The SOGS-RA vs. the MAGS-7: Prevalence Estimates and Classification Congruence

The SOGS-RA vs. the MAGS-7: Prevalence Estimatesand Classification Congruence

Jennifer Langhinrichsen-Rohling

Martin L. Rohling

University of South Alabama, Mobile, AL

Paul Rohde

John R. Seeley

Oregon Research Institute, Eugene, OR

The purpose of this study was to compare the prevalence rate estimates and congru-ence in classification status derived from two popular measures of adolescentgambling (SOGS-RA and MAGS-7). Adolescents from three states (Alabama, Missis-sippi, and Oregon) completed an anonymous questionnaire (n ¼ 1846 high schoolstudents total). Results indicate that the prevalence of probable adolescent pathologi-cal gambling varied both as a function of instrument and cut-off point utilized forclassification (range 1.7%–8.2%). Classification groups (non-problem, at-risk, andproblem gamblers) generated by both instruments were found to be associated withreports of gambling frequency, amount of money lost in one gambling occasion, andparental gambling problems. However, concern was raised because the MAGS-7 andthe SOGS-RA had little congruence in their three-group classification decisions forspecific individuals (e.g., only 20.5% agreement for problem gamblers). To improveclinical utility, an empirical case was made for using the SOGS-RA to generate afourth group of adolescent gamblers, which we labeled ‘‘probable pathological gam-blers’’ (SOGS-RA � 6). This group was differentiated from the remaining gamblinggroups on all the validity indices. The implications and limitations of these findings,as well as future directions, are discussed.

KEY WORDS: adolescent problem gambling; prevalence; measurement; congruence.

Please address corresspondence to Jennifer Langhinrichsen-Rohling, Department of Psychol-ogy, University of South Alabama, 385 Life Sciences Building, Mobile, AL 36688-0002. E-mail:[email protected].

Journal of Gambling Studies, Vol. 20, No. 3, Fall 2004 (� 2004)

259

1050-5350/04/0900-0259/0 � 2004 Human Sciences Press, Inc.

INTRODUCTION

In the late 1980s, the prevalence of gambling disorders in U.S.’adolescents began to capture the attention of clinical, research, andpublic policy professionals. Initial data indicated that adolescentswere engaging in gambling activities at alarming rates (e.g., John-ston, O’Malley, & Bachman, 1993; Lesieur & Klein, 1987; Winters,Stinchfield, & Fulkerson, 1993). In fact, many researchers reportedhigher rates of pathological gambling for adolescents than for adults(e.g., Griffiths & Wood, 2000; Jacobs, 1989; National ResearchCouncil, 1999; Shaffer & Hall, 1996; Shaffer, Hall, & Vander Bilt,1997). Pathological gambling has been defined in the Diagnosticand Statistical Manual- Fourth Edition (DSM-IV, 1994) as persistentand recurrent maladaptive gambling behavior that disrupts personal,family, or vocational pursuits.

Existing self-report measures vary in the degree to which theyare based on DSM criteria for pathological gambling. Differing oper-ational definitions of problem gambling, as determined by differentinstruments, may result both in different prevalence estimates for theproblem and incongruence in determining who is most in need ofprevention or intervention services (Winters & Anderson, 2000). Forexample, the South Oaks Gambling Screen–Revised for Adolescents(SOGS-RA) (Winters, Stinchfield, & Fulkerson, 1993), the mostwidely used measure of adolescent gambling, was not created todirectly reflect DSM criteria for pathological gambling, whereas theMassachusetts Adolescent Gambling Screen (MAGS), was (Shaffer,LaBrie, Scanlan, & Cummings, 1994). Consequently, the currentstudy was designed to compare and contrast the rates of adolescentproblem gamblers identified by these two instruments, as well as theclassification congruence for particular individuals between these twomeasures of adolescent gambling.

A review of 14 independent studies indicates that both the fre-quency of juvenile gambling and the prevalence of adolescent gam-bling disorders have been significantly increasing over the past15 years (Jacobs, 2000). Further substantiating that gambling is aproblem for youth, Shaffer et al.’s (1997) integrative meta-analysisconcluded that between 4.4% and 7.4% of adolescents are probablepathological gamblers, and another 10%–14% of youth are at-risk forserious gambling problems.

260 JOURNAL OF GAMBLING STUDIES

Comparison of prevalence rates for problem gambling across dif-ferent samples (adolescents compared to adults), locations (parts ofthe U.S.), research studies, or time periods (1990s vs. 2000s) requiresa measurement device with excellent psychometric properties, andclear and clinically useful operational definitions of the constructbeing measured. However, the extent to which there is a ‘‘gold stan-dard’’ measure of adolescent problem gambling is still a matter ofsome debate (Derevensky & Gupta, 2000). The main contender forthat honor appears to be the SOGS-RA (Winters et al., 1993). It isthe most widely used measure for adolescent gambling, just as theoriginal SOGS (Lesieur & Blume, 1987) is one of the most popularmeasures of adult problem gambling. The widespread usage of theSOGS-RA makes valuable comparisons between studies of juvenilegambling feasible (Winters, Bengston, Dorr, & Stinchfield, 1998).Furthermore, Winters and colleagues specifically chose to developthe SOGS-RA by revising the SOGS, so that findings from the twoSouth Oaks instruments could be compared easily (e.g., adolescentsto adults). Researchers have noted other advantages to the SOGS-RA,including its 12-month time frame, which facilitates the detection ofcurrent gambling problems. The relatively short and delineated timeperiod also makes the SOGS-RA useful when measuring change insymptoms over time or response to treatment.

Disadvantages to the SOGS have also been noted, however. Forexample, the number of symptoms endorsed is used to determinewhether the individual can be considered a problem gambler onboth the SOGS and the SOGS-RA. However, the appropriate cut-offpoint on the SOGS-RA for determining that an adolescent is a prob-lem gambler has been contested in the literature (e.g., Govoni, Rupc-ich, & Frisch, 1996), as using the original cut-off point for adultsappeared to identify too many adolescents as problem gamblers.Clearly, which SOGS-RA cut point should be used to separate patho-logical adolescent gamblers from at-risk gamblers needs to be deter-mined by empirically establishing the cut-off against an adequatecriterion measure. Second, comparison studies of other gamblingmeasures with the SOGS-RA indicate that the SOGS-RA may providetoo liberal an estimate of problem or pathological gambling in ado-lescents (Derevensky & Gupta, 2000). Third, the degree to which theSOGS-RA can function as a screening device for diagnosing probablepathological adolescent gambling is unclear because the instrument

261JENNIFER LANGHINRICHSEN-ROHLING ET AL.

was not specifically designed to correspond to DSM diagnostic crite-ria for pathological gambling.

A more recently derived measure of adolescent gambling is theMAGS (Shaffer et al., 1994). According to the authors, this instru-ment was specifically designed to function as a brief clinical screen-ing measure that can correctly identify individuals who either arenow or have ever been pathological gamblers across their lifetime,according to the DSM-IV. Thus, the MAGS items were based on preli-minary DSM-IV criteria for pathological gambling and it has alifetime reference for gambling-related problems rather than a12-month timeframe. Preliminary data indicate that the MAGS hasgood psychometric properties. For example, discriminant analysissuccessfully categorized 96% of adolescent gamblers as pathological,in transition, or non-pathological (Shaffer et al., 1994). Furthermore,Shaffer et al. (1994) culled the seven best items from the originalMAGS and concluded that these items would function as an efficientand effective screen to identify adolescents with serious gamblingproblems (i.e., the MAGS-7).

Unfortunately, a recent comparison of many of the importantmeasures of adolescent gambling (Derevensky & Gupta, 2000) didnot include either the MAGS or the MAGS-7, even though Dereven-sky and Gupta suggested that it might be ‘‘the measure of choice forfuture research efforts’’ based on its strong psychometric properties.Other researchers also have not chosen to use the MAGS to assessthe prevalence of pathological gambling, so little is currently knownabout the usefulness and comparability of this measure relative tothe SOGS-RA. Thus, the MAGS-7 was utilized in this study as thecomparison measure of pathological gambling in adolescents.

In summary, the purpose of the current study was to comparethe similarity of the prevalence rates of adolescent gambling thatcould be obtained with the SOGS-RA and the MAGS-7. A priori, itwas expected that both measurement devices would identify similarrates of non-problem, at-risk, and problem gamblers. Second, it wasexpected that there would be good agreement or congruencebetween the two measures regarding classification (i.e., both mea-sures would classify the same individuals as problem gamblers orat-risk gamblers or non-problem gamblers). Third, the extent towhich gambling frequency and amount variables, as well as demo-graphic variables (e.g., sex, ethnicity, age, and family composition)


differentiated among problem, at-risk, non-problem, and non-gam-bling youth was assessed for each instrument. Despite contradictoryresults in the literature (e.g., Derevensky, Gupta, & Cioppa, 1996;Griffins & Wood, 2000; Hraba & Lee, 1996), it was expected thatolder adolescents and males would have higher rates of pathologicalgambling than would younger adolescents and females. These find-ings were expected for both the SOGS-RA and the MAGS-7. No a pri-ori hypotheses were offered for ethnicity and family composition.

METHOD

Participants

One-time anonymous questionnaire data were collected from1,846 adolescents from the three high schools located in Alabama,Mississippi, and Oregon. Sophomores, juniors, and seniors were tar-geted for data collection. We chose to include only these upper levelhigh school students because we wanted individuals who had thepotential to drive or whose peers may be driving. Thus, the 46 indi-viduals who completed the data packet and identified themselves asfreshman were removed from the data set. The data packet alsoincluded a 5-item infrequency scale (Jackson, 1976) to identify inva-lid responders. If three or more items were scored in the highlyinfrequent direction, the person was considered an invalid respon-der. Fifty-eight participants (3%) were removed from the data setdue to their responses on the infrequency questions. Finally, an addi-tional seven individuals were removed because they had incongruousresponses on the gambling measures (e.g., they indicated that theyhad never gambled on one part of the questionnaire but reportedlosing $100 gambling on another part of the questionnaire). Thefinal sample, therefore, consisted of 1,735 high school students (810from Alabama; 623 from Mississippi, 302 from Oregon). However,actual sample sizes vary slightly across analyses due to missing datafor a particular measure or question.

Among participants in the present study, 75.9% were Caucasian;15.5% were African American; 2.0% each were Hispanic and AsianAmerican; 1.2% were Native American, and 2.8% identified them-selves as other. Fifty-three percent were female. Sixty-one percent lived


with both parents. According to the adolescents, the modal familyincome was between $50,000–$100,000. The three grades were approx-imately equally represented (34% Sophomores, 35% Juniors, and 31%Seniors). The mean age of participants was 16.5 years. Differencesamong sub-samples were consistent with the demographics of the statefrom which they were drawn (e.g., the two Southern sites had moreAfrican American participants and more participants living in motheronly households; Oregon had more Asian American participants).

Procedure

Consent to administer the questionnaire was initially obtainedfrom the high school principals. Although this was a conveniencesample, these particular high schools were recruited for participationbecause of their similarity in terms of scholastic test scores (average)and the demographics of their surrounding local community (smalltown). Adult gambling venues were within easy driving distance fromall three sites.

Once permission was obtained from the principal and the corre-sponding school board, a letter was sent to all parents informingthem of the project. Parents were then invited to informational meet-ings about the project that were held in their respective high schools.Using a passive parental consent procedure, parents were given sev-eral methods to prevent their child or children from participating(i.e., at the informational meeting, by telephone to the project direc-tors, and by instructing their child not to participate on the date ofadministration).

On the day of data collection, an informed assent form was readaloud to all students. Assenting participants were then given a self-report packet to complete in class. Participants were recruited fromall classes that contained sophomores, juniors, and seniors. Freshmanclasses were excluded from participation. Furthermore, if studentswere in a mixed grade-level class, freshmen were asked not to partici-pate. All potential participants were also told not to fill out thepacket if their parents had instructed them not to or had expressedany concern about their participation. Additionally, they were toldthey were free to choose not to participate or to stop participating atany time, without penalty. Thus, data collection was voluntary and noidentifying information was collected from the participants. Using


this anonymous procedure, the participation refusal rates were low(i.e., 5.2% in Mississippi, 4.6% in Alabama, and 8.5% in Oregon). Atrained research assistant was present to answer questions and toensure anonymity. Teachers were asked either to leave the room orto remain in the room and stay busy with another activity so as notto interfere with anonymous participation. On average, the self-report packets took about 30 minutes to complete. At the conclusionof the project, a dollar was donated to each school’s PTA for eachindividual from that school who participated in the study.

Measures Determining the Prevalence of Adolescent Gambling

SOGS-RA (Winters et al., 1993) is a 16-item instrument that wasoriginally designed to parallel the SOGS (Lesieur & Blume, 1987).Twelve of the items have typically been used to score the measure(i.e., items 1–4 are excluded from the summary score). However, thenon-scored items can be used to determine the 12-month and life-time rate of a variety of gambling activities as well as the maximumfrequency of any gambling behavior. The largest amount of moneyspent gambling during the past year can also be determined fromthe unscored SOGS-RA items. There is also a question about whetherthe adolescent thinks either of their parents gambles too much. Pre-viously derived psychometric data indicated that the SOGS-RA hadreasonable internal consistency (coefficient a ¼ .80) and could becharacterized by one common dimension (Winters et al., 1993).

Initially, Winters et al. (1993) recommended that any individualwho endorsed three or more items be considered a problem gam-bler. Using this cut-off point, they found that gambling frequencyand amount of money spent differentiated the resulting groups.However, in later publications, Winters and colleagues proposedusing a score of four or more to identify problem gamblers. Scoresof two or three on the SOGS-RA were then used to create an at-riskgroup and all others were considered non-problem gamblers (Win-ters, Stinchfield, & Kim, 1995). In keeping with Winters et al. (1995),subsequent researchers (e.g., Derevensky & Gupta, 2000; Govoni,Rupcich, & Frisch, 1996) have also used the following SOGS-RAscores to delineate three groups: (a) no gambling problems (SOGS-RA Total ¼ 0 or 1); (b) at-risk gamblers (SOGS-RA Total ¼ 2 or 3);(c) problem gamblers (SOGS-RA Total > 4). Even more recently,


however, Ladouceur et al. (2000) suggested using a score of five ormore on the SOGS-RA to determine ‘‘probable pathological gam-blers.’’ To begin with, these three methods of scoring the SOGS-RAwere compared in the current study (a cut-score of 3, 4, or 5 todetermine problem gamblers). Similar to previous research, thecoefficient alpha for the SOGS-RA was .80 for the current sampleand did not vary much for different subgroups of participants.

MAGS-7 (Shaffer et al., 1994) is a 7-item measure chosen fromthe best items of the MAGS. The MAGS was developed on a sampleof high school students as a brief screening device for pathologicalgambling. Items were based on proposed DSM-IV gambling criteriaand uses a lifetime frame for item endorsement. Consistent with theDSM-IV, the MAGS-7 items measure preoccupation with gambling(Item 1), risk of job/education or relationship (Items 5 and 6), lyingto family or family problems related to gambling (Items 2 and 4), lossof gambling control (Item 3), and whether the individual has commit-ted illegal acts related to gambling (Item 7). The authors provide aweighting and scoring system for the seven items in their 1994 article.A single summary score is derived via a linear combination of theseven items, each multiplied by their relative weight as determined bya discriminant function analysis. Total scores less than zero indicatenon-pathological gamblers. Scores greater than or equal to zero, andless than or equal to two, indicate at-risk gamblers. Total scoresgreater than two indicate probable pathological or problem gamblers.The authors reported a coefficient alpha of .83 for the 7-item sub-scale. In the current sample, the coefficient alpha for the weightedseven items was .65. One item was a poor contributor to the totalscore (i.e., ‘‘Are you always able to stop gambling when you want?’’).When this item was removed, the coefficient alpha for the MAGS-7increased to .73. Nonetheless, all seven items were retained for thisstudy in order to replicate the author’s original scoring procedure.

Demographics Characteristics

Participants reported their sex, age (in years), grade in school,race/ethnicity (collapsed to Caucasian, African American, andOther), current living situation (collapsed to Both Parents, MotherOnly, Father Only, and Other), and estimated household income(dichotomized into greater than $25,000 or less than $25,000).


RESULTS

Prevalence of Pathological Gambling as a Function of Measurement Device

SOGS-RA and MAGS-7 scores were used to estimate the preva-lence rates of problem gambling in high school students. As shownin Table 1, the majority of adolescents reported having gambled atleast once in their lives (86.6% endorsed at least one gambling activ-ity on the SOGS-RA). When considering the frequency of gamblingdata, a substantial percentage of the high school students who hadreported gambling indicated that they had gambled either daily orweekly during the past year (22.8%).

As would be expected, use of the three potential cut-off scoresfor the SOGS-RA (greater than or equal to three, four, or five)resulted in different estimates of the prevalence rate of pathologicalgambling in high school students (8.2%, 4.8%, and 2.9%,respectively). The 4.8% prevalence rate is most consistent with theprevious rates of problem gambling reported for adolescents (Shaffer

Table 1Prevalence Rates of Adolescent Gambling as a Function

of Measurement Device

N Valid (%)

Frequency of gambling activityEver gambled in lifetime 1479/1708 86.6Daily/weekly frequency of gambling 337/1479 22.8

SOGS-RA classification ratesSOGS-RA > 3 items (Winters et al., 1993) 137/1672 8.2SOGS-RA > 4 items (Govoni et al., 1996) 81/1672 4.8SOGS-RA > 5 items (Ladouceur et al., 2000) 48/1672 2.9SOGS-RA 5 Groups: Probable Pathological(> 6 items)

38/1672 2.3

MAGS-7 classification ratesWeighted MAGS-7 > 2.00 27/1601 1.7

Note: Ns vary slightly across analyses due to missing data.


et al., 1997), perhaps because this is the most common cut-off score(greater than or equal to 4) used for the SOGS-RA in the gamblingliterature (Govoni et al., 1996; Winters et al., 1995). In spite of itslifetime, rather than 12-month timeframe, a much smaller percent-age of problem gamblers were identified using the weighted MAGS-7(n ¼ 27, 1.7%).

However, the relatively poor agreement between the SOGS-RAand the MAGS-7 in terms of classifying particular individuals isshown in Table 2. For this analysis, only high school studentsreporting some gambling activity during their lifetimes were consid-ered (i.e., non-gamblers were removed from the analysis). The bestagreement between the two instruments occurred when determin-ing that an individual was a non-problem gambler. For example,when examining the total number of individuals classified as beingnon-problem gamblers by either instrument (n ¼ 1304), both instru-ments agreed that 1072, or 82.2% of individuals were non-problemgamblers.

In contrast, there was considerable incongruence between themeasures for designating someone as an at-risk gambler. For exam-ple, of the 269 individuals who were designated as at-risk for problemgambling by either the SOGS-RA or the MAGS-7, there was congru-ence in classification for only 39 people. This is a congruence rate of14.5% for the at-risk gambling designation.

Table 2Contingency Table of Classification Agreement between

the SOGS-RA and the MAGS-7

MAGS Classification Categories

SOGS-RA CategoriesNon-Problem

(<0)At-risk

(>0 or < 2)Problem(>2) Total

No problem (SOGS-RA 0–1) 1072 95 7 1174At-risk (SOGS-RA 2–3) 101 39 1 141Problem (SOGS-RA > 4) 29 33 18 80

Total for sample 1202 167 26 1395

Note: Ns vary slightly across analyses due to missing data. All non-gamblers were removed from thisanalysis.


There was also little agreement between the instruments aboutwho constituted the problem gamblers. Of the total number of indi-viduals identified as problem gamblers by either instrument(n ¼ 88), only 18 (or 20.5%) received this classification from bothinstruments. In fact, a greater proportion of individuals who wereclassified as current problem gamblers by the SOGS-RA were classi-fied as non-problem gamblers by the MAGS-7, rather than problemgamblers as expected (29/88 or 33% incongruence).

Finally, using a less stringent test of congruence, we consideredthe total number of individuals who were classified by either instru-ment as being either at-risk or problem gamblers (n ¼ 323). Only28.2% (n ¼ 91) were identified by both instruments as being eitherat-risk or problem gamblers. This suggests that the two instrumentsare not identifying the same people for additional assessment pur-poses or clinical interventions.

Given the lack of agreement between the two instruments in gen-erating clinically meaningful groups, and given that the SOGS-RAclassifies three times as many adolescents as problem gamblers thandoes the MAGS-7, we decided to generate two groups out of theSOGS-RAs current problem gambling group. Specifically, we exploredthat possibility that there was heterogeneity in the SOGS-RA problemgambling group, such that it contained both problem gamblers andprobable pathological gamblers (i.e., adolescents who would be likelyto meet DSM criteria for pathological gambling). Because of the rela-tively small number of individuals classified as problem gamblers bythe MAGS-7, despite its lifetime timeframe, we thought it was likelythat this instrument was already fairly conservative in its designationof problem gambler and chose to leave its cut-off score intact.

Therefore, SOGS-RA cut-off scores of 5–7 were considered forthe newly defined group of probable pathological gamblers. Basedon the empirical data comparing and contrasting the groups, thechanges in identification concordance between instruments, and thecell sizes of the resulting groups, we chose to use SOGS-RA scoresgreater than or equal to six to generate this fourth group, which welabeled ‘‘probable pathological gamblers’’, as distinguished from the‘‘problem gamblers’’ group, which now contained only individualswho scored 4 or 5 on the SOGS-RA.

When this new group was added, 38 of 81 adolescents movedfrom the SOGS-RA problem gambling group to the SOGS-RA


probable pathological gambling group. Fourteen of these 38 individ-uals were also identified as problem gamblers by the MAGS-7, for anagreement rate of 36.8%. Although this is still low, it is a consider-able improvement over the 20.5% agreement between instrumentsobtained for the unaltered problem gambler groups. Moreover, ofthe 26 problem gamblers identified by the MAGS-7, 14 of them werenow classified as probable pathological gamblers on the SOGS-RA,for an agreement rate of 53.8%.

In the next set of analyses, we considered the validity of deriv-ing the new four gambling groups from the SOGS-RA (i.e., non-problem, at-risk, problem, probable pathological gambler), ratherthan the traditional three SOGS-RA groups (i.e., non-problem, at-risk, problem). Two types of items were used as validity indices:unscored items from the SOGS-RA on gambling behavior and theMAGS-7 items. Adolescents who had never gambled were excludedfrom these analyses. These data are presented in Table 3. Signifi-cant Chi-squares were obtained for all seven MAGS items, indicatingthat each MAGS-7 item differentiated among the four SOGS-RAgroups. Follow-up analyses were conducted with Tukey’s least signifi-cant difference test or with planned Chi-square comparisons of theprobable pathological gamblers vs. all the other gambling youth.The usefulness of separating out probable pathological gamblers(with SOGS-RA scores greater than or equal to six) from the tradi-tional problem gambling group is apparent, as the probable patho-logical gamblers were significantly differentiated from the problemgamblers on six of the seven MAGS-7 items. In particular, theMAGS-7 item, arrested for gambling, did an excellent job of differ-entiating the probable pathological gamblers from all the othergroups, as 23.7% of the probable pathological gamblers endorsedthis item and almost no one from any other group did. Further-more, there were also clear differences among at-risk, problematic,and probable pathological gamblers on these other MAGS-7 items(i.e., neglect obligations, family complaints, family/friend problems,work/school difficulties related to gambling, feeling pressure togamble when not gambling). The final MAGS-7 item (‘‘Are youalways able to stop gambling when you want?’’) was the worst at dif-ferentiating among the groups and did not significantly differenti-ate the probable pathological gamblers from the problem gamblers.Overall, however, these analyses, using each MAGS-7 item as a


Tab

le3

SOGS-RAan

dMAGS-7

Reliabilityan

dValidityData

SOGS-RA

Dependentvariable

Non-problem

(n=11

55)

(0to

1)

At-risk

(n=10

)(2

to3)

Problem

(n=65

)(4

to5)

Probable

Pathological

(n=38

)(6

ormore)

Contingency

(r)

DSM

symptom

sperMAGS-7

1.Arrestedforgambling*

.3a

.5a

.0a

23.7

b.37*

**

2.Neg

lect

obligations

.5a

.5a

3.2b

34.2

c.41*

*

3.Fam

ilyworry/co

mplain

.7a

2.9b

6.3c

26.3

d.29*

**

4.Fam

ily/friendproblem

.9a

6.8b

19.0

c47

.4d

.40*

**

5.Work/schooltrouble

2.8a

9.2b

20.6

c42

.1d

.30*

**

6.Pressure

when

not

4.6a

5.7a

22.2

b55

.3c

.33*

**

7.Can

notstopgambling

18.1

a14

.5b

23.8

c31

.6c

.07*

Max

Freq

ofgamblingpast

12months

%Daily

orweekly

16.5

a59

.8b

64.5

b,c

73.7

c.40*

**

Amount$spentpast12months

%losing$2

00ormore

1.7a

9.1b

10.8

b28

.9c

.54*

**

Meanmoney

spen

t1.9a

3.2b

3.4b

,c3.7c

F=12

3.3*

**

%Paren

tgamblestoomuch

5.8a

9.6b

12.3

b18

.4c

.14*

**


Tab

le3(Continued)

MAGS-7

Nonproblem

(n=14

03)(<0)

At-risk

(n=17

1)(>

0or<2)

Problem

(n=27

)(>2)

Max

freq

ofgamblingpast12months

%Daily

orweekly

22.4

a54

.0b

57.7

b.24*

**

Amount$spentpast12months

%Losing$2

00ormore

2.9a

5.9b

34.6

c.32*

**

Meanmoney

spen

t2.1a

2.9b

3.9c

F=55

.61*

**

%Paren

tgamblestoomuch

5.6a

15.5

b26

.9c

.18*

**

Note:*p

<.05,

**p<.01,

***p

<.001

.Nsvary

slightlyacross

analyses

dueto

missingdata.

Allnon-gam

blers

wereremovedfrom

thesean

alyses.Post-hoc

comparisonsweremad

ewithTuke

y’sleastsign

ificantdifferencesorviaplanned

comparisons;an

alpharate

ofp<.05was

utilized.Meanswithdifferent

superscriptaresign

ificantlydifferentfrom

onean

other.


validity index, provided good initial evidence for the value of gener-ating an additional group with the SOGS-RA data.

Also, as shown in Table 3, the new four SOGS-RA groups alsosignificantly differed in their reports of gambling frequency andamount of money spent gambling. For example, individuals in thenewly created probable pathological gambling group were themost likely to have lost $200 or more gambling, in comparison toall other adolescent gambling groups. Thus, comparison ofresponses to the unscored SOGS-RA gambling items providedadditional validation for our four-group classification strategy withthe SOGS-RA.

Table 3 also presents data on the validity of three groupsderived from the MAGS-7. The three MAGS-7 groups were signifi-cantly differentiated by reports of amount of money spent gamblingin the past 12 months and maximum frequency of gambling in thepast year. These data provide validity evidence for the MAGS-7 three-group classifications.

Demographics and Gambling Group Membership: SOGS-RA and the MAGS-7

As shown in Table 4, demographics were also related to gam-bling group membership as determined by SOGS-RA score. Follow-up analyses were conducted with Tukey’s least significant differencetest or with planned Chi-square comparisons of the pathologicalgamblers vs. all the other gambling youth to determine the nature ofany obtained group differences. In particular, at-risk, problematic,and probable pathological gamblers were significantly more likely tobe male compared to non-problem or never gamblers. African-Ameri-cans and other ethnicities were significantly over-represented in theprobable pathological gambling group, while individuals being raisedby both parents were significantly under-represented in the probablepathological gambling group. Family income was not significantlyrelated to SOGS-RA gambling group membership in the currentsample.

The same set of analyses was conducted with the MAGS-7 gam-bling groups. Results are shown in Table 5. Only two demographicvariables significantly differentiated among the groups: (1) probabil-ity of being male, and (2) family composition. The latter variablehad a very small effect size with the MAGS-7 groupings.


Tab

le4

Dem

ograp

hic

CharacteristicsoftheNew

lyDerived

SOGS-RAGroupsAcross

theTotalSam

ple

Dependentvariable

(%of

total)

Never

(n=20

4)Non-problem

(n=11

55)

At-risk

(n=21

0)Problem

(n=65

)

Probable

Pathological

(n=38

)Chi-squarevalues

Gender

Male(46.8)(%

)24

.6a

44.8

b62

.8c

74.6

c,d

89.5

dv2

(4,16

51)=11

0.6*

**

Grade

inschool

Sophomore

(34.0)(%

)28

.133

.234

.946

.250

.0v2

(8,16

59)=18

.1*

Junior(35.4)(%

)33

.037

.334

.027

.731

.6Se

nior(30.6)(%

)38

.929

.531

.126

.218

.4

Race Cau

casian

(76.7)(%

)79

.478

.070

.575

.057

.9v2

(8,16

53)=22

.4**

African

-American

(15.6)

(%)

12.3

14.0

22.7

15.6

21.1

Other

(7.3)(%)

8.3

7.9

6.8

9.4

21.1


Tab

le4(Continued)

Dependentvariable

(%of

total)

Never

(n=20

4)Non-problem

(n=11

55)

At-risk

(n=21

0)Problem

(n=65

)

Probable

Pathological

(n=38

)Chi-squarevalues

Familycomposition

Both

paren

ts(60.9)(%

)60

.362

.160

.165

.139

.5v2

(12,

1656

)=30

.9**

Mother

only

(25.3)(%

)28

.425

.220

.723

.828

.9%

Father

only

(7.7)

5.4

7.9

8.2

6.3

13.2

Other

(6.1)(%)

5.9

4.8

11.1

4.8

18.4

Est.household

income

<$2

5,00

0(19.7)(%

)23

.219

.519

.110

.027

.3v2

(4,13

37)=6.1

Note:

Nsvary

slightlyacross

analyses

dueto

missingdata.

Post-hocco

mparisonsweremad

ewith

Tuke

y’sLeast

Sign

ificantDifferencesorviaplanned

comparisons;

analpharate

ofp<.05was

utilized.Meanswithdifferentsuperscripts

aresign

ificantlydifferentfrom

onean

other,*p

<.05,

**p<.01,

***p

<.001

.


Tab

le5

Dem

ograp

hic

CharacteristicsoftheMAGS-7

GroupsAcross

theTotalSam

ple

Dependentvariable

(%of

total)

Non-problem

(n=11

55)

At-risk

(n=21

0)Problem

(n=65

)Contingency

(R)

Chi-square

Gender

Male(46.8)(%

)46

.8a

69.9

b96

.2c

R=.19

v2(2,139

7)=53

.2***

Grade

inSchool

Sophomore

(34.0)(%

)33

.543

.238

.5R=.08

v2(4,140

7)=8.7

Junior(35.4)(%

)36

.631

.446

.2Se

nior(30.6)(%

)30

.025

.415

.4

Race Cau

casian

(76.7)(%

)77

.772

.169

.2R=.08

v2(4,139

7)=8.1

African

-American

(15.6)(%

)14

.820

.011

.5Other(7.3)(%)

7.5

7.9

19.2

Familycomposition

Both

paren

ts(60.9)(%

)61

.956

.546

.2R=.09

v2(6,140

0)=12

.5*

Mother

only(25.3)(%

)24

.725

.030

.8Father

only(7.7)(%)

7.8

10.1

3.8

Other(6.1)(%)

5.6

8.3

19.2

Est.household

income

<25,00

0(19

.7)(%)

20.2

15.9

22.7

R=.03

v2(2,115

0)=1.51

Note:

Nsvary

slightlyacross

analyses

dueto

missingdata.

Allnon-gam

blers

wereremovedfrom

thesean

alyses.Post-hocco

mparisonsweremad

ewith

Tuke

y’sLeastSign

ificantDifferencesorviaplanned

comparisons;an

alpharate

ofp<.05was

utilized.Meanswithdifferentsuperscriptsaresign

ificantly

differentfrom

onean

other,*p

<.05.***p<.001

.


DISCUSSION

The current study was designed to compare the prevalence ofprobable pathological gambling among adolescents using twoimportant measures of gambling: the SOGS-RA and the MAGS-7.The results obtained in the current study demonstrate that preva-lence estimates of adolescent gambling problems vary as a functionof measurement device and cut-off score used for classification. Inthe present research, the estimates of rates of problem gamblingranged from 8.2% using the most liberal SOGS-RA cut-off score to1.7% using the weighted MAGS-7. The SOGS-RA cut-off score thathas been most commonly used in the literature (a total score of 4or more) resulted in 4.8% of the adolescents being classified asproblem gamblers. While this estimate is consistent with Shafferet al.’s (1997) findings from a meta-analysis of the existing studiesof the prevalence of adolescent pathological gambling; the preva-lence rate obtained by using the MAGS-7 was considerably lower(1.7%), in spite of its lifetime rather than 12 months frame ofreference.

Concerns emerged regarding congruence in classificationbetween the SOGS-RA and the MAGS-7 gambling groups. A contin-gency table showed that the classification congruence between theinstruments was poor for both the at-risk and problem gamblers. Forexample, of the 88 individuals classified as problem gamblers byeither instrument, there was instrument congruence for only 18(20.5%). Even lower congruence between instruments was obtainedfor the at-risk individuals (14.5%).

To obtain better agreement between the instruments on a groupof high-risk individuals, we separated a high-risk, probable pathologi-cal gambling group out of the SOGS-RA problem gambling group.As hoped for, better agreement in classification between measureswas obtained by delineating this group of 38 individuals. Further-more, initial validation for delineating a probable pathological gam-bling group was offered, as this gambling group was differentiatedwell by six of the MAGS-7 gambling items, and also by gambling fre-quency and money spent indices on the SOGS-RA. For example, thenewly created probable pathological gambling group was the onlygroup that had any individuals who had been arrested for gambling;23.7% of the individuals in this group endorsed this item. Clinically,


these individuals appear to be ideal candidates for gambling treat-ment and clinical interventions.

As stated previously, poor agreement between instruments wasobtained for classifying individuals as at-risk gamblers. For example,only 39 of the 141 individuals placed into the SOGS-RA at-risk groupwere also identified as at-risk by the MAGS-7. Conversely, only 39 ofthe 167 individuals deemed at-risk by the MAGS-7 were similarlydefined as at-risk by the SOGS-RA. Which individuals, as identifiedby which measure, are the best candidates for prevention or earlyintervention efforts will be an important agenda for future researchefforts.

As a whole, these findings generate some concerns for the clini-cian or school administrator who is likely to use only one measure todetermine whether a particular adolescent is in need of preventionor intervention services. Certainly, future studies using follow-up clin-ical interviews will be needed to better understand how to best clas-sify adolescents and which, if any, self-report measure can functionas a ‘‘gold standard’’ for identifying adolescents who are pathologicalgamblers.

The advantages of the MAGS-7 include its brevity and connec-tion with DSM symptomology. Groups created by the MAGS-7 werealso differentiated well on measures of gambling frequency andamount of money spent. However, its disadvantages include a cum-bersome weighted scoring system, and its lifetime frame of referenceif one is looking to measure current symptoms, change in symptomsover time, or response to treatment.

In contrast, the advantages of the SOGS-RA include its inclusionof items that can be used to separate never gamblers from non-prob-lem gamblers. Understanding the characteristics of individuals whohave never experimented with gambling may facilitate preventionefforts for problematic adolescent gambling; as might a fuller under-standing of adolescents who have experimented with gambling with-out developing any risk indicators. Furthermore, the SOGS-RA caneasily be scored to create at-risk, problem and probable pathologicalgambling groups, as was done in the current study. These groups arelikely to benefit from different types of intervention efforts. Finally,the psychometric properties of the SOGS-RA appear to be robustand the SOGS-RA is used so frequently that comparisons betweenstudies are possible. On the other side, the clinical utility of the


SOGS-RA might be enhanced if it mapped on better to the DSM-IVcriteria for pathological gambling. Moreover, it will be important toensure that the range of gambling activities assessed on the SOGS-RA remain current and inclusive, if endorsement of these activities isused to differentiate those who have experimented with gamblingfrom those who have never gambled. Finally, there is still concernabout over-endorsement of some SOGS-RA items, given the highnumber of items that were endorsed by many adolescents in the cur-rent sample. In fact, Ladouceur et al. (2000) contend that theinflated SOGS-RA prevalence rates obtained with youth samplesreflect the tendency of adolescents to misunderstand some of theSOGS-RA items.

Results obtained in the current study also suggest that severaldemographic characteristics are related to gambling group mem-bership as determined by both instruments. Males are more likelyto be at-risk, problem, and probable pathological gamblers thanfemales. Members of minority ethnic groups also have increasedrisk of being classified as probable pathological gamblers; this isparticularly true for individuals who are neither Caucasian norAfrican-American. Family composition is also related to gamblinggroup membership, as individuals from one-parent homes aremore likely to be classified as probable pathological gamblers thanare individuals from two parent homes. Thus, coming from anintact home may function as a protective factor from this type ofrisk-taking behavior.

Care should be exercised when interpreting these findings, how-ever, because this study was conducted using a cross-sectional self-report design. Longitudinal work including a multi-modal assessmentwith multiple informants is needed. This type of design would pro-vide test-retest information for both instruments, while generatingdata to help determine which adolescents, identified by what instru-ments, will be the best candidates for particular gambling preventionand intervention services. In contrast, the strengths of this studyinclude the large number of participants, the multiple measures ofgambling, and the high participation rates that were obtained. Webelieve, that taken as a whole, these findings highlight the impor-tance of refining our ability to assess adolescent gambling problemsboth reliably and validly. These findings also reveal that differentmeasures, even ones generating similar estimates of the rate of


pathological gambling in adolescents, may be identifying differentadolescents as at-risk or high-risk for gambling problems. Futureresearchers comparing instruments should also consider the degreeof congruence with which the instruments classify particular adoles-cents. Finally, there is a continued need for a ‘‘gold standard’’ instru-ment with which to measure adolescent gambling. This instrumentshould be able to delineate non-problem gamblers from non-gam-blers. The instrument should also have an empirically determinedmethod for determining probable pathological gambling, as definedby the DSM-IV, in adolescents.

ACKNOWLEDGMENTS

This research was supported by a grant from the National Cen-ter for Responsible Gaming. We would also like to acknowledge thehelp of the following graduate research assistants: R. Matthew Alder-son and M. Scott Tims. Thanks also to Natalie O’Brien and ouranonymous readers for their careful reading and insightful editing ofthis paper.

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The SOGS-RA vs. the MAGS-7: Prevalence Estimates and Classification Congruence

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