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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.
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Prevalence of compulsive buying
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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
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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
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Keywords: addictive behavior, consumer behavior, cross-cultural comparison, epidemiology,
publication bias
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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
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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
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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
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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).
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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
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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
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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.
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Please insert Figure 1 and Table 1 here
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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).
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Please insert Figure 2 about here
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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,
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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.
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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.
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Please insert Table 2 about here
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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.
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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
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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.
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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
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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
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carefully about how to conceptualise the disorder and to clearly separate out current versus
lifetime prevalence in the samples used.
Prevalence of compulsive buying
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Acknowledgement
The authors would like to thank Dr. Zsofia K. Takacs (Eötvös Loránd University, Hungary)
for her help with the meta-analysis.
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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
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
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
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
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
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
State of Illinois
Paper and
pencil
292
36.5
45.6
48
CBS
8.1
Adult non-representative
9.
Lo and Harvey
(2014)
29
Users of several internet
forums
Online
521
NR
NR
62
CBS
12
Study 1
10.
Online
200
NR
NR
60
CBS
16
11.
Roberts, Manolis and
Pulling (2014)
30
Unknown
Mixed (online
and word-of-
mouth)
409
NR
NR
(Categorical
presentation
of data)
50
CBS
13.9
12.
Leite et al. (2013)
31
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
Unknown
Paper and
pencil
438
NR
36
70
CBS
7.1
14.
Mueller et al. (2011)
33
Newspaper, Web site
postings, and flyers
Online
387
NR
39.1 (18-
74)
66
CBS
17
15.
Lejoyeux et al.
(2008)
34
Fitness club clients
Interview
300
99
28.6
42
QABB
49
16.
Ridgway, Kukar
Kinney and Monroe
(2008)
35
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
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
Undergraduate students
NR
659
97.5
21.7
62,3
GCBS
6.7
21.
Harnish and Bridges
(2014)
38
Undergraduate students
Mixed
184
NR
22.6
100
RCBS
20
22.
Unger et al. (2014)
39
University students
Paper-and-
pencil
233
NR
23.2
56.9
GCBS
10.3
23.
Duroy, Gorse and
Lejoyeux (2014)
40
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
University students
e-mail
2108
35.1
22.6
58.6
MIDI
3.6
25.
Müller et al. (2013)
42
Students
NR
214
NR
23
NR
CBS
3
26.
Harvanko et al.
(2013)
43
Students
Online
1857
31
22.7
(18-58)
58
MIDI
3.6
27.
Mazhari (2012)
44
University students
Paper and
pencil
925
97.3
21.5
67
MIDI
6
28.
Claes et al. (2011)
45
Undergraduate female
students
Paper and
pencil
211
72.3
22.6
(18-34)
100
CBS
5.2
29.
Lejoyeux et al.
(2011)
46
Medical students
NR
203
NR
23
66
QABB
11
Prevalence of compulsive buying
32
30.
Odlaug and Grant
(2010)
47
Undergraduate students
Paper and
pencil
791
20.1
20.0
(17-24)
67.9
MIDI
15
31.
Bohne (2010)
48
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
Undergraduate students
Paper and
pencil
948
NR
19.7
(18-25)
73.6
SPQ
21.8
33.
Norum (2008)
50
Undergraduate students
Online
4429
27.2
18-27
69
CBS
8.1
34.
Wang and Yang
(2008)
51
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
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
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
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
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
Consumers from online
forums
Online
526
NR
42
68.8
RCBS
8.5
43.
Jung and Yi (2014)
56
Individuals with
frequent buying lapses
Online
813
NR
NR (52%
were 20-30)
74.7
CBS
57.6
44.
Alemis and Yap
(2013)
57
Shopping-related online
and off-line sites
Online
162
NR
36.2
79
CBS
8
45.
Lejoyeux et al.
(2012)
58
Customers from a sport
shop
Interview
500
77
29
43
QABB
24
Prevalence of compulsive buying
33
46.
Kukar-Kinney,
Ridgway (59)
59
Customers of an Internet
women's clothing
retailer
E-mail
314
24.3
53
98.5
RCBS
16
47.
Phau and Woo (60)
60
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
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
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

Supplementary resource (1)

... Some authors consider this difference artificial, based on research that women are more open to admitting their pathological behavior (15). A meta-analysis with 49 articles included focused on the prevalence of CS in 16 countries (N = 32,000) and reported a 4.9% pooled prevalence in adults, with higher rates in university students (up to 11.5%) (16). The prevalence of CS in shopping-specific populations (e.g., individuals shopping in supermarkets or malls) had a value of up to 27.8% (16). ...
... A meta-analysis with 49 articles included focused on the prevalence of CS in 16 countries (N = 32,000) and reported a 4.9% pooled prevalence in adults, with higher rates in university students (up to 11.5%) (16). The prevalence of CS in shopping-specific populations (e.g., individuals shopping in supermarkets or malls) had a value of up to 27.8% (16). European surveys reported an increase in B/SD in the adult population over the last two decades, indicating the necessity to develop adequate strategies for early detection and treatment (17). ...
... In conclusion, the necessity of investigating the available treatments for B/SD is derived from multiple factors: the use of excessive buying as a way to cope with stress or isolation (27); negative functional consequences and risk of a chronic course (28); and the relatively high and increasing prevalence of problematic buying in the general population (1,16). The need to assess the efficacy of psychotherapeutic, pharmacological and combined therapies in BAs or SUDs is also motivated by the high societal and personal costs associated with healthcare in B/SD, high risk of psychiatric or somatic comorbidity, and lower quality of life (29). ...
Article
Full-text available
The prevalence of buying/shopping disorder (B/SD) has been increasing in the last two decades, and this disorder has a substantial negative impact on general functioning and quality of life. Therefore, a systematic review of the studies dedicated to the efficacy and tolerability of therapeutic interventions, both psychological and pharmacological, might help clinicians to decide on the most evidence-based treatment for these patients. In order to further increase the clinical usefulness of the current review, GRADE-based recommendations were formulated, where enough evidence was found to support such an approach. A number of five electronic databases were searched for single case reports, case series, open-label and double-blind, placebo/active intervention-controlled trials, but other secondary reports (i.e., systematic reviews and meta-analyses) were also included in this analysis. Studies with unspecified designs or those that do not report either qualitatively or quantitively the evolution of B/SD core manifestations were excluded. All data included in the secondary analysis were evaluated using the Joanna Briggs Institute critical appraisal checklists. A total number of 24 manuscripts (i.e., 12 clinical trials, eight case reports, and four reviews) were included. Most of the reviewed studies were of moderate quality, representing a certain limitation of this review and preventing the formulation of high-validity recommendations. Psychotherapy, especially cognitive behavioral therapy (CBT) seems to be the main intervention supported by the current evidence, followed by the combination of antidepressants and CBT, and serotoninergic antidepressants as monotherapy. There is an obvious need to further develop good-quality trials with a more significant number of participants with B/SD and longer follow-up periods.
... For instance, Koh, Tang, Gan, and Kwonet (2020) found a high rate of CB tendency among students in samples drawn from several universities in China, America, and South Korea. Maraz, Griffiths, and Demetrovics (2016) demonstrate in a meta-analysis that young consumers generally make more compulsive purchases. Tokgöz (2020) discovered that university students' consumption patterns are influenced by current consumer societies, and their tendency for CB has increased in Turkey. ...
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Modern tüketim toplumunda, istenmeyen bir satın alma davranışı olarak nitelendirilen kompulsif satın alma, üniversite öğrencileri arasında oldukça yaygınlaşmıştır. Bu nedenle öğrencilerin kompulsif satın alma davranışlarını etkileyen faktörlerin belirlenmesi önem arz etmektedir. Bu doğrultuda, bu araştırmanın iki amacı bulunmaktadır. Öncelikle materyalist eğilimlerin kompulsif satın alma davranışına etkisinde plansız satın alma davranışının aracı rolünün ortaya konması amaçlanmaktadır. Araştırmanın diğer amacı, dindarlığın bu aracı ilişkiyi düzenleyip düzenlemediğinin belirlenmesidir. Bu amaçlar doğrultusunda, 344 üniversite öğrencisinden çevrimiçi anket yöntemiyle veri elde edilmiştir. Düzenleyici aracılık analizleri, Hayes (2013) tarafından geliştirilen PROCESS makrosu kullanılarak yapılmıştır. Araştırma bulgularına göre, materyalizmin plansız ve kompulsif satın almayı olumlu yönde etkilediği belirlenmiştir. Bunun yanı sıra, plansız satın alma davranışının materyalizm ve kompulsif satın alma davranışı arasındaki ilişkide aracı rolü olduğu tespit edilmiştir. Bununla birlikte, plansız satın almanın materyalizm ile kompulsif satın alma davranışı arasındaki aracılık etkisinin dindarlık düzeyine göre değişmediği ortaya konmuştur. Bu araştırma, materyalizm ile kompulsif satın alma davranışı arasındaki mekanizmanın daha iyi anlaşılmasına imkân sağlayarak literatüre katkı sunmaktadır. Dindarlığın düzenleyici rolünü ele alması yönüyle de tüketici davranışlarında dindarlığın rolünün incelenmesi gerektiği çağrısında bulunan araştırmacılara katkı sağlamaktadır.
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Background Compulsive buying behavior or pathological buying is increasingly being recognized as a psychiatric disorder, and various psychosocial factors have been proposed to contribute to this problem. This study aimed to identify the association between compulsive buying behavior, stress, anxiety, depression, and impulsivity. Methods This cross-sectional, online survey used Google Forms to collect sociodemographic and clinical details of the participants between June 2021 and August 2021. In addition, they were evaluated on Pathological Buying Screener, Depression, Anxiety and Stress Scale – 21 (DASS-21), and Barratt Impulsiveness Scale – Brief (BIS-Brief). Results Out of 426 participants with valid responses, 169 (39.7%) qualified for pathological buying. The participant groups “with” and “without” pathological buying were comparable on sociodemographic characteristics, the preferred mode of shopping, and daily Internet use duration. Those “with” pathological buying scored significantly higher on DASS-21 and BIS-Brief. Both DASS-21 and BIS-Brief scores were predictors of pathological buying scores. Conclusions There is a significant association between pathological buying, psychological distress, and impulsivity.
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Purpose The purpose of this research is to shed light on the chain of psychological and behavioral effects that precipitates from a materialistic state of mind. Specifically, this study examines the psychological impact of materialism on younger consumers and in turn, their compulsive buying (CB) habits. This study also proposes possible interventions that may enhance consumer resistance to materialism and buying impulses and, ultimately, protect consumers’ mental and financial well-being. Design/methodology/approach An online survey was assembled in Qualtrics using reputable scales from extant marketing and psychology literature. The survey was completed by 193 young adults. Structural equation modeling was used to evaluate the proposed model of psychological drivers of CB. Findings The results suggest that young, materialistic consumers are at high risk of developing depression. Moreover, obsessive-compulsive (OC) tendencies and impaired self-esteem (SE) resulting from this depression may facilitate and fuel CB addiction. Despite no evidence for a direct link between depression and CB, the results indicate that this particular relationship is fully mediated by OC behavior and low SE. Social implications The discussion provides a detailed list of various behavioral modifications to help reduce consumer susceptibility to materialistic values and CB addiction. Originality/value This study contributes to consumer research by proposing an alternative conceptualization of the traditionally direct relationship assumed between materialism and CB in the literature. The facilitating roles of depression and its psychological byproducts (i.e. impaired SE and OC disorder) are examined as precursors of CB. Implications and suggestions for consumers battling CB addiction are provided.
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Consumers' inclinations towards materialism and compulsive buying are influenced by a variety of factors. Materialistic consumers face maladies that cause stress and lower subjective well-being and are unable to control their buying behaviour that in turn leads to social and financial issues. This paper aims to investigate the effect of emotional intelligence training on consumers' materialism and compulsive buying. The experimental design involves 36 respondents across both groups. Findings confirm the hypothesis that ability-based training programmes can help consumers improve their emotional intelligence whilst also lowering their levels of materialism and compulsive buying. In sum, the results extend the existing literature on consumer materialism by providing an explanation on how specific emotional ability-based training can diminish materialistic and excessive buying inclinations. The development of emotional intelligence skills-based training programmes contributes to more sustainable consumer behaviour, mitigating the vulnerability to materialism and related addictive behavioural consequences.
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This chapter provides an overview of software Comprehensive Meta‐Analysis (CMA) and shows how to use it to implement the ideas. The same approach could be used with any other program as well. The chapter also provides a sense for the look‐and‐feel of the program. CMA features a spreadsheet view and a menu‐driven interface. As such, it allows a researcher to enter data and perform a simple analysis in a matter of minutes. At the same time, it offers a wide array of advanced features, including the ability to compare the effect size in subgroups of studies, to run meta‐regression, to estimate the potential impact of publication bias, and to produce high‐resolution plots. The program is designed to work with studies that compare an outcome in two groups or that estimate an outcome in one group.
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Such diverse thinkers as Lao-Tze, Confucius, and U.S. Defense Secretary Donald Rumsfeld have all pointed out that we need to be able to tell the difference between real and assumed knowledge. The systematic review is a scientific tool that can help with this difficult task. It can help, for example, with appraising, summarising, and communicating the results and implications of otherwise unmanageable quantities of data. This book, written by two highly-respected social scientists, provides an overview of systematic literature review methods: Outlining the rationale and methods of systematic reviews; Giving worked examples from social science and other fields; Applying the practice to all social science disciplines; It requires no previous knowledge, but takes the reader through the process stage by stage; Drawing on examples from such diverse fields as psychology, criminology, education, transport, social welfare, public health, and housing and urban policy, among others. Including detailed sections on assessing the quality of both quantitative, and qualitative research; searching for evidence in the social sciences; meta-analytic and other methods of evidence synthesis; publication bias; heterogeneity; and approaches to dissemination.
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