Video Data Analysis
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Video Data Analysis (VDA) is an analytical framework designed to facilitate the study of social processes and situational dynamics. It consists of approaches and tools from various social science disciplines for collecting and analyzing video data in the 21st century. Video Data Analysis is employed across disciplines such as sociology, psychology, criminology, and education research.[1]
General approach
VDA makes use of technological and social developments in relation to video recordings. Mobile phone cameras, CCTV surveillance cameras, body-worn cameras, and other types of cameras generate an ever-expanding pool of recordings from real-life situations. More and more of these videos are uploaded to internet platforms such as Snapchat, TikTok, Instagram, LiveLeak, YouTube, Facebook, and many others. Others can be accessed through collaboration with public and private institutions, such as police departments or CCTV providers. These new sources of video data support researchers in unobtrusively collecting video recordings that depict real-life situations, even of extremely rare events that would be otherwise impossible for researchers to observe firsthand. VDA relies on these types of videos to analyze real-life social processes and events—tracing them step-by-step to explain how they unfold.[1]
To do so, VDA draws on methodological approaches such as visual studies, ethnography, psychological laboratory experiments, and multimodal interaction analysis to provide an interdisciplinary approach to using, primarily, found video data.[1] Foci of such analyses include sequences of people’s interactions, movements, fields of vision, exchanges of glances or gestures, and actors’ facial expressions and body postures.[1] The goals of VDA studies are to further our understanding of the rules, processes, and sequential patterns that govern social life on the micro level, both in everyday encounters and extreme situations.[2] At the core of this perspective lies the question: How do social actions and situational dynamics impact social outcomes? Video data offers the possibility to study situational patterns in unprecedented detail and rigor by allowing researchers to replay situations, watch them in slow motion and fast forward, and share primary data of situations with colleagues and readers. VDA outlines a toolkit of analytic dimensions and procedures, introduces criteria of validity, and discusses challenges and limitations.
Analytic dimensions and procedures
Analytic dimensions refer to the content of visual data that are of interest when analyzing situations: facial expressions and body posture, interactions, and context.[1][2] Facial expressions and body postures are any nonverbal information that a person’s face and body convey. Interactions refer to anything people do or say that is geared toward or affects their environment or people within. Context means information on the physical and social setting of a situation. These dimensions should be understood as lenses that help derive information from visual recordings and that might help to understand situational dynamics, provided they draw on a thorough theoretical reflection and employ clear, detailed coding schemes.
Through its analytic procedures, VDA aims to reconstruct a situation step-by-step, analyze its inner dynamics, and establish comprehensive storylines.[2] Developing coding schemes entails a first phase of intensive, detailed labeling of information in a data piece, and a second phase of grouping labels together into categories and dimensions that can be connected to form concepts. If researchers apply a deductive approach, developing concepts and a coding scheme is not part of data analysis, and the analysis starts with assigning codes to the data. Assigning codes to the data means to assign one or several codes to a section of a data piece, thereby indicating the relevant content this section contains. Reconstructing events means breaking down situations or events under study by focusing on who did what, when, and where. This reconstruction should include context as well as process and can be documented in the form of a narrative account, tempo-spatial matrix, and/or visual representation of the situation or event.[2] Looking for patterns means to try to understand what situational path led (through one or several chains of linked actions and stages) from the start of the situation to the occurrence, or absence, of the outcome under study. It can help to (1) look for smoking guns and turning points, (2) count things, (3) analyze rhythm and turn-taking, (4) zoom in on actors, and (5) focus on the role of space.[2] VDA’s detailed case reconstructions and analyses of patterns may be combined with any method of data analysis that helps identify patterns in a formalized and systematic way. Depending on the research question, regression analysis, configurational comparative methods, grounded theory methodology, and sequence analysis can be especially fruitful additions to VDA-type analyses.
Quality criteria
Criteria for validity include neutral or balanced data sources, optimal capture, and natural behavior. Neutral or balanced data sources should not reflect an adherence to particular interests that could lead to the concordant publication or provision of access to biased data; if sources that demonstrate a propensity for specific interests are used, researchers should seek to triangulate various sources representing divergent interests. Optimal capture means visual data should cover the duration of a situation or event, its space, and all actors involved. Natural behavior refers to an actor’s unaltered behavior in a given situation, that is, the researcher should consider the degree to which actors recorded in visual data behave the same way that they would have otherwise behaved, were a camera not present.[2]
Uses
Video Data Analysis has been applied to study how armed store robberies evolve or fail[3][4], to examine the situational dynamics of physical violence[5][6][7], to observe how protests or uprisings turn violent[8][9][10][11] and how massacres or street fights happen[12][13][14], as well as to study military negotiations[15], school yard fights, lynching, or consoling behaviors.[16][17]
VDA is not suited for all types of research questions and theoretical approaches and, like all methodological approaches, it entails limitations and challenges. First, the type of data used by VDA implies limited access to video recordings from private events, such as funerals in Western societies. Second, VDA does not offer the tacit knowledge and immersion in a social context that comes with continuous direct participant observation, and it does not offer the same potential as ethnography for studying the cultural knowledge or narratives of a specific community or group of people. Third, interpretation of certain elements, such as gestures, may be context dependent, making VDA less suitable to study social contexts that a researcher is unfamiliar with.[2] Fourth, a number of research ethics questions remain unclear with the new types of video data VDA often employs[18]; e.g., what types of video from which platforms are admissible to use as research data.
See also
- Participant observation
- Visual studies
- Multimodal interaction analysis
- Systemic social observation
- Process tracing
- Grounded theory
Related approaches
- Derry, Sharon J. et al. 2010. "Conducting Video Research in the Learning Sciences: Guidance on Selection, Analysis, Technology, and Ethics."Journal of the Learning Sciences 19(1):3–53.
- Kissmann, Ulrike Tikvah, ed. 2009. "Video Interaction Analysis: Methods and Methodology." Frankfurt/Main: Peter Lang.
- LeBaron, Curtis, Paula Jarzabkowski, Michael G. Pratt, and Greg Fetzer. 2018. “An Introduction to Video Methods in Organizational Research.” Organizational Research Methods 21(2):239–60.
- Lindegaard, Marie Rosenkranz & Bernasco, Wim (2018). “Lessons Learned from Crime Caught on Camera.” Journal of Research in Crime and Delinquency 55(1): 155-186.
- Margolis, Eric and Luc Pauwels, eds. 2011. The SAGE Handbook of Visual Research Methods. Los Angeles: Sage Publications Ltd.
- Norris, Sigrid. 2004. Analyzing Multimodal Interaction: A Methodological Framework. New York/London: Routledge.
- Pauwels, Luc. 2015. Reframing Visual Social Science: Towards a More Visual Sociology and Anthropology. Cambridge University Press.
- Tuma, René, Bernt Schnettler, and Hubert Knoblauch. 2013. Videographie: Einführung in die interpretative Videoanalyse sozialer Situationen. Springer VS.
Further reading
- Nassauer & Legewie 2019. “Analyzing 21st Century Video Data on Situational Dynamics – Key Issues and Challenges.“ Social Sciences. https://doi.org/10.3390/socsci8030100
- Legewie & Nassauer 2018. “YouTube, Google, Facebook: 21st Century Online Video Research and Research Ethics“. In Hella von Unger and Wolff-Michael Roth: „Research Ethics in Qualitative Research,“ Special Issue Forum: Qualitative Social Research 19(3). http://dx.doi.org/10.17169/fqs-19.3.3130
- Nassauer & Legewie 2018. “Video Data Analysis: A methodological framework for a novel research trend.“ Sociological Methods & Research. Online first: https://doi.org/10.1177/0049124118769093.
References
- ↑ 1.0 1.1 1.2 1.3 1.4 Nassauer, A.; Legewie, N. (2018). "Video Data Analysis: A Methodological Frame for a Novel Research Trend". Sociol. Methods Res. 7. doi:10.1177/0049124118769093.
- ↑ 2.0 2.1 2.2 2.3 2.4 2.5 2.6 Nassauer, A.; Legewie, N. (2019). "Analyzing 21st Century Video Data on Situational Dynamics—Issues and Challenges in Video Data Analysis". Soc. Sci. 8.
- ↑ Mosselman, F.; Weenink, D.; Lindegaard, M.R. (2018). "Weapons, Body Postures, and the Quest for Dominance in Robberies: A Qualitative Analysis of Video Footage". Res. Crime Delinquency. 55: 3–26.
- ↑ Nassauer, A. (2018). "How Robberies Succeed or Fail: Analyzing Crime Caught on CCTV". J. Res. Crime Delinquency. 55.
- ↑ Collins, R, “Violence: A Micro-Sociological Theory”. Princeton University Press, 2008.
- ↑ Collins, R. (2018). "The micro-sociology of Violence". Br. J. Sociol. 60: 566–576.
- ↑ Lindegaard, M.R.; Bernasco, W.; Jacques, S (2015). "Consequences of Expected and Observed Victim Resistance for Offender Violence during Robbery Events". Res. Crime Delinquency. 52: 32–61.
- ↑ Bramsen, I. (2018). "How violence happens (or not): Situational conditions of violence and nonviolence in Bahrain, Tunisia and Syria". Psychol. Violence.
- ↑ Nassauer, A. (2016). "From peaceful marches to violent clashes: a micro-situational analysis". Soc. Mov. Stud. 15: 1–16.
- ↑ Nassauer, A. (2018). "Situational Dynamics and the Emergence of Violence During Protests". Psychol. Violence. 8: 93–304.
- ↑ Nassauer, A, “Situational Breakdowns - Understanding Protest Violence and Other Surprising Outcomes”. Oxford University Press, 2019.
- ↑ Klusemann, S. (2012). "Massacres as process: A micro-sociological theory of internal patterns of mass atrocities". Eur. J. Criminol. 9: 468–480.
- ↑ Levine, M.; Taylor, P.J.; Best, R. (2011). "Third Parties, Violence, and Conflict Resolution: The Role of Group Size and Collective Action in the Microregulation of Violence". Psychol. Sci. 22: 406–412.
- ↑ Philpot, R.; Levine, M. “Street Violence as a Conversation: Using CCTV Footage to Explore the Dynamics of Violent Episodes”. 2016.
- ↑ Klusemann, S. (2009). "Atrocities and Confrontational Tension". Front. Behav. Neurosci. 3: 1–10.
- ↑ Bloch, C.; Liebst, L.S.; Christiansen, J.M.; Heinskou, M.B. (2018). "Caring collectives and other forms of bystander helping behavior in violent situations". Curr. Sociol. 66: 1049–1069.
- ↑ Lindegaard, M.R. (2017). "Consolation in the aftermath of robberies resembles post-aggression consolation in chimpanzees". PLOS ONE. 12.
- ↑ Nassauer, A.; Legewie, N. (2018). "YouTube, Google, Facebook: 21st Century Online Video Research and Research Ethics". Forum Qual. Sozialforschung Forum Qual. Soc. Res. 19.
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