Tests the idea that the frequency distribution typically observed in crosssectional crime victimization data sampled from surveys of general populations is a heterogeneously distributed result of the mixing of two latent processes associated, respectively, with each of the tails of the distribution.
Datasets are assembled from a number of samples taken from the British Crime Survey and the Scottish Crime Victimization Survey. Latent class analysis is used to explore the probable, latent distributions of individual property crime and personal crime victimization matrices that express the frequency and type of victimization that are self-reported by respondents over the survey recall period.
The analysis obtains broadly similar solutions for both types of victimization across the respective datasets. It is demonstrated that a hypothesized mixing process will produce a heterogeneous set of local sub-distributions: a large sub-population that is predominantly not victimized, a very small ‘chronic’ sub-population that is frequently and consistently victimized across crime-type, and an ‘intermediate’ sub-population (whose granularity varies with sample size) to whom the bulk of victimization occurs. Additionally, attention is paid to the position of very high frequency victimization within these sub-populations.
The analysis supports the idea that crime victimization may be a function of two propensities: for immunity, and exposure. It demonstrates that zero-inflation is also a defining feature of the distribution that needs to be set alongside the significance that has been attached to the thickness of its right tail. The results suggest a new baseline model for investigating population distributions of crime victimization.