Content uploaded by Aniko Maraz
Author content
All content in this area was uploaded by Aniko Maraz on Dec 18, 2017
Content may be subject to copyright.
Prevalence of compulsive buying
1
The prevalence of compulsive buying: A meta-analysis
Maraz, A.1,2, *, Griffiths, M. D.3, Demetrovics, Z.,1
1 Institute of Psychology, Eötvös Loránd University, Budapest, Hungary
2 Doctoral School of Psychology, Eötvös Loránd University, Budapest, Hungary
3 Psychology Division, Nottingham Trent University, Nottingham, United Kingdom
Running title: Prevalence of compulsive buying
Word count: 3928
Conflict of interest: none.
*Corresponding author: Aniko Maraz, Department of Clinical Psychology and Addiction,
Institute of Psychology, Eötvös Loránd University, Izabella u. 46, Budapest, Hungary,
+3670/257-1246, aniko.maraz@ppk.elte.hu
Prevalence of compulsive buying
2
ABSTRACT
Aims: To estimate the pooled prevalence of compulsive buying behaviour (CBB) in different
populations and to determine the effect of age, gender, location, and screening instrument on
the reported heterogeneity in estimates CBB and whether publication bias could be identified.
Methods: Three databases were searched (Medline, PsychInfo, Web of Science) using the
terms “compulsive buying”, “pathological buying” and “compulsive shopping” to estimate the
pooled prevalence of CBB in different populations. Forty studies reporting 49 prevalence
estimates from 16 countries were located (n=32,000). To conduct the meta-analysis, data from
non-clinical studies regarding mean age and gender proportion, geographical study location,
and screening instrument used to assess CBB were extracted by multiple independent
observers and evaluated using a random effects model. Four a-priori subgroups were analysed
using pooled estimation (Cohen’s Q) and covariate testing (moderator and meta-regression
analysis). Results: The CBB pooled prevalence of adult representative studies was 4.9%
[3.4%-6.9%, eight estimates, 10,102 participants] although estimates were higher among
university students: 8.3% [5.9%-11.5%, 19 estimates, 14,947 participants], in adult non-
representative samples: 12.3% [7.6%-19.1%, 11 estimates, 3929 participants], and in
shopping-specific samples: 16.2% [8.8%-27.8%, 11 estimates, 4,686 participants]. Being
young and female were associated with increased tendency, but not location (USA vs. non-
USA). Meta-regression revealed large heterogeneity within subgroups, mostly due to diverse
measures and timeframes (current vs. lifetime) used to assess CBB. Conclusions: A pooled
estimate of compulsive buying behaviour in the populations studied is around 5% but there is
large variation between samples largely accounted for by use of different time frames and
measures.
Prevalence of compulsive buying
3
Keywords: addictive behavior, consumer behavior, cross-cultural comparison, epidemiology,
publication bias
Prevalence of compulsive buying
4
INTRODUCTION
Research into shopping has demonstrated that although shopping is a necessity in modern life,
it is also a leisure activity and a form of entertainment with a rewarding value for some people
(1). However, when taken to the extreme, shopping (or buying) can be a harmful and
destructive activity for a minority of individuals. The consequences of compulsive buying
behaviour are often underestimated. Christenson et al. (2) noted that CBB results in (i) large
debts (58.3%), (ii) inability to meet payments (41.7%), (iii) criticism from acquaintances
(33.3%), (iv) legal and financial consequences (8.3%), (v) criminal legal problems (8.3%),
and (vi) guilt (45.8%). Furthermore, individuals with CBB often describe an increasing level
of urge or anxiety that can only be alleviated and lead to a sense of completion when a
purchase is made (3). Compulsive buying is a frequent disorder in a small minority of
shopping mall visitors and is associated with important and robust indicators of
psychopathology such as psychiatric distress, borderline personality disorder, and substance
abuse (4). Compared to non-compulsive buyers, compulsive buyers are over twice as likely to
abuse substances, have any mood or anxiety disorder, and three times more likely to develop
eating disorder than non-compulsive buyers (5). However, it should be noted that these
findings are based on a small number of studies, all of which have sampling limitations.
Despite many studies highlighting the severe negative consequences that compulsive
buying can lead to, the latest (fifth) edition of the Diagnostic and Statistical Manual of Mental
Disorders (DSM-5) does not include compulsive buying disorder due to insufficient research
in the field (6). Individuals with the condition are classified within the residual category of
“Unspecified disruptive, impulse-control, and conduct disorders”. Nevertheless, there are
various consensus-based definitions of compulsive buying in the research literature.
According to Faber O’Guinn and Krych (1987), compulsive consumption corresponds to a
Prevalence of compulsive buying
5
consumer behaviour that is “inappropriate, typically excessive, and clearly disruptive to the
lives of individuals who appear impulsively driven to consume” (p. 132).
McElroy et al. (8) point out that both cognitive and behavioural components play an
important role in the acquisition, development and maintenance of the disorder. Diagnostic
criteria include (i) maladaptive preoccupation with buying or shopping, or maladaptive
buying or shopping impulses; (ii) generation of marked distress by the buying preoccupations,
impulses or behaviours, which are time consuming, significantly interfere with social or
occupational functioning or result in financial problems; and (iii) lack of restriction of the
excessive buying or shopping behaviour to periods of hypomania or mania. Given the lack of
consensus regarding the term, the present study included all pathological consumer behaviour
under the umbrella term of “compulsive buying behaviour” (CBB).
The age of onset for CBB appears to be in the late teens or early twenties, although
some studies have reported a later mean age of 30 years (8). There is also a lack of consensus
relating to gender differences. Most clinical studies report that women are much more likely
to become compulsive buyers than men, but not all surveys have found significant differences
in buying tendencies between men and women (9). Cultural mechanisms have been proposed
to recognize the fact that CBB mainly occurs among individuals living in developed countries
(10). Elements reported as being necessary for the development of CBB include the presence
of a market-based economy, the availability of a wide variety of goods, disposable income,
and significant leisure time (3). For these reasons, Black (3) concluded that CBB is unlikely to
occur in poorly developed countries, except among the wealthy elite.
Given this background, the main aim of the present paper is to review and summarise
the empirical data concerning the prevalence of compulsive buying in non-clinical
populations. Following a systematic literature review, the present study (a) estimated a pooled
prevalence of compulsive buying behaviour (CBB) in different populations across the world
Prevalence of compulsive buying
6
where studies have been carried out. Furthermore, the study examined (b) the effect of age,
gender, geographical location of the study, and the screening instrument used on the reported
heterogeneity in estimates, and (c) whether publication bias could be identified.
METHODS
Sources and search terms
This systematic review conforms with the guidelines of meta-analyses in
epidemiology outlined by Stroup et al. (11). At the end of March 2015, three academic
databases (Medline [PubMed], PsycInfo, and Web of Science) were used to identify all
possible papers concerning CBB. The terms apply were “compulsive buying”, “compulsive
shopping”, or “pathological buying”. Searching all fields in the three aforementioned
databases resulted in 290, 523, and 449 relevant hits respectively. Although the
aforementioned databases incorporate “grey literature” such as dissertations and conference
presentation, these were later excluded. No further grey literature was searched. After
removing duplicates, 638 papers were left for further evaluation.
Inclusion and exclusion criteria
Inclusion and exclusion criteria were established to maximise specificity and sensitivity
across the identified papers. Studies in the papers were considered relevant if they reported
empirical prevalence data concerning compulsive buying as well as data from other peer-
reviewed works (book chapters, letters to editor, etc.). The conventional formula was used to
calculate the minimum required sample size (12, 13) setting precision to 5%, confidence
interval to 99% and expected prevalence to 5.8% that is the most recent representative
prevalence data for the USA. Using the given formula, studies with 145 or more participants
Prevalence of compulsive buying
7
were considered suitable to return reliable prevalence rates. For this reason, in order to be
included in the current review, studies had to be carried out on a non-clinical sample with
more than 145 participants. Only published studies written in English were included with no
constraints regarding participants’ ages. Where multiple publications presented identical data,
the most ‘‘informative version’’ of the study was included so as not to double-count what was
in reality a single prevalence estimate. Two authors categorised the search results (AM and
ZD) and a third author was included in cases of disagreement (MDG).
As noted in the previous section, 638 publications were identified. The first papers
excluded were case studies (n=23) and reviews or theoretical works (n=192) that included no
new empirical data. In the next step, a further 244 clinical samples were excluded such as
studies examining drug effectiveness, comparing compulsive buyers with other populations,
as well as those with other mental health issues such as Parkinson’s disease or eating
disorders. A further 26 studies were excluded because they disseminated qualitative findings
or double-reported prevalence data from the same sample. Another 73 papers were excluded
because they used the compulsive buying scale points as a measure of severity, and did not
report the prevalence rates. The original 638 publications also included 15 written in a foreign
language, seven dissertation or conference abstracts, and 16 studies in which the sample size
was below 145 participants. Given that the present review focuses on adult populations, two
studies that recruited high school children were also excluded (i.e., 14, 15)
1
. This left 40
studies that met the predetermined inclusion criteria for the current review.
Meta-analysis: data analysis
1
Prevalence rates were: 19% (Chinese junior high school students), 25% (Thai junior high school students)
(Guo & Cai, 2011) and 40% among Italian upper intermediate school students (Villella et al., 2011).
Prevalence of compulsive buying
8
The following information was extracted from the studies: sample mean age, proportion of
females (in %), the study’s geographical location, and the screening instrument used to assess
CBB, and the reported prevalence estimate of CBB. Furthermore, the association between
age, gender, and CBB was also extracted as reported by the individual studies.
The unit of data analysis was the estimated prevalence rate for CBB and not the
studies. This was because some studies reported more than one prevalence rate for the same
sample. This approach was opted for in order to avoid bias as a result of having to choose
between the estimates assessed by the different CBB screening measures. Given that
inherently different populations are clearly not comparable, the following subgroups were
formed a-priori: adult representative samples, adult non-representative samples (e.g.,
university staff members), university student samples, and shopping-specific samples (e.g.,
customers of a shopping mall). Using the random effects model, studies were weighted by the
inverse of their variance, so that studies with larger sample sizes and more accurate estimates
of population parameters had a greater weight on the mean effect size. Prevalence estimates
were considered outliers if the standardized residual exceeded ± 3.29 (73). In the current
study, no outliers were identified and all standardised residuals were within the acceptable
range.
To address the issue of publication bias, a funnel plot asymmetry was examined
following the guidelines by Sterne et al. (16). A funnel plot is a scatter plot of the effect
estimates from individual studies against measures of each study’s size. In the absence of
publication bias and between-study heterogeneity, the scatter will be due to sampling
variation alone and the plot will resemble a symmetrical inverted funnel. Following the recent
recommendations by Sterne et al. (16), Egger’s test of the intercept was used to statistically
evaluate publication bias. The more the intercept deviates from zero, the more pronounced the
asymmetry. If the p value of the intercept is 0.1 or smaller, the asymmetry is considered to be
Prevalence of compulsive buying
9
statistically significant. However, Egger’s test, similar to other statistical tests for funnel plot
asymmetry, has low statistical power (16).
The rate of heterogeneity was calculated separately within each of the four groups.
Heterogeneity refers to the differences between the studies in terms of methods, participants,
and other unknown sources (17). This can be tested using the Q statistic with a random effects
model (Cochran’s Q, see: 18-21). Cochran’s Q has an approximate chi-square distribution and
represents the degree of departure from homogeneity. A significant (p<0.05) Q value
indicates that there is statistically significant heterogeneity in the studies.
Covariates were tested when heterogeneity was identified within a subgroup. Meta-
regression was used to assess the association between outcome (prevalence) and continuous
covariates such as sample mean age and the proportion of females (22). Moderator analysis
was used for categorical variables such as study location (i.e., USA vs. non-USA) and
assessment screening tool used (current vs. lifetime prevalence). Only moderator variables
that had at least four estimates in one cell were used (72). Moderators were significant in
cases of categorical variables if Qbetween was significant. The regression coefficient (and its
significance level) was calculated in addition to Tau2 and reflects between-study variance.
The Comprehensive Meta-Analysis Version 3 software (23, 24) was used to calculate
prevalence estimates within groups, publication bias, and to conduct moderator and meta-
regression analysis.
RESULTS
Prevalence by populations
As noted above, 40 relevant studies were identified that met the inclusion criteria,
reporting 49 different prevalence rate estimates for 32,333 participants. Table 1 depicts the
Prevalence of compulsive buying
10
studies in greater detail. As already noted, the sample was a-priori divided into four sub-
samples: adult representative, adult non-representative, university student and shopping-
specific. The mean prevalence of compulsive buying was 4.9% in adult representative
samples [CI: 3.4%-6.9%, 10,102 participants], 12.3% in adult non-representative samples [CI:
7.6%-19.1%, 3929 participants], 8.3% in university student samples [CI: 5.9%-11.5%, 14,947
participants] and16.2% in shopping-specific samples [CI: 8.8%-27.8%, 4,686 participants]
(see Figure 1).
There was significant heterogeneity in each of the four groups (representative:
Qwithin=101.4 p<0.001; non-representative: Qwithin=322.3 p<0.001; student: Qwithin=604.1
p<0.001; specific: Qwithin=1038.7 p<0.001). Thus covariates were tested to explain variability.
------------------------------------------------
Please insert Figure 1 and Table 1 here
------------------------------------------------
Publication bias
The funnel plot of standard error was symmetric in three subgroups (see Figure 2). Following
visual inspection, the most symmetric was the adult representative samples’ funnel plot, and
the least symmetric was the plot of the specific samples. Egger’s p value indicated significant
symmetry in special populations (Intercept = -13.20, p= 0.15), adult representative (Intercept
= -2.96, p= 0.39), and student populations (Intercept = -2.47, p=0.38). There was significant
asymmetry in the adult non-representative (Intercept = -17.80, p=0.03) and likely to be caused
by an extremely high estimate of 49% in one particular study (34).
Prevalence of compulsive buying
11
------------------------------------------------
Please insert Figure 2 about here
------------------------------------------------
Covariate analysis
Age, gender and study location (USA vs. non-USA)
In total, seven studies reported in eight different samples that compulsive buying
tendency decreases with age, of which five estimates were reported in adult representative
samples. Age did not have an effect on CBB in five samples. Only one study reported that
older students were more likely to engage in compulsive buying than younger ones in a
sample of undergraduate students (50). The remaining studies did not test or report the
association between age and CBB. The mean sample age was lower for adult non-
representative and shopping-specific populations than for adult representative ones (weighted
means respectively: 37.4 years, 37.2 years, and 41.7 years). Age had non-significant effects in
all four populations; in the representative (coefficient: 0.02, p=0.77, Q=0.09, Tau2=0.38),
non-representative (coefficient: -0.08, p=0.21, Tau2=0.85), student (coefficient: -0.24,
p=0.051, Tau2=0.36) and shopping-specific samples (coefficient: 0.03, p=0.38, Tau2=0.58).
With regards to gender, women were more prone to CBB than men in 12 different
samples of which four were reported on adult representative samples. No gender difference
was found in four instances, and undergraduate men reported higher CBB tendencies than
women in one sample (50). On average, the samples included more females including 55.5%
of adult representative, 69.4% of adult non-representative, 65.9% of university student, and
69.8% of shopping-specific samples. The proportion of females in the sample had non-
significant effects in the representative (coefficient: -0.002, p=0.96, Tau2=0.35), student
(coefficient: 0.02, p=0.10, Tau2=0.45) and specific populations (coefficient: 0.013, p=0.33,
Prevalence of compulsive buying
12
Tau2=0.58), but significant effect in the non-representative sub-group (coefficient=-0.05,
p<0.01, Tau2=0.32). The higher the proportion of females, the lower the reported prevalence
of compulsive buying in the adult non-representative sample.
With regards to the geographic location of where the study was carried out, most
estimates (n=18) were reported from the United States, followed by Germany (n=6), France
(n=5) and Hungary (n=4). Tested as moderators, USA vs. non-USA study location was
calculated (n≥4), the effect of which was non-significant in two groups: in the non-
representative (point estimates: 0.10 and 0.16, Qbetween=0.957, p=0.33), and in the student
population (point estimates: 0.09 and 0.08, Qbetween=0.05, p=0.82)
2
.
The effect of assessment tool: lifetime versus current prevalence
As shown in Table 2, 39% of the prevalence rate estimates (19/49) were obtained using the
CBS (28) although cut-off scores differed. A considerable amount of variability in estimated
prevalence rates was most likely due to the fact that measures had different time frames. The
CBS, MIDI, and the SPQ contain items regarding lifetime CBB prevalence (that is, if the
individual has ever experienced problems with buying behaviour), whereas the G-CBS,
RCBS, ECBS, ECBS-R, and the PS assess current CBB prevalence (problems with buying
behaviour at the time of assessment). The QABB contains mixed items regarding verb tense
(i.e., 13 out of the 19 items refer to past, and six refer to present behaviour). When calculating
the mean average estimates by the type of instrument, the sample size-weighted mean of
current estimate was 6.99% (assessed by GCBS, RCBS, ECBS, ECBS-R or PS), lifetime
estimate was 11.08% (assessed by CBS, MIDI or SPQ), and the mixed estimate was 11.14%
(assessed by QABB).
2
In the representative populations, estimates were 0.07 and 0.04 but n=2 and n=6 respectively for USA vs. non-
USA, whereas in the specific populations point estimates were both 0.16 but n=2 and n=9.
Prevalence of compulsive buying
13
Unfortunately, in three out of the four groups, there were less than four studies,
therefore differences between estimates (lifetime vs. current) could only be calculated in one
subgroup. Among students, point estimates were 0.09 and 0.08 for current and lifetime
prevalence respectively, which yielded a non-significant difference (Q=0.73, p=0.70)
3
.
However, as a trend, lifetime prevalence estimates were clearly higher than current estimates.
------------------------------------------------
Please insert Table 2 about here
------------------------------------------------
DISCUSSION
The present review aimed to summarise knowledge concerning the prevalence of compulsive
buying in non-clinical adult populations. It also aimed to examine the possible causes of the
varying estimates of CBB disorder and to calculate a pooled prevalence based on all existing
prevalence data. Via systematic literature review, 40 relevant studies were identified reporting
49 different estimates for over 32,000 participants. The estimated prevalence rate of
compulsive buying was 4.9% [3.4%-6.9%, based on eight estimates] in the general adult
representative populations. Prevalence rates were higher in university student samples (8.3%
[5.9%-11.5%], 19 estimates) and in adult non-representative samples (12.3% [7.6%-19.1%]
11 estimates) compared to representative ones. Unsurprisingly, the highest prevalence rates
were among shopping-specific samples (16.2% [8.8%-27.8%], 11 estimates).
A considerable amount of heterogeneity was present in the reported estimates and was
also reflected by the funnel plots. Funnel plots indicated significant asymmetry only in the
3
Point estimates for the representative group were 0.06 (n=5), 0.07 (n=2), for the non-representative group were
0.10 (n=2) and 0.11 (n=8), and for the specific group: 0.09 (n=5) and 0.30 (n=3) for current and lifetime
estimates respectively. Data obtained via mixed instruments (QABB) were treated as missing.
Prevalence of compulsive buying
14
adult non-representative samples that is mainly due to one (although statistically speaking
non-outlier) extremely high value of 49% among fitness club clients (34). Although
asymmetric funnel plots are interpreted as indicators of publication bias, they may give the
wrong impression if high precision studies are different from low precision studies with
respect to effect size (e.g., due to different populations examined) (64). Therefore in this case,
heterogeneity is likely to be accounted for by the heterogeneous populations and instruments
rather than by publication bias.
On one hand, heterogeneity in prevalence rates may be accounted for by the lack of
consensus regarding the definition of compulsive buying. Studies used different measures to
assess CBB, each having a different conceptual background. Most definitions include
cognitive-affective indicators as well as maladaptive behavioural consequences when defining
the disorder (e.g., debts). The screening instruments used across studies differed in indicators
of financial consequences (e.g., credit card use, debts, loan etc.) and are subject to differences
according to countries, sub-cultures and/or age groups. Given the challenges of establishing a
reliable cut-off value for the scales, about twice as many studies (n=73) used the rating scales
as indicators of severity (ignoring the cut-off values) as opposed to those that reported
categorical data (n=40). Researchers have repeatedly noted that compulsive buying tendencies
vary along a spectrum (65, 66) and they argue that psychological problems exist as
dimensions rather than categories. However, a categorical approach has the advantage of
identifying potentially self-harming individuals as well as an estimation of the problem extent
within the given population. Furthermore, knowledge of the proportion of compulsive buyers
in the study sample enables comparison across studies. Therefore future studies should report
the proportion of compulsive buyers in their samples.
On the other hand, cut-off scores were not standard, but differed across measures. In
relation to the Compulsive Buying Scale, Faber and O'Guinn (28) noted that “a cut-off point
Prevalence of compulsive buying
15
at 2 SD is recommended for research purposes. This point is associated with a scale score of -
1.34” (p. 464) thereby suggesting a cut-off value adjusted to the mean score and standard
deviation of the given population. The adjustable cut-off score is suitable to account for
cultural differences in the indicators of CBB (e.g., the use of credit cards) although this
approach was only adhered to in a minority of studies. Application of a fixed cut-off value (-
1.34 for the CBS) was more common, which sets the same standard of buying pathology
across countries without taking into consideration the instrument’s local validity and
reliability. The reporting of local validity is especially important given the culture-dependent
nature of compulsive buying behaviour. For example, some countries have an extended
tradition of bank check usage and others do not have. This difference is ignored when
administering an instrument where using bank check is an indicator of CBB (such as the
CBS).
Thirdly, diversity is due to the fact that measures do not explicitly distinguish current
and lifetime assessment of CBB. Prevalence rates assessed with an instrument that assesses
lifetime prevalence report 1.6 times higher rates on average than those assessing current
prevalence. This proportion is in line with other studies reporting 50% lifetime and 30% of
12-month prevalence of any psychiatric disorder in the USA (67) and 6.9% lifetime vs. 3.4%
12-month prevalence of major depression among Chinese Americans (68). Pooled lifetime
versus current estimates in adult representative samples in the present review were 6.1% and
6.0% respectively (excluding estimates assessed by the QABB), but these estimates were
largely varying therefore more data are needed to establish reliable estimates. Future studies
should therefore explicitly separate out current and lifetime prevalence of the disorder when
assessing CBB.
Non-representative samples (e.g., adults, university students, shoppers) tended to
recruit younger participants who were more likely to be female than representative studies.
Prevalence of compulsive buying
16
The mean age of the sample and the proportion of males and females did not have a reliable
effect on the prevalence estimates. Being of a younger age was predictive of CBB according
to individual study results and also according to the regression analysis in the representative
samples. In the other groups, it is possible that methodological heterogeneity masked the
effects of age. However, it remains open as to whether compulsive buying tendency decreases
with age or this difference reflects generational differences. If the latter was the case, then the
prevalence of compulsive buying behaviour is expected to increase in the future. There is
already evidence in the literature for increasing rates of CBB in Germany (27) and in Spain
(69), but longitudinal studies are needed to clarify this question. Nevertheless, the tendency of
a younger age being associated with higher CBB tendency is also reflected by the fact that
university samples report higher CBB tendencies than do general adult samples. The
overwhelming majority of individual studies report that women are more likely to be
compulsive buyers than men, although this effect was either non-significant or very weak
when tested as a covariate. Again, this is probably due to the large methodological differences
in the studies. The dominance of women in CBB is in line with the evolutionary explanations
of the disorder that CBB might reflect ancient collecting tendencies that had been mainly
assigned to females within their social groupings (70). In any case, the fact that adult non-
representative studies recruit young and female participants is a significant contributing factor
to the elevated prevalence rates reported.
In relation to data collection, estimates from the United States (18 out of 49) were
over-represented compared to countries other than the USA, although there was no difference
in the reported estimates between the U.S. and non-U.S. countries. However, it is difficult to
draw reliable conclusions regarding the cultural variance of CBB given that adult
representative estimates are only available from the USA, Spain, Germany and Hungary.
Prevalence of compulsive buying
17
Future studies are needed to clarify whether the geographical location (or culture) has any
effect on the prevalence rates.
Finally, some estimates deviate largely from the expected values within the given sub-
group. For example, Lejoyeux, Avril (34) reported that 49% of fitness club clients and 33% of
women entering a department store (61) had CBB. Although these estimates are not
representative of the given population, the high estimates raise the concern of
overpathologizing the behaviour (71), especially when no other measures (such as
overspending) were assessed to validate the category of compulsive buyers.
The present study is not without its limitations. The relationship described by a meta-
regression is an observational association across trials; therefore, it is unsuitable for causal
interpretation. Furthermore, because meta-analyses rely on published data, the results suffer
from the same sampling errors and biases in the original observations (aggregation bias, see
22). Furthermore, not every study was designed to report prevalence, and only the
representative estimates are suitable to draw reliable estimates regarding the prevalence rates
of CBB. Finally, even the CBS that has good sensitivity (89.8%) and specificity (85.3%)
values has a positive predictive value of only around 20% when the prevalence is 5%. This
means that only one out of five individuals who screen positive on CBS is problematic in a
clinical sense (74).
The fact that compulsive buying behaviour is a relatively common disorder with
severe consequences for a minority of individuals should not be overlooked. It appears that
approximately one in 20 individuals suffer from CBB at some point in their lives and that
being young and female are associated with a higher risk of CBB. High heterogeneity is likely
to be the result of methodological variability within studies, such as assessment screens with
different time frames and conceptual background. Future studies should therefore think
Prevalence of compulsive buying
18
carefully about how to conceptualise the disorder and to clearly separate out current versus
lifetime prevalence in the samples used.
Prevalence of compulsive buying
19
Acknowledgement
The authors would like to thank Dr. Zsofia K. Takacs (Eötvös Loránd University, Hungary)
for her help with the meta-analysis.
Prevalence of compulsive buying
20
REFERENCES
1. Mukhopadhyay A, Johar GV. Indulgence as self-reward for prior shopping restraint: a
justification-based mechanism. Journal of Consumer Psychology. 2009;19:334–45.
2. Christenson G, Faber R, de Zwaan M, Raymond NC, Specker SM, Ekern MD, et al.
Compulsive buying: Descriptive characteristics and psychiatric comorbidity. Journal of
Clinical Psychiatry. 1994;55(1):5-11.
3. Black DW. A review of compulsive buying disorder. World Psychiatry. 2007;6(1):14.
4. Maraz A, van den Brink W, Demetrovics Z. Prevalence and construct validity of
compulsive buying disorder in shopping mall visitors. Psychiatry Research. In press.
5. Black DW, Repertinger S, Gaffney GR, Gabel J. Family history and psychiatric
comorbidity in persons with compulsive buying: preliminary findings. American
Journal of Psychiatry. 1998;155(7):960-3.
6. American Psychiatric Association. Diagnostic and statistical manual of mental disorders
(DSM-5). Fifth ed. Arlington, VA: American Psychiatric Association; 2013.
7. Faber RJ, O’Guinn TC, Krych R. Compulsive consumption. Advances in consumer
research. 1987;14(1):132-5.
8. McElroy SL, Keck JPE, Pope JHG, Smith JM, Strakowski SM. Compulsive buying: a
report of 20 cases. Journal of Clinical Psychiatry. 1994;55(6):242-8.
9. Koran LM, Faber RJ, Aboujaoude E, Large MD, Serpe RT. Estimated prevalence of
compulsive buying behavior in the United States. The American journal of psychiatry.
2006;163(10).
10. Black DW, Monahan P, Schlosser S, Repertinger S. Compulsive buying severity: an
analysis of compulsive buying scale results in 44 subjects. Journal of Nervous and
Mental Disease. 2001;189(2):123-6.
Prevalence of compulsive buying
21
11. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-
analysis of observational studies in epidemiology: a proposal for reporting. Journal of
the American Medical Association. 2000;283(15):2008-12.
12. Lwanga SK, Lemeshow S. Sample size determination in health studies: a practical
manual. Geneva: World Health Organization; 1991.
13. Naing L, Winn T, Rusli B. Practical issues in calculating the sample size for prevalence
studies. Archives of Orofacial Sciences. 2006;1(1):9-14.
14. Guo Z, Cai Y. Exploring the antecedents of compulsive buying tendency among
adolescents in China and Thailand: A consumer socialization perspective. African
Journal of Business Management. 2011;5(24):10198-209.
15. Villella C, Martinotti G, Di Nicola M, Cassano M, La Torre G, Gliubizzi MD, et al.
Behavioural addictions in adolescents and young adults: results from a prevalence
study. Journal of Gambling Studies. 2011;27(2):203-14.
16. Sterne JA, Sutton AJ, Ioannidis J, Terrin N, Jones DR, Lau J, et al. Recommendations
for examining and interpreting funnel plot asymmetry in meta-analyses of randomised
controlled trials. British Medical Journal. 2011;343.
17. Petticrew M, Roberts H. Systematic reviews in the social sciences: A practical guide.
Chichester: John Wiley & Sons; 2008.
18. Smith TC, Spiegelhalter DJ, Thomas A. Bayesian approaches to random‐effects meta‐
analysis: a comparative study. Statistics in Medicine. 1995;14(24):2685-99.
19. Sutton AJ, Abrams KR, Jones DR, Sheldon TA, Song F. Systematic reviews of trials
and other studies. Health Technology Assessment. 1998;2(19).
20. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-
analyses. British Medical Journal. 2003;327(7414):557-60.
Prevalence of compulsive buying
22
21. Higgins JPT. Assessing statistical heterogeneity: Chi-squared or I-squared. Newsletter
of the Cochrane Wounds Group. 2004:3-4.
22. Thompson SG, Higgins J. How should meta‐regression analyses be undertaken and
interpreted? Statistics in Medicine. 2002;21(11):1559-73.
23. Borenstein M, Hedges L, Higgins J, Rothstein H. Comprehensive meta-analysis version
2. Englewood, NJ: Biostat. 2005;104.
24. Englewood N. Comprehensive Meta-Analysis (Version 3). Available from
http://www.comprehensive.com: Biostat, Icl.; 2014.
25. Maraz A, Eisinger A, Hende B, Urbán R, Paksi B, Kun B, et al. Measuring compulsive
buying behaviour: Psychometric validity of three different scales and prevalence in the
general population and in shopping centres. Psychiatry Research. 2015;225:326-34.
26. Otero-López JM, Villardefrancos E. Five-Factor Model personality traits, materialism,
and excessive buying: a mediational analysis. Personality and Individual Differences.
2013;54(6):767-72.
27. Neuner M, Raab G, Reisch LA. Compulsive buying in maturing consumer societies: An
empirical re-inquiry. Journal of Economic Psychology. 2005;26(4):509-22.
28. Faber RJ, O'Guinn TC. A clinical screener for compulsive buying. Journal of Consumer
Research. 1992:459-69.
29. Lo H-Y, Harvey N. Compulsive Buying: Obsessive Acquisition, Collecting or
Hoarding? International Journal of Mental Health and Addiction. 2014;12(4):453-69.
30. Roberts JA, Manolis C, Pullig C. Contingent Self‐Esteem, Self‐Presentational
Concerns, and Compulsive Buying. Psychology & Marketing. 2014;31(2):147-60.
31. Leite P, Rangé B, Kukar-Kiney M, Ridgway N, Monroe K, Ribas Junior R, et al. Cross-
cultural adaptation, validation and reliability of the brazilian version of the Richmond
Compulsive Buying Scale. Revista Brasileira de Psiquiatria. 2013;35(1):38-43.
Prevalence of compulsive buying
23
32. Tommasi M, Busonera A. Validation of three compulsive buying scales on an Italian
sample. Psychological Reports. 2012;111(3):831-44.
33. Mueller A, Mitchell JE, Peterson LA, Faber RJ, Steffen KJ, Crosby RD, et al.
Depression, materialism, and excessive Internet use in relation to compulsive buying.
Comprehensive Psychiatry. 2011;52(4):420-4.
34. Lejoyeux M, Avril M, Richoux C, Embouazza H, Nivoli F. Prevalence of exercise
dependence and other behavioral addictions among clients of a Parisian fitness room.
Comprehensive Psychiatry. 2008;49(4):353-8.
35. Ridgway NM, Kukar‐Kinney M, Monroe KB. An expanded conceptualization and a
new measure of compulsive buying. Journal of Consumer Research. 2008;35(4):622-39.
36. Roberts JA, Manolis C. Baby boomers and busters: an exploratory investigation of
attitudes toward marketing, advertising and consumerism. Journal of Consumer
Marketing. 2000;17(6):481-97.
37. Li S, Unger A, Bi C. Different facets of compulsive buying among Chinese students.
Journal of behavioral addictions. 2014;3(4):238-45.
38. Harnish RJ, Bridges KR. Compulsive buying: the role of irrational beliefs, materialism,
and narcissism. Journal of Rational-Emotive & Cognitive-Behavior Therapy.
2014;33:1-16.
39. Unger A, Papastamatelou J, Okan EY, Aytas S. How the economic situation moderates
the influence of available money on compulsive buying of students—a comparative
study between Turkey and Greece. Journal of behavioral addictions. 2014;3(3):173-81.
40. Duroy D, Gorse P, Lejoyeux M. Characteristics of online compulsive buying in Parisian
students. Addictive Behaviors. 2014;39(12):1827-30.
Prevalence of compulsive buying
24
41. Leppink E, Odlaug BL, Lust K, Christenson G, Derbyshire K, Grant JE. Losing control:
Assaultive behavior as a predictor of impulse control disorders in young adults.
Comprehensive Psychiatry. 2014;55(8):1831-6.
42. Müller A, Smits DJ, Claes L, Gefeller O, Hinz A, de Zwaan M. The German version of
the Material Values Scale. GMS Psycho-Social-Medicine. 2013;10:1-9.
43. Harvanko A, Lust K, Odlaug BL, Schreiber LR, Derbyshire K, Christenson G, et al.
Prevalence and characteristics of compulsive buying in college students. Psychiatry
Research. 2013;210(3):1079-85.
44. Mazhari S. Association between problematic internet use and impulse control disorders
among Iranian university students. Cyberpsychology, Behavior, and Social Networking.
2012;15(5):270-3.
45. Claes L, Bijttebier P, Mitchell JE, de Zwaan M, Mueller A. The relationship between
compulsive buying, eating disorder symptoms, and temperament in a sample of female
students. Comprehensive Psychiatry. 2011;52(1):50-5.
46. Lejoyeux M, Richoux-Benhaim C, Betizeau A, Lequen V, Lohnhardt H. Money
attitude, self-esteem, and compulsive buying in a population of medical students.
Frontiers in Psychiatry. 2011;2:13-6.
47. Odlaug BL, Grant JE. Impulse-control disorders in a college sample: results from the
self-administered Minnesota Impulse Disorders Interview (MIDI). Primary care
companion to the Journal of clinical psychiatry. 2010;12(2):PCC.09m00842.
48. Bohne A. Impulse-control disorders in college students. Psychiatry Research.
2010;176(1):91-2.
49. MacLaren VV, Best LA. Multiple addictive behaviors in young adults: Student norms
for the Shorter PROMIS Questionnaire. Addictive Behaviors. 2010;35(3):252-5.
Prevalence of compulsive buying
25
50. Norum PS. Student internet purchases. Family and Consumer Sciences Research
Journal. 2008;36(4):373-88.
51. Wang C-C, Yang H-W. Passion for online shopping: The influence of personality and
compulsive buying. Social Behavior and Personality. 2008;36(5):693-706.
52. Yurchisin J, Johnson KK. Compulsive buying behavior and its relationship to perceived
social status associated with buying, materialism, self‐esteem, and apparel‐product
involvement. Family and Consumer Sciences Research Journal. 2004;32(3):291-314.
53. Roberts JA, Jones E. Money attitudes, credit card use, and compulsive buying among
American college students. Journal of Consumer Affairs. 2001;35(2):213-40.
54. Roberts JA. Compulsive buying among college students: An investigation of its
antedecents, consequences, and implications for public policy. Journal of Consumer
Affairs. 1998;32(2):295-319.
55. Mikołajczak-Degrauwe K, Brengman M. The influence of advertising on compulsive
buying—The role of persuasion knowledge. Journal of behavioral addictions.
2014;3(1):65-73.
56. Jung J, Yi S. Assessment of heterogeneity of compulsive buyers based on affective
antecedents of buying lapses. Addiction Research & Theory. 2014;22(1):37-48.
57. Alemis MC, Yap K. The role of negative urgency impulsivity and financial
management practices in compulsive buying. Australian Journal of Psychology.
2013;65(4):224-31.
58. Lejoyeux M, Guillot C, Chalvin F, Petit A, Lequen V. Exercise dependence among
customers from a Parisian sport shop. Journal of Behavioral Addictions. 2012;1(1):28-
34.
59. Kukar-Kinney M, Ridgway NM, Monroe KB. The role of price in the behavior and
purchase decisions of compulsive buyers. Journal of Retailing. 2012;88(1):63-71.
Prevalence of compulsive buying
26
60. Phau I, Woo C. Understanding compulsive buying tendencies among young
Australians: The roles of money attitude and credit card usage. Marketing Intelligence
& Planning. 2008;26(5):441-58.
61. Lejoyeux M, Mathieu K, Embouazza H, Huet F, Lequen V. Prevalence of compulsive
buying among customers of a Parisian general store. Comprehensive psychiatry.
2007;48(1):42-6.
62. Scherhorn G, Reisch LA, Raab G. Addictive buying in West Germany: An empirical
study. Journal of Consumer Policy. 1990;13(4):355-87.
63. Valence G, d'Astous A, Fortier L. Compulsive buying: Concept and measurement.
Journal of Consumer Policy. 1988;11(4):419-33.
64. Lau J, Ioannidis JP, Terrin N, Schmid CH, Olkin I. Evidence based medicine: The case
of the misleading funnel plot. British Medical Journal. 2006;333(7568):597.
65. Dittmar H. Understanding and diagnosing compulsive buying. In: Coombs RH, editor.
Handbook of addictive disorders: A practical guide to diagnosis and treatment.
Hoboken, NJ: Wiley; 2004. p. 411-50.
66. Rose P, Segrist DJ. Difficulty identifying feelings, distress tolerance and compulsive
buying: Analyzing the associations to inform therapeutic strategies. International
Journal of Mental Health and Addiction. 2012;10(6):927-35.
67. Kessler RC, McGonagle KA, Zhao S, Nelson CB, Hughes M, Eshleman S, et al.
Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United
States: results from the National Comorbidity Survey. Archives of General Psychiatry.
1994;51(1):8-19.
68. Takeuchi DT, Chung RC-Y, Lin K-M, Shen H, Kurasaki K, Chun C-A, et al. Lifetime
and twelve-month prevalence rates of major depressive episodes and dysthymia among
Chinese Americans in Los Angeles. American Journal of Psychiatry. 1998;155:1407-14.
Prevalence of compulsive buying
27
69. García I, Jódar L, Merello P, Santonja F-J. A discrete mathematical model for addictive
buying: predicting the affected population evolution. Mathematical and Computer
Modelling. 2011;54(7):1634-7.
70. Saad G. Evolutionary consumption. Journal of Consumer Psychology. 2013;23(3):351-
71.
71. Billieux J, Schimmentic A, Khazaald Y, Mauragea P, Heerena A. Are we
overpathologizing everyday life? A tenable blueprint for behavioral addiction research.
Journal of Behavioral Addictions. In press.
72. Bakermans-Kranenburg M J, van Ijzendoorn M H, Juffer F. Less is more: Meta-
analyses of sensitivity and attachment interventions in early childhood. Psychological
Bulletin. 2003;129:195–215.
73. Tabachnick BG, Fidell AS. Using multivariate statistics. Boston, MA: Pearson
Education. 2007.
74. Maraz A, Király O, Demetrovics Z. The diagnostic pitfalls of surveys: if you score
positive on a test of addiction, you still have a good chance not to be addicted. A
response to Billieux et al. 2015. Journal of Behavioral Addictions. In press.
Prevalence of compulsive buying
28
Fig. 1: Forrest plot of prevalence estimates in non-clinical populations by subgroup
Prevalence of compulsive buying
29
Fig. 2: Funnel plots to account for publication bias
Prevalence of compulsive buying
30
Table 1: Detailed prevalence estimates
Reference
Ref.
number
Country
Target population
Data
collection
method
N
Response
rate (%)
Sample
mean age
(range, if
available)
Women
(%)
Instrument
Prevalence
(%)
Comment
Adult representative
1.
Maraz et al. (2015)
25
Hungary
Population of Hungary
Mixed
(Interview +
self-report)
2710
85.1
40.3
50.8
QABB
1.85
193 completed the QABB, cut-off lowered to 8
2.
Otero-López and
Villardefrancos
(2013)
26
Spain
Population of Galicia
Self-report
(postal)
2159
41.6
35.4
51.9
GCBS
7.1
Quota sampling procedure; cut-off score of two
standard deviations above the mean (45 and
above)
3.
Koran et al. (2006)
9
USA
General population of
the US
Telephone
2513
56.3
47.1
66
CBS
5.8
Data collection method: US random-digit-dial
telephone calls, stratified by state
4.
Neuner, Raab and
Reisch (2005)
27
Germany
Population of former
Western Germany, 1991
Mixed
959
NR
42.9
53.3
GCBS
5.1
5.
Population of former
Eastern Germany, 1991
495
NR
51.9
GCBS
1
6.
Population of former
Western Germany, 2001
Interview
729
NR
42.1
49.3
GCBS
8
7.
Population of former
Eastern Germany, 2001
245
NR
53.3
GCBS
6.5
8.
Faber and O'Guinn
(1992)
28
USA
State of Illinois
Paper and
pencil
292
36.5
45.6
48
CBS
8.1
Adult non-representative
9.
Lo and Harvey
(2014)
29
Great
Britain
Users of several internet
forums
Online
521
NR
NR
62
CBS
12
Study 1
10.
Taiwan
Online
200
NR
NR
60
CBS
16
11.
Roberts, Manolis and
Pulling (2014)
30
USA
Unknown
Mixed (online
and word-of-
mouth)
409
NR
NR
(Categorical
presentation
of data)
50
CBS
13.9
12.
Leite et al. (2013)
31
Brazil
Normal population,
recruited via
advertisement
Online
202
NR
30.8
74
RCBS
10.9
The authors used the cut-off value 24 and
above instead of the conventional value of 25.
Prevalence of compulsive buying
31
13.
Tommasi and
Busonera (2012)
32
Italy
Unknown
Paper and
pencil
438
NR
36
70
CBS
7.1
14.
Mueller et al. (2011)
33
USA
Newspaper, Web site
postings, and flyers
Online
387
NR
39.1 (18-
74)
66
CBS
17
15.
Lejoyeux et al.
(2008)
34
France
Fitness club clients
Interview
300
99
28.6
42
QABB
49
16.
Ridgway, Kukar‐
Kinney and Monroe
(2008)
35
USA
University staff
members
Online
555
46
47
(20-77)
92.7
RCBS
8.9
The correlation between RCBS and CBS was
positive, r=0.62, p<0.01. Difference in
prevalence rates measured by the two
instruments is statistically significant.
17.
Online
CBS
5
18.
Roberts and Manolis
(2000)
36
USA
Baby Boomers
Interview
380
NR
NR (born
1946-1964)
55
CBS
7
No significant difference in prevalence between
“boomers” and “busters”
19.
Baby Busters
Interview
537
NR
NR (born
1965-1976)
59
CBS
11
University students
20.
Li, Unger and Bi
(2014)
37
China
Undergraduate students
NR
659
97.5
21.7
62,3
GCBS
6.7
21.
Harnish and Bridges
(2014)
38
USA
Undergraduate students
Mixed
184
NR
22.6
100
RCBS
20
22.
Unger et al. (2014)
39
Turkey
and
Greece
University students
Paper-and-
pencil
233
NR
23.2
56.9
GCBS
10.3
23.
Duroy, Gorse and
Lejoyeux (2014)
40
France
University students
Self-report
survey
200
NR
20.2
67
Echeburùa's
clinical
screener for
online CB
16
CB+ if at least 2 affirmative answers out of 5
24.
Leppink et al. (2014)
41
USA
University students
e-mail
2108
35.1
22.6
58.6
MIDI
3.6
25.
Müller et al. (2013)
42
Germany
Students
NR
214
NR
23
NR
CBS
3
26.
Harvanko et al.
(2013)
43
USA
Students
Online
1857
31
22.7
(18-58)
58
MIDI
3.6
27.
Mazhari (2012)
44
Iran
University students
Paper and
pencil
925
97.3
21.5
67
MIDI
6
28.
Claes et al. (2011)
45
Belgium
and
Germany
Undergraduate female
students
Paper and
pencil
211
72.3
22.6
(18-34)
100
CBS
5.2
29.
Lejoyeux et al.
(2011)
46
France
Medical students
NR
203
NR
23
66
QABB
11
Prevalence of compulsive buying
32
30.
Odlaug and Grant
(2010)
47
USA
Undergraduate students
Paper and
pencil
791
20.1
20.0
(17-24)
67.9
MIDI
15
31.
Bohne (2010)
48
Germany
College students
Paper and
pencil
571
NR
21.7
(17-48)
64
Screening
questions
based on
the DSM
0.4
32.
MacLaren and Best
(2010)
49
Canada
Undergraduate students
Paper and
pencil
948
NR
19.7
(18-25)
73.6
SPQ
21.8
33.
Norum (2008)
50
USA
Undergraduate students
Online
4429
27.2
18-27
69
CBS
8.1
34.
Wang and Yang
(2008)
51
Taiwan
University students
Paper and
pencil
403
57.6
20.8
(19-40)
NR
CBS
29.8
Correlation between OP and CB is 0.34
(p<0.01)
35.
PS
5.2
36.
Yurchisin and
Johnson (2004)
52
USA
Undergraduate students
Paper and
pencil
305
NR
20
(18-24)
85
ECBS
14.7
93% of compulsive buyers were female
37.
Roberts and Jones
(2001)
53
USA
College students and
Faculty staff
Paper and
pencil
406
NR
19
(17-21)
49
CBS
9
Same weighting scheme was used for each
item.
38.
Roberts (1998)
54
USA
College students in
Texas ("Baby Busters"
born between 1971 and
1975)
Telephone
300
2.5
21
(18-24)
62
CBS
6
Shopping-specific samples
39.
Maraz et al. (2015)
25
Hungary
Shopping mall
customers
Mixed
(personal
contact + e-
mail)
1447
5.1
31.2
63
RCBS
2.5
40.
ECBS-R
8.7
41.
QABB
13.3
Cut-off lowered to 8
42.
Mikołajczak-
Degrauwe and
Brengman (2014)
55
Belgium
Consumers from online
forums
Online
526
NR
42
68.8
RCBS
8.5
43.
Jung and Yi (2014)
56
Korea
Individuals with
frequent buying lapses
Online
813
NR
NR (52%
were 20-30)
74.7
CBS
57.6
44.
Alemis and Yap
(2013)
57
Australia
Shopping-related online
and off-line sites
Online
162
NR
36.2
79
CBS
8
45.
Lejoyeux et al.
(2012)
58
France
Customers from a sport
shop
Interview
500
77
29
43
QABB
24
Prevalence of compulsive buying
33
46.
Kukar-Kinney,
Ridgway (59)
59
USA
Customers of an Internet
women's clothing
retailer
E-mail
314
24.3
53
98.5
RCBS
16
47.
Phau and Woo (60)
60
Australia
Customers of a major
shopping complex
Interview
415
18
17-29
56
CBS
36.8
One item was removed from the CBS and
median-split at 3.51 applied
48.
Ridgway, Kukar‐
Kinney and Monroe
(2008)
35
USA
Customers
of an Internet women’s
clothing retailer
Online
309
23.9
53
(28-75)
98.5
RCBS
16
49.
Lejoyeux et al.
(2007)
61
France
Women entering a
department store
Interview
200
87
40.8
100
QABB
32.5
Notes: Response rate = Target sample size / net sample size. NR= not reported; CB+=compulsive buyer; CBS=Compulsive Buying Scale;
GCBS=German Compulsive Buying Scale; QABB=Questionnaire About Buying Behavior; RCBS=Richmond Compulsive Buying Scale;
MIDI=Minnesota Impulse Disorders Interview; ECBS=Edwards Compulsive Buying Scale; SPQ=Shorter PROMIS Questionnaire; Passion
Scale; NR=not reported
Prevalence of compulsive buying
34
Table 2: Assessment tools used to assess compulsive buying and the frequency of usage
Assessment Tool
Cut-off
Type of
Prevalence
Number of
prevalence
estimates
CBS
Clinically valid
Lifetime
19
GCBS
Clinically valid
Current
7
QABB
Conventional (10) and based
on psychometrics (8)
Mixed
6
RCBS
Clinically valid
Current
7
MIDI
Based on theory
Lifetime
4
ECBS
Conventional
Current
1
ECBS-R
Based on psychometrics
Current
1
SPQ
Based on psychometrics
Lifetime
1
PS
Based on psychometrics
Current
1
Screening questions
based on the DSM
Based on theory
NR
1
ESOCB
Conventional
Unknown
1
Total
49
Notes: PR= Prevalence rates; CBS=Compulsive Buying Scale; GCBS=German Compulsive Buying
Scale; QABB=Questionnaire About Buying Behavior; RCBS=Richmond Compulsive Buying Scale;
MIDI=Minnesota Impulse Disorders Interview; Edwards Compulsive Shopping Scale; SPQ=Shorter
PROMIS Questionnaire, Passion Scale; ESOCB=Echeburua’s screener for online CBB; NR=not
reported
