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R E S E A R C H A R T I C L E Open Access
The protective role of religiosity against
problem gambling: findings from a five-
year prospective study
Seema Mutti-Packer
1*
, David C. Hodgins
1
, Robert J. Williams
2
and Barna Konkolÿ Thege
3,4
Abstract
Background: Little research has examined the potential protective influence of religiosity against problem
gambling; a common addictive behavior, and one with a host of associated negative health and social outcomes.
The aims of this study were to examine (1) the potential longitudinal association between religiosity and problem
gambling among adults and (2) the potential moderating role of gender on this association.
Methods: Data were from five waves of the Quinte Longitudinal Study (QLS), between 2006 and 2010. Participants
were Canadian adults from Belleville, Ontario, Canada (n= 4121). A multiple group (based on gender) latent growth
curve analysis was conducted to examine the overall trajectory of problem gambling severity. Two models were
tested; the first examined the influence of past-year religious service attendance, and the second examined an
overall measure of personal religiosity on the trajectory of problem gambling. The Problem and Pathological
Gambling Measure (PPGM) was used as a continuous measure. The Rohrbaugh-Jessor Religiosity Scale (RJRS) was
used to assess past-year frequency of religious service attendance and personal religiosity. Religious affiliation
(Protestant, Catholic, Atheist/Agnostic, Other, Prefer not to say) was also included in the models.
Results: At baseline, higher frequency of past-year religious service attendance (males: β=−0.54, females: β=−0.68,
p < 0.001 for both) and greater overall personal religiosity (males: β=−0.17, females: β=−0.13, p < 0.001 for both)
were associated with lower PPGM scores. The moderating effect of gender indicated that the influence of past-year
religious service attendance was greater among females (χ
2
diff
(44)
=336.8, p<0.001); however, the effect of overall
religiosity was greater among males (χ
2
diff
(36)
=213.4, p<0.001). Findings were mixed with respect to religious affiliation.
No measures of religiosity or religious affiliation were associated with the overall decline in problem gambling severity.
Conclusions: These findings suggest that religiosity may act as a static protective factor against problem gambling
severity but may play a less significant role in predicting change in problem gambling severity over time.
Keywords: Trajectory of gambling, Adults, Problem gambling, Religion, Religious affiliation, Longitudinal, Latent growth
curve modeling
Background
A large body of evidence indicates that religion and reli-
gious involvement are associated with positive mental
health and physical health outcomes, including de-
creased rates of depression, suicide, and coronary heart
disease [1]. There is also research highlighting the pro-
tective influence of religiosity against addictive behaviors
such as smoking, alcohol and drug use [1, 2]. However,
there has been relatively little research which examines
the potential protective influence of religiosity against
excessive gambling, a common addictive behavior, and
one with a host of associated negative outcomes includ-
ing criminal behavior [3], psychiatric comorbidities [4],
and financial issues [5]. The limited amount of research
in the field has indicated that religious involvement is in-
versely related to ever-gambling [6, 7], past-year gam-
bling [8], gambling frequency, and the amount of money
gambled [9–11].
* Correspondence: seema.mutti@ucalgary.ca
1
Department of Psychology, University of Calgary, 2500 University Drive NW,
Calgary, AB T2N 1N4, Canada
Full list of author information is available at the end of the article
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Mutti-Packer et al. BMC Psychiatry (2017) 17:356
DOI 10.1186/s12888-017-1518-5
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
It is important to note that religiosity is multi-
dimensional in nature, and has both a public and private
component. For example, attending religious services
can be considered a public display or community aspect
of religiousness, whereas the extent to which someone
internalizes their faith and uses it to guide their life and
decision-making can be considered the private compo-
nent. In addition to public and private components of
religiosity, religious affiliation represents another dimen-
sion of religiosity that is important to understand in the
context of gambling. Religious traditions tend to have di-
verse ethical codes of conduct with regards to gambling.
For example, although gambling is not explicitly prohib-
ited in the Bible, conservative and mainline Protestant
groups interpret its teachings as such. Within the Is-
lamic tradition, gambling is explicitly prohibited accord-
ing to the Quran. On the other hand, there are religious
traditions that do not reject gambling behavior categor-
ically, such as the Catholic and Jewish traditions. Over-
all, religious affiliation and its codes of conduct have the
potential to shape cultural norms, beliefs, and ultimately
behavior.
One major methodological limitation of studies exam-
ining the influence of various dimensions of religiosity
on gambling is that most are cross-sectional, and unable
to examine these associations over time. To our know-
ledge, only one study has tested the longitudinal associ-
ation between religiosity and gambling [6]. This study
used data from the US National Longitudinal Study of
Adolescent to Adult Health, and found that religiousness
during adolescence predicted aspects of gambling behav-
ior in young adulthood. Additional longitudinal research
is needed to assess the potential protective influence of
religiosity on subsequent problem gambling behaviors in
other jurisdictions and among different age cohorts.
Moreover, despite evidence showing that men and
women differ in terms of the occurrence, prevalence,
correlates, and consequences of their addictive behaviors
[12, 13], the role of gender has not yet been explicitly ex-
amined in studies related to gambling and religiosity. It
has been hypothesized that religiosity is influenced by
underlying personality traits, namely risk aversion [14,
15]. This line of thinking posits that those who are more
risk-averse, are also more likely to be religious. This no-
tion has been used to explain consistent gender differ-
ences across various dimensions of religiosity indicating
that women tend to be more religious overall [14, 15].
However, a recent study found no evidence supporting
the idea that risk-aversion explains the association be-
tween religion and gambling [6].
The primary goal of the current study was to examine
the potential protective influence of religious service at-
tendance and overall religiosity on problem gambling se-
verity over time, among a sample of Canadian adults.
Specific aims were to: 1) examine the trajectory of prob-
lem gambling severity, and 2) examine the potential in-
fluence of religious service attendance and personal
religiosity on problem gambling severity both at baseline
and over time, and 3) examine the potential moderating
role of gender on these associations.
Methods
Sample and procedure
Data were from the Quinte Longitudinal Study (QLS)
[16], the primary aim of which was to help develop an
etiological model of problem gambling. Ethical approval
was provided by the Human Subject Research Commit-
tee at the University of Lethbridge. A cohort of 4121
adults from Ontario, Canada were assessed yearly over a
five-year period (2006 to 2011).
Recruitment was conducted via random digit tele-
phone dialing from a pool of numbers with area codes
and prefixes estimated to be within 70 km of the city of
Belleville in Ontario, Canada. The response rate in the
QLS was 21.3%, a similar value to those obtained in re-
search with a similar focus from the same country e.g.,
[17]. The retention rate for the QLS was 93.9%, an ex-
ceptionally high value in large scale longitudinal research
of this nature. Two samples were recruited: a ‘general
population’sample (n = 3065) and an ‘at risk’sample for
problem gambling (n = 1056). The purpose of recruiting
the ‘at risk’subsample was to ensure that there were a
sufficient number of people in the cohort who became
problem gamblers during the course of the study. For a
respondent to be considered ‘at-risk’, they had to indicate
one or more of the following: (1) spending $10 or more
per month on lottery, instant win tickets, bingo, casino
table games, or games of skill against other people; (2)
playing either slot machines or betting on horse racing
in the past year; (3) an intention to gamble at a new
slots-at-racetrack facility that was scheduled to be built
sometime in the next few years in the area.
If the person agreed to participate in the survey, they
were sent an email with a link to the online question-
naire or booked into a time slot at the program office
where they completed the survey on a computer on site.
Informed written consent was obtained from all partici-
pants prior to completing the online survey, each year.
A total of 69.5% opted to complete the survey online for
the first assessment, with this proportion steadily in-
creasing to 90.0% for the final assessment. A small per-
centage of people completed a paper and pencil version
of the survey because of their unfamiliarity with com-
puters (1.2% to 1.9% depending on the survey year). Fur-
ther methodological details can be found elsewhere [18].
The focus of the QLS was to examine changes in gam-
bling over time, thus it was not necessary that the sam-
ple be representative of the Ontario or Canadian
Mutti-Packer et al. BMC Psychiatry (2017) 17:356 Page 2 of 10
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population, only that it contained a diverse range of
gamblers. Nonetheless, the demographic profile of the
sample is similar to Canadian adults (15+) as established
by the 2006 Canadian Census, with the exception that
the present sample tends to include slightly fewer people
aged 18 to 24, seniors 65 and older, single people, and
has a somewhat higher level of educational attainment.
Characteristics of the study sample are described in
Table 1.
Measures
Severity of gambling problems was assessed by the
Problem and Pathological Gambling Measure (PPGM)
[19, 20]. The PPGM is a 14-item item instrument
that provides both a continuous measure of problem
gambling severity, as well as a categorical risk assess-
ment that classifies people into the following categor-
ies: non-gambler; recreational gambler; at risk
gambler; problem or pathological gambler. The PPGM
has good internal consistency (Cronbach α=0.76 to
0.81, depending on the dataset) and 1 month test-re-
test reliability (r= 0.78), as well as excellent corres-
pondence to clinical assessment [20].
Religiosity was measured by the Rohrbaugh–Jessor
Religiosity Scale (RJRS) [21]. This scale is an eight-item
instrument assessing ritual, consequential, ideological,
Table 1 Baseline sample characteristics
Overall
(n = 4121)
Males
(n = 1867)
Females
(n = 2253)
Age [M (SD)] 46.1 (14.1) 47.1 (14.7) 45.2 (13.6)
Overall religiosity
a
[M (SD)] 12.1 (7.0) 10.8 (7.1) 13.1 (6.6)
Past year religious service attendance
b
[N (%)]
Missing 162 (3.9) 103 (5.5) 59 (2.6)
Not at all 1654 (41.8) 758 (43.0) 896 (40.8)
Low 1307 (33.1) 568 (32.2) 739 (33.7)
Moderate 462 (11.7) 189 (10.7) 273 (12.4)
High 536 (13.5) 249 (14.1) 287 (13.1)
Religious affiliation [N (%)]
Missing 1 (0.0) –1 (0.0)
Catholic 860 (29.0) 391 (22.3) 469 (22.5)
Protestant 2269 (55.1) 977 (55.8) 1292 (62.1)
Agnostic/atheist 331 (8.0) 195 (11.1) 136 (6.5)
Other
c
372 (9.0) 189 (10.8) 183 (8.8)
Prefer not to say 288 (7.0) 115 (6.2) 173 (7.7)
Education [N (%)]
Less than high school 462 (11.2) 236 (12.6) 226 (10.0)
High school 823 (20.0) 355 (19.0) 468 (20.8)
Some post-secondary or technical school 1106 (26.8) 561 (30.0) 545 (24.2)
Completed college/university or more 1730 (42.0) 715 (38.3) 1015 (45.0)
Annual household income
d
[N (%)]
$0 to $39,999.00 1402 (34.0) 557 (29.8) 845 (37.5)
$40,000.00 to $79,999.00 1716 (41.6) 793 (42.5) 923 (40.9)
$80,000.00 or more 1003 (24.3) 517 (27.7) 486 (21.6)
Marital status [N (%)]
Never married 491 (11.9) 236 (12.6) 255 (11.3)
Married or common-law 2944 (71.4) 1387 (74.3) 1557 (69.1)
Divorced/separated/widowed 686 (16.6) 244 (13.1) 442 (19.6)
a
Items 2–8 of the Rohrbaugh Jessor Religiosity Scale
b
Low = categories “once”and “2 to 5 times”in the past year; Moderate = categories “6 to 10 times”and “once or twice a month”; High: categories “once a week”
and “more than once a week”
c
Other’religious affiliation includes Muslim, Jewish, Buddhist, Hindu, Sikh, and other affiliations
d
‘Unsure’responses (n= 129) were replaced by imputed values: if income stable in next 2 data waves then replaced with that value; elsewhere replaced with the
mean of values from all other survey waves
Mutti-Packer et al. BMC Psychiatry (2017) 17:356 Page 3 of 10
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and experiential religiosity. Internal consistency of the
scale was excellent (Cronbach α= .90). The first item of
the scale, referring to past-year frequency of religious
service attendance was examined separately in order to
examine the community aspect of religious involvement,
similar to previous research [6]. Response options for
the measure of past-year religious service attendance
were: 0 = Not at all; 1 = Once; 2 = Two to five times;
3 = Six to ten times; 4 = Once or twice a month;
5 = Once a week; 6 = More than once a week. Following
Uecker and Stokes [6], responses were categorized as:
‘Not at all’,‘Low’=“Once”and “Two to five times”;
‘Moderate’=“Six to ten times”and “Once or twice a
month”;‘High’=“Once a week”and “More than once a
week”.
The seven remaining items of the RJRS assessed the
magnitude of religious belief in a person’s life, and each
question had four or five response options indicating a
range of agreement or importance. Specifically, five
questions had five response options, and two questions
had four response options. For example, response op-
tions for the following question: “When you have a ser-
ious personal problem, how often do you take religious
advice or teaching into consideration?”, were: “almost al-
ways, usually, sometimes, and never”(scored 3, 2, 1, and
0, respectively). The responses from these seven items
were summed together, with a range of scores falling be-
tween 0 and 26; higher scores indicated greater belief.
To assess one’s religious affiliation, respondents were
asked ‘What is your religious affiliation?’Response op-
tions were: Catholic, Protestant (e.g., Anglican, Baptist,
Lutheran, United, Presbyterian), Atheist, Agnostic, Other
(e.g., Muslim, Jewish, Buddhist, Hindu, Sikh), or Prefer
not to say.
Statistical analyses
Mplus version 6.0 was used for all analyses. Following a
stepwise-procedure [22], the first step was to fit a base-
line or unconditional latent growth curve model to
examine the overall trajectory of problem gambling se-
verity. Next, the variables of interest were added to the
model. To examine the potential protective effects of re-
ligiosity on problem gambling severity over time, two
models were tested. The first model examined the fre-
quency of past-year religious service attendance. The
second model included an overall measure of religiosity.
Both models included religious affiliation, and adjusted
for age, education, household income, and marital status.
This analytic approach employed a full-information
maximum likelihood (FIML) estimator.
Latent growth curve models include two latent factors,
the intercept and slope, as well as repeated measures of
the observed outcome of interest over time. The slope
factor loadings for the unconditional linear model were
fixedat0,1,2,3,and4,definingthestartofthe
study as the intercept. To examine the potential mod-
erating influence of gender, a multiple-group approach
was used, whereby each level of the moderator was
specifiedasagroup(maleandfemale).Themultiple
groups approach estimates growth curves based on
the grouping of interest within a single analysis. Dif-
ferential effects were examined by fixing regression
co-efficients to be equal across groups (also known as
equality constraints) and comparing the constrained
model to the unconstrained model. If the chi-square
statistic of the constrained model had increased sig-
nificantly, the unconstrained model fit the data better
than the constrained model, indicating a significant
moderating effect of the grouping variable (gender in
this case).
The following indices were used to examine model fit:
comparative fit index (CFI), Tucker-Lewis index (TLI),
root mean square error of approximation (RMSEA), and
standardized root mean square residual (SRMR). Ad-
equate fit was indicated by CFI and TLI >0.90, RMSEA
<0.08, and SRMR <0.10. Good fit was indicated by CFI
and TLI >0.95, RMSEA <0.06, and SRMR <0.08 [23].
Important to note is that chi-square test of model fit is
likely to be significant when the sample size is large,
which is the case for the current study. Thus, model fit
was based primarily on the CFI, TLI, RMSEA, and
SRMR.
Results
Unconditional latent growth curve model
An overall decreasing trend that appeared linear was ob-
served in mean scores based on the PPGM assessment
across the five assessments (Table 2). The linear growth
curve model fit the data well [χ
2
(19)
= 42.7, p= 0.001,
CFI = 0.99, TLI = 0.99, RMSEA = 0.03, SRMR = 0.02].
The mean latent variables representing the intercept and
slope indicated similar baseline levels of problem gam-
bling severity for males and females, as well as the same
decreasing slope over time (Table 3). For both males and
females, there was also significant variance in baseline
levels of problem gambling severity, indicating that not
everyone started at the same level of problem gambling
severity. The significant variance in the slope indicated
variability in individual trajectories over time. The sig-
nificant negative correlations between the intercept and
slope indicate that higher levels of problem gambling se-
verity at baseline were associated with steeper declines
over time. To test the moderating effect of gender, the
constrained [χ
2
(21)
= 47.02] and unconstrained
[χ
2
(19)
= 42.72] models were compared, and the results
[χ
2
diff
(2)
= 4.3, p = 0.12] indicated no moderating effect
of gender.
Mutti-Packer et al. BMC Psychiatry (2017) 17:356 Page 4 of 10
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Conditional latent growth curve model: The influence of
past-year frequency of religious service attendance
The conditional latent growth curve model estimating
the effect of past-year religious service attendance
showed good fit [χ
2
(37)
=55.5, p = 0.03, CFI = 0.99,
TLI = 0.99, RMSEA = 0.02, SRMR = 0.01]. Among both
males and females, those with high religious service at-
tendance reported significantly lower levels of problem
gambling severity at baseline (β=−0.62, p < 0.001, and
β=−0.65, p < 0.001, respectively) compared to those
who did not attend religious services at all in the past
year. For females, even those with moderate past year at-
tendance reported significantly lower levels of problem
gambling severity at baseline (β=−0.16, p = 0.04) com-
pared to those who did not attend at all.Furthermore,
past-year religious service attendance influenced the
slope of problem gambling severity for females, meaning
that those with high past-year attendance, experienced a
slower decline in problem gambling severity (β= 0.32,
p = 0.03). In terms of the moderating effect of gender, a
significant difference between the constrained and un-
constrained model [χ
2
diff
(13)
= 192.9, p < 0.001] was ob-
served indicating a moderating effect of gender on
frequency of service attendance, such that the effect of
past-year religious service attendance on problem gam-
bling severity was greater among females.
Table 2 Gambling severity categories and mean scores (SD) based on the Problem and Pathological Gambling Measure (PPGM)
across the five data waves
Time 1 Time 2 Time 3 Time 4 Time 5
Overall
Sample size at given data wave 4120 3939 3901 3829 3799
Non-gambler [N (%)] 309 (7.5) 298 (7.6) 363 (9.3) 423 (11.0) 406 (10.7)
Recreational gambler [N (%)] 3111 (75.5) 3092 (78.5) 3034 (77.8) 2978 (77.8) 2951 (77.7)
At-risk gambler [N (%)] 564 (13.7) 436 (11.1) 401 (10.3) 324 (8.5) 365 (9.6)
Problem/pathological gambler [N (%)] 136 (3.3) 113 (2.9) 103 (2.6) 104 (2.7) 77 (2.0)
PPGM score
a
[M (SD)] 1.13 (0.57) 1.09 (0.54) 1.06 (0.54) 1.03 (0.55) 1.03 (0.53)
Males
Sample size at given data wave 1867 1770 1742 1713 1690
Non-gambler [N (%)] 135 (7.2) 127 (7.2) 165 (9.5) 194 (11.3) 186 (11.0)
Recreational gambler [N (%)] 1385 (74.2) 1376 (77.7) 1321 (75.8) 1322 (77.2) 1278 (75.6)
At-risk gambler [N (%)] 281 (15.1) 222 (12.5) 215 (12.3) 149 (8.7) 187 (11.1)
Problem/pathological gambler [N (%)] 65 (3.5) 45 (2.5) 41 (2.4) 48 (2.8) 39 (2.3)
PPGM score
a
[M (SD)] 1.15 (0.58) 1.10 (0.54) 1.08 (0.55) 1.03 (0.56) 1.05 (0.56)
Females
Sample size at given data wave 2253 2169 2159 2116 2109
Non-gambler [N (%)] 174 (7.7) 171 (7.9) 198 (9.2) 229 (10.8) 220 (10.4)
Recreational gambler [N (%)] 1725 (76.5) 1716 (79.1) 1713 (79.3) 1656 (78.3) 1673 (79.3)
At-risk gambler [N (%)] 283 (12.6) 214 (9.9) 186 (8.6) 175 (8.3) 178 (8.4)
Problem/pathological gambler [N (%)] 71 (3.1) 68 (3.1) 62 (2.9) 56 (2.6) 38 (1.8)
PPGM score
a
[M (SD)] 1.11 (0.56) 1.08 (0.54) 1.05 (0.54) 1.03 (0.54) 1.02 (0.51)
a
Missing values imputed: Non-gamblers coded to 0; infrequent/low spend gamblers coded to 1. Gambling categories are listed for descriptive purposes only; con-
tinuous PPGM scores were used for analyses
Table 3 Unconditional latent growth curve model of problem gambling severity, by gender (n = 4120)
Males (n = 1867) Females (n = 2253)
estimate p estimate p
Means/intercept 1.14 <0.001 1.10 <0.001
Means/slope −0.03 <0.001 −0.03 <0.001
Variances/intercept 0.20 <0.001 0.20 <0.001
Variances/slope 0.005 <0.001 0.003 <0.001
Within-process correlation (intercept < −>slope) −0.32 <0.001 −0.44 <0.001
Unstandardized estimates were used for means and variances; standardized estimates were used for correlations
Mutti-Packer et al. BMC Psychiatry (2017) 17:356 Page 5 of 10
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The model with covariates also indicated good fit
[χ
2
(91)
= 115.5, p = 0.04, CFI = 0.99, TLI = 0.99,
RMSEA = 0.01, SRMR = 0.01]. Table 4 presents the re-
sults from this model. All significant associations be-
tween past-year service attendance and problem
gambling severity, as well as the moderating effect of
gender [χ
2
diff
(44)
= 336.8, p < 0.001], were retained after
including covariates in the model, with the exception of
the influence of high past-year religious service attend-
ance on the slope of problem gambling severity for fe-
males, which lost significance.
Differences also emerged with respect to religious af-
filiation (Table 4 and Additional file 1: Table S1). Cath-
olic males had significantly higher levels of problem
gambling severity at baseline compared to Protestant
males (β= 0.16, p = 0.02), and males who reported other
religious affiliations (β= 0.32, p = 0.002). Catholic fe-
males had significantly higher levels of problem gam-
bling severity at baseline compared to Protestant females
(β= 0.12, p = 0.05), those who reported Other religious
affiliations (β= 0.43, p < 0.001), as well as those who
were Atheist/Agnostic (β= 0.30, p = 0.03). Protestant fe-
males had greater levels of problem gambling severity at
baseline compared to those with Other religious
affiliations (β=0.31, p = 0.001). In addition, males and
females who preferred not to state their religious affili-
ation reported greater levels of problem gambling sever-
ity at baseline compared to those who reported other
religious affiliations (β=0.31, p = 0.02 and β=0.29,
p = 0.02). Religious affiliation did not influence the slope
of problem gambling severity for males or females.
Conditional latent growth curve model: The influence of
overall religiosity on problem gambling severity
The conditional latent growth curve model estimating
the effect of past-year religiosity showed good fit
[χ
2
(25)
= 47.7, p = 0.004, CFI = 0.99, TLI = 0.99,
RMSEA = 0.02, SRMR = 0.02]. For both males and fe-
males, overall religiosity was negatively associated with
baseline levels of problem gambling severity, meaning
that those with higher levels of religiosity reported lower
levels of problem gambling severity at baseline
(β=−0.17, p < 0.001, and β=−0.09, p < 0.001, respect-
ively). The moderating effect of gender was significant
[χ
2
diff
(5)
= 45.9, p < 0.001], indicating that the effect of
religiosity on baseline levels of problem gambling sever-
ity was greater among males.
Table 4 The influence of frequency of religious service attendance on the intercept and slope of problem gambling severity, by
gender (n = 3959)
Males (n= 1764) Females (n= 2195)
Intercept Slope Intercept Slope
Effect p Effect p Effect p Effect p
Frequency of service attendance
a
Not at all (ref.) ––––––––
Low 0.10 0.11 −0.16 0.15 0.03 0.57 0.04 0.68
Moderate −0.07 0.48 −0.005 0.98 −0.16 0.04 0.06 0.68
High −0.54 <0.001 −0.25 0.10 −0.68 <0.001 0.24 0.10
Religious affiliation
b
Atheist/Agnostic (ref.) ––––––––
Catholic 0.22 0.11 0.01 0.96 0.30 0.03 −0.09 0.74
Protestant 0.06 0.66 −0.08 0.73 0.18 0.17 −0.01 0.98
Other −0.10 0.50 0.02 0.93 −0.13 0.41 −0.01 0.97
Prefer not to say 0.21 0.19 −0.38 0.19 0.16 0.29 −0.13 0.66
Age −0.004 0.04 0.01 0.01 0.003 0.17 0.006 0.10
Education −0.12 <0.001 0.02 0.67 −0.14 <0.001 0.05 0.25
Household income 0.07 0.08 −0.04 0.60 0.02 0.465 0.10 0.12
Marital status
Never married (ref.) ––––––––
Married or common law −0.34 <0.001 0.36 0.03 −0.20 0.02 0.42 0.01
Divorced or separated or widowed −0.11 0.33 0.06 0.78 −0.19 0.05 0.45 0.01
Standardized estimates used for regression coefficients
a
Low = categories “once”and “2 to 5 times”in the past year; Moderate = categories “6 to 10 times”and “once or twice a month”; High: categories “once a week”
and “more than once a week”
b
See Additional file 1: Table S1 for contrasts between all levels of religious affiliation
Mutti-Packer et al. BMC Psychiatry (2017) 17:356 Page 6 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The model with covariates fit well [χ
2
(79)
= 113.9,
p = 0.01, CFI = 0.99, TLI = 0.99, RMSEA = 0.01,
SRMR = 0.01]. Table 5 presents the results from this
model. All significant associations between religiosity
and problem gambling severity, as well as the moderat-
ing effect of gender [χ
2
diff
(36)
= 213.4, p < 0.001], were
retained after including covariates in the model. Differ-
ences emerged with respect to religious affiliation (Table
5 and Additional file 2: Table S2). Catholic males had
significantly higher levels of problem gambling severity
at baseline compared to all other religious affiliations
(Protestant: β= 0.14, p = 0.04; Atheist/Agnostic:
β= 0.28, p = 0.01; Other: β= 0.33, p = 0.001), except for
those who preferred to not state their religious affili-
ation. Protestant males reported greater levels of prob-
lem gambling severity compared to those with other
religious affiliations (β= 0.19, p = 0.04). In addition,
males and females who preferred not to state their reli-
gious affiliation, reported higher levels of problem gam-
bling severity at baseline compared to those who
reported other religious affiliations (β=0.28, p = 0.04
and β=0.33, p = 0.01, respectively).
Catholic and Protestant females showed the same pat-
tern of results. Both religious groups reported signifi-
cantly higher levels of problem gambling severity at
baseline compared to those who were atheist/agnostic,
as well as those who reported other religious affiliations
(Catholic females: β= 0.49, p < 0.001 and β= 0.44,
p < 0.001, respectively; Protestant females: β= 0.41,
p < 0.001 and β= 0.36, p < 0.001, respectively). In
addition, females preferred not to state their religious
affiliation reported greater levels of problem gambling
severity at baseline compared to those who reported
they were Atheist/Agnostic (β= 0.38, p = 0.005). Similar
to overall religiosity, religious affiliation did not influ-
ence the slope of problem gambling severity either for
males or females.
Discussion
The findings from the current study are consistent with
previous longitudinal research demonstrating the transi-
ent nature of problem gambling [24]. In addition, there
was a negative correlation between baseline levels of
problem gambling severity and its rate of change: higher
levels of problem gambling severity at baseline were as-
sociated with steeper declines over time. These findings
are also similar to those from a recent study examining
the latent growth trajectory of problem gambling sever-
ity using data from the Manitoba Longitudinal Study of
Young Adults [25]. This naturally occurring, decreasing
pattern has also been observed in trajectories of other
behavioral addictions. For example, one study using data
from the QLS examined trajectories of various norma-
tively counter-indicated habits including excessive eat-
ing, sexual behavior, shopping, online chatting, eating,
and video gaming, and found that the excessive behavior
was highest at the initial assessment, followed by a de-
creasing trend [26]. Together, this set of findings sug-
gests that problem gambling is episodic in nature, and at
the sub-clinical level, may resolve naturally over time.
Additionally, given that the QLS included non-clinical
samples, it is possible that the survey itself may have
Table 5 The influence of overall religiosity on the intercept and slope of problem gambling severity, by gender (n = 4120)
Males (n = 1867) Females (n = 2253)
Intercept Slope Intercept Slope
Effect p Effect p Effect p Effect p
Religiosity total score −0.17 <0.001 −0.002 0.97 −0.13 <0.001 −0.004 0.93
Religious affiliation
a
Atheist/Agnostic (ref.) –– – ––– – –
Catholic 0.28 0.01 −0.15 0.45 0.49 <0.001 −0.15 0.51
Protestant 0.14 0.15 −0.22 0.21 0.41 <0.001 −0.09 0.67
Other −0.05 0.71 −0.11 0.63 0.05 0.71 −0.09 0.71
Prefer not to say 0.23 0.10 −0.46 0.07 0.38 0.005 −0.25 0.33
Age −0.01 0.004 0.01 0.01 0.001 0.51 0.01 0.10
Education −0.15 <0.001 0.02 0.63 −0.14 <0.001 0.06 0.22
Household income 0.07 0.06 −0.05 0.43 0.01 0.75 0.11 0.11
Marital status
Never married (ref.) –– – ––– – –
Married or common law −0.33 <0.001 0.45 0.005 −0.19 0.03 0.43 0.01
Divorced or separated or widowed −0.05 0.66 0.15 0.46 −0.15 0.14 0.48 0.01
a
See Additional file 2: Table S2 for contrasts between all levels of religious affiliation
Mutti-Packer et al. BMC Psychiatry (2017) 17:356 Page 7 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
acted as an intervention; cueing respondents to think
about their behavior and to make changes over time.
The results of the present study are also in line with pre-
vious findings indicating that religious service attendance
may be a more robust protective factor against gambling
than other aspects of religiosity [27–29]. Specifically, we
found that respondents who attended religious services
weekly or more reported lower levels of problem gambling
severity. Similarly, Uecker and Stokes [6] found that ado-
lescents who attended religious services weekly or more
had lower odds of having ever gambled. In terms of longi-
tudinal associations in the current study, only weekly or
more frequent past-year religious service attendance had a
significant association with the slope of problem gambling
severity. This association was significant only for females,
and did not occur in the expected direction; those who
attended religious services weekly or more experienced a
slower decline in problem gambling severity over time. It
is possible that the slower rate of decline was due to the
fact that respondents who attended religious services
weekly or more had lower levels of problem gambling se-
verity to begin with. However, this association lost signifi-
cance once covariates were added to the model, indicating
that religious service attendance did not independently
influence problem gambling severity over time. The mod-
erating effect of gender indicated that the influence of
past-year religious service attendance was greater among
females; however, the effect of overall religiosity was
greater among males. However, it is important to note that
strong conclusions cannot be drawn without also examin-
ing the role of selection factors, such as personality fac-
tors, that might make it more likely that individuals attend
religious services, and less likely to develop gambling
problems. For example, conscientiousness has been found
to be negatively associated with problem gambling [30].
Within the construct of conscientiousness, the facet of
self-discipline may also be reflected in the measure of fre-
quency of religious service attendance, that is, those that
have greater self-discipline might attend religious services
more often. Future research should seek to examine the
potential explanatory role of personality traits, and in par-
ticular, the facet of self-discipline, in the association be-
tween religiosity and problem gambling.
In terms of religious affiliation, overall, our findings
are in line with previous research. In the current study,
Catholic males had greater levels of problem gambling
severity compared to Protestant males. A finding that is
in line with previous research indicating that Catholics
gambled more frequently and were more likely to be
problem gamblers compared to Protestants [10, 11, 31,
32]. Furthermore, those with no religious affiliation
(atheist/agnostic), tended to have lower levels of prob-
lem gambling severity compared to either Protestant or
Catholic groups. This is similar to previous research
indicating that Catholics were more likely to have
gambledcomparedtothosewithnoreligiousaffili-
ation [11, 33]. As a potential explanation for the
somewhat counterintuitive finding that religious affili-
ation can be associated with a greater probability of
engaging in risky behaviors, findings from a recent
study indicated that if a certain behavior is not pro-
hibited by the ethical code of a given religious affili-
ation (which in the case of gambling is largely variant
across denominations), transcendental beliefs might
increase perceived control and thus risk taking [34].
In addition, those who preferred not to state their reli-
gious affiliation reported greater levels of problem gam-
bling severity at baseline, compared to those who
reported ‘Other’religious affiliations. This may be be-
cause those in the ‘Other’group were predominantly
from religious traditions that either explicitly denounce
gambling or deem it as culturally unacceptable (i.e.,
Islam, Buddhism, and Sikhism). In addition, it is also
possible that those who preferred not to report their re-
ligious affiliation had less trust in the world and society,
or had experienced trauma due to religion, both of
which can be significant sources of stress and anxiety.
Since gambling can be a maladaptive way of coping with
negative emotions, it is possible that these potential
heightened levels of stress and anxiety may have trig-
gered an increase in gambling among this group. How-
ever, this is merely speculation, and due to the nature of
self-reported data, it is unknown which category of reli-
gious affiliation (if any), these respondents who preferred
not to state their religious affiliation, might identify with,
nor the specific reasons underlying the decision to not
report one’s religious affiliation.
Also of note is the independent influence of different
measures of religion—religious affiliation, religious ser-
vice attendance, and overall religiosity—on problem
gambling severity among adults. These findings highlight
the multi-dimensional nature of religion, and the im-
portance of examining a variety of religion measures to
accurately capture this construct [10, 27].
Limitations
Given the low response rate (21.3%), the findings from the
current study should be interpreted with caution. In
addition, due to oversampling those at-risk, the QLS sample
contained a somewhat higher proportion of At Risk, Prob-
lem, and Pathological Gamblers, and a lower proportion of
Non-Gamblers compared to the province of Ontario,
Canada (13.7% vs. 6.3%; 2.1% vs. 1.4%; 1.2% vs. 0.8%, re-
spectively) [35]. However, according to the 2006 Canadian
Census, the overall demographic profile of the QLS (i.e.,
gender, age, marital status, education, income, employment,
and race/ethnicity) is similar to Canadian adults (15+), with
some exceptions [16]. The QLS sample has a somewhat
Mutti-Packer et al. BMC Psychiatry (2017) 17:356 Page 8 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
higher level of educational attainment, fewer people in the
18 to 24 and 65 years and older, age groups, as well as fewer
single people, and visible minorities.
Although the religion measures used in the current
study did influence the initial level of problem gambling
severity, they did not influence the rate of change over
time. One explanation for these null findings could be
the use of time-invariant variables. It is likely that mea-
sures of religion are not static but instead change over
time. Unfortunately, the QLS did not assess religiosity
beyond the initial wave of data collection. Future re-
search should seek to address this limitation using longi-
tudinal datasets which capture measures of religiosity
over time, and perhaps with the use of time-varying pre-
dictor variables and covariates, or alternatively with the
use of a parallel-process growth curve model. Further-
more, the assumption in the current study, as well as all
other studies conducted to date on this topic, is that
religiosity predicts current and subsequent problem
gambling severity. However, what has yet to be exam-
ined are potential causal effects in the opposite direc-
tion, and whether gambling predicts religiosity.
Another limitation revolves around the measure of reli-
gious affiliation. Specifically, with regards to the hetero-
geneity of the ‘Other’category, which was comprised of
diverse minority religions despite different perspectives on
gambling. For example, within the Islamic tradition, gam-
bling is explicitly prohibited in the Quran, whereas within
the Jewish tradition, gambling is more culturally accepted
and has a longstanding history. These diverse religions
were grouped together because there was rather low vari-
ability in the population of Quinte overall; the ‘Other’cat-
egory represented only 9% of the total sample.
Conclusions
The findings from the current study suggest that the re-
lationship between religiosity and problem gambling is
complex and nuanced. For example, the findings indicate
that some dimensions of religiosity, including religious
service attendance and personal religiosity may play a
protective role against problem gambling, while other
aspects of religiosity such as one’s religious affiliation
were associated with an increase in problem gambling
severity, such as the case with Catholics compared to
Atheists. Thus, there is a need for additional research to
better understand the relationship between problem
gambling and religiosity over time taking into consider-
ation additional factors that might cause individuals to
select into organized religion, including but not limited
to personality factors such as conscientiousness. Overall,
the findings from this study provide support for the po-
tential influential role of religious affiliation and respect-
ive faith-based doctrines in shaping cultural views on
gambling and ultimately behavior.
Additional files
Additional file 1: Table S1. Contrasts between all categories of
religious affiliation when examining the influence of frequency of
religious service attendance on the intercept and slope of problem
gambling severity, by gender (n= 3959). Table presenting all contrasts
between religious affiliation. (DOCX 14 kb)
Additional file 2: Table S2. Contrasts between all categories of
religious affiliation when examining the influence of overall religiosity on
the intercept and slope of problem gambling severity, by gender
(n= 4120). Table presenting all contrasts between religious affiliation.
(DOCX 14 kb)
Abbreviations
CFI: Comparative Fit Index; FIML: Full Information Maximum Likelihood;
PPGM: Problem and Pathological Gambling Measure; QLS: Quinte
Longitudinal Study; RJRS: Rohrbaugh-Jessor Religiosity Scale; RMSEA: Root
Mean Square Error of Approximation; SRMR: Standardized Root Mean Square
Residual; TLI: Tucker-Lewis Index
Acknowledgements
Not applicable.
Funding
The Quinte Longitudinal Study (QLS) was funded by the Ontario Problem
Gambling Research Centre (OPGRC). The funding body had no involvement
in the study design, analysis, interpretation of the data, in the writing of the
report, and in the decision to submit the article for publication.
Availability of data and materials
Please contact Gambling Research Exchange Ontario (GREO) for access to
data on which the manuscript is based.
Authors’contributions
RJW designed and managed the Quinte Longitudinal Study. BKT, RJW, and
DCH conceptualized the hypotheses for this paper. SMP designed the
analytic approach and conducted the statistical analyses. SMP, BKT, DCH, and
RJW interpreted the results. SMP prepared the first draft of the manuscript.
All authors read, contributed to, and approved the final version of the
manuscript.
Ethics approval and consent to participate
Ethical approval was provided by the Human Subject Research Committee at
the University of Lethbridge. If the person agreed to participate in the
survey, they were sent an email with a link to the online questionnaire or
booked into a time slot at the program office where they completed the
survey on a computer on site. Informed written consent was obtained from
all participants prior to completing the online survey, each year.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Department of Psychology, University of Calgary, 2500 University Drive NW,
Calgary, AB T2N 1N4, Canada.
2
Faculty of Health Sciences, University of
Lethbridge, Lethbridge, AB, Canada.
3
Research and Academics Division,
Waypoint Centre for Mental Health Care, Penetanguishene, ON, Canada.
4
Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
Mutti-Packer et al. BMC Psychiatry (2017) 17:356 Page 9 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Received: 10 July 2017 Accepted: 26 October 2017
References
1. Koenig HG, King DE, Carson VB. Handbook of religion and health. 2nd ed.
New York: Oxford University Press; 2012.
2. Koenig HG. Religion, spirituality, and health: the research and clinical
implications. ISRN Psychiatry. 2012:1–33.
3. Williams RJ, Royston J, Hagen BF. Gambling and problem gambling within
forensic populations: a review of the literature. Crim Justice Behav. 2005;
32(6):665–89.
4. Chou K-L, Afifi TO. Disordered (pathologic or problem) gambling and axis I
psychiatric disorders: results from the National Epidemiologic Survey on
alcohol and related conditions. Am J Epidemiol. 2011;173(11):1289–97.
5. Petry NM, Armentano C. Prevalence, assessment, and treatment of
pathological gambling: a review. Psychiatr Serv. 1999;50(8):1021–7.
6. Uecker JE, Stokes CE. Religious background and gambling among young
adults in the United States. J Gambl Stud. 2016;32(1):341–61.
7. Ghandour LA, El Sayed DS. Gambling behaviors among university youth:
does one’s religious affiliation and level of religiosity play a role? Psychol
Addict Behav. 2013;27(1):279–86.
8. Casey DM, Williams RJ, Mossière AM, Schopflocher DP, el-Guebaly N,
Hodgins DC, et al. The role of family, religiosity, and behavior in adolescent
gambling. J Adolesc. 2011;34(5):841–51.
9. Hodgins DC, Schopflocher DP, Martin CR, el-Guebaly N, Casey DM, Currie
SR, et al. Disordered gambling among higher-frequency gamblers: who is at
risk? Psychol Med. 2012;42(11):2433–44.
10. Ellison CG, Mcfarland MJ. Religion and gambling among U.S. adults:
exploring the role of traditions, beliefs, practices, and networks. J Sci Study
Relig. 2011;50(1):82–102.
11. Diaz JD. Religion and gambling in sin-city: a statistical analysis of the
relationship between religion and gambling patterns in Las Vegas residents.
Soc Sci J. 2000;37(3):453–8.
12. Fattore L, Altea S, Fratta W. Sex differences in drug addiction: a review of
animal and human studies. Women’s Health. 2008;4(1):51–65.
13. MacLaren VV, Best LA. Multiple addictive behaviors in young adults:
student norms for the shorter PROMIS questionnaire. Addict Behav.
2010;35(3):252–5.
14. Miller AS, Hoffmann JP. Risk and religion: an explanation of gender
differences in religiosity. J Sci Study Relig. 1995;34(1):63–75.
15. Miller AS, Stark R. Gender and religiousness: can socialization explanations
be saved? Am J Sociol. 2002;107(6):1399–423.
16. Williams RJ, Hann RG, Schopflocher DP, West BL, McLaughlin P, White N, et
al. Quinte longitudinal study of gambling and problem gambling. Report
prepared for the Ontario Problem Gambling Research Centre. Guelph,
Ontario, Canada. 2015. Available from: http://hdl.handle.net/10133/3641.
Accessed May 2017.
17. Konkolÿ Thege B, Colman I, el-guebaly N, Hodgins DC, Patten SB,
Schopflocher D, et al. Substance-related and behavioural addiction
problems: two surveys of Canadian adults. Addict Res Theory. 2015;
23(1):34–42.
18. McLaughlin P, White N, King K, Hann RG, Williams RJ, Schopflocher D, West
B, Flexhaug T. QLS front-line retention manual: methods for achieving a
94% cohort retention rate in longitudinal research. Report prepared for the
Ontario Problem Gambling Research Centre. Guelph, Ontario, Canada. 2014.
Available from: http://hdl.handle.net/10133/3379. Accessed May 2017.
19. Williams RJ, Volberg R. Best Practices in the Population Assessment of
Problem Gambling. Report prepared for the Ontario Problem Gambling
Research Centre. Guelph, Ontario, Canada. 2010. Available from: http://hdl.
handle.net/10133/1259. Accessed May 2017.
20. Williams RJ, Volberg RA. The classification accuracy of four problem
gambling assessment instruments in population research. Int Gambl Stud.
2014;14(1):15–28.
21. Rohrbaugh J, Jessor R. Religiosity in youth: a personal control against
deviant behavior. J Pers. 1975;43(157):136–55.
22. Bollen KA, Curran PJ. Latent curve models: a structural equation perspective.
New Jersey: John Wiley & Sons; 2006.
23. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure
analysis: conventional criteria versus new alternatives. Struct Equ Modeling.
1999;6(1):1–55.
24. LaPlante DA, Nelson SE, LaBrie RA, Shaffer HJ. Stability and progression of
disordered gambling: lessons from longitudinal studies. Can J Psychiatr.
2008;53(1):52–60.
25. Edgerton JD, Melnyk TS, Roberts LW. Problem gambling and the youth-to-
adulthood transition: assessing problem gambling severity trajectories in a
sample of young adults. J Gambl Stud. 2015;31(4):1463–85.
26. Konkolÿ Thege B, Woodin EM, Hodgins DC, Williams RJ. Natural course of
behavioral addictions: a 5-year longitudinal study. BMC Psychiatry. 2015;
15(1):4.
27. Beyerlein K, Sallaz JJ. Faith’s wager: how religion deters gambling. Soc Sci
Res. 2017;62:204–18.
28. Hoffmann JP. Religion and problem gambling in the U.S. Rev Relig Res.
2000;41(4):488–509.
29. Lam D. The influence of religiosity on gambling participation. J Gambl Stud.
2006;22(3):305–20.
30. Hwang JY, Shin YC, Lim SW, Park HY, Shin NY, Jang JH, et al.
Multidimensional comparison of personality characteristics of the big five
model, impulsiveness, and affect in pathological gambling and obsessive–
compulsive disorder. J Gambl Stud. 2012;28(3):351–62.
31. Hraba J, Lee G. Gender, gambling and problem gambling. J Gambl Stud.
1996;12(1):83–101.
32. Feigelman W, Wallisch LS, Lesieur HR. Problem gamblers, problem
substance users, and dual-problem individuals: an epidemiological study.
Am J Public Health. 1998;88(3):467–70.
33. Eitle D. Religion and gambling among young adults in the United States. J
Sci Study Relig. 2011;50(1):61–81.
34. Chan KQ, Tong EMW, Tan YL. Taking a leap of faith. Soc Psychol Personal
Sci. 2014;5(8):901–9.
35. Williams RJ, Volberg RA. Gambling and problem gambling in Ontario.
Report prepared for the Ontario Problem Gambling Research Centre and
the Ontario Ministry of Health and long term care. Available from: https://
www.uleth.ca/dspace/handle/10133/3378. Accessed May 2017.
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