Conceptualization and validity of the compulsivity construct in potentially addictive behaviors: a replication and extension study with the brief Granada Assessment for Cross-domain Compulsivity (GRACC18) in the gambling and video gaming domains

Conceptualization and validity of the compulsivity construct in potentially addictive behaviors: a replication and extension study with the brief Granada Assessment for Cross-domain Compulsivity (GRACC18) in the gambling and video gaming domains | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Conceptualization and validity of the compulsivity construct in potentially addictive behaviors: a replication and extension study with the brief Granada Assessment for Cross-domain Compulsivity (GRACC18) in the gambling and video gaming domains I. Muela, A. Hernando, J. R. Barrada, A. Lozano-Ruiz, J. López-Guerrero, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6680509/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background. The definition of compulsivity and its role in putative addictive behaviors remains unclear, partly due to previous research conflating its conceptualization as a general transdiagnostic trait with its understanding as an acquired feature of a specific activity as it spirals out of control. Methods. This study aims to validate a short version of the GRACC90 scale (GRACC18), designed to assess the degree to which video gaming and gambling have become compulsive in two independent samples of panel members who regularly engage in one of these activities—ranging in severity from recreational to pathological (though not formally diagnosed)—. Exploratory structural equation modeling (ESEM) was applied to examine the factorial structure of the scale and to test its structural invariance across both domains. Additionally, statistical associations between compulsivity scores and gambling- and gaming-related constructs—including problem severity, positive and negative affect, motives, and quality of life—were explored. Results. Findings indicate that (a) the compulsivity scale exhibits reliability, validity, and a unifactorial structure, (b) its structure remains invariant across domains, (c) compulsivity is strongly correlated with symptom severity in both gambling and gaming, moderately associated with negative affect and quality of life, and not significantly linked to positive affect, and (d) no interaction effect between domain and compulsivity was statistically significant in the regression models tested to predict the two affect dimensions and quality of life. Furthermore, (e) severity shows a stronger correlation with affect and quality of life than compulsivity, and (f) cross-loadings between severity and compulsivity items are notably low. Conclusion. These results support the theoretical validity of compulsivity as distinct from severity, demonstrating an identical structural composition and comparable involvement in symptoms and harms across video gaming and gambling domains. Future research should further investigate the emotional, cognitive, and computational foundations of compulsivity in these and other behavioral contexts. compulsivity addictive behaviors non-substance addiction validation gambling video-gaming Figures Figure 1 Introduction Compulsivity, defined as the excessive drive to engage in perseverative actions with adverse consequences, is a hallmark of behavioral and substance-related addictions (Brooks et al., 2017 ; Luigjes et al., 2019 ; Yücel et al., 2019 ). This construct is theorized to underpin well-established behavioral addictions, such as gambling disorder (GD), as well as putative ones, such as (Internet) gaming disorder (IGD), where individuals fail to control the problematic activity despite significant negative impacts on their daily functioning, interpersonal relationships, and quality of life (Muela et al., 2022 ; Yücel et al., 2019 ). Accurately understanding and measuring compulsivity is thus essential for advancing addiction research and designing effective interventions (López-Guerrero et al., 2025 ). It is important to distinguish between two senses of the term 'compulsivity': as an individual differences trait or as an acquired feature of specific activities or behaviors. The former refers to a transdiagnostic factor characterized by general behavioral inflexibility and feedback insensitivity. This trait manifests as a tendency to develop repetitive, habitual actions that are often difficult to control and persist despite adverse consequences (Hook et al., 2021 ). To measure this construct, researchers have developed psychometric tools (Albertella et al., 2020 ; Chamberlain & Grant, 2018 ), often supplemented by neuropsychological or laboratory-based assessments (e.g., van Timmeren et al., 2018 ). In dimensional diagnostic frameworks, trait compulsivity is viewed as a stable disposition that increases vulnerability to addictive behaviors across domains, or might result from changes in the brain attributable to chronic exposure to an addictive agent (Yücel et al., 2019 ). In the second sense—as an acquired characteristic of a given behavior—compulsivity develops over time, as an initially goal-driven action becomes detached from personal goals and ultimately uncontrolled. Preponderant addiction models have proposed learning and conditioning mechanisms to drive this transition (Perales et al., 2020 ), and, although these behaviors can broadly be described as compulsions, the term ‘compulsion’ has a more specific meaning as a symptom of Obsessive-Compulsive Disorder (OCD). To avoid confusion, we will refrain from using ‘compulsion’ to describe addictive behaviors. The present study restrictedly concerns the conceptualization of compulsivity as an acquired feature of specific behaviors, regarding which two critical gaps remain: the lack of consensus on its definitional boundaries (Berridge & Robinson, 2016 ; Everitt & Robbins, 2016 ; Koob & Schulkin, 2019 ; Luigjes et al., 2019 ), and the absence of psychometric instruments to assess it from an intensional perspective—one that considers the mechanisms underlying compulsive behaviors (Sussman, 2017 ). To tackle these challenges, we designed a multi-step research program. An initial systematic review (Muela et al., 2022 ) aimed to identify and categorize the various ways in which putative and well-established addictive behaviors have been conceptualized as compulsive. Specifically, this study analyzed all available scales for behavioral addictions, carefully examining their items to identify those describing behaviors as compulsive. These items were then systematically categorized into distinct operationalizations. As a result, six fundamental and qualitatively distinct operationalizations of compulsive behavior were identified: (a) automatic or habitual behavior occurring without conscious instrumental goals, (b) an overwhelming urge or desire that compels action and undermines control attempts, (c) an inability to stop or interrupt an activity once started, leading to episodes that are significantly longer or more intense than intended (bingeing), (d) a failure to refrain from the behavior despite conscious recognition that its negative consequences outweigh the positive ones, (e) attentional capture and cognitive hijacking, and (f) rigid rules, stereotyped behaviors, and rituals associated with task execution or completion. In the second study (Muela et al., 2023 ), we translated these operationalizations into an assessment tool: the Granada Compulsivity Assessment Scale (GRACC). This 90-item instrument was designed to capture the primary dimensions of compulsivity from an intensional perspective. The study empirically validated the GRACC90 in two independent samples of individuals regularly engaging in gambling and video gaming activities. Psychometric analyses demonstrated that the GRACC90 is a robust unidimensional measure, invariant across these behaviors, with strong predictive capacity for the severity of symptoms and their impact on affect and quality of life. Despite the sound psychometric properties of the GRACC90, its length poses practical limitations. So, their authors also proposed a shortened version of the scale, the GRACC18, developed by selecting the 18 items with the highest factor loads in the analyzed samples. Interestingly, 15 of the 18 items in the GRACC18 came from only three of the six operationalizations, meaning that these are sufficient to capture the construct: overwhelming urge or desire (5 items), inability to refrain from the behavior despite its negative consequences outweigh the positive ones (5 items), and attentional capture and cognitive hijacking (5 items). Two items reflected automatic or habitual behavior, and one item represented bingeing. None of the 18 selected items referred to stereotyped behavior. The reduced scale exhibited an even clearer unidimensional structure while retaining its predictive capacity for quality of life, severity, and negative affect. However, this shortened version has not yet been formally validated with an independent sample. The present study thus aligns with Muela et al. ( 2023 ) in terms of general methodology while building upon its strengths. Specifically, we employed similar assessment protocols for two groups of participants who engage regularly in either gambling or video gaming (n = 303 and n = 355, respectively) to (a) replicate the validity and unidimensional structure of compulsivity, as measured by the GRACC18 scale, and (b) confirm the scale’s structural invariance across behavioral domains. Furthermore, this study (c) explores whether compulsivity predicts wellbeing-related variables differently across domains. These variables include the severity of gambling- and gaming-related symptoms, general positive and negative affect, and quality of life. Examining how compulsivity correlates with other theoretically and practically relevant constructs is crucial for understanding its differential role in gambling- and gaming-related symptoms and harms. This analysis has the potential to provide empirical support for either divergence or convergence between these potentially addictive activities. In that sense, our hypotheses will remain open. Finally, we will assess (d) the relationship between compulsivity and problem severity. While these constructs are highly correlated, and are often used interchangeably—such as in the cases of problematic internet use (PIU) and compulsive internet use (CIU; Guertler et al., 2014 ; Liu et al., 2024 ; Meerkerk et al., 2009 ; Pérez-Sáenz et al., 2023 )—, we propose that compulsivity precedes and underpins severity, and hypothesize that psychometric evidence will support maintaining them as distinct constructs. Methods Participants and Procedure Adult members (≥ 18 years old) of a Spanish online panel, adhering to UNE ISO 20252 and ESOMAR standards, were invited to participate in an online survey. Confidentiality, anonymity, approximate duration, and incentive conditions were clearly communicated prior to obtaining explicit informed consent. Two distinct samples were recruited: regular gamblers and regular video gamers (excluding lottery-only gamblers and preventing duplicated participation). Once participants consented, they completed an initial ad-hoc questionnaire covering demographics and preferred gaming or gambling modalities, followed by a more extensive behavioral survey. Data quality was ensured through consistency checks and control items. Participants failing these checks were excluded. Participants were recruited until each sample reached a minimum of 300 valid cases., resulting in final samples sizes of 303 gamblers and 355 gamers. In the gambling sample, 42% scored at or above the cut-off for gambling disorder (GD9, a Gambling Disorder measure detailed below; ≥ 4 symptoms), while 21.19% of video gamers scored at or above the cut-off for clinical significance (IGD9, an Internet Gaming Disorder tool described below; ≥ 5 symptoms). Measures Initial ad-hoc survey. The initial survey included questions on sociodemographic characteristics: age, gender, education level, parents’ education level, monthly income (defined by the sum of all family members’ incomes), and their preferred types of video or gambling games. 18 [ -item Granada Assessment for Cross-Domain Compulsivity (GRACC18). This is the target measure of this study. This instrument is the shortened version of the GRACC90 (Muela et al., 2023 ), which was originally developed based on the systematic review presented in Muela et al. ( 2022 ). Responses were collected using 18 5-point Likert-type items (from 1 = totally disagree to 5 = totally agree ). Aside from the target activity specified in the instructions (gambling or video gaming), the gambling and gaming versions were the same. The wording of the items can be found in Muela et al. ( 2023 ), and also it is presented in the Results section (Table 3 ). Please note, however, that this study was carried out with the Spanish version and the English translation is not yet formally validated. Table 3 Item loadings and wordings. Item wording Loadings 15. I can’t stop the desire to play when I’m overpowered by certain bodily or internal sensations. 0.91 05. The game is on my mind even when I’m not playing, and I should be thinking about something else. 0.91 08. I often find myself thinking when I will play again, instead of focusing on what I should be doing. 0.90 12. I keep playing even though I feel guilty for my irrational behavior. 0.90 11. Often playing is something that I want to do so badly that I feel my heart beating faster. 0.90 10. Anything related to playing immediately catches my attention and interferes with what I’m doing at that moment. 0.90 09. Sometimes, the desire to play dominates me. 0.90 17. My thoughts continuously revolve around playing, even when I’m not playing. 0.90 03. I feel an uncontrollable desire to play even right after I’m done. 0.89 07. Spending a lot of time playing has become an almost involuntary habit. 0.88 18. I haven’t stopped playing, even though doing so is causing me more disadvantages than advantages. 0.88 16. Every time I play, I feel like I’m on a slippery slope that I can’t get back up. 0.88 06. I often play because I feel an irrepressible desire to play when a surge of strong emotions take over me. 0.87 04. I can’t stop playing, even though playing has had a negative impact on my life that clearly outweighs its positive impact. 0.87 14. Once I have started, I can’t stop playing unless something external forces me to. 0.86 13. I keep playing even though I am aware that the harm it does me is greater than the benefits. 0.85 02. I feel I can’t get thoughts about playing out of my head. 0.81 01. I continue to play even though I’m fully aware that I have increased the risks in certain aspects of my life so much that it’s not worth it. 0.69 Notes. The present study evaluated the original Spanish version of GRACC18. This translated version to English is presented only for information purposes, and therefore, it has not been formally validated. Quality of Life in Individuals Addicted to Psychoactive Substances Test (TECVASP). This instrument (Lozano-Rojas et al., 2007 ) evaluates the perception of physical and psychological wellbeing and health, both in general terms and in relation to substance use. Responses were collected using 22 5-point Likert-type items (from 1 = a lot to 5 = not at all ). Higher scores indicate better quality of life. In accordance with the purposes of this study (as in Muela et al., 2023 ), the original wording of items referring to substance use was slightly modified to reflect behaviors related to video gaming or gambling. In our study, the internal consistency of the adapted scale was satisfactory, with a Cronbach’s alpha of .89. Diagnostic Questionnaire for Gambling Disorder (GD9). The Spanish version of the GD-9 (Jiménez-Murcia et al., 2019 ), adapted from its earlier form based on DSM-IV-TR criteria (Jiménez-Murcia et al., 2009 ), was used to assess the nine diagnostic criteria for gambling disorder as defined in the DSM-5. The instrument includes 17 yes/no items (e.g., “Have you frequently thought about ways of getting money with which to gamble?”). For diagnostic criteria represented by two items, a criterion was considered met if the participant answered “yes” to at least one of them. Endorsing four or more criteria indicates probable gambling disorder. In this study, the scale demonstrated high internal consistency (α = .91). The total number of criteria endorsed was also treated as a continuous index of symptom severity, regardless of whether the diagnostic threshold was met. Only the gambling sample responded to the GD9. Diagnostic Questionnaire for Internet Gaming Disorder (IGD9). The nine items proposed by Mallorquí-Bagué et al. ( 2017 ) were used to assess the severity of video gaming problems. This scale evaluates the nine IGD criteria (one item for each criterion) proposed in Section III (emerging conditions) of the DSM-5 (American Psychiatric Association, 2013 ). A cut-off point of five or more criteria is used for the diagnosis of IGD. Consistent with the strategy followed in Muela et al. ( 2023 ), comparability between this and the GD9 scale was more effectively preserve using this form of the scale, rather than the IGDS9-BF (Beranuy et al., 2020 ), which assesses the same criteria using Likert-type items. Additionally, internal consistency was also adequate (α = .84 in our sample). As with the previous instrument, the number of symptoms the individual presents will be interpreted as a quantitative measure of problem symptoms severity, regardless of whether this number is sufficient or not for a positive screening. Only the video-gaming sample responded to the IGD9. Brief Gambling Motives Inventory (bGMI). This instrument (Barrada et al., 2019 ) consists of 18 items with a 4-points scale (from 0 = never/almost never to 3 = always/almost always ). This instrument assesses four gambling motives: Affect Regulation (seven items; e.g., "To forget my worries"; α = .94), Financial (four items; e.g., "To win money"; α = .84), Fun/Thrill (four items; e.g., "Because it’s fun"; α = .79), and Social motives (three items; e.g., "Because it makes a social gathering more enjoyable"; α = .75). Only the gambling sample responded to the bGMI. Video-Gaming Motives Questionnaire (VMQ). This instrument (López-Fernández et al., 2020 ) consists of with 24 items with response options ranging from 1 = strongly disagree to 5 = strongly agree . This questionnaire assesses eight video gaming motives: Cognitive Development (e.g., "Game imply a mental challenge"; α = .63), Competition (e.g., "I like to prove that I am better than other players"; α = .74), Coping (e.g., "Gaming helps me improve my mood"; α = .81), Customization (e.g., "I like to create my own word in games"; α = .81), Fantasy (e.g., "I feel immersed in a fantastic/fictitious world"; α = .84), Recreation (e.g., "It is entertaining"; α = .81), Social Interaction (e.g., "I keep in touch with my friends by gaming"; α = .86), and Violent Reward (e.g., "I enjoy the violent fights in video games"; α = .90). Only the video-gaming sample responded to the VMQ. All alpha’s coefficients correspond to the sample of this study. Positive and Negative Affect Scales (PANAS). Designed by Watson et al. ( 1988 ), this self-administered scale evaluates affect using two independent dimensions. The Negative Affect subscale evaluates a general dimension of psychological distress (e.g., "Nervous"), and the Positive Affect subscale evaluates the degree to which a person experiences positive emotions (e.g., “Interested”). This instrument consists of 20 items (10 items per dimension) on a 5-point Likert scale used to rate how accurately the adjective describes how the participant usually feels (1 = very slightly or not at all , 5 = a lot ). Cronbach’s alpha value for Negative Affect was .92, and for Positive Affect, .88. The Spanish adaptation of Sandín et al., ( 1999 ) was used. Statistical analyses An exploratory structural equation model (ESEM; Asparouhov & and Muthén, 2009) was used to evaluate the factor structure of the GRACC18. For unidimensional models without correlated uniqueness, ESEM, exploratory factor analysis, and confirmatory factor analysis lead to the same results. Although Muela et al. ( 2023 ) indicated that the GRACC18 was a unidimensional scale, we further conducted additional analyses –namely the scree-plot and parallel analysis- to evaluate the dimensionality of the scale (Garrido et al., 2013 ). This analysis was done with the full (combined) sample. Additionally, and in correspondence to the approach used in Muela et al. ( 2023 ), we conducted an analysis to evaluate measurement invariance across the type of activity (gambling or video gaming). Invariance was tested by assessing the degree of fit similarity between consecutive models. As a first step, the equality of form was evaluated. Within the ESEM framework, this process involves specifying the number of factors and pairs of correlated uniqueness. Next, we assessed invariance by examining the equality of thresholds and factor loadings across groups. The constraints were considered satisfactory if the change in the comparative fit index (CFI) was lower than .01 and the increase in the root mean square error of approximation (RMSEA) did not exceed .015 (Chen, 2007 ; Cheung & and Rensvold, 2002). That invariance was found by Muela et al. ( 2023 ). Given that we wanted to study the associations between compulsivity and severity, we tested whether GRACC18 and severity questionnaires were ‘pure’ measures of their intended constructs. If that was the case, two-factor models with all the items of both scales should provide an adequate fit and, importantly, the presence of relevant cross-loadings should be minimal. We fitted ESEM models, as those allow the presence of all cross-loadings. Those analyses were conducted with target rotations. To analyze the latent models, the robust weighted least squares was used estimator (WLSMV) implemented in MPlus was employed. To be indicative of an adequate fit to the data, the conventional cut-offs (e.g., Hu & Bentler, 1999 ) suggest values above .95 for CFI and Tucker-Lewis index (TLI). Similarly, values smaller than .06 for RMSEA and smaller than .08 for standardized root mean square residual (SMSR) support acceptable model fit. However, these cut-off values should be considered as general guidance rather than being interpreted as “golden rules” (Marsh et al., 2004 ). On one hand, it is important to emphasize that these cut-offs were originally established for confirmatory models using continuous data, so careful consideration should be given when interpreting these values (Xia & Yang, 2019 ). Furthermore, we need to consider that some fit indices, such as RMSEA, are sensitive to mean item loadings, with higher measurement quality leading to an apparently worse fit (McNeish et al., 2018 ). Modification indices were examined to identify specific areas of misfit. Cronbach’s alpha was used to evaluate internal consistency of each total scale score. We presented descriptives of the samples (sociodemographic information and scale scores). Also, associations between the dimensions of the GRACC18 and the other constructs assessed in this study were examined using Pearson correlations coefficients. These analyses were split by type of activity. A theoretically relevant question was whether compulsivity associations with additional variables varied by type of activity. We checked this in two ways. First, we compared the correlation sizes of the four variables present in both samples (severity, the two affect dimensions, and quality of life) with compulsivity scores, specifically by checking if the subtraction between the correlations with GRACC18 by activity was different from zero. Second, we compared the slopes when regressing the scores of the three constructs that were measured with the same scales (the two affect dimensions and quality of life) on compulsivity for both samples, that is, we tested if there was a compulsivity × sample interaction. We also present the plots for those interaction models. We also tested whether compulsivity or severity showed higher correlations with well-being measures (the two affect dimensions and quality of life). Data analyses were conducted using Mplus version 8.4 (Muthén & Muthén, 2019 ) and R version 4.4.3 (R Core Team, 2025). All datasets and scripts used in the analyses are publicly accessible via the OSF repository ( https://osf.io/nbw9d/?view_only=8ebe7735472846d7bb7f34bb734d7c6b ). Results Descriptive Analyses Sociodemographic data and measures of involvement in the main activity of interest (gambling, video gaming) for the two samples are shown in Table 1 . Table 1 Descriptive statistics for the participants in the two samples Gamblers Gamers n = 303 n = 355 Educational Level Number (percentage) No formal studies 7 (2.3%) 5 (1.4%) Compulsory education not finished 7 (2.3%) 6 (1.7%) Compulsory education finished 26 (8.6%) 32 (9.0%) High school/Professional training not finished 57 (18.8%) 62 (17.5%) High school/Professional training finished 83 (27.4%) 94 (26.5%) University studies not finished 25 (8.3%) 31 (8.7%) University studies finished 98 (32.3%) 125 (35.2%) Household monthly income Less than 600 euros 10 (3.3%) 7 (2.0%) Between 600 and 1000 euros 16 (5.3%) 32 (9.0%) Between 1001 and 1500 euros 58 (19.1%) 49 (13.8%) Between 1501 and 2000 euros 59 (19.5%) 81 (22.8%) Between 2001 and 2500 euros 73 (24.1%) 65 (18.3%) More than 2500 euros 87 (28.7%) 121 (34.1%) Gender Male 128 (42.2%) 206 (58.0%) Female 172 (56.8%) 148 (41.7%) Other 3 (1.0%) 1 (0.3%) Mean (Standard deviation) Age (years) 36.3 (11.7) 40.0 (11.1) Median [25-75th percentile] Weekly time spent (hours) 2.0 [1.0–6.0] 10.0 [5.0–20.0] Monthly expenditure (euros) 40.0 [10.5–110.0] 10.0 [0.0–30.0] Notes: Gambling severity was assessed with GD9 and gaming severity, with IGD9. Both measures are based on DSM-5 criteria. For weekly time and monthly money invested, the median and lower and upper bounds of the interquartile range [25-75th percentile] were calculated instead of mean and standard deviation to avoid the influence of extreme scores. Internal Structure and Consistency of the GRACC18 Both the scree-plot and the results of the parallel analysis clearly showed the convenience of retaining a single factor. In the parallel analysis, this first eigenvalue from the sample (13.89) was markedly larger than the eigenvalue from the randomly generated datasets (1.42), while for the second eigenvalue it was lower (0.69 versus 1.33). Model fit of the different tested factor models is shown in Table 2 . Fit indices of the unidimensional models for the GRACC18 items on the full sample were, overall, satisfactory (CFI = .991, TLI = .990, RMSEA = .079, SRMR = .021), except for the RMSEA, with values slightly above the reference value. Table 2 Goodness-of-fit indices for the different models. χ 2 df CFI TLI RMSEA SRMR ΔCFI ΔRMSEA Full sample M1. 1 factor 692.5 135 0.991 0.99 0.079 0.021 Subsamples M2. Gambling sample 487.3 135 0.988 0.987 0.093 0.027 M3. Video games sample 315.9 135 0.995 0.994 0.061 0.02 Invariance by type of activity M4. Equal form 799.4 270 0.992 0.991 0.077 0.024 M5. Equal loadings and thresholds 917.8 340 0.991 0.992 0.072 0.026 0.001 –.005 Craving and Severity M6. Gambling sample 627.1 298 0.991 0.99 0.06 0.031 M7. Video games sample 457.4 298 0.996 0.995 0.039 0.032 Notes: df = degrees of freedom; CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; SRMR = standardised root mean square residual; Δ = increment in fit index with respect to previous model. The χ 2 test p -value for all the models was < .001. When we looked at the modification indices, the maximum one (clearly over the rest; MI = 76.9, expected standardized parameter change = .39) was for the correlation between the uniqueness of Item 13 (“I keep playing even though I am aware that the harm it does me is greater than the benefits”) and 18 (“I haven’t stopped playing even though doing so is causing me more disadvantages than advantages”). Considering that (a) the other model fit indexes were adequate, (b) when we included this additional parameter model fit improved very little (new RMSEA = .075 for the full sample), (c) the value could be due to the high item loadings, and (d) there was not strong theoretical argument to support increasing the complexity of the model, we decided to retain a single factor without including correlations between items in the model. Items loadings are shown in Table 3 . Overall, loadings were very high in this factor ( M = .87; max = .91, "I can’t stop the desire to play when I'm overpowered by certain bodily or internal sensations"; min = .69, "I continue to play even though I'm fully aware that I have increased the risks in certain aspects of my life so much that it's not worth it"). When we tested this model in the gambling and video gaming samples separately, fit was satisfactory in both (CFI = .988/.995, TLI = .987/.994, RMSEA = .093/.061, SRMR = .027/.020), although slightly better for the video gaming sample. Regarding the model invariance with respect to type of activity, we compared fits of a model with equal form and equal loading and thresholds, and observed no meaningful change in fit between the two models (ΔCFI = .001, ΔRMSEA = –.005; Table 2 ). Finally, the internal consistency was very high for the GRACC18 (α = .98). Separability of Compulsivity (GRACC18) from Problem Symptoms Severity (GD9/IGD9) The model fit of the bidimensional models simultaneously including all the GRACC18 and severity items was adequate for both the gambling and video games samples: CFI = .991/.996, TLI = .990/.995, RMSEA = .060/.039, SRMR = .031/.032. As could be expected if both questionnaires were, to a large degree, pure measures of their intended constructs, the cross-loadings were small. For the gambling sample, the mean unsigned loading of GRACC18 items in the severity dimension was .10, with a maximum of .28, and, thus, no cross-loading over .30; the mean unsigned loading of severity items in the compulsivity dimension was .13, with a maximum of .30, and a single cross-loading over .30. For the video gaming sample, the mean unsigned loading of GRACC18 items in the severity dimension was .12, with a maximum of .38, and a single cross-loading over .30; the mean unsigned loading of severity items in the compulsivity dimension was .09, with a maximum of .20, and, thus, no single cross-loading over .30. The latent correlation between compulsivity and severity was .76 for the gambling sample and .81 for the video gaming sample. Associations with Other Variables Descriptive and Pearson correlations for all measures are shown in Table 4 for the gambling sample and Table 5 for the video gaming sample. The mean scores in the GRACC18 showed no statistically significant difference by type of activity [ M gambling = 2.32, SD gambling = 1.12, M video gaming = 2.29, SD video gaming = 1.09, t (634.5) = 0.339, p = .735, d = − 0.03]. Table 4 Measure descriptives (lower panel) and correlations (upper panel) between measures for the samples of gambling participants GRACC18 GD9 PANAS NA PANAS PA TECVASP bGMI Affect bGMI Financial bGMI fun bGMI Social GRACC18 Severity (GD9) .72 Neg. Affect - PANAS .46 .54 Pos. Affect - PANAS .04 .01 − .08 QoL (TECVASP) − .48 − .58 − .75 .12 bGMI Affect .69 .68 .48 .17 .47 bGMI Financial .26 .31 .36 .12 .35 .36 bGMI Fun .51 .50 .30 .19 .30 .72 .21 bGMI Social .51 .48 .39 .21 .41 .70 .25 .59 GRACC18 GD9 PANAS NA PANAS PA TECVASP bGMI Affect bGMI Financial bGMI fun bGMI Social Mean 2.32 3.09 21.12 28.83 63.66 6.90 6.24 5.61 3.00 Standard deviation 1.12 3.19 8.41 6.71 11.77 6.04 3.44 2.95 2.46 Skewness 0.28 0.51 0.42 -0.20 -0.58 0.46 -0.12 0.06 0.54 Kurtosis -1.31 -1.26 -0.70 -0.11 -0.34 -0.90 -1.00 -0.91 -0.71 Notes. All the correlations were statistically significant, p < .05, except for underlined values. GRACC18 scores were computed as means of item scores. For all other measures, scores are computed as sums of item scores. Table 5 Measure descriptives (lower panel) and correlations (upper panel) between measures for the video games sample. GRACC18 IGD9 PANAS NA PANAS PA TECVASP VMQ Dev. VMQ Comp. VMQ Coping VMQ Custom. VMQ Fantasy VMQ Recr. VMQ Social VMQ Violence GRACC18 Severity (IGD9) 0.73 PANAS Neg. aff. 0.42 0.52 PANAS Pos. aff. 0.04 -0.03 -0.13 QoL (TECVASP) -0.52 -0.61 -0.66 0.14 VMQ Development 0.37 0.37 0.17 0.17 -0.3 VMQ Competition 0.42 0.37 0.16 0.21 -0.23 0.49 VMQ Coping 0.4 0.4 0.26 0.11 -0.28 0.57 0.44 VMQ Customization 0.29 0.28 0.13 0.17 -0.18 0.5 0.43 0.52 VMQ Fantasy 0.37 0.35 0.18 0.11 -0.24 0.57 0.51 0.65 0.67 VMQ Recreation 0.08 0.15 -0.07 0.15 0 0.37 0.4 0.48 0.42 0.55 VMQ Social 0.49 0.32 0.17 0.24 -0.21 0.48 0.6 0.46 0.47 0.53 0.23 VMQ Violence 0.47 0.39 0.3 0.12 -0.28 0.35 0.43 0.28 0.24 0.37 0.13 0.46 GRACC18 IGD9 PANAS NA PANAS PA TECVASP VMQ Dev. VMQ Comp. VMQ Coping VMQ Custom VMQ Fantasy VMQ Recr. VMQ Social VMQ Violence Mean 2.29 2.19 18.39 29.77 66.35 8.27 8.56 8.73 8.68 8.83 10.52 7.03 5.83 Standard deviation 1.09 2.53 7.78 6.41 10.46 2.25 2.38 2.35 2.51 2.56 1.75 2.91 2.86 Skewness 0.46 1.03 0.64 -0.19 -0.53 -0.29 -0.28 -0.48 -0.5 -0.59 -1.28 0.01 0.57 Kurtosis -1.05 0.04 -0.57 -0.16 -0.39 -0.39 -0.83 -0.38 -0.53 -0.46 1.72 -1.26 -1.01 Notes. All the correlations were statistically significant, p < .05, except for underlined values. GRACC18 scores were computed as means of item scores. For all other measures, scores are computed as sums of item scores. Regarding the other measures evaluated in both samples, all the correlations were in line with what could be expected. GRACC18 scores presented the highest correlation with severity scores ( r gambling = 0.72, r videogaming = 0.73), followed by quality of life scores ( r gambling = -0.48, r videogaming = -0.52), and with negative affect ( r gambling = 0.46, r videogaming = 0.42). All correlations were statistically significant (all ps < .001). In both subsamples, correlations with positive affect were not statistically significant ( r gambling = 0.04, p = .478 r videogaming = 0.04, p = .477). For the gambling sample, GRACC18 scores were positively associated with all gambling motives ( M r = .49), with a maximum correlation with affect regulation motives ( r = .69) and a minimum correlation with financial motives ( r = .26; all ps < .001). For the video gaming sample, GRACC18 scores were also positively and statistically significantly ( ps < .001) associated with all video gaming motives ( M r = .36), except with recreation motives ( r = .08, p = .114). The maximum correlation was with social interaction ( r = .49), whereas the minimum (statistically significant) correlation was with customization motives ( r = .29). We compared differences in correlation sizes between the GRACC18 scores and variables for the two samples and there were no statistically significant differences (for severity: r difference = -0.01; z = -0.193, p = .847; for negative affect: r difference = .04; z = 0.624, p = .530); for positive affect: r difference = .00; z = 0.039, p = .969; for quality of life: r difference = .05; z = 0.779, p = .436.). We calculated regression models in which we predicted the two affect dimensions or quality of life including the interaction between sample and compulsivity. No interaction effect was statistically significant: for negative affect, b = -0.46, t (654) = -0.889, p = .374; for positive affect, b = -0.02, t (654) = -0.049, p = 0.961; and for quality of life, b = 0.00, t (654) = 0.006, p = .995. The plots corresponding to those regression models can be seen in Fig. 1 . Additionally, we compared the correlation sizes of GRACC18 scores and severity scores with the two affect dimensions and quality of life for both samples. Severity scores showed a statistically higher correlation compared to GRACC18 scores. For negative affect, gambling: r difference = − .08; z = -2.229, p = .026; and video gaming: r difference = -0.11; z = -2.884, p = .004. For quality of life, gambling: r difference = .10; z = 2.803, p = .005; video gaming: r difference = .08; z = 2.450, p = .014. No significant differences in correlation were found for positive affect (gambling: r difference = .03; z = 0.692, p = .489; video gaming: r difference = .06; z = 1.517, p = .129). Discussion To this date, compulsivity has remained an ambiguous term in the existing literature, making it essential to clearly define its meaning before examining its role in addictive behaviors. This study builds on previous qualitative and quantitative research that has contributed to its conceptualization and measurement—specifically in cases where compulsivity emerges as an acquired characteristic of behaviors that become uncontrollable and resistant to negative consequences. A key contribution of this work is the strong validation of compulsivity as a psychometrically sound construct, as measured by the GRACC18 scale, across at least two potentially escalating activities: video gaming and gambling. Although compulsivity has been conceptualized in various ways, the GRACC18 primarily reflects three core facets—an irrepressible urge, an inability to refrain from the activity despite recognizing its disutility, and a hijacking of attentional and cognitive resources. Together, these elements form a unified construct capable of predicting associated harms, such as problem severity, negative affect, and diminished quality of life, while remaining distinct from them. Crucially, the clear psychometric separation between compulsivity and severity reinforces the conceptualization of severity as a relatively superficial blend of symptoms (including behaviors and consequences; Tseng et al., 2023 ), whereas compulsivity more directly taps onto the underlying processes driving them. In other words, severity is based on an extensional definition of addictive behaviors, while compulsivity has the potential to contribute to an intensional understanding of their mechanisms. While this remains largely speculative, urges and hijacking likely represent two interconnected aspects of the same phenomenon—one embodying the motivational and emotional dimensions of craving (whether appetitive or aversive), and the other capturing its cognitive components. When these forces become sufficiently intense, they can create the subjective impression that the problematic behavior is no longer under voluntary control despite disutility. The second important contribution of the present work is the corroboration that the GRACC18 —now validated in a sample distinct from the one in which it was developed—is structurally invariant across the gaming and gambling domains. Invariance is accompanied by a mostly coincident pattern of correlations between compulsivity and other constructs of interest in the two samples, with compulsivity strongly predicting severity, moderately predicting negative affect and poorer quality of life, and non-significantly predicting positive affect. Moreover, the slope of these effects did not differ across domains for any outcome, which indicates that gambling- and gaming-related severity and harms are equally accounted for by compulsivity in the two samples. In other words, our findings support the convergence of video gaming- and gambling-related problems concerning the role of compulsivity. Contrary to our initial predictions at the origin of this research endeavor, compulsivity does not appear to play a more significant role in problematic gambling than in problematic video gaming—at least within the predominantly subclinical samples examined in this and previous studies. Nonetheless, this conclusion goes only as far as self-report measures can take us. As discussed earlier, compulsivity is primarily characterized by intense urges, an inability to refrain despite subjective disutility, and the hijacking of cognitive and attentional resources. However, although the GRACC scale was carefully designed to ensure clarity, specificity, and validity for the construct of interest (see Muela et al., 2022 , 2023 ), it provides little insight into the origins of these features. Research has shown, for instance, that the experience of craving can vary significantly across different domains. In gambling disorder, this urge typically manifests as an aversive tension state, alleviated only through gambling. In contrast, within the problematic video gaming domain, it may stem from a heightened anticipation of rewards, or a profound sense of boredom and lack of stimulation when gaming is unavailable (King et al., 2016 ). Complementarily, recent evidence shows that the contribution of appetitive and aversive processes to craving may vary, not only across behavioral domains, but also across activity modalities in the same domain (López-Guerrero et al., 2023 ). Further research to assess the qualitative nature of compulsivity and the cognitive and emotional processes responsible for it is thus warranted (López-Guerrero et al., 2024). In all other respects, the present study closely mirrors the primary findings obtained with the original GRACC90 scale. However, it now does so across two independent samples of gamblers and gamers, employing parallel protocols. Beyond the previously discussed associations, compulsivity was found to be linked to gambling and gaming motives in both respective samples. The only notable differences between the two domains were the relatively stronger associations between coping/affect regulation motives and compulsivity in gambling, and between social motives and compulsivity in video gaming. This could be interpreted as support for the proposals that compulsive gambling could be understood as arising from biased choice processes under negative affect (Hogarth, 2020 ), whereas social pressure and comparison processes could be strongly involved in dysfunctional video gaming (Krassen & and Aupers, 2022; Park et al., 2020 ; Yang & Yao, 2025 ). However, since motives were assessed using different scales in each sample, these differences remain difficult to interpret. Additionally, severity scores demonstrated stronger associations with poorer quality of life and increased negative affect than GRACC18 compulsivity scores. This distinction supports the separation between compulsivity and the symptoms of the problematic activity. Severity measures incorporate items reflecting negative individual and social consequences that may lead to impairment across multiple levels, whereas compulsivity is more closely tied to the behavioral characteristics themselves, irrespective of their broader impact on daily functioning. While compulsive behaviors often carry the potential for harm, the extent of harm depends solely on the degree to which the compulsive behavior disrupts essential aspects of an individual's life. Limitations, strengths and final remarks The present study is not without limitations. The first stems from the reliance on self-report measures, which inherently fail to capture etiological differences between superficially similar responses to items. Even though, in this case, the wording has been designed to minimize ambiguities found in previous instruments, self-report methods still pose interpretative challenges. Secondly, concerns regarding sample composition must be acknowledged. The study was conducted on a non-clinical, non-representative sample recruited via an online panel. While this warrants caution in extrapolating findings to clinical populations or the general public, it is noteworthy that nearly identical results were observed not only across the two samples in this study but also in two separate studies using comparable protocols with independent samples. Finally, the cross-sectional nature of the data introduces additional limitations. Most notably, while the findings suggest a distinction between compulsivity, severity, and other relevant constructs (such as motives, affect, and quality of life), they do not imply any specific causal relationships among these factors. The low cross-loadings between the compulsivity and severity scales represent a strength, as they indicate that shared variability is not artificially inflated by item overlap. However, the notion that compulsivity precedes and drives symptoms remains more of a plausible interpretation than an empirical finding. On the other hand, this study also boasts significant strengths, chief among them being its integration into a broader, multistep research effort. This process began with a fundamental conceptual exploration of compulsivity and has culminated in the development of a valid and reliable assessment tool. The GRACC90 and GRACC18 scales were largely developed through a data-driven approach and can be adapted for any putatively addictive activity with minimal rewording. In this regard, the final tool represents a promising advancement in addressing the serious fragmentation problem, and thus the ubiquitous jingle-jangle fallacy, in behavioral addiction measurement. If common mechanisms underpin various behavioral addictions, research will undoubtedly benefit from the availability of standardized measures applicable across different activities, rather than relying on distinct, non-comparable instruments for each. In summary, the structured, progressive development of GRACC is arguably one of the most comprehensive and systematic efforts in the field of compulsivity and behavioral addictions to date. The resulting measure demonstrates exceptional psychometric properties, particularly in terms of reliability and convergent validity. Notably, despite its strong correlation with severity measures, it extends beyond them in terms of intensionality, as it does not rely on an extensional set of features. This serves as compelling evidence that certain symptoms may be incidental or secondary in defining a behavior as compulsive and ultimately problematic. Looking ahead, our research will focus on exploring the etiology of compulsivity across different domains, aiming to determine whether compulsivity in these contexts is driven by shared emotional, cognitive, and computational processes. Abbreviations bGMI Brief Gambling Motives Inventory CFI Comparative Fix Index CIU Compulsive Internet Use DSM Diagnostic and Statistical Manual of Mental Disorders ESEM Exploratory Structural Equation Model GD Gambling Disorder GD9 Diagnostic Questionnaire for Gambling Disorder GRACC Granada Assessment for Cross-Domain Compulsivity GRACC18 18-item Granada Assessment for Cross-Domain Compulsivity GRACC90 90-item Granada Assessment for Cross-Domain Compulsivity IGD Internet Gaming Disorder IGD9 Diagnostic Questionnaire for Internet Gaming Disorder MI Modification Indices OCD Obsessive-Compulsive Disorder PANAS Positive and Negative Affect Scale PIU Problematic Internet Use RMSEA Root Mean Squared Error of Approximation SMSR Standardized Root Mean Square Residual TECVASP Quality of Life in Individuals Addicted to Psychoactive Substances Test TLI Tucker-Lewis Index VMQ Video-gaming Motives Questionnaire WLSMV Weighted Least Square Mean and Variance adjusted Declarations Ethics approval and consent to participate The procedure of this study complies with the ethical standards of the Helsinki Declaration of 1975, as revised in 2008. Both subsamples were recruited as part of the project Gbrain3 (see Funding), approved by the Human Research Ethics Committee of the University of Granada (reference number 1830/CEIH/2020). All participants were informed about the nature of the study and all provided informed consent. Consent for publication Not applicable. Availability of data and materials The database and code files for these analyses are available at the OSF website (https://osf.io/nbw9d/?view_only=8ebe7735472846d7bb7f34bb734d7c6b). Competing interests The authors declare no competing interests. Funding Work by the core team (IM, JLG, FJR and JCP) has been supported by grants from the Spanish Government ( Research costs : Gbrain3, reference PID2020-116535 GB-I00, Convocatoria 2020 de Proyectos de I+D+I de Generación de Conocimiento , funded by Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, MICIU/AEI/10.13039/501100011033; Publication costs : Gbrain 4, reference PID2023-150731NB-I00, Convocatoria 2023 de Proyectos de I+D+I de Generación de Conocimiento , funded by Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación; MICIU/AEI/10.13039/501100011033/, and FEDER-EU “Una manera de hacer Europa”). IM is supported by an individual research grant (PRE2018-085150, Ministerio de Ciencia, Innovación y Universidades). JLG’s work is supported by an individual research grant (PRE2021-100665), funded by MICIU/AEI/10.13039/501100011033 and by “ESF+”. FJR's work is supported by an individual research grant (FPU21/00462), funded by the Agencia Estatal de Investigación and by ESF+. Authors’ contributions IM: Study concept and design, conducting the experiment, writing – original draft. JFN: Study concept and design, conducting the experiment, writing – review and editing. JRB: Study concept and design, analysis and interpretation of data, writing – original draft. AH: Analysis and interpretation of data, writing – review and editing. ALR: Analysis and interpretation of data, writing – review and editing. JLG: Writing – review and editing. FJR: Writing – review and editing. JCP: Principal Investigator, study concept and design, obtained funding, study supervision, writing – original draft. The draft of the manuscript was revised and approved by all the authors. All authors had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. 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Upward social comparison predicts the consumption intention of e-sports among Chinese college students. Social Behav Personality: Int J. 2025;53(2):1–11. https://doi.org/10.2224/sbp.14058 . Yücel M, Oldenhof E, Ahmed SH, Belin D, Billieux J, Bowden-Jones H, Carter A, Chamberlain SR, Clark L, Connor J, Daglish M, Dom G, Dannon P, Duka T, Fernandez-Serrano MJ, Field M, Franken I, Goldstein RZ, Gonzalez R, Verdejo-Garcia A. A transdiagnostic dimensional approach towards a neuropsychological assessment for addiction: An international Delphi consensus study. Addiction. 2019;114(6):1095–109. https://doi.org/10.1111/add.14424 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers invited by journal 04 Jul, 2025 Editor assigned by journal 02 Jul, 2025 Editor invited by journal 06 Jun, 2025 Submission checks completed at journal 04 Jun, 2025 First submitted to journal 04 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6680509","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481883442,"identity":"ab38fdf4-d504-4bcd-b305-434ecf2d2534","order_by":0,"name":"I. Muela","email":"","orcid":"","institution":"1.\tDepartment of Experimental Psychology; Mind, Brain, and Behavior Research Center (CIMCYC), Universidad de Granada","correspondingAuthor":false,"prefix":"","firstName":"I.","middleName":"","lastName":"Muela","suffix":""},{"id":481883444,"identity":"1f8dd099-b6ad-4c12-b5b5-9a5ffa7f962f","order_by":1,"name":"A. Hernando","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYHACNgYeBhsGBmYStaSRruUwCer5Zzc/e/Cm4nzidnb2h58rGGzsCWqRuHPM3HDOmduJO5sZkiXPMKQlNhDUcyPBTJq37XbihsMMByQbGA4nENQhfyP9G1DLOaAWxuafDQz/CTvM4EYOyJYDQC3MbEBbDjASdJjhjZwyyTlnko03HGZjs2wwSCbsF7kb6dsk3lTYyW44f/zxzYYKO8IOQ3cnqRpGwSgYBaNgFGAFAKOdO3pFZZUnAAAAAElFTkSuQmCC","orcid":"","institution":"2.\tDepartment of Psychology and Sociology; Universidad de Zaragoza","correspondingAuthor":true,"prefix":"","firstName":"A.","middleName":"","lastName":"Hernando","suffix":""},{"id":481883446,"identity":"0833ec11-4fdd-4a3d-819b-42c648f6e794","order_by":2,"name":"J. R. Barrada","email":"","orcid":"","institution":"2.\tDepartment of Psychology and Sociology; Universidad de Zaragoza","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"R.","lastName":"Barrada","suffix":""},{"id":481883449,"identity":"fbd068a8-a373-4792-b599-71cfd84483fc","order_by":3,"name":"A. Lozano-Ruiz","email":"","orcid":"","institution":"3.\tFaculty of Health Sciences; Valencian International University – VIU","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"","lastName":"Lozano-Ruiz","suffix":""},{"id":481883450,"identity":"9f9331c9-fecb-40be-b3bb-3b8440884a26","order_by":4,"name":"J. López-Guerrero","email":"","orcid":"","institution":"1.\tDepartment of Experimental Psychology; Mind, Brain, and Behavior Research Center (CIMCYC), Universidad de Granada","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"","lastName":"López-Guerrero","suffix":""},{"id":481883451,"identity":"46a6b06a-36ab-48e7-ac90-2861bf3480c9","order_by":5,"name":"F. J. Rivero","email":"","orcid":"","institution":"1.\tDepartment of Experimental Psychology; Mind, Brain, and Behavior Research Center (CIMCYC), Universidad de Granada","correspondingAuthor":false,"prefix":"","firstName":"F.","middleName":"J.","lastName":"Rivero","suffix":""},{"id":481883452,"identity":"e5dd6a60-8f7e-4507-83b3-567a16bf546b","order_by":6,"name":"J. F. Navas","email":"","orcid":"","institution":"4.\tDepartment of Personality, Assessment, and Clinical Psychology; Complutense University of Madrid","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"F.","lastName":"Navas","suffix":""},{"id":481883453,"identity":"705a38bd-b816-463c-8368-2bd44bf103d6","order_by":7,"name":"J. C. Perales","email":"","orcid":"","institution":"1.\tDepartment of Experimental Psychology; Mind, Brain, and Behavior Research Center (CIMCYC), Universidad de Granada","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"C.","lastName":"Perales","suffix":""}],"badges":[],"createdAt":"2025-05-16 11:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6680509/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6680509/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86326718,"identity":"3d3dac5a-5dfb-4572-b714-48b97ad16575","added_by":"auto","created_at":"2025-07-09 11:05:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":413644,"visible":true,"origin":"","legend":"\u003cp\u003eRegression models for negative affect, positive affect and quality of life, including the interaction between domain (sample) and compulsivity.\u003c/p\u003e\n\u003cp\u003eNotes. No interaction effects were statistically significant (all \u003cem\u003eps\u003c/em\u003e ≥ .374).\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6680509/v1/474f63a945e07a03be4bd9d8.jpeg"},{"id":86328509,"identity":"0bd4e7da-efa1-4a2d-9061-676ae2334379","added_by":"auto","created_at":"2025-07-09 11:29:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2100267,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6680509/v1/604bb92c-12fd-4027-b14f-a92b748eec95.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Conceptualization and validity of the compulsivity construct in potentially addictive behaviors: a replication and extension study with the brief Granada Assessment for Cross-domain Compulsivity (GRACC18) in the gambling and video gaming domains","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCompulsivity, defined as the excessive drive to engage in perseverative actions with adverse consequences, is a hallmark of behavioral and substance-related addictions (Brooks et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Luigjes et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Y\u0026uuml;cel et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This construct is theorized to underpin well-established behavioral addictions, such as gambling disorder (GD), as well as putative ones, such as (Internet) gaming disorder (IGD), where individuals fail to control the problematic activity despite significant negative impacts on their daily functioning, interpersonal relationships, and quality of life (Muela et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Y\u0026uuml;cel et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Accurately understanding and measuring compulsivity is thus essential for advancing addiction research and designing effective interventions (L\u0026oacute;pez-Guerrero et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt is important to distinguish between two senses of the term 'compulsivity': as an individual differences trait or as an acquired feature of specific activities or behaviors. The former refers to a transdiagnostic factor characterized by general behavioral inflexibility and feedback insensitivity. This trait manifests as a tendency to develop repetitive, habitual actions that are often difficult to control and persist despite adverse consequences (Hook et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To measure this construct, researchers have developed psychometric tools (Albertella et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chamberlain \u0026amp; Grant, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), often supplemented by neuropsychological or laboratory-based assessments (e.g., van Timmeren et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In dimensional diagnostic frameworks, trait compulsivity is viewed as a stable disposition that increases vulnerability to addictive behaviors across domains, or might result from changes in the brain attributable to chronic exposure to an addictive agent (Y\u0026uuml;cel et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the second sense\u0026mdash;as an acquired characteristic of a given behavior\u0026mdash;compulsivity develops over time, as an initially goal-driven action becomes detached from personal goals and ultimately uncontrolled. Preponderant addiction models have proposed learning and conditioning mechanisms to drive this transition (Perales et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and, although these behaviors can broadly be described as compulsions, the term \u0026lsquo;compulsion\u0026rsquo; has a more specific meaning as a symptom of Obsessive-Compulsive Disorder (OCD). To avoid confusion, we will refrain from using \u0026lsquo;compulsion\u0026rsquo; to describe addictive behaviors.\u003c/p\u003e\u003cp\u003eThe present study restrictedly concerns the conceptualization of compulsivity as an acquired feature of specific behaviors, regarding which two critical gaps remain: the lack of consensus on its definitional boundaries (Berridge \u0026amp; Robinson, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Everitt \u0026amp; Robbins, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Koob \u0026amp; Schulkin, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Luigjes et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the absence of psychometric instruments to assess it from an intensional perspective\u0026mdash;one that considers the mechanisms underlying compulsive behaviors (Sussman, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo tackle these challenges, we designed a multi-step research program. An initial systematic review (Muela et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) aimed to identify and categorize the various ways in which putative and well-established addictive behaviors have been conceptualized as compulsive. Specifically, this study analyzed all available scales for behavioral addictions, carefully examining their items to identify those describing behaviors as compulsive. These items were then systematically categorized into distinct operationalizations. As a result, six fundamental and qualitatively distinct operationalizations of compulsive behavior were identified: (a) automatic or habitual behavior occurring without conscious instrumental goals, (b) an overwhelming urge or desire that compels action and undermines control attempts, (c) an inability to stop or interrupt an activity once started, leading to episodes that are significantly longer or more intense than intended (bingeing), (d) a failure to refrain from the behavior despite conscious recognition that its negative consequences outweigh the positive ones, (e) attentional capture and cognitive hijacking, and (f) rigid rules, stereotyped behaviors, and rituals associated with task execution or completion.\u003c/p\u003e\u003cp\u003eIn the second study (Muela et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we translated these operationalizations into an assessment tool: the Granada Compulsivity Assessment Scale (GRACC). This 90-item instrument was designed to capture the primary dimensions of compulsivity from an intensional perspective. The study empirically validated the GRACC90 in two independent samples of individuals regularly engaging in gambling and video gaming activities. Psychometric analyses demonstrated that the GRACC90 is a robust unidimensional measure, invariant across these behaviors, with strong predictive capacity for the severity of symptoms and their impact on affect and quality of life.\u003c/p\u003e\u003cp\u003eDespite the sound psychometric properties of the GRACC90, its length poses practical limitations. So, their authors also proposed a shortened version of the scale, the GRACC18, developed by selecting the 18 items with the highest factor loads in the analyzed samples. Interestingly, 15 of the 18 items in the GRACC18 came from only three of the six operationalizations, meaning that these are sufficient to capture the construct: overwhelming urge or desire (5 items), inability to refrain from the behavior despite its negative consequences outweigh the positive ones (5 items), and attentional capture and cognitive hijacking (5 items). Two items reflected automatic or habitual behavior, and one item represented bingeing. None of the 18 selected items referred to stereotyped behavior. The reduced scale exhibited an even clearer unidimensional structure while retaining its predictive capacity for quality of life, severity, and negative affect.\u003c/p\u003e\u003cp\u003eHowever, this shortened version has not yet been formally validated with an independent sample. The present study thus aligns with Muela et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in terms of general methodology while building upon its strengths. Specifically, we employed similar assessment protocols for two groups of participants who engage regularly in either gambling or video gaming (n\u0026thinsp;=\u0026thinsp;303 and n\u0026thinsp;=\u0026thinsp;355, respectively) to (a) replicate the validity and unidimensional structure of compulsivity, as measured by the GRACC18 scale, and (b) confirm the scale\u0026rsquo;s structural invariance across behavioral domains.\u003c/p\u003e\u003cp\u003eFurthermore, this study (c) explores whether compulsivity predicts wellbeing-related variables differently across domains. These variables include the severity of gambling- and gaming-related symptoms, general positive and negative affect, and quality of life. Examining how compulsivity correlates with other theoretically and practically relevant constructs is crucial for understanding its differential role in gambling- and gaming-related symptoms and harms. This analysis has the potential to provide empirical support for either divergence or convergence between these potentially addictive activities. In that sense, our hypotheses will remain open.\u003c/p\u003e\u003cp\u003eFinally, we will assess (d) the relationship between compulsivity and problem severity. While these constructs are highly correlated, and are often used interchangeably\u0026mdash;such as in the cases of problematic internet use (PIU) and compulsive internet use (CIU; Guertler et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Meerkerk et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; P\u0026eacute;rez-S\u0026aacute;enz et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u0026mdash;, we propose that compulsivity precedes and underpins severity, and hypothesize that psychometric evidence will support maintaining them as distinct constructs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants and Procedure\u003c/h2\u003e\u003cp\u003eAdult members (\u0026ge;\u0026thinsp;18 years old) of a Spanish online panel, adhering to UNE ISO 20252 and ESOMAR standards, were invited to participate in an online survey. Confidentiality, anonymity, approximate duration, and incentive conditions were clearly communicated prior to obtaining explicit informed consent. Two distinct samples were recruited: regular gamblers and regular video gamers (excluding lottery-only gamblers and preventing duplicated participation).\u003c/p\u003e\u003cp\u003eOnce participants consented, they completed an initial ad-hoc questionnaire covering demographics and preferred gaming or gambling modalities, followed by a more extensive behavioral survey. Data quality was ensured through consistency checks and control items. Participants failing these checks were excluded. Participants were recruited until each sample reached a minimum of 300 valid cases., resulting in final samples sizes of 303 gamblers and 355 gamers. In the gambling sample, 42% scored at or above the cut-off for gambling disorder (GD9, a Gambling Disorder measure detailed below; \u0026ge; 4 symptoms), while 21.19% of video gamers scored at or above the cut-off for clinical significance (IGD9, an Internet Gaming Disorder tool described below; \u0026ge; 5 symptoms).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eInitial ad-hoc survey.\u003c/b\u003e The initial survey included questions on sociodemographic characteristics: age, gender, education level, parents\u0026rsquo; education level, monthly income (defined by the sum of all family members\u0026rsquo; incomes), and their preferred types of video or gambling games.\u003c/p\u003e\u003cp\u003e\u003cb\u003e18\u003c/b\u003e[\u003cb\u003e-item Granada Assessment for Cross-Domain Compulsivity (GRACC18).\u003c/b\u003e This is the target measure of this study. This instrument is the shortened version of the GRACC90 (Muela et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which was originally developed based on the systematic review presented in Muela et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Responses were collected using 18 5-point Likert-type items (from 1\u0026thinsp;=\u0026thinsp;\u003cem\u003etotally disagree\u003c/em\u003e to 5\u0026thinsp;=\u0026thinsp;\u003cem\u003etotally agree\u003c/em\u003e). Aside from the target activity specified in the instructions (gambling or video gaming), the gambling and gaming versions were the same. The wording of the items can be found in Muela et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and also it is presented in the Results section (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Please note, however, that this study was carried out with the Spanish version and the English translation is not yet formally validated.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eItem loadings and wordings.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem wording\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLoadings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15. I can\u0026rsquo;t stop the desire to play when I\u0026rsquo;m overpowered by certain bodily or internal sensations.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e05. The game is on my mind even when I\u0026rsquo;m not playing, and I should be thinking about something else.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e08. I often find myself thinking when I will play again, instead of focusing on what I should be doing.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12. I keep playing even though I feel guilty for my irrational behavior.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11. Often playing is something that I want to do so badly that I feel my heart beating faster.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10. Anything related to playing immediately catches my attention and interferes with what I\u0026rsquo;m doing at that moment.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e09. Sometimes, the desire to play dominates me.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17. My thoughts continuously revolve around playing, even when I\u0026rsquo;m not playing.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e03. I feel an uncontrollable desire to play even right after I\u0026rsquo;m done.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e07. Spending a lot of time playing has become an almost involuntary habit.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18. I haven\u0026rsquo;t stopped playing, even though doing so is causing me more disadvantages than advantages.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16. Every time I play, I feel like I\u0026rsquo;m on a slippery slope that I can\u0026rsquo;t get back up.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e06. I often play because I feel an irrepressible desire to play when a surge of strong emotions take over me.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e04. I can\u0026rsquo;t stop playing, even though playing has had a negative impact on my life that clearly outweighs its positive impact.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14. Once I have started, I can\u0026rsquo;t stop playing unless something external forces me to.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13. I keep playing even though I am aware that the harm it does me is greater than the benefits.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e02. I feel I can\u0026rsquo;t get thoughts about playing out of my head.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e01. I continue to play even though I\u0026rsquo;m fully aware that I have increased the risks in certain aspects of my life so much that it\u0026rsquo;s not worth it.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eNotes. The present study evaluated the original Spanish version of GRACC18. This translated version to English is presented only for information purposes, and therefore, it has not been formally validated.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuality of Life in Individuals Addicted to Psychoactive Substances Test (TECVASP).\u003c/b\u003e This instrument (Lozano-Rojas et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) evaluates the perception of physical and psychological wellbeing and health, both in general terms and in relation to substance use. Responses were collected using 22 5-point Likert-type items (from 1\u0026thinsp;=\u0026thinsp;\u003cem\u003ea lot\u003c/em\u003e to 5\u0026thinsp;=\u0026thinsp;\u003cem\u003enot at all\u003c/em\u003e). Higher scores indicate better quality of life. In accordance with the purposes of this study (as in Muela et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the original wording of items referring to substance use was slightly modified to reflect behaviors related to video gaming or gambling. In our study, the internal consistency of the adapted scale was satisfactory, with a Cronbach\u0026rsquo;s alpha of .89.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiagnostic Questionnaire for Gambling Disorder (GD9).\u003c/b\u003e The Spanish version of the GD-9 (Jim\u0026eacute;nez-Murcia et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), adapted from its earlier form based on DSM-IV-TR criteria (Jim\u0026eacute;nez-Murcia et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), was used to assess the nine diagnostic criteria for gambling disorder as defined in the DSM-5. The instrument includes 17 yes/no items (e.g., \u0026ldquo;Have you frequently thought about ways of getting money with which to gamble?\u0026rdquo;). For diagnostic criteria represented by two items, a criterion was considered met if the participant answered \u0026ldquo;yes\u0026rdquo; to at least one of them. Endorsing four or more criteria indicates probable gambling disorder. In this study, the scale demonstrated high internal consistency (α\u0026thinsp;=\u0026thinsp;.91). The total number of criteria endorsed was also treated as a continuous index of symptom severity, regardless of whether the diagnostic threshold was met. Only the gambling sample responded to the GD9.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiagnostic Questionnaire for Internet Gaming Disorder (IGD9).\u003c/b\u003e The nine items proposed by Mallorqu\u0026iacute;-Bagu\u0026eacute; et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) were used to assess the severity of video gaming problems. This scale evaluates the nine IGD criteria (one item for each criterion) proposed in Section III (emerging conditions) of the DSM-5 (American Psychiatric Association, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A cut-off point of five or more criteria is used for the diagnosis of IGD. Consistent with the strategy followed in Muela et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), comparability between this and the GD9 scale was more effectively preserve using this form of the scale, rather than the IGDS9-BF (Beranuy et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which assesses the same criteria using Likert-type items. Additionally, internal consistency was also adequate (α\u0026thinsp;=\u0026thinsp;.84 in our sample). As with the previous instrument, the number of symptoms the individual presents will be interpreted as a quantitative measure of problem symptoms severity, regardless of whether this number is sufficient or not for a positive screening. Only the video-gaming sample responded to the IGD9.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBrief Gambling Motives Inventory (bGMI).\u003c/b\u003e This instrument (Barrada et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) consists of 18 items with a 4-points scale (from 0\u0026thinsp;=\u0026thinsp;\u003cem\u003enever/almost never\u003c/em\u003e to 3\u0026thinsp;=\u0026thinsp;\u003cem\u003ealways/almost always\u003c/em\u003e). This instrument assesses four gambling motives: Affect Regulation (seven items; e.g., \"To forget my worries\"; α\u0026thinsp;=\u0026thinsp;.94), Financial (four items; e.g., \"To win money\"; α\u0026thinsp;=\u0026thinsp;.84), Fun/Thrill (four items; e.g., \"Because it\u0026rsquo;s fun\"; α\u0026thinsp;=\u0026thinsp;.79), and Social motives (three items; e.g., \"Because it makes a social gathering more enjoyable\"; α\u0026thinsp;=\u0026thinsp;.75). Only the gambling sample responded to the bGMI.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVideo-Gaming Motives Questionnaire (VMQ).\u003c/b\u003e This instrument (L\u0026oacute;pez-Fern\u0026aacute;ndez et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) consists of with 24 items with response options ranging from 1\u0026thinsp;=\u0026thinsp;\u003cem\u003estrongly disagree\u003c/em\u003e to 5\u0026thinsp;=\u0026thinsp;\u003cem\u003estrongly agree\u003c/em\u003e. This questionnaire assesses eight video gaming motives: Cognitive Development (e.g., \"Game imply a mental challenge\"; α\u0026thinsp;=\u0026thinsp;.63), Competition (e.g., \"I like to prove that I am better than other players\"; α\u0026thinsp;=\u0026thinsp;.74), Coping (e.g., \"Gaming helps me improve my mood\"; α\u0026thinsp;=\u0026thinsp;.81), Customization (e.g., \"I like to create my own word in games\"; α\u0026thinsp;=\u0026thinsp;.81), Fantasy (e.g., \"I feel immersed in a fantastic/fictitious world\"; α\u0026thinsp;=\u0026thinsp;.84), Recreation (e.g., \"It is entertaining\"; α\u0026thinsp;=\u0026thinsp;.81), Social Interaction (e.g., \"I keep in touch with my friends by gaming\"; α\u0026thinsp;=\u0026thinsp;.86), and Violent Reward (e.g., \"I enjoy the violent fights in video games\"; α\u0026thinsp;=\u0026thinsp;.90). Only the video-gaming sample responded to the VMQ. All alpha\u0026rsquo;s coefficients correspond to the sample of this study.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePositive and Negative Affect Scales (PANAS).\u003c/b\u003e Designed by Watson et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), this self-administered scale evaluates affect using two independent dimensions. The Negative Affect subscale evaluates a general dimension of psychological distress (e.g., \"Nervous\"), and the Positive Affect subscale evaluates the degree to which a person experiences positive emotions (e.g., \u0026ldquo;Interested\u0026rdquo;). This instrument consists of 20 items (10 items per dimension) on a 5-point Likert scale used to rate how accurately the adjective describes how the participant usually feels (1\u0026thinsp;=\u0026thinsp;\u003cem\u003every slightly or not at all\u003c/em\u003e, 5\u0026thinsp;=\u0026thinsp;\u003cem\u003ea lot\u003c/em\u003e). Cronbach\u0026rsquo;s alpha value for Negative Affect was .92, and for Positive Affect, .88. The Spanish adaptation of Sand\u0026iacute;n et al., (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) was used.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAn exploratory structural equation model (ESEM; Asparouhov \u0026amp; and Muth\u0026eacute;n, 2009) was used to evaluate the factor structure of the GRACC18. For unidimensional models without correlated uniqueness, ESEM, exploratory factor analysis, and confirmatory factor analysis lead to the same results. Although Muela et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicated that the GRACC18 was a unidimensional scale, we further conducted additional analyses \u0026ndash;namely the scree-plot and parallel analysis- to evaluate the dimensionality of the scale (Garrido et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This analysis was done with the full (combined) sample.\u003c/p\u003e\u003cp\u003eAdditionally, and in correspondence to the approach used in Muela et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we conducted an analysis to evaluate measurement invariance across the type of activity (gambling or video gaming). Invariance was tested by assessing the degree of fit similarity between consecutive models. As a first step, the equality of form was evaluated. Within the ESEM framework, this process involves specifying the number of factors and pairs of correlated uniqueness. Next, we assessed invariance by examining the equality of thresholds and factor loadings across groups. The constraints were considered satisfactory if the change in the comparative fit index (CFI) was lower than .01 and the increase in the root mean square error of approximation (RMSEA) did not exceed .015 (Chen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Cheung \u0026amp; and Rensvold, 2002). That invariance was found by Muela et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven that we wanted to study the associations between compulsivity and severity, we tested whether GRACC18 and severity questionnaires were \u0026lsquo;pure\u0026rsquo; measures of their intended constructs. If that was the case, two-factor models with all the items of both scales should provide an adequate fit and, importantly, the presence of relevant cross-loadings should be minimal. We fitted ESEM models, as those allow the presence of all cross-loadings. Those analyses were conducted with target rotations.\u003c/p\u003e\u003cp\u003eTo analyze the latent models, the robust weighted least squares was used estimator (WLSMV) implemented in MPlus was employed. To be indicative of an adequate fit to the data, the conventional cut-offs (e.g., Hu \u0026amp; Bentler, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) suggest values above .95 for CFI and Tucker-Lewis index (TLI). Similarly, values smaller than .06 for RMSEA and smaller than .08 for standardized root mean square residual (SMSR) support acceptable model fit. However, these cut-off values should be considered as general guidance rather than being interpreted as \u0026ldquo;golden rules\u0026rdquo; (Marsh et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). On one hand, it is important to emphasize that these cut-offs were originally established for confirmatory models using continuous data, so careful consideration should be given when interpreting these values (Xia \u0026amp; Yang, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, we need to consider that some fit indices, such as RMSEA, are sensitive to mean item loadings, with higher measurement quality leading to an apparently worse fit (McNeish et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Modification indices were examined to identify specific areas of misfit.\u003c/p\u003e\u003cp\u003eCronbach\u0026rsquo;s alpha was used to evaluate internal consistency of each total scale score. We presented descriptives of the samples (sociodemographic information and scale scores). Also, associations between the dimensions of the GRACC18 and the other constructs assessed in this study were examined using Pearson correlations coefficients. These analyses were split by type of activity.\u003c/p\u003e\u003cp\u003eA theoretically relevant question was whether compulsivity associations with additional variables varied by type of activity. We checked this in two ways. First, we compared the correlation sizes of the four variables present in both samples (severity, the two affect dimensions, and quality of life) with compulsivity scores, specifically by checking if the subtraction between the correlations with GRACC18 by activity was different from zero. Second, we compared the slopes when regressing the scores of the three constructs that were measured with the same scales (the two affect dimensions and quality of life) on compulsivity for both samples, that is, we tested if there was a compulsivity \u0026times; sample interaction. We also present the plots for those interaction models. We also tested whether compulsivity or severity showed higher correlations with well-being measures (the two affect dimensions and quality of life).\u003c/p\u003e\u003cp\u003eData analyses were conducted using Mplus version 8.4 (Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and R version 4.4.3 (R Core Team, 2025). All datasets and scripts used in the analyses are publicly accessible via the OSF repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/nbw9d/?view_only=8ebe7735472846d7bb7f34bb734d7c6b\u003c/span\u003e\u003cspan address=\"https://osf.io/nbw9d/?view_only=8ebe7735472846d7bb7f34bb734d7c6b\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive Analyses\u003c/h2\u003e\u003cp\u003eSociodemographic data and measures of involvement in the main activity of interest (gambling, video gaming) for the two samples are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics for the participants in the two samples\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGamblers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGamers\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;355\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducational Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNumber (percentage)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal studies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompulsory education not finished\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompulsory education finished\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school/Professional training not finished\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57 (18.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (17.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school/Professional training finished\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (27.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94 (26.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity studies not finished\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity studies finished\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (32.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 (35.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold monthly income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 600 euros\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBetween 600 and 1000 euros\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBetween 1001 and 1500 euros\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49 (13.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBetween 1501 and 2000 euros\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (19.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (22.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBetween 2001 and 2500 euros\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 (24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (18.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than 2500 euros\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87 (28.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e121 (34.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128 (42.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e206 (58.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e172 (56.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148 (41.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMean (Standard deviation)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.3 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.0 (11.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMedian [25-75th percentile]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWeekly time spent (hours)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.0 [1.0\u0026ndash;6.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.0 [5.0\u0026ndash;20.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonthly expenditure (euros)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.0 [10.5\u0026ndash;110.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.0 [0.0\u0026ndash;30.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: Gambling severity was assessed with GD9 and gaming severity, with IGD9. Both measures are based on DSM-5 criteria. For weekly time and monthly money invested, the median and lower and upper bounds of the interquartile range [25-75th percentile] were calculated instead of mean and standard deviation to avoid the influence of extreme scores.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eInternal Structure and Consistency of the GRACC18\u003c/h2\u003e\u003cp\u003eBoth the scree-plot and the results of the parallel analysis clearly showed the convenience of retaining a single factor. In the parallel analysis, this first eigenvalue from the sample (13.89) was markedly larger than the eigenvalue from the randomly generated datasets (1.42), while for the second eigenvalue it was lower (0.69 versus 1.33).\u003c/p\u003e\u003cp\u003eModel fit of the different tested factor models is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Fit indices of the unidimensional models for the GRACC18 items on the full sample were, overall, satisfactory (CFI\u0026thinsp;=\u0026thinsp;.991, TLI\u0026thinsp;=\u0026thinsp;.990, RMSEA\u0026thinsp;=\u0026thinsp;.079, SRMR\u0026thinsp;=\u0026thinsp;.021), except for the RMSEA, with values slightly above the reference value.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGoodness-of-fit indices for the different models.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eΔCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eΔRMSEA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eFull sample\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1. 1 factor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e692.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSubsamples\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2. Gambling sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e487.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM3. Video games sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e315.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInvariance by type of activity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM4. Equal form\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e799.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM5. Equal loadings and thresholds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e917.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCraving and Severity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM6. Gambling sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e627.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM7. Video games sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e457.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNotes: \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;degrees of freedom; CFI\u0026thinsp;=\u0026thinsp;comparative fit index; TLI\u0026thinsp;=\u0026thinsp;Tucker-Lewis index; RMSEA\u0026thinsp;=\u0026thinsp;root mean square error of approximation; SRMR\u0026thinsp;=\u0026thinsp;standardised root mean square residual; Δ\u0026thinsp;=\u0026thinsp;increment in fit index with respect to previous model. The \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e test \u003cem\u003ep\u003c/em\u003e-value for all the models was \u0026lt;\u0026thinsp;.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhen we looked at the modification indices, the maximum one (clearly over the rest; MI\u0026thinsp;=\u0026thinsp;76.9, expected standardized parameter change\u0026thinsp;=\u0026thinsp;.39) was for the correlation between the uniqueness of Item 13 (\u0026ldquo;I keep playing even though I am aware that the harm it does me is greater than the benefits\u0026rdquo;) and 18 (\u0026ldquo;I haven\u0026rsquo;t stopped playing even though doing so is causing me more disadvantages than advantages\u0026rdquo;). Considering that (a) the other model fit indexes were adequate, (b) when we included this additional parameter model fit improved very little (new RMSEA\u0026thinsp;=\u0026thinsp;.075 for the full sample), (c) the value could be due to the high item loadings, and (d) there was not strong theoretical argument to support increasing the complexity of the model, we decided to retain a single factor without including correlations between items in the model.\u003c/p\u003e\u003cp\u003eItems loadings are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Overall, loadings were very high in this factor (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.87; \u003cem\u003emax\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.91, \"I can\u0026rsquo;t stop the desire to play when I'm overpowered by certain bodily or internal sensations\"; \u003cem\u003emin\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.69, \"I continue to play even though I'm fully aware that I have increased the risks in certain aspects of my life so much that it's not worth it\").\u003c/p\u003e\u003cp\u003eWhen we tested this model in the gambling and video gaming samples separately, fit was satisfactory in both (CFI\u0026thinsp;=\u0026thinsp;.988/.995, TLI\u0026thinsp;=\u0026thinsp;.987/.994, RMSEA\u0026thinsp;=\u0026thinsp;.093/.061, SRMR\u0026thinsp;=\u0026thinsp;.027/.020), although slightly better for the video gaming sample.\u003c/p\u003e\u003cp\u003eRegarding the model invariance with respect to type of activity, we compared fits of a model with equal form and equal loading and thresholds, and observed no meaningful change in fit between the two models (ΔCFI\u0026thinsp;=\u0026thinsp;.001, ΔRMSEA = \u0026ndash;.005; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Finally, the internal consistency was very high for the GRACC18 (α\u0026thinsp;=\u0026thinsp;.98).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSeparability of Compulsivity (GRACC18) from Problem Symptoms Severity (GD9/IGD9)\u003c/h3\u003e\n\u003cp\u003eThe model fit of the bidimensional models simultaneously including all the GRACC18 and severity items was adequate for both the gambling and video games samples: CFI\u0026thinsp;=\u0026thinsp;.991/.996, TLI\u0026thinsp;=\u0026thinsp;.990/.995, RMSEA\u0026thinsp;=\u0026thinsp;.060/.039, SRMR\u0026thinsp;=\u0026thinsp;.031/.032.\u003c/p\u003e\u003cp\u003eAs could be expected if both questionnaires were, to a large degree, pure measures of their intended constructs, the cross-loadings were small. For the gambling sample, the mean unsigned loading of GRACC18 items in the severity dimension was .10, with a maximum of .28, and, thus, no cross-loading over .30; the mean unsigned loading of severity items in the compulsivity dimension was .13, with a maximum of .30, and a single cross-loading over .30. For the video gaming sample, the mean unsigned loading of GRACC18 items in the severity dimension was .12, with a maximum of .38, and a single cross-loading over .30; the mean unsigned loading of severity items in the compulsivity dimension was .09, with a maximum of .20, and, thus, no single cross-loading over .30.\u003c/p\u003e\u003cp\u003eThe latent correlation between compulsivity and severity was .76 for the gambling sample and .81 for the video gaming sample.\u003c/p\u003e\n\u003ch3\u003eAssociations with Other Variables\u003c/h3\u003e\n\u003cp\u003eDescriptive and Pearson correlations for all measures are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for the gambling sample and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e for the video gaming sample. The mean scores in the GRACC18 showed no statistically significant difference by type of activity [\u003cem\u003eM\u003c/em\u003e\u003csub\u003egambling\u003c/sub\u003e = 2.32, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003egambling\u003c/sub\u003e = 1.12, \u003cem\u003eM\u003c/em\u003e\u003csub\u003evideo gaming\u003c/sub\u003e = 2.29, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003evideo gaming\u003c/sub\u003e = 1.09, \u003cem\u003et\u003c/em\u003e(634.5)\u0026thinsp;=\u0026thinsp;0.339, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.735, \u003cem\u003ed\u003c/em\u003e = \u0026minus;\u0026thinsp;0.03].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasure descriptives (lower panel) and correlations (upper panel) between measures for the samples of gambling participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGRACC18\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGD9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePANAS NA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePANAS PA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTECVASP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ebGMI Affect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ebGMI Financial\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ebGMI fun\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ebGMI Social\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRACC18\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSeverity (GD9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeg. Affect - PANAS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePos. Affect - PANAS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e.04\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e.01\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026minus;\u0026thinsp;.08\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQoL (TECVASP)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ebGMI Affect\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ebGMI Financial\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ebGMI Fun\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ebGMI Social\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGRACC18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eGD9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ePANAS NA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003ePANAS PA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eTECVASP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ebGMI Affect\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003ebGMI Financial\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003ebGMI fun\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003ebGMI Social\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e63.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStandard deviation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSkewness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKurtosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eNotes. All the correlations were statistically significant, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, except for underlined values. GRACC18 scores were computed as means of item scores. For all other measures, scores are computed as sums of item scores.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasure descriptives (lower panel) and correlations (upper panel) between measures for the video games sample.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"14\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGRACC18\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIGD9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePANAS NA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePANAS PA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTECVASP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVMQ Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eVMQ Comp.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eVMQ Coping\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eVMQ Custom.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eVMQ Fantasy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eVMQ Recr.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eVMQ Social\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eVMQ Violence\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRACC18\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSeverity (IGD9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePANAS Neg. aff.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePANAS Pos. aff.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e0.04\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-0.03\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQoL (TECVASP)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVMQ Development\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVMQ Competition\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVMQ Coping\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVMQ Customization\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVMQ Fantasy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVMQ Recreation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e0.08\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-0.07\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e0\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVMQ Social\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVMQ Violence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGRACC18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eIGD9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ePANAS NA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003ePANAS PA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eTECVASP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eVMQ Dev.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eVMQ Comp.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eVMQ Coping\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eVMQ Custom\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eVMQ Fantasy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eVMQ Recr.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eVMQ Social\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003eVMQ Violence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e10.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e7.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e5.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStandard deviation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e2.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSkewness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKurtosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"14\"\u003eNotes. All the correlations were statistically significant, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, except for underlined values. GRACC18 scores were computed as means of item scores. For all other measures, scores are computed as sums of item scores.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRegarding the other measures evaluated in both samples, all the correlations were in line with what could be expected. GRACC18 scores presented the highest correlation with severity scores (\u003cem\u003er\u003c/em\u003e\u003csub\u003egambling\u003c/sub\u003e = 0.72, \u003cem\u003er\u003c/em\u003e\u003csub\u003evideogaming\u003c/sub\u003e = 0.73), followed by quality of life scores (\u003cem\u003er\u003c/em\u003e\u003csub\u003egambling\u003c/sub\u003e = -0.48, \u003cem\u003er\u003c/em\u003e\u003csub\u003evideogaming\u003c/sub\u003e = -0.52), and with negative affect (\u003cem\u003er\u003c/em\u003e\u003csub\u003egambling\u003c/sub\u003e = 0.46, \u003cem\u003er\u003c/em\u003e\u003csub\u003evideogaming\u003c/sub\u003e = 0.42). All correlations were statistically significant (all \u003cem\u003eps\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). In both subsamples, correlations with positive affect were not statistically significant (\u003cem\u003er\u003c/em\u003e\u003csub\u003egambling\u003c/sub\u003e = 0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.478 \u003cem\u003er\u003c/em\u003e\u003csub\u003evideogaming\u003c/sub\u003e = 0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.477).\u003c/p\u003e\u003cp\u003eFor the gambling sample, GRACC18 scores were positively associated with all gambling motives (\u003cem\u003eM\u003c/em\u003e\u003csub\u003er\u003c/sub\u003e = .49), with a maximum correlation with affect regulation motives (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.69) and a minimum correlation with financial motives (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.26; all \u003cem\u003eps\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). For the video gaming sample, GRACC18 scores were also positively and statistically significantly (\u003cem\u003eps\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) associated with all video gaming motives (\u003cem\u003eM\u003c/em\u003e\u003csub\u003er\u003c/sub\u003e = .36), except with recreation motives (\u003cem\u003er\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.08, p\u0026thinsp;=\u0026thinsp;.114). The maximum correlation was with social interaction (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.49), whereas the minimum (statistically significant) correlation was with customization motives (\u003cem\u003er\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.29).\u003c/p\u003e\u003cp\u003eWe compared differences in correlation sizes between the GRACC18 scores and variables for the two samples and there were no statistically significant differences (for severity: \u003cem\u003er\u003c/em\u003e\u003csub\u003edifference\u003c/sub\u003e = -0.01; \u003cem\u003ez\u003c/em\u003e = -0.193, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.847; for negative affect: \u003cem\u003er\u003c/em\u003e\u003csub\u003edifference\u003c/sub\u003e = .04; \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.624, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.530); for positive affect: \u003cem\u003er\u003c/em\u003e\u003csub\u003edifference\u003c/sub\u003e = .00; \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.969; for quality of life: \u003cem\u003er\u003c/em\u003e\u003csub\u003edifference\u003c/sub\u003e = .05; \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.779, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.436.). We calculated regression models in which we predicted the two affect dimensions or quality of life including the interaction between sample and compulsivity. No interaction effect was statistically significant: for negative affect, \u003cem\u003eb\u003c/em\u003e = -0.46, \u003cem\u003et\u003c/em\u003e(654) = -0.889, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.374; for positive affect, \u003cem\u003eb\u003c/em\u003e = -0.02, \u003cem\u003et\u003c/em\u003e(654) = -0.049, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.961; and for quality of life, \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, \u003cem\u003et\u003c/em\u003e(654)\u0026thinsp;=\u0026thinsp;0.006, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.995. The plots corresponding to those regression models can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAdditionally, we compared the correlation sizes of GRACC18 scores and severity scores with the two affect dimensions and quality of life for both samples. Severity scores showed a statistically higher correlation compared to GRACC18 scores. For negative affect, gambling: \u003cem\u003er\u003c/em\u003e\u003csub\u003edifference\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.08; \u003cem\u003ez\u003c/em\u003e = -2.229, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.026; and video gaming: \u003cem\u003er\u003c/em\u003e\u003csub\u003edifference\u003c/sub\u003e = -0.11; \u003cem\u003ez\u003c/em\u003e = -2.884, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004. For quality of life, gambling: \u003cem\u003er\u003c/em\u003e\u003csub\u003edifference\u003c/sub\u003e = .10; \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.803, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.005; video gaming: \u003cem\u003er\u003c/em\u003e\u003csub\u003edifference\u003c/sub\u003e = .08; \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.450, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.014. No significant differences in correlation were found for positive affect (gambling: \u003cem\u003er\u003c/em\u003e\u003csub\u003edifference\u003c/sub\u003e = .03; \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.692, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.489; video gaming: \u003cem\u003er\u003c/em\u003e\u003csub\u003edifference\u003c/sub\u003e = .06; \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.517, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.129).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo this date, compulsivity has remained an ambiguous term in the existing literature, making it essential to clearly define its meaning before examining its role in addictive behaviors. This study builds on previous qualitative and quantitative research that has contributed to its conceptualization and measurement\u0026mdash;specifically in cases where compulsivity emerges as an acquired characteristic of behaviors that become uncontrollable and resistant to negative consequences.\u003c/p\u003e\u003cp\u003eA key contribution of this work is the strong validation of compulsivity as a psychometrically sound construct, as measured by the GRACC18 scale, across at least two potentially escalating activities: video gaming and gambling. Although compulsivity has been conceptualized in various ways, the GRACC18 primarily reflects three core facets\u0026mdash;an irrepressible urge, an inability to refrain from the activity despite recognizing its disutility, and a hijacking of attentional and cognitive resources. Together, these elements form a unified construct capable of predicting associated harms, such as problem severity, negative affect, and diminished quality of life, while remaining distinct from them.\u003c/p\u003e\u003cp\u003eCrucially, the clear psychometric separation between compulsivity and severity reinforces the conceptualization of severity as a relatively superficial blend of symptoms (including behaviors and consequences; Tseng et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), whereas compulsivity more directly taps onto the underlying processes driving them. In other words, severity is based on an extensional definition of addictive behaviors, while compulsivity has the potential to contribute to an intensional understanding of their mechanisms. While this remains largely speculative, urges and hijacking likely represent two interconnected aspects of the same phenomenon\u0026mdash;one embodying the motivational and emotional dimensions of craving (whether appetitive or aversive), and the other capturing its cognitive components. When these forces become sufficiently intense, they can create the subjective impression that the problematic behavior is no longer under voluntary control despite disutility.\u003c/p\u003e\u003cp\u003eThe second important contribution of the present work is the corroboration that the GRACC18 \u0026mdash;now validated in a sample distinct from the one in which it was developed\u0026mdash;is structurally invariant across the gaming and gambling domains. Invariance is accompanied by a mostly coincident pattern of correlations between compulsivity and other constructs of interest in the two samples, with compulsivity strongly predicting severity, moderately predicting negative affect and poorer quality of life, and non-significantly predicting positive affect. Moreover, the slope of these effects did not differ across domains for any outcome, which indicates that gambling- and gaming-related severity and harms are equally accounted for by compulsivity in the two samples.\u003c/p\u003e\u003cp\u003eIn other words, our findings support the convergence of video gaming- and gambling-related problems concerning the role of compulsivity. Contrary to our initial predictions at the origin of this research endeavor, compulsivity does not appear to play a more significant role in problematic gambling than in problematic video gaming\u0026mdash;at least within the predominantly subclinical samples examined in this and previous studies. Nonetheless, this conclusion goes only as far as self-report measures can take us. As discussed earlier, compulsivity is primarily characterized by intense urges, an inability to refrain despite subjective disutility, and the hijacking of cognitive and attentional resources. However, although the GRACC scale was carefully designed to ensure clarity, specificity, and validity for the construct of interest (see Muela et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), it provides little insight into the origins of these features. Research has shown, for instance, that the experience of craving can vary significantly across different domains. In gambling disorder, this urge typically manifests as an aversive tension state, alleviated only through gambling. In contrast, within the problematic video gaming domain, it may stem from a heightened anticipation of rewards, or a profound sense of boredom and lack of stimulation when gaming is unavailable (King et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Complementarily, recent evidence shows that the contribution of appetitive and aversive processes to craving may vary, not only across behavioral domains, but also across activity modalities in the same domain (L\u0026oacute;pez-Guerrero et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Further research to assess the qualitative nature of compulsivity and the cognitive and emotional processes responsible for it is thus warranted (L\u0026oacute;pez-Guerrero et al., 2024).\u003c/p\u003e\u003cp\u003eIn all other respects, the present study closely mirrors the primary findings obtained with the original GRACC90 scale. However, it now does so across two independent samples of gamblers and gamers, employing parallel protocols. Beyond the previously discussed associations, compulsivity was found to be linked to gambling and gaming motives in both respective samples. The only notable differences between the two domains were the relatively stronger associations between coping/affect regulation motives and compulsivity in gambling, and between social motives and compulsivity in video gaming. This could be interpreted as support for the proposals that compulsive gambling could be understood as arising from biased choice processes under negative affect (Hogarth, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), whereas social pressure and comparison processes could be strongly involved in dysfunctional video gaming (Krassen \u0026amp; and Aupers, 2022; Park et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yang \u0026amp; Yao, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, since motives were assessed using different scales in each sample, these differences remain difficult to interpret.\u003c/p\u003e\u003cp\u003eAdditionally, severity scores demonstrated stronger associations with poorer quality of life and increased negative affect than GRACC18 compulsivity scores. This distinction supports the separation between compulsivity and the symptoms of the problematic activity. Severity measures incorporate items reflecting negative individual and social consequences that may lead to impairment across multiple levels, whereas compulsivity is more closely tied to the behavioral characteristics themselves, irrespective of their broader impact on daily functioning. While compulsive behaviors often carry the potential for harm, the extent of harm depends solely on the degree to which the compulsive behavior disrupts essential aspects of an individual's life.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eLimitations, strengths and final remarks\u003c/h2\u003e\u003cp\u003eThe present study is not without limitations. The first stems from the reliance on self-report measures, which inherently fail to capture etiological differences between superficially similar responses to items. Even though, in this case, the wording has been designed to minimize ambiguities found in previous instruments, self-report methods still pose interpretative challenges.\u003c/p\u003e\u003cp\u003eSecondly, concerns regarding sample composition must be acknowledged. The study was conducted on a non-clinical, non-representative sample recruited via an online panel. While this warrants caution in extrapolating findings to clinical populations or the general public, it is noteworthy that nearly identical results were observed not only across the two samples in this study but also in two separate studies using comparable protocols with independent samples.\u003c/p\u003e\u003cp\u003eFinally, the cross-sectional nature of the data introduces additional limitations. Most notably, while the findings suggest a distinction between compulsivity, severity, and other relevant constructs (such as motives, affect, and quality of life), they do not imply any specific causal relationships among these factors. The low cross-loadings between the compulsivity and severity scales represent a strength, as they indicate that shared variability is not artificially inflated by item overlap. However, the notion that compulsivity precedes and drives symptoms remains more of a plausible interpretation than an empirical finding.\u003c/p\u003e\u003cp\u003eOn the other hand, this study also boasts significant strengths, chief among them being its integration into a broader, multistep research effort. This process began with a fundamental conceptual exploration of compulsivity and has culminated in the development of a valid and reliable assessment tool. The GRACC90 and GRACC18 scales were largely developed through a data-driven approach and can be adapted for any putatively addictive activity with minimal rewording. In this regard, the final tool represents a promising advancement in addressing the serious fragmentation problem, and thus the ubiquitous jingle-jangle fallacy, in behavioral addiction measurement. If common mechanisms underpin various behavioral addictions, research will undoubtedly benefit from the availability of standardized measures applicable across different activities, rather than relying on distinct, non-comparable instruments for each.\u003c/p\u003e\u003cp\u003eIn summary, the structured, progressive development of GRACC is arguably one of the most comprehensive and systematic efforts in the field of compulsivity and behavioral addictions to date. The resulting measure demonstrates exceptional psychometric properties, particularly in terms of reliability and convergent validity. Notably, despite its strong correlation with severity measures, it extends beyond them in terms of intensionality, as it does not rely on an extensional set of features. This serves as compelling evidence that certain symptoms may be incidental or secondary in defining a behavior as compulsive and ultimately problematic. Looking ahead, our research will focus on exploring the etiology of compulsivity across different domains, aiming to determine whether compulsivity in these contexts is driven by shared emotional, cognitive, and computational processes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebGMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBrief Gambling Motives Inventory\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComparative Fix Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCIU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCompulsive Internet Use\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiagnostic and Statistical Manual of Mental Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eESEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExploratory Structural Equation Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGambling Disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGD9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiagnostic Questionnaire for Gambling Disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGRACC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGranada Assessment for Cross-Domain Compulsivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGRACC18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18-item Granada Assessment for Cross-Domain Compulsivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGRACC90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90-item Granada Assessment for Cross-Domain Compulsivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIGD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInternet Gaming Disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIGD9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiagnostic Questionnaire for Internet Gaming Disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModification Indices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObsessive-Compulsive Disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePANAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive and Negative Affect Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePIU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProblematic Internet Use\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRoot Mean Squared Error of Approximation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSMSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandardized Root Mean Square Residual\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTECVASP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuality of Life in Individuals Addicted to Psychoactive\u003c/p\u003e\n \u003cp\u003eSubstances Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTucker-Lewis Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVMQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVideo-gaming Motives Questionnaire\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWLSMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWeighted Least Square Mean and Variance adjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe procedure of this study complies with the ethical standards of the Helsinki Declaration of 1975, as revised in 2008. Both subsamples were recruited as part of the project Gbrain3 (see Funding), approved by the Human Research Ethics Committee of the University of Granada (reference number 1830/CEIH/2020). All participants were informed about the nature of the study and all provided informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe database and code files for these analyses are available at the OSF website (https://osf.io/nbw9d/?view_only=8ebe7735472846d7bb7f34bb734d7c6b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWork by the core team (IM, JLG, FJR and JCP) has been supported by grants from the Spanish Government (\u003cstrong\u003eResearch costs\u003c/strong\u003e: Gbrain3, reference PID2020-116535\u0026thinsp;GB-I00, \u003cem\u003eConvocatoria 2020 de Proyectos de I+D+I de Generaci\u0026oacute;n de Conocimiento\u003c/em\u003e, funded by Ministerio de Ciencia e Innovaci\u0026oacute;n, Agencia Estatal de Investigaci\u0026oacute;n, MICIU/AEI/10.13039/501100011033; \u003cstrong\u003ePublication costs\u003c/strong\u003e: Gbrain 4, reference PID2023-150731NB-I00, \u003cem\u003eConvocatoria 2023 de Proyectos de I+D+I de Generaci\u0026oacute;n de Conocimiento\u003c/em\u003e, funded by Ministerio de Ciencia e Innovaci\u0026oacute;n, Agencia Estatal de Investigaci\u0026oacute;n; MICIU/AEI/10.13039/501100011033/, and FEDER-EU \u0026ldquo;Una manera de hacer Europa\u0026rdquo;). IM is supported by an individual research grant (PRE2018-085150, Ministerio de Ciencia, Innovaci\u0026oacute;n y Universidades). JLG\u0026rsquo;s work is supported by an individual research grant (PRE2021-100665), funded by MICIU/AEI/10.13039/501100011033 and by \u0026ldquo;ESF+\u0026rdquo;. FJR\u0026apos;s work is supported by an individual research grant (FPU21/00462), funded by the Agencia Estatal de Investigaci\u0026oacute;n and by ESF+.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIM: Study concept and design, conducting the experiment, writing \u0026ndash; original draft. JFN: Study concept and design, conducting the experiment, writing \u0026ndash; review and editing. JRB: Study concept and design, analysis and interpretation of data, writing \u0026ndash; original draft. AH: Analysis and interpretation of data, writing \u0026ndash; review and editing. ALR: Analysis and interpretation of data, writing \u0026ndash; review and editing. JLG: Writing \u0026ndash; review and editing. FJR: Writing \u0026ndash; review and editing. JCP: Principal Investigator, study concept and design, obtained funding, study supervision, writing \u0026ndash; original draft. The draft of the manuscript was revised and approved by all the authors. All authors had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlbertella L, Chamberlain SR, Le Pelley ME, Greenwood L-M, Lee RS, Ouden D, Segrave L, Grant RA, J. E., Y\u0026uuml;cel M. 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Addiction. 2019;114(6):1095\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/add.14424\u003c/span\u003e\u003cspan address=\"10.1111/add.14424\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"compulsivity, addictive behaviors, non-substance addiction, validation, gambling, video-gaming","lastPublishedDoi":"10.21203/rs.3.rs-6680509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6680509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e\u003cp\u003eThe definition of compulsivity and its role in putative addictive behaviors remains unclear, partly due to previous research conflating its conceptualization as a general transdiagnostic trait with its understanding as an acquired feature of a specific activity as it spirals out of control.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e\u003cp\u003eThis study aims to validate a short version of the GRACC90 scale (GRACC18), designed to assess the degree to which video gaming and gambling have become compulsive in two independent samples of panel members who regularly engage in one of these activities\u0026mdash;ranging in severity from recreational to pathological (though not formally diagnosed)\u0026mdash;. Exploratory structural equation modeling (ESEM) was applied to examine the factorial structure of the scale and to test its structural invariance across both domains. Additionally, statistical associations between compulsivity scores and gambling- and gaming-related constructs\u0026mdash;including problem severity, positive and negative affect, motives, and quality of life\u0026mdash;were explored.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e\u003cp\u003eFindings indicate that (a) the compulsivity scale exhibits reliability, validity, and a unifactorial structure, (b) its structure remains invariant across domains, (c) compulsivity is strongly correlated with symptom severity in both gambling and gaming, moderately associated with negative affect and quality of life, and not significantly linked to positive affect, and (d) no interaction effect between domain and compulsivity was statistically significant in the regression models tested to predict the two affect dimensions and quality of life. Furthermore, (e) severity shows a stronger correlation with affect and quality of life than compulsivity, and (f) cross-loadings between severity and compulsivity items are notably low.\u003c/p\u003e\u003ch2\u003eConclusion.\u003c/h2\u003e\u003cp\u003eThese results support the theoretical validity of compulsivity as distinct from severity, demonstrating an identical structural composition and comparable involvement in symptoms and harms across video gaming and gambling domains. Future research should further investigate the emotional, cognitive, and computational foundations of compulsivity in these and other behavioral contexts.\u003c/p\u003e","manuscriptTitle":"Conceptualization and validity of the compulsivity construct in potentially addictive behaviors: a replication and extension study with the brief Granada Assessment for Cross-domain Compulsivity (GRACC18) in the gambling and video gaming domains","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 11:05:32","doi":"10.21203/rs.3.rs-6680509/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-07-23T11:08:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161546898041061777460885549863288006086","date":"2025-07-14T06:37:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-04T11:59:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-02T15:25:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-06T14:52:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-04T08:14:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-06-04T08:10:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36881dc4-6346-45a2-a7e5-c04a36fa9bea","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-09T11:05:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-09 11:05:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6680509","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6680509","identity":"rs-6680509","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}