism as a dimension of personality. Oxford, England: Carne & Russak. Fehr, R., Gelfand, M. J., & Nag, M. (2010). The road to forgiveness: A meta-analytic synthesis of its situational and dispositional correlates. Psychological Bulletin, 136, 894–914. Gelman, A. (2016, November 17). Thinking more seriously about the design of exploratory studies: A manifesto [Blog post]. Retrieved from https://statmodeling.stat. columbia.edu/2016/11/17/thinking-more-seriously-about-the-design-ofexploratory-studies/. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). Boca Raton, FL: Chapman & Hall/CRC. Goldberg, L. R. (1999). A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models. In I. Mervielde, I. Deary, F. De Fruyt, & F. Ostendorf (Vol. Eds.), Personality psychology in Europe. Vol. 7. Personality psychology in Europe (pp. 7–28). Tilburg, Netherlands: Tilburg University
5
Personality and Individual Differences 152 (2020) 109605
J.B. Hittner, et al.
smoking, dual use, and problem behaviors among U.S. adolescents: Results from a national survey. Journal of Adolescent Health, 61, 155–162. McCullough, M. E., Bono, G., & Root, L. M. (2007). Rumination, emotion, and forgiveness: Three longitudinal studies. Journal of Personality and Social Psychology, 92, 490–505. McMillen, R. C., Gottlieb, M. A., Shaefer, R. M., Winickoff, J. P., & Klein, J. D. (2015). Trends in electronic cigarette use among U.S. adults: Use is increasing in both smokers and nonsmokers. Nicotine & Tobacco Research, 17, 1195–1202. Mirbolouk, M., Charkhchi, P., Kianoush, S., Uddin, S. M. I., Orimoloye, O. A., Jaber, R., & Blaha, M. J. (2018). Prevalence and distribution of e-cigarette use among U.S. adults: Behavioral risk factor surveillance system, 2016. Annals of Internal Medicine, 169, 429–438. Munafo, M. R., Zetteler, J. I., & Clark, T. G. (2007). Personality and smoking status: A meta-analysis. Nicotine and Tobacco Research, 9, 405–413. Pineiro, B., Correa, J. B., Simmons, V. N., Harrell, P. T., Menzie, N. S., Unrod, M., ... Brandon, T. H. (2016). Gender differences in use and expectancies of e-cigarettes: Online survey results. Addictive Behaviors, 52, 91–97. Prochaska, J. J. (2018). The public health consequences of e-cigarettes: A review by the National Academies of Sciences. A call for more research, a need for regulatory action. Addiction, 114, 587–589. Reynolds, W. M. (1982). Development of reliable and valid short forms of the MarloweCrowne social desirability scale. Journal of Clinical Psychology, 38, 119–125. Ross, S. R., Hertenstein, M. J., & Wrobel, T. A. (2007). Maladaptive correlates of the failure to forgive self and others: Further evidence for a two-component model of forgiveness. Journal of Personality Assessment, 88, 158–167. Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian ttests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16, 225–237. Saddleson, M., Kozlowski, L., Giovino, G., Hawk, L., Murphy, J., MacLean, M., ... Mahoney, M. (2015). Risky behaviors, e-cigarette use and susceptibility of use among college students. Drug and Alcohol Dependence, 149, 25–30. Sijtsma, K. (2016). Playing with data—Or how to discourage questionable research practices and stimulate researchers to do things right. Psychometrika, 81, 1–15. Thompson, L. Y., Snyder, C. R., Hoffman, L., Michael, S. T., Rasmussen, H. N., Billings, L. S., & Roberts, D. E. (2005). Dispositional forgiveness of self, others, and situations. Journal of Personality, 73, 313–359. Toussaint, L., Worthington, E., & Williams, D. R. (2015). Forgiveness and health. Dordrecht, Netherlands: Springer. Trumbo, C. W., & Harper, R. (2013). Use and perception of electronic cigarettes among college students. Journal of American College Health, 61, 149–155. Wagenmakers, E. J., Lee, M. D., Lodewyckx, T., & Iverson, G. (2008). Bayesian versus frequentist inference. In H. Hoijtink, I. Klugkist, & P. A. Boelen (Eds.). Bayesian evaluation of informative hypotheses (pp. 181–207). New York: Springer.
Press. Gostin, L. O., & Glasner, A. Y. (2014). E-cigarettes, vaping, and youth. Journal of the American Medical Association, 312, 595–596. Hakulinen, C., Hintsanen, M., Munafo, M. R., Virtanen, M., Kivimaki, M., Batty, G. D., & Jokela, M. (2015). Personality and smoking: Individual-participant meta-analysis of nine cohort studies. Addiction, 110, 1844–1852. Hampson, S. E., Andrews, J. A., Severson, H. H., & Barckley, M. (2015). Prospective predictors of novel tobacco and nicotine product use in emerging adulthood. The Journal of Adolescent Health, 57, 186–191. Hanewinkel, R., & Isensee, B. (2015). Risk factors for e-cigarette, conventional cigarette, and dual use in German adolescents: A cohort study. Preventive Medicine, 74, 59–62. Hefner, K. R., Sollazzo, A., Mullaney, S., Coker, K. L., & Sofuoglu, M. (2019). E-cigarettes, alcohol use, and mental health: Use and perceptions of e-cigarettes among college students, by alcohol use and mental health status. Addictive Behaviors, 91, 12–20. JASP Team (2017). JASP (Version 0.8.2) [computer software]. Kassel, J. D., Evatt, D. P., Greenstein, J. E., Wardle, M. C., Yates, M. C., & Veilleux, J. C. (2007). The acute effects of nicotine on positive and negative affect in adolescent smokers. Journal of Abnormal Psychology, 116, 543–553. Kline, R. B. (2004). Beyond significance testing: Reforming data analysis methods in behavioral research. Washington: American Psychological Association. Krentzman, A. R., Strobbe, S., Harris, J. I., Jester, J. M., & Robinson, E. A. R. (2017). Decreased drinking and alcoholics anonymous are associated with different dimensions of spirituality. Psychology of Religion and Spirituality, 9, S40–S48 Supplement 1. Lee, H., Lin, H., Seo, D., & Lohrmann, D. K. (2017). Determinants associated with ecigarette adoption and use intention among college students. Addictive Behaviors, 65, 102–110. Lee, M. D., & Wagenmakers, E. J. (2013). Bayesian cognitive modeling: A practical course. Cambridge University Press. Lin, W., Mack, D., Enright, R. D., Krahn, D., & Baskin, T. W. (2004). Effects of forgiveness therapy on anger, mood, and vulnerability to substance use among inpatient substance-dependent clients. Journal of Consulting and Clinical Psychology, 72, 1114–1121. Littlefield, A. K., Gottlieb, J. C., Cohen, L. M., & Trotter, D. R. M. (2015). Electronic cigarette use among college students: Links to gender, race/ethnicity, smoking, and heavy drinking. Journal of American College Health, 63, 523–529. Makalic, E., & Schmidt, D. F. (2016). High-dimensional Bayesian regularized regression with the BayesReg package. Available at https://arxiv.org/pdf/1611.06649.pdf. Malouff, J. M., Thorsteinsson, E. B., & Schutte, N. S. (2006). The five-factor model of personality and smoking: A meta-analysis. Journal of Drug Education, 36, 47–58. Maltby, J., Macaskill, A., & Day, L. (2001). Failure to forgive self and others: A replication and extension of the relationship between forgiveness, personality, social desirability and general health. Personality and Individual Differences, 30, 881–885. McCabe, S. E., West, B. T., Veliz, P., & Boyd, C. J. (2017). E-cigarette use, cigarette
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Contents lists available at ScienceDirect
Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Personality and substance use correlates of e-cigarette use in college students☆
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James B. Hittner , Neha Penmetsa, Vincent Bianculli, Rhonda Swickert Department of Psychology, College of Charleston, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: E-cigarettes Vaping Smoking Substance use Personality Forgiveness
Many studies have examined psychological and substance use correlates of e-cigarette use. However, several potentially important personality and substance use variables have yet to be considered. In an effort to remedy this omission, we studied the associations among e-cigarette use, personality, and substance use in a sample of 380 college students. All associations were examined for both weekday and weekend e-cigarette use. The mean age of the sample was 19.1 with a standard deviation of 1.7. Regarding current e-cigarette use, 11.8% of participants reported using e-cigarettes on weekdays and 13.9% reported weekend use. The variables most strongly associated with being an e-cigarette user versus non-user were amount of tobacco cigarette smoking, being male, taking a prescribed stimulant medication for a diagnosed medical condition, and low levels of forgiveness toward others. Two of the variables (taking a prescribed stimulant medication and low forgiveness) are novel predictors that appear to have not been previously examined. Implications of the results for understanding e-cigarette use are discussed and directions for future research are considered.
1. Introduction Electronic nicotine delivery systems, also known as e-cigarettes, are battery-powered devices that deliver nicotine through inhaled vapor. Although e-cigarettes expose users to fewer toxicants and carcinogenic substances than do tobacco cigarettes (Prochaska, 2018), there are many negative consequences associated with e-cigarette use including headaches, trouble breathing, persistent coughing, dizziness, nose bleeds, and heart palpitations (Gostin & Glasner, 2014). In addition, acute nicotine toxicity is a concern. It is estimated that one prefilled liquid nicotine JUUL pod (a popular e-cigarette device) contains as much nicotine as a full pack of cigarettes (Campaign for Tobacco Free Kids, 2018). The prevalence of e-cigarette use (i.e., vaping) among adolescents and emerging adults has increased in recent years. For example, McCabe, West, Veliz, and Boyd (2017) estimated that 9.9% of U.S high school seniors engaged in past-month e-cigarette use. In a study of over 1400 college students from the state of New York, 29.9% reported using e-cigarettes at least once and 14.9% reported being current e-cigarette users (Saddleson et al., 2015). In a large study of U.S. adults, Mirbolouk et al. (2018) found that 9.2% of individuals aged 18 to 24 were current e-cigarette users, and that this age group had the highest prevalence of current e-cigarette use. Similarly, McMillen,
Gottlieb, Shaefer, Winickoff, and Klein (2015) found that adults aged 18–24, compared to other adult age groups, had the highest prevalence of e-cigarette use. To summarize, young adults in general, and college students in particular, have the highest prevalence rates of e-cigarette use (Baeza-Loya et al., 2014; Littlefield, Gottlieb, Cohen, & Trotter, 2015; McMillen et al., 2015; Mirbolouk et al., 2018; Saddleson et al., 2015; Trumbo & Harper, 2013). Given the growing popularity of e-cigarette use on college campuses, a number of recent studies have examined the psychosocial and substance use correlates of e-cigarette use among emerging adults (e.g., Choi & Forster, 2013; Lee, Lin, Seo, & Lohrmann, 2017; Saddleson et al., 2015). These studies consistently find that e-cigarette use is strongly positively associated with being male and being a tobacco cigarette smoker (Anand et al., 2015; Lee et al., 2017; Mirbolouk et al., 2018; Saddleson et al., 2015). Other variables that are positively associated, albeit less consistently, with e-cigarette use among emerging adults include alcohol use (Hefner, Sollazzo, Mullaney, Coker, & Sofuoglu, 2019; Saddleson et al., 2015), marijuana use (Azagba & Wolfson, 2018; Lee et al., 2017; Saddleson et al., 2015), the anticipated positive consequences associated with e-cigarette use (Pineiro et al., 2016), impulsivity (Cohn et al., 2015), and sensation seeking (Hampson, Andrews, Severson, & Barckley, 2015; Hanewinkel & Isensee, 2015).
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We thank Lisa Ross for her helpful comments on an earlier version of this article. Portions of this research were previously presented at the annual meeting of the Southeastern Psychological Association, Charleston, South Carolina, USA (March, 2018). ⁎ Corresponding author at: Department of Psychology, College of Charleston, 66 George Street, Charleston, SC 29424, USA. E-mail address: [email protected] (J.B. Hittner). https://doi.org/10.1016/j.paid.2019.109605 Received 4 June 2019; Received in revised form 22 August 2019; Accepted 3 September 2019 0191-8869/ © 2019 Elsevier Ltd. All rights reserved.
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exploratory predictors, we extrapolate from the cigarette use literature and hypothesize that four of the Big Five factors (i.e., higher extraversion, higher neuroticism, lower conscientiousness, and lower agreeableness) will predict e-cigarette use. We also hypothesize that low forgiveness of others will predict e-cigarette use. For the remaining exploratory predictors (see Method section), we offer no specific a priori hypotheses.
Somewhat surprisingly, it appears that foundational personality characteristics (i.e., the Big Five factors) have not yet been examined in the e-cigarette use literature. The Big Five personality factors consist of openness to experience, conscientiousness, extraversion, agreeableness and neuroticism (Goldberg, 1999). In the tobacco use literature, at least three metaanalyses found associations between the Big Five and cigarette smoking (Hakulinen et al., 2015; Malouff, Thorsteinsson, & Schutte, 2006; Munafo, Zetteler, & Clark, 2007). Although these meta-analyses are not statistically independent (there are overlapping studies), the main findings were that high extraversion, high neuroticism, low conscientiousness, and low agreeableness all predicted a greater likelihood of cigarette smoking. Given the consistent association between cigarette smoking and e-cigarette use (Anand et al., 2015; Mirbolouk et al., 2018; Saddleson et al., 2015), it is perhaps reasonable to surmise that these same Big Five factors might also predict e-cigarette use. In addition to the Big Five, another personality construct that has yet to be examined in relation to e-cigarette use is dispositional forgiveness. The expression of forgiveness has been shown to promote both psychological and physical health (Toussaint, Worthington, & Williams, 2015). In a study by Ross, Hertenstein, and Wrobel (2007), forgiveness of others was negatively associated with personality characteristics from the emotional/erratic cluster, such as impulsivity, unpredictability, and risk-taking. A meta-analysis by Fehr, Gelfand, and Nag (2010) found negative correlations between forgiveness and rumination, negative mood, and anger. One interpretation of these findings is that behaving in an unforgiving manner is associated with heightened negative affect and that the tendency to ruminate serves to perpetuate one's level of distress. Consistent with this proposition, McCullough, Bono, and Root (2007) found that ruminating about interpersonal transgressions induces negative thoughts and aversive emotional states, which then inhibits forgiveness. Given the association between low forgiveness and heightened negative affect (Fehr et al., 2010; McCullough et al., 2007), and considering previous research indicating that nicotine use can reduce negative affect (Kassel et al., 2007), it might be the case that individuals who are interpersonally unforgiving are more inclined to gravitate toward e-cigarettes. Additional data bearing on the link between forgiveness and substance use (e.g., e-cigarette use) come from the drug and alcohol treatment literature. For example, Lin, Mack, Enright, Krahn, and Baskin (2004) found that drug users who underwent forgiveness therapy showed significant reductions in overall vulnerability to drug use. In a study of alcohol dependent adults, Krentzman, Strobbe, Harris, Jester, and Robinson (2017) found favorable effects of treatment in that active Alcoholics Anonymous involvement was associated with greater forgiveness of others. To sum up thus far, a review of the literature suggests that several personality constructs—specifically, the Big Five and forgiveness—might be associated with e-cigarette use. However, given the paucity of research directly linking these predictors to e-cigarette use and the lack of a theoretical framework for unifying these predictors, any inquiry examining the association between these predictors and ecigarette use would be largely exploratory. While it is true that exploratory research is not well positioned to test theory-driven hypotheses, exploratory work can be instrumental for hypothesis generation (Sijtsma, 2016). As Gelman (2016) wrote: “Data-based exploration and hypothesis generation are central to science”. Building on previous research, the purpose of this study was to examine substance use, personality variables, and gender as predictors of e-cigarette use in a young adult sample. By examining a mix of traditional (e.g., gender, tobacco cigarette use) and exploratory (e.g., forgiveness, Big Five) predictors, we hope to contribute new information to the growing literature on e-cigarette use. Regarding the traditional predictors, we hypothesize that male gender status, cigarette smoking, alcohol use, and marijuana use will predict e-cigarette use. These predictions are consistent with previous research. Regarding the
2. Method 2.1. Participants Participants were college undergraduates (N = 380) attending a medium-sized university in the southeastern United States. There were 299 females and 81 males, and participants ranged in age from 17 to 30 years (M = 19.1, SD = 1.7). Regarding race/ethnicity, the breakdown was: 307 White/Caucasian, 36 Black/African American, 7 Hispanic/Latino, 13 Asian, 1 American Indian or Alaskan Native, 2 Native Hawaiian/Pacific Islander, and 14 Other. The breakdown in terms of educational level (i.e., year in college) was: Freshman (68.1%), Sophomore (20.3%), Junior (6.6%), Senior (5%). 2.2. Measures 2.2.1. Traditional predictors Four variables constitute traditional predictors (defined as variables found in previous studies to be consistently, or at least somewhat consistently, associated with e-cigarette use): (1) gender, (2) alcohol use, (3) tobacco cigarette use, and (4) marijuana use. We assessed gender using the following item (meant to minimize sociocultural constructions of gender): “What is your assigned (at birth) biological sex?” Response options were “male” or “female”. Amount of alcohol use was assessed using the following two items: “How much alcohol do you usually drink on a typical weekday?” and “How much alcohol do you usually drink on a typical weekend day?” For both items, the response options were “1 or 2 drinks”, “3 or 4 drinks”, “5 or 6 drinks”, “7 or more drinks”, “I do not drink alcohol on weekdays” (changed to “weekends” for the weekend alcohol item). Similarly, two items assessed tobacco cigarette use: “How many cigarettes do you usually smoke on a typical weekday?” and “How many cigarettes do you usually smoke on a typical weekend day?” The response options were “Between 1 and 10 cigarettes”, “Between 11 and 15 cigarettes”, “Between 16 and 20 cigarettes”, “Between 21 and 40 cigarettes”, “More than 40 cigarettes”, “I do not smoke cigarettes on weekdays” (changed to “weekends” for the weekend cigarette item). Finally, two items assessed amount of marijuana use: “How many times do you usually smoke marijuana on a typical weekday?” and “How many times do you usually smoke marijuana on a typical weekend day?” The response options were “1 or 2 times”, “3 or 4 times”, “5 or 6 times”, “7 or more times”, “I do not use marijuana on weekdays” (changed to “weekends” for the weekend marijuana item). 2.2.2. Exploratory predictors 2.2.2.1. The Mini-IPIP. The Mini-IPIP (Donnellan, Oswald, Baird, & Lucas, 2006) is a 20-item questionnaire measuring the Big Five Personality Factors (Goldberg, 1999). The Big Five consist of openness to experience (labeled “intellect” in the Mini-IPIP), conscientiousness, extraversion, agreeableness and neuroticism. Each personality dimension is measured using four items, and the degree to which each item is self-descriptive is indicated using a 5-point scale ranging from “very inaccurate” to “very accurate”. For all five personality dimensions, higher scores indicate greater levels of the factor being measured. Internal consistency reliability estimates (Cronbach's alphas) for the five scales were: intellect (0.65), conscientiousness (0.72), extraversion (0.79), agreeableness (0.68), neuroticism (0.58). 2
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2.3. Procedure
2.2.2.2. Heartland forgiveness scale. This 18-item measure assesses one's dispositional tendency toward forgiveness (Thompson et al., 2005). Dispositional forgiveness can be defined as the general tendency to be a forgiving person. For each item, participants respond using a 7-point scale ranging from “Almost Always False” to “Almost Always True.” In addition to a total scale score (Cronbach's alpha = 0.87), the Heartland Forgiveness Scale contains three 6-item subscales (alpha coefficients in parentheses): forgiveness of self (0.77), forgiveness of others (0.82), and forgiveness of situations (0.80). Higher scores on all scales indicate greater levels of forgiveness.
This IRB-approved study was conducted during the 2016 and 2017 academic years. Participants were recruited through the university's Psychology Department participant pool. Each participant completed an anonymous online survey that required 20–30 min and all respondents were granted course credit for their participation. No personal identifying information was collected and all responses were maintained on a secure server. 2.4. Data analyses
2.2.2.3. Stimulant use. We assessed the following two variables: (1) use of prescription stimulant medications for recreational purposes, and (2) use of stimulant medication, such as Ritalin, for a prescribed medical condition. For recreational stimulant medication use, participants were presented with two parallel items: one for weekday and one for weekend use. To avoid sounding redundant we present only the weekday use item, which was: “How often do you use stimulantbased prescription medications, such as Ritalin or Adderall, for nonprescription recreational purposes on a typical weekday?” The response options were “1 or 2 times”, “3 or 4 times”, “5 or 6 times”, “7 or more times”, “I do not use stimulant-based medications for recreational purposes on weekdays”. To assess the second variable, we asked the following question: “Do you currently use a stimulant medication, such as Ritalin or Adderall, for a prescribed medical condition?” The response options were “Yes” or “No”.
First, we calculated a Bayes factor for each Traditional and Exploratory predictor variable. A Bayes factor is a ratio of two probabilities: the probability of the data given the alternative model divided by the probability of the data given the null model. In the case of continuously-scaled predictors (e.g., forgiveness scores), the null model would state that mean forgiveness scores are the same for e-cigarette users and non-users. For nominally-scaled categorical predictors (e.g., gender), the null model would state that the number of e-cigarette users is the same for males and females. The larger the Bayes factor, the greater the evidence in support of the alternative model (e.g., that forgiveness scores differ for e-cigarette users and non-users, or that the number of e-cigarette users differs for males and females). All Bayes factors were calculated twice: once for weekday e-cigarette use vs. nonuse, and once for weekend e-cigarette use vs. non-use. Regarding software implementation, the Bayes factors were computed using the JASP computer program (JASP Team, 2017) and were parameterized as twotailed tests using the default prior specification (a Cauchy distribution with a scaling factor of 0.707, see Rouder, Speckman, Sun, Morey, & Iverson, 2009). Unlike frequentist statistical tests (e.g., t-tests), the process of conducting multiple Bayes factors exerts little, if any, influence on the familywise Type I error rate (Rouder et al., 2009; Wagenmakers, Lee, Lodewyckx, & Iverson, 2008). Furthermore, in contrast to performing null hypothesis significance testing (NHST) using frequentist test statistics, Bayes factors directly consider the alternative hypothesis. NHST procedures, on the other hand, do not empirically evaluate the alternative hypothesis. Rather, they are concerned solely with either refuting or not refuting the null hypothesis (Wagenmakers et al., 2008). Second, as a follow up to the Bayes factors, two Bayesian logistic regression analyses were performed, one for weekday and one for weekend e-cigarette use vs. non-use. One advantage of Bayesian (versus frequentist) logistic regression is that Bayesian-generated estimates tend to have lower variance (Gelman et al., 2013). A second advantage is that a Bayesian estimate is accompanied by a credible interval, which can be directly interpreted as a range of values that contain, with a specified degree of probability, the true parameter estimate/true population value (Gelman et al., 2013). This is in contrast to frequentist confidence intervals, which require the assumption of repeated hypothetical sampling in order to make inferences about the true population parameter. For the present analyses, the predictors consisted of those variables that had large Bayes factors (see Results section, and see Lee & Wagenmakers, 2013 for general guidelines). For each predictor, we calculated the mean parameter estimate (regression coefficient) from the posterior distribution by running 10,000 Markov Chain Monte Carlo (MCMC) samples (discarding the first 1000 as burn-in samples). Regarding the prior distribution of regression coefficient parameters, these parameters were given a joint prior distribution specified by a Gaussian/Normal variance mixture (Makalic & Schmidt, 2016). This is a commonly selected default prior distribution for Bayesian regression (Makalic & Schmidt, 2016). The quantities of interest from both regressions (i.e., weekday and weekend e-cigarette use) are the mean regression coefficients and accompanying 95% credible intervals for each predictor, and the importance ranking (rank-ordering) of the predictors. A mean coefficient is interpreted as being substantively
2.2.2.4. Risk-taking behavior. We asked the following two questions: (1) “How frequently do you engage in risky behavior?”, and (2) “How frequently do you intentionally/knowingly approach potentially harmful situations?” The response options were “very frequently”, “somewhat frequently”, “neither frequently nor infrequently”, “somewhat infrequently”, “very infrequently”. For both items, scores were reverse coded such that higher scores indicate greater risk-taking.
2.2.3. Control variable (covariate) 2.2.3.1. Marlowe-Crowne social desirability scale. The 13-item MarloweCrowne Social Desirability Scale (Reynolds, 1982) was used to assess the tendency that participants might have to portray themselves in an overly socially appropriate manner. For each item, participants indicate whether they believe the statement is “true” or “false” of them, and higher scores indicate stronger social desirability response tendencies. The Cronbach's alpha coefficient was 0.68.
2.2.4. Criterion variables The criterion variables (the outcome variables to be predicted) were a pair of binary variables indicating whether participants (1) currently use or do not use e-cigarettes on weekdays, and (2) currently use or do not use e-cigarettes on weekends. The items used to assess weekday and weekend e-cigarette use were: “How many times do you usually smoke e-cigarettes on a typical weekday?” and “How many times do you usually smoke e-cigarettes on a typical weekend day?” The response options were “1 or 2 times”, “3 or 4 times”, “5 or 6 times”, “7 or more times”, and “I do not smoke e-cigarettes on weekdays” (changed to “weekends” for the weekend e-cigarette item). Although there are multiple response options, the number of e-cigarette users (see Results section) was not large enough to separately statistically model the different frequency of use categories. Consequently, for weekday and weekend use separately, we created a binary variable whereby those participants reporting any e-cigarette use were categorized as “current e-cigarette users” and those reporting no vaping at all were categorized as “e-cigarette non-users”. These two binary variables served as our outcome variables of interest. 3
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Of the 380 participants, 45 (11.8%) stated that they currently use ecigarettes on weekdays (29.6% of males, 7% of females). A similar number (53, 13.9%) reported using e-cigarettes on weekends (30.9% of males, 9.4% of females). There were no differences in social desirability response bias (Marlowe-Crowne scores) between e-cigarette users and non-users.
Interval: [0.01, 1.54], t = 1.93). Turning now to weekend use, the following variables, listed in rankorder starting with the strongest predictor, were best able to discriminate e-cigarette use from non-use: higher levels of weekend tobacco cigarette smoking, taking a prescribed stimulant medication for a diagnosed condition, being male, and lower levels of forgiveness toward others. For these predictors, the mean regression coefficient parameter estimates, the 95% credible intervals, and the Bayesian t-test values were as follows: weekend cigarette smoking (M = 1.27, 95% Credible Interval: [0.44, 2.17], t = 2.89), prescribed stimulant medication (M = 1.12, 95% Credible Interval: [0.31, 1.95], t = 2.67), male gender (M = −0.75, 95% Credible Interval: [−1.46, −0.07], t = −2.12), forgiveness of others (M = −0.67, 95% Credible Interval: [−1.37, −0.02], t = −1.97).
3.2. Bayes factors
4. Discussion
For both weekday and weekend e-cigarette use, large Bayes factors (> 12) were obtained for alcohol use, tobacco cigarette use, marijuana use, recreational stimulant medication use, frequency of risky behavior, and the tendency to approach potentially harmful situations. For all of these behaviors, e-cigarette users reported higher levels/greater frequencies than did non-users. The Bayes factors also indicated that ecigarette users were more likely to be male, less likely to be forgiving of others, and more likely to use a prescribed stimulant medication for a diagnosed medical condition (all Bayes factors > 12). Regarding forgiveness of others, the mean score for non-users (29.2) was similar to normative data reported by Thompson et al., 2005 (30.2, averaged across three samples). In contrast, the mean score for e-cigarette users was 26.2. Regarding prescribed stimulant medication use, 49 participants (13% of the sample) reported being current users of a prescribed stimulant medication. Furthermore, 38.8% of those individuals taking a prescribed stimulant, and 7.9% of those not taking a prescribed stimulant, reported being current weekday e-cigarette users. Similarly, 38.8% of those taking a prescribed stimulant, and 10.3% of those not taking a prescribed stimulant, reported being current weekend e-cigarette users. The Bayes factors for all other predictors were small (< 3.5) and thus not clearly supportive of the alternative model (i.e., a model which posits differences between e-cigarette users and nonusers). Because a Bayes factor is a ratio quantifying the relative weight of evidence for the alternative versus null model, it can be conceptualized as an odds-ratio type statistic. A Bayes factor of 12, for example, indicates that the alternative model is 12 times more likely than the null model. For guidelines concerning which Bayes factor magnitudes constitute small, medium and large effects, see Lee and Wagenmakers (2013).
This study looked at personality, substance use, and gender as predictors of e-cigarette use in a young adult sample. Unlike previous studies, we examined both a mix of traditional predictors that have been directly linked to e-cigarette use, and promising exploratory predictors that, based on related streams of research, might be associated with e-cigarette use. Regarding the traditional predictors, we hypothesized that marijuana use, alcohol use, male gender status, and cigarette smoking would predict e-cigarette use. The Bayes factors indicated that marijuana use predicted both weekday and weekend ecigarette use, but this variable did not emerge as a substantive predictor in the Bayesian regressions. Such a pattern (i.e., predictive in one type of Bayesian analysis but not both) suggests that marijuana use is a moderately important predictor. This interpretation is commensurate with past research indicating that marijuana use is an inconsistent predictor of e-cigarette use (Azagba & Wolfson, 2018; Lee et al., 2017; Saddleson et al., 2015). Regarding alcohol use, the Bayes factors indicated that this variable was a substantive predictor of both weekday and weekend e-cigarette use. Furthermore, alcohol use predicted weekday e-cigarette use in the Bayesian regression. This pattern (i.e., a substantive predictor in both Bayes factor analyses and one of the two regressions) suggests that alcohol use is a stronger predictor of e-cigarette use than is marijuana use. Future research is needed to assess the replicability of these alcohol and marijuana use effects. The remaining two traditional predictors—male gender and cigarette smoking—were the strongest of the four predictors in this category. These two variables were substantive predictors in both Bayes factor analyses and both Bayesian regressions. Moreover, these findings are consistent with previous studies highlighting the importance of male gender and cigarette smoking as predictors of e-cigarette use (Anand et al., 2015; Lee et al., 2017; Mirbolouk et al., 2018; Saddleson et al., 2015). Regarding the exploratory predictors, we hypothesized that four of the Big Five factors (i.e., extraversion, neuroticism, conscientiousness, and agreeableness) would predict e-cigarette use. Contrary to our expectations, none of these variables were substantive predictors in any of the analyses. However, it is important to recall that these predictions were based on findings from previous tobacco cigarette use studies, rather than studies on e-cigarette use per se. Thus, one interpretation is that the predictive ability of the Big Five does not generalize from tobacco cigarette use to e-cigarette use. A different, opposing interpretation is that the Big Five can predict e-cigarette use, but that the instrument used in the present study (the Mini-IPIP) was insufficient to capture such associations. One consideration is the length of the MiniIPIP (20-items, 4-items per Big Five factor). Perhaps a longer scale with more items per factor (greater content coverage) would have yielded different results. Another consideration concerns the suboptimal reliability coefficients that we found for agreeableness (α = 0.68) and neuroticism (α = 0.58). These lower reliability values may have attenuated the associations between these two factors and e-cigarette use. This explanation does not apply to extraversion and conscientiousness
important when the 95% credible interval excludes zero. The Bayesian logistic regression analyses were conducted using the Bayesreg package for R (Makalic & Schmidt, 2016). 3. Results 3.1. Prevalence of e-cigarette use
3.3. Bayesian logistic regressions All of the predictors that had large Bayes factors were examined using Bayesian logistic regression. This multivariate procedure pits the predictors against each other to see which variables are most strongly associated with e-cigarette use. Beginning with weekday use, the following variables, listed in rank-order starting with the strongest predictor, were best able to discriminate e-cigarette use from non-use: taking a prescribed stimulant medication for a diagnosed condition, higher levels of weekend tobacco cigarette smoking, being male, lower levels of forgiveness toward others, and greater weekday alcohol use. For these predictors, the mean regression coefficient parameter estimates, the 95% credible intervals, and the Bayesian t-test values were as follows: prescribed stimulant medication (M = 1.52, 95% Credible Interval: [0.64, 2.42], t = 3.37), weekend cigarette smoking (M = 1.37, 95% Credible Interval: [0.41, 2.36], t = 2.76), male gender (M = −1.09, 95% Credible Interval: [−1.89, −0.32], t = −2.75), forgiveness of others (M = −0.83, 95% Credible Interval: [−1.63, −0.11], t = −2.16), weekday alcohol use (M = 0.75, 95% Credible 4
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important in one, but not both, of our Bayesian analyses, we regard them as being of modest import in predicting e-cigarette use. As with most empirical studies, it is important for future research to replicate these findings, especially since many of the predictors were exploratory-based. A number of scholars have argued that exploratory research is under-utilized but is important for hypothesis generation and identifying potentially important associations that may lead to new insights (Gelman, 2016; Sijtsma, 2016). As Gelman (2016) wrote: “Data-based exploration and hypothesis generation are central to science”. Despite several considerations that limit the generalizability of our results (e.g., all participants sampled from a single university, a moderate number of e-cigarette users), there were also several strengths worth emphasizing. First, two novel predictors that we believe warrant further research attention (taking a prescribed stimulant medication for a diagnosed medical condition and low forgiveness of others) were consistently associated with e-cigarette use vs. non-use. Second, our findings are not based on null hypothesis significance testing, the limitations of which are well documented (e.g., Kline, 2004), but, instead, result from a Bayesian model testing approach whereby the alternative model is directly compared against the null model. Third, and related to the second point above, our two-pronged model testing approach using both Bayes factors and Bayesian logistic regression can serve as a data analytic template for other researchers to follow. In conclusion, we hope that our findings will stimulate both replication efforts and further inquiries regarding the predictors and consequences of e-cigarette use.
because the reliability coefficients for these two factors were acceptable (α's of 0.79 and 0.72, respectively). Further research is needed to evaluate the associations between the Big Five factors and e-cigarette use. In addition to the Big Five, another personality factor that might be worth examining is psychoticism (Eysenck & Eysenck, 1977). Individuals scoring high in psychoticism typically are disagreeable, impersonal, unemotional, and antisocial. Such characteristics, either directly or indirectly through compromised relationships, could predispose one toward negative affect which, as discussed previously, could be a risk factor for e-cigarette use. Future research might benefit by examining psychoticism as a predictor of e-cigarette use. In addition to the four Big Five factors, we also hypothesized that low levels of forgiveness toward others would predict e-cigarette use. This hypothesis was supported across all four Bayesian analyses. Being unwilling to forgive others not only heightens negative affect but is also a stressful experience for the unforgiving person (Fehr et al., 2010; McCullough et al., 2007). According to Maltby, Macaskill, and Day (2001), lack of other forgiveness represents an extrapunitive interpersonal style whereby the unforgiving person seeks revenge and blames others for their transgressions. For the relatively unforgiving person who is already at risk for e-cigarette use (i.e., male, tobacco cigarette smoker, stimulant medication user), e-cigarettes might seem particularly attractive given the well-documented link between nicotine use and mood regulation (Kassel et al., 2007). Further research is needed to test this hypothesis. Overall, our findings were similar for weekday and weekend e-cigarette use. Furthermore, the most important predictors of e-cigarette use were: higher levels of cigarette smoking, being male, taking a prescribed stimulant medication for a diagnosed condition, lower levels of forgiveness toward others, and greater weekday alcohol use (the latter being a substantive predictor of weekday e-cigarette use only). As noted in the Results section, all of these predictors had large Bayes factors and they were the top-ranked predictors in the Bayesian regressions. Several of these variables (i.e., tobacco cigarette smoking, male gender, alcohol use) have previously been identified as important predictors of e-cigarette use (Anand et al., 2015; Choi & Forster, 2013; Hefner et al., 2019; Saddleson et al., 2015). The remaining two predictors—low forgiveness of others and taking a prescribed stimulant medication for a diagnosed condition—have not been the focus of earlier research. In fact, to the best of our knowledge, these findings are novel and represent the first report of substantive associations between these two variables and e-cigarette use. The fact that e-cigarette users were substantially more likely to take a prescribed stimulant medication is interesting, but an explanation for this association is not readily apparent. Given the pharmacological similarity between a prescribed stimulant and inhaled nicotine-infused vapor (both are psychostimulants) and considering the reinforcing effects of stimulant use (Advokat, Comaty, & Julien, 2014), perhaps college-aged users of prescribed stimulants are inclined to gravitate toward e-cigarette use, especially if they are males who smoke tobacco cigarettes. Our working assumption is that prescribed stimulant medication use precedes e-cigarette use, but we did not collect data bearing on temporal sequencing so we can't rule out a reverse pattern whereby e-cigarette use precedes stimulant medication use. Additional research is needed to further address this issue. To summarize, the most robust predictors of e-cigarette use (i.e., those variables that had both large Bayes factors and 95% Bayesian logistic regression credible intervals that excluded zero), were amount of tobacco cigarette smoking, taking a prescribed stimulant medication for a diagnosed medical condition, being male, low forgiveness of others, and amount of weekday alcohol use (the latter being important for weekday e-cigarette use only). There were other variables that had large Bayes factors but did not emerge as substantively important in the Bayesian regressions, most notably, marijuana use, recreational stimulant medication use, frequency of risky behavior, and the tendency to approach potentially harmful situations. Because these predictors were
Informed consent Informed consent was obtained from all participants in the study. Declaration of competing interest The authors declare that they have no conflict of interest. References Advokat, C. D., Comaty, J. E., & Julien, R. M. (2014). Primer of drug action (13th ed.). Duffield, United Kingdom: Worth Publishers. Anand, V., McGinty, K. L., O’Brien, K., Guenthner, G., Hahn, E., & Martin, C. A. (2015). Ecigarette use and beliefs among urban public high school students in North Carolina. Journal of Adolescent Health, 57, 46–51. Azagba, S., & Wolfson, M. (2018). E-cigarette use and quantity of cigarette smoking among adolescent cigarette smokers: A finite mixture model analysis. Drug and Alcohol Dependence, 185, 33–39. Baeza-Loya, S., Viswanath, H., Carter, A., Molfese, D. L., Velasquez, K. M., Baldwin, P. R., ... Salas, M. (2014). Perceptions about e-cigarette safety may lead to e-smoking during pregnancy. Bulletin of the Menninger Clinic, 78, 243–253. Campaign for Tobacco Free Kids (2018). JUUL and youth: Rising e-cigarette popularity. Washington, DC: Campaign for tobacco free kidshttps://www.tobaccofreekids.org/ assets/factsheets/0394.pdf. Choi, K., & Forster, J. (2013). Characteristics associated with awareness, perceptions, and use of electronic nicotine delivery systems among young U.S. Midwestern adults. American Journal of Public Health, 103, 556–561. Cohn, A., Villanti, A., Richardson, A., Rath, J. M., Williams, V., Stanton, C., & Mermelstein, R. (2015). The association between alcohol, marijuana use, and new and emerging tobacco products in a young adult population. Addictive Behaviors, 48, 79–88. Donnellan, M. B., Oswald, F. L., Baird, B. M., & Lucas, R. E. (2006). The mini-IPIP scales: Tiny- yet-effective measures of the big five factors of personality. Psychological Assessment, 18, 192–203. Eysenck, H. J., & Eysenck, S. B. (1977). Psychoticism as a dimension of personality. Oxford, England: Carne & Russak. Fehr, R., Gelfand, M. J., & Nag, M. (2010). The road to forgiveness: A meta-analytic synthesis of its situational and dispositional correlates. Psychological Bulletin, 136, 894–914. Gelman, A. (2016, November 17). Thinking more seriously about the design of exploratory studies: A manifesto [Blog post]. Retrieved from https://statmodeling.stat. columbia.edu/2016/11/17/thinking-more-seriously-about-the-design-ofexploratory-studies/. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). Boca Raton, FL: Chapman & Hall/CRC. Goldberg, L. R. (1999). A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models. In I. Mervielde, I. Deary, F. De Fruyt, & F. Ostendorf (Vol. Eds.), Personality psychology in Europe. Vol. 7. Personality psychology in Europe (pp. 7–28). Tilburg, Netherlands: Tilburg University
5
Personality and Individual Differences 152 (2020) 109605
J.B. Hittner, et al.
smoking, dual use, and problem behaviors among U.S. adolescents: Results from a national survey. Journal of Adolescent Health, 61, 155–162. McCullough, M. E., Bono, G., & Root, L. M. (2007). Rumination, emotion, and forgiveness: Three longitudinal studies. Journal of Personality and Social Psychology, 92, 490–505. McMillen, R. C., Gottlieb, M. A., Shaefer, R. M., Winickoff, J. P., & Klein, J. D. (2015). Trends in electronic cigarette use among U.S. adults: Use is increasing in both smokers and nonsmokers. Nicotine & Tobacco Research, 17, 1195–1202. Mirbolouk, M., Charkhchi, P., Kianoush, S., Uddin, S. M. I., Orimoloye, O. A., Jaber, R., & Blaha, M. J. (2018). Prevalence and distribution of e-cigarette use among U.S. adults: Behavioral risk factor surveillance system, 2016. Annals of Internal Medicine, 169, 429–438. Munafo, M. R., Zetteler, J. I., & Clark, T. G. (2007). Personality and smoking status: A meta-analysis. Nicotine and Tobacco Research, 9, 405–413. Pineiro, B., Correa, J. B., Simmons, V. N., Harrell, P. T., Menzie, N. S., Unrod, M., ... Brandon, T. H. (2016). Gender differences in use and expectancies of e-cigarettes: Online survey results. Addictive Behaviors, 52, 91–97. Prochaska, J. J. (2018). The public health consequences of e-cigarettes: A review by the National Academies of Sciences. A call for more research, a need for regulatory action. Addiction, 114, 587–589. Reynolds, W. M. (1982). Development of reliable and valid short forms of the MarloweCrowne social desirability scale. Journal of Clinical Psychology, 38, 119–125. Ross, S. R., Hertenstein, M. J., & Wrobel, T. A. (2007). Maladaptive correlates of the failure to forgive self and others: Further evidence for a two-component model of forgiveness. Journal of Personality Assessment, 88, 158–167. Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian ttests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16, 225–237. Saddleson, M., Kozlowski, L., Giovino, G., Hawk, L., Murphy, J., MacLean, M., ... Mahoney, M. (2015). Risky behaviors, e-cigarette use and susceptibility of use among college students. Drug and Alcohol Dependence, 149, 25–30. Sijtsma, K. (2016). Playing with data—Or how to discourage questionable research practices and stimulate researchers to do things right. Psychometrika, 81, 1–15. Thompson, L. Y., Snyder, C. R., Hoffman, L., Michael, S. T., Rasmussen, H. N., Billings, L. S., & Roberts, D. E. (2005). Dispositional forgiveness of self, others, and situations. Journal of Personality, 73, 313–359. Toussaint, L., Worthington, E., & Williams, D. R. (2015). Forgiveness and health. Dordrecht, Netherlands: Springer. Trumbo, C. W., & Harper, R. (2013). Use and perception of electronic cigarettes among college students. Journal of American College Health, 61, 149–155. Wagenmakers, E. J., Lee, M. D., Lodewyckx, T., & Iverson, G. (2008). Bayesian versus frequentist inference. In H. Hoijtink, I. Klugkist, & P. A. Boelen (Eds.). Bayesian evaluation of informative hypotheses (pp. 181–207). New York: Springer.
Press. Gostin, L. O., & Glasner, A. Y. (2014). E-cigarettes, vaping, and youth. Journal of the American Medical Association, 312, 595–596. Hakulinen, C., Hintsanen, M., Munafo, M. R., Virtanen, M., Kivimaki, M., Batty, G. D., & Jokela, M. (2015). Personality and smoking: Individual-participant meta-analysis of nine cohort studies. Addiction, 110, 1844–1852. Hampson, S. E., Andrews, J. A., Severson, H. H., & Barckley, M. (2015). Prospective predictors of novel tobacco and nicotine product use in emerging adulthood. The Journal of Adolescent Health, 57, 186–191. Hanewinkel, R., & Isensee, B. (2015). Risk factors for e-cigarette, conventional cigarette, and dual use in German adolescents: A cohort study. Preventive Medicine, 74, 59–62. Hefner, K. R., Sollazzo, A., Mullaney, S., Coker, K. L., & Sofuoglu, M. (2019). E-cigarettes, alcohol use, and mental health: Use and perceptions of e-cigarettes among college students, by alcohol use and mental health status. Addictive Behaviors, 91, 12–20. JASP Team (2017). JASP (Version 0.8.2) [computer software]. Kassel, J. D., Evatt, D. P., Greenstein, J. E., Wardle, M. C., Yates, M. C., & Veilleux, J. C. (2007). The acute effects of nicotine on positive and negative affect in adolescent smokers. Journal of Abnormal Psychology, 116, 543–553. Kline, R. B. (2004). Beyond significance testing: Reforming data analysis methods in behavioral research. Washington: American Psychological Association. Krentzman, A. R., Strobbe, S., Harris, J. I., Jester, J. M., & Robinson, E. A. R. (2017). Decreased drinking and alcoholics anonymous are associated with different dimensions of spirituality. Psychology of Religion and Spirituality, 9, S40–S48 Supplement 1. Lee, H., Lin, H., Seo, D., & Lohrmann, D. K. (2017). Determinants associated with ecigarette adoption and use intention among college students. Addictive Behaviors, 65, 102–110. Lee, M. D., & Wagenmakers, E. J. (2013). Bayesian cognitive modeling: A practical course. Cambridge University Press. Lin, W., Mack, D., Enright, R. D., Krahn, D., & Baskin, T. W. (2004). Effects of forgiveness therapy on anger, mood, and vulnerability to substance use among inpatient substance-dependent clients. Journal of Consulting and Clinical Psychology, 72, 1114–1121. Littlefield, A. K., Gottlieb, J. C., Cohen, L. M., & Trotter, D. R. M. (2015). Electronic cigarette use among college students: Links to gender, race/ethnicity, smoking, and heavy drinking. Journal of American College Health, 63, 523–529. Makalic, E., & Schmidt, D. F. (2016). High-dimensional Bayesian regularized regression with the BayesReg package. Available at https://arxiv.org/pdf/1611.06649.pdf. Malouff, J. M., Thorsteinsson, E. B., & Schutte, N. S. (2006). The five-factor model of personality and smoking: A meta-analysis. Journal of Drug Education, 36, 47–58. Maltby, J., Macaskill, A., & Day, L. (2001). Failure to forgive self and others: A replication and extension of the relationship between forgiveness, personality, social desirability and general health. Personality and Individual Differences, 30, 881–885. McCabe, S. E., West, B. T., Veliz, P., & Boyd, C. J. (2017). E-cigarette use, cigarette
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