Predictors of Problematic Video Game Usage in ... - Hogrefe eContent

SUCHT, 59 (3), 2013, 153 – 164

Special Issue

Predictors of Problematic Video Game Usage in Childhood and Adolescence Thomas Mößle and Florian Rehbein Criminological Research Institute of Lower Saxony, Hannover, Germany Abstract: Aim: The aim of this article is to work out the differential significance of risk factors of media usage, personality and social environment in order to explain problematic video game usage in childhood and adolescence. Method: Data are drawn from the Berlin Longitudinal Study Media, a four-year longitudinal control group study with 1 207 school children. Data from 739 school children who participated at 5th and 6th grade were available for analysis. Result: To explain the development of problematic video game usage, all three areas, i. e. specific media usage patterns, certain aspects of personality and certain factors pertaining to social environment, must be taken into consideration. Video game genre, video gaming in reaction to failure in the real world (media usage), the childrens/adolescents academic self-concept (personality), peer problems and parental care (social environment) are of particular significance. Conclusion: The results of the study emphasize that in future – and above all also longitudinal – studies different factors regarding social environment must also be taken into account with the recorded variables of media usage and personality in order to be able to explain the construct of problematic video game usage. Furthermore, this will open up possibilities for prevention. Keywords: problematic video game usage, risk factors, media usage, personality, social environment

Risikofaktoren problematischer Computerspielnutzung im Kindes- und Jugendalter Zusammenfassung: Fragestellung: Ziel dieses Artikels ist es die differentielle Bedeutung von Risikofaktoren der Mediennutzung, der Persçnlichkeit und des sozialem Umfelds zur Erklrung problematischen Computerspielverhaltens im Kindes- und Jugendalter herauszuarbeiten. Methodik: Die Daten stammen aus dem Berliner Lngsschnitt Medien, einer 4-jhrigen Lngsschnittstudie mit Kontrollgruppendesign mit 1 207 Grundschlerinnen und Grundschlern. Ziel dieser Studie ist die umfassende Beantwortung der Frage, in welcher Weise sich die Mediennutzung auf Kinder und Jugendliche bezglich ihrer Freizeitgestaltung, ihres Sozialverhaltens, ihrer Intelligenzentwicklung, ihrer kçrperlichen Entwicklung sowie ihrer Schulleistungen auswirkt. Die Daten von 739 Schlerinnen und Schlern, welche sich in der fnften und sechsten Klasse an der Untersuchung beteiligten, konnten den verschiedenen Analysen zugrunde gelegt werden. Als Risikofaktoren der Mediennutzung wurden die Prferenz von Onlinerollenspielen bzw. von First- und Third-Person-Shootern sowie die Nutzung von Computerspielen bei realweltlichen Problemen, als Risikofaktoren der Person Hyperaktivitt, das Vorhandensein einer depressiven Verstimmung sowie das Selbstkonzept eigener Schulfhigkeiten, und als Risikofaktoren des sozialen Umfelds das Erleben von Elterngewalt, eine geringere allgemeine elterliche Zuwendung sowie Verhaltensprobleme mit Gleichaltrigen erhoben. Ergebnisse: Neben der Deskription von Gruppenunterschieden von Schlerinnen und Schlern mit und ohne problematisches Computerspielverhalten, welches anhand der Skala CSAS bestimmt wurde, und der Prsentation von quer- wie lngsschnittlichen Korrelationsanalysen, werden die Ergebnisse eines Strukturgleichungsmodels zur einjhrigen lngsschnittlichen Vorhersage problematischen Computerspielverhaltens dargestellt. Zusammengefasst zeigt sich, dass zur Erklrung der Entstehung eines problematischen Computerspielverhaltens auf alle drei Bereiche, d. h. spezifische Mediennutzungsmuster, bestimmte Merkmale der Persçnlichkeit sowie bestimmte Faktoren des sozialen Umfeldes zurckgegriffen werden muss. Einen besonderen Stellenwert haben ein Computerspielen bei realweltlichem Misserfolg (Mediennutzung), das Selbstkonzept eigener Schulfhigkeiten (Persçnlichkeit) sowie Probleme mit Gleichaltrigen und elterliche Zuwendung (soziales Umfeld). Schlussfolgerungen: Die Ergebnisse der Studie verdeutlichen, dass in zuknftigen – und hier vor allem auch lngsschnittlichen – Studien neben den erhobenen Variablen der Mediennutzung und der Persçnlichkeit auch Variablen des sozialen Umfeldes bercksichtigt werden mssen, um das Konstrukt problematisches Computerspielverhalten erklren zu kçnnen. Des Weiteren erçffnet dies auch Mçglichkeiten der Prvention. Schlsselwçrter: problematisches Computerspielverhalten, Risikofaktoren, Mediennutzung, Persçnlichkeit, soziales Umfeld

DOI: 10.1024/0939-5911.a000247

SUCHT 59 (3) 2013 Verlag Hans Huber, Hogrefe AG, Bern

154

T. Mçßle & F. Rehbein: Predictors of Problematic Video Game Usage

Introduction Dating at the latest from the rise in the number of children and adolescents with problematic video game usage seeking counseling and the setting up of the first out- and inpatient facilities to treat internet, video game and media addiction, clear efforts are being made to characterize this problematic behavior more exactly and to distinguish it from other psychiatric disorders.1 For a deeper understanding, it is essential to observe possible risk and protective factors as well as possible co-morbidities. A consideration of risk and protective factors of media usage, personality and social environment seems especially suitable (cf. classical addiction triangle, Kielholz & Ladewig, 1972; see also Rehbein & Mçßle, 2012). The various risk correlates of problematic video game usage have been observed in psychometrical studies in particular (cf. Rehbein & Mçßle, 2012). In addition to long playing times (cf. Tejeiro Salguero & Bersabe Morn, 2002; Gentile, 2009; Choo et al., 2010; Wçlfling, Mller & Beutel, 2011; Gentile et al., 2011), it was thus possible in previous studies to identify a dysfunctional media-related coping (cf. Batthyny, Mller, Benker & Wçlfling, 2009; Wçlfling et al., 2011) and especially the use of online role playing games (MMORPGs) (cf. Wçlfling et al., 2011; Schmidt, Drosselmeier, Rohde & Fritz, 2011) or shooter games (cf. Batthyny et al., 2009) as risk factors specific to media usage. With regard to personality, children with problematic video game behavior stand out – besides male gender (cf. Mçßle, Kleimann & Rehbein, 2007) – on account of their attention problems/impulsivity (cf. Hahn & Jerusalem, 2001a; Gentile, 2009; Choo et al., 2010), a higher acceptance of violence (cf. Choo et al., 2010), a lack of role-taking (cf. Rehbein, Kleimann & Mçßle, 2010), social uncertainty/shyness (cf. Wçlfling et al., 2011) or generally lower social competence (cf. Choo et al., 2010). Adolescents with problematic video game behavior also report more often on the lack of regular leisure time pursuits (cf. Rehbein, Kleimann & Mçßle, 2009; Rehbein et al., 2010), depressive symptoms (cf. te Wildt, Putzig, Zedler & Ohlmeier, 2007) and express thoughts of suicide more frequently (cf. Rehbein et al., 2009, 2010). In the context of school, they show lower academic achievement (cf. Anderson & Dill, 2000; Walsh, 2000; Rehbein et al., 2009, 2010; Batthyny et al., 2009; Gentile, 2009; Choo et al., 2010; Wçlfling et al., 2011; Gentile et al., 2011), increased school absenteeism – with gaming as the main motive – (cf. Rehbein et al., 2009, 2010; Choo et al., 2010; Wçlfling et al., 2011), repeating of classes (cf. Rehbein et al., 2009, 2010) as well as a self-reported school phobia (cf. Batthyny et al., 2009; Wçlfling et al., 2011). Besides 1

In this article problematic video game behavior/usage is used synonymously for video game addiction. This is done on account of the fact that the participants of the study are pupils at the transition between childhood and adolescence. In this way we can avoid the questionable use of the label pathological for a population of 11 to 13-years-olds as would be implied by the term addiction.


low parental care (cf. Baier & Rehbein, 2009), to our knowledge, the closer social environment has been considered only by Rehbein and colleagues (Rehbein et al., 2010) who, in their analysis of the risk factors of problematic video game usage, documented violence experienced by children at the hands of parents. The victimization of children through parental violence, however, did not prove to be statistically significant. To gain a deeper insight into the exact relationship (cause, correlate, consequence) and the significance of the various risk correlates, few studies have investigated several different risk and protective factors of problematic video game usage together. Rehbein and colleagues (Rehbein et al., 2010) implemented this, for example, in a logistic regression model, which was intended to identify variables of personality, situation and video games which could best predict the development of a video game addiction. It was to be examined whether the use of certain game genres would remain a significant risk factor if further variables were included in the regression. The following variables proved to be significant predictors of video game addiction: dysfunctional media usage, the experience of power and control in video games, the use of Massively Multiplayer Online Role Playing Games (MMORPGs), a sense of achievement experienced predominantly in video games, school phobia, the repeating of classes, a lack of role-taking, impulsivity with resulting negative consequences and the reported acceptance of violence (Rehbein et al., 2010). Longitudinal studies, however, are scarce : To our knowledge, a two-year study (Gentile et al., 2011), a sixmonth short interval study (Lemmens, Valkenburg & Peter, 2011) and the study from which the data in this paper are drawn (Mçßle, 2012), are the only longitudinal studies on problematic video game usage available. In their study with N = 3 034 students in which they observed the existing risk correlates of problematic video game usage, Gentile and colleagues (Gentile et al., 2011) were able to identify the development of a depression, anxiety, social phobia and low school performance as the consequences of pathological video game usage, whereas high weekly playing times, low social competence, low empathy and higher impulsivity values were determined as predictors of video game addiction in that study. In the shortterm cross-sectional study of Lemmens and colleagues (Lemmens et al., 2011) a causal effect between lower social competence and problematic video game behavior six weeks later was also shown for a random group of N = 542 adolescents. The aim of the present paper first is to overcome the drawbacks of the multitude of cross-sectional studies on risk factors of problematic video game usage, which all have the limitation that any inference of a causal relationship constitutes a theory-based interpretation. Secondly, this paper aims at determining the significance of the different risk factors of media usage, personality and social environment in explaining problematic video game usage by considering all three factors together.


Methods Design and study sample The Berlin Longitudinal Study Media is a four-year longitudinal control group study with 1 207 school children representative of the (federal) state of Berlin (3rd to 6th grade). From the overall population of 1 042 Berlin third grade classes (Status May 2005: 24 714 children) all classes with n > 15 were selected for sampling (24 352 children in 1 009 classes). 80 classes of differing schools were drawn randomly, 47 of which participated in the longitudinal study (1 129 children). A total of 943 children participated at t1 (84 % participation; for further details see Mçßle, Kleimann, Rehbein & Pfeiffer, 2010; Kleimann & Mçßle, 2008; Mçßle, 2012). The aim of the study was to comprehensively determine the way in which media use (and specifically the use of possibly harmful media content) affects children and adolescents regarding their leisure time activities, their social conduct, the development of their intelligence and their school performance. On five occasions (t1: November 2005, n = 943; t2: May 2006, n = 846; t3: May 2007, n = 835; t4: May 2008, n = 827; t5: May 2009, n = 806) a four-hour paper-pencil interview allocated on two consecutive days was completed within the classroom setting. Data from 739 school children were available for analysis in this article (prerequisite: study participation at t4 and t5, which corresponds to a retention rate of 89 %). Analyses were restricted to t4 and t5, as problematic video game usage (operationalized by CSAS) and some of the predictor variables (computer game genre, Strengths and Difficulties Questionnaire, see below) were included in the questionnaire at t4 for the first time. The average age of participants was 11.5 years at t4 and 12.5 years at t5. About half of the study participants were male (50.6 %), 28.3 % of the children came from immigrant communities.

155

CSAS-II at t5 (for a detailed description of both scales see Mçßle, 2012; Rehbein et al., 2010). The CSAS is based on the Internet Addiction Scale (ISS-20; Hahn & Jerusalem, 2001b, 2010) which has been extended and adapted to the issue of video game addiction and follows the classification of dependency of ICD-10. The CSAS-I (Mt4 = 15.27, SDt4 = 4.88, Cronbachs at4 = .84), administered at t4, consists of 11 items (4-point scale: 1 = incorrect to 4 = absolutely true) and covers the dimensions of preoccupation/salience (2 items), conflict (4 items), loss of control (3 items) and withdrawal symptoms (2 items). The following classification is used: 11 – 27 = unproblematic, 28 – 32 = at risk of becoming addicted, 33 – 44 = addicted. The CSAS-II (Mt5 = 19.46, SDt5 = 7.48, Cronbachs at5 = .94), administered at t5, consists of 14 items (4-point scale: 1 = incorrect to 4 = absolutely true) and additionally covers tolerance (2 items). Preoccupation/ salience was extended by two items, loss of control shortened by one item. The following diagnostic cut-offs are used: 14 – 34 = unproblematic, 35 – 41 = at risk of becoming addicted, 42 – 56 = addicted. The CSAS-II was administered at t5 in order to assure comparability with other samples, anticipating a follow-up of the sample for three further years. To avoid a selective distortion by either instrument, for all longitudinal analyses a sum score Problematic Video Game Usage (PVGU) was used, defined as the sum of those 10 items which had been used at both times (Mt4 = 13.35, SDt4 = 4.32, Cronbachs at4 = .83, Mt5 = 13.95, SDt5 = 5.40, Cronbachs at5 = .91). On a continuum from unproblematic to problematic video game usage, the sum variable PVGU can be seen as a measurement of the degree to which the person being questioned is prone to addictive video gaming. Gaming as dysfunctional coping was assessed by using the item I usually play video games when my life is not going well (4point scale: incorrect = 1 to absolutely true = 4; Mt4 = 1.39, SDt4 = .74, Mt5 = 1.50, SDt5 = .81).

Personality

Measures Media Usage Media ownership (no = 0, yes = 1) at home as well as bedroom media ownership were assessed for games consoles (e. g. Sony PlayStation, Microsoft X-Box or comparable) and portable games consoles (e. g. Nintendo DS or Sony PlayStation Portable). Furthermore, participants were asked about the usage of nine different video game genres including Massively Multiplayer Online Role Playing Games (MMORPGs), shooters, simulation and party games on a 5-point scale (never = 1 to very often = 5; MMORPG: Mt4 = 2.07, SDt4 = 1.46, Mt5 = 1.98, SDt5 = 1.41; shooter: Mt4 = 1.90, SDt4 = 1.43, Mt5 = 1.89, SDt5 = 1.41). There is a preference for a certain genre if students scored very often for one genre or if students scored sometimes or often for one genre and not higher for any other genre. Problematic video game behavior was assessed using the Video Game Addiction Scale CSAS; CSAS-I at t4 and

For assessing the students academic self-concept the sumscore of the 4 most selective of the 15 items of the corresponding FEESS 3 – 4 subscale (SIKS; Rauer & Schuck, 2003) were used (4-point scale: incorrect = 1 to absolutely true = 4; Mt4 = 3.06, SDt4 = .66, Mt5 = 3.03, SDt5 = .70): I learn very slowly (reversed), at school I do most things correctly, I am quite good at learning, I am good at school. With a Cronbachs at4 = .84 and Cronbachs at5 = .87 internal consistency is comparable to the original scale (for further scale characteristics see Mçßle, 2012). For measuring ADHD-symptomatology the subscale hyperactivity of the Strengths and Difficulties Questionnaire, a brief screening questionnaire for assessing behavioral strengths and abnormalities in children and adolescents aged 4 – 16 was used in the German translation (SDQ-Deu; Goodman, 1997; Klasen et al., 2000). The subscale hyperactivity (Cronbachs at4 = .61, Cronbachs at5 = .62) consists of 5 items (3-point scale: 0 = not true, 1 = somewhat true, 2 = certainly true) with the following classification: 0 – 5 = normal, SUCHT 59 (3) 2013 Verlag Hans Huber, Hogrefe AG, Bern

156


6 = borderline, 7 – 10 = abnormal (cf. Goodman, 2005). The students overall wellbeing was assessed using the KiddoKINDLR (Ravens-Sieberer & Bullinger, 2000). To roughly check for depressive symptoms – in the absence of a clinical instrument for diagnosing depression in the questionnaire – a sum scale of the following five items was used (5-point scale; always = 1 to never = 5; Mt4 = 9.54, SDt4 = 3.06, Cronbachs at4 = .63, Mt5 = 10.28, SDt5 = 3.29, Cronbachs at5 = .65): in the past week I laughed a lot, in the past week I was bored (reversed), in the past week I felt alone (reversed; all subscale psychological wellbeing), in the past week I was full of strength and power, in the past week I was tired and limp (reversed; both subscale physical wellbeing, for further scale characteristics see Mçßle, 2012).

Social environment For measuring peer problems the subscale peer problems of the Strengths and Difficulties Questionnaire in the German translation (SDQ-Deu; Goodman, 1997; Klasen et al., 2000) was used. The subscale peer problems (Cronbachs at4 = .42, Cronbachs at5 = .55) consists of 5 items (3-point scale: 0 = not true, 1 = somewhat true, 2 = certainly true) with the following classification: 0 – 3 = normal, 4 – 5 = borderline, 6 – 10 = abnormal (cf. Goodman, 2005). To assess family violence, participants were asked whether they had been physically abused by their mothers or fathers within the last four weeks prior to the interview (4-point scale: never, once or twice, 3 to 6 times, more often) regarding the following incidents: slapped in the face, hit with an object, treated harshly. Children reporting one of these incidents at least once for either father or mother were categorized as having experienced family violence (no = 0, yes = 1; Mt4 = 16 %, Mt4 = 15 %). Overall parental care was operationalized for father and mother using the following five items (4point scale: always = 1 to never = 4): my father/my mother knows what I do in my spare time (monitoring), my father/ my mother is someone I can freely discuss anything with (relationship/emotional closeness), my father/my mother helps with my homework if I need help (support/interest), my father/my mother asks me how it was in school (interest) and my father/my mother gives me praise for good actions (appraisal/relationship). A mean scale of paternal (Cronbachs at4 = .75, Cronbachs at5 = .71) and maternal (Cronbachs at4 = .64, Cronbachs at5 = .68) reported parental care (Mt4 = 16.27, SDt4 = 2.73, Mt5 = 15.82, SDt5 = 2.86) was used for calculations with the following assigned groups: 5 – 10 = low parental care, 11 – 15 = medium parental care, 16 – 20 high parental care (for further scale characteristics see Mçßle, 2012).

Statistical analysis In a first step t-tests and chi-square-tests were performed to test for group differences between problematic (at risk or addicted) and unproblematic video game users with respect SUCHT 59 (3) 2013 Verlag Hans Huber, Hogrefe AG, Bern

to the different variables of media usage, personality and social environment, which may be predictors for problematic video game behavior. To assess the differential significance of the individual variables for the development of problematic video game usage, cross-sectional bivariate intercorrelations at t4 and longitudinal correlations with problematic video game usage at t5 were calculated in a second step. IBM SPSS 19 was used for all of these calculations. Finally, to determine the differential influence of the variables of media usage, of personality and of social environment (including their interactions) on problematic video game usage by using a multivariate approach, a longitudinal path model (manifest variables and maximum likelihood estimations) was calculated with t4 predictors to predict problematic video game usage at t5 while controlling for t4 problematic video game usage. The sum variable Problematic Video Game Usage (PVGU) was used rather than the dichotomous classification (beyond reasons of comparability) due to the low incidence rate of addicted/at risk computer gamers. The structure of the path model resembles that of a cross-sectional path model using the same data set (cf. Mçßle, 2012). For all predictor variables direct effects on PVGU were assumed to assess their individual predictive power (gender, cf. Mçßle et al., 2007; shooter games, cf. Batthyny et al., 2009; MMORPGs, cf. Schmidt et al., 2011; dysfunctional media-related coping, cf. Wçlfling et al., 2011; hyperactivity, cf. Choo et al., 2010; depressive symptoms, cf. te Wildt et al., 2007; academic self-concept, cf. Lemmens et al., 2011; Gentile et al., 2011; family violence, cf. Rehbein et al., 2010; parental care, cf. Baier & Rehbein, 2009). Because of its high explanatory power for other problem behaviors (cf. Lçsel & Bender, 2003; Remschmidt & Walter, 2009; Remschmidt, 2005), family violence was included in the path model despite missing empirical evidence – also for validating the results of Rehbein et al. (2010). Indirect effects on PVGU for gender were presumed via playing video games in reaction to problems, frequent use of shooter games and MMORPGs due to the higher prevalence rate of computer games usage and overall higher affinity of males for computer games (cf. Gentile et al., 2011; Mçßle et al., 2010; Wçlfling et al., 2011). In addition, two potential indirect effects on PVGU for gender were included via hyperactivity and depressive symptoms on account of higher prevalence rates of hyperactivity and lower rates of depression for male children and adolescents (cf. Schlack, Hçlling & Huss, 2007; Mehler-Wex, 2008). Regarding computer game usage, potential indirect effects on PVGU in reaction to real-life problems via the frequent use of shooter games and MMORPGs were included as a media-related coping has been shown to be associated with an overall higher frequency of using computer games in general and MMORPGs in particular (cf. Wçlfling et al., 2011). The frequent use of shooter games and MMORPGs were assumed to be correlated as a frequent use of these genres is associated with a frequent use of computer games in general (cf. Rehbein et al., 2010). Regarding predictor variables of personality, correlations were allowed among


each other due to a high rate of comorbidity (cf. Schlack et al., 2007; Mehler-Wex, 2008; te Wildt et al., 2007). Two indirect effects of hyperactivity and depressive symptoms on PVGU via playing video games in reaction to real-life problems were included, as it was supposed that the use of computer games might be seen to be especially helpful in solving problems caused by hyperactivity or depressive symptoms (cf. te Wildt et al., 2007; Batthyny et al., 2009) and as hyperactivity and depression might reduce the availability of other functional coping strategies. An indirect effect of low academic self-concept on PVGU via frequent use of shooter games with their structural characteristics allowing instantaneous experience of power and self-efficacy was included as it was assumed that children with a low academic self-concept – or a low self-esteem in general – are especially attracted to this computer game genre (cf. Montag et al., 2011). With respect to variables of social environment, correlations were allowed among each other, again due to a high rate of comorbidity – as known, for example, from violent behavior research (cf. Lçsel & Bender, 2003; Remschmidt & Walter, 2009). Between all predictors of personality and all predictors of social environment correlations were allowed due to the complex interaction of peer and family context and variables of personality (cf. Lçsel & Bender, 2003; Remschmidt & Walter, 2009; Remschmidt, 2005). One indirect effect on PGVU was included for low parental care via low academic self-concept, since a causal effect especially of parental appraisal (as being part of parental care as operationalized in this study) on self-concept is expected (cf. Weinert & Helmke, 1997). Three indirect effects of social environment variables via playing video games in reaction to real-life problems were assumed for peer problems, family violence and low parental care, as the amount of this escapist use of computer games can be assumed to be higher because of the stress caused by such problematic relationships (cf. Batthyny & Pritz, 2009) and at the same time depriving from healthy coping strategies usually provided by peers and family. The path model was realized in IBM SPSS Amos 21.

Results At t4 an average of 58 % (t5: 62 %) of the students report having their own games console in their bedroom, 52 % (t5: 61 %) have their own PC and 82 % (t5: 83 %) a portable games console. On average students use video games 41 minutes a day (SD = 71 minutes; boys: 59 minutes, SD = 87 minutes; girls: 22 minutes, SD = 42 minutes) at t4 and 51 minutes a day (SD = 90 minutes; boys: 69 minutes, SD = 108 minutes; girls: 32 minutes, SD = 61 minutes) at t5. The three most favored game genres of boys are sport (t4: 52 %, t5: 51 %), shooter (t4: 29 %, t5: 26 %) and simulation games (t4: 25 %, t5: 27 %). MMORPGs are also quite popular, too: 23 % (t4, 6th of 9), 26 % (t5, 3rd of 9). The three most favored game genres of girls are party (t4: 41 %, t5: 43 %), simulation (t4: 38 %, t5: 36 %) and puzzle/skill games (t4:

157

28 %, t5: 29 %). MMORPGs (t4: 10 %, t5: 7 %, both 5th of 9) are of medium, shooter games of low popularity (t4: 3 %, t5: 3 %, both 8th of 9). Concerning problematic video game usage 1.8 % (n = 13) of the sample are classified as being in danger of becoming addicted (0.3 % of girls, 3.2 % of boys) and 0.8 % as addicted (n = 6; 0 % of girls, 1.7 % of boys) on the basis of the data recorded at t4 – all children were included, i. e. also children who do not play video games (8 %). At t5 – once again all children were included, also those who do not play video games (10 %) – prevalence estimates are as follows: 2.3 % (n = 17) are classified as being in danger of becoming addicted (0.8 % of girls, 3.8 % of boys) and 1.8 % (n = 13) as addicted (0.5 % of girls, 3.0 % of boys). A large correlation of the scale values (r = .52, p < .001) points to a certain stability of problematic video game usage over time: Of 18 school children with problematic video game behavior at t4 (at risk or addicted) for whom data was available at t5, 10 were still classified as being problematic video game users (56 %). On the other hand, during the one-year period of the survey, 3 % of the school children were additionally classified as being addicted to video games: of 597 children with unproblematic video game behavior at t4 for whom data was available at t5, at t5 18 were classified as being problematic video game users (see Table 1). Results of the group comparisons (problematic vs. unproblematic video gamers) are presented in Table 2. Firstly, significant differences regarding preferences for video game genres can be seen. Of all the genres included in the survey, first and third-person shooters and MMORPGs are the genres for which the greatest differences in preferences between the two groups of video game users were recorded. Video game users with problematic behavior also tend to use video games especially when their life “is not going so well“. This difference is even greater at t5 than at t4. In the field of personality, there are corresponding findings for academic self-concept: Children with a problematic video game behavior assess their academic abilities more negatively than the group of unproblematic video game users. With regard to depressive symptoms, children with a problematic video game behavior show a higher depressive symptomatology. This difference, however, becomes statistically significant only at t5. Clear group differences can also be seen with regard to hyperactivity measured in SDQ (Percentage abnormal; t4: 47 % vs. 11 %; t5: 43 % vs. 8 %). In the field of social environment, problematic video game users differ from unproblematic video game users concerning peer problems measured in SDQ. The percentages of children with abnormal (as denoted in SDQ) values in the area of peer problems in the group of problematic video game users equal 26 % at t4 and 38 % at t5 (unproblematic video game users: 6 %). Further findings in the area of social environment for the present sample can be summarized as follows: Children whose usage of video games is problematic report more frequently on less parental care and experience violence in the home more often. SUCHT 59 (3) 2013 Verlag Hans Huber, Hogrefe AG, Bern

158


Table 1 Stability of problematic video game usage t5 unproblematic

at risk

never unproblematic t4 at risk addicted

addicted starts

97 % (579) stops 42 % (5) 50 % (3)

1.8 % (11)

1.2 % (7) stays

33 % (4) 17 % (1)

25 % (3) 33 % (2)

Note. Numbers in brackets are case numbers.

To assess the differential significance of the individual predictor variables, (inter-)correlations were calculated (see Table 3). First of all, it is noticeable that at t4 – with the exception of MMORPGs, which correlate in a statistically relevant way only with the more frequent usage of first and third-person shooters, playing video games in reaction to real life problems and with peer problems – all variables recorded show slight or moderate correlations with one another. The highest correlative values can be seen for academic self-concept. With regard to the dependent variables of problematic video game usage, statistically relevant cross-sectional (t4) and longitudinal (t5) correlations can be found for all variables recorded. The highest correlations with problematic video game usage are found for a usage of video games when life “is not going so well”, the lowest correlations for depressive symptoms. The path model determining the explanatory value of the different predictor variables is shown in Figure 1. The model structure has a good model fit (w2 = 52.54 (25, N = 666), RMSEA = .04, RMR = .17, SRMR = .03, GFI = .99, AGFI = .96) and an explained variance of 34 %. When simultaneously controlling further factors and t4 problematic video game usage (bt4t5 = .25), an independent explanatory value for certain video game patterns with regard to the degree of problematic video game usage can be observed. First and foremost it is video gaming in reaction to failure in the real world which has the highest explanatory value (b = .18) in the model. The more the children agree to using video games especially when their life “is not going so well”, the higher is their risk of problematic video game usage. This predictor touches on the construct of dysfunctional stress regulation, in which gaming takes the place of confronting problems or conflicts in the real world. If this is the case, MMORPGs (b = .21) and first- and thirdperson shooters (b = .17) are played more frequently. The frequent use of MMORPGs (b = .07) – but not the frequent use of first- and third-person shooters (b = .05ns) – thus to a certain extent can explain problematic video game usage, if gender is also taken into account at the same time. Both genres are decidedly male-preferred (first- and thirdperson shooters: b = .42; MMORPGs: b = .16) and boys show a more problematic video game behavior regardless of their genre preference, b = .10).


Among personality variables, a low academic selfconcept (b = .14), a construct which could be more precisely positioned at the interface of personality and social environment, has the highest independent explanatory value for problematic video game usage. Neither hyperactivity (b = .07ns), which is more often shown for boys (b = .12), nor the presence of depressive symptoms (b = .04ns), which are reported more often for girls (b = -.10), can predict problematic video gaming. Hyperactive behavior, however, leads to video games being used when life “is not going so well” (b = .18). A further factor of influence for the development of video game addiction is the social environment of the children. If the children indicate that they experience peer problems, this is associated with a larger degree of problematic video game usage (b = .08). Neither experiencing little parental care (b = .05ns) nor experiencing family violence has an additional independent explanatory value for problematic video game usage (b = .00ns). On account of the connections with the personality variables mentioned above, all three factors of influence furthermore can be observed as moderating background factors (v = .14 to v = .31). Beyond that, there are two important indirect effects of a low parental care on problematic video games usage via a low academic self-concept (b = .33), and via playing video games in reaction to real-life problems (b = .13), the two predictor variables with the highest explanatory value in the model.

Discussion The data of the Berlin Longitudinal Study Media first of all demonstrate that problematic video game usage in childhood and adolescence shows a certain stability, with markedly lower stability values than those reported by Gentile and colleagues (Gentile et al., 2011): 56 % (cf. 84 %; Gentile et al., 2011) of the school children who were classified as problematic (at risk or addicted) at 5th grade remained so at 6th grade. During the one-year duration of the survey from 5th to 6th grade a further 3 % (cf. 1 %; Gentile et al., 2011) were also classified as problematic video game users. The correlation of problematic video game usage observed at t4 and t5 of r = .52,


159

Figure 1. Path model explaining problematic video game use (t5) by t4 variables. however, is more or less of the same order as that of the sixmonth short interval study of Lemmens and colleagues (r = .61, Lemmens et al., 2011). The details on the influence of various risk factors emphasize that to explain the development of problematic video game usage in adolescence, all three areas, i. e. specific media usage patterns (cf. Batthyny et al., 2009; Rehbein et al., 2010; Wçlfling et al., 2011; Schmidt et al., 2011), certain aspects of personality (Hahn & Jerusalem, 2001a; Gentile, 2009; Rehbein et al., 2009, 2010; cf. Choo et al., 2010; Wçlfling et al., 2011) and certain factors of social environment (cf. Rehbein et al., 2010) must be taken into account. Even more so, because these risk factors in part correlate highly with one another and can thus also take on the role of moderating background factors. Five of the risk factors examined deserve to be mentioned again here: two with no direct effect on problematic video game usage, i. e. family violence and depressive symptoms, two having a direct effect on problematic video game usage, i. e. gaming in reaction to failure in the real world (media usage) and academic selfconcept (personality) and one having no direct but two important indirect effects on problematic video game usage, i. e. parental care (social environment). The non-effect of victimization of children through parental violence is consistent with the results of Rehbein and colleagues (Rehbein et al., 2010), who reported no statistically significant influence of family violence on problematic video game behavior, using a logistic regression model, and could therefore be confirmed with a younger study population. Maybe this missing causal relationship distinguishes problematic video game usage from other problem behaviors (cf. Lçsel & Bender, 2003; Remschmidt & Walter, 2009; Remschmidt, 2005). The

additional non-effect of parental violence on gaming in reaction to failure in the real world (the strongest predictor for problematic video game usage) as well as the nonexisting correlation (r = .02ns) with the frequent use of MMORPGs (the game genre predominantly used by children and adolescents with problematic video game usage) in our opinion both underline this interpretation. Observing no direct effect of depressive symptoms on problematic video game usage was somewhat surprising, as a depressive symptomatology is commonly reported with regard to problematic video game usage (cf. Black & Moyer, 1998; Greenfield, 2000; te Wildt et al., 2007). There are several possible explanations to this: 1. Depressive symptoms are only comorbid with problematic video game usage, there is no cause or consequence. 2. Depressive symptoms are the consequence of problematic video game usage (cf. Gentile et al., 2011). 3. The influence of depressive symptomatology is expressed in the observed intercorrelations with other variables of personality and social environment. 4. For this age group depressive symptoms do not unfold explanatory value. To clarify the intricate relationship of depressive symptomatology as well as other psychiatric disorders with problematic video game usage, more longitudinal research is needed. In the simultaneous, multivariate observation in a oneyear longitudinal study, video gaming in reaction to failure in the real world and academic self-concept, were seen as explanatory variables of essential significance (cf. Batthyny et al., 2009; Rehbein et al., 2010; Wçlfling et al., 2011). Gaming in reaction to failure in the real world, however, also is directly connected with low parental care. This is to be judged as especially problematic because, on account of the close proximity of gaming in reaction to failure in the real world to the construct of dysfunctional stress reguSUCHT 59 (3) 2013 Verlag Hans Huber, Hogrefe AG, Bern

160


Table 2 Media usage/personality/social environment by problematic video gaming classification unproblematic

problematic

m (n)

m (n)

1.87 (637), SD = 1.39 1.88 (628), SD = 1.38

3.95 (19), SD = 1.65 3.54 (28), SD = 1.81

t(654) = -6.34, p < .001, d = 1.45, 1-b = .99 t(654) = -6.15, p < .001, d = 1.18, 1-b = .99

2.06 (637), SD = 1.44 1.96 (626), SD = 1.38

4.11 (19), SD = 1.45 3.76 (28), SD = 1.70

t(654) = -6.09, p < .001, d = 1.40, 1-b = .99 t(653) = -6.81, p < .001, d = 1.28, 1-b = .99

1.35 (648), SD = .66 1.44 (635), SD = .72

3.05 (19), SD = 1.17 3.30 (30), SD = .95

t(665) = -10.75, p < .001, d = 2.29, 1-b = .99 t(654) = -6.34, p < .001, d = 1.45, 1-b = .99

3.09 (649), SD = .66 3.06 (632), SD = .69

2.29 (19), SD = .72 2.53 (30), SD = .89

t(666) = 5.21, p < .001, d = 1.21, 1-b = .99 t(660) = 4.05, p < .001, d = .76, 1-b = .77

9.49 (645), SD = 2.97 10.12 (626), SD = 3.08

9.84 (19), SD = 3.83 12.79 (29), SD = 5.22

t(662) = -.498, p = .62, d = .11, 1-b = .66 t(653) = -4.39, p < .001, d = .81, 1-b = .83

% (n)

% (n)

normal borderline abnormal normal borderline abnormal

79 % (500) 10 % (66) 11 % (71) 81 % (502) 11 % (68) 8 % (46)

21 % (4) 32 % (6) 47 % (9) 40 % (12) 17 % (5) 43 % (13)

normal borderline abnormal normal borderline abnormal

73 % (465) 21 % (131) 6 % (38) 74 % (459) 20 % (121) 6 % (37)

32 % (6) 42 % (8) 26 % (5) 34 % (10) 28 % (8) 38 % (11)

no yes no Yes

85 % (550) 15 % (96) 87 % (550) 13 % (86)

68 % (13) 32 % (6) 57 % (17) 43 % (13)

low medium high

2 % (14) 30 % (183) 68 % (414)

0 % (0) 82 % (14) 18 % (3)

frequent use of shooters t4 t5 frequent use of MMORPG t4 t5 playing video games in reaction to real life problems t4 t5 academic self-concept t4 t5 depressive symptoms t4 t5

hyperactivity t4

t5

peer problems t4

t5

family violence t4 t5 parental care t4


w2 = 35.34 (2, N = 656), p < .001, V = .23, 1-b = .99 w2 = 47.28 (2, N = 646), p < .001, V = .27, 1-b = .99

w2 = 20.02 (2, N = 653), p < .001, V = .18, 1-b = .84 w2 = 44.97 (2, N = 646), p < .001, V = .26, 1-b = .99

w2 = 3.97 (1, N = 665), p < .05, V = .08, 1-b = .54 w2 = 20.12 (1, N = 666), p < .001, V = .17, 1-b = .86

w2 = 21.12 (2, N = 628), p < .001, V = .18, 1-b = .82


161

Table 2 Media usage/personality/social environment by problematic video gaming classification (Continued)

t5

low medium high

unproblematic

problematic

m (n)

m (n)

4 % (26) 32 % (185) 64 % (375)

18 % (5) 57 % (16) 25 % (7)

w2 = 21.40 (2, N = 614), p < .001, V = .19, 1-b = .87

Notes. n = case numbers. d = Cohens effect size. 1-ß = achieved power. V = Cramers V. Power was calculated using G*Power 3.1.

Table 3 Cross-sectional and longitudinal intercorrelations t5 t4 sex shooter MMORPG pgp asc ds hy pp fv pac

shooter .44***

MMORPG .19*** .42***

pgp .12** .23*** .23***

asc

ds ns

-.07 -.14** -.05ns -.24***

ns

-.05 -.07ns .02ns .09* -.26***

hy

pp

fv

pac

pvgu

pvgu

.17*** .10** .07ns .24*** -.41*** .17***

.10* .14*** .09* .15*** -.34*** .27*** .28***

.09* .08* .01ns .10** -.15*** .17*** .14*** .17***

-.08* -.14** -.06ns -.20*** .33*** -.19*** -.23*** -.18*** -.26***

.30*** .36*** .29*** .55*** -.35*** .13** .35*** .33*** .23*** -.27***

.27*** .30*** .24*** .42*** -.34*** .08* .31*** .27*** .14*** -.23***

Notes. Shooter = genre preference shooter. MMORPG = genre preference MMORPG. pgp = playing video games in reaction to real life problems. asc = academic self-concept. ds = depressive symptoms. hy = hyperactivity. pp = peer problems. fv = family violence. pac = parental care. pvgu = problematic video game usage. * p < .05, ** p < .01, *** p < .001.

lation, it is to be feared that gaming will take the place of confronting problems in the real world. If this is understood as an attempt at self-medicating, such behavior is not only understandable but reasonable. If, however, problems continue to be “confronted” in the virtual world only, this has little bearing on reality and can for the long term intensify a flight into the virtual world (”Im understood there”, “I experience success there”, “I can defend myself there”) and increase the danger of problematic video game usage. The connections with a low concept of ones own abilities also point to the same direction, a connection which has not yet been reported in this way in the relevant literature; only low school performance has been observed with problematic video game usage (cf. Anderson & Dill, 2000; Walsh, 2000; Rehbein et al., 2009, 2010; Batthyny et al., 2009; Gentile, 2009; Choo et al., 2010; Wçlfling et al., 2011; Gentile et al., 2011). There is, however, a close relationship between academic self-concept and school performance and grades might be the mediator between academic self-concept and problematic video game usage (cf. Mçßle, 2012). If one assumes that a low concept of ones own abilities is also associated with low self-efficacy, it is obvious and also understandable why children with a low concept of their own abilities turn to the virtual world of video games. Therein gamers can generate experiences of success by ensuring that the game, the level, etc. are best

suited to their abilities. Furthermore, whether in a shooter or an online role playing game, gamers get a direct and immediate feedback on the success of their actions, something the parents of these children mostly fail to provide (as indicated by the direct effect of low parental care on low academic self-concept). Should the gamers fail, they can either try again or simply choose another game. But both constructs together (a low concept of ones own abilities and gaming in reaction to failure in the real world) could result in the fact that in extreme cases success and recognition are achieved in the virtual world only. When this happens, this behavior can take on a life of its own and reinforce problematic video gaming. Although having identified the interdependence and importance of the examined predictors on problematic video game usage, some limitations have to be discussed. First, we only can present evidence for such variables that have been included in the study. Maybe there are other variables of equivalent or higher predictive power, e. g. social uncertainty/shyness. Second, having drawn a representative sample of primary grade school children in the state of Berlin, the results of these investigations are representative only for this age group (at least in Berlin). The observed relationships need to be validated with different populations and older age groups. Third, having assessed all constructs via self-report, some results might SUCHT 59 (3) 2013 Verlag Hans Huber, Hogrefe AG, Bern

162


differ if clinical instruments had been used instead. With these limitations in mind, however, it can be inferred that in further studies variables of social environment must be considered together with the often recorded variables of media usage and personality: The interplay of different variables makes the picture. Furthermore, this should also be realized more frequently in longitudinal studies. This is the only way in which the construct of problematic video game usage can be explained comprehensively, thus providing a basis for evidence based intervention and prevention strategies.

Implications for Practice – Problematic video game usage must be met with adequate preventive measures. Children and adolescents should, for example, be strengthened in their social competence and their ability to cope with stress and must, above all, have the opportunity to gain experience in the real world, which builds up their self-esteem. – A significant role is played by parents not just on account of the way in which they themselves use media and the way in which they bring up their children. A lack of parental care and the pattern of media usage within the family are important mediators. Preventive measures must take account of this, as parental care has a considerable effect on the development of a healthy level of self-confidence. – Furthermore, a new assessment of age classifications, including addictive game features as criteria, seems to be a possible way of countering problematic video game usage. Although both shooters and MMORPGs (mostly USK 12) seem to be connected with problematic gaming behavior, in the current age classification system only the higher amount of explicit violence decides on the more rigid age classification.

Acknowledgments The study was financed by the Volkswagen Foundation. No conditions whatsoever were imposed with the financing. We thank the 1 207 Berlin school children and their 1 085 parents who participated in the study and the 47 junior school headmasters and 89 teachers for helping us to carry out the study at their schools and for providing lesson time.

Declaration of Conflicts of Interest The authors declare that there is no conflict of interest relating to this article.


References Anderson, C. A. & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78, 772 – 790. Baier, D. & Rehbein, F. (2009). Computerspielabhngigkeit im Jugendalter [Video game dependency in adolescence]. In C. J. Tuly (Ed.), Multilokalitt und Vernetzung: Beitrge zur technikbasierten Gestaltung jugendlicher Sozialrume (pp. 139 – 155). Weinheim, Mnchen: Juventa. Batthyny, D., Mller, K. W., Benker, F. & Wçlfling, K. (2009). Computerspielverhalten: Klinische Merkmale von Abhngigkeit und Missbrauch bei Jugendlichen [Computer game playing: Clinical characteristics of dependence and abuse among adolescents]. Wiener Klinische Wochenschrift, 121, 502 – 509. Batthyny, D. & Pritz, A. (Eds.). (2009). Rausch ohne Drogen – Substanzungebundene Schte [Intoxication without Drugs – Behavioral Addictions]. Wien: Springer. Black, D. W. & Moyer, T. (1998). Clinical features and psychiatric comorbidity of subjects with pathological gambling behavior. Psychiatric Service, 49, 1434 – 1439. Choo, H., Gentile, D. A., Sim, T., Li, D., Khoo, A. & Liau, A. K. (2010). Pathological video-gaming among Singaporean youth. Ann Acad Med Singapore, 39, 822 – 829. Gentile, D. A. (2009). Pathological video-game use among youth ages 8 to 18: A national study. Psychological Science, 20, 594 – 602. Gentile, D. A., Choo, H., Liau, A., Sim, T., Li, D., Fung, D. et al. (2011). Pathological video game use among youths: A twoyear longitudinal study. Pediatrics, 127, 319 – 329. Goodman, R. (1997). The strengths and difficulties questionnaire: A research note. Journal of Child Psychology and Psychiatry, 38, 581 – 586. Goodman, R. (2005). Auswertung der Selbsteinschtzungen des SDQ-D [Scoring the Self-Report Strengths and Difficulties Questionnaire]. Retrieved May 21, 2013, from http://www. sdqinfo.org/py/sdqinfo/b3.py?language=German Greenfield, D. N. (2000). Virtual Addiction. Oakland: New Harbinger. Hahn, A. & Jerusalem, M. (2001a). Internetsucht: Jugendliche gefangen im Netz [Internet addiction: Adolescents caught in the Net]. In J. Raithel (Ed.), Risikoverhaltensweisen Jugendlicher: Formen, Erklrungen und Prvention (pp. 279 – 294). Opladen: Leske & Budrich. Hahn, A. & Jerusalem, M. (2001b). Internetsucht: Reliabilitt und Validitt in der Online-Forschung [Internet addiction: Reliability and validity in online research]. In A. Theobald, M. Dreyer & T. Starsetzki (Eds.), Handbuch zur Online-Marktforschung. Beitrge aus Wissenschaft und Praxis (pp. 211 – 234). Wiesbaden: Gabler. Hahn, A. & Jerusalem, M. (2010). Die Internetsuchtskala (ISS): Psychometrische Eigenschaften und Validitt [Internet addiction scale ISS: Psychometric properties and validity]. In D. Mcken, A. Teske, F. Rehbein & B. te Wildt (Hrsg.), Prvention, Diagnostik und Therapie von Computerspielabhngigkeit (pp. 185 – 204). Lengerich: Pabst Science Publishers. Kielholz, P. & Ladewig, D. (1972). Die Drogenabhngigkeit des modernen Menschen [Drug Addiction of the Modern Human Being]. Mnchen: Lehmanns. Klasen, H., Woerner, W., Wolke, D., Meyer, R., Overmeyer, S., Kaschnitz, W. et al. (2000). Comparing the German versions of


the Strengths and Difficulties Questionnaire (SDQ-Deu) and the Child Behavior Checklist. European Child and Adolescent Psychiatry, 9, 271 – 276. Kleimann, M. & Mçßle, T. (2008). The logs of eliza and other media stories. Behavioral and developmental effects of a school based media education program – Berlin longitudinal study media. International Journal of Behavioral Development, 32(Suppl. 2), 55 – 59. Lemmens, J. S., Valkenburg, P. M. & Peter, J. (2011). Psychosocial causes and consequences of pathological gaming. Computers in Human Behavior, 27, 144 – 152. Lçsel, F. & Bender, D. (2003). Protective factors and resilience. In D. P. Farrington & J. W. Coid (Eds.), Early Prevention of Adult Antisocial Behaviour (pp. 130 – 204). Cambridge: Cambridge University Press. Mehler-Wex, C. (2008). Depressive Stçrungen [Depressive Disorder]. Heidelberg: Springer Medizin Verlag. Montag, C., Flierl, M., Markett, S., Walter, N., Jurkiewicz, M. & Reuter, M. (2011). Internet addiction and personality in firstperson-shooter video gamers. Journal of Media Psychology: Theories, Methods, and Applications, 23, 163 – 173. Mçßle, T. (2012). ”dick, dumm, abhngig, gewaltttig?” Problematische Mediennutzungsmuster und ihre Folgen im Kindesalter. Ergebnisse des Berliner Lngsschnitt Medien [“fat, stupid, addicted, violent?“ Problematic Media Usage Behavior and its Consequences in Childhood. Results of the Berlin Longitudinal Study Media.]. Baden Baden: Nomos. Mçßle, T., Kleimann, M. & Rehbein, F. (2007). Bildschirmmedien im Alltag von Kindern und Jugendlichen: Problematische Mediennutzungsmuster und ihr Zusammenhang mit Schulleistungen und Aggressivitt [Screen Media in Everyday Life of Children and Adolescents: Problematic Usage Patterns and their Relation to School Success and Aggressive Behavior] (Interdisziplinre Beitrge zur Kriminologischen Forschung, Bd. 33, 1. Aufl.). Baden-Baden: Nomos. Mçßle, T., Kleimann, M., Rehbein, F. & Pfeiffer, C. (2010). Media use and school achievement – boys at risk? British Journal of Developmental Psychology, 28, 699 – 725. Rauer, W. & Schuck, K. D. (2003). FEESS 3 – 4. Fragebogen zur Erfassung emotionaler und sozialer Schulerfahrungen von Grundschulkindern dritter und vierter Klassen [FEESS 3 – 4. Questionnaire for Assessing Emotional and Social School Experiences of 3rd and 4th Grade Primary School Children]. Gçttingen: Hogrefe. Ravens-Sieberer, U. & Bullinger, M. (2000). KINDLR – Fragebogen zur Erfassung der gesundheitsbezogenen Lebensqualitt bei Kindern und Jugendlichen – revidierte Form [KINDLRQuestionnaire for the Assessment of Health-Related Quality of Life in Children and Adolescents – Revised Version]. Unpublished manuscript. Rehbein, F., Kleimann, M. & Mçßle, T. (2009). Exzessives Computerspielen und Computerspielabhngigkeit im Jugendalter – Ergebnisse einer deutschlandweiten Reprsentativbefragung [Video game dependency in childhood and adolescence. Empirical results on causes, diagnostic, and comorbid conditions with special regard to video game characteristics.]. Die Psychiatrie, 6, 140 – 146. Rehbein, F., Kleimann, M. & Mçßle, T. (2010). Prevalence and risk factors of video game dependency in adolescence: Results of a German nationwide survey. Cyberpsychology, Behavior, and Social Networking, 13, 269 – 277.

163

Rehbein, F. & Mçßle, T. (2012). Risikofaktoren fr Computerspielabhngigkeit: Wer ist gefhrdet? [Risk factors for video game addiction: Who is endangered?]. SUCHT, 58, 391 – 400. Remschmidt, H. (2005). Kinder- und Jugendpsychiatrie. Eine praktische Einfhrung [Psychiatry of Childhood and Adolescence. A Practical Introduction.]. Stuttgart: Thieme. Remschmidt, H. & Walter, R. (2009). Kinderdelinquenz. Gesetzesverstçße Strafunmndiger und ihre Folgen [Child Delinquency. Infringements of Children who Are Not of the Age of Criminal Responsibility and their Consequences.]. Heidelberg: Springer Medizin Verlag. Schlack, R., Hçlling, B. M. & Huss, M. (2007). Die Prvalenz der Aufmerksamkeitsdefizit-/Hyperaktivittsstçrung (ADHS) bei Kindern und Jugendlichen in Deutschland [Prevalence of ADHD of children and adolescents in Germany]. Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 5/6, 827 – 835. Schmidt, J.-H., Drosselmeier, M., Rohde, W. & Fritz, J. (2011). Problematische Nutzung und Abhngigkeit von Computerspielen [Problematic use of and addiction to video games]. In J. Fritz, C. Lampert, J.-H. Schmidt & T. Witting (Eds.), Kompetenzen und exzessive Nutzung bei Computerspielern: Gefordert, gefçrdert, gefhrdet (61st ed.). Berlin: Vistas Verlag. te Wildt, B. T., Putzig, I., Zedler, M. & Ohlmeier, M. D. (2007). Internetabhngigkeit als ein Symptom depressiver Stçrungen [Internet addiction as a symptom of depression disorders]. Psychiatrische Praxis, 34(Suppl. 3), S318–S322. Tejeiro Salguero, R. A. & Bersabe Morn, R. M. (2002). Measuring problem video game playing in adolescents. Addiction, 97, 1601 – 1606. Walsh, D. (2000). Interactive media violence and children – Testimony submitted to the Committee on Commerce, Science and Transportation. Minnesota Medicine, 83, 42 – 46. Weinert, F. E. & Helmke, A. (Eds.). (1997). Entwicklung im Grundschulalter [Development in Primary School Age]. Weinheim: Beltz PVU. Wçlfling, K., Mller, K. W. & Beutel, M. E. (2011). Reliabilitt und Validitt der Skala zum Computerspielverhalten (CSV-S) [Reliability and Validity of the Scale for the Assessment of Pathological Computer-Gaming (CSV-S)]. Psychotherapie Psychosomatik Medizinische Psychologie, 61, 216 – 224.

Thomas Mçßle born 1975, is Vice Director of the Criminological Research Institute of Lower Saxony (KFN) in Hanover where he is responsible for research on media effects. He studied Psychology at the Catholic University of Eichsttt-Ingolstadt where he received his doctorate (“Zur Entwicklung des Personenwiedererkennens“ ) in the Department of Developmental and Educational Psychology in 2005. He has been a research associate at the KFN since April 2005. In 2012 he received the authorization to teach psychology at the University of Hildesheim. Florian Rehbein born 1977, is a research associate at the Criminological Research Institute of Lower Saxony (KFN) in Hanover, where he leads the project Video game and Internet addiction. He studied Social Education at SUCHT 59 (3) 2013 Verlag Hans Huber, Hogrefe AG, Bern

164


PD Dr. Thomas Mçßle

the University of Applied Sciences in Bremen from 1996 to 1998 and Psychology at the University of Bremen from 1998 to 2004. In 2010 he received his PHD in Psychology from the University of Hildesheim for his experimental work on the cognitive effects of violent screen media. He currently conducts research on the diagnostics, prevalence and the causes and consequences of video game and Internet addiction. He has additional research interests related to other behavioral addictions, epidemiological research methods and screening tools, criminal psychology and prevention of mental health problems in adolescents.


Kriminologisches Forschungsinstitut Niedersachsen e.V. Criminological Research Institute of Lower Saxony Ltzerodestr. 9 30161 Hannover Germany Tel.: + 49 (0) 511 34836-75 Fax: + 49 (0) 511 34836-10 [email protected]

Submitted: 29.11.2012 Accepted after revision: 16.04.2013