Internet Addiction Test (IAT)

Abstract

The Internet Addiction Test (IAT), developed by Dr. Kimberly S. Young in 1998, represents the pioneering and globally preeminent psychometric instrument engineered to evaluate problematic, compulsive, and pathological computer and Internet usage. Derived from the diagnostic criteria for Pathological Gambling in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), the IAT translates behavioral addiction phenomenology into a 20-item self-report questionnaire. Each item is scored on a 5-point Likert scale (ranging from 1 = “Rarely” to 5 = “Always”, or alternatively 0 = “Does not apply” to 5 = “Always”), yielding total composite scores between 20 and 100 (or 0 to 100). The resultant global score stratifies respondents across distinct clinical tiers: normal/controlled Internet users (scores 20–49), moderate/problematic Internet users experiencing occasional or frequent life interference (scores 50–79), and severe/pathological Internet addicts exhibiting severe daily functional impairment (scores 80–100).

Across nearly three decades of international cross-cultural research, the IAT has demonstrated strong psychometric properties, consistently producing high internal consistency (Cronbach’s alpha coefficients routinely spanning $\alpha = .88$ to $.94$) and robust test-retest reliability ($r > .80$). While originally conceptualized as a unidimensional severity index, subsequent exploratory and confirmatory factor analyses across diverse demographic cohorts have yielded multidimensional models—most notably the foundational six-factor framework established by Widyanto and McMurran (2004) capturing Salience, Excessive Use, Neglect of Work, Anticipation, Lack of Control, and Neglect of Social Life, alongside contemporary bifactor representations. This comprehensive article provides an exhaustive evaluation of the IAT, examining its theoretical lineage, latent structural validity, convergent and discriminant validity parameters, clinical scoring protocols, and psychometric standing in psychiatric and digital behavioral research.

Keywords

Internet Addiction Test, IAT, Kimberly Young, Problematic Internet Use, Behavioral Addiction, Compulsive Technology Use, Psychometrics, Factor Structure, Construct Validity, Digital Health

Authors

The Internet Addiction Test was conceptualized and developed by the late Dr. Kimberly S. Young, Psy.D. (1965–2019), an internationally recognized psychologist, clinical researcher, and pioneer in the empirical study of behavioral technology addictions. Dr. Young was a Professor of Psychology at St. Bonaventure University in Western New York and the founding director of the Center for Internet Addiction (established in 1995) and the Center for Online Addiction.

Dr. Young authored numerous seminal treatises in behavioral health, including Caught in the Net: How to Recognize the Signs of Internet Addiction and a Winning Strategy for Recovery (1998), Tangling the Web (2001), and Internet Addiction: A Handbook and Guide to Evaluation and Treatment (co-edited with Cristiano Nabuco de Abreu, 2010). She presented the initial diagnostic framework for technological dependency at the 104th Annual Convention of the American Psychological Association (APA) in Toronto in 1996, laying the methodological and diagnostic groundwork that directly led to the formalized construction of the 20-item IAT in 1998.

Purpose

The primary purpose of the Internet Addiction Test is to quantitatively measure the presence, severity, and multidimensional life interference associated with compulsive Internet utilization. Emergent during the mid-1990s—a pivotal historical era marked by the rapid global proliferation of personal computers, broadband telecommunications, and digital social platforms—the IAT addressed an urgent clinical void: the total absence of standardized, objective psychometric criteria to distinguish normative, recreational, or occupational Internet engagement from functionally debilitating, compulsive usage patterns.

In clinical settings, the IAT operates both as an initial screening tool and a diagnostic staging metric. Clinicians deploy the instrument to identify individuals whose virtual activities compromise physical well-being, psychological stability, occupational productivity, academic attainment, and interpersonal functioning. By capturing subjective feelings of preoccupation, mood modification, behavioral tolerance, and withdrawal-like irritability upon forced offline transition, the tool provides mental health professionals with an empirical baseline to guide cognitive-behavioral therapy (CBT), motivational interviewing, family systems interventions, and psychiatric management.

In academic and epidemiological research, the IAT serves as the gold-standard benchmark against which emerging behavioral metrics (e.g., social media addiction, gaming disorder, smartphone dependence) are compared. The instrument enables researchers to evaluate worldwide prevalence rates, chart developmental trajectories among adolescents and young adults, identify psychiatric comorbidities—such as major depressive disorder, generalized anxiety, attention-deficit/hyperactivity disorder (ADHD), and neurodevelopmental vulnerabilities—and systematically assess the efficacy of clinical interventions through pre- and post-treatment psychometric testing.

Psychological Construct

The psychological construct evaluated by the IAT is Problematic Internet Use (PIU), historically conceptualized by Young as Internet Addiction Disorder (IAD). Rather than focusing solely on absolute quantitative screen time (which often conflates benign, necessary occupational/academic engagement with dysregulated habits), the IAT captures the qualitative cognitive, affective, and behavioral manifestations of compulsive engagement. This overarching construct is delineated through several interconnected clinical dimensions:

1. Salience and Cognitive Preoccupation

This dimension reflects the degree to which Internet activity dominates an individual’s cognitive bandwidth and emotional focus. It is characterized by persistent intrusive thoughts, anticipatory longing for future online sessions, and rumination while physically engaged in offline obligations. Individuals with high salience experience a subjective pull wherein the virtual environment becomes their primary psychological reality, relegating physical environments to peripheral significance (e.g., Item 11: “Do you find yourself anticipating when you will go online again?” and Item 15: “Do you feel preoccupied with the internet when not online, or fantasize about being online?”).

2. Loss of Control and Compulsive Relapse

At the neurological core of behavioral addiction lies executive dysfunction and impaired impulse regulation. This component measures a respondent’s chronic inability to regulate session duration, adhere to predetermined time boundaries, or successfully curtail digital intake despite explicit personal intentions and repeated promises to self or others. The behavioral manifestation of this construct is typified by the “just a few more minutes” rationalization (e.g., Item 1: “Do you find that you stay online longer than you intended?” and Item 17: “Do you try to cut down on the amount of time you spend online and fail?”).

3. Functional, Occupational, and Academic Impairment

Compulsive behavior inherently incurs direct opportunity costs. This dimension evaluates the severe disruption of real-world responsibilities, career trajectories, academic obligations, and domestic upkeep resulting from time diverted into digital spheres. Clinically, this manifests as chronic procrastination, missed deadlines, decreased work output, and disciplinary or academic warnings (e.g., Item 6: “Does your work suffer because of the amount of time you spend online?” and Item 8: “Does your job performance or productivity suffer because of the internet?”).

4. Interpersonal Alienation and Social Substitution

This sub-construct evaluates the attrition of tangible offline human relationships in favor of virtual relationships or isolated digital activities. It tracks interpersonal friction, marital conflict, diminished marital intimacy, defensiveness, and parental estrangement triggered by excessive connectivity (e.g., Item 3: “Do you prefer the excitement of the internet to intimacy with your partner?”, Item 5: “Do others in your life complain to you about the amount of time you spend online?”, and Item 19: “Do you choose to spend more time online over spending time out with others?”).

5. Emotional Escape and Negative Reinforcement (Mood Modification)

Addictive behaviors frequently function as maladaptive coping mechanisms designed to alleviate negative affective states such as dysphoria, loneliness, chronic stress, or social anxiety. This dimension captures the cognitive-affective process of using the digital sphere for psychological soothing and emotional avoidance (e.g., Item 10: “Do you block disturbing thoughts about your life with soothing thoughts of the internet?”).

6. Withdrawal-Like Dysphoria and Affective Reactivity

Parallel to substance use disorders, abrupt cessation or disruption of the addictive stimulus precipitates physiological and affective dysregulation. Within the IAT, this construct operationalizes emotional instability, irritability, generalized agitation, and depressive affect experienced when disconnected from the Internet, alongside hostility directed at individuals who interrupt ongoing online interactions (e.g., Item 13: “Do you snap, yell, or act annoyed if someone bothers you while you are online?” and Item 20: “Do you feel depressed, moody, or nervous when you are not online…?”).

Theoretical Framework

The conceptual genesis of the Internet Addiction Test rests on a classical nosological parallel: Dr. Kimberly Young recognized that pathological Internet use exhibited structural behavioral homologies not with physical chemical dependence, but with Impulse Control Disorders, specifically Pathological Gambling as operationalized in the DSM-IV. Unlike substance use disorders—which require physiological dependence driven by exogenous chemical ligands (e.g., ethanol, opioids, nicotine)—behavioral addictions operate primarily through intermittent reinforcement schedules, dopaminergic reward-loop adaptations, and impaired prefrontal inhibitory control.

Young argued that the Internet represents an expansive, multi-channel environmental medium capable of delivering instantaneous positive reinforcement (e.g., novel information, social validation, gaming victories, sexual stimulation) alongside powerful negative reinforcement (e.g., avoidance of offline interpersonal conflict, masking of depressive thoughts, dissociation from academic/career pressures). Consequently, the IAT’s theoretical scaffolding reflects four foundational theoretical models in clinical psychology and behavioral neuroscience:

1. The DSM-IV Impulse-Control / Addictive Paradigm

Young mapped the eight diagnostic criteria of DSM-IV Pathological Gambling directly into technological behaviors: (a) cognitive preoccupation, (b) escalating tolerance (needing increased online time to achieve satisfaction), (c) failed efforts to control or cut back, (d) withdrawal symptoms upon restriction, (e) staying online longer than intended, (f) jeopardizing critical relationships or professional opportunities, (g) deception/lying to family members or therapists regarding usage extent, and (h) utilizing digital spaces as a primary escape from dysphoric mood. The 20 items of the IAT represent an expansive psychometric elaboration of these core behavioral diagnostic criteria.

2. Davis’s Cognitive-Behavioral Model of Pathological Internet Use

Richard A. Davis (2001) proposed a definitive cognitive-behavioral etiology distinguishing between Specific Pathological Internet Use (SPIU)—which involves targeted compulsive behaviors using the Internet merely as a delivery medium (e.g., online gambling, online auctioning, cybersex)—and Generalized Pathological Internet Use (GPIU), characterized by multidirectional, aimless, non-specific browsing and social connectivity driven by maladaptive core cognitions. Davis argued that individuals with preexisting psychopathology (such as social phobia, severe loneliness, or depression) develop maladaptive automatic thoughts (e.g., “I am only respected online,” “The world offline is hostile”). The IAT directly indexes these generalized cognitive-behavioral distortions across its items measuring escapism, relational preference for online users, and catastrophic thinking regarding life offline.

3. Griffiths’ Component Model of Addiction

Mark Griffiths (2005) proposed that all behavioral addictions, regardless of modality, share six core operational components: Salience, Mood Modification, Tolerance, Withdrawal Symptoms, Conflict (both intrapsychic and interpersonal), and Relapse. The theoretical structure of the IAT exhibits near-perfect fidelity to Griffiths’ taxonomy, validating the position that problematic Internet consumption is an authentic expression of behavioral addictive pathology underpinned by neurobiological sensitization of the mesolimbic dopamine pathway.

4. The I-PACE Model (Interaction of Person-Affect-Cognition-Execution)

Contemporary psychometrics evaluates the IAT within the updated theoretical framework formulated by Brand et al. (2016, 2019): the I-PACE model. I-PACE posits that problematic technology use emerges from complex, dynamic interactions between personal predisposing variables (genetics, personality traits such as high neuroticism and low conscientiousness), affective and cognitive evaluations (coping mechanisms, implicit reward expectations), and executive control deficits (impaired cognitive flexibility and inhibitory control). As the individual repeatedly experiences conditioned positive reinforcement via online engagement, subjective craving and automatized cue-reactivity escalate, resulting in the manifest symptom profile captured systematically across the 20 IAT items.

Validity

The psychometric validity of the Internet Addiction Test has undergone extensive international empirical scrutiny across North American, European, Asian, and Latin American populations, establishing strong content, construct, convergent, discriminant, and criterion-related validity.

1. Content and Construct Validity

Content validity was initially established via expert clinical consensus during the instrument’s derivation phase, where items were directly adapted from established psychiatric criteria for behavioral addictions. Subsequent construct validation studies employing structural equation modeling have repeatedly demonstrated that the items of the IAT accurately measure the latent construct of problematic Internet use without contamination from generic computer literacy or benign digital utility.

2. Convergent Validity

The convergent validity of the IAT is exceptionally robust. Empirical investigations consistently demonstrate statistically significant, moderate-to-strong positive correlations between IAT total scores and alternative validated measures of problematic technology use, including:

• The Compulsive Internet Use Scale (CIUS; $r = .75$ to $.85, p < .001$)
• The Generalized Problematic Internet Use Scale-2 (GPIUS-2; $r = .68$ to $.81, p < .001$)
• The Smartphone Addiction Scale (SAS; $r = .60$ to $.74, p < .001$)
• Actual objective computer metrics, including logged online hours per week for non-work/non-academic purposes ($r = .45$ to $.62, p < .001$)

Furthermore, convergent validity is verified by strong correlations with prominent psychiatric symptom inventories. IAT scores correlate significantly with depressive symptoms on the Beck Depression Inventory (BDI; $r = .42$ to $.56$), anxiety on the State-Trait Anxiety Inventory (STAI; $r = .38$ to $.52$), and global psychiatric distress on the Symptom Checklist-90-Revised (SCL-90-R; $r = .44$ to $.61$).

3. Discriminant Validity

Discriminant validity has been rigorously demonstrated across studies comparing the IAT against unrelated or divergent psychological constructs. The IAT exhibits low or non-significant correlations with social desirability metrics (e.g., the Marlowe-Crowne Social Desirability Scale, $r = -.08$ to $-.14$), confirming that high scores reflect genuine behavioral dysregulation rather than response biases. Additionally, the instrument demonstrates divergence from healthy passionate activity; studies utilizing Vallerand’s Dualistic Model of Passion reveal that the IAT correlates strongly with obsessive passion ($r = .58$) but shows near-zero or weak negative associations with harmonious passion ($r = -.04$ to $.11$), confirming its capability to distinguish between high-engagement hobbies and pathological compulsion.

4. Criterion and Predictive Validity

Criterion-related validity is evidenced by the IAT’s ability to differentiate known clinical groups. In psychiatric cohort studies, patients diagnosed with impulse control disorders, major depression, or substance use disorders score significantly higher on the IAT than neurotypical community controls ($t > 8.50, p < .001$). In longitudinal academic and occupational research, elevated baseline IAT scores predict significant decrements in cumulative grade point average (GPA) among university undergraduates ($eta = -.28, p < .01$), elevated rates of chronic sleep deprivation, increased workplace absenteeism, and higher incidence of relationship dissolution over 12-month follow-up periods.

Reliability

The Internet Addiction Test exhibits consistently superior reliability across diverse clinical and non-clinical populations worldwide. Numerous psychometric evaluations have scrutinized its internal consistency, split-half reliability, test-retest stability, and standard error of measurement.

1. Internal Consistency

In the seminal psychometric validation study conducted by Widyanto and McMurran (2004), the overall 20-item instrument demonstrated an exceptional Cronbach’s alpha coefficient of $\alpha = .90$. Subsequent cross-cultural adaptations have replicated these high internal consistency metrics across varying languages and settings:

• English original community samples: $\alpha = .89 – .93$ (Young, 1998; Widyanto & McMurran, 2004)
• French validation (Khazaal et al., 2008): $\alpha = .93$
• Italian validation (Ferraro et al., 2007): $\alpha = .91$
• Chinese adolescent validation (Lai et al., 2013): $\alpha = .93$
• Spanish validation (Fernández-Villa et al., 2015): $\alpha = .92$
• German validation (Pawlikowski et al., 2013): $\alpha = .89$

Item-total correlations for the 20 items consistently surpass the standard psychometric threshold of $r = .30$, with the vast majority falling between $r = .45$ and $r = .72$. Modern psychometric analyses using McDonald’s total omega ($\omega_t$) report coefficients exceeding $.92$, demonstrating that common factor variance accounts for the vast majority of total score variance.

2. Test-Retest Reliability and Temporal Stability

Evaluations of temporal stability demonstrate that the IAT provides reliable longitudinal measurement when underlying psychological states remain stable. Khazaal et al. (2008) reported a test-retest correlation coefficient of $r = .83$ ($p < .001$) over a 2- to 4-week interval in an adult population. Similar investigations across adolescent student cohorts (e.g., Lai et al., 2013) demonstrated intraclass correlation coefficients (ICC) ranging between $.82$ and $.88$ across 3-week intervals, confirming robust temporal reproducibility. In long-term intervention monitoring, the standard error of measurement (SEM) has been documented at approximately $3.4$ to $4.2$ points, providing a dependable threshold for the Reliable Change Index (RCI) in clinical intervention trials.

Factor Analysis

Although Kimberly Young initially conceptualized the IAT as a unidimensional severity index, decades of empirical factor analytic research have uncovered meaningful structural nuances. Researchers have extensively examined both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) models to clarify the underlying latent architecture of the instrument.

1. Widyanto and McMurran’s Six-Factor Model (2004)

In the first formal exploratory factor analysis of the English-language IAT, Widyanto and McMurran (2004) administered the questionnaire to a diverse online sample (aged 13–67) and identified a 6-factor structure through Principal Component Analysis with Varimax rotation, explaining 56.4% of the total variance:

• Factor 1: Salience (Items 10, 12, 13, 15, 19) — Captures psychological preoccupation, escapism, and the central role the Internet occupies in emotional life (explained 12.3% of variance).
• Factor 2: Excessive Use (Items 1, 2, 14, 18, 20) — Highlights excessive time allocation, late-night usage, sleep deprivation, and deceptive hiding of use (explained 10.5% of variance).
• Factor 3: Neglect of Work (Items 6, 8, 9) — Measures occupational and academic decrements and productivity decline (explained 9.0% of variance).
• Factor 4: Anticipation (Items 7, 11) — Reflects compulsive forward-thinking and immediate checking behaviors upon waking or starting tasks (explained 8.8% of variance).
• Factor 5: Lack of Control (Items 16, 17) — Focuses specifically on the inability to self-limit and the failed attempts to curtail duration (explained 8.2% of variance).
• Factor 6: Neglect of Social Life (Items 3, 4, 5) — Encompasses social isolation, preferring online peers over physical partners, and complaints from significant others (explained 7.6% of variance).

2. Alternative Factor Solutions (Two-, Three-, and Four-Factor Frameworks)

Subsequent psychometricians argued that several factors in Widyanto and McMurran’s model contained too few items (e.g., Factors 4 and 5 had only two items each), leading to model instability. Consequently, several alternative configurations emerged:

• The Two-Factor Model (Pawlikowski et al., 2013; Watters et al., 2013): CFA supported a parsimonious two-factor conceptualization: (1) Loss of Control / Time Management Issues (Items 1, 2, 6, 8, 14, 16, 17) and (2) Craving / Social Problems / Emotional Relief (Items 3, 4, 5, 9, 10, 11, 12, 13, 15, 18, 19, 20). Model fit indices supported this solution ($\chi^2/df < 2.5, \text{CFI} = .94, \text{TLI} = .93, \text{RMSEA} = .051$).
• The Three-Factor Model (Lai et al., 2013): Comprising (1) Withdrawal and Social Problems, (2) Time Management and Performance, and (3) Reality Substitution.

3. Modern Bifactor Structural Modeling

To resolve the enduring debate between unidimensional versus multidimensional structures, recent psychometric studies have implemented Bifactor Confirmatory Factor Analysis (e.g., Pontes et al., 2014; Bányai et al., 2017). These structural models demonstrate that a single general factor ($g$-factor representing general problematic Internet severity) accounts for over 75–85% of the common variance extracted, while the specific sub-factors account for unique residual variance. The Bifactor model demonstrates superior model fit:

• Comparative Fit Index ($\text{CFI}$) $ge .96$
• Tucker-Lewis Index ($\text{TLI}$) $ge .95$
• Root Mean Square Error of Approximation ($\text{RMSEA}$) $le .045$ ($90% \text{ CI } [.038, .052]$)
• Standardized Root Mean Square Residual ($\text{SRMR}$) $le .038$

These findings provide clear psychometric justification for clinical and research practice: clinicians can confidently compute and interpret the global composite score (20–100) to gauge overall severity, while simultaneously evaluating individual subscale profiles to identify specific functional targets (e.g., social alienation versus executive control deficits).

Instrument / Measurement Tool

• Full Instrument Name: Internet Addiction Test
• Acronym: IAT
• Author: Dr. Kimberly S. Young, Psy.D.
• Year of Formal Publication: 1998 (preliminary 8-item diagnostic questionnaire published in 1996)
• Assessment Type: Self-report questionnaire / Clinical screening instrument
• Target Population: Adolescents (ages 12+) and adults
• Administration Format: Pen-and-paper, computerized psychological testing platforms, or digital survey interfaces
• Administration Time: Approximately 5 to 10 minutes
• Total Number of Items: 20 items
• Item Phrasing: Interrogative behavioral questions (e.g., “Do you find that you stay online longer than you intended?” or “How often do you find that you stay on-line longer than you intended?”)
• Response Format: 5-point Likert rating scale. Standard research scoring utilizes values 1 through 5:
  • 1 = Rarely (or Rarely or never)
  • 2 = Occasionally (or Every once in a while)
  • 3 = Sometimes
  • 4 = Often (or Frequently)
  • 5 = Always
  • (Note: An optional “0 = Not Applicable / Does Not Apply” is utilized in some diagnostic settings, yielding potential scores from 0 to 100)
• Scoring Procedure: The respondent’s ratings for all 20 items are summed linearly to yield an overall composite score. There are no reverse-scored items. The theoretical range on the standard 1–5 scoring format is 20 to 100 points.
• Clinical Cut-Off Categories and Diagnostic Interpretation:
  • 20 – 49 points (Average / Normal Internet User): Indicates a controlled, normative pattern of Internet engagement. While the individual may occasionally surf the Web somewhat longer than planned, they maintain robust personal control, experience negligible daily disruption, and suffer no significant psychological or occupational impairments.
  • 50 – 79 points (Moderate Problematic Internet Use): Indicates occasional or frequent functional problems directly attributable to Internet usage. The individual experiences discernible difficulty regulating screen time, periodic academic or workplace procrastination, sleep disruptions, and interpersonal friction. Comprehensive clinical review of items scored 4 or 5 is recommended.
  • 80 – 100 points (Severe / Pathological Internet Addiction): Indicates that Internet utilization is precipitating severe, life-altering disruptions, significant distress, and pervasive functional impairment. The individual exhibits full clinical dependency, characterized by loss of control, severe occupational or academic decline, social isolation, and withdrawal symptoms. Immediate diagnostic evaluation and structured therapeutic intervention (e.g., CBT, clinical counseling) are strongly indicated.

Permissions & Fee and Test Year

The Internet Addiction Test was developed by Dr. Kimberly S. Young in 1998, evolving from her foundational 8-item Diagnostic Questionnaire (DQ) published in 1996. The instrument appeared in academic literature and was published within Dr. Young’s clinical treatises, notably Caught in the Net (1998, John Wiley & Sons) and subsequent clinical compendiums.

Regarding legal copyright and administration fees, the intellectual property rights associated with commercial applications of Dr. Kimberly Young’s assessments were historically managed through the Center for Internet Addiction (Stoelting Co. currently publishes and distributes commercial diagnostic kits and automated scoring profiles for the IAT). For non-commercial academic research, epidemiological surveys, and scholarly student investigations, the IAT has historically been utilized extensively under academic fair use and open scholarly dissemination principles, provided proper author attribution and scholarly citation are explicitly maintained. Researchers and healthcare organizations planning commercial clinical deployment, enterprise wellness integration, or proprietary electronic health record (EHR) incorporation should consult Stoelting Co. or the estate of Dr. Kimberly Young to obtain formal licensing agreements and administrative permissions.

References

• Bányai, F., Zsila, Á., Király, O., Maraz, A., Elekes, Z., Griffiths, M. D., Andreassen, C. S., & Demetrovics, Z. (2017). Problematic internet use and problematic social media use: The role of accessibility, purpose of internet use, and time spent online. Journal of Behavioral Addictions, 6(4), 589–598. https://doi.org/10.1556/2006.6.2017.072
• Brand, M., Young, K. S., Laier, C., Wölfling, K., & Potenza, M. N. (2016). 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. Neuroscience & Biobehavioral Reviews, 71, 252–266. https://doi.org/10.1016/j.neubiorev.2016.08.033
• Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., & Potenza, M. N. (2019). The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to specific behaviors, 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
• Davis, R. A. (2001). A cognitive-behavioral model of pathological Internet use. Computers in Human Behavior, 17(2), 187–195. https://doi.org/10.1016/S0747-5632(00)00041-8
• Fernández-Villa, T., Alguacil, J., Ayán, C., Almaraz, A., & Martín, V. (2015). Validity and reliability of the Spanish version of the Internet Addiction Test in university students. Gaceta Sanitaria, 29(5), 362–368. https://doi.org/10.1016/j.gaceta.2015.05.004
• Ferraro, G., Caci, B., D’Amico, A., & Blasi, M. D. (2007). Internet addiction disorder: An Italian study. CyberPsychology & Behavior, 10(2), 170–175. https://doi.org/10.1089/cpb.2006.9972
• Griffiths, M. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4), 191–197. https://doi.org/10.1080/14659890500114359
• Khazaal, Y., Becker, J., Billieux, J., Zullino, D., Bowden-Jones, H., & Young, K. (2008). Factor structure and psychometric properties of the Internet Addiction Test in a French-speaking population. CyberPsychology & Behavior, 11(6), 703–707. https://doi.org/10.1089/cpb.2007.0249
• Lai, C. M., Mak, K. K., Watanabe, H., Ang, R. P., Pang, J. S., & Ho, R. C. (2013). Psychometric properties of the Internet Addiction Test in Chinese adolescents. Child Psychiatry & Human Development, 44(3), 358–369. https://doi.org/10.1007/s10578-012-0330-3
• Pawlikowski, M., Altstötter-Gleich, C., & Brand, M. (2013). Validation and psychometric properties of a German version of the Young’s Internet Addiction Test. Computers in Human Behavior, 29(3), 1212–1223. https://doi.org/10.1016/j.chb.2012.12.023
• Pontes, H. M., Király, O., Demetrovics, Z., & Griffiths, M. D. (2014). The conceptualisation and measurement of DSM-5 Internet Gaming Disorder: The development of the IGD-20 Test. PLOS ONE, 9(10), e110137. https://doi.org/10.1371/journal.pone.0110137
• Watters, C. A., Keefer, K. V., Kloosterman, P. H., Summerfeldt, L. J., & Parker, J. D. (2013). Examining the structure of the Internet Addiction Test in adolescents: A check on Young’s original 20-item measure. Computers in Human Behavior, 29(6), 2294–2302. https://doi.org/10.1016/j.chb.2013.05.020
• Widyanto, L., & McMurran, M. (2004). The psychometric properties of the Internet Addiction Test. CyberPsychology & Behavior, 7(4), 443–450. https://doi.org/10.1089/cpb.2004.7.443
• Young, K. S. (1996). Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior, 1(3), 237–244. https://doi.org/10.1089/cpb.1998.1.237
• Young, K. S. (1998). Caught in the net: How to recognize the signs of internet addiction and a winning strategy for recovery. John Wiley & Sons.

13. Items of the Scale (Questionnaire)