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Original paper

Towards comprehending internet gaming disorder: evaluating the psychometric properties of the Polish adaptation of the Craving for Internet Gaming Scale

Tomasz Misiuro
1
,
Agnieszka Misiuro
1
,
Andrzej Cudo
2
,
Mirosław Kowalski
3

  1. Institute of Psychology, University of Zielona Góra, Poland
  2. Institute of Psychology, The John Paul II Catholic University of Lublin, Poland
  3. University of Zielona Góra, Poland
Adv Psychiatry Neurol 2024; 33 (2): 59–66
Online publish date: 2024/07/10
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INTRODUCTION

Video gaming has emerged as one of the most pre­valent forms of leisure worldwide. It is estimated that in 2023 the number of active gamers reached 3.38 billion, exhibiting a 6.3% year-on-year growth [1]. Among this population, a segment demonstrates problematic video game use [2-4] or video game addiction [5-7]. The 11th Re­vision of the International Classification of Diseases incorporates the concept of gaming disorder, encompassing both offline and online gaming [8]. However, specific studies indicate that internet gaming is more frequently associated with addiction than offline gaming [9].
In 2013, the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) introduced Internet Gaming Disorder (IGD) as a concept requiring further investigation [10]. The proposed criteria for IGD have prompted extensive discourse among researchers, highlighting various concerns, including the absence of standardized measurement instruments and the necessity for criteria enhancement [6, 11-14].
One element not explicitly addressed in the DSM-5 criteria for IGD is ‘craving’. This phenomenon has been identified as being clinically significant in various forms of addiction [15, 16]. In general, it can be defined as the conscious experience of a desire to take a substance [17]. Among other things, craving is associated with substance use disorder [15, 16, 18-21], food addiction [22, 23], gambling disorder [24, 25], compulsive pornography use [26, 27], compulsive online shopping [28, 29], problema­tic use of social networking sites [30, 31], and problema­tic mobile phone use [32]. Research findings suggest that craving may play a clinically meaningful role in IGD as well [11, 33, 34]. The measurement and consideration of craving have also been proposed to be of significance in the assessment of clinical intervention outcomes and the treatment of IGD [35-39].
A challenging aspect in assessing the level of craving for internet gaming is the absence of a standardized, uniform measurement method. Various scales used in research are mere modifications of existing instruments [33, 38-41]. This situation hinders or even precludes the comparability of findings across different studies. Moreover, employing scales that lack comprehensive confirmation of their validity and reliability in a specific population carries the risk of obtaining biased results. Hence, it is vital to possess a scale dedicated to measuring IGD with well-established psychometric properties.
One of the candidates for addressing this deficiency is the Craving for Internet Gaming Scale (CIGS) [42]. It encompasses five statements related to the frequency and duration of thoughts about internet gaming, the perceived pleasure associated with it, the intensity of the craving, the challenges in resisting internet gaming, and the overall average craving experienced in the past week.
The scale was developed based on the Penn Alcohol Craving Scale (PACS), which measures the subjective, conscious need to use alcohol [15]. The PACS was used as a conceptual framework for assessing various phenomena related to craving, including problematic smartphone use [43], substance use disorder [44, 45], and compulsive online shopping [46]. The analysis of the psychometric properties of CIGS has confirmed its high reliability and validity. Originally validated on a Turkish sample, the scale has not yet been adapted for use in other cultures.
This study addresses this gap by adapting and validating the CIGS with a Polish sample. It responds to the call for undertaking research on gamers across diverse countries and cultural contexts in order to enhance our comprehension of IGD, refine its criteria, and improve the diagnostic process [6, 42].

METHODS

Participants and procedure

Participants were recruited through Ariadna, a nationwide Polish online research panel, open to individuals aged 15 and up, with rigorous verification procedures in place to prevent automated responses and the creation of multiple accounts. Platform users earned points for completing surveys, which could be redeemed for rewards such as books, games, cosmetics, and electronics.
The participants completed an online questionnaire consisting of seven scales, along with demographic questions and others related to their video gaming activities. The questionnaire also included a control question designed to verify participants’ attentiveness. At the end of the study, participants were requested to indicate their level of engagement in percentage terms.
From the initial sample, one individual was excluded due to their declaring a low level of engagement in completing the questionnaire. Consequently, the analysis was carried out on a sample comprising 575 individuals, including 275 females. The mean age of the participants was M = 29.45 and a standard deviation of SD = 4.25, ranging from 18 to 35. The self-reported average duration of a single uninterrupted gaming session in the last month was M = 2.75 hours, SD = 3.32, with a range from 1 to 39.
To verify the psychometric properties of the CIGS, the sample was randomly divided into two subsamples. The first was used to evaluate structural validity and consisted of 288 individuals, including 135 females, with an average age of M = 29.30 and a standard deviation of SD = 4.20. The second subsample comprised 287 individuals, including 140 females, with an average age of M = 29.60, SD = 4.31. The self-reported average duration of a single uninterrupted gaming session in the last month for these subsamples was M = 2.75 hours, SD = 3.12, and M = 2.74, SD = 3.52, respectively. Detailed information regarding the demographic structure of all samples is provided in Table 1.
The study was conducted in compliance with the Declaration of Helsinki and approved by the Institute of Psychology’s Ethical Committee at John Paul II Catholic University of Lublin. No personal data was collected.

Materials

The Craving for Internet Gaming Scale (CIGS) is a self-report instrument developed based on the Penn Alcohol Craving Scale (PACS) [15]. It comprises five items, with responses provided on a 7-point scale (0-6). The item content was entirely adopted from the PACS, with the distinction that the word ‘alcohol’ was replaced with ‘internet gaming.’ The questions pertain to the frequency and amount of time spent thinking about internet gaming, the pleasure derived from it, the difficulty in resisting it, and the immediate intensity of craving for it. The original scale demonstrated acceptable criterion-related and structural validity. Both exploratory and confirmatory factor analyses confirmed the unidimensional nature of the construct. The scale also exhibited high reliability across three different samples (Cronbach’s α ranging from 0.88 to 0.91).
The translation from English to Polish was performed by two independent translators, who subsequently reconciled their versions into a consistent one. To enable potential comparisons, the existing Polish translation of the PACS was also considered [47]. Finally, a third translator performed the back-translation. The complete translation of the scale is provided in Appendix 1.
The Gaming Disorder Test (GDT) comprises four items related to the diagnostic criteria for gaming disorder as indicated by the World Health Organization [48]. It is acknowledged as the scale that best operationalizes the criteria for this phenomenon [49, 50]. In the present study, we used the Polish adaptation by Cudo et al. [49]. The scale obtained internal consistency coefficients with a Cronbach’s α of 0.89, McDonald’s ω of 0.89, and aver­age variance extracted (AVE) of 0.70.
The Game Transfer Phenomena Scale (GTPS) was created to measure the phenomenon of the experience of altered sensory perceptions, automatic thoughts, and behaviors related to video game content and mechanics when not playing. The GTPS assesses five dimensions, three of which are linked to (i) altered perceptions, which include (ia) altered visual perceptions, (ib) altered body perceptions, and (ic) altered auditory perceptions, along with (ii) automatic mental processes, and (iii) actions and behaviors [51]. The questionnaire comprises 20 items concerning the frequency of occurrences of states that manifest when an individual is not engaged in gaming. In the current study, the Polish adaptation by Cudo et al. was utilized [52]. Regarding the individual subscales, the reliability analysis results were as follows for α, ω, and AVE respectively: (ia) 0.85, 0.85, and 0.59; (ib) 0.89, 0.89, and 0.67; (ic) 0.88, 0.89, and 0.67; (ii) 0.90, 0.90, and 0.70; (iii) actions and behaviors 0.92, 0.92, and 0.74.
The Motives for Online Gaming Questionnaire (MOGQ) [53], in its Polish adaptation by Grzegorzewska and Cierpiałkowska [54], is a scale evaluating the motivational basis of online gaming. The questionnaire consists of 27 items. Participants indicate the frequency with which specific motives drive their engagement in gaming. The scale encompasses seven key motivational factors: (i) social, (ii) escape, (iii) competition, (iv) coping, (v) skill development, (vi) fantasy, and (vii) recreation. The values of α, ω, and AVE were respectively: (i) 0.87, 0.87, 0.63, (ii) 0.90, 0.91, 0.71, (iii) 0.88, 0.88, 0.66, (iv) 0.84, 0.85, 0.60, (v) 0.92, 0.92, 0.74, (vi) 0.91, 0.91, 0.72, and (vii) 0.82, 0.82, 0.61.
The Immersion Questionnaire is the Polish adaptation of a questionnaire designed to measure the sensation of being immersed in the gaming world [55, 56]. The scale comprises 27 questions assessing the unidimensional construct of immersion. The internal consistency coefficients were α = 0.91, ω = 0.92, and AVE = 0.39.
The Depression Anxiety Stress Scales (DASS-21) consists of 21 items that assess symptoms of three negative emotional states [57]. The Depression scale quantifies the construct associated with a loss of self-esteem and motivation, as well as a lack of belief in being able to achieve substantial life goals. Scores on the Anxiety scale are associated with the intensity of experienced anxiety, a predisposition to acute fear reactions, and fear-related physiological factors. Furthermore, individuals obtaining high scores on the Stress scale experience a state of persistent arousal, tension, and exhibit a low threshold for frustration. In the current study, the Polish translation by Zawislak et al. [58] was used. The reliability coefficients for the Depression scale were α = 0.91, ω = 0.92, and AVE = 0.61. For the Anxiety scale, α = 0.89, ω = 0.89, and AVE = 0.55, while for the Stress scale, α = 0.91, ω = 0.91, and AVE = 0.60.
The Brief Self-Control Scale (BSCS), in its Polish adap­tation by Pilarska and Baumeister [59], is a shortened 13-item version of The Self-Control Scale [60]. It measures self-control, defined as an individual’s capacity for self-regulation by controlling their thoughts, emotions, impulses, and regulating performance, in terms of perseverance or speed and accuracy in performing a task. Concerning the internal consistency of the scale, the coefficient values were observed as α = 0.82, ω = 0.82, and AVE = 0.28.

Statistical analysis

Statistical analyses were performed using the R programming language and the jamovi software [61, 62]. The assessment of scale reliability was carried out with the semTools package [63]. To verify structural validity, Confirmatory Factor Analysis (CFA) was calculated using the lavaan package [64]. The second subsample was used for criterion-related validity analysis. Positive correlations were expected between the CIGS and GDT, the Immersion Questionnaire, GTP, MOGQ, DASS-21, and the average duration of a single uninterrupted gaming session in the last month. These indices are related to problematic gaming or gaming addiction [48, 49, 51-53, 55, 65]. Due to the observed poor self-regulation and impulsive behavior in individuals with IDG [66], we also anticipated a negative correlation with BSCS. Furthermore, one-way analysis of variance was used to compare the results on the CIGS scale obtained in distinct groups.

RESULTS

Table 2 presents the correlations between items and the mean and standard deviation values calculated for the entire sample. The analyses conducted revealed statistically significant differences between groups regarding the variable ‘Type of Device’ within the male cohort: F(3, 136) = 3.94, p = 0.010. The post-hoc analysis with the Games-Howell test showed a statistically significant difference between laptops and smartphones. Gamers who use laptops as their primary device demonstrated higher scores (t(161.1) = 2.95, p = .019, Cohen’s d = 0.41). Detailed results are provided in Table 3. Additionally, a statistically significant difference emerged (t(573) = 3.64, p < .001, Cohen’s d = 0.30), indicating that men generally achieved higher scores (M = 2.30) than women (M = 2.00) in the CIGS.

Reliability

Analyses demonstrated the very high reliability of CIGS, defined as internal consistency. The coefficient values in the first subsample were as follows: α = 0.89, ω = 0.90, and AVE = 0.63, while in the second subsample, they were α = 0.90, ω = 0.90, and AVE = 0.65.

Structural validity

The conducted CFA demonstrated an excellent fit of the one-dimensional model to the data: χ² = 10.00, df = 5, p = 0.075, CFI = 0.994, GFI = 0.987, AGFI = 0.961, TLI = 0.988, IFI = 0.994, and SRMR = 0.017. Only the RMSEA value was at an acceptable level: 0.059, 90% CI [0.00; 0.11]. The results indicate a superior fit of the model compared to the original study. However, factor loadings remain similar. The CFA performed on the second subsample also showed an excellent fit of the one-dimensional model to the data across all indicators: χ² = 6.10, df = 5, p = 0.297, RMSEA = 0.028, 90% CI [0.00; 0.09], CFI = 0.999, GFI = 0.992, AGFI = 0.975, TLI = 0.997, IFI = 0.999, and SRMR = 0.012. Factor loadings for both analyses are presented in Table 4.

Criterion-related validity

Table 5 presents the results of the correlation analysis between the CIGS and the other scales, with 95% confidence intervals. The CIGS demonstrated statistically significant associations with all measured variables. The correlation coefficient varied, ranging from a lower limit of a moderate relationship with the Stress scale from the DASS-21 (r = 0.20, p < 0.001) to a strong value with the GDTS (r = 0.58, p < 0.001). The CIGS also showed a moderate positive correlation with the average duration of a single uninterrupted gaming session in the last month. In the case of the relationship with BSCS, a moderate negative correlation was observed (r = –0.31, p < 0.001).

DISCUSSION

This study aimed to verify the psychometric proper­ties of the Polish adaptation of the CIGS. The scale demonstrated high reliability, as assessed in terms of internal consistency. Moreover, the outcomes of the confirmatory factor analysis indicate an excellent fit of the one-factor model to the data, providing substantial support for its structural validity. The Polish translation of the CIGS has also been found to be valid in terms of criterion-related validity. As expected, the scale positively correlates with the measures associated with gaming disorder as well as the average duration of a single uninterrupted gaming session in the last month. In particular, a high correlation with the Gaming Disorder Test indicates a close relationship between the measured construct and the diagnostic criteria for gaming disorder. This aligns with observations regarding the significance of internet gaming craving in the dia­gnosis of IGD [11, 33, 34]. Furthermore, the negative correlation with the Brief Self-Control Scale suggests that individuals with a high level of craving for internet gaming may exhibit lower self-regulation capabilities in controlling their thoughts, affective responses, impulsivity, and regulating their task performance [59, 60]. This finding is consistent with findings from studies on self-control in IGD [67]. Individuals manifesting problematic gaming or gaming disorder tend to pursue instant gratification in the form of gaming, consequently reducing their capacity to exert control over their behavior. In gamers who do not exhibit problematic use, cognitive functions serve to inhibit craving, whereas in those with gaming disorder inhibitory control is notably diminished [66].
The examination of the outcomes derived from the analysis of responses on the CIGS scale suggests the potential presence of gender differences. Male partici­pants achieved significantly higher scores in comparison to females. Moreover, among male participants, a difference was observed between those who used different types of gaming devices most frequently. Those who primarily used laptops scored significantly higher than those whose primary gaming device was a smartphone. This observation may be attributed to the greater accessibility of smartphones, leading to faster satisfaction of the gaming desires and, consequently, a reduced intensity (or duration) of experienced craving.
Our findings are the first to demonstrate that the CIGS maintains its robust psychometric properties in different cultural settings. The development of a widely used questionnaire should not only facilitate a better understanding of the nature of IGD but also contribute to the refinement of addiction criteria and the improvement of the diagnostic process [6, 42]. Consequently, future research should prioritize the adaptation of the CIGS in diverse cultural contexts. Additionally, it is advisable to undertake comprehensive investigations into potential intergroup differences in the levels of craving for internet gaming.
The study had several limitations. It relied on self-report data and followed a cross-sectional design. It is important to note that, despite the inclusion of attention-checking questions, some participants may have responded in a careless or biased manner [68]. Furthermore, participants were recruited online, specifically from the Polish research panel Ariadna, rather than through random population sampling. As a result, caution is warranted when generalizing the findings.

CONCLUSIONS

The present study has successfully conducted the adaptation and validation of the CIGS within a Polish sample. The results have provided robust evidence concerning the scale’s reliability and validity, demonstrating significant correlations between the CIGS and various scales measuring related aspects of video game use, gaming disorder and self-control. We hope that these findings will contribute to a better understanding of IGD and offer a reliable measuring instrument for further research and clinical applications.

Conflict of interest

Absent.

Financial support

The study received financial support from the Institute of Pedagogy at the University of Zielona Góra.

References

1. Newzoo. Newzoo’s Global Games Market Report 2023. Free Version. 2023.
2. King DL, Delfabbro PH, Griffiths MD. The role of structural characteristics in problematic video game play: an empirical study. Int J Ment Health Addict 2011; 9: 320-333.
3. Craig F, Tenuta F, De Giacomo A, Trabacca A, Costabile A. A systematic review of problematic video-game use in people with autism spectrum disorders. Res Autism Spectr Disord 2021; 82: 101726. DOI: 10.1016/j.rasd.2021.101726
4. Mentzoni RA, Brunborg GS, Molde H, Myrseth H, Skouvere KJM, Hetland J, Pallesen S. Problematic video game use: estimated prevalence and associations with mental and physical health. Cyberpsychol Behav Soc Netw 2011; 14: 591-595.
5. Lafreniere MAK, Vallerand RJ, Donahue EG, Lavigne GL. On the costs and benefits of gaming: the role of passion. Cyberpsychol Behav 2009; 12: 285-290.
6. Ancis JR. The age of cyberpsychology: an overview. Technol Mind Behav 2020; 1. DOI: 10.1037/tmb0000009.
7. Ko CH. Internet gaming disorder. Curr Addict Rep 2014; 1: 177-185.
8. World Health Organization (WHO). International Classification of Diseases, Eleventh Revision (ICD-11). 2019/2021. Available at: https://icd.who.int/browse11 (Accessed: 30.08.2023).
9. Lemmens JS, Hendriks SJ. Addictive online games: examining the relationship between game genres and Internet gaming disorder. Cyberpsychol Behav Soc Netw 2016; 19: 270-276.
10. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed, text rev.; 2022.
11. Ko CH, Yen JY, Chen SH, Wang PW, Chen CS, Yen CF. Evaluation of the diagnostic criteria of Internet gaming disorder in the DSM-5 among young adults in Taiwan. J Psychiatr Res 2014; 53: 103-110.
12. King DL, Kaptsis D, Delfabbro PH, Gradisar M. Craving for Internet games? Withdrawal symptoms from an 84-h abstinence from massively multiplayer online gaming. Comput Human Behav 2016; 62: 488-494.
13. Kardefelt‐Winther, Heeren A, Schimmenti A, van Rooij A, Maurage P, Carras M, et al. How can we conceptualize behavioural addiction without pathologizing common behaviours? Addiction 2017; 112: 1709-1715.
14. Kuss DJ, Griffiths MD, Pontes HM. Chaos and confusion in DSM-5 diagnosis of Internet gaming disorder: issues, concerns, and recommendations for clarity in the field. J Behav Addict 2017; 6: 103-109.
15. Flannery BA, Volpicelli JR, Pettinati H. Psychometric properties of the Penn alcohol craving scale. Alcohol Clin Exp Res 1999; 23: 1289-1295.
16. Tiffany ST, Wray JM. The clinical significance of drug craving. Ann N Y Acad Sci 2012; 1248: 1-17.
17. Drummond DC. Theories of drug craving, ancient and modern. Addiction 2001; 96: 33-46.
18. Anton RF. What is craving? Models and implications for treatment. Alcohol Res Health 1999; 23: 165-173.
19. McHugh RK, Fitzmaurice GM, Griffin ML, Anton RF, Weiss RD. Association between a brief alcohol craving measure and drinking in the following week. Addiction 2016; 111: 1004-1010.
20. Sayette MA, Shiffman S, Tiffany ST, Niaura RS, Martin CS, Schadel WG. The measurement of drug craving. Addiction 2000; 95 Suppl 2: 189-210.
21. Lu L, Grimm JW, Hope BT, Shaham Y. Incubation of cocaine craving after withdrawal: a review of preclinical data. Neuropharmacology 2004; 47: 214-226.
22. White MA, Whisenhunt BL, Williamson DA, Greenway FL, Netemeyer RG. Development and validation of the food craving inventory. Obes Res 2002; 10: 107-114.
23. Hill AJ. The psychology of food craving. Proc Nutr Soc 2007; 66: 277-285.
24. Tavares H, Zilberman ML, Hodgins DC, el-Guebaly N. Comparison of craving between pathological gamblers and alcoholics. Alcohol Clin Exp Res 2005; 29: 1427-1431.
25. Young MM, Wohl MJ. The Gambling Craving Scale: psychometric validation and behavioral outcomes. Psychol Addict Behav 2009; 23: 512-522.
26. Kraus S, Rosenberg H. The pornography craving questionnaire: psychometric properties. Arch Sex Behav 2014; 43: 451-462.
27. Allen A, Kannis-Dymand L, Katsikitis M. Problematic internet pornography use: the role of craving, desire thinking, and metacognition. Addiction 2017; 112: 1709-1715.
28. Trotzke P, Starcke K, Müller A, Brand M. Pathological buying online as a specific form of internet addiction: a model-based experimental investigation. PLoS One 2015; 10: e0140296. DOI: 10.1371/journal.pone.0140296.
29. Savci M, Ugur E, Ercengiz M, Griffiths MD. The development of the Turkish craving for online shopping scale: a validation study. Int J Ment Health Addict 2023; 21: 1303-1319.
30. Hormes JM, Kearns B, Timko CA. Craving Facebook? Behavioral addiction to online social networking and its association with emotion regulation deficits. Addiction 2014; 109: 2079-2088.
31. Leng Y, He X, Zhu B, Li P, Xiao C, He W. The craving and excitement of social networking sites addicts: based on cue-reactivity. Front Psychol 2019; 10: 1717. DOI: 10.3389/fpsyg.2019.01717.
32. De-Sola J, Talledo H, Rubio G, de Fonseca FR. Development of a mobile phone addiction craving scale and its validation in a Spanish adult population. Front Psychiatry 2017; 8: 90. DOI: 10.3389/fpsyt.2017.00090.
33. Han DH, Hwang JW, Renshaw PF. Bupropion sustained release treatment decreases craving for video games and cue-induced brain activity in patients with Internet video game addiction. Psychol Pop Media Cult 2011; 1(S): 108-117.
34. Ray LA, Roche DJ. Neurobiology of craving: current findings and new directions. Curr Addict Rep 2018; 5: 102-109.
35. Zhang Y, Ndasauka Y, Hou J, Chen J, Yang LZ, Wang Y, Zhang X. Cue-induced behavioral and neural changes among excessive internet gamers and possible application of cue exposure therapy to internet gaming disorder. Front Psychol 2016; 7: 675. DOI: 10.3389/fpsyg.2016.00675.
36. Zhang JT, Yao YW, Potenza MN, Xia CC, Lan J, Liu L, Fang XY. Effects of craving behavioral intervention on neural substrates of cue-induced craving in Internet gaming disorder. Neuroimage Clin 2016; 12: 591-599.
37. Young KS, Brand M. Merging theoretical models and therapy approaches in the context of Internet gaming disorder: a personal perspective. Front Psychol 2017; 8: 1853. DOI: 10.3389/fpsyg.2017.01853.
38. Li W, Garland EL, Howard MO. Therapeutic mechanisms of Mindfulness-Oriented Recovery Enhancement for internet gaming disorder: reducing craving and addictive behavior by targeting cognitive processes. J Addict Dis 2018; 37: 5-13.
39. Shin YB, Kim JJ, Kim MK, Kyeong S, Jung YH, Eom H, Kim E. Development of an effective virtual environment in eliciting craving in adolescents and young adults with internet gaming disorder. PLoS One 2018; 13: e0195677. DOI: 10.1371/journal.pone.0195677.
40. Deng LY, Liu L, Xia CC, Lan J, Zhang JT, Fang XY. Craving behavior intervention in ameliorating college students’ internet game disorder: a longitudinal study. Front Psychol 2017; 8: 526. DOI: 10.3389/fpsyg.2017.00526.
41. Bonner J, Allen A, Katsikitis M, Love S, Kannis-Dymand L. Metacognition, desire thinking and craving in problematic video game use. J Technol Behav Sci 2022; 7: 532-546.
42. Savci M, Griffiths MD. The development of the Turkish craving for internet gaming scale (CIGS): a validation study. Int J Ment Health Addict 2022; 20: 1-18.
43. Savci M. Validity and reliability of the Smartphone Craving Scale: modifying and adaptation of the Penn Alcohol Craving Scale to the Turkish culture as Smartphone Craving Scale. Anadolu Psikiyatri Derg 2019; 20 (SI 1): 5-15.
44. Tsui JI, Anderson BJ, Strong DR, Stein MD. Craving predicts opioid use in opioid-dependent patients initiating buprenorphine treatment: a longitudinal study. Am J Drug Alcohol Abuse 2014; 40: 163-169.
45. Opozda-Suder S, Karteczka-Witek K, Piasecka M. Psychometric properties and longitudinal measurement invariance of the drug craving scale: modification of the Polish version of the Penn Alcohol Craving Scale (PACS). PLoS One 2021; 16: e0256018. DOI: 10.1371/journal.pone.0256018.
46. Savci M, Ugur E, Ercengiz M, Griffiths MD. The development of the Turkish craving for online shopping scale: a validation study. Int J Ment Health Addict 2023; 21: 1303-1319.
47. Chodkiewicz J, Ziółkowski M, Czarnecki D, Gąsior K, Juczyński A, Biedrzycka A, Nowakowska-Domagała K. Walidacja polskiej wersji Skali Głodu Alkoholu Penn (Penn Alcohol Craving Scale, PACS). Psychiatr Pol 2018; 52: 399-410.
48. Pontes HM, Schivinski B, Sindermann C, Li M, Becker B, Zhou M, Montag C. Measurement and conceptualisation of Gaming Disorder according to the World Health Organization framework: the development of the Gaming Disorder Test. Int J Ment Health Addict 2021; 19: 508-528.
49. Cudo A, Montag C, Pontes HM. Psychometric assessment and gender invariance of the Polish version of the Gaming Disorder Test. Int J Ment Health Addict 2022; 1-24.
50. Karhulahti VM, Martončik M, Adamkovic M. Measuring Internet gaming disorder and gaming disorder: Qualitative content validity analysis of validated scales. Assessment 2023; 30: 402-413.
51. Ortiz de Gortari AB, Pontes H, Griffiths MD. The Game Transfer Phenomena Scale: an instrument for investigating the nonvolitional effects of video game playing. Cyberpsychol Behav Soc Netw 2015; 18: 588-594.
52. Cudo A, Zabielska-Mendyk E, de Gortari ABO. Psychometric assessment and gender invariance of the Polish adaptation of the Game Transfer Phenomena Scale. Adv Cogn Psychol 2022; 18: 48-63.
53. Demetrovics Z, Urbán R, Nagygyörgy K, Farkas J, Zilahy D, Mervó B, Harmath E. Why do you play? The development of the motives for online gaming questionnaire (MOGQ). Behav Res Methods 2011; 43: 814-825.
54. Grzegorzewska I, Cierpiałkowska L. Uzależnienia behawioralne. Warszawa: PWN; 2018.
55. Jennett C, Cox AL, Cairns P, Dhoparee S, Epps A, Tijs T, Walton A. Measuring and defining the experience of immersion in games. Int J Hum Comput Stud 2008; 66: 641-661.
56. Strojny P, Strojny A. Kwestionariusz immersji – polska adaptacja i empiryczna weryfikacja narzędzia. Homo Ludens 2014; 1: 171-186.
57. Lovibond PF, Lovibond SH. The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav Res Ther 1995; 33: 335-343.
58. Zawislak D, Zur-Wyrozumska K, Habera M, Skrzypiec K, Pac A, Cebula G. Evaluation of a polish version of the Depression Anxiety Stress Scales (DASS-21). J Neurosci Cogn Stud 2020; 4: 1015.
59. Pilarska A, Baumeister RF. Psychometric properties and correlates of the Polish version of the Self-Control Scale (SCS). Pol Psychol Bull 2018; 49: 95-106.
60. Tangney JP, Baumeister RF, Boone AL. High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. J Pers 2004; 72: 271-324.
61. R Core Team. R: A Language and environment for statistical computing. Version 4.1. Available at: https://cran.r-project.org. R packages retrieved from CRAN snapshot 2023-04-07.
62. The jamovi project. jamovi. Version 2.3.28. Available at: https://www.jamovi.org (Accessed: 30.08.2023).
63. Jorgensen TD, Pornprasertmanit S, Schoemann AM, Rosseel Y. semTools: Useful tools for structural equation modeling. R package version 0.5-6. Available at: https://CRAN.R-project.org/package=semTools (Accessed: 07.04.2023).
64. Rosseel Y. lavaan: An R Package for Structural Equation Modeling. J Stat Softw 2012; 48: 1-36.
65. Loton D, Borkoles E, Lubman, D, Polman, R. Video game addiction, engagement and symptoms of stress, depression and anxiety: the mediating role of coping. Int J Ment Health Addict 2016; 14: 565-578.
66. Argyriou E, Davison CB, Lee TTC. Response inhibition and Internet Gaming Disorder: a meta-analysis. Addict Behav 2017; 71: 54-60.
67. Dong G, Potenza MN. A cognitive-behavioral model of Internet gaming disorder: theoretical underpinnings and clinical implications. J Psychosom Res 2014; 58: 7-11.
68. Brühlmann F, Petralito S, Aeschbach LF, Opwis K. The quality of data collected online: an investigation of careless responding in a crowdsourced sample. Meth Psychol 2020; 2: 100022.
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