Video Data Analysis

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Video Data Analysis (VDA) is a curated multi-disciplinary collection of tools, techniques, and quality criteria intended for analyzing the content of visuals to study driving dynamics of social behavior and events in real-life settings. It often uses visual data in combination with other data types.[1][2][3][4][5] VDA is employed across the social sciences such as sociology, psychology, criminology, business research, and education research.[2]

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. Parallel to this increase in third-hand video data, advances in camera and data storage technology also enabled new ways of collecting first-hand videos for research, by researchers. In short, humans find themselves in a new era of how social life is captured.[2] 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 first hand. 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.[2]

Areas of application

VDA is employed in disciplines such as sociology, psychology, criminology, business research, and education research[2] to study a variety of phenomena, including armed store robberies or unattended package theft,[5][6][7] the situational dynamics of protests and uprisings,[8][9][10][11][12] or physical violence, such as street fights and massacres[13][14][15][16][17][18][19]. Others have used the approach to study polarization among politicians,[20] YouTuber staged health practices,[21] teacher competence,[22] school yard fights, or consoling behaviors.[23][24] VDA has also been applied to study military negotiations,[25] the unfolding of emergency evacuations,[26] as well as police use of force[4][27][28] and police training[29].

Analytical dimensions and procedures

These dimensions should be understood as lenses that help deriving 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.[2][3]

VDA can be used in indicative and deductive approaches, qualitative in-depth and quantitative large-N, or even computational analyses.[3]

Although these approaches differ in many ways, VDA approaches are united by a number of analytical procedures. First, coding of video data plays a central role in analysis. Coding means to tag a section of data with labels that synthesize content as relevant to a given research project. Some researchers conduct coding in their analysis without using the term itself, and studies differ in whether they develop a coding scheme first, then code data (a deductive approach), or whether they use an iterative approach of data collection, coding, and analysis (an inductive or abductive approach). Still, all types of qualitative and quantitative video analysis include some type of data coding in order to make sense of it and identify patterns.

Second, six analytic lenses[1][3] can move researchers from labeling the data to identifying and interpreting patterns or driving dynamics: counts and quantifications, timing and sequence, rhythm and turn-taking, actors, networks and relations, and spacing. These procedures can help in analyzing video data, regardless of whether the aim is to describe patterns at the micro level, or to study causal links within situations or events. The six procedures all build on coding of the data and they are all interconnected. For instance, one could produce counts and quantifications based on video data that help studying social relations and networks. In other words, the six procedures should not be understood as discrete analytical steps or mutually exclusive ways to analyze video data. Rather, they are a non-exhaustive toolbox from which researchers can pick any combination of tools that work well for what they try to accomplish in their VDA.

Quality criteria

Criteria for validity include neutral or balanced data sources, optimal capture, and natural behavior.[30] Neutral or balanced data sources means that researchers should reflect on possible vested interests of data providers that could lead 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.[1]


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.[1] Fourth, a number of research ethics questions remain unclear with the new types of video data VDA often employs;[30] e.g., what types of video from which platforms are admissible to use as research data.

See also

  • Participant observation
  • Visual studies
  • Mutimodal interaction analysis
  • Systematic social observation
  • Process tracing
  • Grounded Theory
  • Computer Vision

Related approaches

  • 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.
  • Pauwels, Luc. 2015. “Reframing Visual Social Science: Towards a More Visual Sociology and Anthropology.” Cambridge University Press.
  • Sampson Robert, Raudenbush Stephen. 1999. “Systematic Social Observation of Public Spaces: A New Look at Disorder in Urban Neighborhoods.” American Journal of Sociology 105:603–51.

Further readings

  • Nassauer, Anne & Nicolas M. Legewie (2022). “Video Data Analysis: How to use 21st-Century Videos in the Social Sciences.” London: SAGE Publications.


  1. 1.0 1.1 1.2 1.3 Nassauer, A.; Legewie, N. M. (2019). "Analyzing 21st Century Video Data on Situational Dynamics—Issues and Challenges in Video Data Analysis". Social Sciences. 8 (3): 100. doi:10.3390/socsci8030100.
  2. 2.0 2.1 2.2 2.3 2.4 2.5 Nassauer, A.; Legewie, N. M. (2021). "Video Data Analysis: A Methodological Frame for a Novel Research Trend". Sociological Methods & Research. 50: 135–174. doi:10.1177/0049124118769093.
  3. 3.0 3.1 3.2 3.3 Nassauer, A.; Legewie, N. (2022). Video Data Analysis: How to use 21st century video in the social sciences. Sage. ISBN 978-1-5297-2246-8. OCLC 1314331161.
  4. 4.0 4.1 Piza, E. L.; Sytsma, V. A. (2022), "Video Data Analysis of Body-Worn Camera Footage: A Practical Methodology in Support of Police Reform", Justice and Legitimacy in Policing, Routledge, pp. 59–75, doi:10.4324/9781003285267-5.
  5. 5.0 5.1 Stickle, B.; Hicks, M.; Stickle, A.; Hutchinson, Z. (2020). "Porch pirates: examining unattended package theft through crime script analysis". Criminal Justice Studies. 33 (2): 79–95. doi:10.1080/1478601x.2019.1709780.
  6. Mosselman, F.; Weenink, D.; Lindegaard, M. R. (2018). "Weapons, Body Postures, and the Quest for Dominance in Robberies". Journal of Research in Crime and Delinquency. 55 (1): 3–26. doi:10.1177/0022427817706525. PMC 5772446. PMID 29416178.
  7. Nassauer, A. (2018). "How Robberies Succeed or Fail". Journal of Research in Crime and Delinquency. 55: 125–154. doi:10.1177/0022427817715754.
  8. Bramsen, Isabel (2018). "How violence happens (or not): Situational conditions of violence and nonviolence in Bahrain, Tunisia, and Syria". Psychology of Violence. 8 (3): 305–315. doi:10.1037/vio0000178.
  9. Nassauer, Anne (2016). "From peaceful marches to violent clashes: a micro-situational analysis". Social Movement Studies. 15 (5): 515–530. doi:10.1080/14742837.2016.1150161.
  10. Nassauer, Anne (2018). "Situational dynamics and the emergence of violence in protests". Psychology of Violence. 8 (3): 293–304. doi:10.1037/vio0000176.
  11. Nassauer, Anne (2018). Situational breakdowns : understanding protest violence and other surprising outcomes. Oxford University Press. ISBN 978-0-19-092209-2. OCLC 1089969305.
  12. Anisin, Alexei; Ayan Musil, Pelin (2022). "Protester-police fraternization in the 2013 Gezi Park uprisings". Social Movement Studies. 21 (4): 395–412. doi:10.1080/14742837.2021.1884976.
  13. Collins, Randall (2008). Violence. Princeton: Princeton University Press. doi:10.1515/9781400831753. ISBN 978-1-4008-3175-3.
  14. Collins, Randall (2009). "The micro-sociology of violence". The British Journal of Sociology. 60 (3): 566–576. doi:10.1111/j.1468-4446.2009.01256.x. ISSN 0007-1315. PMID 19703175.
  15. Lindegaard, Marie Rosenkrantz; Bernasco, Wim; Jacques, Scott (2014). "Consequences of Expected and Observed Victim Resistance for Offender Violence during Robbery Events". Journal of Research in Crime and Delinquency. 52 (1): 32–61. doi:10.1177/0022427814547639.
  16. Nassauer, Anne (2022). "Video Data Analysis as a Tool for Studying Escalation Processes: The Case of Police Use of Force". Historical Social Research. 47 (2022): 36–57. doi:10.12759/hsr.47.2022.02.
  17. Klusemann, Stefan (2012). "Massacres as process: A micro-sociological theory of internal patterns of mass atrocities". European Journal of Criminology. 9 (5): 468–480. doi:10.1177/1477370812450825.
  18. Levine, Mark; Taylor, Paul J.; Best, Rachel (2011). "Third Parties, Violence, and Conflict Resolution". Psychological Science. 22 (3): 406–412. doi:10.1177/0956797611398495. PMID 21303991.
  19. Philpot, Richard, and Mark Levine. "Street violence as a conversation: using CCTV footage to explore the dynamics of violent episodes." Annual Meeting of the American Society of Criminology, ASC. 2016.
  20. Dietrich, Bryce J. (2021). "Using Motion Detection to Measure Social Polarization in the U.S. House of Representatives". Political Analysis. 29 (2): 250–259. doi:10.1017/pan.2020.25.
  21. del Río Carral, María; Volpato, Lucia; Michoud, Chloé; Phan, Thanh-Trung; Gatica-Pérez, Daniel (2021). "Professional YouTubers' health videos as research material: Formulating a multi-method design in health psychology". Methods in Psychology. 5: 100051. doi:10.1016/j.metip.2021.100051. ISSN 2590-2601.
  22. Aspelin, Jonas; Eklöf, Anders (2022). "In the blink of an eye: understanding teachers' relational competence from a micro-sociological perspective". Classroom Discourse. 14 (1): 69–87. doi:10.1080/19463014.2022.2072354.
  23. Bloch, Charlotte; Liebst, Lasse Suonperä; Poder, Poul; Christiansen, Jasmin Maria; Heinskou, Marie Bruvik (2018). "Caring collectives and other forms of bystander helping behavior in violent situations". Current Sociology. 66 (7): 1049–1069. doi:10.1177/0011392118776365.
  24. Lindegaard, Marie Rosenkrantz; Liebst, Lasse Suonperä; Bernasco, Wim; Heinskou, Marie Bruvik; Philpot, Richard; Levine, Mark; Verbeek, Peter (2017). "Consolation in the aftermath of robberies resembles post-aggression consolation in chimpanzees". PLOS ONE. 12 (5): e0177725. doi:10.1371/journal.pone.0177725. ISSN 1932-6203. PMC 5451014. PMID 28562686.
  25. Klusemann, Stefan (2009). "Atrocities and confrontational tension". Frontiers in Behavioral Neuroscience. 3: 42. doi:10.3389/neuro.08.042.2009. ISSN 1662-5153. PMC 2776490. PMID 19936029.
  26. Philpot, Richard; Levine, Mark (2022). "Evacuation Behavior in a Subway Train Emergency: A Video-based Analysis". Environment and Behavior. 54 (2): 383–411. doi:10.1177/00139165211031193.
  27. Sytsma, Victoria A.; Chillar, Vijay F.; Piza, Eric L. (2021). "Scripting police escalation of use of force through conjunctive analysis of body-worn camera footage: A systematic social observational pilot study". Journal of Criminal Justice. 74: 101776. doi:10.1016/j.jcrimjus.2020.101776.
  28. Piza, Eric L.; Sytsma, Victoria A. (2022). "The Impact of Suspect Resistance, Informational Justice, and Interpersonal Justice on Time Until Police Use of Physical Force: A Survival Analysis". Crime & Delinquency: 001112872211069. doi:10.1177/00111287221106947.
  29. Staller, Mario S; Koerner, Swen; Heil, Valentina; Abraham, Andrew; Poolton, Jamie (2021). "German police recruits' perception of skill transfer from training to the field". International Journal of Police Science & Management. 24 (2): 124–136. doi:10.1177/14613557211064057.
  30. 30.0 30.1 Legewie, N.; Nassauer, A. (2018). "YouTube, Google, Facebook: 21st Century Online Video Research and Research Ethics". Forum Qualitative Sozialforschung / Forum: Qualitative Social Research. 19. doi:10.17169/fqs-19.3.3130.

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