a preliminary investigation into motivational factors associated with older adults’ problem...

A Preliminary Investigation into Motivational FactorsAssociated with Older Adults’ Problem Gambling

Dave Clarke & Joanne Clarkson

Received: 22 February 2007 /Accepted: 12 April 2007 /Published online: 27 April 2007# Springer Science + Business Media, LLC 2007

Abstract Little is known about the relative importance of older problem gamblers’ motivesfor gambling. A questionnaire consisting of demographic items, questions about gamblingbehavior, the past year Revised South Oaks Gambling Screen (SOGS-R), the GeneralHealth Questionnaire (GHQ-12) and the Gambling Motivation Scale (GMS), was com-pleted by a convenience sample of 104 older adults (65+ years) who gambled for money.Frequency of gambling, number of activities, largest amount spent in a single session andparents’ gambling were significantly associated with problem gambling, but not psycholog-ical distress. Hierarchical regression analysis showed that beyond these situational variables,motivation explained approximately 12% of the variance in SOGS-R scores. Uniquemotivational predictors of problem gambling were stimulation and amotivation (meaning-lessness). The results were discussed in terms of activity theory and findings fromcomparable studies with older and younger gamblers. Health professionals and researchersneed to consider risk factors for problem gambling among older adults’ choices of socialactivities.

Keywords Activity theory . GamblingMotivation Scale . General Health Questionnaire .

Older adults . Problem gambling . SOGS-R

Introduction

Recent studies have shown that the growth rate of participation in gambling over the lastfew decades has been the highest among older adults (Gerstein et al. 1999a; McKay 2005;Morgan Research 2000). In the United States, the number of older adults (65+ years)gambling had more than doubled between 1975 and 1998 (National Opinion ResearchCentre [NORC] 1999), and older adults form the largest age group of annual visitors to LasVegas (McNeilly and Burke 2002).

Int J Ment Health Addiction (2009) 7:12–28DOI 10.1007/s11469-007-9079-3

D. Clarke (*) : J. ClarksonSchool of Psychology, Massey University, Albany Campus, 229 State Highway 17, North Shore LibraryBuilding, Albany Village, Private Bag 102 904, North Shore, Auckland 1300, New Zealande-mail: [email protected]

Even though recreational gambling provides opportunities for increased social integration,self-esteem, activity, travel, sensory and cognitive stimulation for older adults (Desai et al.2004; Hope and Havir 2002; Korn and Shaffer 1999, McNeilly and Burke 2002; Munroet al. 2003; Potenza et al. 2002; Vander Bilt et al. 2004; Wiebe et al. 2004), and prevalencerates of problem gambling have been lower than among the general population (Abbott2001; Korn and Shaffer 1999; McNeilly and Burke 2001; NORC 1999; Wiebe 2000, 2003),it may lead to serious financial and personal problems for a minority of older gamblers.

While there was a significant decrease from 4 to 0.7% in lifetime estimates of problemgambling among older adults between the 1991 and 1999 representative New Zealandnational surveys, despite increases in their gambling participation and expenditure (Abbottand Volberg 2000), studies in other countries show significant increases in recent years. Ameta-analysis of all prevalence studies in North America (Shaffer et al. 1999) found astatistically significant increase from 1993 in older adults’ problem gambling rates. Pastyear rates in the United States increased by 23–50% from 1975 to 1998 (Gerstein et al.1999b). Later studies of older gamblers (Bazargan et al. 2000; Erickson et al. 2005; Laddet al. 2003; McNeilly and Burke 2001; Moore 2001; Munro et al. 2003; ProductivityCommission 1999) found relatively large proportions (3–17%) of problem gamblers in theirconvenience samples.

The number of older problem gamblers is expected to increase concomitant with theincrease in their demographic and in their gambling participation (Gerstein et al. 1999a;Kausch 2004; McKay 2005; National Gambling Impact Study Commission 1999; Shaffer etal. 1999; Volberg 2002). They are targeted by casinos with promotions of freetransportation, subsidized meals and discount coupons (McNeilly and Burke 2001; Stittet al. 2003; Tan and Wurtzburg 2004). Many of them are on fixed incomes and have littleopportunity to recover financial losses (Grant et al. 2001; McKay 2005; Petry 2002).Because of moral values, shame and lack of awareness, problem gambling may be morehidden among this age cohort than among younger age groups (Bazargan et al. 2000;McNeilly and Burke 2002; Tan and Wurtzburg 2004).

Activity theory postulates that people who are active in younger years will continue tobe active in older age (Longino and Kart 1982). They should be more satisfied with life andenjoy better mental health as older adults than less active older adults. The theory assumesthe need for social activity in later years, but individual differences in the meaning ofgambling activities for older adults need to be considered, because attitudes, expectationsand mental disengagement from activities may be more important than participation in theactivities. For example, Zaranek and Chapleski (2005) found that older adults who fre-quently visited casinos in Detroit participated in fewer other non-gambling activities,and had poorer mental health, lower income and less social support than older adultsinfrequently or not visiting casinos. The casinos seemed to be giving meaning to the olderadults’ lives.

Gambling Motivation

A few studies have examined the motives of older adults for gambling, but very little isknown about older problem gamblers’ motives for gambling when situational factors forproblem gambling are controlled in statistical analysis, and none has used a standardizedmeasure of gambling motivation with this age group. The purposes of the present studywere to examine situational and motivational correlates of older adult problem gambling,and to ascertain the relative contributions of situational factors and motivations to variationsin their problem gambling scores.

Int J Ment Health Addiction (2009) 7:12–28 13

Motivation involves both internal and external forces that trigger, direct, intensify andlead to persistence of a behavior. Three types of gambling motivation have been identifiedfrom the development of the Gambling Motivation Scale (GMS; Chantal and Vallerand1996; Chantal et al. 1994, 1995; Ladouceur et al. 1997): intrinsic motivation (IM), extrinsicmotivation (EM) and amotivation. IM consists of three facets: (1) toward knowledge vialearning, exploring, or trying to understand something new, (2) toward accomplishment ofthings such as improving one’s skills in a betting activity, and (3) toward stimulation orexcitement. EM involves positive and negative reinforcement: receiving or avoidingsomething. It also consists of three facets: (1) gambling for rewards (external regulation),(2) gambling for release of tension and guilt (introjected regulation), and (3) gambling forinternal values such as social recognition (identified regulation). Amotivation occurs whenone does not perceive relations between one’s own actions and gambling outcomes. Itpertains to activities that are neither intrinsically nor extrinsically motivated, and ischaracteristic of gamblers who have lost their sense of choice and control over their bettinghabits (Chantal and Vallerand 1996). It is displayed by a gambler who continues to gamblefor something to do out of boredom with no real purpose, mentally disengaged and withlittle sense of meaning (apathy).

In Nebraska, older gambling patrons at commercial and charitable bingo parlors, and at acasino were more likely to gamble to relax and have fun (intrinsic motivation), to get awayfor the day (extrinsic motivation) and to pass the time or relieve boredom (amotivation)than a community group of older gamblers not surveyed in gambling venues (McNeilly andBurke 2000). The two groups did not place much emphasis on casino gambling promotionsand incentives such as free transportation, nor on socializing with their friends. The gamblingpatron group had significantly higher 12-month Revised South Oaks Gambling Screen(SOGS-R) scores than the community group. Slot machines were the most common activitiesplayed in casinos, but the bingo patrons spent significantly more money each time theygambled than the casino patrons.

For older casino gamblers in Minnesota (Hope and Havir 2002), intrinsic motivations forsocial stimulation and for trying something new were much more important (35 and 24% ofthe sample, respectively) than the extrinsic motivation of winning money (6%). Similarly,from case studies with casino gamblers (McNeilly and Burke 2002), older adults’ focus wasprimarily on excitement and entertainment rather than on winning money. Compared withyounger gamblers, older recreational gamblers in one study (Desai et al. 2004) were lesslikely to gamble to win money and more likely to report boredom.

While stimulation and rewards were common reasons expressed by older (60+) gamblersin a Manitoba, Canada telephone survey, gambling to escape problems and loneliness(introjected regulation) and to pass the time (amotivation) were reasons that tended topredominate among the problem gamblers in the sample (Wiebe and Cox 2005). Escapingproblems and loneliness is usually associated with older adults’ problem gambling,particularly for women (Boreham et al. 2006; Munro et al. 2003; Sullivan 2001). Electronicgaming machines (EGMs), particularly in hotel bars and lounges is the main activityassociated with increasing SOGS-R scores (McKay 2005; McNeilly and Burke 2000, 2001,2002; Wiebe and Cox 2005; Zaranek and Chapleski 2005). Despite its benefits for mentaland social agilities (Munro et al. 2003), bingo has also been associated with increasingSOGS-R scores (Zaranek and Chapleski 2005).

In an earlier study of gambling motivation of adolescent and young adult gamblers, thereview of literature summarized major situational factors for problem gambling (Clarke2004). Problem gamblers spend more money more frequently and on more activities, aremore likely to have parents who gambled, and are more likely to experience psychological

14 Int J Ment Health Addiction (2009) 7:12–28

distress (depression/anxiety) than non-problem gamblers. The study found that IM forstimulation and EM for rewards were not significantly related to SOGS scores, afterstatistically controlling for situational factors. EM of introjected regulation uniquelyaccounted for the largest percentage of variance (17%) in SOGS scores, followed byamotivation and impulsivity (7% each). Similarly, with the GMS Ladouceur et al. (1997)found that compared to non-problem gamblers from a general population postal survey inQuébec, problem gamblers had greater introjected regulation and amotivation. Theywondered what they got out of gambling. With increasing problem gambling symptoms,stimulation and rewards seem to lose their importance in favor of tension release andapathy.

Frequency, number of activities, amount gambled and psychological distress aresituational factors also associated with older adults’ problem gambling (Erickson et al.2005; McNeilly and Burke 2000, 2002; Vander Bilt et al. 2004; Wiebe 2003; Wiebe andCox 2005). Older men have been more at risk for problem gambling than older women(Vander Bilt et al. 2004; Desai et al. 2004; Gerstein et al. 1999a; Munro et al. 2003; Wiebeand Cox 2005), but older women may be increasingly at risk (Erickson et al. 2005; McKay2005; McNeilly and Burke 2000; Petry 2002; Stitt et al. 2003). Opportunities for casinogambling, bingo, EGMs in hotel lounges, escape from problems and worries (introjectedregulation), and older womens’ reluctance to seek help might be conducive to thedevelopment of their gambling problems.

The present study was a partial replication of the earlier study of adolescent and youngadult gamblers (Clarke 2004). From the findings in that study and from the literature onolder adult problem gambling, it was predicted that for older (65+) gamblers:

(1) There would be no significant differences between men and women on situational andmotivational variables associated with problem gambling;

(2) Frequency of gambling, number of activities, largest amount spent in a single session,parents’ gambling, psychological distress, intrinsic gambling motivation for stimula-tion, extrinsic motivations for external regulation, introjected regulation and identifiedregulation, and amotivation would be positively associated with problem gambling.

(3) Introjected regulation and amotivation would predict additional unique variance inproblem gambling scores beyond the situational factors of gambling frequency,number of activities, amount gambled, parents’ gambling and psychological distress;and

(4) EGM and bingo players would have higher problem gambling scores than gamblersnot playing these activities.

Method

Materials

In addition to demographic questions, an anonymous questionnaire was designed whichconsisted of four parts:

Gambling behavior The participants indicated which of 13 gambling activities they playedfor money at least once in their lifetime, and if they gambled, the frequency for eachactivity during the past 12 months, with ratings of 0 (less than weekly) or 1 (once per weekor more). The activities included: playing EGMs in casinos, other games in casinos, EGMs


and card games not in casinos, bingo, bets on personal skill games such as pool and golf,bets on sports teams, bets on horse or dog races, scratch tickets, Lotto, lotteries, raffles,telephone and Internet gambling. Two total scores ranging from 1 to 13 were tabulated foreach person: the number of activities tried at least once and the frequency for all gamesplayed during the past 12 months. The respondents were also asked to circle either “yes” or“no” if they thought that either of their parents gambled too much, and to indicate thelargest amount of money spent in one gambling session in the past 12 months. They weregiven six options ranging from NZ$1 or less through to NZ$200 or more.

Problem gambling Past year gambling problems were measured by the SOGS-R (Lesieurand Blume 1987, 1993). A score of 3 or more categorizes a person as a problem gambler,and less than 3, a non-problem gambler (Ladouceur et al. 1997). In New Zealand andAustralia, problem gambling also includes the more serious diagnostic classification ofpathological gambling (Abbott and Volberg 2000).

Psychological distress The 12-item General Health Questionnaire (GHQ) has beenvalidated for measuring psychological distress in older people, specifically in the domainsof anxiety, depression, social dysfunction and loss of confidence (Cheung 2002). It has alsobeen shown more suitable for use in community-based surveys and with older people thanlonger versions (Bowling 1997). Global psychological distress scores were used in thepresent statistical analysis which assumes normality because they have a less skewed scoredistribution than caseness scores (Goldberg and Williams 1991). Total scores range from 0to 36 with higher scores indicating a higher probability of general psychological distress.

Gambling motivation The English language version of the Gambling Motivation Scale(GMS) was obtained from the authors (Chantal et al. 1994). It consists of 28 items withseven sub-scales corresponding to the three types of motivation described above: (1)Intrinsic Motivation (IM) toward Stimulation, toward Knowledge, and toward Accom-plishment, (2) Extrinsic Motivation (EM) involving External Regulation, IntrojectedRegulation and Identified Regulation, and (3) Amotivation. At the beginning of the Scale,respondents are asked to select their favorite game from the list of 13 activities and then inanswer to the question, “why have you gambled at your favorite game?”, to rate each itemon a 7-point Likert-type scale ranging from Does not correspond at all (1) to Correspondsexactly (7), with Corresponds moderately (4) as the midpoint. Examples of EM include “Tomake money quickly and easily.” (External Regulation), “Because it’s the best way I knowfor me to relax.” (Introjected Regulation), and “Because it’s the best way I know to gettogether with my friends.” (Identified Regulation); for Amotivation, “I like to gamble, butsometimes I wonder what it does for me.” Scores can range from 4 to 28 on each sub-scale.

Procedure

The convenience sample was obtained in the city of Hamilton, New Zealand fromretirement villages (n=147), senior clubs (n=104), members of the Returned ServicesAssociation (RSA) (n=15) and individuals attending a recently opened casino whodescribed themselves as aged 65 or older (n=25). The questionnaire was distributed withoutattempting to obtain numbers of men and women proportionate to the general population.Participants were included if they had gambled at least once in the past 12 months, fullycompleted the anonymous questionnaire and returned the questionnaire by mail.


An information sheet which included the purposes of the study, the rights of theparticipants and the researchers’ contact details, and the questionnaire were placed in aplain envelope and distributed to convenience samples of older adults. Permission wasgained from site managers of four retirement villages to place information in all residents’letter boxes and from Sky City Hamilton casino’s security manager to approach individualsleaving the casino. Permission was also gained from secretaries of senior clubs to attendone of their meetings and distribute questionnaires to their members. Envelopes were givento the secretary of the RSA for distribution to members willing to complete the ques-tionnaire. Patrons exiting the casino who described themselves as aged 65 or older wereasked by the second author if they were willing to complete a questionnaire in their owntime that looked at the motivations of older gamblers. A pre-paid self-addressed envelopewas provided with each information sheet and questionnaire. The questionnaire took about20 min to complete.

Quantitative data analyses were performed using the Statistical Package for SocialSciences (SPSS) for Windows Version 13. Men were compared to women on the situationalvariables of gambling frequency, number of gambling activities, and psychological distress,the seven aspects of motivation, and problem gambling scores using multivariate analysisof variance (MANOVA). Men were also compared to women on largest amount gambled ina single session and parents’ gambling behavior. Correlations between problem gamblingscores and scores for the situational and motivational variables were computed. Hierarchicalregression analysis was used to assess the relative importance of the aspects of motivationafter controlling for situational variables in the statistical prediction of problem gambling.The problem gambling scores of gamblers on specific activities were compared to theproblem gambling scores of non-gamblers on the respective activities.

Results

Data Transformation

Prior to inferential analyses, the data for each scale were examined for assumptions ofnormality. Gambling frequency, number of activities, the scores on each of the sevenmotivational scales and the SOGS-R scores were skewed, so logarithmic transformationswere computed to approximate normal distributions for these data (Osborne 2002) forcorrelations and regression. Because MANOVAs are highly robust for violations to theassumptions of normality and homogeneity (Spicer 2005), raw data were used in thecomparisons of men’s and women’s scores. Levene’s correction for unequal variances wasused for post-hoc comparisons of means. For the GHQ, the distribution of data was withinthe normal range and raw data were used in the computations.

Respondents

Of the 291 initially delivered questionnaires, a total of 104 fully completed questionnairesand three incomplete ones were returned by mail. Although the overall response rate wasonly 36%, there were no substantial biases in the representativeness of the sample. Exceptfor age over 85 years, the sample was not significantly different from the general NewZealand population 65 years of age and over in terms of gender, age, ethnicity, maritalstatus, employment status and median yearly income ($NZ13,000 vs. $NZ13,100). Thisrate was above the expected rate of 30% for mailed questionnaires (Shaughnessy and


Zechmeister 1985), particularly in surveys of older adults using the present methodologyand looking for specific information (Hope and Havir 2002; Wiebe and Cox 2005).

Table 1 shows the demographic characteristics of the sample and of older adults whocomprise approximately 12% of the total population (New Zealand GovernmentDepartment of Statistics 2001). More women (n=63) participated in the research thanmen (n=41). The sample consisted mainly of retired, widowed/single/divorced Caucasianpersons. Ages ranged from 66 to 87 years with a median age of 73 years (M=74.59, SD=4.50). No participants indicated they were from a Maori or Pacific Island group.

Most of the older adults (64%) gambled weekly or more frequently on one or more ofthe 13 activities, and for most of them (74%) the largest amount spent in one session wasNZ$10 or less. Hence, largest amount spent in a single session was re-grouped intocategories of 0 (NZ$10 or less) and 1 (greater than NZ$10). According to the criterion ofscores of three or greater on the SOGS-R, 9% of the 104 gamblers were classified as currentproblem gamblers, with proportionately equivalent numbers of male (3 of 41) and female(6 of 63) problem gamblers, χ2 (1, N=104) = 0.15, p>0.05. For the present sample, thescale’s coefficient of internal consistency (Cronbach’s α) was 0.65.

For amount spent over $10 in a single session and for parents’ gambling behavior, therewere equivalent percentages of men (20 and 10%, respectively) and women (30 and 13%,respectively), χ2 (1, N=104)=1.47 and 0.21, respectively, p>0.05. There were no significantdifferences between male and female mean scores on any of the variables (Table 2). Thusthe first hypothesis was corroborated.

For the second hypothesis, correlates of problem gambling appear in Table 3. Internalconsistency for the GHQ was 0.88 for the present sample. Coefficients of internal

Table 1 Characteristics of the Sample (N=104) and the Older Adult Population (N=450,426)

Characteristic Present Sample Populationa χ2 df

n % n %

Gender 1.01 1Male 41 39 198,187 44Female 63 61 252,239 56

Age (years) 7.15* 265–74 61 59 246,174 5575–84 41 39 155,613 35>84 2 2 48,639 10

Ethnicity 4.43 2Caucasian 100 96 418,896 93Maori/Pacific Island 0 0 18,017 4Other 4 4 13,513 3

Marital 5.64 2Widowed/Single/Other 55 53 203,508 45Married 41 39 224,100 50Divorced 8 8 22,818 5

Employment 1.49 1Retired 93 89 414,392 92Paid 11 18 36,034 8

a New Zealand Government Department of Statistics (2001). Percentages were rounded to the nearest wholenumber.

*p<0.05


consistency for the seven sub-scales ranged from 0.46 for Identified Regulation to 0.81 forExternal Regulation. Problem gambling was significantly related to frequency, number ofactivities, largest amount gambled, parents’ gambling, intrinsic motivation towardsknowledge and stimulation, extrinsic motivation of external, introjected and identifiedregulation, and amotivation but not psychological distress (r=−0.03). The strongestcorrelation (0.78) was between IM for stimulation and EM for introjected regulation.

For the third hypothesis, hierarchical regression analysis was performed on the data toassess the relative importance of predictors of SOGS-R scores. The first variables enteredwere the situational variables. In the second step, the seven motivational variables wereentered. Table 4 shows the results of the analysis by listing the beta weights for eachpredictor at each step. The R2 statistic gives the percentage of variance accounted for byeach regression model, and the semi-partial r2 the proportion of unique variance explainedby each component. In the first model, the situational variables accounted for 32% of thevariance in SOGS-R scores, F (5, 98) = 9.26, p<0.001. When the motivational variableswere incorporated, the total amount of variance accounted for increased by 12% to 44%,F (7, 91) = 2.74, p<0.05.

Significant individual predictors of problem gambling in the final model were frequency,parents’ gambling, IM towards stimulation and amotivation, F (12, 91) = 5.94, p<0.001.Amotivation functioned as a net suppressor effect, as evidenced by the change in signs fromthe bivariate correlate (r=0.21, Table 3) between amotivation and problem gambling scoresto the beta coefficient (−0.27). This suppressor effect means that the contribution ofamotivation occurred mainly through suppressing the proportions of variance due to other

Table 2 Means and Standard Deviations for Situational Variables, Psychological Distress, Motivation andProblem Gambling of Older Men and Women (N=104)

Men n=41 Women n=63 t (df=102)

Situational VariablesGambling frequency M 1.02 1.24 0.69

SD 1.31 1.27Number of activities M 3.54 3.40 0.14

SD 2.13 1.70Psychological Distress (GHQ) M 10.83 9.84 1.62

SD 4.16 3.66Motivation (GMS)IM knowledge M 4.85 4.73 0.18

SD 1.41 1.45IM accomplishment M 4.07 4.17 0.65

SD 0.26 0.77IM stimulation M 4.78 5.57 1.68

SD 1.82 3.62EM external regulation M 8.71 7.84 0.66

SD 6.03 4.88EM introjected regulation M 4.73 5.56 3.10

SD 1.48 2.75EM identified regulation M 4.46 4.65 0.40

SD 1.34 1.57Amotivation M 5.46 5.54 0.02

SD 2.27 3.06Problem Gambling (SOGS-R) M 0.46 0.54 0.12

SD 1.03 1.18


Tab

le3

Correlatio

ns,Means

andStandardDeviatio

nsof

ScoresforSitu

ationalandMotivationalVariables

(N=10

4)

Variables

12

34

56

78

910

1112

13

1.Gam

blingfrequency

2.Num

berof

activ

ities

0.32**

3.Largestam

ount

spent

0.15

0.41**

*4.

Parents’gambling

0.09

0.21*

0.27

*5.

Psychological

Distress

−0.06

−0.25*

−0.05

0.05

6.IM

know

ledg

e0.25*

0.40**

*0.27

*0.33**

−0.04

7.IM

accomplishm

ent

−0.06

0.21*

0.26

*0.04

−0.14

0.41**

*8.

IMstim

ulation

0.17

0.38**

*0.44

***

0.19

−0.00

0.40**

*0.44

***

9.EM

external

regulatio

n0.35***

0.30**

0.31**

0.27*

0.03

0.41***

0.10

0.47***

10.EM

introjectedregulatio

n0.16

0.31**

0.35

***

0.30**

0.05

0.47**

*0.42

***

0.78**

*0.35**

*11.EM

identifiedregulatio

n0.02

0.13

0.27*

0.38***

0.04

0.25*

0.19

0.18

0.10

0.25*

12.Amotivation

0.17

0.38***

0.37***

0.24*

0.06

0.55***

0.46***

0.52***

0.39***

0.55***

0.15

13.Problem

gambling

0.34**

*0.39**

*0.34

***

0.39**

*−0

.03

0.37**

*0.18

0.47**

*0.28*

0.44

***

0.20*

0.21

*Mean

1.15

3.45

0.26

0.12

10.23

4.78

4.13

5.26

8.18

6.37

4.58

5.51

0.51

Stan

dard

deviation

1.28

1.87

0.44

0.32

3.88

1.43

0.62

3.05

5.31

3.05

1.48

2.76

1.11

Means

andstandard

deviations

arebasedon

raw

data.Point

biserial

correlations

werecomputedforam

ount

spentandparents’

gamblingbehavior.

*p<0.05,**

p<0.01,**

*p<0.001,

one-tailed.


variables in the model that were uncorrelated with problem gambling scores, thus improvingthe prediction of problem gambling scores (Polit 1996). The largest amount of uniquevariance in SOGS-R scores (semi-partial r2) was explained by parents’ gambling behavior(9%), followed by frequency and amotivation (6% each), and IM towards stimulation (5%).

The fourth hypothesis was corroborated. From Table 5, EGM players had significantlyhigher mean problem gambling scores, whether in casinos or in other venues, than non-EGM gamblers. Similarly, bingo players had higher problem gambling scores than non-bingo gamblers. Bettors on horse/dog races had higher scores than non-bettors. Scratchticket players had higher scores than non-players. For card games not in a casino, playersfor money had higher problem gambling scores. There were no significant differences inproblem gambling scores between gamblers and non-gamblers on other games in a casino,Lotto or raffles.

Discussion

In the present study, 9% of the gamblers were classified as current (12-month) problemgamblers, within the range of 3–17% found in North American studies of gamblerssimilarly recruited (Bazargan et al. 2000; Erickson et al. 2005; Ladd et al. 2003; McNeillyand Burke 2001; Moore 2001). The demographic characteristics of the present sample andof older adults in the New Zealand population were similar. However, from the 1999representative national survey of gambling (Abbott and Volberg 2000), 0.3% of the olderpopulation (65+) were estimated to be current (6-month) problem gamblers. The differentpercentage in the present study might be due to the exclusion of non-gamblers, the longertime frame for current status, high rates of gambling in New Zealand cities and theavailability of a casino.

Table 4 Results of Hierarchical Regression Analysis for the Prediction of Problem Gambling fromSituational Variables, Psychological Distress and Motivation (N=104)

Predictor Step 1 Step 2 Step 2Beta Beta Partial r2

Situational VariablesGambling frequency 0.22* 0.22* 0.06Number of activities 0.21* 0.16 0.03Largest amount spent 0.15 0.08 0.01Parents’ gambling 0.29*** 0.28** 0.09Psychological Distress 0.06 0.07 0.01

MotivationIntrinsic towards knowledge 0.14 0.02Intrinsic towards accomplishment 0.03 0.00Intrinsic towards stimulation 0.32* 0.05Extrinsic—external regulation 0.09 0.01Extrinsic—Introjected regulation 0.10 0.01Extrinsic—identified regulation −0.04 0.00Amotivation −0.27* 0.06

R2 0.32*** 0.44**Adjusted R2 0.29*** 0.37**R2 Change 0.32*** 0.12**

*p<0.05, **p<0.01, ***p<0.001


Associations with Problem Gambling

The first hypothesis was supported. Older male and female gamblers in the sample did notdiffer significantly on situational and motivational variables associated with problemgambling, so that older female gamblers might also be at risk for problem gambling (Ericksonet al. 2005; McKay 2005; McNeilly and Burke 2000; Petry 2002; Stitt et al. 2003).

The second hypothesis was partially supported. As in the earlier GMS studies withadolescents and adults (Clarke 2004; Ladouceur et al. 1997), and from parts of other studieswith older gamblers (Erickson et al. 2005; McNeilly and Burke 2000, 2002; Vander Biltet al. 2004; Wiebe 2003; Wiebe and Cox 2005), frequency of gambling, number ofactivities, largest amount spent in a single session, parents’ gambling, intrinsic motivationsfor knowledge and stimulation, extrinsic motivations for external regulation, introjectedregulation and identified regulation, and amotivation were significantly associated withproblem gambling.

It is interesting that the older gamblers’ thinking that their parents gambled too muchaccounted for the largest proportion (9%) of unique variance in problem gambling scores,given that casinos and EGMs were not available in New Zealand until recently. However,compared to most other Western societies, New Zealand and Australia have been very

Table 5 Means and Standard Deviations of Problem Gambling Scores for Gamblers and Non-gamblers onSpecific Activities

Gamblers Non-gamblers t (102)

EGMs in casinos M 0.98 0.18 3.36***SD 1.47 0.59n 43 61

EGMs not in casinos M 1.10 0.28 2.59**SD 1.67 0.71n 29 75

Other games in casinos M 1.33 0.46 1.03SD 2.07 1.03n 6 98

Bingo (housie) M 1.38 0.29 2.65**SD 1.86 0.69n 21 83

Bets on horse/dog races M 0.93 0.25 2.67**SD 1.53 0.64n 40 64

Scratch tickets M 0.69 0.23 2.51**SD 0.70 0.00n 64 40

Non-casino card games M 3.00 0.38 2.61*SD 2.24 0.88n 5 99

Lotto M 0.47 0.60 0.53SD 1.02 1.33n 74 30

Other lotteries or raffles M 0.43 0.68 0.92SD 0.93 1.43n 70 34

*p<0.05, **p<0.01, ***p<0.001, one-tailed tests


tolerant of gambling. On- and off-course betting on horse and dog races have been widelypopular, and a state lottery was established in New Zealand in 1929 (Abbott 2001). Themotives of the present sample’s parents might have been different. For example, in Canada,Chantal and Vallerand (1996) found that all three aspects of internal motivation(stimulation, knowledge, accomplishment) plus a fourth, EM of identified regulation(seeking the approval of others), were the primary motivations of people who preferred togamble on horse races. These motivations were not found with gamblers on games of lucksuch as lotteries. EM of external regulation (rewards) was characteristic of the lottery group,but not of the horse racing bettors. Hence, the present gamblers might have been influencedby parents who gambled too much (Hraba and Lee 1996), but on different activities andwith different motives.

However, there was almost no relationship between psychological distress and problemgambling, contrary to the findings of previous studies (Erickson et al. 2005; McNeilly andBurke 2002; Zaranek and Chapleski 2005). Because the participants completed thequestionnaire within one year of the opening of the Hamilton casino, the novelty effect oftry something new (IM knowledge) and insufficient time for problem gambling symptomssuch as substantial money losses to appear, depression and anxiety might not have yetbecome salient. Further, the participants might have had readily available social support,thus attenuating the relationship between psychological distress and problem gambling(Vander Bilt et al. 2004; Zaranek and Chapleski 2005).

The fifth hypothesis was supported. Further, not only were EGMs and bingo associatedwith older adults’ problem gambling as in previous studies (McKay 2005; McNeilly andBurke 2000, 2001, 2002; Wiebe and Cox 2005; Zaranek and Chapleski 2005), but also cardgames not in a casino, horse/dog races and scratch tickets. As in studies with youngergamblers, continuous forms of gambling, where winnings can be wagered againimmediately within the same session (Abbott 2001; Clarke and Rossen 2000; Welte et al.2004), are risky activities for older adults becoming problem gamblers, particularly forwomen (McKay 2005; McNeilly and Burke 2000; Wiebe and Cox 2005).

Motivation and Problem Gambling

For the fourth hypothesis, when the motivational variables were included together inregression analysis after controlling for the situational variables, IM stimulation andamotivation were significant, unique statistical predictors of problem gambling. Introjectedregulation did not emerge as a significant unique predictor as expected. It was stronglycorrelated with stimulation (0.78), indicating that the internal force of seeking stimulationand the external reinforcement of relieving tension were conjointly related to increasingproblem gambling (McNeilly and Burke 2000). Underlying this combined motive wasamotivation: lack of meaning, mental disengagement, feelings of futility and boredom withgambling activities. With increases in the meaninglessness of gambling, the needs forstimulation and tension release increase, and problem gambling increases. Conversely, fromactivity theory, the meaning that gambling gives to older adults’ lives diminishes whenproblem gambling increases (Zaranek and Chapleski 2005).

Gambling for rewards and for social recognition (identified regulation) was not stronglyrelated to problem gambling. They were insignificant variables in the regression analysisfor both older gamblers in the present sample and younger ones in the earlier study (Clarke2004). However, for both younger and older gamblers, stimulation and amotivation areimportant factors related to problem gambling (Clarke 2004; Ladouceur et al. 1997;McNeilly and Burke 2000; Wiebe and Cox 2005).


Ladouceur and Dubé (1997) also found that with American roulette players, motivationfor external rewards did not distinguish problem from non-problem gamblers. According tooperant theory (Perone et al. 1988) periodically winning provides positive reinforcement ofgambling behavior because variable-ratio schedules of reinforcement tend to produce rapid,steady responding and greater resistance to extinction. It would be expected that suchreinforcements would lead to excessive gambling and thus to gambling problems, butsecondary processes such as strong, negative feelings about the outcomes of gamblingwhich lead to tension are more important for gambling addiction (Carroll and Huxley 1994;Orford et al. 1996). The concept of negative reinforcement applies especially to problemgambling. By gambling, problem gamblers release tension, and the relief reinforces thegambling behavior, especially at their respective favorite gambling activities.

Limitations and Implications

While the sample was similar to older adults in the population, Maori, Pacific Island groupsand Asians were not represented. For younger age groups, Maori and Pacific people are atgreater risk for problem gambling than Caucasians, and very little is know about their olderadults’ gambling behaviors (Abbott and Volberg 2000). Many potential problem gamblerscould have been missed due to apathy, sickness, or being unable to complete the questionnaire.

The small number of problem gamblers (9) in the sample precluded generalizations ofdifferences in motivation and behavior between problem gamblers and non-problemgamblers. The reliability of the SOGS-R with the present sample was low (0.65) and use of3 as a cut-off score might have resulted in high false positive rates (Abbott and Volberg2000). Also, the validity of the SOGS-R for use with older adults has been questioned, withthe recommendation of further refinement or replacement (Wiebe and Cox 2005). It hasfailed to clearly distinguish between problem and pathological gamblers (cut-off score of 5),and older adults tend to under- or over-endorse specific items.

Because the reliability of autobiographical memories decreases over time, the olderadults might have distorted the details of their gambling behavior over 12 months(Bradburn et al. 1987; Rubin et al. 1986). The use of self-administered questionnaires mighthave inflated the correlations among the variables because of participants’ attempts to beconsistent throughout the questionnaire. For example, the perception that their parentsgambled excessively might affect their evaluation of their own gambling behavior in thatthey think they also have a problem with gambling. On the other hand, because ofamotivation and reluctance to admit problems (Wiebe and Cox 2005), older adults may notbe aware of the extent of their gambling problems and thus may not report accurateresponses in relation to their gambling behavior. They might be more likely to hide or denytheir gambling behavior due to such things as religious beliefs and age related perceptionsof how older adults should morally and ethically behave (Bazargan et al. 2000). Futurestudies could corroborate the findings from self-report questionnaires by using informantssuch as friends and family, or by observing older adults’ gambling behavior in gamblingvenues (Wiebe and Cox 2005).

Although motivation contributed significantly to the relationship between situationalvariables and problem gambling, the results need to be treated with caution. The internalconsistency of the Amotivation scale was satisfactory, but marginal (0.69). For IM ofknowledge and accomplishment, and for EM of identified regulation, the internalconsistencies of the GMS scales (0.58, 0.53, and 0.46, respectively) were unacceptable,so that the relationships among curiosity, feelings of accomplishment, social recognitionand problem gambling could not be reliably ascertained. A further limitation was the high


number of significant correlations among the motivational variables, indicating that theGMS scales were not independently measuring different facets of gambling motivation.Wiebe and Cox (2005) have questioned the appropriateness of using the SOGS-R items forolder adults. For example, they found that feeling guilty about gambling was over-endorsedand borrowing money to gamble was under-endorsed, perhaps reflecting the values of olderadults.

Because the data in the present study were cross-sectional, the direction of associationsbetween situational and motivational variables, and problem gambling could not bedetermined. Hence, the relationships among the variables need to be tested in longitudinaland prospective designs. Comparisons to other age groups might determine if the motiva-tional variables are typical or unique to older problem gamblers. Gambling behaviour,including motivation and problem gambling, should be assessed within 12 months ratherthan over a lifetime for more reliable data. Other variables that could influence problemgambling but were not included in the present study also need to be incorporated into thedesigns, such as the personality trait of impulsivity (Clarke 2004; Steel and Blaszczynski1996), social support (Vander Bilt et al. 2004; Zaranek and Chapleski 2005) and incentivessuch as cheap meals, transportation to gambling venues and promotions targeting olderadults (Stitt et al. 2003). Future research also needs to examine the relationship betweendisorders such as dementia and gambling behavior, because a memory disorder may impairjudgment about risks and spending with limited incomes. From a health perspective, furtherresearch should examine the impact and costs of older adults’ gambling in communities,and the prevalence of problem gambling in areas with and without casinos (ProductivityCommission 1999; Ladouceur et al. 1994).

Routine medical examinations should include questions about older adults’ activities(Desai et al. 2004), including SOGS-R items appropriate for them (McNeilly and Burke2000; Zaranek and Chapleski 2005), and about the availability of social support fromfamily and friends (Productivity Commission 1999; Winslow 2002). Signs of problemgambling might include gambling to escape problems and negative feelings, and gamblingout of boredom. Because problem gambling may be a hidden problem among some olderadults, education and publicity about the risks highlighted in the present and other studiescould be explored. Alternative activities such as volunteer work could be encouraged forhealthy older adults who seem to be increasingly reliant on gambling for their entertainmentand meaning of life.

Conclusion

The present study provided further evidence for the consideration of individual differencesin situational and motivational factors that are not included in activity theory. It evaluatedthe relative contribution of older adults’ motives to problem gambling scores, after control-ling for relevant situational variables. Mental disengagement (amotivation) as well asparticipation are important variables related to problem gambling. The results were similarto findings with older gamblers in other countries, except for psychological distress,indicating that similar situational and motivational variables are operating among olderNew Zealand adults. The interrelationships among situational variables and motivation aremore complex and depend upon the favorite gambling activity. Older adults who gamblefrequently on EGMs, at bingo and at cards, who think that their parents gambled too much,who gamble for stimulation and who are bored with gambling activities are at risk forproblem gambling.


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