The American Journal of Drug and Alcohol Abuse, 35:109–114, 2009Copyright © Informa Healthcare USA, Inc.ISSN: 0095-2990 print/ 1097-9891 onlineDOI: 10.1080/00952990902825439

Development and Validation of the Retrospective AlcoholContext Scale

Darren Mays, M.P.H.Department of Behavioral Sciences and Health Education, Emory University, Rollins School of PublicHealth, Atlanta, Georgia, USA

Stuart Usdan, Ph.D.University of Alabama, Tuscaloosa, Alabama, USA

Kimberly Jacob Arriola, Ph.D., M.P.H.Department of Behavioral Sciences and Health Education, Emory University, Rollins School of PublicHealth, Atlanta, Georgia, USA

Jessica Aungst Weitzel, M.P.H.Ciurczak and Company, Inc., Evaluation and Development, Buffalo, New York, USA

Jay M. Bernhardt, Ph.D., M.P.H.National Center for Health Marketing, U.S. Centers for Disease Control and Prevention, Atlanta,Georgia, USA

Background: Few validated measures exist to capture the con-text and consequences of specific drinking events among collegestudents. Objectives: This study sought to describe the develop-ment and validation of the Retrospective Alcohol Context Scale(RACS), a 30-day measure of drinking context among college stu-dents. Methods: A sample of 169 college students completed dailyalcohol assessments for 30 days and completed the RACS at follow-up. Results: The RACS captured fewer negative consequences thandaily assessments; however, high agreement was observed on con-text variables. Conclusion: Results support the validity of the RACSas a measure of drinking context among college students. TheRACS may be most useful when information about drinking needsto be collected under limited time and resources. Scientific Signif-icance: Further research is needed to examine the RACS amongmore diverse, probability samples and over longer time periods.

Keywords Alcohol, alcohol assessment, college students, TimelineFollow-Back (TLFB), universities

Alcohol consumption among U.S. college students is a per-sistent public health concern (1). Negative consequences are

Address correspondence to Darren Mays, M.P.H., Department ofBehavioral Sciences and Health Education, Rollins School of PublicHealth, Emory University, 1518 Clifton Road NE Room 557, Atlanta,GA 30322, USA. E-mail: dmmays@sph.emory.edu

associated with alcohol use by college students, including in-jury, violence, unprotected sex, accidents, drinking and driving,and academic problems (2, 3), and students who binge drink aremore likely to experience such consequences (3). Measures existto assess the negative consequences of alcohol use among col-lege students in hypothetical drinking situations (4). However,no measures exist that assess the contextual factors of specificdrinking events, such as where alcohol was consumed, withwhom alcohol was consumed, and negative consequences thatoccurred as a result of drinking. Knowledge of the contexts ofdrinking events may allow for more targeted prevention effortsto reduce negative consequences related to drinking.

This study describes the development and validation of theRetrospective Alcohol Context Scale (RACS), a calendar-basedmethod of data collection based on the Timeline Follow Back(TLFB) (5), designed to assess the context of drinking eventsamong college students. Data from the RACS were compared todata collected over the same reference period using two differentdaily diary assessments, and to measures of other theoreticalconstructs.

METHODThe RACS was designed to assess the context of individual

drinking events, including where and with whom alcohol wasconsumed, and negative consequences experienced as a result ofeach drinking event. The RACS was created based on a review

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110 D. MAYS ET AL.

of alcohol-related literature, including existing assessments ofthe negative consequences and problems of alcohol use amongcollege students (6–9), focus groups conducted among studentsat the participating institutions (10), the input of the researchteam, and a pilot test of the study methodology and instruments(11, 12).

Research Design and ProceduresThe study protocol was approved by the participating univer-

sity’s Institutional Review Board, and all participants providedwritten, informed consent. Participants were part of a largerstudy designed to evaluate the use of handheld computers toassess alcohol use among college students. College studentswere recruited to participate by placing flyers in buildings cam-pus and by making announcements in large classes. Participantswere randomly assigned to one of two groups prior to baselinesessions: group one completed a daily survey about their drink-ing using a handheld assisted network diary (HAND) (11), andgroup two completed a daily survey about their drinking usinga paper-and-pencil daily social diary (DSD). Participants com-pleted baseline measures at sessions scheduled on campus, wereasked to complete the HAND or DSD assessment each day for a30-day period, and return the assessment and complete follow-up measures at a specified meeting on campus after 30 days.

MeasuresBaseline measures included the TLFB, RACS, Alcohol Con-

sequences Expectancies Scale (ACES), Alcohol ConsequencesSelf-Efficacy Scale (ACSES) (13), other alcohol-related assess-ments, and demographic information. The ACES (α = .812)and the ACSES (α = .853) were measures developed for thepurposes of this study that had high internal consistency in thecurrent study and a previous pilot (12). The same assessmentswere completed at follow-up. Standard drinks were defined onall assessments as 12 ounces of beer, 5 ounces of wine or cham-pagne, 3 ounces of fortified wine, or 1.5 ounces of hard liquor.

The RACS was a self-completed, 30-day, retrospective as-sessment of the context of drinking events. The RACS wasadministered along with the TLFB, using a 30-day calendarwith spaces for respondents to fill in how much alcohol wasconsumed, where and with whom it was consumed, and thenegative consequences that occurred. Instructions for the RACSincluded a description of the information that was to be pro-vided in each field for the drinking context variables. For ex-ample, the field examining with whom alcohol was consumedincluded codes for drinking alone, with friends, with strangers,with roommates, etc.

AnalysesData were analyzed using SPSS 15.0. Agreement for context

variables between the HAND and the RACS, and the DSD andthe RACS was assessed by calculating percent agreement. Whileother statistical methods designed to examine agreement exist

(e.g., Cohen’s Kappa), these tests were not applied because thedata did not meet distributional assumptions. Days on whichalcohol consumption was reported on both assessments wereselected for comparison to minimize inflation in agreement thatwould occur as a result of including nondrinking days.

Wilcoxon Signed Rank tests were performed to comparethe total negative consequences and negative consequences perdrinking day on the HAND and DSD to the RACS. Spearmanrank-order correlations were calculated to assess the relation-ship between theoretical constructs of self-efficacy and outcomeexpectancies measured at baseline and negative consequencesreported on the RACS. Correlations were not calculated forconsequences with too few reported occurrences on either ofthe assessments.

RESULTS

Study SampleThe sample consisted of 169 college students between 18

and 24 years of age enrolled at a large public university in thesoutheastern U.S. The characteristics of the sample are displayedin Table 1.

RACS Validity: DSD GroupThe mean number of completed surveys on the DSD was

29.29 (Standard Deviation [SD] = 0.91). The DSD recorded729 drinking days and the TLFB recorded 652 drinking days(out of a total possible 2,490 study days). The DSD recordeda total of 478 negative consequences and 204 negative conse-quences were reported on the RACS (Table 2). The majority ofparticipants reported significantly more negative consequences(59.0%, z = −5.374, p < .001) and negative consequences perdrinking day (66.2%, z = 5.16, p < .001) on the DSD comparedto the RACS.

Drinking days were recorded on both the DSD and TLFB as-sessments on a total of 385 study days. The percent agreementfor context variables is displayed in Table 3. Correlations be-tween negative consequences and baseline outcome expectancymeasures were in the hypothesized direction for all negative con-sequences except getting into trouble with the police and doingpoorly on a test or project (Table 4). Correlations between neg-ative consequences and baseline self-efficacy levels at avoidingeach of the negative consequences were in the hypothesized di-rection for all negative consequences except getting into troublewith police (Table 4). Correlations were significant for outcomeexpectancies and self-efficacy related to several negative conse-quences (Table 4).

RACS Validity: HAND GroupThe mean number of completed assessments on the HAND

was 25.75 (SD = 5.90). The HAND recorded 763 drinkingdays and the TLFB recorded 753 drinking days (of a possible2,580 study days). In total, 455 negative consequences were

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DEVELOPMENT AND VALIDATION 111

TABLE 1Participant characteristics

DSD Group HAND Group

Demographics n Percent n Percent

GenderFemale 40 48% 43 50%Male 41 49% 42 48%Other/Missing 2 2% 1 1.2%

RaceWhite/Caucasian 64 77% 66 77%Black/African Am. 8 10% 7 8%Other/Missing 11 13% 13 15%

HousingResidence Hall 60 73% 55 64%House/Apt 17 21% 28 32%Fraternity/Sorority 4 5% 2 2%Other/Missing 2 1% 1 1%

Live withAlone 9 11% 7 8%Roommates 66 80% 70 81%Other/Missing 8 9% 9 10%

Alcohol Use n Mean (SD) n Mean (SD)Baseline TLFB

Drinking days 83 5.89 (3.29) 86 5.93 (3.22)Drinks/Drinking day 83 4.87 (3.02) 86 5.77 (2.87)

DSDDrinking days 83 8.31 (4.59) – –Drinks/Drinking day 83 5.24 (3.09) – –

HANDDrinking days – – 86 8.84 (4.80)Drinks/Drinking day – – 86 5.87 (2.91)

Follow-up TLFBDrinking days 83 7.71 (4.77) 86 8.65 (4.80)Drinks/Drinking day 83 5.13 (3.42) 86 5.74 (2.88)

reported on the HAND and 209 negative consequences were re-ported on the RACS (Table 2). The majority of participantsreported significantly more negative consequences (65.1%, z =−5.87,p < .001) and negative consequences per drinking day(70.9%, z = −6.13, p < .001) on the HAND compared to theRACS.

Drinking days were recorded on both the HAND and TLFBassessments on a total of 417 study days. The percent agreementfor context variables is presented in Table 3. Correlations be-tween negative consequences and baseline outcome expectancymeasures were in the hypothesized direction for all negative con-sequences except hurt/injured or getting sick (Table 4). Correla-tions between negative consequences and baseline self-efficacylevels were in the hypothesized direction for all negative con-sequences except missing class (Table 4). Correlations were

TABLE 2Total reported negative consequences on DSD, HAND, and

RACSa

DSD Group HAND Group

Negative consequence DSD RACS HAND RACS

Missed class 34 9 31 17Poor test/Project 10 2 5 2Campus trouble 0 0 2 1Drove car 80 55 86 24Police trouble 2 1 4 0Hurt/Injured 12 1 7 4Kicked out of bar/Party 1 2 3 0Hangover 133 73 123 101Vandalize/Broke

something 11 3 9 3Sick 33 16 19 11Pass out/Black out 36 10 41 18Emotional 34 7 28 4Fight 40 13 20 10Embarrassed/Regret

something 52 12 77 14TOTAL 478 204 455 209

aDSD = Daily Social Diary, HAND = Handheld Assisted NetworkDiary, RACS = Retrospective Alcohol Context Scale.

significant for outcome expectancies and self-efficacy related toseveral negative consequences (Table 4).

DISCUSSIONThis study examined the validity of the RACS by analyzing

the agreement between the RACS and two different forms ofdaily assessments, and by comparing behaviors reported on theRACS to theoretical constructs related to the same behaviors(self-efficacy and outcome expectancies). The HAND and DSDcaptured significantly more negative consequences and negativeconsequences per drinking day than the RACS over the course ofthe study. There are a number of possible explanations for theseresults. The RACS may be affected by memory recall bias due tothe retrospective nature, and many of the negative consequencesthat are reported in lower frequencies on the RACS are thosewhich may be seen as embarrassing, are stigmatized, or areagainst the law. Since the RACS was completed in a room withother study participants, these consequences may have beenreported with less frequency on the RACS compared to theHAND and DSD.

The percent agreement between the HAND, DSD, and RACSfor contextual variables was generally high. While the HANDand DSD assessments captured more negative consequencescompared to the RACS, the analysis of days when drinking wasreported on both assessments indicates that the two methods

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TABLE 3Percent agreement contextual variables for concordant drinking days

DSD Groupa HAND Groupa

Variable RACS Frequency % Agreement RACS Frequency % Agreement

Negative ConsequencesMissed class 3 94.3% 11 93.7%Poor test/Project 1 97.2% 0 –Drove car 31 82.9% 14 88.2%Campus trouble 0 – 1 99.5%Hurt/Injured 0 – 2 98.3%Hangover 35 83.5% 58 77.0%Vandalize 2 97.2% 2 98.1%Sick/Threw up 8 93.4% 3 96.9%Pass out/Black out 5 92.4% 12 90.9%Emotional/Upset 2 93.7% 2 95.7%Fight 4 92.1% 6 96.6%Regret 1 91.7% 10 92.1%Kicked out 1 – 0 –Police trouble 0 – – –Mean – 91.8% – 93.34%Standard Deviation – 4.7% – 6.1%

Drinking Locations/EventsParty 39 76.9% 41 78.9%Bar/Club 76 77.2% 143 78.7%Restaurant 22 86.7% 39 87.8%House/Apartment 198 43.7% 225 65.7%Tailgate/Sports 7 94.3% 12 95.9%Mean – 76.8% – 80.2%Standard Deviation – 26.8% – 25.1%

Co-drinkersBy yourself 13 90.8% 17 90.4%Girlfriend/Boyfriend 84 77.5% 64 83.4%Friend(s) 221 94.1% 304 79.1%Roommate(s) 46 73.7% 36 72.7%Teammate(s) 1 100.0% 4 98.3%Greek bros/sisters 33 90.5% 47 83.4%Stranger(s) 20 77.5% 16 79.6%Family 13 90.2% 16 92.8%Mean – 78.7% – 79.6%Standard Deviation – 20.3% – 18.0%

aDSD = Daily Social Diary, HAND = Handheld Assisted Network Diary.

have a relatively high level of agreement on contextual variablesincluding drinking events and locations, drinkers, and negativeconsequences on days when alcohol use was concurrently re-ported. Percent agreement does not take into account agreementthat may occur due to chance (14); however, the frequenciesobserved for many of the context variables of interest limitedthe statistical analyses that could be conducted. Future studiesshould examine the use of the RACS with studies with longerfollow-up periods to capture more drinking events.

Nearly all of the correlations between reported negative con-sequences related to alcohol use and the theoretical constructs ofself-efficacy and outcome expectancies were in the hypothesizeddirection, and a number of these relationships were statisticallysignificant. In most of these instances where theoretical con-structs assessed did not result in correlations in the expecteddirection, the negative consequences were reported with suchlow frequency on the RACS that the Spearman’s rho valueis not interpretable. These results suggest that the RACS has

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DEVELOPMENT AND VALIDATION 113

TABLE 4Spearman’s rho alcohol-related negative consequences and baseline theoretical measures

DSD Group HAND Group

Negative Consequence Expectancy (ACES) Self-Efficacy (ACSES) Expectancy (ACES) Self-Efficacy (ACSES)

Campus trouble – – .099 .178Drinking and driving .531∗∗ .314∗∗ .349∗∗ .096Emotional/Upset .166 .102 .136 .237∗

Fight/Argument .140 .123 .173∗ 0.58Hangover .324∗∗ .357∗∗ .311∗∗ .175Hurt/Injured .004 .177 −.058 .137Kicked out bar/Party .047 .201 .063 –Miss class .313∗ .211 .161∗ −.081Pass out/Black out .254∗∗ .200 .318∗∗ .156Police trouble −.098 −.086 −.069 –Regret .169 .153 .129 .162Break/Vandalize .084 .240∗∗ .126 .195Poor test/Project −.012 .100 .014 .179Sick/Throw up .162 .048 −.67 .096

∗Significant at p < .05; ∗∗Significant at p < .01.

demonstrable construct validity based on constructs from So-cial Cognitive Theory (15), which have been previously demon-strated to be predictive of drinking-related behaviors amongcollege students (16).

The results should be considered in light of several limita-tions. The study used a convenience sample of college studentswho reported alcohol consumption at least one to two times perweek, thus the results are not generalizable to all college stu-dents. All drinking behaviors were based on self-report. Whilepast research has illustrated that self-reported alcohol use be-haviors are reliable (17), self-reported behaviors may be subjectto biases. The HAND was subject to some minor technical diffi-culties, which may have impacted completion rates. While tech-nical support was provided to ensure surveys were completedthroughout the study, it is unclear the extent to which these minortechnical difficulties impacted study findings. The retrospectivenature of the RACS is also subject to memory recall bias. How-ever, the TLFB method of data collection, on which the RACSwas designed, has shown to be a reliable and psychometricallysound method to measure alcohol consumption for a variety ofpopulations (5, 18).

The results indicate that the RACS may represent a validassessment of the context of drinking behaviors among collegestudents. Such an instrument is particularly important in thispopulation because the contexts in which alcohol is consumedmay have an impact on adverse outcomes related to alcoholuse. The RACS may be most useful when information aboutdrinking behaviors needs to be collected under limited time andresources. Information about drinking contexts can be collectedquickly and inexpensively using the RACS, as the assessmentrequires minimal time to complete and is economical to produce.

Many health education campaigns on college campuses havefound little success in reducing alcohol use by college students;however, many colleges and universities do not use appropriateinformation on alcohol use behaviors on their own campusesto design prevention efforts (19). Knowledge of the specificcontexts of alcohol consumption among college students, espe-cially those that often lead to adverse consequences, may allowhealth education professionals focusing on college alcohol useto target alcohol use prevention campaigns on individual col-lege campuses. An instrument such as the RACS can revealinformation regarding the drinking environment on specific col-lege campuses, and the contexts in which heavy drinking andnegative outcomes occur, potentially leading to more targetedprevention efforts. Future studies should examine the RACS inlarger, probability samples of college students to further exam-ine its validity and reliability and understand the patterns andcontexts of drinking behaviors among this population.

ACKNOWLEDGEMENTSThis research was conducted while the authors J.M.B. and

J.A.W. were at the Emory University Rollins School of PublicHealth, Atlanta, Georgia.

The findings and conclusions in this article are those of theauthor and do not necessarily represent the views of the Centersfor Disease Control and Prevention.

This research was supported by Grant Number5R21AA013969-03 from the National Institute on AlcoholAbuse and Alcoholism.

This project was supported in part by an appointment tothe Research Participation Program for the Centers for Disease

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114 D. MAYS ET AL.

Control and Prevention administered by the Oak Ridge Institutefor Science and Education through an agreement between theDepartment of Energy and CDC.

Declaration of InterestThe authors report no conflicts of interest. The authors alone

are responsible for the content and writing of the article.

REFERENCES1. Johnston LD, O’Malley PM, Bachman JG, Schulenberg JM. Monitoring the

Future: National Survey Results on Drug Use, 1975–2004. College Studentsand Adults Ages 19–45. Bethesda, MD: National Institute on Drug Abuse,2005.

2. Hingson R, Heeren T, Winter M, Wechsler H. Magnitude of alcohol-relatedmortality and morbidity among U.S. college students: Changes from 1998–2001. Annual Review of Public Health 2005; 26:259–279.

3. Wechsler H, Lee JE, Kuo M, Seibring M, Nelson TF, Lee H. Trends incollege student binge drinking during a period of increased preventionefforts. Findings from 4 Harvard School of Public Health College AlcoholStudy surveys: 1993–2001. Journal of College Health 2002; 50(5):203–217.

4. Carrigan G, Samoluk SB, Stewart SH. Examination of the short form ofthe Inventory of Drinking Situations (IDS-42) in a young adult universitystudent sample. Behaviour Research and Therapy 1998; 36:789–807.

5. Sobell MB, Sobell LC, Klajner F, Pavan D, Basian E. The reliability ofa timeline method for assessing normal drinking college students’ recentdrinking history: Utility for alcohol research. Addictive Behaviors 1986;11(2):149–161.

6. Engs RC. Drinking patterns and drinking problems of college students.Journal of Studies on Alcohol 1977; 38:2144–2156.

7. Hurlbut SC, Sher KJ. Assessing alcohol problems in college students. Amer-ican Journal of College Health 1992; 41:49–58.

8. O’Hare T. Measuring problem drinking in first time offenders. Developmentand validation of the College Alcohol Problems Scale (CAPS). Journal ofSubstance Abuse Treatment 1997; 14:383–387.

9. White H, Lavoubie E. Towards the assessment of adolescent problem drink-ing. Journal of Studies on Alcohol 1989; 50:30–37.

10. Usdan S, Martin R, Mays D, Cremeens J, Weitzel JA, Bernhardt JM. In-cidents of intoxication among college students: Implications for harm re-duction approaches to high-risk drinking. Journal of Drug Education, InPress.

11. Bernhardt JM, Usdan S, Mays D, Arriola KJ, Martin RJ, Cremeens J, McGillT, Weitzel JA. Alcohol assessment using wireless handheld computers: Apilot. Addictive Behaviors 2007; 32:3065–3070.

12. Weitzel JA, Bernhardt JM, Usdan S, Mays D Glanz K. Using wireless hand-held computers to reduce the negative consequences of drinking alcohol.Journal of Studies on Alcohol and Drugs 2007; 68:534–537.

13. Arriola KJ, Usdan S, Mays D, Weitzel JA, Cremeens J, Martin R, BorbaCPC, Bernhardt JM. Reliability and validity of the alcohol consequencesexpectations scale. American Journal of Health Behavior 2009; 33:504–512.

14. Watkins MW, Pacheo M. Inter-observer agreement in behavioral research:Importance and calculation. Journal of Behavioral Education 2001; 10:205–212.

15. Bandura A. Social Foundations of Thought and Action: A Social CognitiveTheory. Englewood Cliffs, NJ: Prentice-Hall, 1986.

16. Dijkstra A, Sweeney L, Gebhardt W. Social cognitive determinants of drink-ing in young adults: Beyond the alcohol expectancies paradigm. AddictiveBehaviors 2001; 26:689–706.

17. Midanik LT. Validity of self-report alcohol use: A literature review andassessment. British Journal of Addiction 1988; 83:1019–1030.

18. Sobell LC, Maisto SA, Sobell MB, Cooper AM. Reliability of alcoholabusers’ self-reports of drinking behaviors. Behaviour Research and Ther-apy 1979; (2):157–160.

19. National Institute of Alcohol Abuse and Alcoholism. A Call to Action:Changing the Culture of Drinking at U.S. Colleges. Bethesda, MD: NationalInstitutes of Health, 2002.

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