Problem Gambling, Gambling Correlates, and Help-Seeking Attitudes in a Chinese Sample: An Empirical Evaluation

doi:10.4236/psych.2011.24054

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Psychology

2011. Vol.2, No.4, 342-354

Problem Gambling, Gambling Correlates, and Help-Seeking

Attitudes in a Chinese Sample: An Empirical Evaluation

Jasmine M. Y. Loo, Tian Po Oei, Namrata Raylu

School of Psychology, The University of Queensland; Brisbane, Australia.

Email: jasminelmy@help.edu.my

Received March 17th, 2011; revised April 19th, 2011; accepted May 21st, 2011.

There is an increasing consensus that problem gambling (PG) is a serious social issue among the Chinese, but

little is known of the factors associated with PG among the Chinese using validated and improved PG measure-

ments. This study examined the patterns of PG and the PG predictive ability of variables such as gam-

bling-related cognitions, gambling urge, depression, anxiety, stress, and help-seeking attitudes among Chinese

individuals living in Taiwan. The participants consisted of 801 Taiwanese Chinese student and community indi-

viduals (Mean age = 25.36 years). The prevalence of PG (Problem Gambling Severity Index; PGSI) and patho-

logical gambling (South Oaks Gambling Screen; SOGS) are higher in this Taiwanese Chinese sample as com-

pared with past prevalence research. Significant differences were found between PGSI groups (i.e., non-PG,

low-risk, moderate-risk, and PG) in socio-demographic variables. Erroneous gambling-related cognitions and

overall negative psychological states significantly predicted PG. In addition, interaction effects of gender, me-

diation effects, and the predictive ability of help-seeking attitudes were discussed. The findings of this study

have important implications in the understanding of PG among the Chinese. Gambling-related cognitions and

negative psychological states are important factors that should be addressed in intervention programs.

Keywords: Chinese, Gambling, Problem Gambling, Help-Seeking, Cognitions, Psychological States

Introduction

The earliest documented accounts of gambling were recorded

in China where “keno” was first played 3000 years ago to fund

the building of the Great Wall (National Policy Toward Gam-

bling, 1974). Gambling was very popular in ancient China and

throughout Chinese history despite the fact that it was under

strict legislative controls and banned in some regions. Despite

being illegal in mainland China (except in Macau where casino

gambling is legalized) and Taiwan (except outlying Penghu

islands), gambling remains popular among the Chinese around

the world (i.e., Chinese Diaspora) due to the fact that it is an

acceptable form of social activity in the community (Hobson,

1995; Lai, 2006; Raylu & Oei, 2004b). In fact, social gambling

is expressed as a form of entertainment, often occurring during

festive seasons (e.g., Chinese New Year), birthday gatherings,

or wedding celebrations. This activity usually happens with

friends, family, or colleagues, and the gambling episode lasts

for a limited period of time without loss of control (Clarke, Tse

et al., 2006). Nevertheless, social gambling can escalate to se-

rious social gambling, problem gambling, and pathological

gambling.

To date, most researchers have concurred that the term prob-

lem gambling (PG) refers to gambling behaviour that is severe

enough to create negative outcomes for the problem gambler,

immediate family, and social networks (Brooker, Clara, & Cox,

2009; Raylu & Oei, 2002). Similarly, in this study, problem

gambling will be used in a broader sense to define the situation

where an individual is experiencing gambling problems that

causes disruption to the individual’s functioning that may ex-

tend to affect family members and social networks (Lesieur &

Blume, 1987). The term pathological gambling will be used to

define individuals who meet the diagnostic criteria in the Di-

agnostic and Statistical Manual, Fourth Edition, Text Revision

(DSM-IV-TR; American Psychiatric Association, 2000). For

the purpose of this study, gambling problems are conceptual-

ized on a latent continuum of duration and severity.

When gamblers win or lose, a range of readily perceptible

cognitions, emotions, and behaviours are evoked. In turn, they

drive a vicious cycle of excessive gambling with detrimental

consequences such as financial debt, work and health issues,

and strained relationships (Loo, Raylu, & Oei, 2008). These

detrimental effects of PG affects both problem gamblers and

their significant others (Raylu & Oei, 2002). Furthermore, the

availability and legalisation of gambling activities in various

countries (e.g., recent legalisation of gambling in Singapore)

perpetuates the frequency and severity of gambling-related

social issues among the Chinese and has raised concerns among

practitioners and governmental authorities (Tan, Yen, & Nayga,

2010). It is also not uncommon to encounter anecdotal media

coverage on PG among Chinese individuals with speculations

of prostitution and drug-dealing to repay debts, and parental

neglect of young children stemming from gambling addiction

(Blaszczynski, Huynh, Dumlao, & Farrell, 1998). Moreover,

empirical evidence of PG among the Chinese in Australia and

Hong Kong do suggest that gambling is a popular recreational

activity and prevalence rates are higher in this population in

comparison to Western populations (Blaszczynski et al., 1998;

Chen et al., 1993; Loo et al., 2008).

It has also been argued that there are differences in the de-

velopment of PG, perpetuation of gambling problems, and

help-seeking attitudes between individuals from the West and

Chinese individuals (Loo et al., 2008; Raylu & Oei, 2004a). To

J. M. Y. LOO ET AL. 343

date, much research has been conducted on Western samples

and results obtained were commonly used to guide research and

interventions among Chinese samples. As the Chinese ethnic

group is the largest ethnic group representing 22% of the

world’s population (Tseng, Lin, & Yeh, 1995) and as gambling

is popular among this group, it is important that we specifically

investigate the patterns of PG among the Chinese and highlight

the socio-demographic variables and correlates of PG such as

gambling-related cognitions, gambling urge, depression, anxi-

ety, stress, and help-seeking correlates in order to assist in iden-

tification of PG cases for early detection and intervention. In-

vestigating correlates of help-seeking attitudes is also an im-

portant step in future development of PG intervention that will

increase positive help-seeking propensity among Chinese prob-

lem gamblers.

Different conceptual understanding and theories of PG pro-

duces diverse forms of measurement tools; therefore, creating

varied empirical findings about the prevalence of PG (McMil-

len & Wenzel, 2006). Hence, utilising valid screening tools for

Chinese PG is essential in the advancement of our understand-

ing of PG among the Chinese. The predecessors of the Cana-

dian Problem Gambling Index (CPGI) such as South Oaks

Gambling Scale (SOGS) and Diagnostic and Statistical Manual

(DSM-IV) have been used widely in gambling prevalence re-

search among the Chinese residing in Hong Kong and Macao

(Fong & Ozorio, 2005; Wong & So, 2003), and also among

studies investigating PG correlates (e.g., Liao, 2008; Oei, Lin,

& Raylu, 2008; Oei & Raylu, 2010). These studies used cut

scores validated among Western samples, which often overes-

timate the rates of pathological gambling among the Chinese

(Tang, Wu, Tang, & Yan, 2010). Hence, this study will adopt

the Chinese SOGS cut scores proposed by Tang and colleagues

(2010) as an attempt to improve the accuracy of estimating

pathological gambling in this population.

A recent comparison of four gambling measures such as

SOGS, CPGI, Gamblers Anonymous-20 (GA-20), and DSM-IV

on a sample in Singapore have found CPGI to be the most reli-

able and valid in measurement construct (Arthur et al., 2008).

As a whole the CPGI consists of 31 items measuring gambling

involvement, PG assessment, and PG correlates (Ferris &

Wynne, 2001). However, only nine items are scored, which is

collectively named the Problem Gambling Severity Index

(PGSI) under the CPGI. Although many studies uses the term

CPGI synonymously with PGSI; for clarity reasons, PGSI will

be used in this study to identify the scored 9-items scale. To

date, there is a lack of research that investigates the patterns of

relationship between PG and other correlates utilizing the PGSI.

In this study, the Chinese validated version of PGSI will be

used for the above reasons to measure PG. For the purposes of

estimating the prevalence of PG and pathological gambling,

SOGS will be used concurrently with PGSI as a comparison.

Also, PGSI will be used to examine differences in socio-

demographic variables and to investigate the predictive ability

of gambling correlates and help-seeking attitudes on PG. Being

a relatively new scale (i.e., Chinese cut scores unavailable) and

as the PGSI was originally developed to remediate the problem

of overestimation in other scales, the original cut scores (Ferris

& Wynne, 2001) will be utilised in this study.

Considering measurement issues described here, there is a

gap in literature using newer PG screening tools on the patterns

of PG and important factors concerning PG such as psycho-

logical states, socio-demographic variables, gambling-related

cognitions, gambling urges, and help-seeking attitudes. Fur-

thermore, this study will extend our current knowledge of Chi-

nese help-seeking attitudes by exploring the impact of psycho-

logical openness, help-seeking propensity, and indifference to

stigma on PG among Chinese individuals. The subsequent sec-

tions discuss these factors in detail.

Past Prevalence Estimates, Types of Gambling, and

Gender Differences

To date, the estimates of reported gambling participation

varied from 26.6% (Lai, 2006; Sin, 1997) to 92% (Clarke et al.,

2006) in Chinese samples from Canada and New Zealand re-

spectively. However, estimates of PG and pathological gam-

bling closely resembled each other. In past research, the esti-

mates of PG using SOGS ranged from 2.5% (Fong & Ozorio,

2005; Sin, 1997) to 4.0% (Wong & So, 2003). Meanwhile,

using the DSM-IV Gambling-Behaviour Index and SOGS, the

estimated percentages of pathological gambling in the pool of

empirical studies ranged from 1.78% in a sample of Macao

residents (Fong & Ozorio, 2005), 1.8% among Hong Kong

residents (Wong & So, 2003), and Canadian residents (Sin,

1997) to 2.9% in an Australian Chinese speaking sample

(Blaszczynski et al.1998). Due to the possibility of false posi-

tive cases in prevalence studies using either DSM or SOGS, it

has been suggested that more research needs to be conducted to

clarify prevalence results (Blaszczynski et al., 1998). Hence,

both SOGS using Chinese cut scores and PGSI will be meas-

ured in this study in an attempt to address the issue of

over-estimation. It is important to note also that the underre-

porting of problems among Chinese PGs are common issues

particularly among males in past research (Blaszczynski et al.,

1998) and hence will be investigated in this study by comparing

self-rated score and actual scores from screening tools.

Studies have reported that males participated more in gam-

bling than females and were at higher risk of gambling prob-

lems (Blaszczynski et al., 1998; Chen et al., 1993); however,

there is a lack of research on the preferred types of gambling

according to gender. Hence, the patterns of PG and frequency

of participation in various forms of gambling will be further

investigated in this study. The moderating effects of gender

among gambling-related correlates in predicting PG will also be

investigated. In a research among Australian Chinese individu-

als, more entertaining forms of gambling such as Lotto, Keno,

Powerball, and poker machines at the casino (in that order)

were found to be the most common gambling activities (VCGA,

2000). As the country in which a problem gambler resides in

influences legislations and the forms of gambling activities

available, it is important to examine the popular forms of gam-

bling among the Chinese and also between genders in their

respective countries. Also, there is a lack of research reporting

on the participation frequency according to different types of

gambling and the average amount of money spent in each gam-

bling type by Chinese individuals in Taiwan, which will be thus

investigated in this study. Such information on the popularity of

certain gambling activities will prove valuable in making in-

formed decisions about policy regulations, channelling of re-

sources, and intervention strategies targeting specific at-risk

type gamblers.

J. M. Y. LOO ET AL.

344

Socio-Demographic Factors, Psychological States,

Gambling-Related Cognitions, and Gambling Urge

Research evidences suggest that gambling-related cognitions

such as erroneous beliefs, expectancies, illusion of control and

perpetuating gambling thoughts play a crucial role in the de-

velopment and maintenance of gambling behaviour (Griffiths,

1994; Loo et al., 2008; Oei et al., 2008). The influences of

gambling urge have also been considered as an important factor

in the development of PG (Raylu & Oei, 2002, 2004; Sharpe,

2002). Although previous studies have discussed the role of

urges in the development of cognitions (Niaura, 2000; Skinner

& Aubin, 2010; Tiffany, 1999; Tiffany & Conklin, 2000), these

mediating effects of gambling-related cognitions have not been

empirically investigate in the literature. Hence, the mediating

process of gambling-related cognitions by which gambling

urges impacts on PG severity (i.e., gambling urges impacts on

PG via gambling-related cognitions) will be examined in this

study among Chinese individuals living in a traditionally Chi-

nese country (i.e., Taiwan).

Furthermore, in Western samples, an individual’s gambling

severity typically differ with socio-demographic factors such as

age, gender, marital status, income, education, and employment

status (Ocean & Smith, 1993). However, results are varied

across different ethnic groups and countries, and consistent

evidences are lacking among Chinese samples as compared

with Western samples. This study will further investigate the

patterns of socio-demographic factors according to the severity

of PG.

In the gambling literature, negative psychological states (e.g.,

depression, anxiety, and stress) have been found to play an

important role in the development and maintenance of PG (Loo

et al., 2008; Moodie & Finnigan, 2006a; Oei et al., 2008; Raylu

& Oei, 2002). Affective disorders often comorbid with PG

(Coman, Burrows, & Evans, 1997; El-Guebaly et al., 2006);

however, the direction of effects is often unclear, especially

among the Chinese as limited research has been conducted in

this sample. In Western samples, problem gamblers have con-

sistently reported higher rates of affective disorders, particu-

larly for depression (Becona, Lorenzo, & Fuentes, 1996;

Blaszczynski & McConaghy, 1989; Moodie & Finnigan,

2006a). Similarly, research has postulated the contribution of

anxiety and stress in the impairment of decision-making (Miu,

Heilman, & Houser, 2008), and the development, maintenance,

and relapse of problem gambling (Blaszczynski, McConaghy,

& Frankova, 1991; Raylu & Oei, 2004b). This study will ex-

amine the predictive ability of psychological states in PG with

modifications made in screening tools (described above) in a

sample of Chinese residents in Taiwan.

Help-Seeking Attitudes

It is well-documented that Chinese gamblers have difficulty

admitting that PG is an issue that has to be dealt with, and

viewing gambling as an avenue of gaining financial wealth

despite having financial difficulties (Loo et al., 2008; Papineau,

2001; Scull & Woolcock, 2005). Difficulty in admitting the

problem and seeking help are common characteristics among

the Chinese, which affects their propensity for help-seeking

behaviour and in turn increase their susceptibility to mental

health issues (Pagura, Fotti, Katz, & Sareen, 2009). Such be-

haviours are seen as a sign of weakness and vulnerability,

which produces the feared reality of losing respect in the com-

munity (Basu, 1991; GAMECS Project, 1999). Hence, this

study will empirically investigate the predictive ability of three

aspects of attitudes toward seeking mental health services—

psychological openness, help-seeking propensity, and indiffer-

ence to stigma, which will contribute to our understanding of

the antecedents of help-seeking among Chinese problem gam-

blers. Psychological openness explores an individual’s ability

in acknowledging psychological problems and willingness to

seek professional help for it if deemed necessary, while

help-seeking propensity explores the extent of willingness and

ability to seek psychological help (Mackenzie, Knox, Gekoski,

& Macaulay, 2004). Finally, indifference to stigma represents

the degree of concern for the opinions of others when they are

found to be experiencing psychological problems.

Rationale for This Study

One of the main objectives of this study is to provide a de-

tailed evaluation of similarities and differences in patterns of

PG, pathological gambling, and socio-demographic correlates.

This study will also examine the PG predictive ability of gam-

bling-related cognitions, gambling urge, negative psychological

states, and help-seeking attitudes among Chinese individuals

residing in Taiwan. As an attempt to remediate the issue of PG

over-estimation in past Chinese studies, modifications in PG

screening tools will be made (i.e., Chinese SOGS cut scores

and PGSI scale). Patterns of pathological gambling, PG, fre-

quency of gambling participation according to types of gam-

bling activities, amount spent on gambling, and gender differ-

ences will also be detailed in this study. It is hypothesized that

gambling-related cognitions, gambling urges, depression, anxi-

ety, and stress will be able to predict PG (i.e., higher scores on

these correlated will predict higher PGSI score). Furthermore, it

is predicted that gambling-related cognitions will mediate the

relationship between gambling urges and PG. Based on past

research that have consistently found gender differences in PG,

it is hypothesized that there will be a moderating effect of gen-

der in these gambling correlates, which will be investigated

using interaction effects. In help-seeking attitudes, it is hy-

pothesized that more positive help-seeking attitudes (i.e., high

psychological openness, high help-seeking propensity, and low

indifference to stigma) will predict lower PG severity. An ac-

curate understanding of the influences of these factors is im-

portant in making informed decisions among policy makers and

devising early intervention strategies that target high-risk indi-

viduals that minimises the detrimental effects of PG. Further-

more, a solid understanding of patterns of gambling among the

Chinese and the effect of gambling correlates on PG is inter-

esting in its own right but, just as important, it is fundamental

for future development of effective treatment programs among

the Chinese.

Method

Participants

The participants consisted of 801 Chinese participants from

Taiwan (i.e., Taiwanese Chinese; 52.38% were males and

47.62% were females). All 801 participants can read and write

J. M. Y. LOO ET AL. 345

in Chinese language (i.e., Mandarin). The mean age was 25.36

years (SD = 10.25) with an age range of 18 to 74 years. In rela-

tion to employment status, 69.50% of participants were stu-

dents; while 20.10% were employed full-time and 5.10% were

employed part-time. The remainder participants were either job

hunting (1.70%), under disability pension (2.50%), or retired

(1.10%). Most participants have never married (82.7%), while

15.5% are currently married, 1.1% was separated or divorced,

0.4% was widowed, and 0.3% was in a domestic partnership.

In relation to education, 57.2% of participants have had some

college education, 28% completed a Bachelor’s degree, 12.3%

had up to high school education, and 2.5% completed a Post-

graduate degree. Most participants were ancestor worshippers

(30.2%), while 23.5% were Buddhists, 22.3% had no religion,

18.4% were Taoists, 4.6% were Catholics or Christians, and 1%

believed in other religions. The majority of participants earned

less than Taiwan Dollar (TWD) 100,000 (73.8%), while 8.1%

earned between TWD 100,000 - TWD 300,000; 11.5% earned

between TWD 300,000 - TWD 700,000; 4.5% earned between

TWD 500,000 - TWD 700,000; and 2.1% earned more than

TWD 1,000,000.

Measures

As part of a larger project, the materials included a demo-

graphics form and a set of self-report questionnaires. All meas-

ures without existing validated Chinese versions were trans-

lated into Chinese from English and back-translated again (i.e.,

reverse translation) to check for consistency and face validity.

A bilingual psychologist and graduate student who underwent

both Western and Chinese education completed the translations.

The scales were also checked by two bilingual clinical psy-

chology PhD candidates who were blind to the study to ensure

accuracy of translation. Pilot tests were conducted on 10 uni-

versity students to verify the semantic integrity of each item

and to ensure ease of understanding. The preceding steps were

then repeated for highlighted items after pilot testing. Discrep-

ancies between the versions were thoroughly discussed and

resolved until a translated version was found to have semantic

equivalence with the original English version.

The Problem Gambling Severity Index (PGSI; Ferris &

Wynne, 2001) The PGSI is a 9-item measure of PG, derived

from the 31-item CPGI. Five items of PGSI originated from

SOGS, 2 items from DSM-IV, and 2 items were developed for

the PGSI. It uses a 4-point rating scale ranging from “0—Nev-

er” to “3—Almost always.” Items are totalled and the total of 0

identifies a non-gambler, 1-2 identifies a low-risk gambler, 3-7

identifies a moderate-risk gambler, and 8 or more identifies a

problem gambler. The Cronbach’s alpha was good, at 0.84,

with a test-retest reliability of 0.78 (Ferris & Wynne, 2001).

The PGSI has good criterion-related validity because it matches

up fairly well with the DSM-IV and the SOGS, correlating at

0.83 with both measures (Ferris & Wynne, 2001). The Cron-

bach’s alpha in a Chinese sample was reported to be 0.77 with

good concurrent, predictive, and discriminant validities (Loo,

Oei, & Raylu, under review).

South Oaks Gambling Screen (SOGS; Lesieur & Blume,

1987) is the most commonly used 20-item self-administered

instrument for assessing “pathological gambling,” which was

developed based on DSM-III criteria. Cronbach’s alpha was

0.97 and the test-retest reliability was 0.71 (Lesieur & Blume,

1987). This 20-item scale uses yes/no responses and scores

ranged from 0 to 20. A score of 0 indicates no pathological

gambling, 1-4 indicates at-risk gambling behaviour or possible

pathological gambling, and a score of 5 or more indicates

pathological gambling (Stinchfield, 2002). In Chinese samples,

the Cronbach’s alpha was 0.75 with good construct validity

(Blaszczynski et al., 1998).

Gambling Frequency, Amount Spent, and Self-Rated PG be-

haviour. The gambling frequency questions consisted of four

items measuring frequency of gambling and amount of money

(TWD) spent per day in (1) gaming machines, (2) table games,

(3) animals such as horse racing, and (4) other forms of gam-

bling such as bingo, lottery, and sports betting. Frequency re-

sponses were measured on a 5-point scale ranging from

“1—Never,” “2—monthly or less,” “3—2 to 4 times a month,”

“4—2 to 3 times per week,” to “5—4 or more times per week.”

Amount of money spent required open-ended responses. The

final item asked participants to indicate on a scale whether they

consider themselves to be a non-problem gambler or a problem

gambler.

The Gambling Related Cognitions Scale-Chinese Version

(GRCS-C; Oei, Lin, & Raylu, 2007a; Raylu & Oei, 2004b).

The GRCS-C is a 23-item scale measuring erroneous gambling

cognitions and included items such as “I have some control

over predicting my gambling wins.” There are five subscales in

the GRCS-C: (1) GE—Gambling expectancies, (2) IC—Illu-

sion of control, (3) PC—Predictive control, (4) IS—Inability to

stop gambling, and (5) IB—Interpretative bias. The responses

were measured on a 7-point Likert scale (1 = strongly disagree

to 7 = strongly agree) with higher scores indicating more cog-

nitive distortions held by the individual. The GRCS-C reported

a Cronbach’s alpha 0.95 and ranged from 0.83 to 0.89 for the

five factors (Oei et al., 2007a). The GRCS-C also reported good

concurrent, predictive, and discriminant validities.

The Gambling Urge Scale-Chinese Version (GUS-C; Oei,

Lin, & Raylu, 2007b; Raylu & Oei, 2004). The 6-item ques-

tionnaire measured gambling urges, which has been found to be

important in the maintenance of PG. The GUS-C included

items such as “All I want to do now is to gamble” and “Nothing

would be better than having a gamble right now.” Participants

responded on a 7-point Likert scale (1 = strongly disagree to 7

= strongly agree) with higher scores indicating a stronger urge

to gamble. The Cronbach’s alpha for the Chinese version was

reported to be 0.87 and has adequate concurrent, predictive, and

criterion validities (Oei et al., 2007b).

Depression, Anxiety, and Stress Scale (DASS; Lovibond &

Lovibond, 1995). This 21-item scale (rated on a 4-point scale

ranging from 0 “did not apply to me at all” to 3 “applied to me

very much or most of the time”) assesses symptoms of depres-

sion (DASS-D), anxiety (DASS-A), and stress (DASS-S). The

scale reported good internal consistency (α = 0.94, 0.87 and

0.91 for the subscales of depression, anxiety and stress respec-

tively) (Antony, Bieling, Cox, Enns, & Swinson, 1998). In a

Chinese sample, the Cronbach’s alpha for the three subscales

were also high (α depression = .85; α anxiety = .87; α stress

= .82) and the Chinese version showed good criterion and pre-

dictive validities (Oei et al., 2008).

Inventory of Attitudes toward Seeking Mental Health Ser-

vices (IASMHS; Mackenzie et al., 2004). The IASMHS is a

24-item scale measuring mental health help-seeking behaviour

J. M. Y. LOO ET AL.

346

and included items such as “If I were to experience psycho-

logical problems, I could get professional help if I wanted to”.

The responses were measured on a 5-point Likert scale (0 =

disagree to 4 = agree) with higher total scores indicating more

positive attitudes toward mental health help-seeking. There are

three subscales in the IASMHS: 1) psychological openness

(higher scores represent higher psychological openness), 2)

help-seeking propensity (higher scores represent stronger

help-seeking propensity), and 3) indifference to stigma (lower

scores represent indifference; higher scores represent fear of

stigma). Cronbach’s alpha for IASMHS was .87 (α = 0.82, 0.76

and 0.79 for the subscales of psychological openness,

help-seeking propensity, and indifference to stigma respec-

tively). The three factors are significantly positive correlated.

The IASMHS reported good discriminant, predictive, and con-

current validity. The Cronbach’s alphas in a Chinese sample

were reported to be 0.74 for the total scale (Atkinson, 2007).

The scale reported good criterion, predictive, and discriminant

validities.

Procedure

As a part of a larger study, university participants from Tai-

wan were recruited from universities in Southern Taiwan and

Northern Taiwan. Ethical clearances were provided by the re-

spective organisational ethics committee and all procedures

were carried out accordingly. The university participants were

recruited from these departments: 1) Nursing, 2) General Edu-

cation, 3) Mechanical Engineering, 4) Electrical Engineering, 5)

Recreation Administration, 6) Business Administration, and 7)

Medicine. The community participants were recruited from

Southern and Northern Taiwan by word-of mouth and commu-

nity contacts with companies. Voluntary participation was fol-

lowed by an introduction to the research study, explanation on

informed consent, and freedom to withdraw participation. No

personal identification information was requested and privacy

was assured. Paper and pencil questionnaires were administered

individually and participants were thanked and debriefed upon

completion. All participants were reimbursed with TWD

100.00 (i.e., approximately AUD 4.00) and average time taken

to complete the questionnaire was 30 minutes.

Results

Preliminary Data Analysis

All data cleaning and descriptive analyses were conducted

using SPSS version 17 (SPSS Inc., 1988). Data cleaning in-

cluded checking accuracy of data entry, missing values, and

assumptions of multivariate analysis. All outliers were checked

for accurate data entry and were retained as each case is from

the intended sample and is a true reflection of the data collected

from participants (Tabachnick & Fidell, 2007). There were 396

males and 360 females (45 missing data). Missing gender data

were not imputed for the same reasons. Non-systematic and

minor missing data (less than 5% missing) for all variables

were replaced using mean substitution (Tabachnick & Fidell,

2007). Four participants’ entries were removed due to more

than 40% of missing items in each entry. Visual screening of

the histogram and statistical tests indicated that there was some

univariate kurtosis and skewness. Data was positively skewed

and hence, logarithm transformation was performed (Tabach-

nick & Fidell, 2007). All analyses were conducted with both

non-transformed and transformed data—as no substantive dif-

ferences were found only the non-transformed results are re-

ported. As shown in Table 1, all scales used in this study re-

ported good Cronbach’s alpha with α ranging from 0.66 to 0.98,

reflecting good internal consistency within each measurement

scale.

A Comparison of Patt erns of Patho logical Gambling

(SOGS) and Problem Gam bling (PGSI)

As shown in Table 2 and according to the Chinese SOGS

cut-offs (Tang et al., 2010), 89.3% of participants are non-

pathological gamblers, 6.5% are at-risk pathological gamblers,

and 4.2% are pathological gamblers. Using the PGSI cut-offs

(Ferris & Wynne, 2001), 42.1% of participants are classified as

non-problem gamblers, 21.6% are low-risk gamblers, 27.5%

are moderate-risk gamblers, and 8.9% are problem gamblers

(see Table 2). The Goodness-of-Fit Chi-Square tests were used

to test whether the observed pattern of events described below

differs significantly. There is a significant difference between

the lower percentage of estimated pathological gamblers ac-

cording to SOGS (4.2%) as compared to the percentage of es-

timated problem gamblers in PGSI (8.9%), Δ χ2 (3) = 35, p

< .001. In the non-problem gambler (PGSI) group, there are

significantly more females (47.8%) than males (36.9%), Δ χ2 (1)

= 26, p < .001. The opposite was true for the non-pathological

gambler (SOGS) group as there are significantly less females (n

Table 1.

Reliability analyses for each scale and subscale.

Chinese Scale α

Problem Gambling Severity Index PGSI 0.77

South Oaks Gambling Screen (SOGS) 0.83

Gambling frequency 0.66

Gambling Cognitions (GRCS-C) 0.98

GRCS-GE 0.91

GRCS-IC 0.87

GRCS-PC 0.91

GRCS-IS 0.94

GRCS-IB 0.88

Gambling Urge (GUS-C) 0.94

DASS total 0.95

Depression (DASS-d) 0.88

Anxiety (DASS-a) 0.86

Stress (DASS-s) 0.86

Inventory of Attitudes toward Seeking

Mental Health Services (IASMHS) total 0.72

IASMHS-Psychological openness 0.71

IASMHS-Help-seeking propensity 0.82

IASMHS-Indifference to stigma 0.78

J. M. Y. LOO ET AL. 347

Table 2.

Frequency and percentage of participants in each category of SOGS and PGSI.

Scale Males Females Total

SOGS

Non-pathological gambler (SOGS = 0 to 4) 349 (88.1%) 326 (90.6%) 675 (89.3%)

At-risk pathological gambler (SOGS = 5 to 7) 29 (7.3%) 20 (5.6%) 49 (6.5%)

Pathological gambler (SOGS = 8 or more) 18 (4.5%) 14 (3.9%) 32 (4.2%)

PGSI

Non-problem gambler (PGSI=0) 146 (36.9%) 172 (47.8%) 318 (42.1%)

Low-risk problem gambler (PGSI = 1 to 2) 89 (22.5%) 74 (20.6%) 163 (21.6%)

Moderate-risk problem gambler (PGSI = 3 to 7) 114 (28.8%) 94 (26.1%) 208 (27.5%)

Problem gambler (PGSI = 8 or more) 47 (11.9%) 20 (5.6%) 67 (8.9%)

= 326) than males (n = 346), Δ χ2 (1) = 20, p < .001. There is a

general trend in the PGSI groups to have significantly more

males in all three low-risk (Δ χ2 (1) = 15, p < .001), moder-

ate-risk (Δ χ2 (1) = 20, p < .001), and problem gambling (Δ χ2

(1) = 27, p < .001) groups. In the SOGS groups, there are sig-

nificant differences between the frequency of males and fe-

males in the at-risk pathological gambler group (Δ χ2 (1) = 9, p

= .005) and the pathological gambler group (Δ χ2 (1) = 4, p

= .05). It is interesting to note that among the males, the fre-

quency of non-pathological gamblers (SOGS; n = 349) equals

the summed frequency of non-PG, low-risk, and moderate-risk

PG (PGSI; total n = 349). However, the frequencies among

females are significantly different (Δ χ2 (3) = 14, p < .005).

Also, among the males, the summed frequency of at-risk

pathological gamblers and pathological gamblers (SOGS; n =

49) equals the frequency of problem gambler (PGSI; total n =

49). However, the frequencies among females are significantly

different (Δ χ2 (3) = 14, p < .005).

In the sample, 330 males (83.3% of males) and 304 females

(84.4%) rated themselves to be non-problem gamblers. These

frequencies are significantly different when compared with the

actual PGSI scores (males Δ χ2 (3) = 184, p < .001; females Δ

χ2 (3) = 132, p < .001) and SOGS scores (males Δ χ2 (2) = 19, p

< .001; females Δ χ2 (2) = 14, p < .001). Self-rating scores also

showed that 14 males (3.5%) and 14 females (3.9%) rated

themselves to be problem gamblers. These frequencies are not

significantly different when compared with the actual SOGS

scores for males (Δ χ2 (2) = 4, p > .05, ns), while the frequen-

cies are exactly the same for females. The self-rated problem

gambler scores were significantly different from the PGSI

scores for males (Δ χ2 (3) = 33, p < .001), but not significantly

different for females (Δ χ2 (3) = 6, p > .05, ns). In sum,

self-rated PG was similar to actual PGSI and SOGS scores

respectively with the exception of PGSI scores for males (i.e.,

according to PGSI scores, males tended to significantly under-

estimate their problem gambling). Meanwhile, non-problem

gambling was significantly overestimated by the participants

when compared with both SOGS and PGSI scores.

The frequency, percentages, and average amount spent on

each type of gambling are illustrated in Table 3. The rate of

gambling varies according to gender and type of gambling.

Table games are the most popular gambling activity, followed

by other forms of gambling such as lottery and sports betting,

then gaming machines, and the least popular was animals gam-

bling. However, the highest amount of money was spent on

gaming machines (Δ χ2 (3) = 1667.52, p < .001) although it was

not the most popular type of gambling activity, as followed by

other forms of gambling such as lottery (Δ χ2 (3) = 126922.80,

p < .001), table games (Δ χ2 (3) = 424.65, p < .001). The least

amount of money was spent on animals gambling. Males gam-

bled significantly more frequently than females on table games

(Δ χ2 (1) = 4.00, p < .05) with the exception of significantly

more females reported having gambled monthly or less on table

games (Δ χ2 (1) = 15.00, p < .001) and other forms of gambling

such as lottery (Δ χ2 (1) = 7.00, p < .01).

Socio-Demographic Variables and Problem Gambling

as Measured by PG SI

Results were analysed to examine differences between PGSI

groups (i.e., non-problem gambler, low-risk PG, moderate-risk

PG, and PG) in socio-demographic variables such as age, gen-

der, marital status, education, employment status, annual in-

come, and religion. A One-way ANOVA revealed that there

was a statistically significant difference in age according to

PGSI cut-off groups, F(3,793) = 7.55, p < .0001, partial ή2 =

0.028. Employing the Bonferroni post-hoc test, significant dif-

ferences in age were found between non-problem gamblers (M

age = 26.75, SD = 11.27) and moderate-risk gamblers (M =

23.10, SD = 7.15, p < .0001); and between moderate-risk gam-

blers (M = 23.10, SD = 7.15) and problem gamblers (M = 27.75,

SD = 12.82, p = .005).

The Multi-Dimensional Chi-Square test was used to examine

characteristics of problem gamblers in various socio-demo-

graphic variables described below. There is a significant rela-

tionship between PG behaviour and gender, χ²(3, N = 756) =

14.63, p = .002. Males (N = 47, 70.1%) are more likely to be

problem gamblers as compared to females (N = 20, 29.9%). It

is a small association (Phi, φ = 0.139, p = .002) and thus gender

accounted for 1.9% of the variance in PGSI score. Never mar-

ried (N = 55, 76.4%) individuals are more likely to be problem

amblers as compared to married (N = 13, 18.1%) individuals, g

J. M. Y. LOO ET AL.

348

Table 3.

Frequency and percentage of participants according to gambling types.

Gambling type and frequency Males Females Total

Gaming machines (e.g., pokies)a

Never 308 (78.2%) 280 (78.4%) 588 (78.3%)

Monthly or less 77 (19.5%) 71 (19.9%) 148 (19.7%)

2 - 4 times a month 5 (1.3%) 2 (0.6%) 7 (0.9%)

2 - 3 times per week 0 (0%) 2 (0.6%) 2 (0.3%)

4 or more times per week 4 (1.0%) 2 (0.6%) 6 (0.8%)

Table games (e.g., cards)b

Never 196 (49.9%) 156 (43.6%) 352 (46.9%)

Monthly or less 157 (39.9%) 172 (48.0%) 329 (43.8%)

2 - 4 times a month 27 (6.9%) 22 (6.1%) 49 (6.5%)

2 - 3 times per week 5 (1.3%) 4 (1.1%) 9 (1.2%)

4 or more times per week 8 (2.0%) 4 (1.1%) 12 (1.6%)

Animals (e.g., horse bet)c

Never 378 (95.9%) 346 (96.9%) 724 (96.4%)

Monthly or less 13 (3.3%) 8 (2.2%) 21 (2.8%)

2 - 4 times a month 1 (0.3%) 0 (0%) 1 (0.1%)

2 - 3 times per week 0 (0%) 3 (0.8%) 3 (0.4%)

4 or more times per week 2 (0.5%) 0 (0%) 2 (0.3%)

Other forms (e.g., lottery)d

Never 231 (58.6%) 207 (57.8%) 438 (58.2%)

Monthly or less 121 (30.7%) 128 (35.8%) 249 (33.1%)

2-4 times a month 28 (7.1%) 15 (4.2%) 43 (5.7%)

2-3 times per week 9 (2.3%) 5 (1.4%) 14 (1.9%)

4 or more times per week 5 (1.3%) 3 (0.8%) 8 (1.1%)

aAverage money spent on gaming machines = TWD 143, 171.05; bAverage money spent on table games = TWD 14, 580.73; cAverage money spent on animals = TWD 14,

156.08; dAverage money spent on other forms of gambling = TWD 141, 503.53.

χ²(15, N = 796) = 28.22, p = .02. It is a small association (Phi, φ

= 0.188, p = .02) and thus marital status accounted for 3.5% of

the variance in PGSI score. There is no significant relationship

between PG behaviour and education, χ²(18, N = 793) = 23.98,

p = .156, ns. Analysis revealed that there is a significant asso-

ciation between PG behaviour and employment status, χ²(18, N

= 792) = 33.17, p = .016. Students (N = 47, 66.2%) are more

likely to be classified as problem gamblers as compared to in-

dividuals in full-time employment (N = 15, 21.1%). It is a small

association (Phi, φ = 0.205, p = .016) and thus employment

status accounted for 4.2% of the variance in PGSI score. There

is no significant association between PG behaviour and annual

income, χ²(18, N = 794) = 27.54, p = .069, ns. Similarly, no

significant relationship was reported between PG and religion,

χ²(24, N = 728) = 31.00, p = .154, ns. In sum, there are signifi-

cant associations between PG and age, gender, marital status,

and employment status respectively.

Predictor Variables of Problem Gambling Behaviour

Examination of the linear relationships between socio-de-

mographic variables (i.e., age, gender, marital status, employ-

ment) and PG showed significant correlations between PG and

gender; hence, gender will be controlled for in the analysis. All

variables except the help-seeking attitudes subscales showed

significant positive correlations with PGSI. However, IASMHS

total and Indifference to stigma showed significant negative

correlation with PGSI (i.e., positive help-seeking attitude pre-

dicts lower PGSI scores and stronger indifference to stigma

J. M. Y. LOO ET AL. 349

predicts higher PGSI scores, respectively).

Preliminary steps were taken in all analyses to check for ad-

herence to assumptions. To reduce problems associated with

multicollinearity, all independent variables and moderator var-

iables were centred (i.e., standardised) (Frazier, Tix, & Barron,

2004; Jaccard, Wan, & Turrisi, 1990). Centred variables were

created by subtracting the mean value from the variable while

the interaction variable was created by multiplying the two

mean centred independent and moderator variables together

(Jaccard et al., 1990). A moderation effect was considered evi-

dent only when the interaction term (e.g., gender x GRCS total)

in the regression was significant. With the use of a p < .001

criterion (i.e., values larger than 24.322, df = 7) for Mahalano-

bis distance (Tabachnick & Fidell, 2007), eight outliers were

identified and deleted from dataset. Using the variation infla-

tion factor (VIF), multi-collinearity was checked and all vari-

ables reported values below 10 (Field, 2000), which indicate

that the data did not violate the assumption of multi-collinea-

rity.

First, Hierarchical Multiple Regression (HMR) was used to

assess the extent to which these variables could predict PG.

PGSI total score was used as the dependent variable (DV). The

independent variables (IV) and interaction variables were:

(Step 1) Gender, (Step 2) GRCS-total, GUS-total, DASS-total,

and IASMHS-total, and (Step 3) Two-way interactions between

gender and total scores. Table 5 displays the unstandardised

regression coefficients (B), standard error of B, the standardised

regression coefficients (β), R2 change, R, R2, and Adjusted R2

after entry of all independent variables. R was significantly

different from zero at each step. HMR results showed that the

model was significant F (9, 738) = 19.34, p < .001. The R2

value of 0.19 indicates that 19% of the variability in PGSI

scores is accounted for by the predictors. Only these variables

significantly predicted and accounted for variance in PGSI

scores: 1) gender (accounted for 1.8% of variance); 2) GRCS

total (17%); and 3) DASS total (3.3%).

Predictor Variables of Problem Gambling Behaviour

Examination of the linear relationships between socio-de-

mographic variables (i.e., age, gender, marital status, employ-

ment) and PG showed significant correlations between PG and

gender; hence, gender will be controlled for in the analysis.

Table 4 displays the correlations between the variables. All