Cross-Classified Multilevel Models: An Application to the Criminal Case Processing of Indicted Terrorists

Abstract

This study provides an application of cross-classified multilevel models to the study of early case processing outcomes for suspected terrorists in U.S. federal district courts. Because suspected terrorists are simultaneously nested within terrorist organizations and criminal court environments, they are characterized by overlapping data hierarchies that involve cross-nested ecological contexts. Cross-classified models provide a useful but underutilized approach for analyzing such data. Using the American Terrorism Study (ATS), this research examines case dismissals, trial adjudications and criminal convictions for a sample of 574 terrorist suspects. Findings indicate that diverse factors affect case processing outcomes, including legal factors such as the number of counts, number of co-defendants, and statute of indictment, extralegal factors such as the ethnicity of the offender, and incident characteristics such as the type of terrorism target. Case processing outcomes also vary significantly across both terrorist groups and criminal courts and are partially explained by select group and court characteristics including the type of terrorist organization and the terrorism trial rate of the court. Results are discussed vis-à-vis contemporary research on terrorism punishments and future directions are suggested for additional applications of cross-classified models in criminological research.

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Fig. 1

Notes

  1. 1.

    86% of dismissals followed government motions. One case was dismissed due to civil rights violations, two cases were nolle prosequi and five cases were dismissed due to mistrials.

  2. 2.

    To investigate potential selection effects (Bushway et al. 2007), alternative models for trial dispositions and convictions were initially estimated using the subset of cases that were not dismissed (n = 518) along with Heckman’s (1976) two-step selection model (estimated with “heckprob” in Stata 9.2). The results indicated that the selection and substantive equations were statistically independent for both outcomes, suggesting selection bias was not a problem. Although this result raises important questions about the ubiquitous concerns surrounding selection bias in criminal case processing (e.g. Berk 1983), it should be interpreted cautiously. Given the small degree of censoring, the relatively small sample size, the fact that there was not a clear theoretically-defensible exclusion restriction, and concern over the order in which the outcomes occur (e.g. some cases may be dismissed after a plea is entered), results are based on the total sample of n = 574 cases and therefore represent the unconditional estimates for all models reported.

  3. 3.

    A small number of cases (<5%) involved an unusually large number of counts (e.g. 300 or more). Based on the frequency distribution, 50 was selected as the upper cutoff value to prevent these small number of outliers from having undue influence.

  4. 4.

    Included in racketeering offenses are the 35 cases prosecuted explicitly under the new terrorism chapter of the Federal Criminal Code introduced in 1995. These cases were not coded separately because there are relatively few cases prosecuted under terrorism statutes and because no comparable chapter existed prior to 1995. Supplemental analyses were conducted examining the independent effects of these cases and their findings are noted where relevant.

  5. 5.

    Although omission of these measures is unfortunate, it is almost certainly less important for research on early case processing decisions than for research on final sentences. Offense severity is typically the strongest predictor of sentencing, but not necessarily for earlier case processing decisions like those examined in this paper. For instance, Albonetti (1986, 1987) finds little evidence that statutory severity is related to U.S. Attorneys’ decisions to prosecute federal crimes. Some research does suggest that criminal history is related to federal charging behavior (Albonetti 1986; Shermer and Johnson 2010), but the current focus on terrorism suspects largely obviates this concern because the vast majority of convicted terrorists are known to have little or no prior criminal history. For example, 83% of post-guidelines terrorists fall into the lowest criminal history category (Category I) and over 90% fall into either the first or second category (Category I or Category II).

  6. 6.

    All but 8 offenders fell into one of these three categories so other racial/ethnic groups could not be separately examined. Twenty two offenders were also missing information on race/ethnicity and are included in the referent, though supplemental analysis demonstrated that this coding strategy was unrelated to the findings for race/ethnicity.

  7. 7.

    Robust standard errors provide standard error estimates that are adjusted to account for potential violations of model assumptions. Statistical packages such as SAS and STATA provide canned routines for producing cluster-adjusted robust standard errors, as well as other approaches for dealing with clustered data (see Stata Library 2011a).

  8. 8.

    Although the terms hierarchical linear models (HLM) and multilevel models (MLM) are often used interchangeably, they are not necessarily equivalent. Hierarchical models refer to data structures involving exact nesting of lower levels of analysis within higher order units. Multilevel models, on the other hand, represent a broader class of statistical models designed to analyze any nested data structures involving multiple units of analysis. As such, multilevel models encompass hierarchical linear models along with related statistical models, such as the cross-classified models presented here, which are designed to account for nested data that does not follow strict hierarchical ordering.

  9. 9.

    In the classic example of educational research, for instance, students may be nested within both classrooms and within schools, which would represent a three-level hierarchical data structure; however, if students are simultaneously nested with the schools they attend and the neighborhoods in which they live, then the data hierarchy would be cross-classified as in the current research example (see e.g. Goldstein 1994).

  10. 10.

    The total level 2 variance is equal to the sum of terrorist group and district court variances, where var(\( u_{1j} \)) = τu1, var(\( u_{2k} \)) = τu2, and var(\( e_{i(jk)} \)) = σ 2e . If either level 2 variance is zero, then model (2) reduces to the standard two-level hierarchical model. Since the levels are independent (group, district), their variances can be added to the level 1 variance in the intercept-only model to estimate the total variance. For continuous outcomes, this value can then be used to calculate separate intraclass correlations for both group ρu1 = (τu1/(σ 2e  + τu1 + τu2) and district ρu2 = (τu2/(σ 2e  + τu1 + τu2) levels of analysis respectively.

  11. 11.

    The same statistical tests used for random coefficients in standard multilevel models can be applied to cross-classified models. In HLM these include a likelihood ratio test for the deviance statistics of nested models (that vary in their random parameters) as well as χ2 significance tests for variance components included in the model (Raudenbush and Bryk 2002: 63–65). However, specifying random coefficients in cross-classified data can be particularly time consuming and complicated because the number of available random parameters is multiplied by the cross-classification. Ultimately, specification of random effects should be driven by theoretical considerations and tested independently in each nested structure before specifying the full cross-nested random effects (Hox 2002: 136).

  12. 12.

    The model could be further expanded to include cross-level interactions by incorporating level-2 variables (leftwing and/or caseload) in the model equations for individual predictors, (\( \beta_{1(jk)} \)) and (\( \beta_{2(jk)} \)), or by including cross-hierarchy terms to allow the effects of level 2 predictors at one hierarchy to vary across the other. To prevent the sample model from becoming overly complicated, these additional effects are not presented in Eq. 5, but they are briefly discussed here to help illustrate the full versatility and complexity of cross-classified models.

  13. 13.

    The proportion of cases that eventuate in trial differs from the average trial rate across districts because the first is averaged across all 574 indictees and the latter is averaged across the 52 district courts.

  14. 14.

    All multilevel models are estimated as random intercept models because investigation of theoretically-specified random coefficients produced null results for all models. In part, this likely reflects the relatively small sample size and suggests that future research with larger samples is needed to better investigate the extent to which individual terrorist suspect characteristics exert varying effects across group and district contexts.

  15. 15.

    The coefficient for a black defendant approached but did not exceed marginal levels of significance for this model (p = .11).

  16. 16.

    The terrorism trial rate was omitted from this model because by definition it is related to the outcome. Not surprisingly, when included, it is positively, strongly and significantly related to the individual odds of individual trial disposition (b = 5.21, SE = .73).

  17. 17.

    Crimes prosecuted specifically under terrorism statutes were separated from racketeering crimes in this analysis because unlike for other outcomes, they exerted countervailing effects on case conviction.

  18. 18.

    Comparisons of ordinary logistic regression, clustered robust standard error models, and cross-classified multilevel models in the current data showed that cluster-corrected models produce identical coefficients but larger standard errors than ordinary logistic regression models, whereas the cross-classified models produce larger standard errors but also unique coefficient estimates. For additional information on comparisons of different analytical approaches to clustered data see Stata Library (2011b).

  19. 19.

    Ordinary logistic regression typically employs Maximum Likelihood Estimation (MLE) whereas multilevel models often use some variation of this approach, depending upon the statistical program employed and the model specified. In the current analysis, Stata 11.1 is used to estimate the logistic regressions and HLM 6.02 is used for the cross-classified multilevel models. HLM uses full Penalized Quasi-Likelihood (PQL) to generate parameter estimates, which produces approximate empirical Bayes estimates for the random coefficients, generalized least squares estimates for the level 2 coefficients, and approximate maximum likelihood estimates of the variance and covariance parameters in the model (see Raudenbush and Bryk 2002, for a detailed elaboration).

  20. 20.

    For an interesting empirical example of this type of research see Jayasinghe et al. (2003) who used cross-classified models to assess the overlapping influence of grant reviewers and grant submitters on assessment ratings within academic disciplines.

  21. 21.

    Larger samples would also provide an important opportunity to expand the complexity of the cross-classified model to investigate additional research questions involving such things as cross-level and cross-hierarchy interactions. In the current study, initial attempts were made to examine cross-level interactions between offender race/ethnicity and terrorist group affiliation, but the small sample size precluded estimation of these effects.

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Correspondence to Brian D. Johnson.

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This material is based upon work supported by the Science and Technology Directorate of the U.S. Department of Homeland Security under Grant Award Number 2008-ST-061-ST0004, made to the National Consortium for the Study of Terrorism and Responses to Terrorism (START, www.start.umd.edu). The views and conclusions contained in this document are those of the author and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security or START.

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Johnson, B.D. Cross-Classified Multilevel Models: An Application to the Criminal Case Processing of Indicted Terrorists. J Quant Criminol 28, 163–189 (2012). https://doi.org/10.1007/s10940-011-9157-3

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Keywords

  • Terrorism
  • Prosecution
  • Punishment
  • Cross-classified models
  • Cross-nested models
  • Multilevel models