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.


Analytic dimensions refer to the content of visual data that are of interest when analyzing situations: facial expressions and body posture, interactions, and context. 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 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.

Through its analytic procedures, VDA aims to reconstruct a situation step-by-step, analyze its inner dynamics, and establish comprehensive storylines. 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. Looking for patterns means to try 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.

Copyright: “A female using a smartphone to take photographs“, by Kuba Bożanowski,  is licensed under CC BY 2.0

VDA’s detailed case reconstructions and analyses of patterns may be combined with any method of data analysis that helps identifying 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.


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.


Research ethics concern the application of ethical principles to the research process as a reflection of moral rules and values, with core goals being protecting participants from harm, avoiding conflict of interest and misrepresentation, respecting common laws, and adhering to standards such as professional competence and nondiscrimination. These provide the overall frame of reference for reflections on online video research. In the context of VDA, five ethical areas are specifically relevant: (1) informed consent; (2) privacy; (3) unique opportunities; (4) potential harm; and (5) transparency.

Informed consent means that people should know that they are being researched, receive relevant information on the planned research in a comprehensible format, and should then voluntarily agree to participate, or decline to do so. When assessing issues of informed consent, we suggest asking three questions of the data: Does the space filmed require informed consent? If not: Was the focus on the space or on people? And if the space filmed requires consent: did people give consent?

Privacy means respect people’s private information and anonymity, both of which are complex concepts that need to be re-evaluated for online contexts. To assess issues of privacy in livestreams and online video research, we suggest reflecting on the online and situational context in which the footage was found and taken, and the content of the video.

Unique opportunities focuses on the potential benefits of a study. We suggest asking: does a study offer unique potential for scientific insights and/or real-life benefits, and could other data could replace the video fully or in parts of the analysis?

Potential harm refers to possible ways in which a study may hurt the study subjects, researchers, or third parties. Since one of the main goals in research ethics is to minimize harm, a rigorous assessment of potential harm is essential for any study. We suggest three for assessing potential harm specific to video research: What kind of behavior is depicted? Could data harm people or groups depicted? How publicly available is the video prior to research?

Finally, transparency refers to making goals, procedures, and data as accessible to the public as possible, thereby improving traceability and openness of scientific processes and findings. We suggest four questions to assess whether a given study can actualize the immense potential for transparency inherent in video research: can permanent access to the video be assured? Can the researcher share the data with reviewers? Can the researcher share the data with the broader research community? And can the researcher share the data during talks at conferences or workshop?

These ethical areas and principles always have to be evaluated in relation to each other and weighed against each other in the context of a specific study. Of course, an outcome of such an assessment may very well be that a study is too unethical to be implemented, or needs substantial revisions.

Copyright: “crowd”, by oatsy40, is licensed under CC BY 2.0


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

Header image copyright: IFacility CCTV Cameras, by RickySpanish, licensed under CC BY-SA 4.0