Fluent Essays

Assistance with this assignment  Note from instructor: I would suggest putting thoughts in your own words by paraphrasing, summarizing, and putting ideas into your own words. Changing a few words in a sentence wont get it. Vol.:(0123456789)1 3 European Child & Adolescent Psychiatry (2020) 29:839–847 https://doi.org/10.1007/s00787-019-01398-2 O R I G I N A L CO N T R I B U T I O N Time spent gaming and psychiatric symptoms in childhood: cross‑sectional associations and longitudinal effects Frode Stenseng1,2  · Beate Wold Hygen3 · Lars Wichstrøm3,4 Received: 26 March 2019 / Accepted: 30 August 2019 / Published online: 6 September 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract There is sparse knowledge on how the amount of gaming overlaps with—and is longitudinally related to—psychiatric symp- toms of ADHD and emotional problems throughout early and middle childhood. In this prospective study of 791 Norwegian children, we investigated the amount of electronic gaming at ages 6, 8, and 10 while also measuring DSM symptoms of such disorders. Cross-lagged longitudinal analyses showed that more ADHD symptoms at age 8 predicted more gaming at age 10, whereas gaming did not predict more psychiatric symptoms, controlled for gender and socio-economic status. Cross- sectional overlaps between gaming and symptoms were marginal but nonetheless increased with each age level. Hence, time spent gaming did not forecast more psychiatric problems at these ages, but children with more ADHD symptoms were more likely to increase their amount of gaming throughout middle childhood. Results indicate that the sheer amount of gaming is not harmful to children’s mental health, but that poorly regulated children become more attracted to games throughout childhood. Findings are discussed in light of the coexistence of problematic gaming and psychiatric problems reported among adolescents and adults, as well as the potential beneficial psychological outcomes from gaming. Keywords ADHD · Internet gaming disorder · Cross-lagged analyses · Structural equation modeling · Community sample · Video games Introduction The majority of 10-year-olds spend more than 1 h per day playing digital games, both in Europe [41] and in the United States [13, 42]. At the same time, little is known with regards to whether the amount of gaming may alter children’s psy- chological development, and concerns have been raised that gaming may negatively impact young children’s cognition and temperament [22]. It is also important to identify factors that make some children more vulnerable to develop a patho- logical pattern of gaming, including excessive time used on gaming, so that these children can be protected from gaming addiction and the problems emerging from it [35]. The number of reports from this field is growing, for example finding cross-sectional links between time spent gaming and impulsivity [14] and self-harm [36], and lon- gitudinally, that more video game exposure predicts more attention problems and impulsivity traits [15, 43]. On the social level, studies have found that more gaming is cross- sectionally associated with more conduct problems [5] and predicts more loneliness over a 6-month period [26]. It has also been shown that boys with attention-deficit/hyperactiv- ity disorder (ADHD) are more at risk for developing prob- lematic video game use than their non-diagnosed peers [29]. It seems plausible that children with limited self-regulation resources—in particular—may be attracted to such games because of the sense of control that emerges when gaming, as well as the reward systems implemented in such games [28]. Playing digital games may provide pleasurable states of flow [10] or a state of escape [3, 38], which is related to cog- nitive narrowing, reduced self-awareness, and diminished * Frode Stenseng [email protected] Beate Wold Hygen [email protected] Lars Wichstrøm [email protected] 1 Department of Education and Lifelong Learning, NTNU, Trondheim, Norway 2 Queen Maud University College, Trondheim, Norway 3 NTNU Social Research, Trondheim, Norway 4 Department of Psychology, NTNU, Trondheim, Norway http://orcid.org/0000-0002-6581-1133 http://crossmark.crossref.org/dialog/?doi=10.1007/s00787-019-01398-2&domain=pdf 840 European Child & Adolescent Psychiatry (2020) 29:839–847 1 3 negative affect. Such a state of immersion [16] maybe par- ticularly valued among children with higher levels of nega- tive self-perceptions, as found frequently among children with ADHD [4]. Moreover, the positive effects of immersion or escapism through gaming has been stressed by several authors as understated in the discussion around the inclusion of internet gaming disorder in DSM-5 and ICD-11 [21, 44]. Notably, building from the technical possibilities in digital games, studies have also tested whether video game-based interventions can improve real-life abilities among adults with ADHD, but results are per now mixed [40]. ADHD may lead to more gaming, but the opposite causal direction—that excessive gaming may lead to, or elevate, ADHD symptoms in early childhood—has also found some support [34]. Theoretically, this latter outcome is based on the idea that gaming offers stimuli at a fast pace, also typi- cal of TV series and movies for children. According to the scan-and-shift hypothesis [20], exposure to media with high informative pace leads children to adopt a high-speed mode of attentional focus. This alteration of cognition may hinder children’s ability to develop sustained attention. Likewise, it has been suggested that gaming and other types of media, through their intense cognitive stimulation, may habituate children to elevated levels of arousal, so that environments with low stimulation provoke restlessness [25]. The easiest way to uphold such arousal may be through sustained and/ or more intense gaming. Taken together, research indicates that children with ADHD are more vulnerable to spend substantially more time on gaming than their peers, and conversely, that increased gaming may cause more ADHD symptoms. It also seems conceivable that such a reciprocal relationship depends on a threshold of the amount of gaming, so that a mutually rein- forcing relationship is evident among “dedicated” gamers, but not among “casual” gamers [18]. This was also indi- cated in a large community study in the USA on 16-year-olds [11], finding less negative socio-emotional outcomes among those who gamed an average amount, compared to those who gamed most and those who did not game at all. Emotional problems have also been linked to excessive gaming, although mainly among adolescents and adults. Brunborg et al. [5] found in a community sample of ado- lescents in Norway that gaming addiction was associated with higher levels of self-reported depression, whereas the amount of gaming in itself was unrelated to such problems. Likewise, Mentzoni et al. [32] reported that problematic gaming was related to lower life satisfaction and higher levels of anxiety and depression in a nationwide sample in Norway. Furthermore, Mehroof and Griffiths [31] reported in a sample of the university student in the United King- dom that problematic gaming was related to both trait and state anxiety. Also relevant, in a study on users of massively multiplayer online games, Cole and Hooley [7] found that participants with higher gaming addiction had higher levels of social phobia, state and trait anxiety, and neuroticism. A recent study on Norwegian 10-year-olds [45] found that poor emotion regulation skills were associated with Inter- net gaming disorder, which points toward the comorbidity seen between emotional problems and pathological gam- ing among adolescents and adults. Another recent study by Coyne et al. [9] found that time spent on violent video games at age fourteen predicted less empathetic concerns for others and less benevolence two years after, supporting the notion that gaming may undermine the development of social skills. There is empirical evidence that excessive gaming is cor- related with regulation deficiencies and emotional problems. Nevertheless, studies investigating the time relationship between gaming and such problems are lacking. This gap of knowledge on the long-term relationship between gaming and mental health in early life is partly due to the fact that studies in the field often have one or more of the following limitations: (a) have used cross-sectional designs; (b) have used trait-based measures as opposed to mapping psychiatric symptoms from a diagnostic interview, and also; (c) have predominantly been conducted on adolescents (aged 12–18) and not prepubertal children, who presumably are more susceptible to the potential effects from gaming. However, at least two studies are notable exceptions. First, Gentile et al. [15] followed more than 2000 Singaporean children and adolescents over 3 years in three waves of data collec- tions, while measuring self-reported time spent gaming and ADHD symptoms (inattention and hyperactivity). Measures of gaming and symptoms were included at Wave 2 and 3, with mean ages of approximately 12 and 13 years, and data were analyzed in reciprocal models. Analyses showed that time spent gaming predicted more attention and impulsiv- ity symptoms, and such symptoms predicted more gaming. In other words, a bidirectional relationship was evidenced at these ages. Also notable, Lemmens et  al. [26]—using longitudinal data from a Dutch sample of adolescents— investigated reciprocal relationships between pathological gaming and several psychosocial constructs. Specifically, they found that low social competence and self-esteem, and more loneliness, predicted pathological gaming 6 months later. They also reported that more pathological gaming pre- dicted more loneliness during the same time period. Time spent gaming, however, did not predict such outcomes in their study. Despite the fact that social competence, self- esteem, and loneliness are moderately overlapping with ADHD symptoms and emotional problems, these factors are known to forestall psychiatric symptoms (e.g., [39]). Most importantly, both the Gentile et al. study and Lemmens et al. study conveyed that gaming and psychological development existed in a bidirectional relationship in adolescence, beg- ging the following question: Is such an interplay evident at 841European Child & Adolescent Psychiatry (2020) 29:839–847 1 3 earlier ages? And accordingly, does it apply to both dedi- cated and casual gamers? In the current prospective study including 791 Norwegian children, we investigated cross-sectional associations and the longitudinal interplay of the amount of time spent on screen games and symptoms of ADHD, and emotional prob- lems (depression and anxiety) from age 6 through age 8, and up to age 10. More specifically, in a reciprocal model, we tested the extent to which gaming at age 6 and 8 predicted more symptoms at age 8 and 10, and likewise, whether more symptoms at age 6 and 8 predicted more gaming at age 8 and 10, controlling for their respective subsequent levels of gam- ing and symptoms, as well as gender and socio-economic status. We also tested to what extent significant longitudi- nal effects were evident both among casual and dedicated gamers. Methods Participants and procedure The present study is based on data from the second, third, and fourth wave of the Trondheim Early Secure Study (for a comprehensive description, please see [37]. The first wave of this study was conducted in 2007 and 2008 (T1) and included participants from two birth cohorts of children in Trondheim, Norway (born in 2003 or 2004). The project has been approved—for each wave of data collection—by the Regional Committee for Research Ethics, Mid-Norway (www.etikk om.no, REK 4.2008.2632). There were 1250 Norwegian-speaking children in the birth register for 2003 and 2004 in Trondheim, and all were recruited to participate in the study. Of these nine-hundred and five children were tested at the time of study enroll- ment (mean age = 4.55 years; 50.6\% boys). At T1, 81\% of the children were accompanied by their biological mother in the clinic, and more than 99\% of the children were of West- ern ethnic origin, and 86\% of their parents lived together. Socio-economic status was assessed through the maternal educational level at T1 (5 categories: lower secondary, upper secondary, vocational school, college, and university). Approximately 2 years later, a total of 752 children par- ticipated in the follow-up assessment (T2), resulting in a lon- gitudinal participation rate of 75.6\% (mean age = 6.72 years; 50.5\% boys). At the second follow-up (T3), 661 children participated (mean age = 8.8 years; 48.7\% boys), which cor- responded to a participation rate of 87.9\%, and at the third follow-up, 699 children participated (T1–T3 participants were recruited to T4). The retention rate in the data used in the present study, from the first wave (T2) to the last wave (T4), was 86.7\% (including subjects participating at T2, missing at T3, but returning at T4). Measures Digital gaming At age 6 and 8, parents estimated the amount of time their child spent on digital games on different platforms, such as computers and tablets. They were asked to report how many days per week their child spent gaming and the average amount of hours and minutes per day when gaming, resulting in a measure of hours of gaming per day. At age 10, the children reported their gaming the last 30 days at a very specific level by means of an interview (typical amount of gaming i.e., before school, after school but before dinner, after dinner, and after bedtime, with adapted classifications for weekends, i.e. excluding before/ after school). The average amount of hours of gaming per day was then computed. ADHD symptoms The Preschool Age Psychiatric Assessment/Child and Ado- lescent Psychiatric Assessment (PAPA/CAPA; [1, 12]) is a semi-structured diagnostic interview developed for assessing DSM-IV diagnoses [2]. The interview follows a structured protocol using parents as informants. According to the DSM- IV and DSM-5, we computed one Hyperactivity-Impulsivity score and one Inattention score consisting of their respec- tive symptoms. Interviewers (n = 7) had at least a bachelor’s degree in a relevant field and had been trained by the team that developed the PAPA/CAPA. To calculate the interrater reliability, the audio of 9\% of the interviews was recorded by pairs of blinded raters. The reliability (intraclass correla- tions) for ADHD among multiple pairs of blinded raters was 0.96 for the PAPA and 0.90 for the CAPA. Emotional problems As with ADHD, the PAPA/CAPA [1, 12] was used to meas- ure emotional problems. Because the core aspects of emo- tional problems, depression and anxiety, are highly over- lapping in childhood [8], we created a latent construct of symptoms of depression and generalized anxiety from the PAPA/CAPA interview. We also tested the factorial validity of a second order latent factor intended to measure emo- tional problems, including for example social anxiety, but this model did not converge due to low occurrence of such symptoms in our sample. However, a latent factor consist- ing of the sum of depression symptoms and the sum of http://www.etikkom.no 842 European Child & Adolescent Psychiatry (2020) 29:839–847 1 3 generalized anxiety symptoms yielded sufficient factorial validity (see Fig. 1). Results Preliminary analyses, including mean-level differences between times of measurement and bivariate correlations, are presented first. Analyses of the structural equation model follows, first to test whether cross-lagged effects of gaming and symptoms from age 6 to age 10 were identified in the total sample, and then to determine whether children who gamed more/less than average displayed different paths in the model. Descriptives The amount of gaming increased substantially from age 6 (M = 35.7 min) to age 8 (M = 67.5 min; t = 11.42, p < 0.001), and from age 8 to age 10 (M = 100.1 min; t = 6.96, p < 0.001). Hyperactivity-impulsivity symptoms decreased from age 6 (M = 0.84) to age 8 (M = 0.61; t = 2.98, p < 0.01), but not sig- nificantly from age 8 to age 10 (M = 0.49; t = 1.63, p = 0.10). Inattentiveness symptoms did not increase significantly from age 6 (M = 0.80) to age 8 (M = 0.91; t = 1.35, p = 0.18) and remained stable from age 8 to age 10 (M = 0.92). Depression symptoms did decrease significantly from age 6 to age 10 (age 6: M = 0.58; age 8: M = 0.29; age 10: M = 0.36; t = 5.35, p < 0.001), whereas anxiety symptoms increased signifi- cantly (age 6: M = 0.45; age 8: M = 0.62; age 10: M = 0.81; t = 7.19, p < 0.001). Socio-economic status was uncorrelated to gaming at all three-time points (r’s ranging from − 0.08 to 0.07, p > 0.01), but significantly related to some of the DSM-measures (r’s ranging up to 0.12, p < 0.01). Correla- tion analyses of study variables are shown in Table 1. Longitudinal cross‑lagged analyses In order to control for repeated-measures invariance of symptoms, corresponding sums of symptoms were allowed to correlate between time points. All structural analyses were performed using the maximum likelihood estimator (MLR). Missing values were treated according to the full informa- tion maximum likelihood procedure. Judgment of model fit was made according to the recommendations of Hu and Bentler [19], see also [27]. Regarded as reasonable indica- tors of good fit of a model are values of the comparative fit index (CFI) and the Tucker-Lewis index (TLI) close to 0.95, and values of the root mean squared error of approximation (RMSEA) and the standardized root mean squared residual (SRMR) less than 0.06 and 0.08, respectively. Analyses of the autoregressive cross-lagged model were performed in Mplus 7.1. (Muthén & Muthén, 2012). In addition, gaming, ADHD symptoms, and emotional problems at age 8 were auto-regressed on the identical measures at age 6. Fig. 1 Structural equation model illustrating stability and reciprocal effects of gaming, ADHD symptoms, and emotional problems at ages 6, 8, and 10 in the total sample. Cross-sectional correlations are not shown. *p < .0.05. **p < .0.01 843European Child & Adolescent Psychiatry (2020) 29:839–847 1 3 Ta bl e 1 M ea ns , s ta nd ar d de vi at io ns , a nd z er o- or de r c or re la tio ns fo r s tu dy v ar ia bl es N = 6 33 –7 91 (d iff er s du e to v ar yi ng p ar tic ip at io n ra te s) *p < .0 .0 5. * *p < 0 .0 1 a 1 , b oy s; 2 , g ir ls M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1. G en de ra 1. 49 0. 50 – 2. T im e ga m in g, a ge 6 0. 36 0. 41 −  0 .2 7* * – 3. H yp er -I m pu ls iv ity , a ge 6 0. 84 1. 49 −  0 .1 1* * 0. 03 – 4. In at te nt iv en es s, a ge 6 0. 80 1. 50 −  0 .1 0* * 0. 05 0. 57 ** – 5. D ep re ss io n, a ge 6 0. 58 0. 90 −  0 .0 6 0. 06 0. 27 ** 0. 37 ** – 6. G en er al a nx ie ty , a ge 6 0. 45 0. 75 −  0 .0 9* 0. 10 ** 0. 38 ** 0. 44 ** 0. 59 ** – 7. T im e ga m in g, 8 y ea rs 0. 68 0. 65 −  0 .2 4* * 0. 17 ** 0. 03 0. 08 * 0. 04 0. 10 * – 8. H yp er -I m pu ls iv ity , a ge 8 0. 61 1. 50 −  0 .1 4* * 0. 05 0. 54 ** 0. 48 ** 0. 23 ** 0. 31 ** 0. 04 – 9. In at te nt iv en es s, a ge 8 0. 91 1. 65 −  0 .1 5* * 0. 06 0. 49 ** 0. 55 ** 0. 32 ** 0. 38 ** 0. 12 ** 0. 64 ** – 10 . D ep re ss io n, a ge 8 0. 29 0. 62 −  0 .0 6 0. 01 0. 17 ** 0. 27 ** 0. 34 ** 0. 24 ** 0. 06 0. 26 ** 0. 37 ** – 11 . G en er al a nx ie ty , a ge 8 0. 62 0. 95 −  0 .0 3 −  0 .0 1 0. 33 ** 0. 41 ** 0. 30 ** 0. 35 ** 0. 01 0. 50 ** 0. 51 ** 0. 43 ** – 12 . T im e ga m in g, a ge 1 0 1. 00 1. 08 −  0 .2 0* * 0. 17 ** 0. 07 0. 09 * 0. 05 0. 06 0. 30 ** 0. 10 ** 0. 15 ** 0. 01 0. 08 * – 13 . H yp er -I m pu ls iv ity , a ge 1 0 0. 49 1. 34 −  0 .1 0* * 0. 09 * 0. 51 ** 0. 42 ** 0. 18 ** 0. 23 ** − 0 .0 1 0. 61 ** 0. 51 ** 0. 22 ** 0. 39 ** 0. 09 * – 14 . I na tte nt iv en es s, a ge 1 0 0. 92 1. 68 −  0 .1 9* * 0. 08 0. 40 ** 0. 52 ** 0. 25 ** 0. 28 ** 0. 11 ** 0. 43 ** 0. 62 ** 0. 30 ** 0. 39 ** 0. 17 ** 0. 62 ** – 15 . D ep re ss io n, a ge 1 0 0. 36 0. 68 0. 01 −  0 .0 5 0. 13 ** 0. 25 ** 0. 26 ** 0. 17 ** 0. 03 0. 14 ** 0. 22 ** 0. 32 ** 0. 25 ** 0. 05 0. 15 ** 0. 30 ** – 16 . G en er al a nx ie ty , a ge 1 0 0. 81 1. 15 −  0 .1 1* * 0. 01 0. 30 ** 0. 36 ** 0. 25 ** 0. 29 ** 0. 05 0. 35 ** 0. 39 ** 0. 24 ** 0. 39 ** 0. 10 * 0. 41 ** 0. 47 ** 0. 47 ** – 844 European Child & Adolescent Psychiatry (2020) 29:839–847 1 3 The cross-lagged model was first tested on the total sample (see Fig. 1). The model had good fit with the data: χ2(54, N = 791) = 84.80, p < 0.001, CFI = 0.98, TLI = 0.97, RMSEA = 0.027, SRMR = 0.027. The stability of ADHD symptoms was high (betas were 0.84 and 0.78, both p < 0.01, at age 8 and 10, respectively). The stability of emotional problems was moderate for emotional problems (betas were 0.25, p < 0.05, and 0.39, p < 0.01), as well as for gaming (betas were 0.13, p < 0.01, and 0.35, p < 0.01). The latent measure of ADHD symptoms was significantly correlated with the measure of emotional problems at all three-time points (age 6: r = 0.60; age 8: r = 0.55; age 10: r = 0.34, all p’s < 0.01). The amount of gaming was significantly corre- lated with emotional problems at age 6 (r = 0.13 p < 0.05), but not significantly cross-sectionally correlated to the other measures of ADHD symptoms and emotional problems in the model. Because there have been mixed results regarding the effect of gaming on mental health, and longitudinal studies in early childhood are lacking, we examined the effects of gaming on ADHD symptoms and emotional problems in the model. No such negative effects were found in the present study. More ADHD symptoms and emotional problems at age 6 did not predict more gaming at age 8, but more ADHD symptoms at age 8 predicted more gaming at age 10 (β = 0.16 p < 0.01). There was no such effect involving symptoms of emotional disorders. Then, because gender was correlated with gaming as well as ADHD and emotional problems, we reran the analyses with gender as a covari- ate. This did not impose any substantial changes in model fit or in the paths, and the effect of ADHD symptoms at age 8 effect on age 10 gaming remained (β = 0.12 p < 0.05). Mediation was also tested, based on the idea that the effect of gender on gaming may have been mediated by ADHD symptoms, and vice versa, but there was no evidence for this (gender → gaming at age 6 → ADHD at age 8, z = − 0.07, p = 0.44; gender → ADHD at age 6 → gaming at age 8, z = − 0.013, p = 0.59). Notably, we also run a model controlling for socio-economic status at T1, which slightly attenuated the effect of ADHD-symptoms at age 8 on more gaming at age 10, but the main finding remained (β = 0.14 p < 0.05). Finally, we tested the idea that the interplay of variables in the model may be different for those children who game substantially more than their peers. To test this potential non-linear distribution, we added new variables computed from the mean-centered square sums of gaming at age 6 and at age 8, separately, which then were included in the model as predictors of the relevant outcomes (predicting the effect of the residuals of the linear regression effect). If these vari- ables contributed significantly in predicting the outcomes, this would indicate the existence of a non-linear effect in the amount of gaming (see e.g. [33]). First, when we added the squared interaction variable for gaming at age 6 on age 8 variables; this did not have a significant effect (Gaming T12 → ADHD symptoms at age 8: β = − 0.07 p = 0.20; Gam- ing T12 at age 6 → emotional symptoms at age 8: β = 0.02 p = 0.69). Second, when we included the corresponding ×2 variable for gaming at age 8 as a predictor; it did not have a significant effect on ADHD symptoms at age 10 (β = 0.04, p = 0.30) or emotional problems at age 10 (β = − 0.02, p = 0.63). Hence, specific analyses did not identify a differentiated effect for ADHD symptoms among dedicated versus casual gamers [18]. The pattern was, however, clear with regards to the direction of effects in the total sample: ADHD symp- toms predicted more gaming, but gaming did not predict more ADHD symptoms. Thus, there was no evidence for a reciprocal—or mutually reinforcing—relationship between ADHD symptoms and gaming. Discussion In this longitudinal study in a large community sample, we tested the reciprocal relationships of digital gaming, symp- toms of ADHD, and emotional problems. More ADHD symptoms at age 8 predicted more gaming at age 10, but this effect was not found from ages 6 to 8. Digital gaming did not predict more ADHD symptoms at any age, and there were no prospective bi-directional relationships between gaming and symptoms of emotional disorders. Previous studies have found that gaming is cross-section- ally correlated with poor self-regulation, including attention problems and impulsivity [15, 43], but only among older children. The present results are partly in line with these findings in that gaming was related to more general anxi- ety and inattentiveness in the bivariate correlation analyses. Moreover, this link was nonsignificant at age 6, but signifi- cant and stronger at age 10 than age 8, indicating that this association may increase with age. This also points to the possibility that specific elements [23] of inattentiveness may be affected by the amount of gaming, although the determi- nation of such underlying elements was beyond the scope of this study. Empirical studies have advocated that ADHD is a risk factor for developing pathological gaming [29], and that gaming may lead to more ADHD symptoms in early child- hood [34]. Both of these assertions were supported by Gen- tile et al. [15] in their study of Singaporean adolescents, which found a bidirectional relationship between time spent gaming and self-reported ADHD symptoms. In the present study, the only assertion supported was the one suggest- ing that more ADHD symptoms are a risk factor for more gaming. Hence, as gaming did not predict ADHD symp- toms, a bi-directional relationship was not evidenced at this 845European Child & Adolescent Psychiatry (2020) 29:839–847 1 3 developmental stage. Accordingly, neither the scan-and-shift hypothesis [20] nor the elevated arousal-hypothesis [25]— two models which postulate that screen use may provoke ADHD-related behavior—were supported. The amount of gaming overlapped cross-sectionally with more anxiety symptoms at ages 6 and 10, but only weakly (both r’s 0.10). However, no cross-lagged effects between gaming and emotional problems were documented in the longitudinal model. Overall, in our sample of prepubertal children, the substantial comorbidity of gaming problems and emotional disorders typically found among adolescents [5] and adults [32] was not evident in terms of time spent gaming. Likewise, emotional problems did not predict more gaming over time, nor did gaming predict more emotional problems. Although there are indices suggesting that gaming may indirectly forecast emotional problems in adolescence (i.e., through more loneliness, [26], there was no direct rela- tionship found in the present study. Several community studies have documented that boys tend to game more than girls [13, 14], and this was also supported in the present study. Boys gamed more than girls at all time points, and the discrepancy between the genders was stable across age groups. In fact, the amount of gaming was almost tripled from age 6 to age 10 in the total sample. This is in accordance with expectations, as many games are designed to capture the interest of older children. As chil- dren get older, they are also typically more autonomous in their time management and hence given more freedom to choose their own activities throughout the day. At the same time, there was great variation in the amount of gaming, which indicates that children choose different paths in their orientation toward gaming. Future studies may investigate how gaming is intertwined with other spare-time activities [17], and perhaps disentangle how and when gaming may become a problem in some children’s lives, in the sense that their interest becomes a source for intra- and/or … PSY 499 Annotated Bibliography Rubric Instructor comments: First and foremost, please follow the organization of material outlined in the grading rubric. Always start with the purpose of the study. This is the normal flow of a research paper. End with limitations/critique and implications for future research. Criterion #1 Please be sure to address the purpose of the study you are reviewing. If there is a hypothesis or research question, please be sure to include it in your own words (summarize). For articles that are case studies or are qualitative research, there may goals and objectives rather than a purpose and hypothesis. Criterion #2 Please address the methods adequately. Discuss the participants and include the demographics, specific material used, and procedures for the study. There should be enough detail in the methods so that the reader could design a replication. For meta-analyses, you may list the number of studies used, the inclusion or exclusion criteria, and the total number of participants. For articles with qualitative analyses there are unique issues that need to be addressed. If there were subjects, they need to be discussed. If the authors used interviews or documents, how where they coded and analyzed? Describe the methods used as clearly as you can. Criterion #3 Describe the results. Were there significant differences between groups or treatments, correlations between variables etc. Be sure to address in a statement what the results mean with regard to past research (does it support or refute it?). Also, please address the significance of the findings. What are the implications for the research (e.g., policy change, educational reform, future research, etc.). NOTE: There are a few areas where students tend to have questions regarding the grading rubric. First, criterion #3, “Results and Significance,” is in regard to the study you are reviewing, not your thesis paper. The rubric does not cover how you are going to use the research, so it does not need to be covered in the annotated bibliography 11 Criterion #4 This criterion addresses strengths and limitations or critique of the article. Limitations are generally discussed in the discussion section of the article. APA guidelines for both qualitative and quantitative articles calls for a discussion of limitations (Cooper, 2020; Levitt, 2019). Look in the discussion section of the article for this information. Remember, no study is perfect. Critique can also be based on theoretical differences or inconsistencies, etc. Please be sure to critically evaluate the article objectively (based on methodology, etc.). Think of the critique as limitations of the study in terms of rigor (threats to internal validity) and generalizability (threats to external validity). Limitations are neither good or bad, do not be judgmental, rather factual, dispassionate, and objective. Some Common Limitations • The article is supported by a for profit organization, such as the pharmaceutical industry. The industry controls the data and what gets published and what doesn’t (potentially biased data). • The subject pool or participant pool was drawn from a convenience sample, is too small, or not representative of the population (potential threat to external validity). • Treatment not having sufficient follow up, such as a 12-week treatment, but no follow-up after 3 or 6 months (does not reflect data on long term effects of treatment). • Insufficient length of the treatment (treatment may have not been sufficient to show effect). • Lack of a control or comparison group in a within group pre-test post-test design. • Using a questionnaire or survey without established reliability and validity. Construct and content validity information can be found in the methods section of the article. • A pilot study that is not based on previous research (insufficient comparison data with other studies available). • Theoretical critique of the study based on the fact that another theory may explain the results. • Subject or participant bias can occur with self-report questionnaires and interviews. Halo effect. 12 • Single subjects experimental designs only have one participant. Without comparing results to other studies, it’s difficult to determine if the results are due to the experimental manipulation or chance. You should draw from your knowledge of Research Methods I & II to address these issues. NOTE: In the case of qualitative studies, you may see the term “transferability,” or do these results transfer to similar situations. Also, rather than rigor, you may see terms like transparency, fidelity, and/or utility. In other words, is the study presented in such a way that you can follow it from start to finish? Are all descriptions adequate? Do they follow from one section to another? Do the results have utility in similar “real world” situations? Potential Threats to Internal Validity (experimental rigor) History (local history) common in cross-sectional designs by age. Some event in one group’s history caused the results, not the treatment. Maturation can occur in longitudinal studies where something in the group’s development causes the results, not the treatment. Testing (listed above) does the test have adequate established reliability and validity. Faulty testing causes the results, not the treatment. Instrumentation, similar to testing has to do with the accuracy of the instruments used to collect the data. If the instruments are not accurate, neither will the results. Statistical regression can occur with test retest studies, scores will tend to cluster around the mean. Selection (covered above) criteria and whether the sample is a convenience sample, stratified sample, etc. Lack of random assignment or matched subjects. Potential Threats to External Validity (generalization) Sample size and demographic composition can impact the results of a study’s ability to generalize to the population. For more on threats to internal and external validity see link below https://web.pdx.edu/~stipakb/download/PA555/ResearchDesign.html https://www.scribbr.com/methodology/internal-vs-external-validity/ NOTE: Your annotated bibliographies are NOT part of your thesis paper. Use the information from the article, do not copy and paste annotated bibliographies together to make up the body of your paper. Consider the annotated bibliographies article summaries only. Criterion #5 Make sure that your annotated bibliographies are 200 - 300 words. Less than 200 words may not cover everything on the grading rubric. Helpful Links The links below will be helpful with setting up your annotated bibliographies https://owl.purdue.edu/owl/general_writing/common_writing_assignments/annotated_bibliograp hies/annotated_bibliography_samples.html This link gives an example for formatting of a book. Please noted that it does not share the same grading criteria for your assignments, so the content is different (see Grading Rubric). http://libguides.enc.edu/writing_basics/annotatedbib/apa This link also provides information on formatting, but does not include a complete criterion for grading your assignment (see Grading Rubric) http://advice.writing.utoronto.ca/types-ofwriting/annotated-bibliography/ This link gives an example of the correct format and some of the criteria that you will be required to include, for a full list of criteria, see the Grading Rubric. https://sites.umuc.edu/library/libhow/bibliography_apa.cfm This website has two examples of an annotated bib in APA format. Note it does not follow the same criteria as our assignment. https://www.youtube.com/watch?reload=9&v=lPhWhRlEWtI Putting an Annotated Bibliography into APA format. This is good example using MS Word. There are a few things to note. First, acceptable formats are Times New Roman, Arial, and Courier 12 font. The other issue presenter forgot the issue number. An issue number should be in parentheses (2) between the volume number and the page numbers. The Assignment Needs During this module, you will submit your third annotated bibliography. This is a clear and concise summary (200 to 300 words) of a journal article, book, or other primary academic source that will be used in your thesis paper. Each submission must also include a brief critique of the source (e.g., how could the study be improved, criticism of the author(s) assertions, ideas for future studies, etc.).