Several points pertaining to our operationalization of persistent offender are worth clarifying. First, studies of a shorter length are likely to produce slightly shorter estimates of criminal career durations. Conversely, lengthier follow-ups produce longer, and more realistic, estimates of the true average criminal career duration in a population. For example, the average criminal career duration in the CSDD is 2.7 years longer at the 56-year follow-up compared to the 40 year follow-up (Farrington, Piquero, & Jennings, 2013). Likewise, the
average criminal career length in the Ontario study of male offenders increased by 1.1 years when an additional six years of observations were included (Ward et al., 2010; Day et al., 2012). Simply put, the true minimum duration for persistent offending in a population, as identified by x̅ccl + 1 SD, is more likely to be found in samples that allow for the observation of criminal careers in their entirety.
Second, the identification of persistent offenders in relation to other people in the sample, such as the approach presented in this paper, is conceptually more suitable than a static one. Static definitions and operationalizations assume that an arbitrary age or offence frequency cut-off (e.g., ‘offending before and after the age of 21’ or ‘the 10 percent most frequent offenders’) can accurately identify all persistent offenders, regardless of the sample used. These approaches yield unreliable results. To demonstrate, self-reports of offending reveal more offenders, longer criminal careers, and more frequent offending than found using official convictions (Kazemian, LeBlanc, Farrington, & Pease, 2007; Farrington, Ttofi, Crago, & Coid, 2014). In the self-report literature, persistent offending has been statically defined as offending that occurs before a range of different ages, including offending before and after the age of 21 (29% identified as persistent in Pulkkinen, Lyyra, and Kokko, 2009), offending before the age of 16 (14% identified as persistent in Decker and Salert, 1986), and offending before the age of 15 and at least twice after the age of 17 (25.3% identified as persistent in Turner, Hartman, and Bishop, 2007). All of these approaches lead to estimates of the prevalence of persistent offenders that are much greater than the expected five to 10 percent.
Persistent offenders represent a minority of offenders in the wider population. Studies that contradict this line of thinking (for example: Turner, Hartman, & Bishop, 2007; Pulkkinen, Lyyra, & Kokko, 2009) are likely to be using a static operationalization that overestimates the true number of persistent offenders. On the other hand, the exact minimum
duration for persistent offending as identified by a relative operationalization is sample specific. Although the minimum duration of persistent offending identified by x̅ccl + 1 SD may not be the same across samples, this operationalization has the advantage of more accurately identifying what constitutes persistent offending in different populations and ensure that the number of persistent offenders are not overestimated.
One of the assumptions underlying the concept of persistent offending is that unique factors are associated with, and predict, persistent criminal careers. If, however, there were but a few years differentiating a persistent and non-persistent offender’s criminal career, then the factors differentiating the two offender groups are likely minimal. Therefore, a possible drawback of the x̅ccl + 1 SD operationalization is that it produces an abrupt cut-off separating persistent and non-persistent offenders. For example, in the CSDD data, 24 years was the minimum criminal career duration for persistent offenders identified by x̅ccl + 1 SD. Some of the non-persistent offenders identified by default may have a criminal career duration that is only a few years less than 24 years. The small differences in duration between persistent and these ‘almost’ persistent offenders is likely to not be enough time to produce any qualitative differences. Hence, the inclusion of almost persistent offenders in the non-persistent group may dilute the number and strength of the variables that differentiate the persistent and non- persistent offenders.
Although static operationalizations also produce an abrupt cut-off, we argue that, for three reasons, our cut-off is less arbitrarily defined and more conceptually suitable. First, the +1 SD cut-off was derived from research on criminal career durations in different sample populations, and therefore is underpinned by consistent empirical evidence. No other operationalisation of persistent offending has been created through such a process. This, we argue, makes our operationalisation considerably less arbitrary than other approaches. Second, the +1 SD cut-off does not stipulate a specific duration, but instead presents a cut-off
that is relative to the data set and measure of offending being used. This ensures that a small portion of the population is consistently identified as persistent. Operationalisations that specify a specific cut-off point, such as offending before and after the age of 21 or committing five or more offences, are not relative to the data set being used, and do not consistently identify a small portion of the population as persistent (Whitten et al., 2017). Third, compared to 34 other operationalisations, the +1 SD cut-off was the only approach not based on onset age or offence frequency to consistently identify those with the longest criminal careers as persistent (Whitten et al., 2017).
The final point for clarification is whether the mean and standard deviation are suitable methods for identifying persistent criminal careers. Considering that criminal career durations are positively skewed, using percentiles may intuitively appear to be a better alternative. For instance, a simple method could be to identify the top 10 percent of offenders with the longest criminal careers as persistent. Yet as previously argued, using an arbitrary cut-off may not accurately or reliably identify all persistent offenders. Furthermore, persistent offenders are outliers in the criminal career distribution (Moffitt, 1993). In other words, the true number of outliers (persistent offenders) may be fewer or greater than what is identified by a static cut-off. Therefore, the mean and standard deviation may be a more suitable method for identifying persistent criminal career lengths. Under a standard normal distribution, x̅ccl + 1 SD identifies 15.4% of the sample. However, because criminal career durations are positively skewed, x̅ccl + 1 SD consistently identifies a smaller portion of the population as persistent. In turn, the persistent criminal career lengths identified by our sample specific method are the outliers that generally approximate five to ten percent of the population.
The exact percentage of persistent offenders identified by x̅ccl + 1 SD is partially dependent on the skewness of the criminal career duration distribution. In lengthy data sets,
this distribution is likely to have more extreme outliers and a longer tail than distributions from shorter data sets. In other words, the population of offenders who identify as x̅ccl + 1 SD may decrease the more a distribution is skewed. It is hypothetically possible that when using x̅ccl + 1 SD, longer data sets that are extremely skewed may be at risk of identifying less than five per cent of the sample as persistent, while shorter data sets that are more normally distributed may be at risk of identifying more than 10 per cent of the sample as persistent.
6.8 ADVANTAGES OF A TEMPORAL DEFINITION AND OPERATIONALISATION