Since the early 2000s, the proliferation of cameras, whether in mobile phones or CCTV, led to a sharp increase in visual recordings of human behavior. This vast pool of data enables new approaches to analyzing situational dynamics. Application is both qualitative and quantitative and ranges widely in fields such as sociology, psychology, criminology, and education. Despite the potential and numerous applications of this approach, a consolidated methodological frame does not exist. This article draws on various fields of study to outline such a frame, what we call video data analysis (VDA). We discuss VDA’s research agenda, methodological forebears, and applications, introduce an analytic tool kit, and discuss criteria for validity. We aim to establish VDA as a methodological frame and an interdisciplinary analytic approach, thereby enhancing efficiency and comparability of studies, and communication among disciplines that employ VDA. This article can serve as a point of reference for current and future practitioners, reviewers, and interested readers.
This paper considers the quantitative assessment of ecological settings such as neighborhoods and schools. Available administrative data typically provide useful but limited information on such settings. We demonstrate how more complete information can be reliably obtained from surveys and observational studies. Survey-based assessments are constructed by aggregating over multiple item responses of multiple informants within each setting. Item and rater inconsistency produce uncertainty about the setting being assessed, with definite implications for research design. Observation-based assessments also have a multilevel error structure. The paper describes measures constructed from interviews, direct observations, and videotapes of Chicago neighborhoods and illustrates an “ecometric” analysis—a study of bias and random error in neighborhood assessments. Using the observation data as an illustrative example, we present a three-level hierarchical statistical model that identifies sources of error in aggregating across items within face-blocks and in aggregating across face-blocks to larger geographic units such as census tracts. Convergent and divergent validity are evaluated by studying associations between the observational measures and theoretically related measures obtained from the U.S. Census, and a citywide survey of neighborhood residents.
