Elements of multivariation analysis of crime on scarce meetings

Crime represents a harmful occurrence that is specific for each culture and socioeconomic formation. Due to this it is no surprise that there is an interest for crime in different areas of social sciences and life. Since crime is a burning topic of numerious scientific and expert analyses or researches, it can be concluded that it is an old mutual problem of all countries, old and new ones. It doesn't depend only on the sociopolitical order, but economic development and characteristical social factors and circumstances that exist in respective countries as well. Surprisingly large diversity of causes and circumstances that lead to the occurrence of crime demands a different approach to its research. It is possible and consists of the application of multivariate analysis, since it allows for many different ways of observing crime and taking more efficient actions aimed at its prevention and enabling of normal functioning and development of state and society. Application of multivariation analysis in crime, is based on consideration of indirect (implicit) knowledge as a method applied on a sample that consists of data from forty seven US countries. Depending on their individuality, different crime rates were determined for each federal state. The mentioned sample was used to answer a simple question through multivariation analysis: would its application discover hidden causes of crime rates, which aren't visible from the data contained in the selected sample? In order to achieve the set goal, factor analysis was used as well the method of inverse factoral (segmentation) sample analysis. Discovering such hidden samples of crime would be a very important way of achieving total control on it. Since full control of crime represents a very modern way in contemporary approach to fighting rime in the world, it is obvious that control is impossible without considering such hidden factors (causes and conditions) which favour occurrence of criminal behavior, which can be shown by multivariation analysis based on the aforementioned sample.