The primary objectives of the present cross-sectional study were to translate and culturally adapt the YouTube Addiction Scale (YAS) into Malay and to evaluate its reliability, factorial validity, concurrent validity, and measurement invariance across gender and ethnicity among Malaysian university students. A total of 690 students participated (mean age = 21.29 years [SD ± 2.42]; 74% female). Confirmatory factor analysis supported a unidimensional YAS structure with good fit (CFI = 0.98, TLI = 0.97, SRMR = 0.06, RMSEA = 0.07). The scale exhibited strong reliability (α = 0.83, ω = 0.84) and measurement invariance across gender and ethnicity. YAS scores showed correlation with Smartphone Application-Based Addiction Scale score (r = 0.17, p < 0.001) and Bergen Social Media Addiction Scale score (r = 0.23, p < 0.001), a weak correlation with time spent on social media (r = 0.09, p = 0.02), and no significant relationship with time spent on online gaming (r = 0.03, p = 0.47). The Malay YAS is a reliable and valid instrument for assessing YouTube addiction among Malaysian university students. Its unidimensional structure, strong reliability, robust measurement invariance across gender and ethnicity, and satisfactory concurrent validity support its application in research, screening, and intervention programs.
AhorsuD. K. (2024). Pathways to social media addiction: Examining its prevalence, and predictive factors among Ghanaian youths. Journal of Social Media Research, 1(1), 47–59. https://doi.org/10.29329/jsomer.9
2.
AndreassenC.BillieuxJ.GriffithsM. D.KussD. J.DemetrovicsZ.MazzoniE.PallesenS. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30(2), 252–262. https://doi.org/10.1037/adb0000160
3.
BalakrishnanJ.GriffithsM. D. (2017). Social media addiction: What is the role of content in YouTube?Journal of Behavioral Addictions, 6(3), 364–377. https://doi.org/10.1556/2006.6.2017.058
4.
BeatonD. E.BombardierC.GuilleminF.FerrazM. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186–3191. https://doi.org/10.1097/00007632-200012150-00014
5.
BorsaJ. C.DamásioB. F.BandeiraD. R. (2012). Cross-cultural adaptation and validation of psychological instruments: Some considerations. Paideia, 22, 423–432. https://doi.org/10.1590/S0103-863X2012000300014
6.
BottaroR.GriffithsM. D.FaraciP. (2025). Meta-analysis of reliability and validity of the Bergen Social Media Addiction Scale (BSMAS). International Journal of Mental Health and Addiction, 1–28. https://doi.org/10.1007/s11469-025-01461-x
7.
BrandM.WegmannE.StarkR.MüllerA.WölflingK.RobbinsT. W.PotenzaM. N. (2019). The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neuroscience & Biobehavioral Reviews, 104, 1–10. https://doi.org/10.1016/j.neubiorev.2019.06.032
8.
ByrneB. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge.
9.
ByrneB. M. (2013). Structural equation modeling with mplus: Basic concepts, applications, and programming. Routledge.
10.
ChenF. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://doi.org/10.1080/10705510701301834
11.
ChenI. H.StrongC.LinY. C.TsaiM. C.LeungH.LinC. Y.PakpourA. H.GriffithsM. D. (2020). Time invariance of three ultra-brief internet-related instruments: Smartphone Application-Based Addiction Scale (SABAS), Bergen Social Media Addiction Scale (BSMAS), and the nine-item Internet Gaming Disorder Scale-Short Form (IGDS-SF9) (study part B). Addictive Behaviors, 101, 105960. https://doi.org/10.1016/j.addbeh.2019.04.018
12.
ChenY.-C.ChenP.-Y.ChiangC.-M.SungP.-C.ChenP.-Y.HsiehJ.-H.TsaiW.-C.HoC.-H.ChowJ. C.ChangT.-H. (2025). Adolescent and young adult suicide and self-harm: Exploring changes in risk factors from 2016 to 2023. Asian Journal of Social Health Behavior, 8(3), 133–140. https://doi.org/10.4103/shb.shb_368_24
13.
ClarkL.ZackM. (2023). Engineered highs: Reward variability and frequency as potential prerequisites of behavioural addiction. Addictive Behaviors, 140, Article 107626. https://doi.org/10.1016/j.addbeh.2023.107626
14.
CsibiS.GriffithsM. D.CookB.DemetrovicsZ.SzaboA. (2018). The psychometric properties of the Smartphone Application-Based Addiction Scale (SABAS). International Journal of Mental Health and Addiction, 16(2), 393–403. https://doi.org/10.1007/s11469-017-9787-2
DinçE.RuckwongpatrK.KaradumanA.ChenI.-H.KaradumanA.WangX. L. (2025). YouTube Addiction Scale (YAS): Adaptation to Turkish culture, validity and reliability study. Jouranl of Social Media Research, 2(1), 13–23. https://doi.org/10.29329/jsomer.20
17.
EikelboomR. (2017). Irresistible: The rise of addictive technology and the business of keeping Us hooked. Perspectives on Science and Christian Faith, 69(4), 253–256.
18.
EllithorpeM. E.ThamS. M.MeshiD. (2023). Problematic media use: Comparisons by media type and with other addictive behavior and substance use domains. Technology, Mind, and Behavior, 4(4), 335–351. https://doi.org/10.1037/tmb0000116
19.
ErnalaS. K.BurkeM.LeavittA.EllisonN. B. (2022). Mindsets matter: How beliefs about Facebook moderate the association between time spent and well-being. In Proceedings of the 2022 CHI conference on human factors in computing systems.
20.
FedeleM.Aran-RamspottS.SuauJ. (2021). YouTube preferences and practices of preadolescents: Findings from a study carried out in Catalonia. Comunicación Y Sociedad, 39, 145–166. https://doi.org/10.17231/comsoc.39
21.
GerbingD. W.AndersonJ. C. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research, 25(2), 186–192. https://doi.org/10.2307/3172650
22.
GriffithsM. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4), 191–197. https://doi.org/10.1080/14659890500114359
23.
HuangP. C.ChenC. Y.ChenI. H.ChenJ. K.PramuktiI.YuR. L.ChenJ. S.HuangC. H.WanX. L.PakpourA. H.PotenzaM. N.LinC. Y. (2025). Psychometric properties and measurement invariance of video addiction scales: The Chinese YouTube Addiction Scale for Taiwan and Hong Kong. Cyberpsychology, Behavior, and Social Networking, 28(10), 672–679. https://doi.org/10.1177/21522715251378312
24.
HuangP. H.ChenH.WengL. J. (2017). A penalized likelihood method for structural equation modeling. Psychometrika, 82(2), 329–354. https://doi.org/10.1007/s11336-017-9566-9
JafferyN. S. N.AnnuarS. S.RajJ. A. T. (2020). The influence of YouTube advertising on the attitude towards fruits and vegetable consumption among university students in Malaysia. Jurnal Komunikasi, 36(3), 353–372. https://doi.org/10.17576/JKMJC-2020-3603-22
27.
JheeJ. H.LyeQ. T.WooK. (2019). Not yet game over: A reappraisal of video game addiction. Loading, 12(19). https://doi.org/10.7202/1058317ar
28.
KhanM. L. (2017). Social media engagement: What motivates user participation and consumption on YouTube?Computers in Human Behavior, 66, 236–247. https://doi.org/10.1016/j.chb.2016.09.024
29.
KimE. S.DedrickR. F.CaoC.FerronJ. M. (2016). Multilevel factor analysis: Reporting guidelines and a review of reporting practices. Multivariate Behavioral Research, 51(6), 881–898. https://doi.org/10.1080/00273171.2016.1228042
30.
KwokC.LeungP. Y.PoonK. Y.FungX. C. C. (2021). The effects of internet gaming and social media use on physical activity, sleep, quality of life, and academic performance among university students in Hong Kong: A preliminary study. Asian Journal of Social Health and Behavior, 4(1), 36–44. https://doi.org/10.4103/shb.shb_81_20
31.
LubkeG. H.DolanC. V.KeldermanH.MellenberghG. J. (2003). Weak measurement invariance with respect to unmeasured variables: An implication of strict factorial invariance. British Journal of Mathematical and Statistical Psychology, 56(2), 231–248. https://doi.org/10.1348/000711003770480020
32.
MahendranR.SubramoniamA.SubbarajA. (2025). Addiction-like behavior associated with YouTube among adolescent boys and girls in South India. Journal of the Scientific Society, 52(2), 163–167. https://doi.org/10.4103/jss.jss_287_24
33.
MariE.BiondiS.VarchettaM.CricentiC.FraschettiA.PizzoA.BarchielliB.RomaP.VilarM. M. i.SalaF. G.GianniniA. M.QuaglieriA. (2023). Gender differences in internet addiction: A study on variables related to its possible development. Computers in Human Behavior Reports, 9, Article 100247. https://doi.org/10.1016/j.chbr.2022.100247
34.
MarinoC.CanaleN.MelodiaF.SpadaM. M.VienoA. (2021). The overlap between problematic smartphone use and problematic social media use: A systematic review. Current Addiction Reports, 8(4), 469–480. https://doi.org/10.1007/s40429-021-00398-0
35.
MitropoulouE. (2024). Exploration of the association between social media addiction, self-esteem, self-compassion and loneliness. Journal of Social Media Research, 1(1), 25–37. https://doi.org/10.29329/jsomer.2
36.
MoghavvemiS.SulaimanA. B.JaafarN. I. B.KasemN. (2017). Facebook and YouTube addiction: The usage pattern of Malaysian students. In 2017 international conference on research and innovation in information systems (ICRIIS).
37.
Mohamad YusofN.Tani BenetB.JamioonJ. A. K. (2023). The use of YouTube in developing speaking skills among undergraduates in Malaysia. International Journal of Modern Language and Applied Linguistics, 7(3), 1–14. https://doi.org/10.24191/ijmal.v7i3.7471
38.
MüllerS. M.WegmannE.OelkerA.StarkR.MüllerA.MontagC.WölflingK.RumpfH. J.BrandM. (2022). Assessment of Criteria for Specific Internet-use Disorders (ACSID-11): Introduction of a new screening instrument capturing ICD-11 criteria for gaming disorder and other potential internet-use disorders. Journal of Behavioral Addictions, 11(2), 427–450. https://doi.org/10.1556/2006.2022.00013
39.
MuniraL.HermanB. (2025). Factors associated with loneliness among adolescents in Thailand, Indonesia, Brunei Darussalam, and the Philippines. Asian Journal of Social Health and Behavior, 8(3), 152–159. https://doi.org/10.4103/shb.shb_370_24
40.
NadoluB.NadoluD. (2020). Homo interneticus—The sociological reality of Mobile online being. Sustainability, 12(5), 1800. https://doi.org/10.3390/su12051800
41.
PakpourA. H.JafariE.ZanjanchiF.PotenzaM. N.LinC.-Y. (2025). The YouTube Addiction Scale: Psychometric evidence for a new instrument developed based on the component model of addiction. International Journal of Mental Health and Addiction, 23(3), 2052–2069. https://doi.org/10.1007/s11469-023-01216-6
42.
PallantJ. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Routledge.
43.
PengP.ChenZ.RenS.LiuY.HeR.LiangY.TanY.TangJ.ChenX.LiaoY. (2023). Determination of the cutoff point for Smartphone Application-Based Addiction Scale for adolescents: A latent profile analysis. BMC Psychiatry, 23(1), 675. https://doi.org/10.1186/s12888-023-05170-4
44.
PhongphanngamS.LachH. W. (2019). Cross-cultural instrument translation and adaptation: Challenges and strategies. Pacific Rim International Journal of Nursing Research, 23(2), 170–179.
RuckwongpatrK.ChirawatP.GhavifekrS.GanW. Y.TungS. E.NurmalaI.NadhirohS. R.PramuktiI.LinC.-Y. (2022). Problematic internet use (PIU) in youth: A brief literature review of selected topics. Current Opinion in Behavioral Sciences, 46, Article 101150. https://doi.org/10.1016/j.cobeha.2022.101150
47.
RuckwongpatrK.NurmalaI.MuthmainnahM.SalimL. A.NadiaA.DeviY. P.HsiehG. J.LinY. C.PramuktiI.ChenC. Y.PakpourA. H.GriffithsM. D.LinC. Y. (2025). Indonesian validation of the YouTube Addiction Scale among young adults. Psychiatry Investigation, 22(10), 1121–1130. https://doi.org/10.30773/pi.2025.0081
48.
RumpfH.-J.BrandtD.DemetrovicsZ.BillieuxJ.CarragherN.BrandM.Bowden-JonesH.Rahimi-MovagharA.AssanangkornchaiS.Glavak-TkalicR.BorgesG.LeeH. K.RehbeinF.FinebergN. A.MannK.PotenzaM. N.SteinD. J.HiguchiS.KingD.PoznyakV. (2019). Epidemiological challenges in the study of behavioral addictions: A call for high standard methodologies. Current Addiction Reports, 6(3), 331–337. https://doi.org/10.1007/s40429-019-00262-2
49.
ShinN. Y. (2022). Psychometric properties of the Bergen Social Media Addiction Scale in Korean young adults. Psychiatry Investigation, 19(5), 356–361. https://doi.org/10.30773/pi.2021.0294
50.
SinghJ. P.KronerD. G.WormithJ. S.DesmaraisS. L.HamiltonZ. (2018). Handbook of recidivism risk/needs assessment tools. John Wiley & Sons.
51.
SongT. J.ZhaoH. (2025). Psychometric properties and measurement invariance across gender of the Chinese version of the Smartphone Application-Based Addiction Scale (SABAS) among Chinese college students. PLoS One, 20(5), e0323215. https://doi.org/10.1371/journal.pone.0323215
52.
SoraciP.FerrariA.AntoninoU.GriffithsM. D. (2021). Psychometric properties of the Italian version of the Smartphone Application-Based Addiction Scale (SABAS). International Journal of Mental Health and Addiction, 19(4), 1261–1273. https://doi.org/10.1007/s11469-020-00222-2
53.
TanC. N.-L. (2023). Toward an integrated framework for examining the addictive use of smartphones among young adults. Asian Journal of Social Health and Behavior, 6(3), 119–125. https://doi.org/10.4103/shb.shb_206_23
54.
VarchettaM.TagliaferriG.MariE.QuaglieriA.CricentiC.GianniniA. M.Martí-VilarM. (2024). Exploring gender differences in internet addiction and psychological factors: A study in a Spanish sample. Brain Sciences, 14(10), 1037. https://doi.org/10.3390/brainsci14101037
55.
VidalC.SussmanC. (2023). Overlap between behavioral addictions and substance use and potential interventions in pediatric populations. Journal of the American Academy of Child & Adolescent Psychiatry, 62(10), S94–S95. https://doi.org/10.1016/j.jaac.2023.07.443
56.
VujićA.VolarovM.LatasM.GriffithsM. D.SzaboA. (2024). Psychometric properties of the Serbian Smartphone Application-Based Addiction Scale (SABAS) and validation of the English version among non-native English speakers. International Journal of Mental Health and Addiction, 22(5), 2682–2705. https://doi.org/10.1007/s11469-023-01013-1
57.
WolgastM.AdlerH.WolgastS. N. (2025). Motives for social media use in adults: Associations with platform-specific use, psychological distress, and problematic engagement. Journal of Social Media Research, 2(3), 179–194. https://doi.org/10.29329/jsomer.45
58.
YueH.ZhangX.ChengX.LiuB.BaoH. (2022). Measurement invariance of the Bergen Social Media Addiction scale across genders. Frontiers in Psychology, 13, 879259. https://doi.org/10.3389/fpsyg.2022.879259
59.
ZendleD. (2020). Too much of a good thing? Excessive use across behaviours and associations with mental health. OSF Preprints. https://doi.org/10.31234/osf.io/pnx2e
60.
ZhangJ.ZhangE.CuiS.ZhangL.RenB.JinQ.LiH.ChenM.LiR.XuY.LiuM.ZhuJ.SuY.WuF.XiangM. (2025). Association of internet addiction severity with anxiety, depression, and suicidal ideation among civil aircrew members: A nationwide cross-sectional survey. Asian Journal of Social Health and Behavior, 8(3), 116–124. https://doi.org/10.4103/shb.shb_302_24
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