In general, data coding involved pre-coded classifications of closed-ended items that fall under the four traditional types. As well, we also see the application of what Kerlin (2002) calls “Selective Coding” (reflected in the structural relationship between categories - the relationship between a core category and related categories - which are integrated to form the theoretical structure of Identity Theft analysis) or “Factual/Descriptive Coding” (ideas that lean more toward the concrete; - such as Actions, Definitions, Events, Properties, Settings, Conditions, Processes, etc.).
The coding of identity theft types, for instance, is an example of nominal scaling because the types of identity theft vulnerabilities or exploitable loopholes must fall into categories that are mutually exclusive and collectively exhaustive. At the same time, it is a selective coding because the separate instances define the structure of the problem: Identity theft Existing credit card Other existing accounts
Personal information Multiple types during same episode No identity theft Unknown Distinct from the other variables, “ways victims became aware of identity theft” is the classic case of open-ended responses coded in factual or descriptive fashion because responses are about events or processes such as noticing “missing money/unfamiliar charges on account,” “contacted about late/unpaid bills,” or “banking problems”.