Measuring Specialisation

Specialisation is most often measured by examining participation in general crime or by analysing specific kinds of offence types like sex offending. As such, specialisation does not simply rely on a count of different types of crime engaged in, rather it requires a

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measure of the general tendency to repeat offend within particular offence type. Early studies in specialisation derived conclusions from data patterns and/or transition matrices (McGloin et al., 2009). Wolfgang et al. (1972), in particular, popularised the method of using offence to offence matrices of transition probabilities to assess specialisation. The matrix was a two-way table of crime types where the row indicate the offence type committed at kth arrest and the column indicates the arrest type at k+1th arrest. In the matrices, the probability of committing one offence of a particular offence type on arrest k and then again on arrest k+1 was calculated. This probability was displayed along the diagonal element. This was done for each transition where each transition took into account an offender moving from one arrest to another. Wolfgang et al. (1972) then calculated the average across the transition matrices with each matrix given equal weight. This final probability matrix was an indication of the specialisation in the sample.

Wolfgang et al. (1972) method of measuring specialisation looked only at the previous arrest in comparison to the present one. This was considered a first-order Markov chain analysis. Stander et al. (1989) also used first-order Markov chain analysis to examine specialisation in their data set. They however went on to examine the possibility of second-order Markov chain analysis. They found that not only was the future arrest affected by the current arrest but also by the past arrest, indicating that second-order Markov analysis might be more useful in determining specialisation in criminal career research.

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Bursik (1980) also examined transition matrices. He developed one of the first indices of specialisation, using the ratio of the observed frequency to the expected frequency by chance in each diagonal cell of the transition matrix. This method Bursik called “residual analysis.” Bursik used Adjusted Standardised Residual (ASR), developed by Haberman (1973), to test the statistical significance of the deviation of the observed frequency from the expected frequency by chance to determine whether the level of specialisation observed was significant. Bursik (1980) specified that the ASR took into account the overall size of the sample and gave a very good indication of how far off the observed count was from the expected count and therefore was a suitable technique to determine statistical significance.

Farrington (1986) expanded on Bursik‟s index by developing the Forward Specialisation Coefficient (FSC), which provided another means of quantifying patterns in transition matrices. With the FSC, specialisation was said to occur when the actual number of offences significantly exceeds the expected number by chance (Lussier, 2005). The value ranged from 0 to 1 with 0 indicating no specialisation and 1 indicating perfect specialisation. However, a few limitations of the FSC have been noted. Osgood and Schreck (2007) observed that the FSC was based on sequential offences and was an aggregate measure. They also remarked that the concept of time was not properly accounted for as offences could be a day apart or years. Furthermore, if there were more than one offence recorded for the same event, the principal one was chosen. Although very popular, the limitations of the FSC led future researchers to develop other measures of specialisation.

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Farrington (1986) also utilised the Backward Specialisation Coefficient (BSC) as another possible measure of specialisation. The BSC also ranged from 0 to 1. A value of 0 indicated complete versatility and 1 indicated perfect backward specialisation. Backward specialisation occurred when every offence of offence A on referral k+1 was preceded by an offence A on referral k. The BSC was found to be highly correlated with the FSC.

An alternative approach to studying specialisation was to investigate the complete offending career (Farrington et al., 1988). Whereas, transition matrices examined consecutive arrests, this approach looked at the proportion of each type of offence committed over the entire career. Bursik (1980) also examined specialisation by assigning all juveniles with more than half of their offences of the same type as specialists. This method was called the percentage rule where an offender was labelled a specialist if 50% or more of his offences were of the same offence type. With this method specialisation could be examined for each offender rather than the entire offending group.

The diversity of offending index (D) was developed by Agresti and Agresti (1978) as another individual level measure of specialisation (Mazerolle et al, 2000; Piquero et al., 1999; Sullivan et al, 2006). The diversity index indicated the probability that any two offences drawn randomly from an individual‟s offence history belonged to separate offending categories. The minimum value of 0 indicated complete specialisation. The maximum value indicating complete generality was calculated using the formula Dmax= (k-1)/k (k = the number of offending categories).

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McGloin and colleagues (2009) argued that the previous methods of measuring specialisation were not adequate and they proposed latent transition analysis (LTA) as a substitute. LTA assumes that a discrete latent variable underlies the population of interest. The method attempts to specify mutually exclusive and exhaustive categories by the available data (Muthén, 2002; Vermunt & Magidson, 2004). This procedure is similar to factor analysis but is designed for classification of offenders.

Osgood and Schreck (2007) also used the regression approach to investigate specialisation. They introduced a model that extends the item response theory of measurement to a multi-level regression framework (Sullivan et al., 2009). In the regression approach, they focused on a specific type of offence, then a term was developed measuring the offence type for example prior violent offending could be included in a multilevel regression model. This was a two-level approach in which level one determined the presence of specialisation and level two investigated the relationship of covariates of this latent variable. Osgood and Schreck (2007) determined that this type of analysis was best suited for analysing particular types of specialisation like specialisation in violent or drug offences.