# Why Do University Students Smoke Tobacco? French Validity of Brief Wisconsin Inventory of Smoking Dependence Motives among (non-) Daily Smokers and Associations with Psychological Variables

Maxime Mauduy, Nicolas Mauny, Helene Beaunieux, Jessica Mange

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Maxime Mauduy, Nicolas Mauny, Helene Beaunieux, Jessica Mange. Why Do University Students Smoke Tobacco? French Validity of Brief Wisconsin Inventory of Smoking Dependence Motives among (non-) Daily Smokers and Associations with Psychological Variables. 2023. hal-04120073

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# Why Do University Students Smoke Tobacco?
## French Validity of Brief Wisconsin Inventory of Smoking Dependence Motives among (non-) Daily Smokers and Associations with Psychological Variables

Maxime Mauduy<sup>ab</sup>, Nicolas Mauny<sup>a</sup>, Hélène Beaunieux<sup>a</sup>, and Jessica Mange<sup>a</sup>

<sup>a</sup>Psychology, University of Caen Normandy, Laboratoire de Psychologie Caen Normandie (LPCN, EA 7452), Caen, France

<sup>b</sup> University of Lausanne, Faculté des sciences sociales et politiques, 1015 Lausanne, Suisse

## Author note

Supplementary material is available online at

https://osf.io/2zh6c/?view_only=76269d9b547642d6849a291e3ee24212.

Maxime Mauduy, ORCID iD: https://orcid.org/0000-0002-4604-5314.

Nicolas Mauny, ORCID iD: https://orcid.org/0000-0001-5156-3530

Hélène Beaunieux, ORCID iD: https://orcid.org/0000-0003-1354-6558

Jessica Mange, ORCID iD: https://orcid.org/0000-0001-6279-4721

Correspondence concerning this article should be addressed to Maxime Mauduy, Université de Caen Normandie, UNICAEN, Laboratoire de Psychologie Caen Normandie (LPCN, EA 7452), Esplanade de la Paix, 14032 CAEN cedex 5 – France. Email: maxime.mauduy@unicaen.fr. Phone Number: +33659576492
**Abstract**

**Introduction:** This study investigates the validity of the Brief Wisconsin Inventory of Smoking Dependence Motives (B-WISDM) among French daily and non-daily university student smokers, and its associations with psychological variables related to smoking, namely attitude, social norms, perceived behavioral control, identity, and psychopathological variables, namely eating disorders, anxiety, and depression.

**Methods:** We tested three measurement models of the B-WISDM using robust confirmatory factor analysis, measurement invariance for daily vs. non-daily smokers, and predictive and convergent validity using a bootstrap multivariate regression model with tobacco dependence and psychological variables as outcomes.

**Results:** The results (1) confirmed the B-WISDM dimensionality in 11 first-order intercorrelated factors among university students; (2) showed the B-WISDM measurement invariance for the types of smokers; (3) showed that tobacco dependence is only and positively predicted by primary dependence motives, namely tolerance, craving, and lack of control, which confirm that they are core components of tobacco dependence; and (4) highlighted specific associations between smoking motives and psychological variables, such as smoking identity and perceived behavioral control with some primary dependence motives, social goals with social norms, weight control with eating disorders, and affiliative attachment and affective enhancement with psychopathological variables.

**Conclusions:** In addition to showing the French B-WISDM good psychometric qualities among university students, this study extends the B-WISDM external validity to non-daily smokers, meaning that tobacco dependence motivations are conceptually identical for (non-)daily student smokers, and shows specific associations of psychological variables with some smoking motives, which is of great interest for inducing smoking reduction.

**Keywords:** Brief Wisconsin Inventory of Smoking Dependence Motives, University student, Smoking motives, (Non-)Daily smokers, Psychological variables
Introduction

Given the consequences it has on health, tobacco use is considered a global health problem (World Heald Organization, 2018). Tobacco dependence, generally defined as the experience of a strong need to smoke (West, 2017), can lead to neuropsychological impairments even among young people (Chamberlain et al., 2012). Therefore, young people, especially university students, constitute a particularly interesting population group for researchers because, they are at risk of developing tobacco dependence in decades to come (Kenford et al., 2005; Schulenberg et al., 2019). In France, 28% of students are smokers (Pasquereau et al., 2017), and among a sample of French student smokers, 15% are already heavily tobacco dependent (Mauduy et al., 2022). Knowing that a basic step for helping people quit smoking consists of identifying their underlying motivations for tobacco dependence, this study focuses on university students’ smoking motives.

Drawing on a large body of work suggesting that dependence is a multidimensional and motivational phenomenon (e.g., Colby et al., 2000), Piper et al. (2004) assumed that tobacco dependence could be measured on the basis of the different motivations underlying tobacco use. As a result, the Wisconsin Inventory of Smoking Dependence Motives (WISDM, 68-item, Piper et al., 2004), and later on, its 37-item brief version (the B-WISDM, Smith et al., 2010) were developed. Numerous studies have been conducted on heavy and daily tobacco users to test the psychometric qualities of the WISDM (e.g., Piasecki et al., 2010a, 2010b, 2011; Piper et al., 2008; Shenassa et al., 2009) and the B-WISDM (e.g., Ma et al., 2012; Smith et al., 2010; Tombor et al., 2010). Researcher prefer this shorter version for practical purposes, but also because it shows good psychometric qualities. First, its dimensionality on 11 motivational factors has been confirmed, namely, loss of control, automaticity, craving, tolerance, affiliative attachment, cognitive enhancement, cue exposure/associative processes, social/environmental goals, affective enhancement, taste, and weight control (Pancani et al., 2015). In their study,
Pancani et al. (2015) compared a model with these 11 first-order factors with error covariances to a model with 11 first-order and 2 second-order factors with error covariances. The rationale for testing these two second-order factors lies in the early work of Piper et al. (2008) which highlights two subscales of the WISDM, namely primary dependence motives (PDM) and secondary dependence motives (SDM). PDM represent the core features of advanced tobacco dependence and consist of four scales (loss of control, automaticity, craving, tolerance). SDM represents instrumental motives for tobacco use and is composed of the seven remaining scales. Pancani et al.’s (2015) results support the 11-factor first-order model. Furthermore, these 11 motivational factors have satisfactory internal consistencies (e.g., Pancani et al., 2015; Smith et al., 2010) and are conceptually similar for men and women (gender measurement invariance, Vajer et al., 2011). Finally, the B-WISDM has good predictive validity with measures of tobacco dependence, such as the Cigarette Dependence Scale (CDS; Pancani et al., 2015), the Tobacco Dependence Screener (Vajer et al., 2011), the Fagerström Test for Nicotine Dependence (FTND; Tombor et al., 2010), and biochemical measures (Smith et al., 2010). In sum, the B-WISDM is an essential tool with good psychometric properties for understanding the different motivations underlying tobacco dependence. Nevertheless, there are two elements that limit the identification of these motivations among university students with the B-WISDM.

First, although the psychometric properties of the B-WISDM have always been tested on heavy or daily smokers (e.g., Pancani et al., 2015; Vajer et al., 2011), it has never been tested on university students. This limits the B-WISDM external validity to this specific population (Vajer et al., 2011), as a majority of student smokers are not daily smokers, but rather smoke on occasions and are thus considered as “light” smokers (Levinson et al., 2007; Moran et al., 2004; Thompson et al., 2007). While the original version of the WISDM was used with college student smokers (e.g., Piasecki et al., 2011), and the B-WISDM has already been used to study the characteristics of non-daily smokers (e.g., Scheuermann, Mburu, et al., 2015; Scheuermann,
Nollen, et al., 2015) or tested in a nonclinical sample (Adkison et al., 2016), only the PDM and SDM scores were used, and neither the dimensionality nor the measurement invariance of B-WISDM according to the type of smoker (daily vs. non-daily) were pre-tested. Testing the psychometric qualities of B-WISDM with non-daily smokers is thus necessary to justify its relevance to this particular smoker population.

Second, although the B-WISDM has good psychometric properties in terms of dimensionality, internal consistency and predictive validity with tobacco dependence, researchers have shown little interest in its convergent validity. Several psychological variables associated with tobacco dependence have been identified among university students: attitude towards smoking (i.e., students' evaluation of smoking, e.g., Mao et al., 2009), perceived smoking social norms (i.e., students' perceptions about how significant others approve of smoking, e.g., Riou França et al., 2009), perceived behavioral control to resist smoking (i.e., students' perception of their ability not to smoke, e.g., Jalilian et al., 2016), smoker identity (i.e., the extent to which students view themselves as smokers, e.g., Levinson et al., 2007; Moran et al., 2004), eating disorders (Eisenberg et al., 2011), anxiety (e.g., Bierhoff et al., 2019), and depression (e.g., Schleicher et al., 2009). Hence, on the one hand, the B-WISDM highlights several motivations for tobacco dependence, and, on the other hand, several psychological variables predict tobacco dependence, but little is known about the associations between these smoking dependence motives and psychological variables. We believe these smoking motives to be associated with specific psychological variables, making these latter the psychological matter of the motivations for tobacco dependence. First, we expect both smoking identity and perceived behavioral control to be two core psychological components of the PDM (e.g., Falomir-Pichastor et al., 2020; Lee et al., 2014; Mauduy et al., 2022). Second, we expect the other psychological variables to be more related to instrumental motivations to smoke, namely SDM. Specifically, attitudes would be primarily associated with taste motives, social norms
with social goals motives, eating disorders with weight control motives, and anxiety and
depression with coping motives, that is affective and cognitive enhancements, and affiliative
attachment. Thus, investigating the associations between the smoking motives and the
psychological variables would be of great interest because it would allow us to (1) provide
additional evidence of the good psychometric properties of the B-WISDM, (2) better
understand the processes underlying tobacco dependence among university students, and (3)
better assist university students to quit smoking by focusing prevention efforts on the
psychological processes underlying their motivations for smoking.

Thus, the present study aims to test the validity of the B-WISDM among a French
university student population. For this purpose, we test the B-WISDM dimensionality in 11
first-order motivational factors, its measurement invariance for types of smokers, namely daily
and non-daily smokers, as well as its concurrent validity with tobacco dependence and
convergent validity with several psychosocial variables already identified in the literature as
being associated with tobacco dependence.

Methods

Procedure and Participants

This study was carried out on a convenience sample of 687 student smokers from the
University of Caen Normandy (France). The participants were recruited per mail on their
institutional address and were asked if they wished to participate in an online survey on tobacco
smoking (via the Limesurvey® application, November 2021). No compensation was provided
to the participants.
## Ethics

All participants were volunteers and gave their consent before starting the survey. The study was notified to and authorized by the “Commission Nationale de l’Informatique et des Libertés” with the registration number u24- 20171109-01R1. Besides, the participants’ anonymity was guaranteed by the University Information System Direction (DSI). This survey was conducted in full agreement with the Declaration of Helsinki (2008) and the ethical standards set by the Psychology Department, that follows the American Psychological Association Ethical Principles of Psychologists and the Code of Conduct (APA, 2017) for the ethical treatment of human participants.

## Measures

Details of all measures and items described below are available on the supplemental online material at https://osf.io/2zh6c/?view_only=76269d9b547642d6849a291e3ee24212.

### Demographics and Smoking History

We measured gender, age, academic level, age of smoking onset, smoking parents, cigarette consumption per day, recent attempts to quit smoking (Perski et al., 2018), and motivation to quit smoking (MTSS, Kotz et al., 2013).

### French Brief Wisconsin Inventory of Smoking Dependence Motives

The B-WISDM, developed by Smith et al. (2010), was translated into French and then back-translated into English by a professional translation service for verification purposes. The B-WISDM is a questionnaire composed of 37 items rated on a 7-point scale ranging from 1 = *Not true for me at all* to 7 = *Extremely true for me*. It comprised 11 subscales that represent different smoking dependence motivations (Smith et al., 2010; Pancani et al., 2015): loss of control, automaticity, craving, tolerance, affiliative attachment, cognitive enhancement, cue
exposure/associative processes, social/environmental goods, affective enhancement, taste, and
weight control (see the supplemental material for the items translated).

Concurrent Validity Measures

Tobacco dependence was measured using the validated French-language Cigarette Dependence Scale (CDS-5; Etter, 2005). This 5-item self-report questionnaire provides a continuous score for cigarette addiction. The CDS has shown high internal consistency, a good predictive validity, as well as a high test-retest reliability (e.g., Etter, 2005, 2008; Etter et al., 2003), thus overcoming the psychometric limitations of the FTND.

Convergent Validity Measures

Attitudes, perceived social norms and perceived behavioral control to resist smoking were assessed based on the Theory of Planned Behavior (TPB, Ajzen, 1991; Jalilian et al., 2016; Mauduy et al., 2022). The assessment included *attitude* towards smoking (4-item, Cronbach α = .92), perceived social norms for smoking (3-item, Cronbach α = .81), and perceived behavioral control to resist smoking (3-item, Cronbach α = .75) rated on Likert-type scale scored from 1 = *do not agree* to 5 = *strongly agree*. This scale measuring three factors of the TPB has acceptable fits in French (Mauduy et al., 2022).

Smoker identity was assessed using the Smoker Self-Concept Scale (SSCS; 5-item Likert-type scale scored from 1 = *do not agree* to 5 = *strongly agree*; Cronbach α =.85, Shadel & Mermelstein, 1996). The SSCS assesses the importance of being a smoker for one's self-concept and has shown great predictive and discriminant validities, a good internal consistency (Shadel & Mermelstein, 1996), and excellent fits in French (Mauduy et al., 2022).

Eating disorders were assessed with the French version of the SCOFF questionnaire (Parker et al., 2010). It consists in responding to five questions related to food in a yes/no format. Scores can range from 0 to 5 (i.e., answer yes to all 5 questions), with a score of 2 or higher indicating a likely case of an eating disorder.
Anxiety was measured with the French version of the State-Trait Anxiety Inventory (STAI, Spielberger et al., 1983) which is composed of 20 items (Cronbach α = .91) on a 4-point scale ranging from no (1) to yes (4). It has shown good validity and acceptable reliability (Barnes et al., 2002).

Depression was measured with the short French version of the Beck Depression Inventory (BDI, Beck et al., 1988). It consists of 13 items, each composed of four statements reflecting various severity degrees of depression-related symptoms. The BDI has shown high internal consistency, good predictive validity, and high test-retest reliability (Beck et al., 1988; Beck & Steer, 1984).

## Statistical Analyses

First, the dimensionality and reliability of the B-WISDM have been tested. Three models derived from the literature were tested using a robust confirmatory factor analysis (CFA). Model 1 contained two first-order correlated factors, namely the PDM and SDM. Model 2 contained 11 first-order correlated factors. Model 3 included 11 first-order correlated factors (as model 2) and 2 second-order factors, namely PDM and SDM. Robust CFA were used to manage the multivariate non-normality of the data (i.e., Kurtosis test > 20, Mardia, 1974). As standard maximum likelihood estimation assumes multivariate normality, we used the maximum likelihood estimation with robust (Huber-White) standard errors. Five fit indices were used to evaluate each model. The Satorra-Bentler Chi-square ($\chi^2$) test indicate the fit goodness of the model when the value is statistically significant. The robust comparative fit index (CFI) and robust Tucker-Lewis index must be greater than 0.95 to demonstrate a good model fit, but values above 0.90 are still considered as acceptable ((Boateng et al., 2018; Brown, 2006). The robust root mean squared error of approximation (RMSEA) indicates a good model fit when its values are lower than 0.05 and an adequate fit when they are lower than 0.08. The standardized root mean square residual (SRMR) had to be lower than 0.08 (Boateng et al.,
2018). To identify whether one more restricted model has a better fit than another, three criteria have to be met. First, we compared the models using the specialized computation for the Satotta-Bentler $\chi^2$ difference (Satorra & Bentler, 2001). The value of the $\chi^2$ difference must be significant (i.e., *p* < .05). However, since the $\chi^2$ test may be sensitive to the sample size, we also used CFI and RMSEA values. The CFI must show an increase higher than .01 (CFI<sub>diff</sub> > .01, Cheung & Rensvold, 2002), and the RMSEA must show a decrease of over 0.015 (RMSEA<sub>diff</sub> < .015, Chen, 2007). The internal consistencies for the optimal model identified were then calculated using Cronbach's alpha. Values above 0.90 are considered excellent, and values above 0.70 are considered satisfactory (Boateng et al., 2018; Nunnally & Bernstein, 1994).

Second, to test the measurement invariance of types of smokers (daily and non-daily), we conducted a series of multigroup CFAs on the previously identified model. Three nested models with increasing constraints were estimated. First, the configural invariance, which corresponds to a model without constraints, tests whether the dimensionality of the FB-WISDM and the item composition of the factors are identical between groups. A configural invariance between groups indicates that the groups (non-daily or daily smokers) have a similar representation of the underlying construct. Second, the metric invariance, which constrained the factor loadings to be identical in all groups, tests whether the contribution of each item to a factor (as measured by the factor loadings or standardized regression coefficient) is identical in all groups. Metric invariance indicates that the understanding of the content of each item is not influenced by the type of smokers. Third, scalar invariance, which constrained both factor loadings and intercepts to be identical in all groups, tests whether the intercept for each item is not influenced by the groups. Scalar invariance indicates that the differences between groups on the means of measure variables (i.e., items) are due to group differences on the mean of the common latent variable (i.e., factor). Thus, scalar invariance is required to compare groups in terms of latent means. To test measurement invariance, we compared configural to metric
invariance, and then, if metric invariance is retained, we compared metric to scalar invariance.

To show measurement invariance, at least one of the three following criteria had to be met.

First, the specialized computation for the Satotta-Bentler $\chi^2$ difference between the two models
has to be not significant, second, the CFI<sub>diff</sub> between the two models should be less than or equal
to .01, and third, the RMSEA<sub>diff</sub> between the two models should be less than or equal to .015.

It should be noted that, since the Satotta-Bentler $\chi^2$ difference test may be sensitive to the sample
size, more attention has been given to the CFI<sub>diff</sub> and RMSEA<sub>diff</sub> indicators to evaluate the
measurement invariance. Furthermore, we also tested the measurement invariance for gender
(men and women) in order to verify that the gender invariance previously shown among adult
daily smokers (Vajer et al., 2011) is replicated among university students.

Third, the concurrent and convergent validity were tested using a multivariate regression
model that includes all the FB-WISDM dimensions of the identified model as predictors, and
tobacco dependence, attitudes, social norms, perceived behavioral control to resist smoking,
smoker identity, eating disorders, anxiety, and depression as outcome variables. The Bootstrap
method (N = 1000) was used to assess the stability of the results.

All statistical analyses were conducted using the R software (version 4.05; R Core
Team). The package *mvn* (Korkmaz et al., 2014) was used to assess the multivariate
nonnormality, the package *lavaan* (Rosseel, 2012) to test the dimensionality, the measurement
invariance, the reliability and concurrent and convergent validities of the FB-WISDM.

## Results

### Characteristics of the Sample

Table 1 presents the characteristics of the sample. Students were considered current smokers when they indicated that they had already smoked 100 cigarettes in their lifetime and had smoked at least one time in the past 30 days (Adkison et al., 2016; Riou França et al., 2009).
Among the 687 smokers ($M_{age} = 20.06$, $SD = 2.39$), 63.5% were female (2.2% did not choose an answer). On average, they started smoking at age 16.1 years ($SD = 2.03$) and currently smoke 6.34 cigarettes per day ($SD = 16.3$). A majority of participants are non-daily smokers (63.5%). Finally, almost half of the participants reported having made one attempt to quit during the past year (41.4%) and are motivated to quit smoking (42.6%).

## Tests of FB-WISDM Dimensionality

Among the 687 participants, 29 had at least one missing data on the FB-WISDM. To address the problem of missing data, we used the full-information maximum likelihood estimation (T. Lee & Shi, 2021).

### Robust Confirmatory Factor Analyses

The fit indices of the three tested model are depicted in Table 2. Robust CFAs of Model 1 indicated an inadequate fit with the data, while Model 2 and Model 3 displayed acceptable fit indices. The specialized computation for the Satorra-Bentler $\chi^2$ difference as well as fit indices indicated that Model 2 fitted significantly better the data than Model 1 ($\chi^2_{diff} = 1721.60$, $df_{diff} = 11$, $p < .001$; $CFI_{diff} = 0.271$; $RMSEA_{diff} = -0.061$), indicating that a two-dimension scale, namely, the composite of four subscales related to the PDM and the composite of nine subscales related to the SDM, is not adequate to account for the data. Model 2 showed a significantly better fit than Model 3 on Satorra-Bentler $\chi^2$ difference ($\chi^2_{diff} = 277.91$, $df_{diff} = 43$, $p < .001$) but not on CFI and RMSEA indices ($CFI_{diff} = 0.015$; $RMSEA_{diff} = 0.004$). These results suggest that the 11 first-order dimensions of the FB-WISDM (Model 2) represent the optimal solution, although we cannot reject Model 3. We further compared models 2 and 3 by examining the factor loadings and correlations between factors. No differences were found between the two models for the first-order factors: they displayed standardized factor loadings that are all above 0.60 as well as positive and significant correlations between all factors. However, two elements
weaken the relevance of Model 3. First, the loadings of the second-order factors are different, with PDM values ranging from 0.77 to 0.98 and SDM ranging from 0.27 to 86. The dimensionality of the SDM is challenged by the three following sub-scales: social/environmental goals, taste and weight control. Second, discriminant validity is also challenged by the high correlations between the PDM and SDM factors (r =0.86). Thus, we consider Model 2 as the optimal solution to fit the data. The results of the robust CFA for Model 2 are displayed in Table 3.

### Cronbach's Alpha

All cronbach's alpha values of the 11 first-order factors were higher than 0.70 (see Table 3), indicating an excellent internal consistency of the FB-WISDM.

## Tests of Measurement Invariances through Multiple Group Robust CFA

Multiple group CFA were used to test the measurement invariance (configural, metric, scalar) of the FB-WISDM for types of smokers (daily and non-daily smokers) and gender (men and women).

### Measurement Invariance for Types of Smokers

For all three invariance levels of types of smokers, the models have good fit indices (see Table 4). The Satorra-Bentler $\chi^2$ difference between the first model (configural invariance) and the second one (metric invariance) is significant ($\chi^2_{diff} = 137.80, df_{diff} = 37, p < .001$) but CFI and RMSEA values do not differ (CFI$_{diff}$ = .005; RMSEA$_{diff}$ = .001). Then, Satorra-Bentler $\chi^2$ difference between the second and the third model (scalar invariance) is significant ($\chi^2_{diff} = 92.62, df_{diff} = 26, p < .001$), but there was no significant change in fit indices (CFI$_{diff}$ = .005; RMSEA$_{diff}$ = .001). Thus, scalar invariance is not rejected, which means that the factor loadings and intercepts of the 11 first-order factors are invariant between types of smokers. Concretely,
daily and non-daily smokers have a similar understanding of the different smoking motivations measured by the FB-WISDM.

## Measurement Invariance for Gender

For all three gender invariance levels, the models have good fit indices (see Table 4). The Satorra-Bentler $\chi^2$ difference between the first model (configural invariance) and the second one (metric invariance) is significant ($\chi^2_{diff} = 55.11, df_{diff} = 37, p = .028$) but the CFI and RMSEA values do not differ. Then, fit indices did not change between the second and the third model (scalar invariance, $\chi^2_{diff} = 29.96, df_{diff} = 26, p = .27$; $CFI_{diff} = 0.0$ ; $RMSEA_{diff} = 0.001$). Thus, scalar invariance is not rejected, which means that the factor loadings and intercepts of the 11 first-order factors are gender invariant. Men and women have a similar understanding of the different motivations for smoking

## Tests of FB-WISDM Concurrent and Convergent Validity

The results, displayed in Table 5, support both concurrent and convergent validity of the FB-WISDM. First, tobacco dependence is mainly and positively predicted by PDM, namely, lack of control, craving, and tolerance, and is also predicted by associative processes. Second, attitudes are mainly and positively associated with taste and affective enhancement, and, to a lesser extent, negatively associated with lack of control; social norms are only positively associated with social goals; perceived behavioral control is mainly and negatively associated with craving and associative processes; smoker identity is mainly associated with lack of control and affiliative attachment, and, to a lesser extent, associative processes and tolerance; eating disorders are only associated with weight control; anxiety is mainly associated with affective enhancement, and, to a lesser extent, with affiliative attachment, weight control and negatively associated with tolerance; and depression is mainly associated with affective
enhancement, weight control and affiliative attachment, and, to a lesser extent, negatively
associated with taste.

Discussion

This study aimed at testing the psychometric qualities of the B-WISDM on a sample of daily and non-daily university student smokers. The questionnaire had already been validated but only on heavy or daily adult smokers. Overall, the results highlight the good psychometric qualities of the FB-WISDM among university students. In line with previous studies, our results confirm the dimensionality in 11 first-order motivational factors, their satisfactory internal consistencies (Ma e al., 2012; Pancani et al., 2015; Smith et al., 2010; Vajer et al., 2011) and gender invariance (Vajer et al., 2011). On the other hand, and beyond being having carried out the first study to test the validity of the B-WISDM on university students, we have also contributed to the literature by highlighting two new findings that we will discuss.

First, this study highlights the applicability of the B-WISDM to a non-daily, occasional smoking population. Tested only among heavy or daily smokers (e.g., Pancani et al., 2015; Smith et al., 2010; Vajer et al., 2011), the validity of the B-WISDM among occasional smokers was lacking. Based on measurement invariance by types of smokers, this study is the first one to show that motivations underlying tobacco dependence are conceptually identical for daily and non-daily university student smokers. This result, beyond extending the applicability of the B-WISDM to different types of smokers, is of great interest because it would allow us to investigate motivational changes explaining the development of daily smoking in occasional smokers (Kenford et al., 2005; Schulenberg et al., 2019), using the B-WISDM.

Second, this study highlights the convergent validity of the B-WISDM with psychological variables, well-known to be associated with tobacco use and dependence. This result is particularly interesting for prevention purposes, as it potentially makes the B-WISDM a very useful diagnostic tool to identify the underlying psychological reasons for an individual's
smoking, in the first stage of prevention. Then, in the second stage, the B-WISDM would be
able to guide prevention actions targeting the psychological variables that are specifically
associated with people's reasons for smoking through the use of adapted strategies. In our
results, two categories of psychological variables seem to emerge, depending on whether they
are more associated with PDM or with SDM. On the one hand, smoker identity and perceived
behavioral control to resist smoking are mainly associated with motivations related to lack of
control, tolerance and craving, thus accounting for three out of four PDM. These findings are
not surprising considering that perceived behavioral control (Lee et al., 2014; Mauduy et al.,
2022) and smoker identity (Falomir-Pichastor et al., 2020; Mauduy et al., 2022) are key
determinants of tobacco dependence and cessation success. As PDM are the core components
of tobacco dependence (Piper et al., 2008), it seems essential for any tobacco dependence
prevention program to target these two psychological variables. The development of new
prevention strategies, such as the social modeling technique (Burn, 1991; see Webb & Sheeran,
2006 for a meta-analysis) for increasing people's confidence to resist smoking, or the multi-
categorization technique (Kang & Bodenhausen, 2015) for encouraging people who smoke to
define themselves not only as smokers but also as members of other social groups and then
reduce the potential role of the problematic identity in driving behaviors, could be considered
for this purpose. On the other hand, attitude towards smoking, perceived smoking social norms,
eating disorders, anxiety, and depression are uniquely associated with specific SDM. Thus, our
results would suggest targeting positive smoking attitudes among student smokers because of
taste and affective enhancement, with for instance the framing technique (Gallagher &
Updegraff, 2012), the perceived smoking social norms because of social goads, with the
personalized normative feedback technique (Vallentin-Holbech et al., 2018), eating disorders
because of weigh control, with dissonance-based prevention programs (see Stice et al., 2019
for a meta-analytic review), and anxiety and depression because of affiliative attachment and
affective enhancement, with mindfulness-based interventions (Hofmann & Gómez, 2017).

This study, however, has two main limitations that we must highlight. First, a
biochemical measure of tobacco dependence (Bize et al., 2009) or a combination of measures
(Hughes et al., 2004) would have allowed us to ensure that the participants are actually smokers.
Second, given that our study was conducted with French students and that it is a correlational
study, further research could attempt to replicate our results.

In conclusion, after having been validated on heavy smokers or daily smokers in
English, Italian and Hungarian participants, this study shows that the B-WISDM has also good
psychometric qualities among French daily and non-daily university student smokers.
Examining its convergent validity with different psychological variables (attitude, social norms,
perceived behavioral control, smoker identity, and eating disorders) has provided a better
understanding of the underlying motivations for tobacco dependence and a guide for smoking
reduction efforts among university students. Future research must now extend the external
validity of the B-WISDM to other populations of non-daily smokers, and investigate the
involvement of other psychological variables in understanding motivations underlying tobacco
dependence.

Funding

The work reported was supported by RIN Tremplin Grant 19E00906 of Normandie Région, France.

**Declaration of Interest Statement**

The authors report no conflict of interest.

Data Availability Statement

Data from this study will be provided by the first author upon request.
References

Adkison, S. E., Rees, V. W., Bansal-Travers, M., Hatsukami, D. K., & O'Connor, R. J. (2016).

Psychometric Characteristics of the Brief Wisconsin Inventory of Smoking Dependence Motives
Among a Nonclinical Sample of Smokers. *Nicotine & Tobacco Research*, 18(4), 470–476.

https://doi.org/10.1093/ntr/ntv113

Ajzen, I. (1991). The theory of planned behavior. *Organizational Behavior and Human Decision

Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Barnes, L. L. B., Harp, D., & Jung, W. S. (2002). Reliability generalization of scores on the

Spielberger state-trait anxiety inventory. *Educational and Psychological Measurement*, 62(4),
603–618.

Beck, A. T., & Steer, R. A. (1984). Internal consistencies of the original and revised Beck Depression

Inventory. *Journal of Clinical Psychology*, 40(6), 1365–1367.

Beck, A. T., Steer, R. A., & Carbin, M. G. (1988). Psychometric properties of the Beck Depression

Inventory: Twenty-five years of evaluation. *Clinical Psychology Review*, 8(1), 77–100.

https://doi.org/10.1016/0272-7358(88)90050-5

Bierhoff, J., Haardörfer, R., Windle, M., & Berg, C. J. (2019). Psychological Risk Factors for Alcohol,

Cannabis, and Various Tobacco Use among Young Adults: A Longitudinal Analysis. *Substance
Use and Misuse*, 54(8), 1365–1375. https://doi.org/10.1080/10826084.2019.1581220

Bize, R., Burnand, B., Mueller, Y., Rège Walther, M., & Cornuz, J. (2009). Biomedical risk

assessment as an aid for smoking cessation. In R. Bize (Ed.), *Cochrane Database of Systematic
Reviews* (Issue 2). John Wiley & Sons, Ltd. https://doi.org/10.1002/14651858.CD004705.pub3

Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018).

Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer. *Frontiers in Public Health*, 6(June), 1–18. https://doi.org/10.3389/fpubh.2018.00149
Brown, T. A. (2006). *Confirmatory Factor Analysis for Applied Research*. Guilford Publications.

Burn, S. M. (1991). Social Psychology and the Stimulation of Recycling Behaviors: The Block Leader Approach. *Journal of Applied Social Psychology*, 21(8), 611–629. https://doi.org/10.1111/j.1559-1816.1991.tb00539.x

Chamberlain, S. R., Odlaug, B. L., Schreiber, L. R. N., & Grant, J. E. (2012). Association between Tobacco Smoking and Cognitive Functioning in Young Adults. *American Journal on Addictions*, 21, S14–S19. https://doi.org/10.1111/j.1521-0391.2012.00290.x

Cheung, G. W., & Rensvold, R. B. (2002). Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance. *Structural Equation Modeling*, 9(2), 233–255.
https://doi.org/10.1207/S15328007SEM09025

Colby, S. M., Tiffany, S. T., Shiffman, S., & Niaura, R. S. (2000). Measuring nicotine dependence among youth: A review of available approaches and instruments. *Drug and Alcohol Dependence*, 59(SUPPL. 1), 23–39. https://doi.org/10.1016/S0376-8716(99)00163-5

Eisenberg, D., Nicklett, E. J., Roeder, K., & Kirz, N. E. (2011). Eating disorder symptoms among college students: Prevalence, persistence, correlates, and treatment-seeking. *Journal of American College Health*, 59(8), 700–707. https://doi.org/10.1080/07448481.2010.546461

Etter, J.-F. (2005). A comparison of the content-, construct- and predictive validity of the cigarette dependence scale and the Fagerström test for nicotine dependence. *Drug and Alcohol Dependence*, 77(3), 259–268. https://doi.org/10.1016/j.drugalcdep.2004.08.015

Etter, J.-F. (2008). Comparing the validity of the Cigarette Dependence Scale and the Fagerström Test for Nicotine Dependence. *Drug and Alcohol Dependence*, 95(1–2), 152–159.
https://doi.org/10.1016/j.drugalcdep.2008.01.017

Etter, J.-F., Houezec, J. Le, & Perneger, T. V. (2003). A self-administered questionnaire to measure
dependence on cigarettes: The cigarette dependence scale. *Neuropsychopharmacology*, 28(2),
359–370. https://doi.org/10.1038/sj.npp.1300030

Falomir-Pichastor, J. M., Blondé, J., Desrichard, O., Felder, M., Riedo, G., & Folly, L. (2020).
Tobacco dependence and smoking cessation: The mediating role of smoker and ex-smoker self-
concepts. *Addictive Behaviors*, 102, 106200. https://doi.org/10.1016/j.addbeh.2019.106200

Gallagher, K. M., & Updegraff, J. A. (2012). Health Message Framing Effects on Attitudes, Intentions, and Behavior: A Meta-analytic Review. *Annals of Behavioral Medicine, 43*(1), 101–116. https://doi.org/10.1007/s12160-011-9308-7

Hofmann, S. G., & Gómez, A. F. (2017). Mindfulness-Based Interventions for Anxiety and Depression. *Psychiatric Clinics of North America*, 40(4), 739–749.
https://doi.org/10.1016/j.psc.2017.08.008

Hughes, J. R., Oliveto, A. H., Riggs, R., Kenny, M., Liguori, A., Pillitteri, J. L., & MacLaughlin, M. A. (2004). Concordance of different measures of nicotine dependence: Two pilot studies. *Addictive Behaviors*, 29(8), 1527–1539. https://doi.org/10.1016/j.addbeh.2004.02.031

Jalilian, F., Joulaei, H., Mirzaei-Alavijeh, M., Samannezhad, B., Berimvandi, P., Matin, B. K., & Mahboubi, M. (2016). Cognitive factors related to cigarettes smoking among college students: An application of theory of planned. *Social Sciences (Pakistan)*, 11(7), 1189–1193.
https://doi.org/10.3923/sscience.2016.1189.1193

Kang, S. K., & Bodenhausen, G. V. (2015). Multiple Identities in Social Perception and Interaction: Challenges and Opportunities. *Annual Review of Psychology*, 66(1), 547–574.
https://doi.org/10.1146/annurev-psych-010814-015025

Kenford, S. L., Wetter, D. W., Welsch, S. K., Smith, S. S., Fiore, M. C., & Baker, T. B. (2005).
Progression of college-age cigarette samplers: What influences outcome. *Addictive Behaviors*,
30(2), 285–294. https://doi.org/10.1016/j.addbeh.2004.05.017
Korkmaz, S., Goksuluk, D., & Zararsiz, G. (2014). MVN: An R Package for Assessing Multivariate Normality. *The R Journal*, 6(2), 151–162. https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf.

Kotz, D., Brown, J., & West, R. (2013). Predictive validity of the Motivation To Stop Scale (MTSS): A single-item measure of motivation to stop smoking. *Drug and Alcohol Dependence*, 128(1–2), 15–19. https://doi.org/10.1016/j.drugalcdep.2012.07.012

Lee, H. S., Catley, D., & Harris, K. J. (2014). Improving understanding of the quitting process: Psychological predictors of quit attempts versus smoking cessation maintenance among college students. *Substance Use and Misuse*, 49(10), 1332–1339.
https://doi.org/10.3109/10826084.2014.901386

Lee, T., & Shi, D. (2021). A Comparison of Full Information Maximum Likelihood and Multiple Imputation in Structural Equation Modeling With Missing Data. *Psychological Methods*, 26(4), 466–485.

Levinson, A. H., Campo, S., Gascoigne, J., Jolly, O., Zakharyan, A., & Tran, Z. V. (2007). Smoking, but not smokers: Identity among college students who smoke cigarettes. *Nicotine and Tobacco Research*, 9(8), 845–852. https://doi.org/10.1080/14622200701484987

Ma, J. Z., Li, M. D., & Payne, T. J. (2012). Evaluation of the brief Wisconsin Inventory of smoking dependence motives in African-American and European-American heavy smokers. *Frontiers in Psychiatry*, 3(APR), 1–7. https://doi.org/10.3389/fpsyt.2012.00036

Mao, R., Li, X., Stanton, B., Wang, J., Hong, Y., Zhang, H., & Chen, X. (2009). Psychosocial correlates of cigarette smoking among college students in China. *Health Education Research*, 24(1), 105–118. https://doi.org/10.1093/her/cyn002

Mardia, K. V. (1974). Applications of Some Measures of Multivariate Skewness and Kurtosis in Testing Normality and Robustness Studies. *The Indian Journal of Statistics*, 36(2), 115–128.
Mauduy, M., Mauny, N., & Mange, J. (2022). Tobacco Dependence Among French University Students: A Cluster Analytic Approach to Identifying Distinct Psychological Profiles of Smokers. *Journal of Drug Issues*, 1–21. https://doi.org/10.1177/00220426221107560

Moran, S., Wechsler, H., & Rigotti, N. A. (2004). Social smoking among US college students. *Pediatrics*, *114*(4), 1028–1034. https://doi.org/10.1542/peds.2003-0558-L

Nunnally, J. C., & Bernstein, I. (1994). *Psychometric theory* (McGraw-Hill). McGraw-Hill.

Pancani, L., D'addario, M., Cappelletti, E. R., Greco, A., Monzani, D., & Steca, P. (2015). Smoking behavior: A cross-sectional study to assess the dimensionality of the brief Wisconsin Inventory of Smoking Dependence Motives and identify different typologies among young daily smokers. *Nicotine and Tobacco Research*, *17*(1), 98–105. https://doi.org/10.1093/ntr/ntu143

Parker, S. C., Lyons, J., & Bonner, J. (2010). Eating Disorders in Graduate Students: Exploring the SCOFF Questionnaire as a Simple Screening Tool. *Journal of Ame*, *54*(2), 103–107.
https://doi.org/10.3200/JACH.54.2.103-107

Pasquereau, A., Gautier, A., Andler, R., Guignard, R., Richard, J. B., & Nguyen-Thanh, V. (2017). Tobacco and e-cigarette in France: levels of consumption according to the preliminary results from the 2016. *Health Barometer*.

Perski, O., Herd, N., Brown, J., & West, R. (2018). Does consistent motivation to stop smoking improve the explanation of recent quit attempts beyond current motivation? A cross-sectional study. *Addictive Behaviors*, *81*, 12–16. https://doi.org/10.1016/j.addbeh.2018.01.037

Piasecki, T. M., Piper, M. E., & Baker, T. B. (2010a). Refining the tobacco dependence phenotype using the Wisconsin Inventory of Smoking Dependence Motives: II. Evidence from a laboratory self-administration assay. *Journal of Abnormal Psychology*, *119*(3), 513–523.
https://doi.org/10.1037/a0020235

Piasecki, T. M., Piper, M. E., & Baker, T. B. (2010b). Tobacco dependence: Insights from
investigations of self-reported motives. *Current Directions in Psychological Science*, 19(6), 395–401. https://doi.org/10.1177/0963721410389460

Piasecki, T. M., Piper, M. E., Baker, T. B., & Hunt-Carter, E. E. (2011). WISDM primary and secondary dependence motives: Associations with self-monitored motives for smoking in two college samples. *Drug and Alcohol Dependence*, 114(2–3), 207–216.
https://doi.org/10.1016/j.drugalcdep.2010.10.005

Piper, M. E., Bolt, D. M., Kim, S.-Y., Japuntich, S. J., Smith, S. S., Niederdeppe, J., Cannon, D. S., & Baker, T. B. (2008). Refining the tobacco dependence phenotype using the Wisconsin Inventory of Smoking Dependence Motives. *Journal of Abnormal Psychology*, 117(4), 747–761.
https://doi.org/10.1037/a0013298

Piper, M. E., Piasecki, T. M., Federman, E. B., Bolt, D. M., Smith, S. S., Fiore, M. C., & Baker, T. B. (2004). A Multiple Motives Approach to Tobacco Dependence: The Wisconsin Inventory of Smoking Dependence Motives (WISDM-68). *Journal of Consulting and Clinical Psychology*, 72(2), 139–154. https://doi.org/10.1037/0022-006X.72.2.139

Radzius, A., Moolchan, E. T., Henningfield, J. E., Heishman, S. J., & Gallo, J. J. (2001). A factor analysis of the Fagerstrom Tolerance Questionnaire. *Addictive Behaviors*, 26, 303–310.

Riou França, L., Dautzenberg, B., Falissard, B., & Reynaud, M. (2009). Are social norms associated with smoking in French university students? A survey report on smoking correlates. *Substance Abuse Treatment, Prevention, and Policy*, 4(4). https://doi.org/10.1186/1747-597X-4-4

Rosseel, Y. (2012). lavaan: An R Package for Structural Equation. *Journal of Statistical Software* Software May, 48(2).

Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. *Psychometrika*, 66(4), 507–514.

Scheuermann, T. S., Mburu, W. E., Mathur, C., & Ahluwalia, J. S. (2015). Correlates of converted and
native nondaily smoking. *Nicotine and Tobacco Research*, 17(9), 1112–1119.

https://doi.org/10.1093/ntr/ntu272

Scheuermann, T. S., Nollen, N. L., Cox, L. S., Reitzel, L. R., Berg, C. J., Guo, H., Resnicow, K., & Ahluwalia, J. S. (2015). Smoking dependence across the levels of cigarette smoking in a multiethnic sample. *Addictive Behaviors*, 43(1), 1–6.

https://doi.org/10.1016/j.addbeh.2014.11.017

Schleicher, H., Harris, K., Catley, D., & Nazir, N. (2009). The role of depression and negative affect regulation expectancies in tobacco smoking among college students. *Journal of American College Health*, 57(5), 507–512. https://doi.org/10.3200/JACH.57.5.507-512

Schulenberg, J., Johnston, L. D., O'Malley, P., Bachman, J., Miech, R. A., & Patrick, M. E. (2019).
*Monitoring the Future national survey results on drug use, 1975-2018: Volume II, college
students and adults ages 19-60.* (Ann Arbor). The University of Michigan.

http://monitoringthefuture.org/pubs.html#monographs

Shadel, W. G., & Mermelstein, R. (1996). Individual differences in self-concept among smokers attempting to quit: Validation and predictive utility of measures of the smoker self-concept and abstainer self-concept. *Annals of Behavioral Medicine*, 18(3), 151–156.

https://doi.org/10.1007/BF02883391

Shenassa, E. D., Graham, A. L., Burdzovic, J. B., & Buka, S. L. (2009). Psychometric properties of the Wisconsin inventory of Smoking Dependence Motives (WISDM-68): A replication and extension. *Nicotine and Tobacco Research*, 11(8), 1002–1010. https://doi.org/10.1093/ntr/ntp109

Smith, S. S., Piper, M. E., Bolt, D. M., Fiore, M. C., Wetter, D. W., Cinciripini, P. M., & Baker, T. B. (2010). Development of the Brief Wisconsin Inventory of Smoking Dependence Motives.

*Nicotine & Tobacco Research*, 12(5), 489–499. https://doi.org/10.1093/ntr/ntq032

Spielberger, C. D., Gorsuch, R. L., Lushene, R., Vagg, P. R., & Jacobs, G. (1983). *Manual for the*
*State-Trait Anxiety Inventory*. Consulting Psychologists Press.

Stice, E., Marti, C. N., Shaw, H., & Rohde, P. (2019). Meta-analytic review of dissonance-based eating disorder prevention programs: Intervention, participant, and facilitator features that predict larger effects. *Clinical Psychology Review*, 70*(February 2018), 91–107.
https://doi.org/10.1016/j.cpr.2019.04.004

Thompson, B., Coronado, G., Chen, L., Thompson, L. A., Halperin, A., Jaffe, R., McAfee, T., & Zbikowski, S. M. (2007). Prevalence and characteristics of smokers at 30 Pacific Northwest colleges and universities. *Nicotine and Tobacco Research*, 9*(3), 429–438.
https://doi.org/10.1080/14622200701188844

Tombor, I., Urbán, R., Berkes, T., & Demetrovics, Z. (2010). Denial of smoking-related risk among pregnant smokers. *Acta Obstetricia et Gynecologica Scandinavica*, 89(4), 524–530.
https://doi.org/10.3109/00016341003678427

Vajer, P., Urbán, R., Tombor, I., Stauder, A., & Kalabay, L. (2011). Psychometric properties and construct validity of the brief wisconsin inventory of smoking dependence motives in an internet-based sample of treatment-seeking Hungarian smokers. *Nicotine and Tobacco Research*, 13(4), 273–281. https://doi.org/10.1093/ntr/ntq254

Vallentin-Holbech, L., Rasmussen, B. M., & Stock, C. (2018). Effects of the social norms intervention *The GOOD Life* on norm perceptions, binge drinking and alcohol-related harms: A cluster-randomised controlled trial. *Preventive Medicine Reports*, 12*(June), 304–311.
https://doi.org/10.1016/j.pmedr.2018.10.019

Webb, T. L., & Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. *Psychological Bulletin*, 132(2), 249–268.
https://doi.org/10.1037/0033-2909.132.2.249

West, R. (2017). Tobacco smoking: Health impact, prevalence, correlates and interventions.
*Psychology and Health*, 32(8), 1018–1036. https://doi.org/10.1080/08870446.2017.1325890

World Heald Organization. (2018). *Who global report on trends in prevalence of tobacco smoking*
*2000-2025, second edition.*

Tables

<caption>**Table 1**</caption>

*Sample Characteristics*

<table><thead><tr><th></th><th><em>M</em> (SD)</th><th>Range</th></tr></thead><tbody><tr><td><strong>Socio demographics</strong></td><td></td><td></td></tr><tr><td>Females, N (%)</td><td>434 (63.17%)</td><td></td></tr><tr><td>Males, N (%)</td><td>235 (34.21%)</td><td></td></tr><tr><td>No answer sex, N (%)</td><td>18 (2.62%)</td><td></td></tr><tr><td>Age, years</td><td>20.06 (2.39)</td><td>18 - 35</td></tr><tr><td><strong>Smoking-related variables</strong></td><td></td><td></td></tr><tr><td>First use, year</td><td>16.12 (2.03)</td><td>10 - 31</td></tr><tr><td>Cigarettes per day</td><td>6.34 (16.3)</td><td>0 - 60</td></tr><tr><td>Days of use per month</td><td>20.52 (10.38)</td><td>1 - 30</td></tr><tr><td>CDS –Tobacco dependence</td><td>11.72 (4.50)</td><td>1 - 24</td></tr><tr><td>MTSS – Motivation to quit smoking</td><td>2.85 (1.69)</td><td>1 - 7</td></tr><tr><td>Recent attempts to quit smoking, N (%)</td><td>214 (41.39%)</td><td></td></tr><tr><td><strong>Smokers Status</strong></td><td></td><td></td></tr><tr><td>Non-daily, N (%)</td><td>436 (63.5%)</td><td></td></tr><tr><td>Daily, N (%)</td><td>251 (36.5%)</td><td></td></tr><tr><td><strong>Psychological variables</strong></td><td></td><td></td></tr><tr><td>Attitude related to smoking</td><td>3.70 (1.08)</td><td>1 - 7</td></tr><tr><td>Social norms related to smoking</td><td>3.86 (1.17)</td><td>1 - 7</td></tr><tr><td>Perceived behavioral control to resist smoking</td><td>3.51 (1.60)</td><td>1 - 7</td></tr><tr><td>Smoker identity</td><td>2.69 (1.64)</td><td>1 - 7</td></tr><tr><td>SCOFF – Eating Disorders</td><td>1.02 (1.18)</td><td>1 - 5</td></tr><tr><td>STAI-T – Anxiety</td><td>50.90 (14.35)</td><td>20 - 80</td></tr><tr><td>BDI – Depression</td><td>9.40 (8.35)</td><td>0 - 36</td></tr></tbody></table>

*Note.* Except for gender, recent attempts to quit, and smokers' status, data show means (standard deviations); CDS: Cigarette Dependence Scale; MTSS: Motivation to stop smoking; SCOFF: Sick Control One Fat Food; STAI-T: State-Trait Anxiety Inventory; BDI: Beck Depression Inventory
<caption>**Table 2**</caption>

*The Fit Indices for the Three Confirmatory Factor Analysis Models of FB-WISDM*

<table><thead><tr><th rowspan="2"></th><th rowspan="2">Model description</th><th rowspan="2">Satorra-Bentler χ<sup>2</sup></th><th rowspan="2">df</th><th rowspan="2">CFI</th><th rowspan="2">TLI</th><th rowspan="2">RMSEA</th><th colspan="2">90% Confidence Interval</th><th rowspan="2">SRMR</th></tr><tr><th>Lower</th><th>Upper</th></tr></thead><tbody><tr><td>Model 1</td><td>2 first-order factors with error covariances</td><td>7291.134</td><td>628</td><td>0.655</td><td>0.634</td><td>0.126</td><td>0.123</td><td>0.129</td><td>0.089</td></tr><tr><td>Model 2</td><td>11 first-order factors with error covariances</td><td>2087.628</td><td>563</td><td>0.926</td><td>0.913</td><td>0.061</td><td>0.058</td><td>0.065</td><td>0.043</td></tr><tr><td>Model 3</td><td>11 first-order and 2 second-order factors with error covariances</td><td>2442.526</td><td>617</td><td>0.911</td><td>0.903</td><td>0.065</td><td>0.061</td><td>0.068</td><td>0.057</td></tr></tbody></table>

*Note.* CFI = comparative fit index; RMSEA = root mean squared error of approximation; SRMR = standardized root mean square residual; TLI = Tucker-Lewis index.
<caption>**Table 3**</caption>

Factor Loadings and Internal Consistencies of the French B-WISDM

<table><thead><tr><th>Factor</th><th>Estimate</th><th>SE</th><th>Standard Estimate</th><th>Z</th><th>p</th><th>α</th></tr></thead><tbody><tr><td><strong>Affiliative Attachment</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Item 11</td><td>1.821</td><td>0.268</td><td>0.821</td><td>6.794</td><td>&lt; .001</td><td>.86</td></tr><tr><td>Item 22</td><td>1.336</td><td>0.191</td><td>0.830</td><td>6.982</td><td>&lt; .001</td><td></td></tr><tr><td>Item 26</td><td>1.498</td><td>0.221</td><td>0.844</td><td>6.766</td><td>&lt; .001</td><td></td></tr><tr><td><strong>Automaticity</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Item 1</td><td>1.628</td><td>0.109</td><td>0.776</td><td>14.975</td><td>&lt; .001</td><td>.89</td></tr><tr><td>Item 10</td><td>1.612</td><td>0.103</td><td>0.774</td><td>15.648</td><td>&lt; .001</td><td></td></tr><tr><td>Item 14</td><td>1.732</td><td>0.118</td><td>0.900</td><td>14.698</td><td>&lt; .001</td><td></td></tr><tr><td>Item 25</td><td>1.519</td><td>0.118</td><td>0.827</td><td>12.845</td><td>&lt; .001</td><td></td></tr><tr><td><strong>Loss of Control</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Item 2</td><td>1.513</td><td>0.285</td><td>0.872</td><td>5.318</td><td>&lt; .001</td><td>.91</td></tr><tr><td>Item 16</td><td>1.338</td><td>0.258</td><td>0.783</td><td>5.185</td><td>&lt; .001</td><td></td></tr><tr><td>Item 21</td><td>1.883</td><td>0.346</td><td>0.911</td><td>5.439</td><td>&lt; .001</td><td></td></tr><tr><td>Item 35</td><td>1.547</td><td>0.290</td><td>0.835</td><td>5.342</td><td>&lt; .001</td><td></td></tr><tr><td><strong>Cognitive Enhancement</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Item 6</td><td>1.786</td><td>0.066</td><td>0.896</td><td>27.128</td><td>&lt; .001</td><td>.92</td></tr><tr><td>Item 13</td><td>1.818</td><td>0.069</td><td>0.917</td><td>26.205</td><td>&lt; .001</td><td></td></tr><tr><td>Item 32</td><td>1.566</td><td>0.067</td><td>0.855</td><td>23.237</td><td>&lt; .001</td><td></td></tr><tr><td><strong>Craving</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Item 4</td><td>1.678</td><td>0.242</td><td>0.855</td><td>6.928</td><td>&lt; .001</td><td>.91</td></tr><tr><td>Item 17</td><td>1.665</td><td>0.243</td><td>0.851</td><td>6.862</td><td>&lt; .001</td><td></td></tr><tr><td>Item 23</td><td>1.723</td><td>0.253</td><td>0.840</td><td>6.823</td><td>&lt; .001</td><td></td></tr><tr><td>Item 29</td><td>1.490</td><td>0.217</td><td>0.841</td><td>6.855</td><td>&lt; .001</td><td></td></tr><tr><td><strong>Cue Exposure / Associative Processes</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Item 8</td><td>1.555</td><td>0.173</td><td>0.612</td><td>8.973</td><td>&lt; .001</td><td>.73</td></tr><tr><td>Item 12</td><td>2.022</td><td>0.155</td><td>0.692</td><td>13.022</td><td>&lt; .001</td><td></td></tr><tr><td>Item 24</td><td>2.079</td><td>0.167</td><td>0.793</td><td>12.435</td><td>&lt; .001</td><td></td></tr><tr><td><strong>Taste</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Item 5</td><td>1.505</td><td>0.116</td><td>0.790</td><td>12.956</td><td>&lt; .001</td><td>.84</td></tr><tr><td>Item 15</td><td>1.706</td><td>0.136</td><td>0.857</td><td>12.575</td><td>&lt; .001</td><td></td></tr><tr><td>Item 20</td><td>1.519</td><td>0.110</td><td>0.747</td><td>13.771</td><td>&lt; .001</td><td></td></tr><tr><td><strong>Tolerance</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Item 3</td><td>1.433</td><td>0.141</td><td>0.718</td><td>10.176</td><td>&lt; .001</td><td>.87</td></tr><tr><td>Item 28</td><td>1.732</td><td>0.146</td><td>0.863</td><td>11.821</td><td>&lt; .001</td><td></td></tr><tr><td>Item 31</td><td>1.555</td><td>0.158</td><td>0.676</td><td>9.841</td><td>&lt; .001</td><td></td></tr><tr><td>Item 36</td><td>1.803</td><td>0.148</td><td>0.894</td><td>12.182</td><td>&lt; .001</td><td></td></tr><tr><td><strong>Weight Control</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Item 7</td><td>1.665</td><td>0.095</td><td>0.867</td><td>17.566</td><td>&lt; .001</td><td>.88</td></tr><tr><td>Item 19</td><td>1.073</td><td>0.092</td><td>0.752</td><td>11.615</td><td>&lt; .001</td><td></td></tr><tr><td>Item 34</td><td>1.755</td><td>0.100</td><td>0.912</td><td>17.534</td><td>&lt; .001</td><td></td></tr></tbody></table>
<table><tbody><tr><td colspan="7">Affective Enhancement</td></tr><tr><td>Item 9</td><td>1.595</td><td>0.251</td><td>0.774</td><td>6.345</td><td>&lt; .001</td><td>.82</td></tr><tr><td>Item 33</td><td>1.690</td><td>0.273</td><td>0.753</td><td>6.199</td><td>&lt; .001</td><td></td></tr><tr><td>Item 37</td><td>1.656</td><td>0.263</td><td>0.795</td><td>6.301</td><td>&lt; .001</td><td></td></tr><tr><td colspan="7">Social / Environmental Goads</td></tr><tr><td>Item 18</td><td>1.622</td><td>0.091</td><td>0.866</td><td>17.829</td><td>&lt; .001</td><td>.92</td></tr><tr><td>Item 27</td><td>1.560</td><td>0.092</td><td>0.848</td><td>17.001</td><td>&lt; .001</td><td></td></tr><tr><td>Item 30</td><td>1.812</td><td>0.083</td><td>0.965</td><td>21.788</td><td>&lt; .001</td><td></td></tr></tbody></table>

Note. N = 687. The table shows the results of the CFA and internal consistency for Model 2.
<caption>**Table 4**</caption>

*The Fit Indices for the Three Multigroups Confirmatory Factor Analysis Models of FB-WISDM for Type of Smokers and Gender*

<table><thead><tr><th rowspan="2"></th><th rowspan="2">Model description</th><th rowspan="2">Satorra-Bentler χ²</th><th rowspan="2">df</th><th rowspan="2">CFI</th><th rowspan="2">TLI</th><th rowspan="2">RMSEA</th><th colspan="2">90% Confidence Interval</th><th rowspan="2">SRMR</th></tr><tr><th>Lower</th><th>Upper</th></tr></thead><tbody><tr><td colspan="10">Type of smokers (daily vs. non-daily)</td></tr><tr><td>Model 1</td><td>Configural Invariance</td><td>2644.08</td><td>1126</td><td>0.919</td><td>0.905</td><td>0.059</td><td>0.056</td><td>0.063</td><td>0.052</td></tr><tr><td>Model 2</td><td>Metric Invariance</td><td>2766.83</td><td>1163</td><td>0.914</td><td>0.901</td><td>0.060</td><td>0.057</td><td>0.064</td><td>0.063</td></tr><tr><td>Model 3</td><td>Scalar Invariance</td><td>2875.13</td><td>1189</td><td>0.914</td><td>0.901</td><td>0.060</td><td>0.057</td><td>0.064</td><td>0.063</td></tr><tr><td colspan="10">Gender (men vs. women)</td></tr><tr><td>Model 1</td><td>Configural Invariance</td><td>2916.475</td><td>1126</td><td>0.917</td><td>0.902</td><td>0.065</td><td>0.062</td><td>0.069</td><td>0.049</td></tr><tr><td>Model 2</td><td>Metric Invariance</td><td>2972.161</td><td>1163</td><td>0.916</td><td>0.904</td><td>0.065</td><td>0.061</td><td>0.068</td><td>0.054</td></tr><tr><td>Model 3</td><td>Scalar Invariance</td><td>3002.432</td><td>1189</td><td>0.916</td><td>0.906</td><td>0.064</td><td>0.061</td><td>0.067</td><td>0.054</td></tr></tbody></table>

*Note.* CFI = comparative fit index; RMSEA = root mean squared error of approximation; SRMR = standardized root mean square residual; TLI = Tucker-Lewis index.
<caption>**Table 5**</caption>

Standardized Coefficients from a Multivariate Regression Model Describing the Association between the French B-WISDM and Tobacco Dependence and Psychological Variables

<table><thead><tr><th></th><th>Tobacco dependence</th><th>Attitude</th><th>Social norms</th><th>PBC</th><th>Smoker identity</th><th>Eating disorders</th><th>Anxiety</th><th>Depression</th></tr></thead><tbody><tr><td>Adjusted R<sup>2</sup></td><td>.761</td><td>.319</td><td>.317</td><td>.520</td><td>.447</td><td>.211</td><td>.247</td><td>.282</td></tr><tr><td>Loss of Control</td><td>.252<sup>***</sup></td><td>-.199<sup>*</sup></td><td>.046</td><td>-.050</td><td>.252<sup>***</sup></td><td>-.081</td><td>-.108</td><td>-.113</td></tr><tr><td>Craving</td><td>.342<sup>***</sup></td><td>.023</td><td>-.004</td><td>-.319<sup>***</sup></td><td>-.013</td><td>-.005</td><td>.215</td><td>.169</td></tr><tr><td>Automaticity</td><td>.044</td><td>-.002</td><td>.025</td><td>-.048</td><td>-.010</td><td>.056</td><td>-.002</td><td>.006</td></tr><tr><td>Tolerance</td><td>.376<sup>***</sup></td><td>.045</td><td>-.047</td><td>.015</td><td>.156<sup>*</sup></td><td>-.030</td><td>-.212<sup>*</sup></td><td>-.123</td></tr><tr><td>Affiliative Attachment</td><td>-.058<sup>*</sup></td><td>.047</td><td>.025</td><td>-.008</td><td>.192<sup>***</sup></td><td>.071</td><td>.182<sup>*</sup></td><td>.234<sup>**</sup></td></tr><tr><td>Associative Processes</td><td>.017</td><td>-.003</td><td>-.051</td><td>-.387<sup>***</sup></td><td>.123<sup>*</sup></td><td>.038</td><td>.043</td><td>-.044</td></tr><tr><td>Social Goads</td><td>-.004</td><td>.001</td><td>.538<sup>***</sup></td><td>.046</td><td>-.009</td><td>-.019</td><td>.028</td><td>.057</td></tr><tr><td>Taste</td><td>.024</td><td>.481<sup>***</sup></td><td>.053</td><td>.009</td><td>.082</td><td>-.049</td><td>-.119</td><td>-.143<sup>*</sup></td></tr><tr><td>Weight Control</td><td>.035</td><td>-.015</td><td>.064</td><td>.025</td><td>-.024</td><td>.332<sup>***</sup></td><td>.129<sup>*</sup></td><td>.226<sup>***</sup></td></tr><tr><td>Affective Enhancement</td><td>-.082</td><td>.181<sup>***</sup></td><td>.008</td><td>-.067</td><td>.009</td><td>.047</td><td>.305<sup>***</sup></td><td>.269<sup>***</sup></td></tr><tr><td>Cognitive Enhancement</td><td>-.012</td><td>.049</td><td>.026</td><td>.003</td><td>.059</td><td>-.002</td><td>.024</td><td>.029</td></tr></tbody></table>

Note. PBC: Perceived Behavioral Control. Cigarettes/day: Number of cigarettes smoked per day. Smoking days/month: Number of smoking days per month. Statistically significant at ***p < .001; **p < .01; *p < .05