Standardizing the Display of IBR Data: Drug Offenses
Additional Examples of Displaying Drug Offense Information Variables
The following tables were chosen as examples that may be of interest to researchers. SPSS code is presented with each table.
Example 1. Drug Offenses Occurring in Conjunction with Other Offenses
Of the drug violation incidents involving more than one offense, most are other Crimes Against Society offenses.
To create these tables, the administrative and offense segments are linked by agency identifier and incident number. Various classifications and counts are created. All incidents involving Drug/Narcotic offenses are selected and the frequency tables are created.
Example 2. Demographics of Offenders in Drug Offenses
Most drug offenders are white, male, young adults.
Since there are 100 possible age entries, it is helpful to group the ages into categories. In the first table, we see that most of the offenders are over the age of 18.
If you'd like to view the entire frequency table, the table is provided in the syntax below.
When age is broken out into categories, we see that most offenders are between the ages of 19 and 25. We can also see that several offenders are under the age of 10, indicating that there may be some coding errors in the data.
Not surprisingly, most of the offenders in drug cases are male.
The vast majority of offenders are white.
Dealers vs Users
The demographics of drug offenders change when we compare users and dealers. Since many offenders are charged with offenses that could fit in either category, or both, the following comparisons include only offenders who are reported as only using or buying (users) or only distributing (dealers).
From this table, we can see that, in general, juveniles are more likely to commit user-related offenses, while adults are more likely to commit dealer-related offenses.
Where sex is known, males and females are both almost equally split between committing user- and dealer-related offenses. The sex of offenders committing dealer-related offenses is more likely to be unknown or not reported.
There is a clear relationship between race and the type of drug offense committed, which was not seen with the other variables. Black and Asian minorites are more likely to be reported in dealer-related offenses than whites. This holds especially true for Blacks, with 74% reported in conjunction with dealer-related offenses. Since race has a relationship to the type of offense reported, it is possible that the previous tables, if broken out by race, would provide clearer patterns.
To create these tables, the matched offense and offender file created above is used, with comparison categories being added. Drug offenses are selected, and frequency tables then display the age, sex, and race of offenders.
Example 3. Location of Drug Offenses
The vast majority of drug offenses take place on the street and in the home.
We can also compare locations of user-related and dealer-related drug offenses. Perhaps surprisingly, dealer-related offenses are more likely to occur in residences than user-related offenses.
To create these tables, drug offenses are selected in the offense segment. Frequencies of location data are then run. To compare user- and dealer-related offense locations, variables are created, the file is split, and another frequency table is run.