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Psychology of Violence

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Journal scope statement

Psychology of Violence is a multidisciplinary research journal devoted to violence and extreme aggression, including identifying the causes of violence from a psychological framework, finding ways to prevent or reduce violence, and developing practical interventions and treatments.

As a multidisciplinary forum, Psychology of Violence recognizes that all forms of violence and aggression are interconnected and require cross-cutting work that incorporates research from psychology, public health, neuroscience, sociology, medicine, and other related behavioral and social sciences.

Research areas of interest include:

• child maltreatment
• children's exposure to violence
• community violence
• intimate partner violence
• international violence
• sexual violence
• systematic violence against marginalized populations
• workplace violence
• youth violence

Disclaimer: APA and the editors of Psychology of Violence assume no responsibility for statements and opinions advanced by the authors of its articles.

Equity, diversity, and inclusion

Psychology of Violence supports equity, diversity, and inclusion (EDI) in its practices. More information on these initiatives is available under EDI Efforts .

Editor's Choice

One article from each issue of Psychology of Violence will be highlighted as an “ Editor’s Choice ” article. Selection is based on the recommendations of the associate editors, the paper’s potential impact to the field, the distinction of expanding the contributors to, or the focus of, the science, or its discussion of an important future direction for science. Editor's Choice articles are featured alongside articles from other APA published journals in a bi-weekly newsletter and are temporarily made freely available to newsletter subscribers.

Author and editor spotlights

Explore journal highlights : free article summaries, editor interviews and editorials, journal awards, mentorship opportunities, and more.

Prior to submission, please carefully read and follow the submission guidelines detailed below. Manuscripts that do not conform to the submission guidelines may be returned without review.

To submit to the editorial office of Brad J. Bushman, PhD, please submit manuscripts electronically through the Manuscript Submission Portal in Microsoft Word format (.doc or .docx) or LaTex (.tex) as a zip file with an accompanied Portable Document Format (.pdf) of the manuscript file.

Prepare manuscripts according to the guidelines stated below, following the 7 th  edition of the Publication Manual of the American Psychological Association  and the  Psychology of Violence journal requirements.

Submit Manuscript

General correspondence may be directed to the editor’s office .

In addition to addresses and phone numbers, please supply email addresses for use by the editorial office and later by the production office. Most correspondence between the editorial office and authors is handled by email, so a valid email address is important to the timely flow of communication during the editorial process.

Also, please be sure to provide names and contact information for each of your co-authors in the cover letter.

Keep a copy of the manuscript to guard against loss.

If you encounter difficulties with submission, please email the peer review coordinator, Davia Tanelus .

Submission policies

Authors should verify in their cover letter that manuscripts submitted to Psychology of Violence have not been published previously and are not currently under consideration for publication elsewhere. The cover letter should also list any other publications from the same dataset and describe how the manuscript overlaps with and is different from other publications, if any, based on the same dataset. See the APA Publication Manual for a detailed discussion of this issue. The cover letter is not shared with reviewers so this information should not be masked.

Authors of accepted articles will be required to complete APA's Publication Rights form and Full Disclosure of Interests form. All studies involving human participants or animal subjects must also adhere to the Ethical Principles of APA. Authors must disclose any potential conflicts of interest with their research or certify that they have none. All publication forms are available on the Forms for Journals Publication page.

Masked review policy

This journal uses a masked reviewing system for all submissions. The first page of the manuscript should omit the authors’ names and affiliations but should include the title of the manuscript and the date it is submitted. Footnotes containing information pertaining to the authors' identities or affiliations should not be included in the manuscript but may be provided after a manuscript is accepted. Make every effort to see that the manuscript itself contains no clues to the authors’ identities.

Equity, diversity, and inclusion statement

Psychology of Violence recognizes that members of marginalized and minoritized groups experience extremely high levels of violence due to a host of legal, social, cultural, economic, and other structural factors. These marginalized and minoritized groups include (but are not limited to) women, individuals who are LGBTQIA2S+, individuals who are members of marginalized ethnic/racial groups, individuals who are low income, individuals with disabilities, individuals from religious minority groups, and individuals who are impacted by privilege and oppression at the intersection of their identities.  We are committed to reducing biases within violence research and ourselves. Psychology of Violence is also committed to recruiting a diverse editorial team and publishing articles authored by individuals from marginalized and minoritized groups. Psychology of Violence encourages scholarship that uses theoretical perspectives, study designs, study samples, and analytical methods that promote equity by representing diversity and inclusion of marginalized and minoritized groups in the United States and internationally. Authors are encouraged to consider system-level factors that influence individuals’ behavior.

Journal Article Reporting Standards

Authors should review the updated APA Style Journal Article Reporting Standards (JARS) for quantitative, qualitative, and mixed methods research before submitting. These standards offer ways to improve transparency in reporting to ensure that readers have the information necessary to evaluate the quality of the research and to facilitate collaboration and replication. Further resources, including flowcharts, are available on the APA Style Journal Article Reports Standards  page.

Sharing of data and stimulus materials

Psychology of Violence encourages authors of accepted manuscripts to make their materials and data publicly available on a third-party repository, including (but not limited to) APA's data-sharing repository . APA's data sharing policy does not require public posting, so you are free to decide what is best for your project in terms of public data, materials, and conditions on their use. Maintaining participants' anonymity is an important concern that may preclude public sharing of a dataset. However, APA policy does require authors to make their data available to other researchers upon request.

Manuscript types

Psychology of Violence  publishes three types of articles:

• Full-length articles (10,000-word limit)
• Research reports (2,500-word limit)
• Short reports (1,000-word limit)

The word limits exclude the abstract, references, tables, and figures. Psychology of Violence publishes both quantitative and qualitative research. The journal also publishes review articles, including meta-analyses, and theoretical pieces.

Commentaries

Commentaries are considered for publication in Psychology of Violence . There are two types. The first type is when a reader submits an unsolicited comment on an article published in Psychology of Violence . The primary purpose of the commentary would be to provide a meaningful insight, concern, alternative interpretation, clarification, or critical analysis of the original article. It is not intended to simply be a critique of the literature review or basic methodology or statistics (e.g., suggesting articles that should have been included in the literature review, suggesting a different type of data analysis, noting that a study is statistically underpowered). Instead, the commentary should provide a richer and more comprehensive context for understanding the article that significantly adds to the literature by focusing on conceptual issues, methodological issues, and/or the policy implications of the findings. If a commentary is accepted, then the original author is invited to write a reply to the comment.

Commentaries should be submitted no later than 12 months after publication of the original article. If the editor determines that the commentary meets the criteria described above, then it will be subject to the same process of peer review and the same editorial criteria and standards as any other manuscript. Commentaries may be no more than half the length of the original article, and replies may be no more than half the length of the commentary. A commentary and reply will be published together. Except under rare circumstances, there will be only one round of comment and reply. The title of a commentary should include a subtitle reflecting the actual title and year of publication of the article that is the focus of the comment.

The second type of commentary is initiated by the editor, who identifies an accepted article as one for which a commentary might be useful (e.g., controversial theoretical perspective or empirical findings; groundbreaking topic). The editor will invite one or more individuals to comment on the accepted article; and the author of the original piece is then invited to submit a reply or rebuttal to the comment. Comments and rebuttals go through the peer review and editorial process as described above. The original article is then published along with the comments and reply in chronological order.

Manuscript preparation guidelines

Manuscripts must be prepared according to the Publication Manual of the American Psychological Association using the 7th edition.

All APA requirements are important; the ones listed below are particularly noteworthy:

• Disclose all prior publications with this dataset in the cover letter (which is only seen by editors). 
• Use bias-free language.
• Double space the entire text and references, putting only one table or figure on a page at the end of the manuscript, using one-inch margins all around, and including a header and page numbers.  
• Minimize the use of acronyms so your manuscript is accessible to a wide audience.

All manuscripts that remain under consideration at Psychology of Violence will be asked to include the following:

• A structured abstract divided into four sections with the headings: Objective, method, results, and conclusions. The objective should clearly communicate the novel contribution of the manuscript. Do not, however, claim that "this is the first study ever to..." Such a claim cannot be substantiated. In the conclusion, please identify at least one specific implication and avoid boilerplate language such as "Implications will be discussed." Target length is no more than 250 words.
• 4 to 5 keywords for all manuscripts.
• A statement that clearly describes the study's purpose must be provided in the first 3 paragraphs of the paper.
• The introduction needs to end with numbered statements of hypotheses or research questions, and these need to be explicitly revisited in the results and discussion.
• Authors are expected to review the APA Style Journal Article Reporting Standards (JARS) and verify that they have included all relevant methodological information for the type of study they conducted, including effect sizes when they can be calculated.
• Number of items, response categories, alpha, and scoring need to be presented for all measures. Validity should be addressed.
• Sample size: Qualitative studies need to provide a rationale for the sample size based on their specific methodology. Quantitative studies need to include a formal power analysis that corresponds to the hypotheses and data analytic approach whenever possible; alternative methods used to determine the precision of parameter estimates should be used when power analyses are not appropriate for the data analytic technique. For both qualitative and quantitative studies, methodological citations should be provided to justify the technique used.
• The discussion needs separate subsections (in this order) for limitations, future research directions, and prevention, clinical, and policy implications. Regarding the implications subsection, at least one of the three types of implications listed above must be addressed and the heading should be modified to indicate those included (e.g., "Prevention and policy implications" or "Clinical implications").
• An honest assessment of the study's limitations is essential in the limitations subsection of the discussion. This section needs to describe the study's major methodological limitations and include a statement regarding the generalizability to other populations and contexts. The need to replicate exploratory or unexpected findings should be explicitly stated.

Per APA policy, authors presenting the results of randomized trials should rely on CONSORT guidelines .

Prospective authors are welcome to direct inquiries regarding these instructions, potential paper topics, journal policy, or manuscript preparation to the editor, Brad J. Bushman .

Additional instructions for all authors

Review APA's Journal Manuscript Preparation Guidelines before submitting your article.

If your manuscript was mask reviewed, please ensure that the final version for production includes a byline and full author note for typesetting.

Double-space all copy. Other formatting instructions, as well as instructions on preparing tables, figures, references, metrics, and abstracts, appear in the Manual . Additional guidance on APA Style is available on the APA Style website .

Below are additional instructions regarding the preparation of display equations, computer code, and tables.

Display equations

We strongly encourage you to use MathType (third-party software) or Equation Editor 3.0 (built into pre-2007 versions of Word) to construct your equations, rather than the equation support that is built into Word 2007 and Word 2010. Equations composed with the built-in Word 2007/Word 2010 equation support are converted to low-resolution graphics when they enter the production process and must be rekeyed by the typesetter, which may introduce errors.

To construct your equations with MathType or Equation Editor 3.0:

• Go to the Text section of the Insert tab and select Object.
• Select MathType or Equation Editor 3.0 in the drop-down menu.

If you have an equation that has already been produced using Microsoft Word 2007 or 2010 and you have access to the full version of MathType 6.5 or later, you can convert this equation to MathType by clicking on MathType Insert Equation. Copy the equation from Microsoft Word and paste it into the MathType box. Verify that your equation is correct, click File, and then click Update. Your equation has now been inserted into your Word file as a MathType Equation.

Use Equation Editor 3.0 or MathType only for equations or for formulas that cannot be produced as Word text using the Times or Symbol font.

Computer code

Because altering computer code in any way (e.g., indents, line spacing, line breaks, page breaks) during the typesetting process could alter its meaning, we treat computer code differently from the rest of your article in our production process. To that end, we request separate files for computer code.

In online supplemental material

We request that runnable source code be included as supplemental material to the article. For more information, visit Supplementing Your Article With Online Material .

In the text of the article

If you would like to include code in the text of your published manuscript, please submit a separate file with your code exactly as you want it to appear, using Courier New font with a type size of 8 points. We will make an image of each segment of code in your article that exceeds 40 characters in length. (Shorter snippets of code that appear in text will be typeset in Courier New and run in with the rest of the text.) If an appendix contains a mix of code and explanatory text, please submit a file that contains the entire appendix, with the code keyed in 8-point Courier New.

Use Word's insert table function when you create tables. Using spaces or tabs in your table will create problems when the table is typeset and may result in errors.

Academic writing and English language editing services

Authors who feel that their manuscript may benefit from additional academic writing or language editing support prior to submission are encouraged to seek out such services at their host institutions, engage with colleagues and subject matter experts, and/or consider several vendors that offer discounts to APA authors .

Please note that APA does not endorse or take responsibility for the service providers listed. It is strictly a referral service.

Use of such service is not mandatory for publication in an APA journal. Use of one or more of these services does not guarantee selection for peer review, manuscript acceptance, or preference for publication in any APA journal.

Submitting supplemental materials

APA can place supplemental materials online, available via the published article in the PsycArticles ® database. Please see Supplementing Your Article With Online Material for more details.

Abstract and keywords

Psychology of Violence uses structured abstracts divided into the following sections, with headings: objective, method, results, and conclusions. The objective should clearly communicate the novel contribution of this manuscript. The conclusions should identify at least one specific implication and avoid boilerplate language such as "Implications will be discussed." Target length is no more than 250 words.

Each manuscript needs five keywords for indexing. Please consider keywords that are common synonyms for the forms of violence addressed in your manuscript. For example, if your study is on "intimate partner violence," it may help some readers find your work if you list "domestic violence" as a keyword.

Public significance statements

Authors submitting manuscripts to Psychology of Violence are required to provide two to three brief sentences regarding the relevance or public health significance of their manuscript. This description should be included within the manuscript on the abstract/keywords page. It should be written in easy-to-understand language for members of the lay public.

To be maximally useful, these statements of public significance should not simply be sentences lifted directly from the manuscript. This statement supports efforts to increase the dissemination and usage of research findings by larger and more diverse audiences. In addition, they should be able to be translated into media-appropriate statements for use in press releases and on social media.

Authors may refer to the Guidance for Translational Abstracts and Public Significance Statements page for help writing their statement.

List references in alphabetical order. Each listed reference should be cited in text, and each text citation should be listed in the references section.

Examples of basic reference formats:

Journal article

McCauley, S. M., & Christiansen, M. H. (2019). Language learning as language use: A cross-linguistic model of child language development. Psychological Review , 126 (1), 1–51. https://doi.org/10.1037/rev0000126

Authored book

Brown, L. S. (2018). Feminist therapy (2nd ed.). American Psychological Association. https://doi.org/10.1037/0000092-000

Chapter in an edited book

Balsam, K. F., Martell, C. R., Jones. K. P., & Safren, S. A. (2019). Affirmative cognitive behavior therapy with sexual and gender minority people. In G. Y. Iwamasa & P. A. Hays (Eds.), Culturally responsive cognitive behavior therapy: Practice and supervision (2nd ed., pp. 287–314). American Psychological Association. https://doi.org/10.1037/0000119-012

Preferred formats for graphics files are TIFF and JPG, and preferred format for vector-based files is EPS. Graphics downloaded or saved from web pages are not acceptable for publication. Multipanel figures (i.e., figures with parts labeled a, b, c, d, etc.) should be assembled into one file. When possible, please place symbol legends below the figure instead of to the side.

• All color line art and halftones: 300 DPI
• Black and white line tone and gray halftone images: 600 DPI

Line weights

• Color (RGB, CMYK) images: 2 pixels
• Grayscale images: 4 pixels
• Stroke weight: 0.5 points

APA offers authors the option to publish their figures online in color without the costs associated with print publication of color figures.

The same caption will appear on both the online (color) and print (black and white) versions. To ensure that the figure can be understood in both formats, authors should add alternative wording (e.g., “the red (dark gray) bars represent”) as needed.

For authors who prefer their figures to be published in color both in print and online, original color figures can be printed in color at the editor's and publisher's discretion provided the author agrees to pay:

• $900 for one figure
• An additional $600 for the second figure
• An additional $450 for each subsequent figure

Permissions

Authors of accepted papers must obtain and provide to the editor on final acceptance all necessary permissions to reproduce in print and electronic form any copyrighted work, including test materials (or portions thereof), photographs, and other graphic images (including those used as stimuli in experiments).

On advice of counsel, APA may decline to publish any image whose copyright status is unknown.

• Download Permissions Alert Form (PDF, 13KB)

Open science badges

Articles are eligible for open science badges recognizing publicly available data, materials, and/or preregistered plans and analyses. These badges are awarded on a self-disclosure basis.

At submission, authors must confirm that criteria have been fulfilled in a  signed badge disclosure form (PDF, 42KB)  that must be submitted as supplemental material. If all criteria are met as confirmed by the editor, the form will then be published with the article as supplemental material.

Authors should also note their eligibility for the badge(s) in the cover letter.

For all badges, items must be made available on an open-access repository with a persistent identifier in a format that is time-stamped, immutable, and permanent. For the preregistered badge, this is an institutional registration system.

Data and materials must be made available under an open license allowing others to copy, share, and use the data, with attribution and copyright as applicable.

Available badges are:

Note that it may not be possible to preregister a study or to share data and materials. Applying for open science badges is optional.

Publication policies

For full details on publication policies, including use of Artificial Intelligence tools, please see APA Publishing Policies .

APA policy prohibits an author from submitting the same manuscript for concurrent consideration by two or more publications.

See also APA Journals ® Internet Posting Guidelines .

APA requires authors to reveal any possible conflict of interest in the conduct and reporting of research (e.g., financial interests in a test or procedure, funding by pharmaceutical companies for drug research).

• Download Full Disclosure of Interests Form (PDF, 41KB)

In light of changing patterns of scientific knowledge dissemination, APA requires authors to provide information on prior dissemination of the data and narrative interpretations of the data/research appearing in the manuscript (e.g., if some or all were presented at a conference or meeting, posted on a listserv, shared on a website, including academic social networks like ResearchGate, etc.). This information (2–4 sentences) must be provided as part of the author note.

Ethical Principles

It is a violation of APA Ethical Principles to publish "as original data, data that have been previously published" (Standard 8.13).

In addition, APA Ethical Principles specify that "after research results are published, psychologists do not withhold the data on which their conclusions are based from other competent professionals who seek to verify the substantive claims through reanalysis and who intend to use such data only for that purpose, provided that the confidentiality of the participants can be protected and unless legal rights concerning proprietary data preclude their release" (Standard 8.14).

APA expects authors to adhere to these standards. Specifically, APA expects authors to have their data available throughout the editorial review process and for at least 5 years after the date of publication.

Authors are required to state in writing that they have complied with APA ethical standards in the treatment of their sample, human or animal, or to describe the details of treatment.

• Download Certification of Compliance With APA Ethical Principles Form (PDF, 26KB)

The APA Ethics Office provides the full Ethical Principles of Psychologists and Code of Conduct electronically on its website in HTML, PDF, and Word format. You may also request a copy by emailing or calling the APA Ethics Office (202-336-5930). You may also read "Ethical Principles," December 1992, American Psychologist , Vol. 47, pp. 1597–1611.

Other information

See APA’s Publishing Policies page for more information on publication policies, including information on author contributorship and responsibilities of authors, author name changes after publication, the use of generative artificial intelligence, funder information and conflict-of-interest disclosures, duplicate publication, data publication and reuse, and preprints.

Visit the Journals Publishing Resource Center for more resources for writing, reviewing, and editing articles for publishing in APA journals.

Incoming (2025) editor

Brad J. Bushman, PhD The Ohio State University, United States of America

Incoming (2025) associate editors

Matt DeLisi, PhD Iowa State University, United States of America  

Ines Keygnaert, PhD Ghent University, Belgium

Miguel T. Villodas, PhD San Diego State University , United States of America  

Incoming (2025) senior consulting editors

Edward Alessi, PhD Rutgers University, United States of America

Christopher Barlett, PhD Kansas State University, United States of America

Laurent Bègue, PhD Université Grenoble Alpes, France

Massil Benbouriche, PhD Université de Lille, France

Henriette Bergstrøm, PhD University of Derby, United Kingdom

Nicola Bowes, PhD Cardiff Metropolitan University, United Kingdom

Enrique Chaux, PhD Universidad de los Andes, Colombia

David Chester, PhD Virginia Commonwealth University, United States of America

Wendy Cukier, PhD Toronto Metropolitan University, Canada

Ilse Derluyn, PhD Ghent University, Belgium

Nathan DeWall, PhD University of Kentucky, United States of America

Meagan Docherty, PhD Bowling Green State University, United States of America

Hedwig Eisenbarth, PhD Victoria University of Wellington, New Zealand

Dorothy Espelage, PhD University of North Carolina, United States of America

Kostas A. Fanti, PhD University of Cyprus, Cyprus

Carlo Garofalo, PhD Università degli Studi di Perugia, Italy

Ángel Gómez Jiménez, PhD Universidad Nacional de Educación a Distancia, Spain

Tobias Hecker, PhD Bielefeld University, Germany

Jennifer A. Livingston, PhD University at Buffalo, United States of America

Mirra N. Milla, PhD Universitas Indonesia, Indonesia

Laura Miller-Graff, PhD University of Notre Dame, United States of America

Sophia Moskalenko, PhD Georgia State University, United States of America

Lindsay Orchowski, PhD Brown University, United States of America

Daniel Romer, PhD University of Pennsylvania, United States of America

Karen Slade, PhD Nottingham Trent University, United Kingdom

Zhaojun Teng, PhD Southwest University, China

Nicholas Thomson, PhD Virginia Commonwealth University, United States of America

Carmen Vives-Cases, PhD University of Alicante, Spain

Incoming (2025) consulting editors

Erinn Acland, PhD Université de Montréal, Canada

Amarnath Amarasingam, PhD Queen’s University, Canada

Farida Anwar, PhD Åbo Akademi University, Finland

John Archer, PhD University of Central Lancashire, United Kingdom

Kathryn M. Bell, PhD Acadia University, Canada

Inti Brazil, PhD Radboud University, The Netherlands

Paul Boxer, PhD Rutgers University—Newark, United States of America

Kai Li Chung, PhD University of Reading Malaysia, Malaysia

Carlos Iván Orellana Calderón, PhD Universidad Don Bosco, El Salvador

Dewey Cornell, PhD University of Virginia , United States of America

Ashlee Curtis, PhD Deakin University, Australia

Sarah DeGue, PhD Centers for Disease Control and Prevention, United States of America

Tom Denson, PhD University of New South Wales, Australia

Kelly P. Dillon, PhD Wittenberg University, United States of America

Bojana Dinić, PhD University of Novi Sad, Serbia

James Effiong, PhD University of Uyo, Nigeria

Deinera Exner-Cortens, PhD University of Calgary, Canada

Theresa Gannon, DPhil University of Kent, United Kingdom

Oluf Gøtzsche-Astrup, PhD Aarhus University, Denmark

Nicola (“Niki”) Graham-Kevan, PhD University of Central Lancashire,   United Kingdom

Tobias Greitemeyer, PhD Universität Innsbruck, Austria

Thomas Gumpel, PhD Hebrew University of Jerusalem, Israel

Jonathan Hall, PhD Uppsala University, Sweden

Muhammad M. Haj-Yahia, PhD Hebrew University of Jerusalem, Israel

Nafees Hamid, PhD King's College London,  United Kingdom

Eddie Harmon-Jones, PhD University of New South Wales, Australia

David Hawes, PhD University of Sydney, Australia

Kathryn Holland, PhD University of Nebraska—Lincoln, United States of America

Ernest Jouriles, PhD Southern Methodist University, United States of America

Sandy Jung, PhD MacEwan University, Canada

Sophie Kjærvik, PhD Virginia Commonwealth University, United States of America

Sander Koole, PhD Vrije Universiteit Amsterdam, The Netherlands

Barbara Krahé, PhD University of Potsdam, Germany

Ulrike Krämer, PhD University of Lübeck, Germany

Dorottya Lantos, PhD University of Technology Sydney, Australia

Ljiljana B. Lazarevic, PhD Institute of Psychology, Serbia

Jill Lobbestael, PhD Maastricht University, The Netherlands

Tailson E. Mariano, PhD Universidade Católica de Pernambuco, Recife, Brazil

Cecilia Martinez-Torteya, PhD University of Michigan , United States of America

Pam McAuslan, PhD University of Michigan—Dearborn, United States of America

Sarah McMahon, PhD Rutgers University, United States of America

André Melzer, PhD University of Luxembourg, Luxembourg

Franklin Moreno, PhD Temple University, United States of America

Christopher Murphy, PhD University of Maryland, Baltimore County, United States of America

Roghieh Nooripour, PhD Alzahra University, Iran

Raymond W. Novaco, PhD University of California, Irvine, United States of America

Tochukwu Charles Orjiakor, PhD University of Nigeria, Nigeria

Dominic Parrott, PhD Georgia State University, United States of America

Carlos E. Pimentel, PhD Federal University of Paraíba , Brazil

Galina Portnoy, PhD Yale School of Medicine, VA Connecticut Healthcare System, United States of America

Joanna Rajchert, PhD Maria Grzegorzewska University, Poland

Joseph Richardson, PhD University of Maryland, United States of America

Daniel Rijo, PhD Universidade de Coimbra, Portugal

Paolo Riva, PhD Università degli Studi di Milano — Bicocca, Italy

Eva M. Romera, PhD Universidad de Córdoba, Spain

Reema Safadi, PhD University of Jordan, Jordan

Esteban Eugenio Esquivel Santoveña, PhD Universidad Autónoma de Ciudad Juárez, Mexico

Sebastian Scherr, PhD University of Augsburg, Germany

Aaron Sell, PhD Heidelberg University, United States of America

Ryan Shorey, PhD University of Wisconsin—Milwaukee, United States of America

Danique Smeijers, PhD Radboud University, The Netherlands

Terri Sullivan, PhD Virginia Commonwealth University, United States of America

Elizabeth Summerell, PhD University of New South Wales, Australia

Jeff R. Temple, PhD University of Texas Medical Branch, United States of America

Martie P. Thompson, PhD Appalachian State University, United States of America

Tracy Vaillancourt, PhD University of Ottawa, Canada

Eduardo A. Vasquez, PhD University of Kent, United Kingdom

Anca Velicu, PhD Institute of Sociology Romania Academy, Romania

Irene Vitoroulis, PhD University of Ottawa, Canada

Ajwang' Warria, PhD University of Calgary, Canada

Samantha Wells, PhD Centre for Addiction & Mental Health, Canada

Jacqueline Woerner, PhD University of Central Florida, United States of America

Anna Zajenkowska, PhD The University of Economics and Human Sciences in Warsaw, Poland

Shaoling Zhong, PhD Guangzhou Medical University, China

Outgoing editor

(handling invited revisions only in 2024)

Antonia Abbey, PhD Wayne State University, United States

Outgoing associate editors

David DiLillo, PhD University of Nebraska–Lincoln, United States

Heather L. McCauley, ScD Michigan State University, United States

Miguel T. Villodas, PhD San Diego State University, United States

Outgoing senior consulting editors

Christina M. Dardis, PhD Towson University, United States

Emily Dworkin, PhD University of Washington, United States

Katie M. Edwards, PhD University of Nebraska–Lincoln, United States

Jun Sung Hong, PhD Wayne State University, United States

Angela Jacques-Tiura, PhD Wayne State University, United States

Erika L. Kelley, PhD Case Western Reserve University, United States

Angie Kennedy, PhD Michigan State University, United States

Traci M. Kennedy, PhD University of Pittsburgh, United States

Julia M. Kobulsky, PhD Temple University, USA

Heather Littleton, PhD University of Colorado Colorado Springs, United States

Laura Miller-Graff, PhD University of Notre Dame, United States

Kathleen A. Parks, PhD University at Buffalo, United States

Zoë Peterson, PhD Indiana University Bloomington, United States

Dennis Reidy, PhD Georgia State University, United States

Scott Ronis, PhD University of New Brunswick, United States

Chiara Sabina, PhD University of Delaware, United States

Cynthia A. Stappenbeck, PhD Georgia State University, United States

David B. Sugarman, PhD Rhode Island College, United States

Rachel A. Wamser-Nanney, PhD University of Missouri St. Louis, United States

Susan H. Yoon, PhD Ohio State University, United States

Outgoing consulting editors

Craig Anderson, PhD Iowa State University, United States

Sarah Bacon, PhD Centers for Disease Control and Prevention, United States

Victoria Banyard, PhD Rutgers University,  United States of America

Tricia Bent-Goodley, PhD Howard University, United States

Gerd Bohner, DPhil Bielefeld University, Germany

Paul Boxer, PhD Rutgers University, United States

Ernestine Briggs-King, PhD Duke University, United States

Brad Bushman, PhD Ohio State University, United States

Michele Cascardi, PhD William Patterson University, United States

Shannon D. Chaplo, PhD University of North Carolina at Chapel Hill, United States

Ruby Charak, PhD The University of Texas Rio Grande Valley, United States

Preeti Chauhan, PhD City University of New York, United States

Kimberly A. Crossman, PhD California State University, Monterey Bay, United States

Carlos A. Cuevas, PhD Northeastern University, United States

Leah Daigle, PhD Georgia State University, United States

Kelly Cue Davis, PhD Arizona State University, United States

Sarah DeGue, PhD Centers for Disease Control and Prevention, Atlanta, Georgia, United States

Thomas Denson, PhD University of New South Wales Sydney, Australia

Christopher Eckhardt, PhD Purdue University, United States

Jeffrey L. Edleson, PhD University of California, Berkeley, United States

Deinera Exner-Cortens, PhD, MPH University of Calgary, Canada

Albert D. Farrell, PhD Virginia Commonwealth University, United States

William Flack, PhD Bucknell University, United States

Thomas Frissen, PhD Maastricht University, Netherlands

Maria Galano, PhD University of Massachusetts Amherst, United States

Carlo Garofalo, PhD University of Perugia, Italy

Rachel Garthe, PhD University of Illinois Urbana, United States

Christine Gidycz, PhD Ohio University, United States

Amanda K. Gilmore, PhD Georgia State University, United States

Lisa A. Goodman, PhD Boston College, United States

Sandra A. Graham-Bermann, PhD University of Michigan, United States

Melanie D. Hetzel-Riggin, PhD Pennsylvania State University Behrend, United States

Kathryn Holland, PhD University of Nebraska–Lincoln, United States

John Horgan, PhD Georgia State University, United States

Anna E. Jaffe, PhD University of Nebraska-Lincoln, United States

Veronica E. Johnson, PhD City University of New York, United States

Wendi L. Johnson, PhD Oakland University, United States

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Anger and aggression treatments: a review of meta-analyses

Affiliations.

• 1 Department of Psychology, St. John's University, 8000 Utopia Pkwy, Queens, NY 11439, United States.
• 2 Department of Psychology, St. John's University, 8000 Utopia Pkwy, Queens, NY 11439, United States. Electronic address: [email protected].
• PMID: 29279226
• DOI: 10.1016/j.copsyc.2017.04.004

In the last several decades, researchers have begun to recognize dysregulated anger as a common and debilitating psychological problem among various psychiatric populations. Accordingly, the treatment of anger and aggression has received increasing attention in the literature. The current article reviews existing meta-analyses of psychosocial intervention for anger and aggression with the aims of (1) synthesizing current research evidence for these interventions, and (2) identifying interventions characteristics associated with effectiveness in specific populations of interest. Results demonstrate that cognitive behavioral treatments are the most commonly disseminated intervention for both anger and aggression. Anger treatments have consistently demonstrated at least moderate effectiveness among both non-clinical and psychiatric populations, whereas aggression treatment results have been less consistent. We discuss the implication of these findings and provide directions for future research in the treatment of anger and aggression.

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Research Article

Determinants of aggressive behavior: Interactive effects of emotional regulation and inhibitory control

Affiliation Institute of Cognitive Neuroscience, National Central University, Taoyuan City, Taiwan

* E-mail: [email protected]

• I-Ju Hsieh, 
• Yung Y. Chen

• Published: April 11, 2017
• https://doi.org/10.1371/journal.pone.0175651
• Reader Comments

Aggressive behavior can be defined as any behavior intended to hurt another person, and it is associated with many individual and social factors. This study examined the relationship between emotional regulation and inhibitory control in predicting aggressive behavior. Seventy-eight participants (40 males) completed self-report measures (Negative Mood Regulation Scale and Buss-Perry Aggression Questionnaire), a stop signal task, and engaged in a modified version of Taylor Aggression Paradigm (TAP) exercise, in which the outcome was used as a measure of direct physical aggression. We used a hierarchical, mixed-model multiple regression analysis test to examine the effects of emotion regulation and inhibitory control on physical reactive aggression. Results indicated an interaction between emotion regulation and inhibitory control on aggression. For participants with low inhibitory control only, there was a significant difference between high and low emotion regulation on aggression, such that low emotion regulation participants registered higher aggression than high emotion regulation participants. This difference was not found among participants with high inhibitory control. These results have implications for refining and targeting training and rehabilitation programs aimed at reducing aggressive behavior.

Citation: Hsieh I-J, Chen YY (2017) Determinants of aggressive behavior: Interactive effects of emotional regulation and inhibitory control. PLoS ONE 12(4): e0175651. https://doi.org/10.1371/journal.pone.0175651

Editor: Antonio Verdejo-García, University of Granada, SPAIN

Received: November 2, 2016; Accepted: March 29, 2017; Published: April 11, 2017

Copyright: © 2017 Hsieh, Chen. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data is available at the Figshare Repository. Please find the data at the following link: https://figshare.com/s/9e6d4a1c852d5c9b3aa9 .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Regardless of culture or geographical location, various manifestations of aggression and forms of aggressive behavior may be found among groups of individual. Aggressive behavior has been defined as any behavior intended to harm or injure another organism which is motivated to avoid such treatment [ 1 ]. This common yet destructive behavior may be the cause of financial loss, emotional distress, physical injury, or even death, to those exposed to it, not to mention other more indirect interpersonal and social costs to individuals and social groups associated with the victims. Therefore, much effort has been devoted to the understanding and prediction of aggressive behavior.

Previous research has proposed several types of aggressive behavior. The mostly used categories are physical, verbal, and indirect aggression [ 2 ]. As each category’s name indicates, physical aggression involves physical harm to others, verbal aggression includes the use of language to hurt people, and indirect aggression involves social manipulation, such as social exclusion that ultimately results in harm to others. Other types of aggression include reactive and instrumental aggression. Reactive aggression (sometimes also referred to as emotional aggression) is a response to provocation or threat, while instrumental aggression (sometimes referred to as proactive aggression) refers to using aggression as means to an end. In the current study, the focus would be on physical reactive aggression.

Previous research has shown that physical aggression can be affected by many factors, such as gender (e.g.,[ 3 , 4 ]), alcohol use (e.g., [ 5 ]), exposure to violent media (e.g., [ 6 ]). Several theories have been developed in order to integrate these factors and explain their relationships with one another. Finkel [ 7 ] proposed a model (I 3 theory) aimed at explaining aggressive behavior, taking into consideration many of the known factors that were associated with aggressive behavior. According to the theory, the commission of aggressive behavior can be determinate by the interactions among instigating trigger, impelling forces, and inhibiting force. Instigating trigger is defined as situational events or circumstances with the potential to lower the threshold of carrying out aggressive acts. Instigating factors would increase the likelihood of aggression. Impelling force refers to personality traits or dispositional factors that would increase the likelihood of aggression, particularly when individuals with these traits are confronted with instigating triggers. Finally, inhibiting force is one that would override an aggressive urge. Together, instigating trigger and impelling force would strengthen an aggressive impulse. Inhibiting force, on the other hand, would determine the threshold above which aggressive impulses would transform into actual aggressive behaviors.

Individual components in the I 3 theory have indeed been found to be associated with aggressive behavior in previous research. For example, instigating triggers, such as provocation [ 8 ], social rejection [ 9 ], and unpleasant temperature [ 10 ], have been found to be related to higher incidents of aggressive behavior and increased aggression. Impelling force such as high impulsivity [ 11 , 12 ] and trait aggressiveness [ 13 ] were also found to predict aggressive behavior. Finally, inhibiting force, such as high self-control [ 14 , 15 ] and better executive functioning [ 16 , 17 ] have been found to be inversely associated with incidents of aggressive behavior. However, fewer studies have examined the potential interactions among the three factors in predicting aggressive behavior. One previous study reported an interaction between provocation and self-regulatory process, such that participants with self-regulatory failure allowed their partners to maintain in painful yoga pose for longer periods of time, but only when they were provoked [ 18 ]. However, the simplicity of I3 theory’s also presents limitations in understanding the multidimensional nature of the constructs that contribute to a complex behavior such as aggression. For example, it would be challenging to fit the theory into the context of instrumental aggression, where someone would use aggression to get to an ultimate goal. Alternative models have raised possibility of potential interactive effects among predictors of aggression (e.g., [ 19 ]).

The current study examined the effects of two inhibiting forces, operationalized as emotion regulation and inhibitory control, in predicting physical reactive aggression. The link between negative emotions and aggressive behavior has been extensively researched. Emotions such as anger, fear, and other generally negative affect have been found to be associated with aggression [ 20 ]. Therefore, the ability to regulate negative emotions can be, and has been found to reduce aggressive behavior [ 21 ]. Emotion regulation is the ability to modulate ones emotional experience and/or responses [ 22 ]. Among the many approaches to examine emotion regulation, one approach is the distinction between top-sown vs. bottom up strategies. Efforts aimed at reframing or reinterpreting emotion provoking stimuli, such as cognitive reappraisal, would involve the top-down (prefrontal areas) modulation of emotion generating brain regions, such as the amygdala [ 23 ]. Bottom-up strategies, such as exposure or conditioning, would directly affect the emotion generating brain regions, with less degrees of involvement from the higher brain regions [ 24 ]. The current study sought to investigate possible effects of top-down emotion regulation on inhibitory control in predicting aggression.

Inhibitory control is the ability to suppress a strong tendency to act, and is an important component of suppressing aggressive behavior. Raaijmakers, Smidts [ 25 ] found that children who showed higher levels of aggressive behavior were significantly more impaired in inhibition than children who showed lower levels of aggressive behavior. Though inhibitory control, usually examined in a motor or behavioral response prevention context, is thought to involve the brain’s prefrontal network, the “top” brain region’s involvement has been found to vary between inhibitory control and other relatively more cognitive tasks, such as dynamic decision making and response selection [ 26 , 27 ]. In addition, top-down emotion regulation strategies, such as cognitive appraisal, has been found to be associated with the ability to recruit various appropriate brain regions [ 28 ]. While both emotion regulation and inhibitory control may be critical components in predicting aggression, they may interact in the process leading up to the execution of aggressive behavior.

One purpose of the current study was to investigate the potential interaction between emotion regulation and inhibitory control in predicting aggressive behavior. Specifically, we examined emotion regulation as a moderating factor for the effects of inhibitory control on aggression. We expected that when provoked, participants high on both emotion regulation and inhibitory control would show the least levels of aggression, while those low on both would show the highest. Due to the relatively more cognitive or top-down nature of emotion regulation, we also expected that it might help reduce aggressive behavior in participants with low inhibitory control.

Another purpose of the current study was to examine aggressive behavior and relationships among the factors in the I 3 model in an Asian sample. Although much of the previous research on aggression had been conducted in western cultures, the norms for aggressive behavior have been found to vary across cultures. For example, a study by Crystal, Chen [ 29 ] compared 11-grade students in three countries: the U.S., Japan, and Taiwan. In this study, Asian students reported less aggression than American students, but Taiwanese students reported more aggression than Japanese students. Another study showed that compared with Chinese or Polish college students, American students reported the highest levels of aggression, followed by Polish and Chinese students [ 30 ]. Some have suggested that the differences in levels of aggression among various cultures may be even greater than gender differences in aggressive behavior [ 31 ]. This study aimed to examine aggressive behavior and its contributing factors in a Taiwanese sample. As such, we expected that a Taiwanese sample might require a higher provocation level before aggression is shown.

Participants

Participants were 80 undergraduate college students who completed the study for monetary compensation or course extra credit. The study was approved by the Research Ethics Committee at the Research Ethics Office of National Taiwan University. In examining the data for normal distribution, we identified two outliers for NMR based on interquartile rang (IQR), which was computed from Turkey’s hinges. NMR scores lower than 1.5 IQR’s but less than 3 IQR’s from end of the boxplot were labeled as outliers. After removing the two outliers, the sample consisted of 41 men and 39 women. Their mean age was 21.57 ( SD = 1.75).

Participants’ initial sessions were conducted in a private room, where they were given an introduction to the study and guided through the process and content of informed consent. Participants were assured that all of their questionnaire responses would be kept confidential. After participants signed the consent form, they were asked to complete study questionnaires. Then participants performed a stop signal task and Taylor aggression paradigm in a counterbalanced order. At the completion of the study, the experimenter debriefed the participants and thanked them for their participation.

Aggressive behavior was measured using the modified version of Taylor aggression paradigm [ 3 ]. Participants were told that they were going to play a competitive reaction time game with an unknown opponent, and that whoever was slower would receive a high-pitched noise through the headphone, as punishment. Unknown to the participants at that time, however, was that their opponents were virtual, and their wins/losses were predetermined. At the beginning of each trial, participants would select the level of punishment that their opponents would receive, if their opponents were to lose. The noise setting buttons were labeled on the keyboard from 1 (corresponding to 55 dB) to 10 (corresponding to100 dB). After each trial, participants were informed of the outcome of the trial (i.e., “you won” or “you lost”). Then, participants were shown the punishment level that their opponents had assigned them for that particular trial. For participants who have lost, they would then be exposed to the punishment noise, corresponding to the level that their opponents assigned them.

The frequency of wins and losses (set at 50% for the current study) and the intensities of noise received were predetermined. Participants competed in 96 reaction time (RT) trials, which were divided into three blocks. The average intensity of noise was 2.5 for block one, 5.5 for block two, and 8.5 for block three. Aggressive behavior was measured by the average levels of punishment participants assigned to their opponents for each block.

Emotional regulation was measured using the Negative Mood Regulation (NMR) Scale [ 32 ]. The NMR is a 30-item scale scored on a 5-point Likert-type scale: 1 ( strong disagree ) to 5 ( strong agree ). The NMR is used to assess one’s ability to relieve or terminate bad moods and emotional distress. Higher score on the NMR would indicate better negative mood regulation. In the current sample, Cronbach’s alpha ( α ) = 0.85.

Inhibitory control was measured using stop signal reaction time (SSRT) in a stop signal task [ 33 ]. The task included two conditions, a “go” condition and a “stop” condition. The go condition required participants to respond to a visual go signal on the computer screen, by pressing on a designated button on the keyboard. The stop condition involved an audible stop signal, which was presented after the visual go signal, and required participants to inhibit their intention to respond to the go signal. Stop condition occurred on 25% of the trials. Inhibitory control was measured by the latency of the response to stop signal (SSRT). Longer SSRT would indicate worse inhibitory control.

Trait aggression was included as a controlling variable, and was measured using the Buss-Perry Aggression Questionnaire, BPAQ [ 34 ]. The BPAQ is a 29-item inventory scored on a 5-point Likert-type scale: 1 ( extremely uncharacteristic of me ) to 5 ( extremely characteristic of me ). Higher scores on the BPAQ would indicate higher trait aggression. In the current sample, Cronbach’s alpha ( α ) = 0.85.

Provocation was assessed at the end of the experiment, by asking the participants how much they felt “provoked” by their opponents during the experiment.

Demographic information was also collected from the participants, which included their age and gender.

Statistical analysis

Using the SPSS 18.0 software package (IBM, Armonk, NY, USA), we employed one repeated-measures Analysis of Variance Analysis test (ANOVA) to check the effect of provocation. Then we used a hierarchical, mixed-model multiple regression analysis test to examine the effects of emotion regulation and inhibitory control on physical reactive aggression. In Step 1, we included provocation, age, gender, and trait aggression as controlling variables. In Step 2, we included negative mood regulation (NMR) and inhibitory control (SSRT) as predictors. Finally, in Step 3 we added the two-way interaction between emotion regulation (NMR) and inhibitory control (SSRT) as predictors.

Preliminary analysis

The mean score for NMR was 103.03 (SD = 12.06), for BPAQ was 75.74 (SD = 13.88), and for SSRT was 219.57ms (SD = 25.95). No significant correlations were found among main study variables, as showed in Table 1 .

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https://doi.org/10.1371/journal.pone.0175651.t001

A repeated-measures Analysis of Variance Analysis (ANOVA) was conducted to test main effect of block on the punishment levels participants assigned to their opponents. Results showed a significant main effect of block on assignment of punishment ( p = .000). Participants’ average punishment (i.e., aggression) increased significantly from Blocks 1 to 3. Post-hoc test (LSD) revealed a significant increase from Block 1 (B1, M = 3.03) to Block 2 (B2, M = 4.63, p < .001), and from B2 to Block 3 (B2, M = 6.55, p < .001).

As expected, there was a greater increase in punishment level from B2 to B3, than from B1 to B2, indicating higher provocation level in the current sample, possibly due to participants being provoked by greater noise levels received from their virtual opponents [ 8 ]. Therefore, the difference in punishment assignment from B2 to B3 was calculated and used as an indicator of aggressive behavior in the current study.

Main effects analysis

Effects of main study variables on aggressive behavior were then examined using hierarchical, mixed-model multiple regression analysis. Step 1 of the Model included controlling variables of provocation, age, gender, and trait aggression. Step 2 included main study variables of negative mood regulation and inhibitory control. Results indicated no significant main effects for any of the main study variable. Further analysis involving hierarchical entry of the predictors indicated that main effects described above were unaltered by the sequence in which they were entered into the model.

Moderation analyses

Next, in Step 3 we added the two-way interaction between emotion regulation (NMR) and inhibitory control (SSRT) in predicting aggressive behavior. Step 3 significantly improved the main effects model, R 2 = .309, p < .02, reflecting a significant NMR x SSRT interaction ( Fig 1 ). For participants with high inhibitory control (low SSRT), there was no significant difference between those high vs. low on NMR scores in predicting aggressive behavior. However, for participants with low inhibitory control (high SSRT), those with higher scores on NMR showed less aggressive behavior than those with lower scores on NMR. In addition, no other significant interactions were found among variables included in Steps 1 and 2 in predicting aggression (ps > .40). We also examined these interactive effects for increases in punishment between Blocks 1 and 2, but did not find any significant interactions.

a. Predicted values of increase in punishment are plotted 1 standard deviation above and below the mean for NMR [ 35 ]. b. The slope for high SSRT (low inhibitory control) is significantly different from zero ( p = .002), and the slope for low SSRT (high inhibitory control) is not, ( p > .33).

https://doi.org/10.1371/journal.pone.0175651.g001

Result from the current study showed an interactive effect of emotional regulation and inhibitory control in predicting aggressive behavior. For individuals with low inhibitory control, those who were better at emotional regulation showed less aggressive behavior than those with worse emotional regulation. For individuals with better inhibitory control, however, emotional regulation had no significant effect on aggressive behavior.

Emotion regulation is a process that involves relatively higher order brain functions, such as cognitive reappraisal, future planning, and anticipation of behavioral consequences. While individuals may experience difficulty transitioning out of negative emotions, through learning, experience, and practice, the ability to regulate negative emotions can be acquired and strengthened. This cognitively based ability, if well practiced, may also modulate less cognitively based, physiological states or reactions. For example, mindfulness meditation has been found to affect autonomic activities, such as blood pressure, heart rate, and heart rate variability [ 36 , 37 ]. In addition, emotion regulation may moderate the effects of less cognitively based factors on aggressive behavior. For example, Castro, Bosch [ 38 ] reported that emotion regulation exercises reduced aggressive behavior, but only for children with severe behavior problems, and not for the normal control group. Results from the current study showed that emotion regulation moderated the effects of inhibitory control on aggressive behavior and significantly benefited individuals with low inhibitory control.

Also, as previously described, aggressive behavior has been found to vary across cultures [ 29 – 31 ]. Results from the current study did not show significant interactive effects for increases in punishment between Blocks 1 and 2, despite of significant increases in punishment in each block (i.e., Block 1 to 2, and Block 2 to 3), corresponding to increases in provocation. Interactive effects were only significant when the provocation level was increased from medium (Block 2) to high (Block 3). One potential explanation is cultural differences. As eastern culture emphasizes social harmony or avoidance of open conflict, they have been found to report less aggression and register lower on measures of aggressive behavior (e.g.,[ 29 ]). It is possible that participants from an eastern cultural are more tolerant of lower level provocation, but would become reactive only to higher level provocation.

Limitations

There are several limitations to the current research. First, this study used an undergraduate sample. Future research may replicate and extend this research to include different age and demographic groups. Also, this study didn’t include other potentially relevant variables, such as retaliatory tendencies. Finally, the current study examined aggressive behavior in a setting when individuals were alone. Future research may include social factors in predicting aggressive behavior. Group dynamic, social desirability or social competition may serve to modulate the associations among our main study variables.

Conclusions

Inhibitory control is a determinant in the execution of aggressive behavior. It appears that emotion regulation may be an asset, particularly for individuals with inhibitory lower control, in modulating the effects of inhibitory control on aggression. These findings have implications for allocating training resources and refining or targeting rehabilitation programs aimed at reducing aggressive behavior in adults.

Author Contributions

• Conceptualization: YYC.
• Data curation: IJH.
• Formal analysis: IJH YYC.
• Investigation: IJH.
• Methodology: YYC IJH.
• Project administration: IJH.
• Resources: YYC.
• Supervision: YYC.
• Writing – original draft: IJH YYC.
• Writing – review & editing: YYC IJH.
• 1. Baron RA. Human Aggression: New York: Plenum; 1977.
• 2. Haller J. Neurobiological Bases of Abnormal Aggression and Violent Behaviour. Berlin: Springer; 2014.
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• 7. Finkel EJ. The I3 Model: Metatheory, theory, and evidence. Advances in experimental social psychology. 49. In Olson J. M., & Zanna M. P. (Eds.) ed. San Diego: Academic Press; 2014.
• 35. Aiken LS, West SG. Multiple regression: Testing and interpreting interactions. Newbury Park, London: Sage; 1991.

• Research article
• Open access
• Published: 18 November 2014

Aggression in children with behavioural/emotional difficulties: seeing aggression on television and video games

• Oana Mitrofan 1 ,
• Moli Paul 1 ,
• Scott Weich 1 &
• Nicholas Spencer 1  

BMC Psychiatry volume  14 , Article number:  287 ( 2014 ) Cite this article

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Mental health professionals are often asked to give advice about managing children’s aggression. Good quality evidence on contributory environmental factors such as seeing aggression on television and in video games is relatively lacking, although societal and professional concerns are high. This study investigated possible associations between seeing aggression in such media and the aggressive behaviour of children attending specialist outpatient child and adolescent mental health services (CAMHS).

In this mixed methods study, forty-seven British children aged 7-11 years with behavioural/emotional difficulties attending CAMHS and their carers participated in a survey; twenty purposively-selected children and a parent/carer of theirs participated in a qualitative study, involving semi-structured interviews, analysed using the Framework Analysis Approach; findings were integrated.

Children attending CAMHS exhibit clinically significant aggression, of varying types and frequency. They see aggression in multiple real and virtual settings. Verbal aggression was often seen, frequently exhibited and strongly associated with poor peer relationships and low prosocial behaviour. Children did not think seeing aggression influences their own behaviour but believed it influences others. Carers regarded aggression as resulting from a combination of inner and environmental factors and seeing aggression in real-life as having more impact than television/video games.

Conclusions

There is yet no definitive evidence for or against a direct relationship between aggression seen in the media and aggression in children with behavioural/emotional difficulties. Future research should take an ecological perspective, investigating individual, developmental and environmental factors. Carers, professional organisations and policy makers should address aggression seen in all relevant area of children’s lives, primarily real-life and secondly virtual environments.

Peer Review reports

Aggression and violence among children and adolescents are of worldwide public health importance [ 1 ]. Aggression is a common reason for referrals to child and adolescent mental health services (CAMHS) [ 2 ]. Referred children may have higher frequency and severity of aggression compared with non-referred peers [ 3 ].

CAMHS professionals are often asked to give advice about managing aggression in children, including psycho-education about contributory environmental factors. Possible associations between exposure to violence in the media and aggression, especially in younger children, have raised public health concern [ 4 ]. The impact on violent media on aggressive behaviour has been much debated, and the methodological quality of many studies and some early meta-analyses extensively criticised. Methodological problems, such as the use of non-standardised measures of aggression that were not tested for validity or reliability, the use of proxy measures of aggression that involved no direct physical aggression or violent behaviour, the lack of controlling for other factors and publication bias effects mean that the evidence remains inconclusive [ 5 ],[ 6 ]. Recent meta-analyses indicate small overall effects for exposure to violence in passive media such as television (TV) and film and newer, interactive media such as video games on aggressive behaviour in children and adults (effect sizes range between r = .03 and .20, with a corrected effect size of r = .08 for children) [ 5 ]-[ 7 ].

Authors have suggested that research should focus on children at increased risk for aggression rather than the general population [ 4 ],[ 8 ]. Children with pre-existing mental health problems, such as behavioural and emotional difficulties, (BED) were reported to be more susceptible to watching and/or being affected by media violence [ 4 ],[ 9 ]. A systematic review focused on children with BED, however, found insufficient, contradictory and methodologically flawed evidence on such an association [ 10 ]. Most studies were North American and school-based; the few health-based studies focused on psychiatric diagnoses associated with aggression, such as conduct disorder, but not aggression per se . Yet, aggression is a non-specific behaviour commonly associated with various, but not equivalent to any, psychiatric diagnoses; aggression can be objectively measured and targeted for intervention, regardless of any associated diagnoses [ 3 ]. Levels of aggression in children with BED attending CAMHS are unknown. Frequencies of other factors that may account for or explain any observed relationship between seeing aggression in the media and aggressive behaviour (so-called third variables) are also unsubstantiated.

Research has been hindered by the lack of valid and reliable measures of seeing aggression in TV programmes and video games, the challenges of operationalizing various definitions of aggression and separating it from concepts such as violence and antisocial behaviour [ 2 ],[ 10 ]. This paper focuses on direct or overt, other-directed aggression because of its high internal and external validity [ 2 ]. Overt aggression has two categories of physical and non-physical aggression, the latter encompassing verbal (e.g., saying hurtful things to another individual), symbolic (i.e., attempting to hurt an individual in a non-verbal manner e.g. making threatening gestures) and object (e.g., hitting an object) aggression [ 10 ]. The evidence base could also be improved by having children’s, potentially different, perspectives in addition to information from carers and professionals [ 11 ],[ 12 ].

This complexity and numerous gaps in knowledge, especially about primary school-aged children, prompted this mixed-methods study of 7-11-year olds, aimed at better understanding possible associations between aggression in children with BED attending CAMHS and their seeing aggression in TV programmes and video games. The research questions were: What are the frequency and characteristics of children’s aggression? Where do children see aggression in their lives? What are the children’s and carers’ perspectives on associations between aggression seen in TV programmes and video games and children’s aggression?

We conducted a survey on aggressive behaviour of children attending CAMHS, a qualitative study on the views of some of these children and their carers on where children saw aggression and possible associations between seen and exhibited aggression, and integrated the findings. The study was conducted in four specialist, multi-disciplinary, outpatient CAMHS in Coventry and Warwickshire. People in this mixed urban and rural area of the United Kingdom (UK) are broadly representative of the general UK population [ 13 ].

Participants

Survey participants were recruited from all children referred to participating CAMHS, between November 2006 and May 2008, who met the following inclusion criteria: referred for behavioural difficulties (e.g., disruptive/challenging/aggressive/antisocial behaviour, hyperactivity, conduct problems) and/or emotional symptoms (e.g., depression, anxiety, withdrawal); aged between 7-11 years at referral. Children with generalised learning difficulties, psychoses, pervasive developmental, eating and substance-related disorders were excluded because of potentially different associations of aggression in such conditions. Other exclusion criteria included: having sensory impairments preventing TV/video game use; contemporaneous child protection issues; and non-English speakers.

Thirty-nine (17%) of the 226 eligible children and 47 (21%) of their main carers participated in the quantitative study. Age ranged between 7-11 years (mean 9 years, SD 1.4) at referral and 8-12 years (mean 10.2, SD 1.4) at time of participation. Almost 3/4 of the children were boys ( n = 35; 75%); all but one were of White British ethnicity. Emotional problems were the most common reasons for CAMHS referral ( n = 22; 47%), followed by non-specific behavioural ( n = 10; 21%), hyperkinetic ( n = 8, 17%) and conduct ( n = 7, 15%) problems. All children watched TV ( n = 47, 100%), most played video games on a console ( n = 42, 89%) or handheld games ( n = 38, 81%) and used computers ( n = 40, 85%), mobile phones ( n = 34, 72%) and the Internet ( n = 38, 81%).

Qualitative study participants were purposively sampled for varying levels of aggression and difference in age, gender, ethnicity and family income [ 14 ]. Fifteen boys and five girls aged 8-12 years, and their main carers contributed to forty interviews, a number likely to deliver saturation of themes and facilitate triangulation of data (child interviews, carer interviews and quantitative data) [ 15 ]. All children but one were of White British ethnicity and had a wide range of family income.

The study was approved by a local research ethics committee. Permission for children’s participation was sought from the child (verbal assent) and a parent with Parental Responsibility (consent). Carers gave consent for their own participation.

Carers completed the Children’s Aggression Scale, parent version (CAS-P), a measure of type, frequency and severity of aggression in psychiatrically referred children aged 7-11 years, in outpatient settings [ 16 ]. Most of its 33 items are rated on a 5-point severity/frequency scale (from “never” = 0 to “most days” or “more than 10 times” = 4). The CAS-P has 5 subscales: Verbal Aggression (e.g., from “snapped or yelled” to “verbally threatened to hit” others), Aggression Against Objects and Animals (e.g., from “slammed a door when angry” to “tortured a pet”), Provoked Physical Aggression (e.g., fighting with others when provoked resulting in mild to serious injuries), Initiated Physical Aggression (e.g., starting fights resulting in mild to serious injuries) and Use of Weapons (e.g., from carrying to injuring another with a weapon). Summing the products of the frequency of behaviour by severity weight for each item yields a total score: higher scores indicate greater aggression. With good internal reliability, the CAS-P significantly correlates with ratings of aggression on the Inattention/Overactivity with Aggression Scale and Child Behavior Checklist (CBCL) [ 16 ].

Children and carers completed the Measure of Aggression, Violence, and Rage in Children (MAVRIC), a measure of frequency and severity of aggression in children aged 5-18 years, in psychiatric outpatient/inpatient settings [ 17 ]. The 19 items on the MAVRIC-C (child version), which parallel those on the MAVRIC-P (parent version), contain between one and eight yes-no questions each, covering verbal and physical aggression and aggression against objects. Higher scores are assigned to “yes” answers indicating longer history and duration of aggressive outbursts and greater severity of potential harm to others. Items are summed to yield a total score (0-30). MAVRIC has good internal reliability and convergent validity with the Aggressive Behavior subscale of the CBCL [ 17 ]. A clinical cut-off of 10 was used [ 17 ],[ 18 ]. The MAVRIC-C was read and explained to children, ensuring understanding.

Carers completed the standardised, validated and reliable Strengths and Difficulties Questionnaire (parent version, P4-16-SDQ), a brief behavioural screening measure of children aged 4-16 years [ 19 ]. The 25 items, each rated on a 3-point scale, are allocated to five subscales, generating scores (0-10) for Emotional Symptoms, Conduct Problems, Hyperactivity, Peer Problems and Prosocial Behaviour. Summing all but the last generates a Total Difficulties Score (0-40).

Semistructured, individual interviews explored children’s and carers’ views on the nature of aggression and where children see aggression in their lives, their feelings on seeing aggression, any relationship between seeing aggression and behaving aggressively and any influencing factors. Children were first asked about TV programmes/video games they liked/did not like. Probing questions, using cartoons, explored programme/game content, what the “goodies” and “baddies” did, whether anything from the programmes/games scared them, and any programmes/games they were not allowed to watch/play. Children were then shown a set of pictures illustrating aggression and asked to describe what they thought was happening in each picture, whether and where they previously had seen such things happening and how they felt at the time, and whether children, including themselves, do such things after seeing them. Most pictures were taken from the Violence Exposure Scale-Revised [ 20 ]. Carers were asked similar open-ended questions.

All children’s study measures were completed on the same day, either at CAMHS or the child’s home, either with the child alone (carer in a room nearby) or in the carer’s presence (as facilitator). The interviews took approximately 40 minutes (10 minutes for the MAVRIC-C). Carers’ interviews were up to 90 minutes long (including approximately 5-10 minutes per questionnaire), completed on the same or later date, depending on carers’ availability. Interviews were audiotaped and transcribed verbatim. Respondent validation for researcher interpretation was sought during interviews.

Data analysis

Descriptive statistics were compiled frequency and characteristics of aggression. Spearman’s correlation and group comparisons using Kruskal-Wallis, Mann–Whitney U tests and Wilcoxon test for matched samples were used to examine associations between scores on aggression measures, socio-demographic variables and SDQ scores. The child’s age at referral was used in statistical analyses as this was a sampling criterion.

Interview transcripts were analysed using the five-staged Framework Approach, a form of thematic qualitative analysis [ 21 ], and NVivo software (version 8). The central component of the Framework Approach is the thematic framework, a series of thematic headings sorted into main and sub-themes, generated from anticipated and emerging issues. The framework is systematically applied to every transcript. The analyst moves back and forth between levels of abstraction without losing sight of the original data, ensuring consistency and transparency. The full range of views can be compared and contrasted within and across participants, thus patterns can be identified and explored.

The quantitative analysis preceded and facilitated qualitative analysis. Issues identified through qualitative analysis informed further quantitative analysis, e.g. exploring links between aggression and the child’s age and family income.

Frequency and characteristics of children’s aggression

Children exhibited various types of aggression: aggression against objects and animals, verbal and physical aggression, including severe forms e.g., attempting to kill someone. Mean CAS-P scores, reflecting frequency and severity of aggression, ranged from 0.43 (weapon use) to 8.83 (verbal aggression) (Table 1 ). MAVRIC mean scores were above the cut-off of 10 (child report 14.59, SD 5.34, range 2-23; carer report 14.65, SD 5.53, range 3-26), suggesting clinically significant aggression in this population. About three-quarters of children ( n = 28, 72% on self-report; n = 36, 78% on carer report) scored above this cut-off. Eleven children (28%) reported thoughts of killing other people when angry and three (8%) having tried to do so. Seventeen carers (37%) reported their children having such thoughts, while three (6%) reported their children’s attempts. Two carers (6%) reported gang-related weapon use. Carer reports were strongly associated with each other (ρ ranging from 0.52 to 0.72, p < .05), but not with child reports.

Several socio-demographic factors were associated with children’s aggression: age, average family income, household size (number of people living in the home), main carer’s highest level of formal education and household type (family headed by lone parent or couple). Seven-nine year-olds scored higher than 10-11 year-olds on Aggression against Objects and Animals, with a similar trend for Initiated Physical Aggression. Children from below national-average family income homes scored higher than others on most aggression measures (Table 1 ). Children in the lowest income group (mean 0.96, SD 1.88) scored higher than others (mean 0.04, SD 0.14) on Use of Weapons ( p < .05). Larger household size was linked to high scores on overall CAS-P, Verbal Aggression and Initiated Physical Aggression (Table 2 ). There was a trend for children whose carers’ highest level of formal education was secondary school (mean 0.54, SD 0.97), living in a family headed by a lone parent (mean 1.34, SD 2.47) to score higher on Use of Weapons than children whose carers’ highest level of formal education was university (mean 0, SD 0; p < .10), living in a family headed by a couple (mean 0.17, SD 0.47; p < .10). Aggression scores were not linked to gender.

High aggression scores on most subscales were associated with high levels of peer relationship problems and low prosocial behaviour levels on the SDQ. High Verbal Aggression scores were more strongly linked to high Conduct Problems and Peer Problems and low Prosocial Behaviour SDQ scores than most of the other aggression subtypes (Table 2 ).

Where do children see aggression?

Children and carers described many places where children see aggression. They fall into two broad categories: real-life, mainly school, playground and home; and the virtual world, mainly television and video games (Figure 1 ). Video games and TV programmes were the most often reported sources of seeing aggression.

Where do children see aggression in their lives. Sources of seeing aggression. Main sources of seeing aggression. Main types of aggression. Important factors related to sources of seeing aggression.

Real-life aggression is mostly verbal e.g., people “shouting” at each other, but also physical, e.g., “fighting”, with some children seeing “a fight every day”. Carers tend to see this as characteristic behaviour for children’s developmental stage and gender, particularly for boys. One mother talked about the “aggression between boys in the sort of pecking order to see who is the toughest”. Three children witnessed severe domestic violence. Low family income appears related to seeing aggression in the community (the street or by neighbours).

Children tend to see more severe forms of aggression, e.g. “stabbing” or “shooting”, in the virtual world. Aggression is present in seemingly age-appropriate programmes and games, i.e., those recommended by rating boards as suitable for the child’s age or broadcasted before `the watershed' (e.g., cartoons). Children also see aggression on inappropriate media, e.g. “shooting games” rated suitable only for those over 18 years, e.g., a 12-year-old boy described playing Grand Theft Auto (18+): “you can go round shooting people for no reason”.

Children sometimes have access to age-inappropriate media accidentally, e.g., free games with a purchased game console, or intentionally, sometimes against parental rules, e.g. at friends’ houses. Carers talked about a generation gap, remembering growing up with less, or a “different breed” of, virtual aggression (e.g., Tom and Jerry). Carers are sometimes unaware of the aggressive content of video games they buy for their children. Some carers thought it difficult to protect children: aggression “is everywhere” and continuous monitoring of what is watched or played is impossible, although letting children decide may be risky. A few carers were radical, saying video games “should all be taken completely off the shelf so nobody can go on them. They only bring violence.”

Boys, more than girls, are interested in video games depicting aggression. Children and carers perceive boys’ preferences to be related to gender-specific competitiveness: the competitive nature of the games challenge boys to move "on to the next level", they “love to win” and “aggression is the excitement”. Virtual settings also permit things impossible in real-life “because you”d just get arrested'. Children and carers talked about “peer pressure”, “like a stigma” and "getting picked on at school" for not playing such games. Society and the media market, accessibility and appeal of games and the lack of exciting but non-aggressive games, fathers' and peers' similar preferences, and lack of outdoor activities, are also believed to influence boys' preferences. Parental restrictions sometimes have the opposite effect: boys play forbidden games more.

Perspectives of children and carers

Children and carer views on associations between aggression seen and exhibited aggression inform two distinct models of thinking: the child model of others but not me (Figure 2 ) and the carer model of nature and nurture (Figure 3 ). Children thought they could be “aggressive” when “angry” or “stressed” rather than because of seeing aggression. The virtual world is clearly separated from real-life: children feel scared or upset by aggression seen “for real” when they empathise with someone being “hurt”, but have neutral feelings towards aggression seen in TV programmes or video games, even if severe. They feel the latter is “just a game” or “just a cartoon” and “not hurting anyone”; carers were aware of this attitude: "He understands they are make-believe" [he thinks] they don't affect him.' Most children see themselves as being at an age or developmental stage where they have learned to differentiate reality from fiction and to understand the potential consequences of aggression.

Child model: Others but not me. Contributing factors to children’s aggression. Protective factors against influences of aggression seen in the virtual world. Risk factors related to influences of aggression seen in the virtual world.

Carer model: Nature and nurture. Factors related to influences of aggression seen in the virtual world. Possible explanations for influences of aggression seen in the virtual world.

Children distinguish between non-realistic/cartoon-like and realistic/human-like aggression. The first typically involves “goodies” fighting “baddies” whose “bodies disappear”, thus being “not really violent” and “funny”. Examples include The Simpsons or Lego games. “Too graphic” media depicting “proper human” people that “move the way they”d get shot in real-life' or “body parts” are considered “realistic” and “violent”; a video game is “violent” only if they “see any blood” in it, according to some children.

Children and carers shared views on other people’s aggression. `Other' children may behave aggressively because they are “mean” or “evil”, angry or stressed, were "taught the wrong way by their parents", or because of parental separation, domestic violence or physical abuse. Children provoked by others exemplified peer influence. Most children thought “really young people”, who cannot distinguish between reality and fiction, could “imitate” their favourite virtual “hero”. Significance was placed on early exposure (3-4 year olds) to aggression, e.g., a 10-year-old boy said, "Little children tend to copy what they see on TV or play. So if they pick up violent stuff, they play violent games, it’s more likely they’re gonna grow up to be like that". One child noted a positive, protective aspect of virtual aggression: 'it’s got to have violence because then you can see how bad it actually is and the effects of it so then people wouldn“t do it”.

Carers thought of aggression as a combination of inner and environmental factors. Inner factors include an “aggressive predisposition”, “something inbuilt” in a child’s “genes” or "mental make-up". Outside influences, or “nurture”, include, more importantly, real-life and the virtual world. The latter was considered to add to children’s predispositions, pre-existing behavioural problems and real-life aggression: "in a child that was already going to be aggressive it might exacerbate it, but I don't think it would cause it alone in a child who wasn't aggressive." One mother prevented her son from watching certain TV programmes because 'there’s anger inside him anyway", not wanting to “feed that anger”. Most carers emphasised the vital role of family and community in helping children use the virtual world “within a controlled environment”, i.e., to explain that aggression “isn”t a good act", and the nature and consequences of aggression, thus limiting its behavioural influences.

Carers felt the distinction between reality and fiction sometimes becomes blurred, especially when aggression is depicted without its “negative side”. One mother noted the way her son "thinks `if Bart can do it, I can do it.' Bart Simpson is a real person to him." Another mother had prevented her 9-year old son from playing an aggressive game: "He says he can distinguish between what’s real and what’s not real but what’s not real is coming into the real world in the form of his aggression". Carers also distinguished between non-realistic/cartoon-like and realistic/human-like aggression, the latter possibly having stronger behavioural effects. Both children and carers see aggression and violence as distinct: violence is “physically doing something” to another person, thus at the more severe end of the aggression spectrum.

carers’ explanations for any behavioural influences of virtual aggression included “desensitisation” (becoming used to aggression and think "it’s the norm"); provision of “role models” to be “copied”; and “mirroring” or reinforcement of real-life aggression. “Addiction” and gender were raised by two mothers, whose sons' aggression was seemingly caused by attempts to interfere with their “obsession” with playing video games. This behaviour was compared to that of a “drug addict”, linked, in part, to boys' competitiveness. Video game playing and sometimes watching TV, regardless of content, were said to make some children “hyper”, "their brains on the go all the time", contributing to aggression, especially in boys.

Carers also discussed the potential role of age and developmental stage (see above). Older children "already developed their own sets of morals and values" and seeing aggression “wouldn't affect them so much”, although the “impressionable” teenage stage was mentioned.

To our knowledge, this study is the first to report on aggression, seen or exhibited, in a UK-based sample of children with BED attending CAMHS. Our sample’s mean scores on most CAS-P subscales were higher than those for children with oppositional defiant disorder, but below the means for children with conduct disorder on all subscales, in an American clinic-based sample of children of similar age [ 16 ]. We found similar low frequencies of weapon use, perhaps related to the young age of the sample.

Our findings of multiple real and virtual sources of seeing aggression, with severe forms seen more often in the latter, agree with earlier Israeli research with primary school children [ 22 ]. Our sample’s reports of seeing aggression on TV, including age-inappropriate programmes, contradict Lowdermilk’s findings that American, primary school children with BED reported mainly watching positive, family-friendly programmes to escape the reality of their sometimes violent home lives [ 10 ].

Our findings that children of this age/ developmental stage make a clear distinction between, and appear to have different emotional responses to real-life and fictional aggression, with potentially different behavioural consequences cohere with recent research in adults. Ramos and colleagues’ findings support the key role of the fantasy-reality distinction in mediating viewers’ cognitive and emotional processing of violence, and go against the idea of desensitisation i.e., media violence does not necessarily reduce viewers’ empathy towards real-life violence [ 23 ]. Younger children’s potential lack of ability to distinguish between the fictional and the real, mentioned by the children in this study, was also discussed by Byron [ 8 ]. This has been disputed, however, by other researchers who argued that children start to develop the ability to use context to make the fantasy-reality distinction between the ages of 3 and 5 years [ 24 ].

Byron also found that children often talk about playing 18-rated games [ 8 ]. Her findings re-iterate ours on children’s notion of "it’s only a game’; parents" lack of awareness of game content; parental concerns about desensitisation and risk of addiction; and the relevance of a child’s individuality. Her similar findings on parental concerns over children getting more access to video games surreptitiously if playing is restricted at home parallel the "forbidden-fruit effect" described by Bijvank and colleagues: restricting young people’s access to video games by using age and violent-content labels may increase their attractiveness [ 25 ].

Our finding that children of this generation are more familiar with video games than their carers echoes Hulme’s concept of “digital natives”: those growing-up with new information and communication technologies are fundamentally different from previous generations in the way they communicate, seek information, interact and entertain themselves [ 26 ]. They may watch TV or play video games when lacking alternative, e.g. outdoor activities [ 27 ], perhaps explaining in part carers’ difficulties in controlling children’s access to the virtual world. Generational differences in video game experience may have contributed to the observed distinction between children’s and carers’ views on the behavioural impact of violent games [ 28 ].

Our results suggest that aggression results from a combination of inner and environmental factors, where family and community have a key role in limiting the influence of aggression on children’s behaviour. This coheres with multiple risk factor models of aggression and the ecological model of child development, which integrates individual, family and environmental factors [ 4 ],[ 29 ]. carers’ own explanations for any association between aggression seen in the media and children’s aggression (aggressive predisposition, “copying” negative role models, reinforcement of real-life aggression, desensitisation, being “hyper” after watching TV or playing video games) appear to be consistent with theories such as social learning theory, the cognitive neo-association model, social information-processing model and arousal theory [ 30 ]-[ 33 ]. But their suggested role of virtual aggression i.e., only additional to children’s aggressive predisposition, pre-existing behavioural problems and secondary to aggression seen in real-life, coheres with the more recently proposed “catalyst” model [ 34 ]. This model suggests that severe forms of aggression results from a combination of genetic and proximal environmental influences (family and peers), with distal environmental factors such as the media having a less important role of modelling the form of aggressive behaviour.

Recent research has also focused on understanding individuals’ motivations for video game play, including violent games, and placing it within the context of normal development [ 35 ]. Our findings on reasons for children’s, particularly boys’ interest in aggressive video games (competition, challenge, fun, excitement, doing things they cannot do in real-life, peers’ preferences, lack of other activities) cohere with studies on motivations for, and experiences of video game play among children [ 35 ] and adults [ 36 ], and the motivational model of video game play [ 37 ]. They are also consistent with Adachi and Willoughby’s findings on the competitive nature of video games as a third variable in the observed longitudinal association between violent video game play and aggression [ 38 ].

Our study augments findings of a systematic review that reported differences between children’s and parents’ views on whether seeing aggression on TV affects children; and possible associations between watching TV, regardless of aggressive content, and children’s aggression [ 10 ]. The way children perceive cartoon-like aggression as “not really violent” and “funny” could partly explain the contradictory results of earlier experimental studies investigating the effects of watching aggressive cartoons on children’s behaviour.

Our quantitative study is limited by the small size and gender and ethnic imbalance of the sample, potentially affecting its generalizability. The qualitative findings also may be less representative of the views of children and carers of other than White British ethnicity. The qualitative data analysis was informed by the researchers’ experiences as mental health professionals and a priori reasoning about possible links between seen and exhibited aggression.

The low recruitment rate reflects challenges in researching this doubly hard to reach population: recruiting children in mental health contexts. Identified barriers were related to: participants, e.g., the children’s complex mental health problems and subsequent burden on families; the research topic (sensitive, raising ethical dilemmas); and mental health services, e.g., clinicians acting as over-zealous gatekeepers [ 39 ], restricting access to families. Nonetheless, there were no significant differences between participants and non-respondents/those opting-out, on child’s age, gender and referral reasons.

There is yet no definitive evidence for or against a direct relationship between aggression seen in the media and children’s aggression. Our study indicates that future research in this field should take a broader, ecological perspective, investigating individual, developmental, family and environmental factors. Virtual aggression seems to play a secondary role to real-life aggression, hence validated measures of aggression in both contexts, distinguishing between types of aggression (verbal and physical) are still needed. The potential role of gender and aggressive predisposition, as mediating or moderating factors in any observed relationship between exposure to media violence and aggression needs nuanced investigation. Third variables that may be key, and need to be further operationalized in terms of research methods, are the abilities to distinguish reality from fantasy, and realistic from non-realistic fiction, and competitiveness. Several other factors should be controlled for in future studies: peer relationships, family income, type and size of household, and parental formal education level. A child’s cognitive, social and emotional developmental stage may be more significant than chronological age. Further research among children with pre-existing mental health problems is warranted, in view of ongoing debate regarding their potential vulnerability [ 40 ].

Our quantitative and qualitative findings indicate that when seeking subjective data from informants, complementarity of child/parent reports rather than inter-rater reliability is worth seeking, as child and adult perspectives potentially uncover different underlying phenomena. Example are children’s distinction between real-life and fictional aggression, and their views that the depiction of blood differentiates between what is and what is not violent.

Children, regardless of their socio-demographic background or aggressive behaviour levels, see a lot of aggression in many parts of their lives. Future research may either confirm or refute the existence of an association between media violence and children’s aggression. Until then, clinicians, professional organizations and policy-makers should address aggression coming into children’s lives through all relevant means, primarily real-life and secondly virtual. For example, in clinical contexts, children are often asked about experiences of abuse but much less about seeing aggression at home, at school, in the neighbourhood or in the media. Children’s access to programmes and games should be supervised [ 35 ] and accompanied by developmentally appropriate discussion about the aggression seen and its potential real-life consequences. Carers should be careful, particularly when a child has an aggressive predisposition and at an earlier developmental stage, as aggression seen could have greater impact on these children.

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This research was funded by the University of Warwick, UK. Researchers would like to thank all families who were involved in the study for their valuable contribution.

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Mitrofan, O., Paul, M., Weich, S. et al. Aggression in children with behavioural/emotional difficulties: seeing aggression on television and video games. BMC Psychiatry 14 , 287 (2014). https://doi.org/10.1186/s12888-014-0287-7

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Aggressive and Violent Behavior

2015, Handbook of Child Psychology and Developmental Science

This is a pre-print version of the Chapter Prepared for the Handbook of Child Psychology and Developmental Science, 7th Edition, Volume 3: Social, emotional and personality development, Edited by Michael E. Lamb

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Digital Technology in Nutrition Education and Behavior Change: Opportunities and Challenges

Alexandra l. macmillan uribe.

Institute for Advancing Health Through Agriculture, Texas A&M AgriLife Research

Emily Welker Duffy

University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Department of Nutrition

Basheerah Enahora

Department of Agricultural and Human Sciences, North Carolina State University, NC State Extension

Phrashiah Githinji

Texas A&M AgriLife Research, Institute for Advancing Health through Agriculture

Jared McGuirt

Department of Nutrition, University of North Carolina Greensboro

Gina L. Tripicchio

Center for Obesity Research and Education, College of Public Health, Temple University

The incorporation of digital technology (digitech) within nutrition education and behavior change interventions (NEBI) has markedly increased, and COVID-19 rapidly accelerated advancement and acceptability in this area. 1 The proliferation of digitech, including devices and platforms, creates novel ways for end-users to engage with NEBI and presents unique opportunities for increasing reach and engagement of underrepresented populations. 2 - 5 While a “digital divide” exists with some digitech, like desktop/laptop ownership and home broadband internet access, most people own smart phones (≥ 76%) or use social media (≥ 65%), regardless ofincome, race and ethnicity, or age. 6 - 8 Furthermore, digitech can resolve common barriers to NEBI participation (e.g., lack of transportation or time) and can increase NEBI scalability. 9 Prior to developing or adapting NEBI that incorporate digitech, it is important to consider challenges that might impact their effectiveness and approaches that enhance equitable access.

Digitech-specific, evidence-based frameworks are critical for developing effective NEBI. In user-centered design, for example, end users’ needs and preferences are prioritized and used to guide design processes, 10 leading to improved participant engagement and an increased likelihood of an effective intervention. 11 Researchers may also consider implementation process models to guide development and optimize sustained digitech utilization. For example, the Exploration, Adoption/Preparation, Implementation, Sustainment (EPIS) model helps identify whether NEBI-related digitech is feasible, adoptable, and relevant to the intended population. 12 Classic theories, like diffusion of innovation, can also be applied to understand how digitech innovations are adopted. Also, engagement strategies like reminders, coaching, and personalized information 13 are important considerations in NEBI digitech. Digital inequities, such as inconsistent internet access or low digital literacy, disproportionately burden the same populations burdened by diet-related disease inequities. Employing user-centered design and leveraging digitech already adopted by the intended audience (e.g., among Hispanics, 80% use social media and 85% own smartphones) 6 , 8 could help reach populations most at risk of diet-related diseases.

Another key challenge is the financial cost of developing and maintaining digitech. For example, a mobile application with simple features can cost $16,000 to $32,000, not including maintenance and updates. 14 This is coupled with the competition, money, and fast pace of digitech in industry that is often misaligned with the scrutinous, slow pace of research. Rigorous digitech-focused funding mechanisms could help support the development and maintenance of innovations. However, continued funding for maintenance and updates may require further testing or expansion of digitech, as part of additional research proposals. Another strategy is leveraging industry’s financial assets and audience reach through collaborative projects that navigate and consider the often-divergent interests of research and industry.

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Biographies

MacMillan Uribe

Contributor Information

Alexandra L. MacMillan Uribe, Institute for Advancing Health Through Agriculture, Texas A&M AgriLife Research.

Emily Welker Duffy, University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Department of Nutrition.

Basheerah Enahora, Department of Agricultural and Human Sciences, North Carolina State University, NC State Extension.

Phrashiah Githinji, Texas A&M AgriLife Research, Institute for Advancing Health through Agriculture.

Jared McGuirt, Department of Nutrition, University of North Carolina Greensboro.

Gina L. Tripicchio, Center for Obesity Research and Education, College of Public Health, Temple University.

Research on Hot Deformation Behavior of Ti-5Al-5Mo-5V-1Cr-1Fe Titanium Alloy with Basket-Weave Microstructure

• Advanced Characterization on Thermodynamic and Kinetics of Complex Alloys
• Published: 05 September 2024

Cite this article

• Yingying Liu   ORCID: orcid.org/0000-0001-9185-3380 1 , 2 ,
• Siyu Tan 1 ,
• Jian Yang 3 ,
• Xuan Liu 1 ,
• Junjie Yang 1 ,
• Chun Li 2 ,
• Shifeng Liu 1 ,
• Wen Wang 1 , 4 &
• Kuaishe Wang 1 , 4  

The hot deformation behavior of Ti-5Al-5Mo-5V-1Cr-1Fe titanium alloy with basket-weave microstructure was studied by the thermal compression experiment conducted at temperatures ranging from 1073 K to 1193 K and strain rates from 0.001 s −1 to 1 s −1 . The results indicate that the stable flow state of the stress–strain curve of Ti-5Al-5Mo-5V-1Cr-1Fe alloy is rapidly reached within a small strain range (< 0.1). The majority of flow curves exhibit horizontal plateaus, which is attributed to the rapid equilibrium between dislocation accumulation and annihilation. Within the experimental parameters, the constitutive equation at ε  = 0.3, thermal activation energy of 346.017 KJ mol −1 and the strain rate sensitivity exponents between 0.25 and 0.4 for basket-weave microstructureTi-5Al-5Mo-5V-1Cr-1Fe titanium alloy with basket-weave microstructure were calculated. During hot deformation, there are phase transformations and several softening mechanisms, including lamellar spheroidization, dynamic recrystallization, dynamic recovery and their interactions. The β transition structures are cut by a large number of interleaved α lamellar structures, and the α lamellar structures are participated in coordinated deformation, which promotes the enhancement of strength and plasticity of Ti-5Al-5Mo-5V-1Cr-1Fe alloy. The model and microstructure analysis in this research can provide data reference for material selection and processing optimization of aircraft structural parts with high impact toughness and strength.

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Acknowledgements

The article is protected by copyright. This work was supported by the fund of the National Defence Key Discipline Laboratory of Light Alloy Processing Science and Technology, Nanchang Hangkong University (EG202380294), Natural Science Foundation of Shaanxi Province of China (2022GY-232) and the Xi’an Science and Technology Program (Grant No.24LLRHZDZX0008). The authors would also like to thank Lei Yang and Wenjuan Niu of Xi’an University of Architecture and Technology for their assistance.

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Yingying Liu, Siyu Tan, Xuan Liu, Junjie Yang, Shifeng Liu, Wen Wang & Kuaishe Wang

National Defense Key Disciplines Laboratory of Light Alloy Processing Science and Technology, Nanchang Hangkong University, Nanchang, 330063, China

Yingying Liu & Chun Li

The First Aircraft Institute of AVIC, Xi’an, 710089, China

National and Local Joint Engineering Research Center for Functional Materials Processing, Xi’an University of Architecture and Technology, Xi’an, 710055, China

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Liu, Y., Tan, S., Yang, J. et al. Research on Hot Deformation Behavior of Ti-5Al-5Mo-5V-1Cr-1Fe Titanium Alloy with Basket-Weave Microstructure. JOM (2024). https://doi.org/10.1007/s11837-024-06855-1

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Received : 30 May 2024

Accepted : 19 August 2024

Published : 05 September 2024

DOI : https://doi.org/10.1007/s11837-024-06855-1

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