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Lutz Wartberg German Center for Addiction Research in Childhood and Adolescence (DZSKJ), University Medical Center Hamburg-Eppendorf, Hamburg, Germany

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Levente Kriston Department of Medical Psychology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

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Katharina Kegel German Center for Addiction Research in Childhood and Adolescence (DZSKJ), University Medical Center Hamburg-Eppendorf, Hamburg, Germany

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Rainer Thomasius German Center for Addiction Research in Childhood and Adolescence (DZSKJ), University Medical Center Hamburg-Eppendorf, Hamburg, Germany

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Background and aims

The surge of problematic Internet use in adolescents is a continuously growing problem across the globe. To our knowledge, to date valid questionnaire-based measurement of problematic Internet use is possible only by self-assessment. The objective for the present study was to adapt an established instrument for a parental assessment of adolescent problematic Internet use and to evaluate the psychometric properties of this questionnaire.

Methods

Data were collected from a representative German sample of 1,000 parents of adolescents aged between 12 and 17 years using a standardized questionnaire. To assess problematic Internet use, we adapted the established Young Diagnostic Questionnaire by rewording the items to survey a parental rating instead of a self-report (“Parental version of the Young Diagnostic Questionnaire,” PYDQ). Additionally, we assessed the Internet usage time, parental monitoring, family functioning, school performance of the adolescent, and parent–adolescent conflicts. We conducted a confirmatory factor analysis based on the 8 items of the PYDQ modeled as categorical indicators and one latent factor using a robust weighted least squares estimator. We also calculated a reliability coefficient, the acceptance of the instrument, and performed correlation analyses.

Results

The unidimensional model showed excellent global goodness-of-fit (χ2/df = 1.65, RMSEA = 0.03, CFI = 0.99, TLI = 0.99) and satisfactory factor loadings (standardized values ranged from 0.60 to 0.77). We observed a reliability coefficient of 0.70, a good acceptance of the instrument, and the correlation analyses indicated the construct validity of the PYDQ.

Discussion and conclusion

The proposed PYDQ is a suitable instrument for parental assessment of adolescent problematic Internet use.

Abstract

Background and aims

The surge of problematic Internet use in adolescents is a continuously growing problem across the globe. To our knowledge, to date valid questionnaire-based measurement of problematic Internet use is possible only by self-assessment. The objective for the present study was to adapt an established instrument for a parental assessment of adolescent problematic Internet use and to evaluate the psychometric properties of this questionnaire.

Methods

Data were collected from a representative German sample of 1,000 parents of adolescents aged between 12 and 17 years using a standardized questionnaire. To assess problematic Internet use, we adapted the established Young Diagnostic Questionnaire by rewording the items to survey a parental rating instead of a self-report (“Parental version of the Young Diagnostic Questionnaire,” PYDQ). Additionally, we assessed the Internet usage time, parental monitoring, family functioning, school performance of the adolescent, and parent–adolescent conflicts. We conducted a confirmatory factor analysis based on the 8 items of the PYDQ modeled as categorical indicators and one latent factor using a robust weighted least squares estimator. We also calculated a reliability coefficient, the acceptance of the instrument, and performed correlation analyses.

Results

The unidimensional model showed excellent global goodness-of-fit (χ2/df = 1.65, RMSEA = 0.03, CFI = 0.99, TLI = 0.99) and satisfactory factor loadings (standardized values ranged from 0.60 to 0.77). We observed a reliability coefficient of 0.70, a good acceptance of the instrument, and the correlation analyses indicated the construct validity of the PYDQ.

Discussion and conclusion

The proposed PYDQ is a suitable instrument for parental assessment of adolescent problematic Internet use.

Introduction

Assessment of problematic Internet use

During the last several years, the surge of problematic Internet use [also known as Internet addiction (e.g., Van Rooij & Prause, 2014), pathological Internet use (e.g., Durkee et al., 2012), or compulsive Internet use (e.g., Quinones & Kakabadse, 2015)] in adolescents is a growing problem across the globe (e.g., Wang et al., 2013). Validated assessment instruments are required to perform empirical investigations in order to gain a better understanding of this rather new phenomenon (Chang & Law, 2008). Some measures demonstrated promising psychometric properties, but most of the existing scales for problematic Internet use require further investigations (Laconi, Rodgers, & Chabrol, 2014). To our knowledge, to date only questionnaires for the self-assessment of problematic Internet use have been published. In addition to self-reports external ratings of problematic Internet use by caregivers or relatives could be useful, like it is common in the assessment of psychological well-being of children and adolescents [e.g., the frequently used Strengths and Difficulties Questionnaire (SDQ, Goodman, 1997) can be applied to adolescents, their parents, and teachers].

The most frequently used self-report instruments to assess problematic Internet use include the Internet Addiction Test (IAT, Young, 1998a; consisting of 20 items), the Chen Internet Addiction Scale (CIAS, Chen, Weng, Su, Wu, & Yang, 2003; 26 items), the Young Diagnostic Questionnaire (YDQ, Young, 1998b; 8 items), and the Compulsive Internet Use Scale (CIUS, Meerkerk, van den Eijnden, Vermulst, & Garretsen, 2009; 14 items). According to Laconi et al. (2014), most often a two-factor solution for the IAT and a five-factor structure for the CIAS were reported, whereas a unidimensional structure for the YDQ and the CIUS was observed. Thus, the YDQ is one of the most widely utilized unidimensional instruments of problematic Internet use.

Adaption of a self-report instrument

The objective for the present study was to develop and to evaluate an instrument for a parental assessment of adolescent problematic Internet use. The YDQ as a brief, established, and unidimensional measure appeared to be suitable for an adaptation. To our knowledge, the proposed Parental version of the Young Diagnostic Questionnaire (PYDQ) is the first standardized instrument to assess adolescent problematic Internet use from the parent’s point of view.

Characteristics and psychometric properties of the YDQ self-report version

The YDQ is a very brief questionnaire with a binary response format (“yes” vs. “no”). The instrument has been translated into multiple languages and is used by researchers worldwide (Alavi, Maracy, Jannatifard, & Eslami, 2011; Bakken, Wenzel, Gotestam, Johansson, & Øren, 2009; Cao & Su, 2006; Cao, Su, Liu, & Gao, 2007; Chou & Hsiao, 2000; Dowling & Quirk, 2009; Durkee et al., 2012; Fischer et al., 2012; Fisoun et al., 2012; Frangos, Frangos, & Kiohos, 2010; Huang et al., 2009; Johansson & Götestam, 2004; Kesici & Sahin, 2010; Li, Zhang, Lu, Zhang, & Wang, 2014; Osada, 2013; Siomos et al., 2013; Stavropoulos, Alexandraki, & Motti-Stefanidi, 2013; Strittmatter et al., 2014, 2015; Yang & Tung, 2007; Young, 1998b; Zhou, Yuan, & Yao, 2012). The 8 items of the YDQ are based on the criteria for pathological gambling in the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV, American Psychiatric Association, 1994).

Five published studies have evaluated the factor structure of the YDQ. In all five surveys (Frangos et al., 2010; Johansson & Götestam, 2004; Li et al., 2014; Siomos, Dafouli, Braimiotis, Mouzas, & Angelopoulos, 2008; Stavropoulos et al., 2013) an exploratory factor analysis was conducted. For the Greek version of the YDQ, two authors (Frangos et al., 2010; Stavropoulos et al., 2013) described a two-factor structure. Still, most of the investigations (Johansson & Götestam, 2004; Li et al., 2014; Siomos et al., 2008) reported a single factor solution for three different translations (Chinese, Greek, and Norwegian) of the YDQ. Reliability coefficients (internal consistency) of the respective YDQ translations ranged from 0.68 (Stavropoulos et al., 2013) to 0.79 (Fisoun et al., 2012). Correlations between the YDQ sum score and external criteria (to verify construct validity) were rarely reported. For example, Johansson and Götestam (2004) observed a statistically significant correlation (r = 0.28, p < .01) between the YDQ and the frequency of Internet use (number of hours per week) in a sample of Norwegian adolescents.

To summarize, the established self-report version (YDQ) has a unidimensional structure, showed acceptable reliability, and the few published correlations with external criteria indicate construct validity of this assessment instrument. We assumed that for a parental assessment of problematic Internet based on the YDQ (PYDQ), comparable psychometric properties as reported for the self-report version could be attainable.

Aim and research questions of the study

The aim of the present study was to investigate the psychometric properties of the proposed PYDQ in a representative general population sample of German parents.

We explored the following three research questions:

  1. 1. Can the unidimensional structure of the YDQ be supported for the PYDQ by confirmatory factor analysis (CFA)?
  2. 2. How reliable is the PYDQ?
  3. 3. Can construct validity be supported by associations between the PYDQ score and alternative criteria of problematic Internet use (e.g., Internet usage time per week of the adolescent)?

Methods

Procedure

Data were collected from a representative German general population sample of 1,000 parents of adolescents aged between 12 and 17 years using a standardized questionnaire. The data collection was carried out by an experienced market research institute. Computer assisted telephone interviews with the parents were conducted in July and August 2015. For inclusion, parents had to live in a household with an adolescent aged between 12 and 17 years. If more than one adolescent in this age range lived in the same household together with his or her parent, the survey was only conducted for the child having his or her birthday more recently (pseudo-random sampling within the adolescents).

Measures

Problematic Internet use in adolescents from a parent’s point of view was measured using the PYDQ. No changes in content of the instrument were made, but all 8 items of the original YDQ (Young, 1998b) were reworded (changes in grammar) to gather an external instead of a self-report rating (see Table 1 for exact formulation of the items of the PYDQ). For example, the third YQD item of Young (1998b) reads “Have you repeatedly made unsuccessful efforts to control, cut back, or stop Internet use?” whereas the PYDQ asks “Has your child repeatedly made unsuccessful efforts to control, cut back, or stop Internet use?” The binary response format of the YDQ (0 = “no,” 1 = “yes”) was maintained for the PYDQ. The criteria of problematic Internet use assessed by the YDQ and the PYDQ are: “preoccupation” (item 1), “tolerance” (item 2), “loss of control” (items 3 and 5), “withdrawal” (item 4), “risk/lose relationships/opportunities” (item 6), “lies to conceal extent of involvement” (item 7), and “dysfunctional coping” (item 8) (Strittmatter et al., 2014). By summing up the values of all 8 items of the instrument, a PYDQ sum score was calculated with a higher sum indicating higher risk levels of adolescent problematic Internet use.

Table 1.

Items, response distributions, and standardized factor loadings for the Parental version of the Young Diagnostic Questionnaire (PYDQ) in a representative sample of parents of adolescents aged 12–17 years (n = 964)

Relative response frequencies (%) Standardized factor loadings
No/Yes
Item 1 Does your child feel preoccupied with the Internet (think about previous online activity or anticipate next online session)? 80.6 0.61
19.4
Item 2 Does your child feel the need to use the Internet with increasing amounts of time in order to achieve satisfaction? 87.9 0.77
12.1
Item 3 Has your child repeatedly made unsuccessful efforts to control, cut back, or stop Internet use? 89.5 0.60
10.5
Item 4 Does your child feel restless, moody, depressed, or irritable when attempting to cut down or stop Internet use? 77.1 0.75
22.9
Item 5 Does your child stay online longer than originally intended? 49.8 0.65
50.2
Item 6 Has your child jeopardized or risked the loss of a significant relationship, job, educational, or career opportunity because of the Internet? 92.4 0.67
7.6
Item 7 Has your child lied to family members, a therapist, or others to conceal the extent of involvement with the Internet? 84.3 0.66
15.7
Item 8 Does your child use the Internet as a way of escaping from problems or of relieving a dysphoric mood (e.g., feelings of helplessness, guilt, anxiety, depression)? 87.6 0.65
12.4

The Family APGAR (Smilkstein, 1978) was applied to measure functioning of the family. APGAR is an acronym for the five domains of family functioning (Adaptability, Partnership, Growth, Affection, and Resolve) being assessed by the standardized questionnaire. The instrument consists of 5 items (3-point scale: 0 = “hardly ever,” 1 = “some of the time,” 2 = “almost always”). The Family APGAR is scored by summing the values of the items for a total score (range: 0–10). A higher total score indicates a greater degree of satisfaction with self-perceived family functioning.

Additionally, parents were requested to specify the average hours per day (separately assessed for Monday to Friday and Saturday and Sunday) their child used the Internet. Based on these values an average adolescent Internet usage time per week was calculated. Furthermore, the parents were asked to rate their monitoring of adolescents’ Internet use (1 =“strongly agree,” 2 = “tend to agree,” 3 = “tend to disagree,” 4 = “strongly disagree”; a lower rating indicates a higher monitoring of adolescent Internet use). The parents were also requested to rate how the school performance of the adolescent has developed due to his or her Internet use with a 5-level response format (1 = “strongly worsened,” 2 = “worsened,” 3 = “remained unchanged,” 4 = “improved,” 5 = “strongly improved”). Furthermore, parents were asked how often (1 =“never,” 2 = “seldom,” 3 = “sometimes,” 4 = “often,” 5 =“very often”) they had conflicts with the adolescent concerning his or her Internet use. Demographic data (e.g., age and gender) were also collected.

Participants

The representative sample included 1,000 parents in Germany with an adolescent aged between 12 and 17 years. The sample consisted of 567 mothers (56.7%) and 433 fathers (43.3%). The mean age of adolescents was 14.21 (SD = 1.61, range: 12–17) and of the parents was 47.08 (SD = 6.32, range: 31–75) years. Eighty-eight percent of the interviewed parents lived with a partner in a household. In total, 41% of the parents had achieved “Abitur” (high educational level), 38% had achieved “Realschulabschluss” (medium educational level), and 20% had achieved “Hauptschulschulabschluss” or left school without qualification (low educational level). Overall, 86% of the sample was employed.

Statistical analysis

After excluding 36 of the 1,000 cases (3.6% of the whole sample), who did not provide a valid response to any of the PYDQ items, statistical analyses were performed on 964 cases. CFA was conducted with categorical factor indicators using a robust (mean- and variance-adjusted) weighted least squares estimator in Mplus 7.2 (Muthén & Muthén, 2012). This approach for dichotomous and ordered categorical variables was presented in detail by Muthén (1984). According to published findings for the YDQ (Johansson & Götestam, 2004; Li et al., 2014; Siomos et al., 2008), we postulated unidimensional structure with the 8 items of the PYDQ loading on a single latent factor. The χ2 test of model fit, the normed χ2 index, the root mean square error of approximation (RMSEA), the weighted root mean square residual (WRMR), the Comparative Fit Index (CFI), and the Tucker–Lewis Index (TLI) were used to assess the global goodness-of-fit of the model. Additionally, as local parameters of model fit standardized factor loadings were explored. We used SPSS version 22.0 (IBM, 2013, New York, USA) to calculate the Kuder–Richardson coefficient of reliability (K-R 20, measurement of internal consistency), the completion rate for each item (measure of acceptance), an unpaired t-test, and Pearson’s product–moment correlations between the PYDQ sum score and other criteria (related to problematic Internet use).

Ethics

The study procedures were carried out in accordance with the Declaration of Helsinki. All subjects of our general population sample of adults were informed about the study and all provided informed consent.

Results

Descriptive statistics for the PYDQ

The response rates for the 8 items ranged between 93.2% (item 1 of the PYDQ) and 96.2% (item 3) indicating good acceptance of the instrument by the parents. The average PYDQ sum score in the sample of the present study was 1.46 (SD = 1.66, range: 0–8). We did not observed a statistically significant difference (t = −0.20, df = 863, p = .844) in the PYDQ sum score between the ratings of mothers (M = 1.45, SD = 1.69) and fathers (M = 1.48, SD = 1.63).

Factorial validity of the PYDQ

Response distributions and standardized factor loadings of the PYDQ items are presented in Table 1. The standardized factor loadings varied between 0.60 (item 3) and 0.77 (item 2). The normed χ2 index (χ2/df) was 1.65 (χ2 = 29.75, df = 18, p = .040), fulfilling the recommended threshold for categorical data of falling below two or three (Schreiber, Stage, King, Nora, & Barlow, 2006, p. 330). The values for the RMSEA (0.03), the WRMR (0.78), the CFI (0.99), and the TLI (0.99) clearly reached the cut-off values for good model fit recommended by Schreiber et al. (2006) (RMSEA < 0.06, WRMR < 0.90, CFI > 0.95, and TLI > 0.96). Considering these results, unidimensionality of the PYDQ was strongly supported.

Reliability and construct validity of the PYDQ

According to Kuder–Richardson 20 formula for binary items, reliability of the PYDQ was 0.70 in our sample. There was no improvement in internal consistency with deletion of any item (range: 0.65–0.68). Statistically significant Pearson’s product–moment correlations between the PYDQ sum score and the average adolescent Internet usage time per week (r = 0.31, p < .001), parental monitoring of adolescent Internet use (r = 0.21, p < .001), family functioning (r = −0.36, p < .001), the development of school performance of the adolescent due to his or her Internet use (r = −0.32, p < .001), and the frequency of conflicts with the adolescent concerning his or her Internet use (r = 0.60, p < .001) are the first indications for the construct validity of the PYDQ.

Discussion

In the present survey, the psychometric properties of a PYDQ were assessed in a representative sample of 1,000 parents with adolescents aged 12–17 years in Germany. To our knowledge, the suggested PYDQ is the first standardized instrument to assess problematic Internet use of adolescents from a parent’s point of view.

Our goal was to develop an efficient and unidimensional assessment instrument. For this purpose, we adapted the established YDQ (Young, 1998b). Most of the published results (Johansson & Götestam, 2004; Li et al., 2014; Siomos et al., 2008) indicate a one-factor structure for the YDQ. Results from the CFA of the PYDQ support these findings. Except for one value (statistically significant χ2 test of model fit), we found good values for all other global fit indices (RMSEA, WRMR, CFI, TLI, and χ2/df) and local parameters of model fit. A unidimensional model for the PYDQ seems to fit the data very well.

We observed a reliability of 0.70 for the PYDQ, which is in the range of the published reliability coefficients for the self-assessment version of the YDQ (0.68–0.79). Aiken and Groth-Marnat (2006) suggest that a questionnaire with a reliability coefficient above 0.60 is sufficiently appropriate for the examination of groups, and this criterion was met by the PYDQ. Furthermore, we observed high response rates (above 90% for every item) indicating a good acceptance of the PYDQ in our sample of parents.

Based on adolescent self-reports in several studies associations between problematic Internet use and a higher average Internet usage time (e.g., Johansson & Götestam, 2004), a lower parental monitoring (e.g., Yen, Ko, Yen, Chang, & Cheng, 2009), a lower functioning of the family (e.g., Ko, Yen, Yen, Lin, & Yang, 2007), a lower school performance (e.g., Stavropoulos et al., 2013), and a higher frequency of familial conflicts (e.g., Wartberg, Kriston, Kammerl, Petersen, & Thomasius, 2015) were reported. Johansson and Götestam (2004) observed in a sample of Norwegian adolescents a correlation of 0.28 between the YDQ sum score and the average Internet usage time per week. In the present study, we found a correlation in a comparable size (0.31) between the PYDQ sum score and the parental estimation of the weekly Internet usage time of his or her adolescent (indicating a moderate but statistically significant association between problematic Internet use and a longer duration of adolescent Internet use). Furthermore, we found a statistically significant relation between a higher PYDQ value and a lower parental monitoring. This finding is in accordance with the result of Yen et al. (2009), indicating that less parental monitoring promotes adolescent problematic Internet use.

In some cross-sectional surveys (e.g., Ko et al., 2007), lower family functioning was associated with adolescent problematic Internet use. To our knowledge, the available findings had been solely based on adolescent self-reports. Our study confirmed the association between lower family functioning and problematic Internet use in parent-reports. Following Ko et al. (2007), the observed problematic Internet use in adolescents can be interpreted as an attempt to compensate familial problems (e.g., if certain needs of the adolescent are not fulfilled due to a low functioning in the family).

It is further conceivable that a very intense use of the Internet and the related neglect of other activities may lead to less commitment in school and fewer contacts or more arguments in the family. In our study, we observed an association between a higher PYDQ value and a change for the worse in the school achievement of the adolescent caused by his or her Internet use. This finding is in line with the result of Stavropoulos et al. (2013), whereupon problematic Internet use (measured by the self-report version of the YDQ) is related to worse academic achievement in adolescents. However, in the present survey the school achievement was based on subjective parental ratings, whereas Stavropoulos et al. (2013) used a more objective measure (student’s grade point average based on assessments of several different teachers for every adolescent). Furthermore, we found a statistically significant relation between a higher PYDQ value and more frequent parent–adolescent conflicts concerning the adolescent Internet use. This result is in line with several studies (e.g., Wartberg et al., 2015), describing a higher frequency of conflicts between parents and their children reporting problematic Internet use as opposed to control families.

We hold the view that an assessment of the PYDQ items by the caregiver is possible and meaningful, if the parent and the adolescent live together in the same household. We did not find a statistically significant difference in the PYDQ sum scores comparing mothers and fathers. Therefore, it seems to be possible to use the rating of a female or a male caregiver to assess adolescent problematic Internet use from a parent’s point of view.

Concerning other aspects of mental health in youth (e.g., internalizing and externalizing problems), it is quite a common procedure to assess the parental perspective in addition to adolescent ratings with adapted screening instruments (e.g., the SDQ, Goodman, 1997), and to compare these two perspectives. Combining external with self-reported ratings seems to be a promising approach for a deeper understanding of problematic Internet use or the new DSM-5 diagnosis “Internet gaming disorder” (American Psychiatric Association, 2013). In a next step, empirically validated norm values for the PYDQ should be established.

Noteworthy, Vadlin, Åslund, Rehn, and Nilsson (2015) had recently presented a new measure for video game addiction (gaming addiction identification test, GAIT) in a self-report and a parent version. The evaluated version of the GAIT assesses 7 out of 9 criteria of Internet gaming disorder (Vadlin et al., 2015) and the authors reported a high concordance between the ratings of the parents and the adolescents. On the one hand there are differences between the nosological entities of problematic Internet use and problematic online gaming (Király et al., 2014) or Internet gaming disorder (Griffiths & Pontes, 2014), but on the other hand the findings of Vadlin et al. (2015) could be interpreted as indications that the ratings of parents could be important for the assessment of adolescent problematic Internet use.

The present study has several limitations. The PYDQ assesses problematic Internet use of adolescents from a parent’s point of view and was applied in a sample of parents. To verify the construct validity, a comparison with the ratings of adolescents would have been of great value, but we were unable to realize this dyadic approach in our survey. In general, the approach to assess adolescent problematic Internet use by a parent requires further empirical investigation. It cannot be excluded that other (not media-related) familial aspects or interactions (e.g., overprotective parenting style) influence parental response behavior in the PYDQ. It is conceivable that some PYDQ items considering observable behavior (e.g., “Has your child lied to family members, a therapist, or others to conceal the extent of involvement with the Internet?”) are easier to rate for parents than other questions regarding thoughts and feelings of the adolescent, like “Does your child feel preoccupied with the Internet (think about previous online activity or anticipate next online session)?” Compared to the other questions of the PYDQ item 5 seems to distinguish worse between problematic and unproblematic Internet use. But to ensure comparability between the self-reported YDQ and the ratings of the parents in the PYDQ, currently a change of the wording of this item seems not useful. Furthermore, it cannot be ruled out that with increasing age of the adolescent a parental rating of his or her problematic Internet use would become more difficult and less accurate. This was the first investigation using the PYDQ, but the psychometric properties should necessarily be tested in other samples. Concerning the self-report version of the instrument (YDQ), some contradictory findings were reported. For example, in most studies a single factor solution (e.g., Li et al., 2014) was reported, but two studies described a two-factor structure (Frangos et al., 2010; Stavropoulos et al., 2013) of the YDQ.

Despite the listed limitations, the PYDQ could be an interesting alternative to the established self-report instruments (e.g., IAT, CIAS, YDQ, and CIUS). According to our experience in the treatment of children and adolescents showing problematic Internet use, the first contact or even the first visit in the treatment facility is often made by parents. A validated and standardized measure to assess adolescent problematic Internet use from a parent’s point of view could provide important notes for the planning of future interventions.

In summary, in the present study we found in the psychometric evaluation of the new measure PYDQ a good evidence of a unidimensional structure, a good acceptance, a sufficient reliability for the examination of groups, and reasonable correlations indicating construct validity of the suggested screening instrument. Accordingly, the PYDQ seems suitable to assess problematic Internet use in adolescents from the parent’s point of view.

Authors’ contribution

LW designed the study. LW and LK performed the statistical analysis. LW wrote the first draft of the manuscript. KK, RT, and LK revised the article critically. All authors reviewed the manuscript for intellectual content, read and approved the final version of the manuscript. All authors had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Conflict of interest

The authors declare no conflict of interest.

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  • Fischer, G. , Brunner, R. , Parzer, P. , Klug, K. , Durkee, T. , Carli, V. , Wasserman, D. , Vonderlin, E. , Resch, F. , & Kaess, M. (2012). Depressivität, selbstverletzendes und suizidales Verhalten bei Jugendlichen mit riskanter und pathologischer Internetnutzung [Depression, deliberate self-harm and suicidal behaviour in adolescents engaging in risky and pathological Internet use]. Praxis der Kinderpsychologie und Kinderpsychiatrie, 61(1), 1631. doi:10.13109/prkk.2012.61.1.16

    • Search Google Scholar
    • Export Citation
  • Fisoun, V. , Floros, G. , Geroukalis, D. , Ioannidi, N. , Farkonas, N. , Sergentani, E. , Angelopoulos, N. , & Siomos, K. (2012). Internet addiction in the island of Hippocrates: The associations between Internet abuse and adolescent off-line behaviours. Child and Adolescent Mental Health, 17(1), 3744. doi:10.1111/camh.2011.17.issue-1

    • Search Google Scholar
    • Export Citation
  • Frangos, C. C. , Frangos, K. C. , & Kiohos, A. (2010). Internet addiction among Greek University students: Demographic associations with the phenomenon, using the Greek version of Young’s Internet addiction test. International Journal of Economic Sciences and Applied Research, 3(1), 4974.

    • Search Google Scholar
    • Export Citation
  • Goodman, R. (1997). The strengths and difficulties questionnaire: A research note. Journal of Child Psychology and Psychiatry, 38(5), 581586. doi:10.1111/jcpp.1997.38.issue-5

    • Search Google Scholar
    • Export Citation
  • Griffiths, M. , & Pontes, H. (2014). Internet addiction disorder and Internet gaming disorder are not the same. Journal of Addiction Research and Therapy, 5(4), e124. doi:10.4172/2155-6105.1000e124

    • Search Google Scholar
    • Export Citation
  • Huang, R. L. , Lu, Z. , Liu, J. J. , You, Y. M. , Pan, Z. Q. , Wei, Z. , He, Q. , & Wang, Z. Z. (2009). Features and predictors of problematic Internet use in Chinese college students. Behaviour & Information Technology, 28(5), 485490. doi:10.1080/01449290701485801

    • Search Google Scholar
    • Export Citation
  • Johansson, A. , & Götestam, K. G. (2004). Internet addiction: Characteristics of a questionnaire and prevalence in Norwegian youth (12–18 years). Scandinavian Journal of Psychology, 45(3), 223229. doi:10.1111/sjop.2004.45.issue-3

    • Search Google Scholar
    • Export Citation
  • Király, O. , Griffiths, M. D. , Urban, R. , Farkas, J. , Kökönyei, G. , Elekes, Z. , Tamás, D. , & Demetrovics, Z. (2014). Problematic Internet use and problematic online gaming are not the same: Findings from a large nationally representative adolescent sample. CyberPsychology, Behavior, & Social Networking, 17, 749754. doi:10.1089/cyber.2014.0475

    • Search Google Scholar
    • Export Citation
  • Kesici, S. , & Sahin, I. (2010). Turkish adaptation study of Internet addiction scale. CyberPsychology, Behavior, & Social Networking, 13(2), 185189. doi:10.1089/cyber.2009.0067

    • Search Google Scholar
    • Export Citation
  • Ko, C. H. , Yen, J. Y. , Yen, C. F. , Lin, H. C. , & Yang, M. J. (2007). Factors predictive for incidence and remission of Internet addiction in young adolescents: A prospective study. CyberPsychology & Behavior, 10(4), 545551. doi:10.1089/cpb.2007.9992

    • Search Google Scholar
    • Export Citation
  • Laconi, S. , Rodgers, R. F. , & Chabrol, H. (2014). The measurement of Internet addiction: A critical review of existing scales and their psychometric properties. Computers in Human Behavior, 41, 190202. doi:10.1016/j.chb.2014.09.026

    • Search Google Scholar
    • Export Citation
  • Li, Y. , Zhang, X. , Lu, F. , Zhang, Q. , & Wang, Y. (2014). Internet addiction among elementary and middle school students in China: A nationally representative sample study. CyberPsychology, Behavior, & Social Networking, 17(2), 111116. doi:10.1089/cyber.2012.0482

    • Search Google Scholar
    • Export Citation
  • Meerkerk, G. J. , van den Eijnden, R. J. , Vermulst, A. A. , & Garretsen, H. F. (2009). The Compulsive Internet Use Scale (CIUS): Some psychometric properties. Cyberpsychology & Behavior, 12(1), 16. doi:10.1089/cpb.2008.0181

    • Search Google Scholar
    • Export Citation
  • Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49(1), 115132. doi:10.1007/BF02294210

    • Search Google Scholar
    • Export Citation
  • Muthén, L. K. , & Muthén, B. O. (2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.

  • Osada, H. (2013). Internet addiction in Japanese college students: Is Japanese version of Internet Addiction Test (JIAT) useful as a screening tool? Bulletin of Senshu University School of Human Sciences, 3(1), 7180.

    • Search Google Scholar
    • Export Citation
  • Quinones, C. , & Kakabadse, N. K. (2015). Self-concept clarity and compulsive Internet use: The role of preference for virtual interactions and employment status in British and North-American samples. Journal of Behavioral Addictions, 4(4), 289298. doi:10.1556/2006.4.2015.038

    • Search Google Scholar
    • Export Citation
  • Schreiber, J. B. , Stage, F. K. , King, J. , Nora, A. , & Barlow, E. A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323338. doi:10.3200/JOER.99.6.323-338

    • Search Google Scholar
    • Export Citation
  • Siomos, K. E. , Dafouli, E. D. , Braimiotis, D. A. , Mouzas, O. D. , & Angelopoulos, N. V. (2008). Internet addiction among Greek adolescent students. CyberPsychology & Behavior, 11(6), 653657. doi:10.1089/cpb.2008.0088

    • Search Google Scholar
    • Export Citation
  • Siomos, K. , Paradeisioti, A. , Hadjimarcou, M. , Mappouras, D. G. , Kalakouta, O. , Avagianou, P. , & Floros, G. (2013). The impact of Internet and PC addiction in school performance of cypriot adolescents. Studies in Health Technology and Informatics, 191, 9094. doi:10.3233/978-1-61499-282-0-90

    • Search Google Scholar
    • Export Citation
  • Smilkstein, G. (1978). The family APGAR: A proposal for family function test and its use by physicians. Journal of Family Practice, 6(6), 12311239

    • Search Google Scholar
    • Export Citation
  • Stavropoulos, V. , Alexandraki, K. , & Motti-Stefanidi, F. (2013). Recognizing Internet addiction: Prevalence and relationship to academic achievement in adolescents enrolled in urban and rural Greek high schools. Journal of Adolescence, 36(3), 565576. doi:10.1016/j.adolescence.2013.03.008

    • Search Google Scholar
    • Export Citation
  • Strittmatter, E. , Brunner, R. , Fischer, G. , Parzer, P. , Resch, F. , & Kaess, M. (2014). Der Zusammenhang von Mobbingerfahrungen, Copingstilen und pathologischem Internetgebrauch bei Jugendlichen [Association of peer victimization, coping, and pathological Internet use among adolescents]. Zeitschrift für Kinder und Jugendpsychiatrie und Psychotherapie, 42(2), 8594. doi:10.1024/1422-4917/a000275

    • Search Google Scholar
    • Export Citation
  • Strittmatter, E. , Kaess, M. , Parzer, P. , Fischer, G. , Carli, V. , Hoven, C. W. , Wasserman, C. , Sarchiapone, M. , Durkee, T. , Apter, A. , Bobes, J. , Brunner, R. , Cosman, D. , Sisask, M. , Värnik, P. , & Wasserman, D. (2015). Pathological Internet use among adolescents: Comparing gamers and non-gamers. Psychiatry Research, 228, 128135. doi:10.1016/j.psychres.2015.04.029

    • Search Google Scholar
    • Export Citation
  • Vadlin, S. , Åslund, C. , Rehn, M. , & Nilsson, K. W. (2015). Psychometric evaluation of the adolescent and parent versions of the Gaming Addiction Identification Test (GAIT). Scandinavian Journal of Psychology, 56(6), 726735. doi:10.1111/sjop.2015.56.issue-6

    • Search Google Scholar
    • Export Citation
  • Van Rooij, A. J. , & Prause, N. (2014). A critical review of “Internet addiction” criteria with suggestions for the future. Journal of Behavioral Addictions, 3(4), 203213. doi:10.1556/JBA.3.2014.4.1

    • Search Google Scholar
    • Export Citation
  • Wang, L. , Luo, J. , Bai, Y. , Kong, J. , Gao, W. , & Sun, X. (2013). Internet addiction of adolescents in China: Prevalence, predictors, and association with well-being. Addiction Research and Theory, 21(1), 6269. doi:10.3109/16066359.2012.690053

    • Search Google Scholar
    • Export Citation
  • Wartberg, L. , Kriston, L. , Kammerl, R. , Petersen, K. U. , & Thomasius, R. (2015). Prevalence of pathological internet use in a representative German sample of adolescents: Results of a latent profile analysis. Psychopathology, 48(1), 2530. doi:10.1159/000365095

    • Search Google Scholar
    • Export Citation
  • Yang, S. C. , & Tung, C. J. (2007). Comparison of Internet addicts and non-addicts in Taiwanese high school. Computers in Human Behavior, 23(1), 7996. doi:10.1016/j.chb.2004.03.037

    • Search Google Scholar
    • Export Citation
  • Yen, C. F. , Ko, C. H. , Yen, J. Y. , Chang, Y. P. , & Cheng, C. P. (2009). Multi-dimensional discriminative factors for Internet addiction among adolescents regarding gender and age. Psychiatry and Clinical Neurosciences, 63(3), 357364. doi:10.1111/j.1440-1819.2009.01969.x

    • Search Google Scholar
    • Export Citation
  • Young, K. S. (1998 a). Caught in the net. New York: John Wiley & Sons.

  • Young, K. S. (1998 b). Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior, 1(3), 237244. doi:10.1089/cpb.1998.1.237

    • Search Google Scholar
    • Export Citation
  • Zhou, Z. , Yuan, G. , & Yao, J. (2012). Cognitive biases toward Internet game-related pictures and executive deficits in individuals with an Internet game addiction. PLoS ONE, 7(11), e48961. doi:10.1371/journal.pone.0048961

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    • Export Citation
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  • Fischer, G. , Brunner, R. , Parzer, P. , Klug, K. , Durkee, T. , Carli, V. , Wasserman, D. , Vonderlin, E. , Resch, F. , & Kaess, M. (2012). Depressivität, selbstverletzendes und suizidales Verhalten bei Jugendlichen mit riskanter und pathologischer Internetnutzung [Depression, deliberate self-harm and suicidal behaviour in adolescents engaging in risky and pathological Internet use]. Praxis der Kinderpsychologie und Kinderpsychiatrie, 61(1), 1631. doi:10.13109/prkk.2012.61.1.16

    • Search Google Scholar
    • Export Citation
  • Fisoun, V. , Floros, G. , Geroukalis, D. , Ioannidi, N. , Farkonas, N. , Sergentani, E. , Angelopoulos, N. , & Siomos, K. (2012). Internet addiction in the island of Hippocrates: The associations between Internet abuse and adolescent off-line behaviours. Child and Adolescent Mental Health, 17(1), 3744. doi:10.1111/camh.2011.17.issue-1

    • Search Google Scholar
    • Export Citation
  • Frangos, C. C. , Frangos, K. C. , & Kiohos, A. (2010). Internet addiction among Greek University students: Demographic associations with the phenomenon, using the Greek version of Young’s Internet addiction test. International Journal of Economic Sciences and Applied Research, 3(1), 4974.

    • Search Google Scholar
    • Export Citation
  • Goodman, R. (1997). The strengths and difficulties questionnaire: A research note. Journal of Child Psychology and Psychiatry, 38(5), 581586. doi:10.1111/jcpp.1997.38.issue-5

    • Search Google Scholar
    • Export Citation
  • Griffiths, M. , & Pontes, H. (2014). Internet addiction disorder and Internet gaming disorder are not the same. Journal of Addiction Research and Therapy, 5(4), e124. doi:10.4172/2155-6105.1000e124

    • Search Google Scholar
    • Export Citation
  • Huang, R. L. , Lu, Z. , Liu, J. J. , You, Y. M. , Pan, Z. Q. , Wei, Z. , He, Q. , & Wang, Z. Z. (2009). Features and predictors of problematic Internet use in Chinese college students. Behaviour & Information Technology, 28(5), 485490. doi:10.1080/01449290701485801

    • Search Google Scholar
    • Export Citation
  • Johansson, A. , & Götestam, K. G. (2004). Internet addiction: Characteristics of a questionnaire and prevalence in Norwegian youth (12–18 years). Scandinavian Journal of Psychology, 45(3), 223229. doi:10.1111/sjop.2004.45.issue-3

    • Search Google Scholar
    • Export Citation
  • Király, O. , Griffiths, M. D. , Urban, R. , Farkas, J. , Kökönyei, G. , Elekes, Z. , Tamás, D. , & Demetrovics, Z. (2014). Problematic Internet use and problematic online gaming are not the same: Findings from a large nationally representative adolescent sample. CyberPsychology, Behavior, & Social Networking, 17, 749754. doi:10.1089/cyber.2014.0475

    • Search Google Scholar
    • Export Citation
  • Kesici, S. , & Sahin, I. (2010). Turkish adaptation study of Internet addiction scale. CyberPsychology, Behavior, & Social Networking, 13(2), 185189. doi:10.1089/cyber.2009.0067

    • Search Google Scholar
    • Export Citation
  • Ko, C. H. , Yen, J. Y. , Yen, C. F. , Lin, H. C. , & Yang, M. J. (2007). Factors predictive for incidence and remission of Internet addiction in young adolescents: A prospective study. CyberPsychology & Behavior, 10(4), 545551. doi:10.1089/cpb.2007.9992

    • Search Google Scholar
    • Export Citation
  • Laconi, S. , Rodgers, R. F. , & Chabrol, H. (2014). The measurement of Internet addiction: A critical review of existing scales and their psychometric properties. Computers in Human Behavior, 41, 190202. doi:10.1016/j.chb.2014.09.026

    • Search Google Scholar
    • Export Citation
  • Li, Y. , Zhang, X. , Lu, F. , Zhang, Q. , & Wang, Y. (2014). Internet addiction among elementary and middle school students in China: A nationally representative sample study. CyberPsychology, Behavior, & Social Networking, 17(2), 111116. doi:10.1089/cyber.2012.0482

    • Search Google Scholar
    • Export Citation
  • Meerkerk, G. J. , van den Eijnden, R. J. , Vermulst, A. A. , & Garretsen, H. F. (2009). The Compulsive Internet Use Scale (CIUS): Some psychometric properties. Cyberpsychology & Behavior, 12(1), 16. doi:10.1089/cpb.2008.0181

    • Search Google Scholar
    • Export Citation
  • Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49(1), 115132. doi:10.1007/BF02294210

    • Search Google Scholar
    • Export Citation
  • Muthén, L. K. , & Muthén, B. O. (2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.

  • Osada, H. (2013). Internet addiction in Japanese college students: Is Japanese version of Internet Addiction Test (JIAT) useful as a screening tool? Bulletin of Senshu University School of Human Sciences, 3(1), 7180.

    • Search Google Scholar
    • Export Citation
  • Quinones, C. , & Kakabadse, N. K. (2015). Self-concept clarity and compulsive Internet use: The role of preference for virtual interactions and employment status in British and North-American samples. Journal of Behavioral Addictions, 4(4), 289298. doi:10.1556/2006.4.2015.038

    • Search Google Scholar
    • Export Citation
  • Schreiber, J. B. , Stage, F. K. , King, J. , Nora, A. , & Barlow, E. A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323338. doi:10.3200/JOER.99.6.323-338

    • Search Google Scholar
    • Export Citation
  • Siomos, K. E. , Dafouli, E. D. , Braimiotis, D. A. , Mouzas, O. D. , & Angelopoulos, N. V. (2008). Internet addiction among Greek adolescent students. CyberPsychology & Behavior, 11(6), 653657. doi:10.1089/cpb.2008.0088

    • Search Google Scholar
    • Export Citation
  • Siomos, K. , Paradeisioti, A. , Hadjimarcou, M. , Mappouras, D. G. , Kalakouta, O. , Avagianou, P. , & Floros, G. (2013). The impact of Internet and PC addiction in school performance of cypriot adolescents. Studies in Health Technology and Informatics, 191, 9094. doi:10.3233/978-1-61499-282-0-90

    • Search Google Scholar
    • Export Citation
  • Smilkstein, G. (1978). The family APGAR: A proposal for family function test and its use by physicians. Journal of Family Practice, 6(6), 12311239

    • Search Google Scholar
    • Export Citation
  • Stavropoulos, V. , Alexandraki, K. , & Motti-Stefanidi, F. (2013). Recognizing Internet addiction: Prevalence and relationship to academic achievement in adolescents enrolled in urban and rural Greek high schools. Journal of Adolescence, 36(3), 565576. doi:10.1016/j.adolescence.2013.03.008

    • Search Google Scholar
    • Export Citation
  • Strittmatter, E. , Brunner, R. , Fischer, G. , Parzer, P. , Resch, F. , & Kaess, M. (2014). Der Zusammenhang von Mobbingerfahrungen, Copingstilen und pathologischem Internetgebrauch bei Jugendlichen [Association of peer victimization, coping, and pathological Internet use among adolescents]. Zeitschrift für Kinder und Jugendpsychiatrie und Psychotherapie, 42(2), 8594. doi:10.1024/1422-4917/a000275

    • Search Google Scholar
    • Export Citation
  • Strittmatter, E. , Kaess, M. , Parzer, P. , Fischer, G. , Carli, V. , Hoven, C. W. , Wasserman, C. , Sarchiapone, M. , Durkee, T. , Apter, A. , Bobes, J. , Brunner, R. , Cosman, D. , Sisask, M. , Värnik, P. , & Wasserman, D. (2015). Pathological Internet use among adolescents: Comparing gamers and non-gamers. Psychiatry Research, 228, 128135. doi:10.1016/j.psychres.2015.04.029

    • Search Google Scholar
    • Export Citation
  • Vadlin, S. , Åslund, C. , Rehn, M. , & Nilsson, K. W. (2015). Psychometric evaluation of the adolescent and parent versions of the Gaming Addiction Identification Test (GAIT). Scandinavian Journal of Psychology, 56(6), 726735. doi:10.1111/sjop.2015.56.issue-6

    • Search Google Scholar
    • Export Citation
  • Van Rooij, A. J. , & Prause, N. (2014). A critical review of “Internet addiction” criteria with suggestions for the future. Journal of Behavioral Addictions, 3(4), 203213. doi:10.1556/JBA.3.2014.4.1

    • Search Google Scholar
    • Export Citation
  • Wang, L. , Luo, J. , Bai, Y. , Kong, J. , Gao, W. , & Sun, X. (2013). Internet addiction of adolescents in China: Prevalence, predictors, and association with well-being. Addiction Research and Theory, 21(1), 6269. doi:10.3109/16066359.2012.690053

    • Search Google Scholar
    • Export Citation
  • Wartberg, L. , Kriston, L. , Kammerl, R. , Petersen, K. U. , & Thomasius, R. (2015). Prevalence of pathological internet use in a representative German sample of adolescents: Results of a latent profile analysis. Psychopathology, 48(1), 2530. doi:10.1159/000365095

    • Search Google Scholar
    • Export Citation
  • Yang, S. C. , & Tung, C. J. (2007). Comparison of Internet addicts and non-addicts in Taiwanese high school. Computers in Human Behavior, 23(1), 7996. doi:10.1016/j.chb.2004.03.037

    • Search Google Scholar
    • Export Citation
  • Yen, C. F. , Ko, C. H. , Yen, J. Y. , Chang, Y. P. , & Cheng, C. P. (2009). Multi-dimensional discriminative factors for Internet addiction among adolescents regarding gender and age. Psychiatry and Clinical Neurosciences, 63(3), 357364. doi:10.1111/j.1440-1819.2009.01969.x

    • Search Google Scholar
    • Export Citation
  • Young, K. S. (1998 a). Caught in the net. New York: John Wiley & Sons.

  • Young, K. S. (1998 b). Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior, 1(3), 237244. doi:10.1089/cpb.1998.1.237

    • Search Google Scholar
    • Export Citation
  • Zhou, Z. , Yuan, G. , & Yao, J. (2012). Cognitive biases toward Internet game-related pictures and executive deficits in individuals with an Internet game addiction. PLoS ONE, 7(11), e48961. doi:10.1371/journal.pone.0048961

    • Search Google Scholar
    • Export Citation
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Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

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2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%.
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

Editorial Board

  • Sophia ACHAB (Faculty of Medicine, University of Geneva, Switzerland)
  • Alex BALDACCHINO (St Andrews University, United Kingdom)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Maria BELLRINGER (Auckland University of Technology, Auckland, New Zealand)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Damien BREVERS (University of Luxembourg, Luxembourg)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Julius BURKAUSKAS (Lithuanian University of Health Sciences, Lithuania)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Silvia CASALE (University of Florence, Florence, Italy)
  • Luke CLARK (University of British Columbia, Vancouver, B.C., Canada)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Geert DOM (University of Antwerp, Belgium)
  • Nicki DOWLING (Deakin University, Geelong, Australia)
  • Hamed EKHTIARI (University of Minnesota, United States)
  • Jon ELHAI (University of Toledo, Toledo, Ohio, USA)
  • Ana ESTEVEZ (University of Deusto, Spain)
  • Fernando FERNANDEZ-ARANDA (Bellvitge University Hospital, Barcelona, Spain)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Sally GAINSBURY (The University of Sydney, Camperdown, NSW, Australia)
  • Belle GAVRIEL-FRIED (The Bob Shapell School of Social Work, Tel Aviv University, Israel)
  • Biljana GJONESKA (Macedonian Academy of Sciences and Arts, Republic of North Macedonia)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Joshua GRUBBS (University of New Mexico, Albuquerque, NM, USA)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Zsolt HORVÁTH (Eötvös Loránd University, Hungary)
  • Susana JIMÉNEZ-MURCIA (Clinical Psychology Unit, Bellvitge University Hospital, Barcelona, Spain)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Chih-Hung KO (Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Taiwan)
  • Shane KRAUS (University of Nevada, Las Vegas, NV, USA)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Bernadette KUN (Eötvös Loránd University, Hungary)
  • Katerina LUKAVSKA (Charles University, Prague, Czech Republic)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Gemma MESTRE-BACH (Universidad Internacional de la Rioja, La Rioja, Spain)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Ståle PALLESEN (University of Bergen, Norway)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Michael SCHAUB (University of Zurich, Switzerland)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Hermano TAVARES (Instituto de Psiquiatria do Hospital das Clínicas da Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN (Ariel University, Israel)
  • Anise WU (University of Macau, Macao, China)

 

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