Psychometric evaluation of the five-factor Modified Drinking Motives Questionnaire — Revised in...

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Psychometric evaluation of the five-factor Modified DrinkingMotives Questionnaire — Revised in undergraduates☆

Valerie V. Grant a,⁎, Sherry H. Stewart b, Roisin M. O'Connor c,Ekin Blackwell d, Patricia J. Conrod e

a Department of Psychology, Life Sciences Centre, Dalhousie University, Halifax, Nova Scotia, Canada B3H 4J1b Departments of Psychiatry, Psychology, and Community Health and Epidemiology,

Dalhousie University, Halifax, Nova Scotia, Canadac Department of Psychology, University of Washington, Seattle, Washington, USA

d Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canadae National Addiction Centre, Institute of Psychiatry, King's College London, University of London, London, United Kingdom

Abstract

The psychometric properties of the Modified Drinking Motives Questionnaire — Revised (Modified DMQ-R)[Blackwell, E., & Conrod, P. J. (2003). A five-dimensional measure of drinking motives. Unpublished manuscript,Department of Psychology, University of British Columbia], based on a five-factor model of drinking motives withseparate coping-anxiety and coping-depression factors, were evaluated in undergraduates. In Study 1, confirmatoryfactor analyses supported the correlated five-factor model in two samples of undergraduate drinkers (N=726 andN=603). Furthermore, the five-factor model fit the data better than a four-factor model conceptually equivalentto that of Cooper [Cooper, M. L. (1994). Motivations for alcohol use among adolescents: Development andvalidation of a four-factor model. Psychological Assessment, 6, 117–128] (i.e., with coping-anxiety and coping-depression items constrained to a single factor). In Study 1, drinking motives were predictive of concurrent drinkingfrequency and typical number of alcoholic beverages per occasion, over and above demographics. In Study 2, theModified DMQ-R scores showed good to excellent test–retest reliability in a sample of undergraduates who wererelatively frequent drinkers (N=169). Also, drinking motives prospectively predicted number of drinks consumed

Addictive Behaviors 32 (2007) 2611–2632

☆ This study was supported by a grant from the Social Sciences and Humanities Research Council of Canada (SSHRC) awardedto Sherry H. Stewart and by a dissertation grant award from the Society for a Science of Clinical Psychology awarded to ValerieV. Grant. This study was conducted as a component of a doctoral dissertation by Valerie V. Grant under the supervision of SherryH. Stewart. The initial Modified Drinking Motives Questionnaire— Revised (Modified DMQ-R) work was supported by a start-up grant from the Department of Psychology, University of British Columbia awarded to Patricia J. Conrod.⁎ Corresponding author. Tel.: +1 902 494 3793; fax: +1 902 494 6585.E-mail address: [email protected] (V.V. Grant).

0306-4603/$ - see front matter © 2007 Elsevier Ltd. All rights reserved.doi:10.1016/j.addbeh.2007.07.004


per week and alcohol-related problems, over and above demographics and initial alcohol use. Notably, coping-anxiety and coping-depression motives were distinctly related to alcohol consumption and alcohol problems.© 2007 Elsevier Ltd. All rights reserved.

Keywords: Drinking motives; Psychometrics; Coping-anxiety motives; Coping-depression motives; Risk factors; Alcohol

Recent reports indicate that there are high rates of heavy drinking and alcohol-related problems amongNorth American university students (e.g., Knight et al., 2002; Kuo et al., 2002). Thus, there has beenincreasing interest in determining risk factors for heavy and/or problematic alcohol consumption. One lineof inquiry in this area has focused on alcohol-use motives, or reasons why individuals consume alcohol(e.g., Cooper, 1994; Cooper, Russell, Skinner, & Windle, 1992).

Early models of drinking motives consisted of two (i.e., positive reinforcement [social] and negativereinforcement [coping]; Farber, Khavari, & Douglass, 1980) or three (i.e., external positive reinforcement[social], internal positive reinforcement [enhancement], and internal negative reinforcement [coping];Cooper et al., 1992) factors. More recently, inspired by Cox and Klinger's (1988) theoretical structure forclassifying alcohol-use motives, Cooper (1994) posited that a four-factor model might better describealcohol-use motives, particularly among adolescents. In this four-factor model, motives are categorizedaccording to two dimensions: type of reinforcement (positive or negative) and source of reinforcement(external or internal). The external motives include positive-reinforcement social (i.e., affiliative) andnegative-reinforcement conformity (i.e., to fit in with admired group; avoid peer rejection) motives. Theinternal, or emotional alcohol-use motives include positive-reinforcement enhancement (i.e., to elevatepositive mood) and negative-reinforcement coping (i.e., to cope with, or to relieve negative emotionalstates, including sadness or anxiety) motives.

In an extensive community-based study of adolescents, Cooper (1994) found that the 20-item DrinkingMotives Questionnaire—Revised (DMQ-R), designed to tap the four-factor model of alcohol-use motives,was psychometrically sound. Results of confirmatory factor analyses (CFAs) supported the hypothesizedfour-factor model, which provided a good fit to the data, better than that of competing models. Similarly,MacLean and Lecci (2000), examining the DMQ-R in a sample of undergraduates, found that the four-factor model fit the data well and provided a better fit compared to alternative models, including a three-factor model that constrained the external motives onto one factor.

In addition, Cooper (1994) found that each of these four types of alcohol-use motives was associatedwith a unique pattern of concurrent alcohol use and alcohol-related problems, even after accounting forbasic demographic differences. Social motives, comprising the most heavily endorsed motive type, werepositively related to frequency and quantity of alcohol consumption, but not to heavy drinking or toalcohol-related problems. Conformity motives were negatively associated with quantity and frequency ofalcohol use and heavy drinking, but positively related to drinking problems. Both of the emotionalalcohol-use motives (enhancement and coping) were positively related to typical quantity and frequencyof alcohol use, heavy drinking, and alcohol-related problems. However, when typical levels of alcohol usewere statistically controlled, only coping motives were associated with alcohol-related problems.

Nonetheless, there are mixed research findings with regard to the associations among negative affect,coping motives, alcohol use, and alcohol-related problems, suggesting significant complexity in theserelationships. Cooper, Frone, Russell, and Mudar (1995) found that coping motives were more proximal

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determinants of alcohol use and alcohol-related problems in adolescents and adults than the underlyingnegative emotional states (i.e., symptoms of depression) driving them. This finding indicates thatknowledge about levels of drinking motives, including coping motives, offers valuable information aboutalcohol use and alcohol-related problems over and above that provided by mood alone. However, in asimilar study with an undergraduate student sample, Read, Wood, Kahler, Maddock, and Palfai (2003)found weak support for the importance of coping motives. In a cross-sectional model, broadly definednegative affect predicted coping motives, which in turn predicted alcohol problems, but not alcohol use.Nonetheless, coping motives did not prospectively predict alcohol problems when initial alcohol use andalcohol problems were statistically controlled. Similarly, Bradizza, Reifman, and Barnes (1999) foundthat, while controlling for basic demographic variables and psychological distress, coping motives fordrinking predicted concurrent, but not prospective, alcohol misuse in mid-adolescence. Coping motivesdid not, however, predict concurrent alcohol misuse in older adolescents. Perkins (1999) found thathaving a prominent stress-related motivation for drinking was associated with higher levels of alcoholconsumption and related problems among some postcollegiate adults, but not among undergraduates. TheRead et al., Bradizza et al., and Perkins findings may reflect the less important role of coping motives foralcohol misuse in populations in which heavy alcohol use is normative (i.e., older adolescents orundergraduate students). However, Kassel, Jackson, and Unrod (2000) found that coping motives didprospectively predict problem drinking in undergraduate students, even while controlling for social andenhancement drinking motives, basic demographics, symptoms of anxiety and depression, alcoholconsumption, and generalized negative mood regulation expectancies.

The mixed findings with respect to the associations among negative affect, coping drinking motives,alcohol use, and alcohol problems, particularly in undergraduate students, suggest that the current copingmotive construct and its measurement deserve closer examination. One limitation of Cooper's (1994)DMQ-R is that it does not contend with the complex nature of coping motives: the DMQ-R copingmotives subscale is generic, with anxiety-related and depression-related coping items merged onto onefactor. It is possible that the use of a generic measure of coping motives obscures the true relationshipsamong coping motives, negative affect, alcohol use, and alcohol problems.

At the diagnostic level, it is clear that both anxiety and depression are linked to alcohol-use disorders(AUDs) in the general adult population (see respective reviews by Kushner, Abrams, & Borchardt, 2000;Swendsen & Merikangas, 2000) and in college students (Dawson, Grant, Stinson, & Chou, 2005).However, there is some evidence to suggest that anxiety and depression are each associated with distinctpatterns of alcohol use. For instance, depression has often been found to be positively related to level ofalcohol consumption (as reviewed by Graham, Massak, Demers, & Rehm, 2007). In a recent Canadianpopulation survey, depression scores were most strongly positively associated with quantity of alcoholconsumed per drinking occasion, as compared to a number of other alcohol-use variables, includingfrequency of drinking (Graham et al.). On the other hand, despite the high rates of comorbidity betweenanxiety disorders and AUDs, there is some evidence of a negative relationship between levels of anxiety(particularly social anxiety) and alcohol-consumption variables, including drinking frequency (seeMorris, Stewart, & Ham, 2005).

The negative affect inherent in depression and anxiety may put individuals at risk for drinking fornegative-reinforcement purposes (i.e., self-medication or coping reasons). Given that depression andanxiety may be associated with different alcohol-use patterns, the mechanisms underlying depression-related drinking may be distinct from those underlying anxiety-related drinking. Accordingly, it stands toreason that separate measures of anxiety-related and depression-related coping drinking motives might

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provide important additional information about the antecedents and consequences of drinking relative tothat provided by a single measure of generic coping motives. Recently, Blackwell and Conrod (2003; alsosee Blackwell, Conrod, & Hansen, 2002) modified and extended Cooper's drinking motives measures(Cooper, 1994; Cooper et al., 1992) to create a 28-item, five-factor measure of drinking motives (theModified DMQ-R; see Table 1 for component items) that differentiates drinking to cope with anxiety(coping-anxiety) from drinking to cope with depression (coping-depression). To achieve this distinction,one new item relevant to coping-anxiety (Item 19) and six new items relevant to coping-depression (Items16, 20, 21, 22, 23, and 27) were added. In addition, an item included in both the three-factor DMQ(Cooper et al., 1992) and the four-factor DMQ-R (Cooper, 1994) related to using alcohol to help withfeeling depressed or nervous was divided into one coping-anxiety item (Item 11) and one coping-depression item (Item 17).

The three-factor DMQ (Cooper et al., 1992), validated on adults, and the four-factor DMQ-R (Cooper,1994), validated on adolescents, both contain social, coping, and enhancement subscales. However, someof the item content for each of these subscales differs between the DMQ and the DMQ-R. Undergraduatestudents are on the cusp between adolescence and adulthood. Thus, Blackwell and Conrod (2003)selected DMQ item content for the Modified DMQ-R social, coping, and enhancement subscales as thewording seemed most appropriate for undergraduates moving into adulthood. They also includedconformity items (only available in the DMQ-R) because conformity has previously been shown to beimportant in undergraduate drinking (MacLean & Lecci, 2000).1

Item generation for the additional Modified DMQ-R coping-depression items was grounded in theory.Conrod, Pihl, Stewart, and Dongier (2000) speculated that a specific personality profile, introversion-hopelessness, may help to explain the link between depression and AUDs. They found that individuals withthis profile held many of the self-defeating beliefs and expectations that characterize individuals at risk fordepression based on their negative cognitive style. Using cognitive vulnerability theories of depression as astarting point, Blackwell and Conrod (2003) reasoned that among some depression-prone individuals,alcohol use would be motivated by a desire to reduce pessimistic and ruminative thinking patterns, negativeself-referent thoughts andmemories, and depressive affect.Modified DMQ-R coping-depression itemswereconstructed in an effort to best reflect these aspects of depression-proneness and the characteristics of theintroversion–hopelessness profile identified by Conrod et al. (2000).

Blackwell and Conrod (2003) administered the Modified DMQ-R to 591 (69.9% women)undergraduates at the University of British Columbia. They transformed the data in an effort to improvethe symmetry of the distributions (many items were positively skewed) and to control for alcoholexperience. Specifically, unstandardized residual scores were created by regressing (within sex) each itemon an alcohol-use severity measure that tapped both frequency of use and problematic use. An exploratoryfactor analysis (using the unweighted least squares estimation method with oblique rotation) on theseunstandardized residual scores resulted in a relatively good simple factor structure, with correlated factorsrepresenting social, coping-anxiety, coping-depression, enhancement, and conformity motives; inter-factor rs ranged from .16 to .54. However, unexpectedly, Item 19 (“To reduce my anxiety”), which wasintended for the coping-anxiety factor and had its highest loading on that factor, also had a salient loading(i.e., ≥ .30; e.g., Brown, 2006) on the coping-depression factor. Furthermore, Item 2 (“To relax”),intended for the coping-anxiety factor, did not have a salient loading on any of the factors. Each of the

1 Relative to the earlier DMQ (Cooper et al., 1992) and DMQ-R (Cooper, 1994) versions, the Modified DMQ-R items wereshifted in grammatical person (from second-to first-person pronouns) and had some other minor wording differences.



Table 1Item endorsement, standardized factor loadings (SL), and standard errors (SE) for the hypothesized five-factor model of drinkingmotives in Sample 1/Sample 2

Social Coping-anxiety

Coping-depression

Enhancement Conformity

Item % Endorsement a SL SE SL SE SL SE SL SE SL SE

1. As a way tocelebrate 93/95 .27/.25 .04/.04

4. Because it iswhat most ofmy friends dowhen we gettogether 72/73 .55/.50 .04/.04

7. To be sociable 88/91 .63/.60 .04/.0410. Because it is

customary onspecial occasions

63/66 .30/.26 .04/.05

13. Because it makesa social gatheringmore enjoyable 90/89 .81/.79 .03/.04

2. To relax 56/52 .43/.42 .04/.058. Because I feel

more self-confidentor sure of myself 56/61 .74/.70 .05/.05

11. Because it helpsme when I amfeeling nervous 41/37 .72/.73 .04/.05

19. To reduce myanxiety 32/28 .62/.63 .04/.04

5. To forget myworries 40/34 .75/.75 .04/.05

14. To cheer me upwhen I'm ina bad mood 46/43 .72/.67 .04/.04

16. To numb my pain 20/16 .65/.72 .04/.0417. Because it helps

me when I amfeeling depressed 26/20 .75/.76 .04/.04

20. To stop me fromdwelling on things 33/28 .67/.74 .04/.04

21. To turn offnegative thoughtsabout myself 21/21 .68/.71 .04/.05

22. To help me feelmore positiveabout thingsin my life 27/21 .71/.65 .04/.04

(continued on next page)



subscales had acceptable internal consistency (Cronbach's αs based on raw scores ranged from .73 to.89). If the Modified DMQ-R is to be used as a tool to identify drinkers with risky alcohol-consumptionmotives (i.e., motives associated with heavy or problematic alcohol use), then a limitation of theBlackwell and Conrod study was the adjustment of scores to control for alcohol-use severity.

The aim of the current investigation was to provide a comprehensive evaluation of the psychometricproperties of the Modified DMQ-R in two separate studies. The purpose of Study 1 was to perform a morestringent test of the hypothesized five-factor structure of the Modified DMQ-R (not adjusted for alcoholexperience). To accomplish this, CFAwas used to test the structure in two separate samples of undergraduates.The CFA approach was appropriate given the strong empirical and theoretical basis of this five-factor model ofdrinking motives. Also, the CFA approach has the advantage of adjusting for measurement error (Brown,2006). It was hypothesized that the five subscales would each be internally consistent, that the correlated five-factor structure would provide a good fit to the data, and that the five-factor model would fit the data better than

Table 1 (continued )

Social Coping-anxiety

Coping-depression

Enhancement Conformity

Item % Endorsement a SL SE SL SE SL SE SL SE SL SE

23. To stop mefrom feelingso hopelessabout the future 13/11 .61/.58 .04/.05

27. To forget painfulmemories 18/14 .56/.57 .04/.05

3. Because I likethe feeling 84/82 .79/.71 .03/.04

6. Because it isexciting 70/75 .70/.72 .04/.04

9. To get a high 52/54 .63/.66 .04/.0412. Because it's fun 93/93 .77/.79 .04/.0426. Because it

makes me feelgood 74/72 .76/.71 .04/.04

15. To be liked 18/17 .72/.61 .05/.0518. So that others

won't kid meabout not using 10/10 .53/.43 .04/.05

24. Because myfriends pressureme to use 13/12 .51/.44 .04/.04

25. To fit in with agroup I like 20/18 .77/.62 .04/.04

28. So I won't feelleft out 34/32 .77/.76 .05/.05

Note. Sample 1: N=726; Sample 2: N=603. SL = standardized factor loading. Percentages of endorsement, factor loadings, andSEs for Sample 1 and Sample 2 are presented before and after the slashes, respectively. All factor loadings are significant atpb .001.a “% Endorsement” for an item represents the percentage of participants who responded at least 2 = some of the time to thatitem (i.e., the percentage who endorsed drinking for that reason more often than 1 = almost never/never).



a model that corresponded conceptually with Cooper's (1994) four-factor model (i.e., with coping-anxiety andcoping-depression items constrained to load on a single generic coping factor). Cooper found that boys reportedmore drinking for social, enhancement, and conformity motives than girls, but that both sexes reportedstatistically equivalent levels of copingmotives. Hence, we also examined potential sex differences in drinkingmotives. However, it would only be appropriate tomake these cross-sex comparisons if the five-factor structurewas first found to be reasonably invariant across sex. Furthermore, in Study 1, the concurrent validity of theModified DMQ-R was tested using two alcohol-use criterion variables: frequency of drinking occasions andquantity of alcoholic beverages consumed per occasion (both in the past 30 days).

One goal of Study 2 was to examine the test–retest reliability of the Modified DMQ-R. This wasaccomplished using a third sample of undergraduate students. Given the conceptualization of drinkingmotives as relatively trait-like variables (e.g., Birch et al., 2004), we anticipated reasonable stability of theModified DMQ-R subscale scores over time. Furthermore, we examined the degree to which eachindividual Modified DMQ-R subscale prospectively predicted two alcohol-related criterion variables –number of alcoholic beverages consumed per week and alcohol-related problems – above and beyond theeffects of basic demographic variables (i.e., sex and age) and initial alcohol use.

The Modified DMQ-R, should it prove to be reliable and valid, could serve as a screening tool foridentifying relatively at-risk undergraduate drinkers and provide the basis for targeted prevention/treatment programs. The decomposition of coping motives into anxiety- and depression-related com-ponents in theModified DMQ-Rmay have particularly important clinical benefits. For instance, if coping-anxiety and coping-depressionmotives were found to be uniquely positively related to heavy drinking and/or alcohol-related problems, alcohol interventions could be targeted at undergraduates with relatively highlevels of each of these subtypes of coping motives. Given that drinkers with coping-anxiety and coping-depression motives consume alcohol to cope with anxious and depressed affect, respectively, alcoholinterventions could target the particular negative affective state (i.e., either anxiety or sadness/depression)underlying the specific coping motive subtype. The expectation is that a reduction in anxious or depressedaffect would result in a decrease in the associated coping motive subtype along with the risk it confers. It isimportant to determine which specific type of negative affect to target because: (1) cognitive-behaviouraltherapeutic interventions that are empirically supported for anxiety symptoms (e.g., exposure to fearedstimuli/situations) are different from those for depressive symptoms (e.g., behavioural activation) (seeChambless & Ollendick, 2001), and (2) specificity in the affective target of treatment allows for brieferinterventions for alcohol misuse or problems. Brief alcohol interventions have been gaining popularitybecause they have higher acceptability among treatment consumers, they can be delivered by a wider arrayof providers in a broader range of clinical settings, and they are less costly, compared to longerinterventions (Moyer, Finney, Swearingen, & Vergun, 2002). Additionally, the Modified DMQ-R couldserve as a treatment outcome measurement tool in investigations of the efficacy of programs designed tointervene with risky motives if it proved to be a stable measure of drinking motives.

1. Study 1: Confirmatory factor analysis and concurrent validity of the Modified DMQ-R

1.1. Method

1.1.1. ParticipantsSample 1 participants were drawn from a pool of 868 Dalhousie University undergraduates in an

introductory psychology course who completed the Modified DMQ-R in 2004 as part of a battery of



questionnaires administered in a mass screening. Those indicating that they did not drink alcohol(n=109), or who did drink, but did not provide complete data on the Modified DMQ-R (n=33) wereexcluded from the analyses, leaving 726 (68.0% women) participants in Sample 1. The mean age was19.30 years (SD=2.96).2 Given the small proportion (4%) of participants with missing data, it isacceptable to use listwise deletion to handle missing data (Roth, 1994).

Sample 2 participants came from a pool of 778 students who completed the Modified DMQ-R in 2005as part of a screening battery administered in an introductory psychology class.3 Of the students screened,75 were excluded from the analyses because they did not give permission for their data to be used inanonymous psychometric analyses, 93 were excluded because they reported that they did not drinkalcohol, and 7 were excluded because they did not provide complete data on the Modified DMQ-R,leaving 603 (69.3% women) participants with a mean age of 19.25 years (SD=3.14) in Sample 2. Thoughinformation about participants' race was not collected, given the demographic make-up of DalhousieUniversity's undergraduate population, a large majority of participants were presumed to be Caucasian(e.g., in Birch et al. (2004), the sample was 94.2% Caucasian).

1.1.2. Materials and procedureAs part of the mass screenings, participants provided written informed consent and then provided basic

demographic information (i.e., sex and age). Next, they completed several screening questionnaires,including the author-compiled Lifestyles Questionnaire, which assesses a variety of lifestyle behaviours,and the Modified DMQ-R.

The Lifestyles Questionnaire is a 12-item measure designed to gather information about an array oflifestyle behaviours performed in the past 30 days. To reduce their salience, quantity and frequency ofalcohol use items were embedded among items tapping a variety of lifestyle behaviours (Babor, Brown,& Del Boca, 1990). Also, to ensure that all respondents were using the same metric in reportingalcohol-use quantity, the definition of one alcoholic beverage was explicit (one alcoholic beverage =one glass of wine, one can/bottle of beer, one shot of hard liquor, or one cooler). For the purposes of thecurrent study, only the two alcohol-related questions were analyzed. One alcohol-related item askedabout the frequency of alcohol consumption in the past 30 days. Scale anchors were 0 (Not Applicable[Only if you did NOT drink alcohol in the past 30 days]) and 4 (6 or more times). The other alcohol-relevant item asked about the average number of alcoholic beverages consumed per typical drinkingoccasion in the past 30 days. Sample 1 participants responded on a scale of 0 (Not Applicable[Only if you did NOT drink alcohol in the past 30 days]) to 5 (10 or more) and Sample 2 participantsresponded on a scale of 0 (Not Applicable [Only if you did NOT drink alcohol in the past 30 days]) to 4(6 or more).

The Modified DMQ-R consists of 28 items, each contributing to one of five subscales: social,coping-anxiety, coping-depression, enhancement, or conformity (see Table 1 for items). Taking intoconsideration all the times they drink, participants indicated how often they drink for the reason

2 One participant in Sample 1 did not provide an age value. This participant was included in the CFAs but excluded from theconcurrent validity analyses.3 The Modified DMQ-R data collected during the mass screenings in 2004 and 2005 were anonymous; thus, there was no way

to confirm that the two samples drawn from these screenings were completely non-overlapping. Based on the fact thatapproximately 1000 students are typically enrolled in the first-year psychology class per year, and that approximately 10students repeat the course each year, the upper limit of overlap between Sample 1 and Sample 2 is approximately 1%.



specified in each item on a 5-point Likert scale ranging from 1 (almost never/never) to 5 (almostalways/always).

1.2. Results

1.2.1. Model evaluation in Sample 1EQS (Version 6.1) software was used to perform all CFAs (Bentler, 1995; Bentler & Wu, 1995). First,

the data were screened to determine the appropriate model estimation method. For the Sample 1 ModifiedDMQ-R scores, the normalized estimate of Mardia's coefficient of multivariate kurtosis (Mardia, 1970)indicated significant non-normality in the data (Bentler & Wu). Thus, the widely used normal theorymaximum likelihood estimator was deemed to be inappropriate. An inspection of the item kurtosesrevealed significant variability (i.e., kurtoses ranged from −1.27 to 23.90). Accordingly, theheterogeneous kurtosis (HK) estimator was used. The geometric mean approach (developed by Bentler,Berkane, & Kano, 1991) to HKmodel estimation was employed. The variance–covariance matrix was thebasis of the analyses and the metric of the latent factors was defined by setting factor variances to 1.0.

We evaluated model goodness of fit using fit statistics that have performed well in simulation studies(Hu & Bentler, 1998): the standardized root mean square residual (SRMR; Bentler, 1995), the root meansquare error of approximation (RMSEA; Steiger, 1990), the comparative fit index (CFI; Bentler, 1990),and the incremental fit index (IFI; Bollen, 1989; referred to as Bollen's fit index [BL89] by Hu & Bentler,1998). There are no absolute criteria for appraising these goodness-of-fit indexes, though Hu and Bentler(1999) recommend the following cutoffs: close to .08 (or lower) for SRMR, close to .06 (or lower) forRMSEA, and close to .95 (or higher) for CFI and IFI. The fit-evaluation criteria proposed by Hu andBentler (1999) for SRMR, RMSEA, CFI, and IFI are more stringent than some traditional rules of thumb(i.e., SRMR≤ .10, RMSEA≤ .10, and CFI≥ .90; reviewed byWeston & Gore, 2006). We followed recentsuggestions that a model meeting the older, conventional cutoff criteria should be deemed to haveadequate fit and that a model meeting the newer criteria proposed by Hu and Bentler (1999) should beconsidered to have excellent fit (Longley, Watson, & Noyes, 2005).

Overall, the hypothesized correlated five-factor model of drinking motives provided an adequateto excellent fit to the data: χ2(340, N=726)=1299.70, pb .001; SRMR=.09; RMSEA= .06 (90%confidence interval [CI]= .059, .066); CFI= .95; IFI= .95. All indicator variables had significantunstandardized loadings on their hypothesized factors (z statistics ranged from 6.49 to 32.89,psb .001). Moreover, the standardized loadings of the indicator variables on their hypothesized factorswere salient (i.e.,≥ .30; Brown, 2006), with the exception of Item 1 on the social factor (see Table 1 forthe standardized factor loadings). Nonetheless, the multivariate Lagrange multiplier (LM) tests foradding parameters suggested some localized areas of strain in the solution (i.e., cross-loadings ofItem 13 on the enhancement factor and Item 8 on the social factor). Given the generally good fitof the model, the statistical significance of all unstandardized factor loadings, the salience of themajority of the standardized factor loadings, and the absence of a substantive basis for makingchanges implied by the LM tests, we did not make post hoc modifications to the hypothesized model(Brown; Byrne, 2006).

Next, using the Sample 1Modified DMQ-R scores, we tested a correlated four-factor model of drinkingmotives that conceptually corresponds to Cooper's (1994) model, with the coping-anxiety and coping-depression items constrained to load on a single generic coping factor. Overall, the goodness-of-fit indexesfor the four-factor model suggested an adequate fit to the data: χ2(344, N=726)=1540.87, pb .001;



SRMR=.10; RMSEA=.07 (90% CI= .066, .073); CFI= .93; IFI= .93. However, the χ2 difference test(χ2diff) revealed a significant decrement in model fit from the five- to the four-factor model, suggesting thatthe former was superior, χ2diff (4)=241.17, pb .001. Likewise, the Akaike information criterion (AIC;Akaike, 1987), which considers parsimony in its assessment of model fit, was lower for the five-factormodel (model AIC=619.70) than for the four-factor model (model AIC=852.87), suggesting that the five-factor model provides a better fit to the data than the four-factor model (Brown, 2006).

1.2.2. Factor correlations, internal consistency, and item endorsement in Sample 1As expected, there were significant correlations among the five factors (rs ranged from .29 to .82,

psb .001, see Table 2). At .82, the correlation between the social and enhancement factors was highest,but it remained below the .85 cutoff often considered to signify redundancy (e.g., Brown, 2006). Asshown in Table 2, the subscale internal consistencies (Cronbach's αs) ranged from .66 (social) to .91(coping-depression). Though the social subscale internal consistency is below the widely accepted .70cutoff, it is acceptable by Loewenthal's (1996) standard, which indicates that a Cronbach's α≥ .60 isadequate for short scales (i.e., scales with less than 10 items). For each item, Table 1 displays (for bothSample 1 and Sample 2) the percentage of participants who endorsed at least a 2 (some of the time). Thisinformation is provided because low endorsement rates could contribute to low reliabilities. As shown inTable 1, for each item, at least 10% of each sample responded that they drank for the reason specified atleast some of the time.

1.2.3. Testing for factorial invariance across sex in Sample 1Factorial invariance of the Modified DMQ-R across sex was tested using the hierarchical, cumulative

steps recommended by Byrne (2006). First, we tested the five-factor model separately in each sex.For men, model fit was adequate to excellent: χ2(340, N=232)=508.02, pb .001; SRMR=.09;RMSEA=.05 (90% CI= .038, .054); CFI= .97; IFI= .97. For women, model fit was adequate: χ2(340,N=494)=1151.07, pb .001; SRMR=.10; RMSEA=.07 (90% CI=.065, .074); CFI= .93; IFI= .93. Next,

Table 2Descriptive statistics and Modified DMQ-R factor correlations for Samples 1 and 2

Factor correlations

Modified DMQ-R subscale M SD α 1 2 3 4 5

(a) Sample 1 (N=726)1. Social 2.73 0.79 .66 – .63 .45 .82 .422. Coping-anxiety 1.83 0.81 .73 – .76 .64 .623. Coping-depression 1.42 0.61 .91 – .53 .374. Enhancement 2.70 1.01 .85 – .295. Conformity 1.27 0.48 .81 –

(b) Sample 2 (N=603)1. Social 2.75 0.74 .61 – .65 .33 .84 .472. Coping-anxiety 1.78 0.78 .73 – .77 .64 .413. Coping-depression 1.36 0.59 .91 – .38 .274. Enhancement 2.62 0.92 .83 – .285. Conformity 1.24 0.40 .72 –

Note. Means, standard deviations, and Cronbach's αs are based on subscale scores calculated from the observed variables. Factorcorrelations are among latent factors. All factor correlations are significant at pb .001.



we tested for configural invariance (i.e., equivalent number of factors and factor-loading patterns)across sex and found adequate fit to the data: χ2(680, N=726)=1659.11, pb .001; SRMR=.10;RMSEA=.06 (90% CI= .059, .067); CFI= .94; IFI= .95.4 The addition of cross-sex equivalenceconstraints for factor loadings did not result in significant degradation in fit (compared to the configuralmodel), χ2diff (23)=13.03, pN .50, suggesting invariance. Additionally constraining factor variances tobe equivalent across sex did not result in a significant decrease in fit, χ2diff (5)=6.79, pN .10,suggesting invariance. However, adding the factor covariance constraints (of cross-sex equivalence)resulted in a significant decrement in fit, χ2diff (10)=23.32, pb .05, suggesting variance across sex.According to the multivariate LM tests, only the enhancement-conformity covariance varied across sex(women N men).

1.2.4. Mean differences in drinking motives across sexThe Modified DMQ-R demonstrated at least partial measurement invariance across sex, including the

most critical form of invariance, configural invariance (Horn, McArdle, & Mason, 1983). Consequently,meaningful cross-sex comparisons of drinking motives were possible. To investigate potential sexdifferences in endorsement of the Modified DMQ-R subscales, we used a series of between-subjectsanalyses of variance. The only significant sex difference that emerged in Sample 1 was on the socialsubscale, on which men (M=2.83; SD=0.80) scored higher than women (M=2.68; SD=0.78), F(1, 724)=5.83, pb .05.

1.2.5. Cross-validation of five-factor structure in Sample 2To ascertain the generalizability of the hypothesized five-factor model of drinking motives, we tested it

in another sample of undergraduate student drinkers. In Sample 2, the five-factor model providedan adequate fit to the Modified DMQ-R data: χ2(340, N=603)=1225.61, pb .001; SRMR=.10;RMSEA=.07 (90% CI= .062, .070); CFI= .92; IFI= .92. As shown in Table 1, the standardized factorloadings were salient for all items save Items 1 and 10. See Table 2 for Sample 2 descriptive statistics.

1.2.6. Concurrent validity in Samples 1 and 2Sequential multiple regression analyses were performed to determine the contribution of Modified

DMQ-R drinking motives (Step 2) over and above sex (men = 0; women = 1) and age (Step 1) to theconcurrent prediction of: (1) drinking frequency, and (2) number of alcoholic drinks consumed perdrinking occasion in Sample 1 and Sample 2 (see Table 3). Demographic variables were significantpredictors of drinking frequency and drinking quantity in both samples (see Table 3). The block ofdrinking motives significantly predicted concurrent drinking frequency and quantity in both samples,even after demographic variables were accounted for. In both samples, enhancement motives (vs. othermotives) were the strongest predictors of elevated drinking frequency and quantity. Social motives werealso predictive of higher drinking frequency and quantity in both samples, whereas conformity motiveswere predictive of lower drinking frequency and quantity. However, non-significant (psN .05) frequency-conformity and quantity-conformity bivariate correlations in both Sample 1 and Sample 2 suggest that the

4 For the tests of factorial invariance across sex, marker indicator variables were used to set the metric of the latent factors. Foreach factor, the indicator variable with the smallest difference between the unstandardized factor loading for men and that forwomen was selected as the marker indicator (i.e., Item 4 for social, Item 11 for coping-anxiety, Item 21 for coping-depression,Item 9 for enhancement, and Item 28 for conformity).



negative association between conformity and drinking behaviour in the regression analyses is a result ofsuppression.5 In Sample 2 only, coping-depression motives also independently predicted increaseddrinking quantity.

Table 3Sequential linear regression analyses predicting concurrent alcohol-use criterion variables in Samples 1 and 2

Sample 1 (N=725) Sample 2 (N=603)

Outcomevariable

Step Indicatorvariable(s)

B SEB

β AdjustedR2

ΔR2 B SEB

β AdjustedR2

ΔR2

Frequencyof drinkingoccasions(past 30 days)

1 (Constant) 3.47⁎⁎⁎ .30 3.71⁎⁎⁎ .31Sex − .20 .09 − .08⁎ − .42 .10 − .16⁎⁎⁎Age − .03 .02 − .07 .01 .01⁎ − .03 .02 − .09⁎ .03 .03⁎⁎⁎

2 (Constant) 1.97⁎⁎⁎ .34 1.25⁎⁎ .37Sex − .15 .09 − .06 − .39 .09 − .15⁎⁎⁎Age − .01 .01 − .02 .00 .01 − .01Social .18 .06 .12⁎⁎ .30 .07 .19⁎⁎⁎

Coping-anxiety .14 .07 .09 .13 .08 .08Coping-depression .10 .09 .05 .11 .10 .06Enhancement .35 .05 .30⁎⁎⁎ .39 .06 .30⁎⁎⁎

Conformity − .55 .10 − .22⁎⁎⁎ .19 .18⁎⁎⁎ − .27 .12 − .09⁎ .24 .22⁎⁎⁎

Typical # ofdrinks perdrinkingoccasion(past 30 days)a

1 (Constant) 4.12⁎⁎⁎ .30 3.85⁎⁎⁎ .29Sex − .53 .10 − .20⁎⁎⁎ − .52 .10 − .21⁎⁎⁎Age − .05 .02 − .12⁎⁎ .05 .05⁎⁎⁎ − .04 .01 − .11⁎⁎ .05 .06⁎⁎⁎

2 (Constant) 2.44⁎⁎⁎ .35 1.68⁎⁎⁎ .34Sex − .48 .09 − .18⁎⁎⁎ − .53 .09 − .22⁎⁎⁎Age − .02 .01 − .06 − .01 .01 − .03Social .17 .06 .11⁎⁎ .14 .07 .09⁎

Coping-anxiety − .03 .08 − .02 − .06 .08 − .04Coping-depression .11 .09 .06 .28 .09 .15⁎⁎

Enhancement .42 .05 .35⁎⁎⁎ .49 .06 .40⁎⁎⁎

Conformity − .41 .10 − .16⁎⁎⁎ .21 .17⁎⁎⁎ − .29 .11 − .10⁎⁎ .26 .22⁎⁎⁎

a In Sample 1, this itemwas scored on a scale of 0 (Not Applicable [Only if you did NOT drink alcohol in the past 30 days]) to 5 (10ormore). However, in Sample 2, this itemwas scored on a scale of 0 (Not Applicable [Only if you did NOT drink alcohol in the past30 days]) to 4 (6 or more).⁎pb .05. ⁎⁎pb .01. ⁎⁎⁎pb .001.

5 In multiple regression, suppression describes a situation in which a predictor variable that is uncorrelated with the outcomevariable nevertheless adds significantly to its prediction when (an)other predictor variable(s), correlated with the initialpredictor, is (are) added to the regression equation. The additional predictor variable(s) essentially suppress(es) outcome-irrelevant variance in the first predictor, allowing it to more efficiently predict the outcome variable. For a detailed discussion ofsuppressor situations, see Paulhus, Robins, Trzesniewski, and Tracy (2004).



2. Study 2: Test–retest reliability and predictive validity of the Modified DMQ-R

2.1. Method

2.1.1. ParticipantsStudy 2 participants (i.e., Sample 3) came from a pool of 177 students enrolled in undergraduate

psychology courses at Dalhousie University who were participating in an internet-based daily diary studyof the emotional antecedents of drinking behaviour, which was advertised as “An Examination of DailyHealth and Daily Activities.” The participants were recruited from the 2005 paper-and-pencil screeningdescribed in Study 1 and from an online screening conducted in 2006–07 which was open toundergraduates taking all levels of psychology courses. Similar to the earlier paper-and-pencil screenings,the 2006–07 online screening consisted of a battery of questionnaires that researchers in the DalhousiePsychology Department used to screen students for eligibility for a variety of studies. Sample 3 waslimited to those reporting (on the Lifestyles Questionnaire) that they had consumed alcohol at least four orfive times in the 30 days prior to screening (Time 1). Of the full sample of 177 students, 5 studentsindicated that they preferred to have their data excluded from the analyses. Additional participants wereexcluded from the analyses because they reported drinking only two or three times in the 30 days prior toscreening (n=1), because their screening (Time 1) data were deleted due to technical problems with thescreening website (n=1), or because they did not complete the alcohol-problems measure at Time 2(n=1). This resulted in a final sample of 169 (76.3% women), predominantly Caucasian (93.5%)participants with a mean age of 19.74 years (SD=3.17).

Of the 169 final participants, 78 had participated in the 2005 mass screening (described in Study 1).The data for 71 of those were actually included in Study 1 (Sample 2), thus their Study 2 (Sample 3)Time 1 data were taken from Study 1. The other 7 participants indicated that they did not want their2005 screening data to be used in anonymous psychometric analyses, and thus were excluded fromSample 2. They were included in Study 2 (Sample 3) analyses because they had provided informedconsent to participate in “An Examination of Daily Health and Daily Activities” which subsumesStudy 2. The remainder of Sample 3 (n=91) provided their Time 1 data in the online screeningconducted in 2006–07.

Table 4Intraclass correlations measuring subscale test–retest reliabilities and paired t tests to determine significance and direction ofchange in drinking motives in Sample 3

Time 1 Time 2

Modified DMQ-R subscale M SD α M SD α ICCT1T2 t

Social 2.94 0.70 .58 3.02 0.81 .69 .67⁎⁎⁎ −1.54Coping-anxiety 1.93 0.83 .72 1.95 0.76 .69 .65⁎⁎⁎ −0.28Coping-depression 1.49 0.62 .89 1.51 0.78 .94 .68⁎⁎⁎ −0.42Enhancement 2.87 0.94 .83 2.93 1.03 .84 .78⁎⁎⁎ −0.86Conformity 1.34 0.46 .74 1.42 0.73 .91 .61⁎⁎⁎ −1.47

Note. Sample 3 (N=169) contains 71 participants from Sample 2. ICCT1T2 = Intraclass correlation coefficient betweencorresponding subscales at Time 1 and Time 2. All paired-samples t statistics are non-significant ( psN .10).⁎⁎⁎pb .001.



Table 5Sequential linear regression analyses predicting prospective alcohol use and alcohol-related problems in Sample 3

Sample 3 (N=169)a

Outcome variable Step Indicator variable(s) B SE B β Adjusted R2 ΔR2

Drinks per weekat Time 2

1 (Constant) −9.56 5.87Sex −2.96 1.37 − .15⁎Ageb −0.19 0.18 − .07Frequency of drinkingoccasions (Time 1)c,d 6.79 1.17 .41⁎⁎⁎

Typical # of drinks peroccasion (Time 1)c 0.67 0.57 .08Time 1 to Time 2 intervale 0.00 0.01 − .02 .20 .23⁎⁎⁎

2 (Constant) −13.58⁎ 6.22Sex −3.99 1.30 − .20⁎⁎Age −0.14 0.18 − .06Frequency of drinkingoccasions (Time 1) 6.63 1.11 .40⁎⁎⁎

Typical # of drinksper occasion (Time 1) −0.15 0.55 − .02Time 1 to Time 2 interval 0.00 0.01 .02Social (Time 1) 0.35 0.91 .03Coping-anxiety (Time 1) −2.98 0.97 − .30⁎⁎Coping-depression (Time 1) 5.67 1.21 .42⁎⁎⁎

Enhancement (Time 1) 2.24 0.72 .25⁎⁎

Conformity (Time 1) −2.75 1.35 − .15⁎ .33 .14⁎⁎⁎

Alcohol-related problemsat Time 2 (NOT controlling forTime 2 drinks per week)f,g

1 (Constant) 1.39 11.06Sex −0.02 2.58 .00Age −0.18 0.34 − .04Frequency of drinkingoccasions (Time 1) 6.02 2.21 .21⁎⁎

Typical # of drinks peroccasion (Time 1) −0.46 1.07 − .03Time 1 to Time 2 interval −0.02 0.02 − .07 .02 .05

2 (Constant) −14.87 11.94Sex 0.49 2.50 .02Age −0.17 0.34 − .04Frequency of drinkingoccasions (Time 1) 5.68 2.13 .20⁎⁎

Typical # of drinks peroccasion (Time 1) −1.23 1.05 − .09Time 1 to Time 2 interval −0.01 0.02 − .04Social (Time 1) 1.91 1.75 .09Coping-anxiety (Time 1) 1.31 1.86 .08Coping-depression (Time 1) 6.84 2.33 .30⁎⁎

Enhancement (Time 1) 1.00 1.39 .07Conformity (Time 1) −2.07 2.59 − .07 .14 .14⁎⁎⁎



2.1.2. Materials and procedureSample 3 participants provided informed consent and then, as part of a battery of screening

questionnaires, completed the Time 1Modified DMQ-R, alongwith the two alcohol-use variables from theLifestyles Questionnaire that were used in Study 1. In Sample 3, the response scale for the item inquiringabout the average number of alcoholic drinks consumed on a typical drinking occasion was 0 (NotApplicable [Only if you did NOT drink alcohol in the past 30 days]) to 4 (6 or more), the same as that usedin Sample 2. At Time 2, after a mean interval of 94.8 days (SD=64.5; range = 1–179), participants

Table 5 (continued )

Sample 3 (N=169)a

Outcome variable Step Indicator variable(s) B SE B β Adjusted R2 ΔR2

Alcohol-related problemsat Time 2 (controlling forTime 2 drinks per week)g

1 Same as Step 1 in previousregression model.

2 (Constant) 9.32 10.05Sex 2.44 2.36 .07Age −0.02 0.31 − .01Frequency of drinkingoccasions (Time 1) 0.39 2.18 .01Typical # of drinks peroccasion (Time 1)

−1.01 0.97 − .07

Time 1 to Time 2 interval −0.01 0.02 − .06Drinks per week (Time 2) 0.83 0.13 .49⁎⁎⁎ .21 .18⁎⁎⁎

3 (Constant) −4.34 11.12Sex 3.59 2.36 .11Age −0.05 0.31 − .01Frequency of drinkingoccasions (Time 1) 0.54 2.16 .02Typical # of drinks peroccasion (Time 1) −1.11 0.97 − .08Time 1 to Time 2 interval −0.01 0.01 − .05Drinks per week (Time 2) 0.78 0.14 .46⁎⁎⁎

Social (Time 1) 1.64 1.61 .08Coping-anxiety (Time 1) 3.62 1.76 .21⁎

Coping-depression (Time 1) 2.44 2.28 .11Enhancement (Time 1) −0.74 1.31 − .05Conformity (Time 1) 0.07 2.40 .00 .28 .09⁎⁎

a Sample 3 contains 71 participants from Sample 2.b Age = age (in years) at Time 2, as Time 1 age data were not available.c Items from Lifestyles Questionnaire, referring to 30 days prior to Time 1 data collection.d Sample 3 was selected on the basis of this item. Only participants endorsing 3 = 4 or 5 times or 4 = 6 or more times in responseto the question How often did you consume alcohol in the past 30 days? were eligible to participate in this study.e Interval, in days, between Time 1 data collection and collection of Time 2 drinks-per-week information.f Alcohol-related problems were measured with Rutgers Alcohol Problem Index (RAPI) total scores at Time 2.g The pattern of results is the same when the interval (in days) between Time 1 and Time 2 RAPI assessment is entered as apredictor instead of the interval between Time 1 and Time 2 assessment of alcohol use.⁎pb .05. ⁎⁎pb .01. ⁎⁎⁎pb .001.



completed (online) a second Modified DMQ-R, a demographics questionnaire that included itemsconcerning typical alcohol-use patterns, and the Rutgers Alcohol Problem Index (RAPI; White &Labouvie, 1989), amid other questionnaires.

The demographics questionnaire, administered at Time 2, included questions about usual drinkingbehaviour embedded among typical demographics items to reduce their salience (Babor et al., 1990). InStudy 2 at Time 2, we improved upon the methodology used in the Study 1 concurrent validity analysesby asking quantity and frequency items in an open-ended manner to further improve the accuracy of theself-reports (Babor et al.). With respect to alcohol-use frequency, participants indicated their typicalnumber of drinking occasions per week (or per month, if less than once per week; or per year, if less thanonce per month). In terms of quantity, participants reported the number of alcoholic beverages (onealcoholic beverage = one bottle of beer, one cooler, one small [4-ounce] glass of wine, or one shot/mixeddrink containing an ounce of hard liquor) that they typically consumed per occasion. A drinks-per-weekcomposite variable was created by multiplying typical drinking occasions per week by typical number ofdrinks per occasion.

The RAPI (White & Labouvie, 1989) is a 23-item measure designed to measure alcohol-relatedproblems among adolescents (up to age 21 years) during the past three years. On a scale of 0 (never) to 4(more than 10 times), participants indicated how often they experienced each alcohol-related problem.Item scores were summed to create a total RAPI score (Sample 3 Cronbach's α=.92).

2.2. Results

2.2.1. Test–retest reliability in Sample 3The Sample 3 Modified DMQ-R subscale means, standard deviations, and Cronbach's αs are

presented in Table 4. The intraclass correlation coefficients (ICCs) between corresponding subscales atTime 1 and Time 2 were all significant ( psb .001). With the exception of the ICC for the enhancementsubscale, which was in the excellent range, the ICCs for the Modified DMQ-R subscales were all in thegood range (Cicchetti, 1994). A series of paired-samples t tests revealed no significant differences inscores across the two testing times.

2.2.2. Predictive validity in Sample 3In Sample 3 at Time 2, the mean number of drinks per week was 9.81 (SD=8.33) and the mean RAPI

total score was 16.12 (SD=14.18). See Table 5 for the results of the predictive validity analyses andTable 6 for bivariate correlations among variables included in the predictive validity analyses. In all of thepredictive validity regression analyses, the Time 1–Time 2 interval (in days) was entered as a predictor inthe step prior to the block of drinking motives to control for interval variability.6 Over and above theeffects of demographic variables (i.e., sex and age) and Time 1 alcohol-use variables, Time 1 coping-depression and enhancement motives significantly positively predicted, and coping-anxiety andconformity motives significantly negatively predicted, number of alcoholic drinks per week at Time 2.However, the non-significant bivariate correlations of coping-anxiety and conformity with Time 2 drinksper week indicate that the negative prediction of Time 2 drinks per week by coping-anxiety and

6 Time 2 alcohol use data (i.e., drinks per week) and Time 2 RAPI scores were not necessarily collected on the same day.However, the majority of participants (97%) did complete the alcohol-use measure and the RAPI on the same day. Themaximum time interval between Time 2 alcohol-use reports and Time 2 RAPI reports was 6 days.



conformity suggested by the regression analyses is actually due to suppression (see Footnote 5). Withoutcontrolling for Time 2 alcohol use (i.e., drinks per week), only Time 1 coping-depression motivessignificantly positively predicted Time 2 alcohol-related problems (i.e., RAPI total scores) over and abovethe demographic variables and Time 1 alcohol-use variables. However, when Time 2 drinks per week wascontrolled for in the model, only Time 1 coping-anxiety motives significantly positively predicted Time 2alcohol-related problems.

3. Discussion

In the present investigation, we examined the psychometric properties of the Modified DMQ-R, whichextends previous research (i.e., Cooper, 1994) by distinguishing between drinking to cope with anxietyand drinking to cope with depression. In Study 1, CFAs indicated that the hypothesized five-factor modelof alcohol-use motives, with correlated factors representing social, coping-anxiety, coping-depression,enhancement, and conformity motives, provided a good fit to the Modified DMQ-R scores of under-graduate student drinkers. In addition, this five-factor model provided a superior fit to the data ascompared to a model conceptually equivalent to Cooper's (1994) four-factor model of drinking motives(i.e., with one generic coping factor containing all of the coping-anxiety and coping-depression items).

The Study 1 findings supported factorial invariance of the Modified DMQ-R across sex. Thus, it waspossible to make meaningful cross-sex comparisons. We found that undergraduate men, relative toundergraduate women, more strongly endorsed drinking for social motives, consistent with Cooper's(1994) findings with adolescents. However, in the current investigation, there were no sex differences inendorsement of any of the other types of drinking motives. Conversely, Cooper found that in addition toendorsing social motives more strongly than female adolescents, male adolescents also endorsedenhancement and conformity motives more strongly than females, though these sex differences were

Table 6Bivariate correlations among Time 1 drinking motives subscales, Time 1 alcohol use, Time 2 alcohol use, and Time 2 alcohol-related problems in Sample 3

Bivariate correlations

Subscales/items 1 2 3 4 5 6 7 8 9

1. Social (Time 1) – .27⁎⁎⁎ .11 .48⁎⁎⁎ .36⁎⁎⁎ .11 .04 .12 .18⁎

2. Coping-anxiety (Time 1) – .69⁎⁎⁎ .35⁎⁎⁎ .40⁎⁎⁎ .08 .08 .09 .31⁎⁎⁎

3. Coping-depression (Time 1) – .20⁎⁎ .30⁎⁎⁎ − .03 .11 .23⁎⁎ .34⁎⁎⁎

4. Enhancement (Time 1) – .22⁎⁎ .24⁎⁎ .25⁎⁎ .31⁎⁎⁎ .22⁎⁎

5. Conformity (Time 1) – − .03 − .08 − .05 .116. Frequency of drinking occasions(past 30 days; Time 1)a – .21⁎⁎ .43⁎⁎⁎ .21⁎⁎

7. Typical # of drinks per drinking occasion(past 30 days; Time 1) – .20⁎ .01

8. Drinks per week (Time 2) – .47⁎⁎⁎

9. Alcohol-related problems (Time 2)b –

Note. Sample 3 (N=169) contains 71 participants from Sample 2.a Sample 3 was selected on the basis of this item. Only participants endorsing 3 (4 or 5 times) or 4 (6 or more times) in response tothe question How often did you consume alcohol in the past 30 days? were eligible to participate in this study.b Alcohol-related problems were measured with Rutgers Alcohol Problem Index total scores at Time 2.⁎pb .05. ⁎⁎pb .01. ⁎⁎⁎pb .001.



smaller than the social motives sex difference. This discrepancy could reflect a developmental differencebetween adolescents and young adults, a difference in the sensitivity of the two instruments to sexdifferences in enhancement and conformity motives, or a difference in power between the current studyand Cooper's study.

In Study 1, tests of concurrent validity of the Modified DMQ-R demonstrated the distinctiveness ofcoping-anxiety and coping-depression motives. In Sample 2, coping-depression motives, but not coping-anxiety motives, were predictive of higher typical quantity of drinks consumed per occasion (in the past30 days), over and above demographic variables and controlling for levels of the other drinking motives.This finding is consistent with the Graham et al. (2007) finding that depression scores were most stronglypositively related to quantity of drinks consumed per occasion (vs. drinking frequency and other alcohol-use variables) and the Blackwell et al. (2002) finding that coping-depression motives were related tohigher typical number of drinks per drinking occasion. Coping-depression motives did not predict typicalquantity of drinks in Sample 1. This discrepancy between Sample 1 and Sample 2 might be accounted forby the differences in response scales across the two samples.

Neither coping-anxiety motives nor coping-depression motives were predictive of concurrent drinkingfrequency (in the past 30 days) once demographic variables and the other drinking motives werecontrolled (Study 1). These results are inconsistent with Cooper's (1994) finding that generic copingmotives were positively related to usual frequency of alcohol use in the past six months. It is possible thatany effects of coping-anxiety and coping-depression motives on usual concurrent frequency of alcoholuse were masked by the inclusion of both coping motives subtypes in the same block of predictors in theregression analyses. Alternatively (or in addition), our use of a relatively insensitive measure of drinkingfrequency (with a 5-point response scale) versus Cooper's use of a more sensitive drinking frequencymeasure (with a 9-point response scale) might account for this discrepancy in coping motives findings.Consistent with Cooper's (1994) results though, social and (particularly) enhancement motives were eachrelated to higher drinking frequency and drinking quantity per occasion (in the past 30 days) andconformity motives were related to lower frequency and quantity of alcohol consumption (afteraccounting for the effects of sex, age, and the other motives).

In Study 2, consistent with a view of drinking motives as relatively trait-like variables (Birch et al.,2004), we found that the Modified DMQ-R subscales had good to excellent test–retest reliabilities in anundergraduate sample of relatively frequent drinkers. Furthermore, each of the five types of drinkingmotives prospectively predicted a distinct pattern of alcohol use and alcohol-related problems amongthese relatively frequent drinkers. Over and above the impact of demographics and initial alcohol use andcontrolling for the effects of the other drinking motives, stronger coping-depression and enhancementmotives at Time 1 prospectively predicted higher drinks per week at Time 2, whereas stronger coping-anxiety and conformity motives at Time 1 prospectively predicted lower drinks per week at Time 2.

Controlling for the effects of the other drinking motives, over and above the effects of demographics andinitial alcohol use, only Time 1 coping-depression motives significantly prospectively predicted Time 2alcohol-related problemswithout accounting for Time 2 drinks per week. However, interestingly, whenTime2 drinks per week were accounted for, only Time 1 coping-anxiety motives significantly prospectivelypredicted alcohol-related problems over and above demographics and initial alcohol use, while controllingfor the effects of the other drinking motives. These results are consistent with Cooper's (1994) finding thatonly coping motives were related to concurrent drinking problems once usual alcohol use was controlled.Furthermore, the present findings indicate that it is specifically drinking to cope with anxiety that is directlyrelated to prospective alcohol-related problems. Conversely, coping-depression motives appear to be



associatedwith prospective alcohol-related problems only indirectly, via their association with higher typicalconsumption of alcohol. These predictive validity results suggest that it is essential to separate anxiety-related and depression-related coping motives. In addition, the current results are consistent with researchindicating that depression is positively related to both alcohol consumption (Graham et al., 2007) and alcoholdependence (Dawson et al., 2005), whereas anxiety may be negatively related to alcohol consumption(Morris et al., 2005), despite evidence of a positive relationship with alcohol-related problems (Stewart,Morris, Komar, & Mellings, 2006) and alcohol dependence (Dawson et al.).

A limitation of the prospective validity analyses in Study 2 was that participants were asked to reportalcohol-related problems during the previous three years (as per usual procedure with the RAPI), a periodwhich extended back before the Time 1 drinking motive reports. Thus, although the alcohol-relatedproblems (RAPI) data were collected at Time 2, after the Time 1 drinking motives data were collected, theprediction of alcohol-related problems by drinking motives reported herein is not entirely prospective.Nonetheless, the mean Time 1–Time 2 interval was over 3 months, so it is likely that at least some of thealcohol-related problems reported at Time 2 did in fact arise after Time 1 reporting. It is important that, infuture, researchers test the validity of the Modified DMQ-R using truly prospective measures of alcohol-related problems. Future researchers could also improve on the current methodology by including a Time 1measure of alcohol-related problems. Also, if Time 1measures of depressive and anxious symptomatologywere included, researchers could distinguish individuals who endorse low levels of coping-depression orcoping-anxiety motives because they simply do not experience symptoms of depression or anxiety fromthose who experience the symptoms but do not drink to cope with them. In addition, in order to more fullyestablish the clinical utility of the Modified DMQ-R, its predictive validity should be tested using actualAUD diagnoses as outcome variables. It is also important to note that the large majority of participants inthe current investigation was Caucasian; thus, it is unclear how the results might apply to more racially andethnically diverse undergraduate populations.

In general, the Modified DMQ-R appears to be a reliable and valid measure of undergraduates'drinking motives. However, non-salient loadings of one (Sample 1) or two (Sample 2) items on the socialmotives subscale in Study 1 and barely acceptable internal consistencies of the social motives subscaleacross Study 1 and Study 2 are cause for some concern. The Modified DMQ-R social subscale is moresimilar to the three-factor DMQ (Cooper et al., 1992) social subscale than to the four-factor DMQ-R(Cooper, 1994) social subscale. However, Cooper's work suggests that the DMQ-R social subscale(Cronbach's α=.85) was more reliable than the DMQ social subscale (Cronbach's α=.77). Thus, infuture, we suggest that researchers using the Modified DMQ-R should replace its current social subscalewith that from the four-factor DMQ-R.

Overall, the results of the current investigation suggest that the Modified DMQ-R shows promise as aclinical tool for use with young adult undergraduate populations. Specifically, the Modified DMQ-Rcould first be used to screen undergraduate drinkers and to identify those with relatively strongendorsement of risky alcohol-use motives. According to the present results, the risky drinking motives areenhancement (predictive of higher alcohol consumption, even within a sample of relatively frequentdrinkers), coping-depression (predictive of greater alcohol-related problems indirectly through higherconsumption), and coping-anxiety (predictive of greater alcohol-related problems when alcoholconsumption is accounted for, despite a negative unique relationship with alcohol consumption) motives.Though higher social motives were related to higher concurrent frequency and quantity of alcohol use inStudy 1, they are not considered to be risky motives for several reasons: (1) they were not prospectivelyrelated to higher alcohol consumption in Study 2 in a sample of relatively frequent (i.e., higher risk)



drinkers, (2) they were not linked to alcohol-related problems in the present investigation, and (3) priorresearch has found them to be associated with normative drinking patterns (Cooper, 1994).

The present results provide direction for targeted preventive or treatment interventions. Clinicians couldassist drinkers with primarily coping-anxiety drinking motives in finding alternative, more adaptive ways ofcombatting symptoms of anxiety (e.g., exposure to feared stimuli or relaxation strategies; Antony &Swinson, 2000). Similarly, clinicians could help drinkers with relatively high levels of coping-depressionmotives to develop alternative strategies for handling depressive symptomatology (e.g., engaging inbehavioural activation; Dimidjian et al., 2006). Undergraduate drinkers with relatively high levels ofenhancement motives could be encouraged to explore other sources (apart from alcohol) of positiveemotional experience (Cooper et al., 1995). For evidence of efficacy of an early intervention approach thattargeted personality risk factors for alcohol abuse, as well as associated risky drinking motives inadolescents, see Conrod, Stewart, Comeau, and Maclean (2006). Further research is required to test theclinical utility of the Modified DMQ-R as a screening tool and as the basis of targeted preventive andtreatment interventions. The current findings provide a solid psychometric foundation for such future studies.

Acknowledgments

Valerie V. Grant has been funded by a Canadian Institutes of Health Research (CIHR) Canada GraduateScholarship Master's Award, a Nova Scotia Health Research Foundation Student Award, a SSHRCDoctoral Fellowship, and Killam Predoctoral Scholarships over the course of the completion of thisresearch. Sherry H. Stewart is supported by an Investigator Award from CIHR and a Killam ResearchProfessorship from the Faculty of Science at Dalhousie University. Roisin M. O'Connor is supported by aCIHR Post-Doctoral Fellowship. Ekin Blackwell was supported by a Michael Smith Foundation forHealth Research Master's Trainee Award at the time of the initial Modified DMQ-R work.

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Psychometric evaluation of the five-factor Modified Drinking Motives Questionnaire — Revised in...

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