Dataset Information

A tool for investigating the differential functions of aggressive behavior in the face-to-face and cyber context: Extending the Cyber-Aggression Typology Questionnaire.

ABSTRACT: Aggressive behavior in the face-to-face and cyber contexts is driven by underlying aggression (i.e., functions of aggressive behavior). Common theories of aggression distinguish between reactive (e.g., rage) and proactive (e.g., seeking to achieve power and affiliation) aggression. However, according to the quadripartite violence typology, this distinction conflates aspects of motivational valence with self-regulatory processes. The Cyber-Aggression Typology Questionnaire (CATQ; Runions et al., 2017, Aggress Behav, 43(1), pp. 74-84) overcomes this weakness by identifying four types of cyber-aggression (impulsive-aversive/rage, controlled-aversive/revenge, controlled-appetitive/reward, and impulsive-appetitive/recreation cyber-aggression). However, the CATQ only considers aggression in cyberspace. We extended the CATQ to the face-to-face context by developing a corresponding Face-to-Face Aggression Typology Questionnaire (FATQ). The aim of this study was to investigate factorial and convergent validity and metric measurement invariance between four-factorial cyber and face-to-face aggression. In total, 587 students from six Austrian universities filled out the CATQ, the FATQ, and additional scales during regular university lectures to examine convergent validity. Confirmatory factor analysis supported the four-factor structure of both questionnaires, after excluding inconclusive items from the impulsive-aversive/rage subscale of the FATQ. These items were also removed from the CATQ to obtain two symmetric questionnaires. Metric measurement invariance between the CATQ and the FATQ was confirmed. Convergent validity was largely observed. Our results support an extended four-factor model of aggression. Having two parallel questionnaires, the FATQ and CATQ, enables future studies to investigate commonalities and differences in underlying drivers of aggressive behavior in the cyber and face-to-face contexts.

SUBMITTER: Graf D

PROVIDER: S-EPMC7496625 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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A tool for investigating the differential functions of aggressive behavior in the face-to-face and cyber context: Extending the Cyber-Aggression Typology Questionnaire.

Graf Daniel D Yanagida Takuya T Maschler Albert A Spiel Christiane C

Aggressive behavior 20200507 5

Aggressive behavior in the face-to-face and cyber contexts is driven by underlying aggression (i.e., functions of aggressive behavior). Common theories of aggression distinguish between reactive (e.g., rage) and proactive (e.g., seeking to achieve power and affiliation) aggression. However, according to the quadripartite violence typology, this distinction conflates aspects of motivational valence with self-regulatory processes. The Cyber-Aggression Typology Questionnaire (CATQ; Runions et al., ...[more]

PMID: 32383267

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Project description:BackgroundEnabling the use of spatial context is vital to understanding today's digital health problems. Any given location is associated with many different contexts. The strategic transformation of population health, epidemiology, and eHealth studies requires vast amounts of integrated digital data. Needed is a novel analytical framework designed to leverage location to create new contextual knowledge. The Geospatial Analytical Research Knowledgebase (GeoARK), a web-based research resource has robust, locationally integrated, social, environmental, and infrastructural information to address today's complex questions, investigate context, and spatially enable health investigations. GeoARK is different from other Geographic Information System (GIS) resources in that it has taken the layered world of the GIS and flattened it into a big data table that ties all the data and information together using location and developing its context.ObjectiveIt is paramount to build a robust spatial data analytics framework that integrates social, environmental, and infrastructural knowledge to empower health researchers' use of geospatial context to timely answer population health issues. The goal is twofold in that it embodies an innovative technological approach and serves to ease the educational burden for health researchers to think spatially about their problems.MethodsA unique analytical tool using location as the key was developed. It allows integration across source, geography, and time to create a geospatial big table with over 162 million individual locations (X-Y points that serve as rows) and 5549 attributes (represented as columns). The concept of context (adjacency, proximity, distance, etc) is quantified through geoanalytics and captured as new distance, density, or neighbor attributes within the system. Development of geospatial analytics permits contextual extraction and investigator-initiated eHealth and mobile health (mHealth) analysis across multiple attributes.ResultsWe built a unique geospatial big data ecosystem called GeoARK. Analytics on this big table occur across resolution groups, sources, and geographies for extraction and analysis of information to gain new insights. Case studies, including telehealth assessment in North Carolina, national income inequality and health outcome disparity, and a Missouri COVID-19 risk assessment, demonstrate the capability to support robust and efficient geospatial understanding of a wide spectrum of population health questions.ConclusionsThis research identified, compiled, transformed, standardized, and integrated multifaceted data required to better understand the context of health events within a large location-enabled database. The GeoARK system empowers health professionals to engage more complex research where the synergisms of health and geospatial information will be robustly studied beyond what could be accomplished today. No longer is the need to know how to perform geospatial processing an impediment to the health researcher, but rather the development of how to think spatially becomes the greater challenge.

Dataset Information

A tool for investigating the differential functions of aggressive behavior in the face-to-face and cyber context: Extending the Cyber-Aggression Typology Questionnaire.

Publications

A tool for investigating the differential functions of aggressive behavior in the face-to-face and cyber context: Extending the Cyber-Aggression Typology Questionnaire.

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