Longitudinal and Reciprocal Relations of Cyberbullying With Depression, Substance Use, and Problematic Internet Use Among Adolescents
TL;DR: CB is predictive of some significant psychological and behavioral health problems among adolescents and intervention efforts should pay attention to these in the prevention and treatment of consequences of CB.
About: This article is published in Journal of Adolescent Health.The article was published on 2013-10-01. It has received 413 citations till now.
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TL;DR: Using structural equation modeling, it was found that both FOMO and SNI mediate the link between psychopathology and CERM, but by different mechanisms, and for girls, feeling depressed seems to trigger higher SNS involvement.
480 citations
TL;DR: In this paper, a systematic literature review is presented to map the longitudinal research in the field of Internet Use and Problematic Internet Use (PIU) in adolescents and emergent adults.
Abstract: The aim of this systematic literature review is to map the longitudinal research in the field of Internet Use (IU) and Problematic Internet Use (PIU) in adolescents and emergent adults. Further, th...
403 citations
Cites background from "Longitudinal and Reciprocal Relatio..."
...Adolescents who were cyber-bullied used the Internet significantly more than their peers who were not subjected to online bullying, while individuals who were both cyber-bullied and acted as cyber-bullies themselves, reported higher IU than those who were solely bullied over the same six-month period (Gámez-Guadix et al., 2013)....
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...The research also suggests different aspects of peer relationships and use of the Internet for social purposes can be predictive and protective of PIU behaviours (Chen et al., 2015; Gámez-Guadix et al., 2013; Thorsteinsson & Davey, 2014; Willoughby, 2008)....
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TL;DR: An overview of social aspects of depression using the NIMH Research and Domain Criteria 'Systems for Social Processes' as a framework describes the bio-psycho-social interplay regarding impaired affiliation and attachment, impaired social communication, and impaired social perception.
380 citations
Cites background from "Longitudinal and Reciprocal Relatio..."
...Cyberbullying victimization led to an increase in depressive symptoms, which in turn increased the probability of cyberbullying (Gámez-Guadix et al., 2013)....
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Posted Content•
TL;DR: In this article, the authors examined the nature and prevalence of such risks, and evaluated the evidence regarding the factors that increase or protect against harm resulting from these risks, so as to inform the academic and practitioner knowledge base.
Abstract: Aims and scope: The usage of mobile phones and the internet by young people has increased rapidly in the past decade, approaching saturation by middle childhood in developed countries. Besides many benefits, online content, contact or conduct can be associated with risk of harm; most research has examined whether aggressive or sexual harms result from this. We examine the nature and prevalence of such risks, and evaluate the evidence regarding the factors that increase or protect against harm resulting from such risks, so as to inform the academic and practitioner knowledge base. We also identify the conceptual and methodological challenges encountered in this relatively new body of research, and highlight the pressing research gaps. Methods: Given the pace of change in the market for communication technologies, we review research published since 2008. Following a thorough bibliographic search of literature from the key disciplines (psychology, sociology, education, media studies and computing sciences), the review concentrates on recent, high quality empirical studies, contextualizing these within an overview of the field. Findings: Risks of cyberbullying, contact with strangers, sexual messaging (‘sexting’) and pornography generally affect fewer than one in five adolescents. Prevalence estimates vary according to definition and measurement, but do not appear to be rising substantially with increasing access to mobile and online technologies, possibly because these technologies pose no additional risk to offline behaviour, or because any risks are offset by a commensurate growth in safety awareness and initiatives. While not all online risks result in self-reported harm, a range of adverse emotional and psychosocial consequences is revealed by longitudinal studies. Useful for identifying which children are more vulnerable than others, evidence reveals several risk factors: personality factors (sensation-seeking, low self-esteem, psychological difficulties), social factors (lack of parental support, peer norms) and digital factors (online practices, digital skills, specific online sites). Conclusions: Mobile and online risks are increasingly intertwined with pre-existing (offline) risks in children's lives. Research gaps, as well as implications for practitioners, are identified. The challenge is now to examine the relations among different risks, and to build on the risk and protective factors identified to design effective interventions.
302 citations
TL;DR: Findings indicate that self-esteem and empathy oriented interventions may successfully address cyberbullying behaviour.
296 citations
References
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Book•
21 Jul 2011
TL;DR: Structural Equation Models: The Basics using the EQS Program and testing for Construct Validity: The Multitrait-Multimethod Model and Change Over Time: The Latent Growth Curve Model.
Abstract: Psychology is a science that advances by leaps and bounds The impulse of new mathematical models along with the incorporation of computers to research has drawn a new reality with many methodological progresses that only a few people could imagine not too long ago Such progress has no doubt revolutionized the panorama of research in the behavioral sciences Structural Equation Models are a clear example of this Under this label are usually included a series of state-of-the-art multivariate statistical procedures that allow the researcher to test theoryguided hypotheses with clearly confi rmatory ends as well as to establish causal relations among variables Confi rmatory factor analysis, the study of measurement invariance, or the multitraitmultimethod models are some of the procedures that stem from this methodology In this sense, it would be diffi cult to fi nd a scientifi c journal that publishes empirical works in psychology that does not address some of these issues, so their current transcendence is undeniable The manual written by the Full Professor of the University of Ottawa, Barbara M Byrne, is a link in a series of books that address this topic Throughout her long academic trajectory, Professor Byrne developed interesting and popular work focused on bringing the researcher and the professional layman—and not so layman—closer to the diverse statistical programs available on the market for data analysis from the perspective of structural equation models (ie, LISREL, AMOS, EQS) (Byrne, 1998, 2001, 2006) Bearing this in mind, the main goal of this work is to introduce the reader to the basic concepts of this methodology, in a simple and entertaining way, avoiding mathematical technicisms and statistical jargon For this purpose, we used the statistical program Mplus 60 (Muthen & Muthen, 2007-2010), an extremely suggestive software that incorporates interesting applications The authoress provides a practical guide that leads the reader through illustrative examples of how to proceed step by step with the Mplus, from the initial specifi cations of the model to the interpretation of the output fi les On the one hand, we underline that the data used proceed from prior investigations and can be consulted in the Internet, offering the reader the possibility of practicing with them (http://wwwpsypresscom/sem-with-mplus/ datasets/); on the other hand, updating the information with novel and apt bibliographic references allows the reader to study in more depth the diverse topics that are presented in the manual, if he or she so desires The book consists of four sections, with a total of 12 chapters The fi rst section, Chapters 1 and 2, addresses introductory terms related to structural equation models and working with the Mplus program at a user-level The second unit focuses on data analysis with a single group In Chapter 3, the factor validity of the self-concept is tested by means of confi rmatory factor analysis In Chapter 4, the authoress performs a fi rst-order confi rmatory factor analysis, in which she examines the validity of the scores of the Maslach Burnout Inventory (MBI) in a sample of teachers In Chapter 5, the internal structure of the scores on the Beck Depression Inventory-II is analyzed by means of second-order confi rmatory factor analysis in a sample of Chinese adolescents In the next chapter, the complete model of structural equations is tested, and the authoress examines the causal relation established between diverse variables (ie, work climate, self-esteem, social support) and Burnout The third section of the manual is, in my opinion, the most interesting, not only because of the expansion of the study of measurement invariance in recent years but also because of the expansion it may possibly have in the future In this section, Professor Byrne goes into multigroup comparisons Specifi cally, in Chapter 7, she examines the factor equivalence of the MBI in two samples of teachers by means of the analysis of covariance structures In this chapter, she introduces relevant concepts, such as types of invariance (confi gural, metric, and strict) or the invariance of partial measurement In Chapter 8, she also analyzes measurement invariance, using for this purpose the analysis of mean and covariance structures This analysis, in comparison to the analysis of covariance structures, allows contrasting the latent means of two or more groups With this goal, she verifi es whether there is measurement invariance between the scores on the Self-description Questionnaire-I in Nigerian and Australian adolescents In Chapter 9, she proposes a complete model of structural equations in which she tests the causal structure through the procedure of cross validation Lastly, in the fourth section, she reveals three very interesting topics, that are also up-to-date and that, to some degree, go beyond the initial goal of the book, such as the multitrait-multimethod models, latent growth curves, and multilevel models Summing up, the work “Structural Equation Modeling with Mplus: Basic concepts, applications, and programming” is of enormous interest and utility for all professionals of psychology and related sciences who, without having exhaustive knowledge of the details of structural equation models, wish to test their hypothetical models by means of the Mplus program No doubt, this is a reference manual, a must-read that is accessible and that has a high degree of methodological rigor We hope that the readers
16,616 citations
"Longitudinal and Reciprocal Relatio..." refers background or methods in this paper
...Table 2 presents the descriptive statistics (mean and SD) and the Pearson correlations for the variables in this study....
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...The adolescents filled in the questionnaires in their classrooms....
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Book•
01 Nov 2000TL;DR: In this article, the EQS program is used to test the factorial verifiability of a theoretical construct and its invariance to a Causal Structure using the First-Order CFA model.
Abstract: Contents: Part I: Introduction. Structural Equation Models: The Basics. Using the EQS Program. Part II: Single-Group Analyses. Application 1: Testing for the Factorial Validity of a Theoretical Construct (First-Order CFA Model). Application 2: Testing for the Factorial Validity of Scores From a Measuring Instrument (First-Order CFA Model). Application 3: Testing for the Factorial Validity of Scores from a Measuring Instrument (Second-Order CFA Model). Application 4: Testing for the Validity of a Causal Structure. Part III: Multiple-Group Analyses. Application 5: Testing for the Factorial Invariance of a Measuring Instrument. Application 6: Testing for the Invariance of a Causal Structure. Application 7: Testing for Latent Mean Differences (First-Order CFA Model). Application 8: Testing for Latent Mean Differences (Second-Order CFA Model). Part IV: Other Important Topics. Application 9: Testing for Construct Validity: The Multitrait-Multimethod Model. Application 10: Testing for Change Over Time: The Latent Growth Curve Model. Application 11: Testing for Within- and Between-Level Variance: The Multilevel Model.
13,439 citations
TL;DR: In this paper, Satorra and Bentler's scaling corrections are used to improve the chi-square approximation of goodness-of-fit test statistics in small samples, large models, and nonnormal data.
Abstract: A family of scaling corrections aimed to improve the chi-square approximation of goodness-of-fit test statistics in small samples, large models, and nonnormal data was proposed in Satorra and Bentler (1994). For structural equations models, Satorra-Bentler's (SB) scaling corrections are available in standard computer software. Often, however, the interest is not on the overall fit of a model, but on a test of the restrictions that a null model sayM
0 implies on a less restricted oneM
1. IfT
0 andT
1 denote the goodness-of-fit test statistics associated toM
0 andM
1, respectively, then typically the differenceT
d
=T
0−T
1 is used as a chi-square test statistic with degrees of freedom equal to the difference on the number of independent parameters estimated under the modelsM
0 andM
1. As in the case of the goodness-of-fit test, it is of interest to scale the statisticT
d
in order to improve its chi-square approximation in realistic, that is, nonasymptotic and nonormal, applications. In a recent paper, Satorra (2000) shows that the difference between two SB scaled test statistics for overall model fit does not yield the correct SB scaled difference test statistic. Satorra developed an expression that permits scaling the difference test statistic, but his formula has some practical limitations, since it requires heavy computations that are not available in standard computer software. The purpose of the present paper is to provide an easy way to compute the scaled difference chi-square statistic from the scaled goodness-of-fit test statistics of modelsM
0 andM
1. A Monte Carlo study is provided to illustrate the performance of the competing statistics.
4,011 citations