American Psychiatric Association. Diagnostic and statistical manual of mental disorders (5th ed.) (DSM-5). Arlington, VA: American Psychiatric Publishing; 2013.
Book
Google Scholar
Asparouhov T, Muthén B. Auxiliary variables in mixture modeling: three-step approaches using Mplus. Struct Equ Model Multidiscip J. 2014;21:329–41.
Article
Google Scholar
Asparouhov, T. muthén, B. 2018. Auxiliary Variables in Mixture Modeling: Using the BCH Method in Mplus to Estimate a Distal Outcome Model and an Arbitrary Secondary Model. Mplus Web Notes: No. 21, Version 3. [online]. [accessed].
Baggio S, Starcevic V, Studer J, Simon O, Gainsbury SM, Gmel G, Billieux J. Technology-mediated addictive behaviors constitute a spectrum of related yet distinct conditions: A network perspective. Psychol Addict Behav. 2018;32:564–72.
Article
PubMed
Google Scholar
Bakk Z, Vermunt JK. Robustness of stepwise latent class modeling with continuous distal outcomes. Struct Equ Model Multidiscip J. 2016;23:20–31.
Article
Google Scholar
Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57:289–300.
Google Scholar
Brand M, Laier C, Young KS. Internet addiction: coping styles, expectancies, and treatment implications. Front Psychol. 2014;5:1256.
PubMed
PubMed Central
Google Scholar
Brand M, Young KS, Laier C, Wolfling K, Potenza MN. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific internet-use disorders: an interaction of person-affect-cognition-execution (I-PACE) model. Neurosci Biobehav Rev. 2016;71:252–66.
Article
PubMed
Google Scholar
Byrne BM. Structural equation modeling with Mplus: basic concepts, applications, and programming. New York: Routledge; 2012.
Google Scholar
Byrne BM, Shavelson RJ, Muthén B. Testing for the equivalence of factor covariance and mean structures: the issue of partial measurement invariance. Psychol Bull. 1989;105:456–66.
Article
Google Scholar
Carli V, Durkee T, Wasserman D, Hadlaczky G, Despalins R, KRAMARZ E, Wasserman C, Sarchiapone M, Hoven CW, Brunner R, Kaess M. The association between pathological internet use and comorbid psychopathology: a systematic review. Psychopathology. 2013;46:1–13.
Article
CAS
PubMed
Google Scholar
Chamberlain SR, Ioannidis K, Grant JE. The impact of comorbid impulsive/compulsive disorders in problematic internet use. J Behav Addict. 2018:1–7.
Chang MK, SP ML. Factor structure for Young’s internet addiction test: A confirmatory study. Comput Hum Behav. 2008;24:2597–619.
Article
Google Scholar
Cheung GW, Rensvold RB. Evaluating goodness-of-fit indexes for testing measurement invariance. Struct Equation Modeling-a Multidisciplinary J. 2002;9:233–55.
Article
Google Scholar
Cohen AS, Bolt DM. A mixture model analysis of differential item functioning. J Educ Meas. 2005;42:133–48.
Article
Google Scholar
Costello AB, Osborne JW. Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Pract Assess Res Eval. 2005;10:1–9.
Google Scholar
Diallo TM, Morin AJ, Lu H. The impact of total and partial inclusion or exclusion of active and inactive time invariant covariates in growth mixture models. Psychol Methods. 2017;22:166–90.
Article
PubMed
Google Scholar
Edelen MO, Reeve BB. Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Qual Life Res. 2007;16(Suppl 1):5–18.
Article
PubMed
Google Scholar
Eysenck HJ. The continuity of abnormal and normal behavior. Psychol Bull. 1958;55:429–32.
Article
CAS
PubMed
Google Scholar
Finch WH, Bronk KC. Conducting confirmatory latent class analysis using Mplus. Struct Equ Model. 2011;18:132–51.
Article
Google Scholar
Fineberg NA, Demetrovics Z, Stein DJ, Ioannidis K, Potenza MN, Grünblatt E, Brand M, Billieux J, Carmi L, King DL, Grant JE, Yücel M, Dell'osso B, Rumpf HJ, Hall N, Hollander E, Goudriaan A, Menchon J, Zohar J, Burkauskas J, Martinotti G, VAN Ameringen M, Corazza O, Pallanti S, Chamberlain SR. Manifesto for a European research network into problematic usage of the internet. Eur Neuropsychopharmacol. 2018;28:1232–46.
Article
CAS
PubMed
PubMed Central
Google Scholar
Fineberg NA, Potenza MN, Chamberlain SR, Berlin HA, Menzies L, Bechara A, Sahakian BJ, Robbins TW, Bullmore ET, Hollander E. Probing compulsive and impulsive behaviors, from animal models to endophenotypes: a narrative review. Neuropsychopharmacology. 2010;35:591–604.
Article
PubMed
Google Scholar
Forbes MK, Tackett JL, Markon KE, Krueger RF. Beyond comorbidity: toward a dimensional and hierarchical approach to understanding psychopathology across the life span. Dev Psychopathol. 2016;28:971–86.
Article
PubMed
PubMed Central
Google Scholar
Frangos CC, Frangos CC, Sotiropoulos I. A meta-analysis of the reliability of Young's internet addiction test. Proceedings of the World Congress on Engineering. 2012:368–71.
Gerbing DW, Hamilton JG. Viability of exploratory factor analysis as a precursor to confirmatory factor analysis. Struct Equ Model. 1996;3:62–72.
Article
Google Scholar
Gillan CM, Daw ND. Taking psychiatry research online. Neuron. 2016;91:19–23.
Article
CAS
PubMed
Google Scholar
Griffiths MD. 1995. Technological addictions. Clinical Psychology Forum. Division of Clinical Psychology of the British Psychol Soc, 14–14.
Griffiths MD, VAN Rooij AJ, Kardefelt-Winther D, Starcevic V, Kiraly O, Pallesen S, Muller K, Dreier M, Carras M, Prause N, King DL, Aboujaoude E, Kuss DJ, Pontes HM, Lopez Fernandez O, Nagygyorgy K, Achab S, Billieux J, Quandt T, Carbonell X, Ferguson CJ, Hoff RA, Derevensky J, Haagsma MC, Delfabbro P, Coulson M, Hussain Z, Demetrovics Z. Working towards an international consensus on criteria for assessing internet gaming disorder: a critical commentary on Petry et al. (2014). Addiction. 2016;111:167–75.
Article
PubMed
PubMed Central
Google Scholar
Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL. Multivariate data analysis (Vol. 7), NJ, Pearson Prentice Hall Upper Saddle River; 2010.
Google Scholar
Ho RC, MWB Z, Tsang TY, Toh AH, Pan F, Lu Y, Cheng C, Yip PS, Lam LT, Lai CM. The association between internet addiction and psychiatric co-morbidity: A meta-analysis. BMC Psychiatry. 2014;14:183.
Article
PubMed
PubMed Central
Google Scholar
Ioannidis K, Chamberlain SR, Treder MS, Kiraly F, Leppink EW, Redden SA, Stein DJ, Lochner C, Grant JE. Problematic internet use (PIU): associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry. J Psychiatr Res. 2016;83:94–102.
Article
PubMed
PubMed Central
Google Scholar
Ioannidis K, Hook R, Goudriaan A, Vlies S, Fineberg N, Grant JE, Chamberlain SR. Cognitive deficits in problematic internet use: a meta-analysis of 40 studies. Br J Psychiatry. 2019; In press.
Ioannidis K, Treder MS, Chamberlain SR, Kiraly F, Redden SA, Stein DJ, Lochner C, Grant JE. Problematic internet use as an age-related multifaceted problem: evidence from a two-site survey. Addict Behav. 2018;81:157–66.
Article
PubMed
PubMed Central
Google Scholar
Jang KL. The behavioral genetics of psychopathology: A clinical guide. New York: Routledge; 2005.
Book
Google Scholar
Kessler RC, Adler L, Ames M, Demler O, Faraone S, Hiripi E, Howes MJ, Jin R, Secnik K, Spencer T, Ustun TB, Walters EE. The World Health Organization adult ADHD self-report scale (ASRS): a short screening scale for use in the general population. Psychol Med. 2005;35:245–56.
Article
PubMed
Google Scholar
Kiraly O, Nagygyorgy BK, Koronczai B, Griffiths MD, Demetrovics Z. Assessment of problematic internet use and online video gaming. In: Aboujaoude E, Starcevic V, editors. Mental health in the digital age: Oxford University Press; 2015.
Kline RB. Principles and practice of structural equation modeling. New York: The Guilford Press; 2015.
Google Scholar
Korkeila J, Kaarlas S, Jääskeläinen M, Vahlberg T, Taiminen T. Attached to the web — harmful use of the internet and its correlates. Eur Psychiatry. 2010;25:236–41.
Article
CAS
PubMed
Google Scholar
Kotov R, Krueger RF, Watson D, Achenbach TM, Althoff RR, Bagby RM, Brown TA, Carpenter WT, Caspi A, Clark LA, Eaton NR, Forbes MK, Forbush KT, Goldberg D, Hasin D, Hyman SE, Ivanova MY, lynam DR, Markon K, Miller JD, Moffitt TE, Morey LC, Mullins-Sweatt SN, Ormel J, Patrick CJ, Regier DA, Rescorla L, Ruggero CJ, Samuel DB, Sellbom M, Simms LJ, Skodol AE, Slade T, South SC, Tackett JL, Waldman ID, Waszczuk MA, Widiger TA, Wright AG, Zimmerman M. The hierarchical taxonomy of psychopathology (HiTOP): A dimensional alternative to traditional nosologies. J Abnorm Psychol. 2017;126:454–77.
Article
PubMed
Google Scholar
Kraemer HC, Noda A, O'hara R. Categorical versus dimensional approaches to diagnosis: methodological challenges. J Psychiatr Res. 2004;38:17–25.
Article
PubMed
Google Scholar
Krueger RF, Nichol PE, Hicks BM, Markon KE, Patrick CJ, Lacono WG, Mcgue M. Using latent trait modeling to conceptualize an alcohol problems continuum. Psychol Assess. 2004;16:107–19.
Article
PubMed
Google Scholar
Laconi S, Rodgers RF, Chabrol H. The measurement of internet addiction: A critical review of existing scales and their psychometric properties. Comput Hum Behav. 2014;41:190–202.
Article
Google Scholar
Lanza ST, Patrick ME, Maggs JL. Latent transition analysis: benefits of a latent variable approach to modeling transitions in substance use. J Drug Issues. 2010;40:93–120.
Article
PubMed
PubMed Central
Google Scholar
Lanza ST, Tan X, Bray BC. Latent class analysis with distal outcomes: A flexible model-based approach. Struct Equ Modeling. 2013;20:1–26.
Article
PubMed
PubMed Central
Google Scholar
Lortie CL, Guitton MJ. Internet addiction assessment tools: dimensional structure and methodological status. Addiction. 2013;108:1207–2016.
Article
PubMed
Google Scholar
Lucke JF. Positive trait item response models. In: New Developments in Quantitative Psychology. New York: Springer; 2013.
Google Scholar
Lucke JF. Unipolar item response models. In: Handbook of item response theory modeling: Applications to typical performance assessment. New York: Routledge; 2015.
Google Scholar
Mchorney CA, Fleishman JA. Assessing and understanding measurement equivalence in health outcome measures - issues for further quantitative and qualitative inquiry - epilogue. Med Care. 2006;44:S205–10.
Article
PubMed
Google Scholar
Meade AW, Johnson EC, Braddy PW. Power and sensitivity of alternative fit indices in tests of measurement invariance. J Appl Psychol. 2008;93:568–92.
Article
PubMed
Google Scholar
Milosevic A, Ledgerwood DM. The subtyping of pathological gambling: a comprehensive review. Clin Psychol Rev. 2010;30:988–98.
Article
PubMed
Google Scholar
Muthén L, Muthén B. Mplus User’s Guide, Los Angeles, CA, USA; 2016.
Google Scholar
Nylund-Gibson K, Choi AY. Ten frequently asked questions about latent class analysis. Transl Issues Psychol Sci. 2018;4:440–61.
Article
Google Scholar
Nylund KL, Asparouhov T, Muthén B. Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Struct Equ Model. 2007;14:535–69.
Article
Google Scholar
Pawlikowski M, Altstotter-Gleich C, Brand M. Validation and psychometric properties of a short version of Young's internet addiction test. Comput Hum Behav. 2013;29:1212–23.
Article
Google Scholar
Pawlikowski M, Nader IW, Burger C, Stieger S, Brand M. Pathological internet use – it is a multidimensional and not a unidimensional construct. Addict Res Theory. 2014;22:166–75.
Article
Google Scholar
Pearson JS, Kley IB. Discontinuity and correlation: a reply to Eysenck. Psychol Bull. 1958;55:433–5.
Article
CAS
PubMed
Google Scholar
Podsakoff PM, Mackenzie SB, Lee J, Podsakoff NP. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J Appl Psychol. 2003;88:879–903.
Article
PubMed
Google Scholar
Podsakoff, P. M., Mackenzie, S. B. & Podsakoff, N. P. 2012. Sources of method Bias in social science research and recommendations on how to control it. In: FISKE, S. T., SCHACTER, D. L. & TAYLOR, S. E. (eds.) Annual Review of Psychology, Vol 63.
Reise SP. Invited paper: the rediscovery of Bifactor measurement models. Multivariate Behav Res. 2012;47:667–96.
Article
PubMed
PubMed Central
Google Scholar
Reise SP, Ainsworth AT. Item response theory: fundamentals, applications, and promise in psychological research. Curr Dir Psychol Sci. 2005;14:95–101.
Article
Google Scholar
Reise SP, Morizot J, Hays RD. The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Qual Life Res. 2007;16(Suppl 1):19–31.
Article
PubMed
Google Scholar
Reise SP, Revicki DA. Introduction: Age-old problems and modern solutions. In: Handbook of item response theory modeling: Applications to typical performance assessment. New York: Routledge; 2015.
Google Scholar
Reise SP, Waller NG. Item response theory and clinical measurement. Annu Rev Clin Psychol. 2009;5:27–48.
Article
PubMed
Google Scholar
Rodriguez A, Reise SP, Haviland MG. Applying Bifactor statistical indices in the evaluation of psychological measures. J Pers Assess. 2016;98:223–37.
Article
PubMed
Google Scholar
Saunders TJ, Vallance JK. Screen time and health indicators among children and youth: current evidence, limitations and future directions. Appl Health Econ Health Policy. 2017;15:323–31.
Article
PubMed
Google Scholar
Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, Dunbar GC. The Mini-international neuropsychiatric interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59(Suppl 20):22–33 quiz 34-57.
PubMed
Google Scholar
Silvia ESM, Maccallum RC. Some factors affecting the success of specification searches in covariance structure modeling. Multivar Behav Res. 1988;23:297–326.
Article
CAS
Google Scholar
Starcevic V, Aboujaoude E. Internet addiction: reappraisal of an increasingly inadequate concept. CNS Spectr. 2017;22:7–13.
Article
PubMed
Google Scholar
Stark S, Chernyshenko OS, Drasgow F. Detecting differential item functioning with confirmatory factor analysis and item response theory: toward a unified strategy. J Appl Psychol. 2006;91:1292–306.
Article
PubMed
Google Scholar
Stein DJ. Internet addiction, internet psychotherapy. Am J Psychiatry. 1997;154:890.
Article
CAS
PubMed
Google Scholar
Teresi JA. Overview of quantitative measurement methods. Equivalence, invariance, and differential item functioning in health applications. Med Care. 2006;44:S39–49.
Article
PubMed
Google Scholar
Teresi JA, Fleishman JA. Differential item functioning and health assessment. Qual Life Res. 2007;16(Suppl 1):33–42.
Article
PubMed
Google Scholar
Tiego J, Oostermeijer S, Prochazkova L, Parkes L, Dawson A, Youssef G, Oldenhof E, Carter A, Segrave RA, Fontenelle LF, Yucel M. Overlapping dimensional phenotypes of impulsivity and compulsivity explain co-occurrence of addictive and related behaviors. CNS Spectr. 2018:1–15.
Ulbricht CM, Chrysanthopoulou SA, Levin L, Lapane KL. The use of latent class analysis for identifying subtypes of depression: A systematic review. Psychiatry Res. 2018;266:228–46.
Article
PubMed
PubMed Central
Google Scholar
Vandenberg RJ, Lance CE. A review and synthesis of the measurement invariance literature: suggestions, practices, and recommendations for organizational research. Organ Res Methods. 2000;3:4–70.
Article
Google Scholar
Widyanto L, griffiths MD, Brunsden V. A psychometric comparison of the internet addiction test, the internet-related problem scale, and self-diagnosis. Cyberpsychol Behav Soc Netw. 2011;14:141–9.
Article
PubMed
Google Scholar
Widyanto L, Griffiths MD, Brunsden V, Mcmurran M. The psychometric properties of the internet related problem scale: A pilot study. Int J Ment Heal Addict. 2008;6:205–13.
Article
Google Scholar
Widyanto L, Mcmurran M. The psychometric properties of the internet addiction test. CyberPsychol Behav. 2004;7:443–50.
Article
PubMed
Google Scholar
Wurpts IC, Geiser C. Is adding more indicators to a latent class analysis beneficial or detrimental? Results of a Monte-Carlo study. Front Psychol. 2014;5:920.
Article
PubMed
PubMed Central
Google Scholar
Young KS. Caught in the net: how to recognize the signs of internet addiction--and a winning strategy for recovery. New York: John Wiley & Sons; 1998a.
Google Scholar
Young KS. Internet addiction: the emergence of a new clinical disorder. Cyberpsychology & Behavior. 1998b;1:237–44.
Article
Google Scholar
Young KS. The research and controversy surrounding internet addiction. CyberPsychol Behav. 1999;2:381–3.
Article
CAS
PubMed
Google Scholar