FULL-LENGTH REPORT Journal of Behavioral Addictions 8(1), pp. 123–134 (2019) DOI: 10.1556/2006.8.2019.08 Spanish validation of the pathological buying screener in patients with eating disorder and gambling disorder FERNANDO FERNÁNDEZ-ARANDA1,2,3*, ROSER GRANERO2,4, GEMMA MESTRE-BACH1,2, TREVOR STEWARD1,2, ASTRID MÜLLER5, MATTHIAS BRAND6,7, TERESA MENA-MORENO1,2, CRISTINA VINTRÓ-ALCARAZ1,2, AMPARO DEL PINO-GUTIÉRREZ1,8, LAURA MORAGAS1, NÚRIA MALLORQUÍ-BAGUÉ1,2, NEUS AYMAMÍ1,9, MÓNICA GÓMEZ-PEÑA1, MARÍA LOZANO-MADRID1,2, JOSÉ M. MENCHÓN1,3,10 and SUSANA JIMÉNEZ-MURCIA1,2,3* 1 Department of Psychiatry, Bellvitge University Hospital-IDIBELL, Barcelona, Spain 2 Ciber Fisiopatología Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain 3 Department of Clinical Sciences, School of Medicine, University of Barcelona, Barcelona, Spain 4 Departament de Psicobiologia i Metodologia de les Ciències de la Salut, Universitat Autònoma de Barcelona, Barcelona, Spain 5 Department of Psychosomatic Medicine and Psychotherapy, Hannover Medical School, Hannover, Germany 6 General Psychology: Cognition and Center for Behavioral Addiction Research (CeBAR), University of Duisburg-Essen, Duisburg, Germany 7 Erwin L. Hahn Institute for Magnetic Resonance Imaging, Essen, Germany 8 Departament d’Infermeria de Salut Pública, Salut Mental i Maternoinfantil, Escola Universitària d’Infermeria, Universitat de Barcelona, Barcelona, Spain 9 Departament de Psicologia Clínica i Psicobiologia, Facultat de Psicologia, Universitat de Barcelona, Barcelona, Spain 10 CIBER Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain (Received: April 16, 2018; revised manuscript received: November 2, 2018; second revised manuscript received: January 17, 2019; accepted: February 2, 2019) Background and aims: Pathological buying (PB) is a behavioral addiction that presents comorbidity with several psychiatric disorders. Despite the increase in the prevalence estimates of PB, relatively few PB instruments have been developed. Our aim was to assess the psychometric properties of the Spanish version of the pathological buying screener (PBS) and to explore the associations between PB, psychopathology, and personality traits. Methods: A total of 511 participants, including gambling disorder (GD) and eating disorder (ED) patients diagnosed according to DSM-5 criteria, as well as healthy controls (HCs), took part in the study. Results: Higher PB prevalence was obtained in ED patients than in the other two study groups (ED 12.5% vs. 1.3% HC and 2.7% GD). Confirmatory factor analysis (CFA) verified the 13-item structure of the PBS, and indexes of convergent and discriminant capacity were estimated. CFA confirmed the structure in two factors (excessive buying behavior and loss of control) with excellent internal consistency (α = .92 and .86, respectively). Good convergent capacity was obtained with external psychopathology and personality measures (positive correlations with novelty seeking and negative associations with self-directedness and harm avoidance were found). Good discriminative capacity to differentiate between the study groups was obtained. Discussion and conclusions: This study provides support for the reliability and validity of the Spanish adaptation of the PBS. Female sex, higher impulsivity, and higher psychopathology were associated with PB. Keywords: pathological buying, gambling disorder, eating disorder, pathological buying screener, validation, psychometric properties INTRODUCTION * Corresponding authors: Susana Jiménez-Murcia, PhD; Head of Pathological buying (PB) is characterized by impulsive drives Gambling Disorder Unit, Department of Psychiatry, Bellvitge and compulsive behaviors that lead to excessive shopping. University Hospital-IDIBELL, c/Feixa Llarga s/n, 08907 L’Hos- As a consequence of this excessive behavior, individuals with pitalet de Llobregat, Barcelona, Spain; Phone: +34 93 260 79 88; PB suffer significant psychological distress and legal/financial Fax: +34 93 260 76 58; E-mail: sjimenez@bellvitgehospital.cat; problems (Müller, Mitchell, & de Zwaan, 2013). No consensus Fernando Fernández‑Aranda, PhD; Head of ED Unit, Department has been reached regarding the classification of PB (Zadka & of Psychiatry, Bellvitge University Hospital‑IDIBELL, c/Feixa Olajossy, 2016) and the current version of the fifth edition of Llarga s/n, 08907 L’Hospitalet de Llobregat, Barcelona, Spain; Diagnostic and Statistical Manual of Mental Disorders Phone: +34 93 260 72 27; Fax: +34 93 260 76 58; E‑mail: (DSM-5; American Psychiatric Association [APA], 2013) ffernandez@bellvitgehospital.cat This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium for non-commercial purposes, provided the original author and source are credited, a link to the CC License is provided, and changes – if any – are indicated. ISSN 2062-5871 © 2019 The Author(s) Fernández-Aranda et al. did not categorize PB as a mental disorder (Piquet-Pessôa, Compulsive Buying Scale (Faber, O’Guinn, & Guinn, Ferreira, Melca, & Fontenelle, 2014; Potenza, 2014). Howev- 1992), the 13-item questionnaire developed by Edwards’ er, PB is often clinically classified as a behavioral addiction, group (Edwards, 1993); the Richmond Compulsive Buying due to its shared phenotype with other recognized behavioral Scale (RCBS; Ridgway, Kukar-Kinney, & Monroe, 2008), addictions, such as gambling disorder (GD; Granero, the 13-item Canadian Compulsive Buying Measurement Fernández-Aranda, Steward, et al., 2016; Grant, Potenza, Scale (Valence, d’Astous, & Fortier, 1988); the 14-item Weinstein, & Gorelick, 2010; Kellett & Bolton, 2009; Müller German Addictive Buying Scale (GABS; Scherhorn, et al., 2019). They are characterized by an early onset of Reisch, & Raab, 1990); and the 12-item Compulsive problematic behavior, significant comorbidity with other psy- Acquisition Scale (Frost, Steketee, & Williams, 2002), chiatric disorders, emotional dysregulation, high levels of while having their respective strengths, suffer from distinct novelty seeking, loss of control, and compulsivity (Black, shortcomings. For example, the RCBS is based on explan- Shaw, McCormick, Bayless, & Allen, 2012; Granero, atory models of compulsive buying behavior by linking PB Fernández-Aranda, Mestre-Bach, et al., 2016; Granero, to obsessive–compulsive spectrum disorders. Similarly, Fernández-Aranda, Steward, et al., 2016). In addition, a some studies have also criticized that the assessment of the dimensional categorization of these disorders, based on the PB construct (which includes components like impulse impulsive–compulsive spectrum, has received significant em- control, distress caused by others knowing a person’s pirical support in recent years (Dell’Osso, Allen, Altamura, purchasing habits, tension when not shopping, spending to Buoli, & Hollander, 2008; Di Nicola et al., 2014; Petruccelli improve mood, or irrational spending habits) could be et al., 2014). Behavioral addictions like GD, as well as other exclusively based on a single-dimension factor, and that it pathologies, such as eating disorders (EDs), are included can be next interpreted in a binary classification system within this framework (Chamberlain, Stochl, Redden, & (present vs. absent) based on a cut-off point [although Grant, 2018; Hollander et al., 1996).Taking personality empirically chosen based on a statistical criterion, 2 standard patterns into account, all these disorders show common deviations (SDs) above the mean value found in a general vulnerability factors, including temperamental traits and high population; Faber et al., 1992]. levels of impulsivity (Claes et al., 2012; Jiménez-Murcia et al., In order to overcome such limitations, Müller, Trotzke, 2015; Kim, Ranson, Hodgins, McGrath, & Tavares, 2018), Mitchell, De Zwaan, and Brand (2015) recently developed a although there are some differences between groups in per- new self-report scale to identify patients who present PB, the sonality dimensions, especially novelty seeking and harm pathological buying screener (PBS). One initial 20-item avoidance (del Pino-Gutiérrez et al., 2017; Jiménez-Murcia version was created to cover multiple compulsive buying- et al., 2015). related areas during the past 6 months: preoccupations/ Focusing specifically on PB, epidemiological studies indi- craving, lack of control, resistance against excessive spend- cate that its prevalence has risen in the past three decades ing, hiding purchases, emotional dysregulation, lies about although PB prevalence estimates can be rather imprecise, spending, degree of suffering, interference with daily func- with values ranging from 1% to 20% (Aboujaoude, 2014; tioning, and financial consequences. The validation analysis Maraz, Griffiths, & Demetrovics, 2016), with a pooled prev- of this tool in a large German community sample (n = 2,539) alence of 5% being the best estimate provided to date (Maraz through exploratory and confirmatory factorial analyses et al., 2016; Müller et al., 2019). The diverse origin of the revealed a satisfactory final version based on the selection samples, the variability in the definitions of PB, and a lack of of 13 items, structured into two factors (Müller et al., 2015): valid assessment tools are likely to be the main reasons why loss of control/consequences (10 items) and excessive such unreliable estimates have been reported (Duroy, Gorse, buying behavior (3 items). The authors provide psychometric & Lejoyeux, 2014; Maraz, Eisinger, et al., 2015; Maraz, evidence regarding the adequate reliability and validity of the van den Brink, & Demetrovics, 2015; Sussman, Lisha, & 13-item PBS, and suggest two thresholds to be the best Griffiths, 2011). When considering face-to-face interviews cut-off points for the PBS total score (29 and 39, which and strict diagnostic criteria, recent literature has highlighted represent 2 SDs above the mean and the average scoring for the appearance of PB as a comorbid feature in other disorders all scale items). (such as ED or other behavioral addictions), (Fernández- In comparison with the GABS (which is centered on the Aranda et al., 2008; Granero, Fernández-Aranda, Steward, concept of PB as a behavioral addiction, with the conse- et al., 2016; Mestre-Bach, Steward, Jiménez-Murcia, & quence of underestimating the loss-of-control aspects of Fernández-Aranda, 2017). This association suggests the buying episodes), the PBS has the advantage of considering existence of shared vulnerabilities between these disorders, both impulse control and addictive features of excessive especially in female clinical populations (Granero, Fernández- buying behavior. Moreover, the PBS includes items to Aranda, Steward, et al., 2016). In general terms, those cases measure craving and loss of control. Finally, a global with both conditions generally have higher psychopathology advantage of the PBS in contrast with all the other available and more dysfunctional personality traits (del Pino-Gutiérrez measures for PB symptoms is the inclusion of a time period et al., 2017; Granero, Fernández-Aranda, Mestre-Bach, et al., (behavior during the past 6 months). 2016; Jiménez-Murcia et al., 2015). Given the advantages of the PBS, the aim of this study Regarding the problem of assessment measures, few was to evaluate the psychometric properties of the Spanish self-rating scales have been developed to specifically mea- adaptation of this screening tool in a large clinical sample sure PB, and most of the existing tools have been developed including patients who met diagnostic criteria for GD, or by consumer researchers instead of psychopathology ED, when compared to healthy controls (HCs). The choice researchers (Black, 2011). Some self-report scales, such as of these disorders was based on the impulsive–compulsive 124 | Journal of Behavioral Addictions 8(1), pp. 123–134 (2019) Spanish validation of the PBS spectrum framework, since both disorders would be part of 2006). This interview also gathered sociodemographic data the spectrum. Furthermore, previous work by our group has (e.g., education and marital status) and additional relevant found compulsive buying behavior to be prevalent in clinical information. If patients were unable to complete the patients with EDs (Fernández-Aranda et al., 2006, 2008; evaluation on their own (e.g., due to being illiterate), these Jiménez-Murcia et al., 2015). In this vein, several studies on instruments were administered verbally by research staff. the etiology of behavioral addictions observed the existence of common risk and protective factors associated with these Measures disorders, such as personality traits, high levels of sensitivity to punishment and reward, materialism, etc. (Guerrero-Vaca The assessment included the Spanish PBS as well as et al., 2018). In previous studies on PB, carried out by our measures of global psychopathology and personality traits. group, we observed that in patients with GD, the prevalence Supplementary Table S1 includes the Cronbach’s α coeffi- of PB was low (less than 1%). However, when we consid- cients estimated in study sample. ered the PB subsample, the rate of the co-occurrence of Pathological buying screener (PBS; Müller et al., 2015). PB + GD increased to almost 19%. In addition, when sex The 13-item PBS was translated from English into Spanish in was taken into account, the comorbidity between BD and accordance with the International Test Commission Guidelines GD rose to 37.5% (Granero, Fernández-Aranda, Steward, for Translating and Adapting Tests (ITC, 2010). Experienced et al., 2016). bilingual clinical psychologists with extensive experience in We hypothesize that the Spanish version of the PBS would behavioral addictions translated the items from the original be structured into two factors as found in the original version PBS into Spanish. The translated items were then of the questionnaire, measuring loss of control/consequences back-translated by an independent native English speaker (TS), and excessive buying behavior. In addition, based on the and the observed differences between the both versions were shared vulnerabilities and comorbidity between behavioral discussed and resolved by common consensus. The Spanish addictions (including PB) with many psychopathological version of the PBS was finally reviewed by two other inde- conditions and personality traits documented in scientific pendent Spanish-speaking clinical psychologists, who had not research, we hypothesize the existence of a positive been involved in the previous back-translation process. association between higher PBS factor scores with worse Symptom Checklist-90 Items – Revised (SCL-90-R; psychopathological state [higher Symptom Checklist-90 Derogatis, 1990). This questionnaire evaluates a broad Items – Revised (SCL-90-R) scales] and more dysfunctional range of psychological problems and psychopathological personality traits (particularly, higher novelty seeking symptoms. This questionnaire contains 90 items and mea- scores and lower self-directedness and cooperativeness sures nine primary symptom dimensions: somatization, scores), and that these associations will emerge in three obsession–compulsion, interpersonal sensitivity, depres- groups. Finally, we hypothesize that a higher prevalence of sion, anxiety, hostility, phobic anxiety, paranoid ideation, PB will be present in the clinical groups compared with and psychoticism. It also includes three global indices: (a) a the HC group. global severity index, designed to measure overall psycho- logical distress; (b) a positive symptom distress index, to measure the intensity of the symptoms; and (c) a positive METHODS symptom total (PST), which reflects self-reported symp- toms. A validated Spanish version was used (González de Participants and procedure Rivera, De las Cuevas, Rodríguez, & Rodríguez, 2002). The Spanish validation scale obtained good psycho- The study was conducted between March 2016 and 2017. metrical indexes, with a mean internal consistency of .75 The total sample included n = 511 participants [two clinical (Cronbach’s α). In the study sample, the consistency was groups (n = 176 ED and n = 184 GD) and a HC group (n = between good (α = .782 for the paranoia subscale) and 151)]. Patients were referred to our Department of Psychiatry excellent (α = .981 for the composite scales). through general practitioners or via another healthcare pro- Temperament and Character Inventory – Revised (TCI-R; fessional and both clinical groups were consecutively admit- Cloninger, 1999). This is a reliable and valid 240-item ted to an outpatient psychiatric treatment. The hospital is a questionnaire that measures seven personality dimensions: public university hospital certified as a tertiary care center for four temperament (novelty seeking, harm avoidance, reward the treatment of GD and it oversees the treatment of highly dependence, and persistence) and three character dimensions complex cases through the state public healthcare system. (self-directedness, cooperativeness, and self-transcendence). Patients were diagnosed according to DSM-5 criteria (APA, All items are measured on a 5-point Likert-type scale. A 2013) by licensed clinical psychologists and psychiatrists. validated Spanish version was used (Gutiérrez-Zotes et al., The HC groups (n = 151) were volunteers recruited from the 2004). The scales in the Spanish revised version showed same catchment area who were screened for the presence of adequate internal consistency (Cronbach’s α mean value of ED, GD, or any psychiatric disorder. .87). In the study, consistency indices ranged from good For the clinical groups, the assessment was conducted (α = .70 for novelty seeking subscale) to very good (α = .859 prospectively in a single session. In addition to the assessment for persistence subscale). battery, patients underwent a semi-structured face-to-face Other sociodemographic and clinical variables. Addi- interview regarding their psychopathological symptoms tional data (clinical and social/family variables related (Fernández-Aranda & Turon, 1998; Jiménez-Murcia, to gambling) were measured using a semi-structured Aymamí-Sanromà, Gómez-Peña, Álvarez-Moya, & Vallejo, face-to-face clinical interview described elsewhere Journal of Behavioral Addictions 8(1), pp. 123–134 (2019) | 125 Fernández-Aranda et al. (Fernández-Aranda & Turon, 1998; Jiménez-Murcia et al., Bellvitge’s Ethics Committee of Clinical Research approved 2006). Some of the disorder-related variables covered in- the study. All subjects were informed about the study and all cluded the age of onset and the duration of the disorder. provided informed consent. Statistical analysis RESULTS Statistical analysis was carried out using Mplus 8 for Windows (Muthén & Muthén, 1998–2017). A multiple- Characteristics of the sample indicators multiple-causes (MIMIC) CFA tested the bifactor structure of the Spanish version of the PBS. MIMIC is a Table 1 includes the frequency distribution of sociodemo- factorial model used for structural-measurement invariance graphic variables and other main clinical variables (age and in multiple groups (i.e., the equivalence of structural and body mass index), duration of the disorder, and the raw PBS measurement coefficients over groups). In this study, MIM- factor scores. Significant differences emerged for all the IC CFA included the covariates participants’ sex and age, measures comparing the study groups, except for employ- and assessed the measurement of non-invariance by groups ment status. defined by group (HC, ED, and GD). In the ED subsample, a Supplementary Table S1 includes the distribution of the MIMIC CFA was also carried out to assess equivalence of psychometrical measures in the study (psychopathological the factor structure between the eating diagnostic subtypes state measured through the SCL-90-R and the personality [anorexia (AN), bulimia nervosa (BN), binge eating disorder traits registered through the TCI-R). (BED), and other specified feeding or eating disorder (OSFED)]. The CFA modeling was run in two steps: (a) Factor structure of the PBS an initial CFA model was obtained without including the measurement of invariance by groups; and (b) a second Table 2 contains the results of the CFA in the entire sample MIMIC-CFA model was obtained including measurement and Figure 1 shows the path diagram. The initial CFA two- of invariance by groups. Goodness-of-fit was evaluated using factor model, adjusted for the participants’ sex and age without standard statistical measures (Barrett, 2007): the root mean considering group, obtained adequate goodness-of-fit square error of approximation (RMSEA), Bentler’s compar- (RMSEA = 0.087, CFI = 0.901, TLI = 0.902, and SRMR = ative fit index (CFI), the Tucker–Lewis Index (TLI), and the 0.068), with an excellent Cronbach’s α (.92 and .86). The standardized root mean square residual (SRMR). RMSEA < MIMIC CFA (also adjusted by sex and age) including the 0.10, TLI > 0.9, CFI > 0.9, and SRMR < 0.1 were considered diagnostic group (GD-ED-HC) also achieved adequate adequate fit (Kline, 2016). The global predictive capacity of fitting (RMSEA = 0.099, CFI = 0.912, TLI = 0.922, and the model was measured by the coefficient of determination SRMR = 0.068), and it did not have a better fit to the (CD), an estimate of the global R2 for the model. data compared with the initial CFA model (χ2 = 133.31, The discriminative capacity of the PBS to differentiate df = 180, p = .996). Cronbach’s α estimates for the MIMIC between the study groups was studied via analysis of CFA measuring invariance by GD-ED-HC were also variance (ANOVA) for the raw factor scores (metric scale) between very good to excellent, being the highest in the ED and with logistic regression for the binary screening classi- group (.94 and .87) and the lowest in the HC group (.79 and fication (positive vs. negative, categorical scale, employing .82). Finally, the results of the MIMIC CFA obtained in the the cut-off points of 29 and 39 derived from the original ED subsample (also adjusted for sex and age) valuing invari- version, as well as the cut-off obtained in this study). ance for the ED diagnosis (AN-BN-BED-OSFED) showed Analyses were adjusted for the covariates of sex and age. equivalent factor structure by groups, with joint test for Effect size was estimated through Cohen’s d coefficient structural coefficients of χ2 = 7.70 (p = .808) and for measure- (|d| > 0.50 was considered moderate effect size and |d| > ment coefficients of χ2 = 5.29 (p = .508). 0.80 good effect size; Kelley & Preacher, 2012). These results as a whole confirm that the structure of the The convergent discriminative validity of the PBS raw PBS in two dimensions (measuring loss of control/ factor scores with the psychopathology (SCL-90-R scales) consequences and excessive buying behavior) is adequate and personality (TCI-R scales) scales was estimated via to assess PB in heterogeneous Spanish samples including partial correlations, adjusted for sex and age. Estimations GD, ED, and HC groups. In fact, to rule out the possible were obtained for the whole sample, as well as stratified by existence of a valid most parsimonious structure for the data, the study group. The following thresholds for effect size an additional MIMIC CFA was carried out for a were used: moderate = |r| > .24, good = |r| > .30, and one-factor solution, obtaining non-adequate adjustment large = |r| > .37 (Rosnow & Rosenthal, 1996). (RMSEA = 0.14, CFI = 0.814, TLI = 0.782, SRMR = Increases in type-I error due to multiple statistical 0.087, and CD = 0.046). comparisons were controlled with Simes’ correction method, a familywise error rate stepwise procedure, which offers a Capacity of the PBS to discriminate between study groups more powerful test than Bonferroni correction (Simes, 1986). Table 3 contains the results of the ANOVA (adjusted for the Ethics participants’ sex and age) comparing the PBS mean raw scores between the groups. The factor loss of control/ The study procedures were carried out in accordance with consequences achieved statistical discriminative capacity the Declaration of Helsinki. The University Hospital of to differentiate between HC and the clinical groups, but no 126 | Journal of Behavioral Addictions 8(1), pp. 123–134 (2019) Spanish validation of the PBS Table 1. Sample description Healthy control Eating disorder Gambling disorder (n = 151) (n = 176) (n = 184) n % n % n % χ2 df p Sex Female 130 86.1 157 89.2 12 6.5 320.50 2 <.001* Male 21 13.9 19 10.8 172 93.5 Origin Spanish 151 100.0 159 90.3 176 95.7 16.48 2 <.001* Foreign 0 0.0 17 9.7 8 4.3 Civil status Single 146 96.7 123 69.9 95 51.6 82.80 4 <.001* Married – partner 3 2.0 38 21.6 67 36.4 Divorced – 2 1.3 15 8.5 22 12.0 separated Education level Primary 3 2.0 94 53.4 127 69.0 205.79 4 <.001* Secondary 148 98.0 65 36.9 43 23.4 University 0 0.0 17 9.7 14 7.6 Employment Unemployed 46 35.4 44 28.9 64 37.2 2.63 2 .268 Employed 84 64.6 108 71.1 108 62.8 Mean SD Mean SD Mean SD F df p Age (years old) 21.44 3.48 31.71 12.84 41.04 12.86 132.90 2, 508 <.001* Body mass index (kg/m2) 22.11 4.05 28.40 11.57 26.51 4.73 25.18 2, 508 <.001* Duration of the disorder (years) – – 9.12 9.75 14.62 11.22 20.82 1, 358 <.001* PBS: loss of control – consequences 3.66 4.22 8.79 9.23 4.98 6.83 23.14 2, 508 <.001* PBS: excessive buying behavior 3.91 2.73 4.21 3.37 2.10 2.62 26.97 2, 508 <.001* Note. SD: standard deviation; df: degrees of freedom; PBS: pathological buying screener; –: not applicable for the group. *Significant comparison (.05 level). Table 2. Confirmatory factor analysis (standardized results): results adjusted for sex and age Model assessing invariance by study group Total sample Healthy control Eating disorder Gambling disorder (n = 511) (n = 151) (n = 176) (n = 184) Coef. SE p Coef. SE p Coef. SE p Coef. SE p Factor F1: loss of control – consequences Cannot stop thinking 0.706 0.024 <.001 0.361 0.035 <.001 0.769 0.028 <.001 0.692 0.035 <.001 about buying Feel embarrassed due to 0.754 0.021 <.001 0.531 0.040 <.001 0.800 0.026 <.001 0.699 0.032 <.001 buying Financial difficulties due 0.818 0.016 <.001 0.634 0.043 <.001 0.864 0.019 <.001 0.757 0.030 <.001 to buying Suffer distress from 0.771 0.020 <.001 0.493 0.037 <.001 0.807 0.027 <.001 0.753 0.029 <.001 buying habits Problems at work, school, 0.724 0.023 <.001 0.748 0.033 <.001 0.669 0.036 <.001 0.653 0.038 <.001 and other areas Feel better when go buying 0.602 0.030 <.001 0.246 0.027 <.001 0.689 0.040 <.001 0.613 0.038 <.001 Hide buying habits from 0.787 0.018 <.001 0.597 0.041 <.001 0.757 0.032 <.001 0.793 0.025 <.001 others Cannot stop buying 0.851 0.014 <.001 0.732 0.040 <.001 0.873 0.018 <.001 0.832 0.024 <.001 despite economy Try to limit buying 0.836 0.015 <.001 0.553 0.043 <.001 0.853 0.021 <.001 0.813 0.024 <.001 unsuccessfully Problems with others due 0.703 0.024 <.001 0.664 0.035 <.001 0.584 0.042 <.001 0.645 0.040 <.001 to buying (Continued) Journal of Behavioral Addictions 8(1), pp. 123–134 (2019) | 127 Fernández-Aranda et al. Table 2. (Continued) Model assessing invariance by study group Total sample Healthy control Eating disorder Gambling disorder (n = 511) (n = 151) (n = 176) (n = 184) Coef. SE p Coef. SE p Coef. SE p Coef. SE p Factor F2: excessive buying behavior Spend more time buying 0.737 0.023 <.001 0.648 0.037 <.001 0.727 0.036 <.001 0.772 0.029 <.001 than intended Buy more than needed 0.876 0.014 <.001 0.926 0.023 <.001 0.837 0.024 <.001 0.861 0.026 <.001 Buy more than planned 0.879 0.014 <.001 0.857 0.025 <.001 0.891 0.021 <.001 0.818 0.029 <.001 Internal consistency (α) F1 .923 .793 .942 .918 F2 .863 .819 .870 .843 Correlation between factors .774 .680 .860 .841 Fitting indexes RMSEA 0.087 0.099 CFI 0.901 0.912 TLI 0.902 0.922 SRMR 0.068 0.068 CD 0.330 0.071 Note. SE: standard error; RMSEA: root mean squared error of approximation; CFI: comparative fit index; TLI: Tucker–Lewis index; SRMR: standardized root mean squared residual; CD: coefficient of determination; α: Cronbach’s α. Figure 1. Path diagram of the MIMIC CFA in the study Table 3. Comparison of the PBS between study groups: ANOVA adjusted for sex and age Healthy Eating Gambling Pairwise comparisons control (HC; disorder (ED; disorder (GD; n = 151) n = 176) n = 184) HC vs. ED HC vs. GD ED vs. GD Mean SD Mean SD Mean SD p |d| p |d| p |d| F1: Loss of control – 3.15 4.22 7.96 9.23 6.20 5.13 <.001* 0.67** <.001* 0.65** .140 0.24 consequences F2: Excessive buying behavior 3.35 2.73 3.74 3.37 3.01 2.62 .263 0.13 .524 0.14 .137 0.23 Note. SD: standard deviation; PBS: pathological buying screener; ANOVA: analysis of variance. *Significant comparison (.05 level). **Effect size in the moderate (|d| > 0.50) to high range (|d| > 0.80). 128 | Journal of Behavioral Addictions 8(1), pp. 123–134 (2019) Spanish validation of the PBS statistical differences emerged for this factor comparing GD Regarding cut-off of 35, point prevalences were 0.7% for versus ED. The factor excessive buying behavior registered HC, 6.8% for ED, and 1.1% for GD, and no statistical statistically equal means in the three study groups. differences emerged in the pairwise comparisons (goodness- The first part of Supplementary Table S2 contains the of-fit achieved, with Hosmer–Lemeshow test: p = .143). results of the logistic regression models (also adjusted for The first part of Supplementary Table S3 assessed the sex and age) assessing the discriminative capacity of the capacity of the cut-off points 29 and 39 (obtained in the PBS global score, with the cut-off points of 29 and 39 original validation) to discriminate between the ED subtypes (considered as the most optimal in the original version of the included in the study (AN, BN, BED, and OSFED). Preva- scale). Using the cut-off point 29, the prevalence of positive lence for positive screening scores based on the cut-off 29 screening scores was 1.3% for HC, 2.7% for GD patients, ranged between 9.9% for OSFED and 17.1% for BN, and 12.5% for ED patients. This threshold obtained dis- whereas prevalence for the cut-off point 39 ranged between criminative capacity to identify ED compared with both HC 2.7% for AN and 7.3% for BN. No statistical differences and GD, but it was not able to differentiate between GD and were found in the pairwise comparisons between groups for HC. The cut-off point 39 obtained very low prevalence for any of the two thresholds (good fit was obtained with non- positive screening scores (0% in the HC group, 1.1% in significant results in Hosmer–Lemeshow tests: p = .156 and the GD group, and 5.7% in the ED group). This threshold p = .715). Considering the cut-off points estimated in the did not achieve discriminative capacity between the groups. study sample (20 and 35), prevalence with a cut-off of 20 Goodness-of-fit was obtained for both logistic regressions was 18.9% for AN, 29.3% for BN, 18.5% for BED, and measuring PBS accuracy to discriminate between groups for 28.2% for OSFED. Considering a cut-off of 35, 5.4% the cut-off points 29 and 39 (non-significant results in the prevalence was found for AN, 7.3% for BN, 7.4% for BED, Hosmer–Lemeshow test: p = .122 and p = .348). and 7.0% for OSFED. No statistical pairwise comparison The second part of Supplementary Table S2 contains the was obtained for these thresholds (good fitting was study of the discriminative capacity for the PBS considering achieved, with p values in the Hosmer–Lemeshow tests of the cut-offs obtained in this own study (estimated as the p = .495 and p = .864). mean + 2 SD) into the HC (cut-off 20) and into the clinical subsample (cut-off 35). Prevalences for the cut-off of 20 Associations between the PBS with external measures were 5.3% for HC, 25.0% for ED, and 13.0% for GD, with statistical capacity to differentiate between ED versus HC Table 4 contains the partial correlations (adjusted for sex and between GD versus HC (adequate fitting was obtained and age) between the PBS raw scores and global psycho- for this model, with Hosmer–Lemeshow test: p = .498). pathological state (SCL-90-R scores) and personality traits Table 4. Association between PBS and external measures: partial correlations adjusted for sex and age Total sample Healthy control Eating disorder Gambling disorder (n = 511) (n = 151) (n = 176) (n = 184) F1 F2 F1 F2 F1 F2 F1 F2 Psychopathology: SCL-90-R Somatization .294* .147 .407* .332* .187 .099 .184 .106 Obsessive–compulsive .318* .177 .240* .124 .240* .208 .285* .184 Interpersonal sensitivity .343* .215 .361* .270* .245* .239 .267* .176 Depressive .288* .138 .286* .161 .176 .139 .207 .149 Anxiety .336* .157 .385* .250* .247* .135 .291* .162 Hostility .306* .165 .302* .287* .201 .119 .286* .175 Phobic anxiety .384* .182 .430* .225 .315* .187 .326* .198 Paranoid ideation .326* .209 .357* .294* .284* .219 .242* .154 Psychotic .324* .167 .310* .210 .276* .207 .239* .141 GSI score .367* .194 .422* .298* .265* .192 .297* .189 PST score .361* .235 .395* .325* .274* .232 .293* .244* PSDI score .261* .098 .291* .189 .170 .100 .145 .054 Personality: TCI-R Novelty seeking .283* .254* .248* .261* .399* .342* .121 .160 Harm avoidance .159 .097 .139 .090 .126 .169 .004 −.021 Reward dependence −.127 −.082 −.070 −.045 −.136 −.095 −.087 −.102 Persistence −.075 −.078 .075 −.006 −.147 −.212 .025 .041 Self-directedness −.359* −.235 −.276* −.163 −.289* −.297* −.321* −.239 Cooperativeness −.306* −.230 −.297* −.182 −.234 −.200 −.379* −.314* Self-transcendence .192 .094 .069 .108 .175 .028 .288* .153 Note. F1: loss of control – consequences; F2: excessive buying behavior; SCL-90-R: Symptom Checklist-90 Items – Revised; TCI-R: Temperament and Character Inventory – Revised; GSI: global severity index; PST: positive symptom total; PSDI: positive symptom distress index; PBS: pathological buying screener. *Effect size in the moderate (|r| > .24) to high range (|r| > .30). Journal of Behavioral Addictions 8(1), pp. 123–134 (2019) | 129 Fernández-Aranda et al. (TCI-R scores). In the total sample, the factor loss of especially relevant in this clinical condition, given that the control/consequences obtained positive associations with prevalence of women who present PB is generally higher all SCL-90-R scales and novelty seeking, and negative than in men (Granero, Fernández-Aranda, Mestre-Bach, associations with self-directedness and cooperativeness. The et al., 2016; Jimenez-Murcia et al., 2015). factor excessive buying behavior only obtained positive The association between the PBS dimension scores with associations with novelty seeking scores. psychopathological state and personality is also a relevant Considering the partial correlations stratified by group, result, and it is congruent with empirical findings in the the results for the factor loss of control/consequences were scientific literature. In fact, one of the most salient char- similar across groups, but no association was found between acteristics of PB as a clinical condition within the this factor and cooperativeness in the ED group and an impulsive–compulsive spectrum is the nature of the behav- additional association emerged with self-transcendence in ior in itself. The impulsive–compulsive nature of PB, as well the GD group. For the clinical subsamples, this factor did as in other behavioral addictions such as GD, is character- not obtain relevant correlations with SCL-90-R subscales. ized by a failure to resist the impulse to carry out a specific Regarding the factor excessive buying behavior, in the HC act to obtain immediate gratification or relieve negative group, many positive associations emerged with the SCL- emotions, despite the harmful long-term (Choi et al., 2014; 90-R subscales, whereas in the ED group, a negative associ- El-Guebaly, Mudry, Zohar, Tavares, & Potenza, 2012; ation with self-directedness was found, and in the GD group, Granero, Fernández-Aranda, Baño, et al., 2016; Grant & this factor positively correlated with the SCL-90-R Chamberlain, 2014; Thompson & Prendergast, 2015; Yi, PST score and negatively correlated with cooperativeness. 2013). This can explain the positive association between Supplementary Table S4 compares ED patients with a PBS scores and novelty seeking, a personality trait measur- negative screening and a positive screening on the PBS (for ing an individual’s tendency to explore and impulsiveness, the cut-off point 29) in psychopathological and personality as described previously in samples with EDs (Jimenez- scores. Statistical differences emerged for all the compar- Murcia et al., 2015). The same argument could be isons on the SCL-90-R and the TCI-R, except for in self- the rationale of the negative association between PBS scores transcendence. As a whole, the clinical groups presented and self-directedness, a domain measuring the ability to higher levels of psychopathology than the HC group, as well auto-regulate and to reach chosen goals. In fact, other studies as more dysfunctional personality traits than HCs. have found a moderate association between impulsiveness– compulsiveness and deficits in self-regulatory capacity (Billieux et al., 2012; Claes et al., 2010). As previously DISCUSSION suggested (Jimenez-Murcia et al., 2015), multiple comorbid conditions around the impulsive spectrum are generally This study, besides aiming to test the validity of the PBS in a associated with higher psychopathology and poorer prog- large clinical Spanish sample including GD and ED patients, nosis (Kim et al., 2018). The extent to which this clinical and in HC participants, explored the prevalence of PB cluster could be a homogeneus endophenotype needs to be among the groups and the associations between PB, psycho- further tested in future studies, while also considering pathological symptoms, and personality traits. Three specific biomarkers and genetic factors (Slane, Klump, strengths of the study are its sample size, the inclusion of McGue, & Iacon, 2014). different clinical groups (including GD patients and ED Due to past indications of PB comorbidity with ED patients), but also HC, having used a validated comprehen- (prevalence between 12 and 18%) or GD (prevalence around sive psychometric screening procedure, and the use of 0.75%; Fernández-Aranda et al., 2006, 2008; Jimenez- MIMIC CFA procedures adjusted for the covariates parti- Murcia et al., 2015), the administration of instruments with cipants’ sex and age. The rationale of selecting these clinical adequate psychometric properties, such as the PBS, is conditions is based on the evidence of shared comorbidity essential in order to provide a more accurate diagnosis of and clinical features, but also some personality traits, be- PB. Further research should be undertaken to detect PB in tween PB, GD and ED (Claes et al., 2012; Davenport, clinical populations and in understudied groups, such as Houston, & Griffiths, 2012; Fernández-Aranda et al., older adults. The higher clinical severity shown in multiple 2008; Granero, Fernández-Aranda, Mestre-Bach, et al., diagnostic conditions associated with high impulsivity (e.g., 2016; Granero, Fernández-Aranda, Steward, et al., 2016; ED or GD with comorbid PB), which are frequently associ- Jiménez-Murcia et al., 2015; Mestre-Bach et al., 2017; ated with more psychopathology, dysfunctional personality Müller et al., 2011; Robbins & Clark, 2015). traits, and poorer prognosis, may lead us to design new The first main finding of the study was that the CFA strategies to combine with usual therapy approaches (e.g., confirmed the original structure of PBS in two-factors (loss CBT) in order to target underlying vulnerabilities of control/consequences and excessive buying behavior), (e.g., impulsivity or emotional dysregulation; Fernández- while additional psychometrical analyses obtained good Aranda et al., 2015; Giner-Bartolomé et al., 2015; Granero, convergent validity compared with external measures and Fernández-Aranda, Baño, et al., 2016; Tárrega et al., 2015). adequate discriminative capacity to differentiate between It is also worth noting that the excessive buying behavior study groups. factor did not discriminate between HC and the clinical The second main finding was that the prevalence levels groups. This suggests that loss of control may be more of PB obtained in the ED and GD groups in this study clinically relevant in discriminating between conditions than coincided with previous studies (Fernandez-Aranda et al., self-reported buying habits. Regarding the cut-off points, we 2008; Jimenez-Murcia et al., 2015), with sex being have assessed the discriminative capacity of the thresholds 130 | Journal of Behavioral Addictions 8(1), pp. 123–134 (2019) Spanish validation of the PBS 29 and 39 defined in the original PBS, because it is relevant accuracy of the Spanish version of the PBS and sheds light on to obtain psychometrical evidence about the effectiveness– its clinical correlates in different patient populations, mainly usefulness of previously available versions during the ad- higher psychopathology and dysfunctional personality traits. aptation phase of the assessment tools. If the factor structure Its application in moderate- to high-risk populations would and the cut points are equally valid in the different adapta- enable early identification of individuals with high vulnera- tions (and ideally with the original version), the instrument bility for developing PB and its related adverse psychosocial will feature greater external/ecological validity because it consequences. Future research should assess the usefulness of will allow for the comparison of results obtained in studies this self-report in samples of treatment-seeking patients who carried out in multiple/different populations. In this study, meet criteria for PB, to confirm the best cut-off points of this the cut-off point of 39 was found to be too high and lacked tool and to assess its capacity to change according to the sensitivity to identify the presence of the disorders analyzed developmental course of the disorder. in our work. The cut-off point of 29 resulted optimal to differentiate between ED with the other two diagnostic subtypes (GD and HC), as well as to differentiate the Funding sources: This manuscript and research were psychopathological and personality traits in the ED supported by grants from the Ministerio de Economía y subsample. Competitividad (PSI2015-68701-R), Ministerio de Sanidad, Servicios Sociales e Igualdad (PR338/17), Instituto de Salud Limitations Carlos III (ISCIII) (PI17/01167), Delegación del Gobierno para el Plan Nacional sobre Drogas (2017I067), and co- The two main limitations of this work are the absence of an funded by FEDER funds /European Regional Development external reference measure for PB and the lack of a sub- Fund (ERDF), a way to build Europe. sample of participants who met clinical criteria for PB. However, it must be argued that despite the high prevalence Authors’ contribution: FF-A, RG, AM, SJ-M, and JMM of PB in the population (Maraz et al., 2016), the number of contributed to the development of the study concept and patients seeking treatment for this condition is low, even in a design and supervised the study. RG performed the statisti- hospital unit specialized in behavioral addictions. This may cal analysis. FF-A, GM-B, TS, CV-A, and MB aided with be explained by shame and embarrassment due to financial our interpretation of data. TM-M, CV-A, GM-B, AdP-G, problems (or even illegal behaviors) those patients exhibit. LM, NM-B, NA, MG-P, and ML-M collected data. FF-A The low frequency of treatment-seeking patients in hospital and RG shared first authorship. settings makes the recruitment of a sample to validate an instrument especially difficult, still the second factor evalu- Conflict of interest: The authors declare no conflict of ating excessive buying behavior could be especially relevant interest. in populations in patients with PB, since it could serve as an indicator of self-reported distress stemming from buying habits. Future studies are needed to assess the validity of this factor in clinical PB patients. Other potential limitations REFERENCES could be the lack of adjustment for other sociodemographic variables apart from sex and age, which were significantly Aboujaoude, E. (2014). Compulsive buying disorder: A review and different between the groups, but it must be argued that this update. Current Pharmaceutical Design, 20(25), 4021–4025. study includes two MIMIC CFAs. 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