CJA
NEW YORK CITY CRIMINAL JUSTICE AGENCY
Jerome E. McElroy Executive Director
PREDICTION OF PRETRIAL FAILURE TO APPEAR AND ALTERNATIVE PRETRIAL RELEASE RISK-CLASSIFICATION SCHEMES IN NEW YORK CITY: A VALIDATION STUDY
Qudsia Siddiqi, Ph.D. Project Director
FINAL REPORT October 2001
52 Duane Street, New York, NY 10007
(646) 213-2500
PREDICTION OF PRETRIAL FAILURE TO APPEAR AND ALTERNATIVE PRETRIAL RELEASE RISK-CLASSIFICATION SCHEMES IN NEW YORK CITY: A VALIDATION STUDY
Qudsia Siddiqi, Ph.D. Project Director
Research Assistance: Taehyon Kim Senior Research Assistant Elyse J. Revere Junior Research Analyst Raymond Caligiure Graphics & Production Specialist Alexandra Gorelik Senior Research Assistant Systems Programming: Wayne Nehwadowich Senior Programmer/Analyst Administrative Support: Bernice Linen-Reed Administrative Associate
October 2001
This report can be downloaded from www.nycja.org\research\research.htm Š 2001 NYC Criminal Justice Agency
ACKNOWLEDGMENTS The author wishes to thank Jerome E. McElroy, Executive Director of the New York City Criminal Justice Agency, Barbara Geller Diaz, Director for Systems, and Richard Peterson, Chairperson of Research Department for sharing their expertise. A special debt of gratitude is owed to Dr. Mary Eckert, former Associate Director for Research, for her invaluable input to the project. The author would also like to thank the New York State Division of Criminal Justice Services (DCJS) for providing supplemental data. DCJS is not responsible for the methodology or conclusion of the report.
Table of Contents LIST OF TABLES ...................................................................................................ii INTRODUCTION....................................................................................................1 CURRENT RECOMMENDATION SCHEME…………………………………….3 METHODOLOGY ...................................................................................................5 A. Sampling and Data Sources ...............................................................................5 B. Dependent Variable ..........................................................................................6 C. Independent Variables ......................................................................................6 D. Statistical Methods ............................................................................................7 RESULTS ................................................................................................................9 A. Defendant Characteristics .................................................................................9 B. Applying the final Model from the ’89 Sample to the ’98 Sample ...................11 C. Construction of the Point Scale..........................................................................13 D. Alternative Risk-Classification Schemes...........................................................19 E. Alternative Risk-Classification Schemes: A Comparison with the Current CJA ROR Recommendation Scheme .................................................................24 F. Conclusion..........................................................................................................24 BIBLIOGRAPHY………………………………………….……………………….26 TABLES
LIST OF TABLES
Table 1:
Arraignment Outcome (Defendant-based)
Table 2:
Characteristics of Defendants Released Pretrial Regardless of Court of Disposition: A Comparison of the 1998 Sample with the 1989 Sample
Table 3:
Multiple Logistic Regression Analysis Predicting Pretrial FTA: The Final Model from the 1989 sample applied to the 1998 sample, A Comparison of the Two samples
Table 4:
Multiple Logistic Regression Analysis Predicting Pretrial FTA Regardless of Court of Disposition: The Re-Estimated Model Used to Create the New Point Scale, Excludes Length of Time at Current Address
Table 5:
Multiple Logistic Regression Analysis Predicting Pretrial FTA Regardless of Court of Disposition: Classification Table for Model 2 at a .25 Cutpoint
Table 6:
Points Derived from Model 2
Table 7:
New Point Scale: A Comparison of the 1998 Sample with the 1989 Sample
Table 8:
Pretrial FTA by Current CJA ROR Recommendation Scheme
Table 9:
Alternative Risk-Classification Schemes for Defendants Regardless of Court of Disposition
Table 10:
A Comparison of the Current CJA ROR Recommendation Scheme with the Alternative Risk-Classification Schemes Suggested for Defendants Regardless of Court of Disposition: The Difference in the Proportion and FTA Rate of Low-, Moderate-, and High-Risk Defendants
Table 11:
Alternative Risk-Classification Scheme 4: A Comparison with the Current CJA Recommendation Scheme
PREDICTION OF PRETRIAL FAILURE TO APPEAR AND ALTERNATIVE PRETRIAL RELEASE RISK-CLASSIFICATION SCHEMES IN NEW YORK CITY: A VALIDATION STUDY INTRODUCTION The New York City Criminal Justice Agency, Inc. (CJA) uses an objective “point scale” to make pretrial release-on-recognizance (ROR) recommendations for defendants arrested in New York City and held for arraignment in the lower court (Criminal Court). Although Section 510.30 of the New York State Criminal Procedure Law permits judges to consider many factors including a defendant’s community-ties, prior criminal record, the seriousness of the offense, previous record of flight, and the weight of the evidence against the defendant, in their pretrial release (and bail) decisions, CJA’s current release recommendation scheme is based solely upon a defendant's ties to the community, leaving to the arraignment judge consideration of all other factors from other sources. Defendants having strong community ties are considered “good risks” to return for scheduled court appearances. Through interviews with arrestees and verification of the information they provide with a third party, CJA determines a defendant’s risk by assigning “points” for specific community ties found in previous research to distinguish defendants who are more likely to appear at subsequent court dates from those less likely to appear. The points are then summed to arrive at the recommendation category on a “scale” assigned to the defendant. In November 1989, CJA began a research project to examine the predictive ability of the current point scale. Data for that study were drawn from a random sample of 15,359 arrests made in the calendar year 1989 in which defendants were held by the police until Criminal Court arraignment. The study was conducted in several analytical phases. The first phase focused on FTA in Criminal Court. The second phase of the analysis concentrated on FTA among Supreme Court defendants. In the final phase of
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the study, FTA was examined regardless of the court of disposition (combined-court analysis). The primary task in each analytical phase was to identify significant predictors of pretrial FTA. To achieve that objective, a number of models were constructed. With the exception of the Supreme Court analysis, the most predictive model from each analytical phase was used to guide the construction of a new point scale. Finally, four riskclassification schemes were developed by dividing the sample of defendants at various points on the new scale. The alternative risk-classification schemes had the potential of improving prediction over the current CJA ROR recommendation scheme by either 1) decreasing the FTA rate, while decreasing the percentage of defendants classified as good risks for an ROR recommendation, 2) decreasing the FTA rate, while increasing the percentage of good-risk defendants, or 3) increasing the proportion of good-risk defendants, while keeping the FTA rate at the same level. Scheme 4, in particular, was able to classify a larger proportion of defendants as good risks for an ROR recommendation, while keeping the FTA at the same level. This was true for both the Criminal Court and the combined-court Schemes 4. The combined-court Scheme 4, however, had the added advantage of identifying at-risk defendants regardless of court of disposition. In 1999, CJA decided to statistically test the findings from the point-scale research on a new and more recent sample of defendants. The data collected in the third quarter of the calendar year 1998 were used for the validation analysis. Using that dataset, the most predictive combined-court model from the ’89 research was replicated. The objective was to determine whether the model was stable across different samples. This report presents findings from the validation analysis, following a brief description of the methodology. detailed.
First, the categories of the current recommendation scheme are
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CURRENT RECOMMENDATION SCHEME
Under the current recommendation scheme the items used to assess a defendant’s ties to the community include: 1. whether there is a working telephone in the defendant’s residence; 2. whether the defendant has resided at his or her current address for one and onehalf years or longer; 3. whether the defendant expects someone (other than the complainant or defense attorney) at Criminal-Court arraignment; 4. whether the defendant lives with parent(s), spouse, or common-law spouse of sixmonths, grandparent, or legal guardian; 5. whether the defendant is employed, in school, or in a job-training program (or some combination of these) full time; 6. whether the defendant’s address is in the New York City area (the five boroughs of the City, and Nassau, Suffolk, and Westchester counties). With the exception of item 3, there are five possible outcomes for the aforementioned items: "yes", "yes verified", "no", "no verified", or "unresolved conflict." The "yes" and "no" outcomes indicate that the defendant’s response has not been verified. The "yes verified" and "no verified" outcomes are used when the information provided by the defendant has been verified through a third-party contact. "Unresolved conflict" means that the information provided by the defendant in the interview does not match with the information given by the verifier and attempts to resolve the conflict were unsuccessful. Item 3 has only "yes" and "no" responses. The responses to the items are used to classify defendants into the various categories that are included in the CJA ROR recommendation scheme. Based on the classification category, a release recommendation is made to a Criminal Court arraignment judge.
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The current CJA ROR recommendation scheme for adult defendants (i.e., sixteen years of age or older) consists of four main categories, two of which have subcategories, as follows: 1. "Recommended": Verified Community Ties (defendant must have a verified New York City area address, have items 2, 4, or 5 verified, and have at least two other true items); 2. "Qualified": Unverified Community Ties (defendant has an unverified New York City area address and has three other items assessed in the affirmative); 3. No Recommendation due to: A. Insufficient community ties (less than three items were answered affirmatively) B. Residence outside the New York City area C. Conflicting residence information (defendant and verifier did not agree) D. Incomplete interview; 4. No Recommendation due to: A. Open bench warrant attached to the New York State criminal history sheet ("rap sheet") B. Criminal history not available C. Bail jumping charge D. For information Only: murder charge E. For information Only: juvenile offender.1 The first three categories summarize the strength of the defendant’s community ties. The fourth major category of the risk-assessment scheme consists primarily of excluding from the ROR recommendation those defendants who have demonstrated that they will not show up for scheduled court appearances on a previous, pending case or those for whom the absence of a rap sheet precludes ascertaining that information. Defendants are also excluded if arrested on a bail jumping offense, which may be charged in New York State after a defendant does not return to court for thirty days or more after failing to appear while on bail or ROR. Previous failures to appear, for which
1
In April 1996, based on new research, a separate new recommendation scheme was introduced for juveniles (under sixteen years of age).
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the defendant returned to court and the warrant was vacated, do not preclude ROR recommendation on a new arrest.
METHODOLOGY A. Sampling and Data Sources Data for the validation analysis were drawn from a cohort of 89,524 arrests affected between July 1, 1998 and September 30, 1998, in which the defendants were prosecuted on new charges. The data set excludes cases that were not docketed in the CJA database (UDIIS), unless there was an indication that they were prosecuted as “A” dockets in Manhattan,2 or as direct indictments, cases for which prosecution information is not available in CJA’s database (Eckert and Curbelo, 2000). The primary data source was the CJA database.3 The Criminal Court data were tracked through August 6, 1999.4 By that time, 98 percent of the cases had reached final dispositions. The cutoff date for Supreme Court data was September 22, 1999. Approximately 12 percent of the cases had not reached final outcomes by that date. The criminal history information was supplemented with data from New York State Division of Criminal Justice Services (DCJS).5 In order to be consistent with the ’89 sample, defendants given Desk Appearance Tickets were excluded from the ’98 sample. Furthermore, the arrest-based sample was 2
CJA’s database does not contain court data for dockets with the same docket number. Thus, court data for “A” dockets in Manhattan (the designation used in Manhattan to distinguish between two court cases with the same docket number, one of which receives a suffix “A”) were not available for analysis. Felony prosecution in the Supreme Court as the result of a direct indictment by a grand jury is also unavailable. Arrest information is available for both these types of records, and defendant information may be available for arrests receiving “A” dockets. To the extent that these records could be distinguished from other types of non-docketed arrests, they were retained in the dataset to maintain a complete cohort of prosecuted arrests. Where the court information could be determined from other sources, it was added to the data set. 3 Information about the arrest is provided by an on-line feed from the New York City Police Department. 4 In the 1998 sample, if a case had multiple dockets, the Criminal Court information including warrants was pulled on the docket having most severe affidavit charge (Penal Law severity). In the 1989 sample, the most severe docket was defined as the one with the most severe first final disposition. In addition, information pertaining to pretrial Criminal Court warrants was collected on the docket with the latest final disposition. 5 DCJS did not provide data for sealed cases. The New York City Police Department, DCJS, or any agency providing data bear no responsibility for the methods of analysis used in this report or its conclusions.
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converted into a defendant-based sample (n=71,273), where only the defendant’s first arrest during the sampling period was included in the study sample (even if the defendant had multiple arrests). In order to examine pretrial FTA, the study focused on defendants whose cases were not completed at Criminal Court arraignment and who were released pretrial: i.e., ROR'd or made bail prior to the disposition of all charges in Criminal or Supreme Court. Table 1 shows that in 1998, 52.6 percent of the cases had not reached a final disposition at Criminal Court arraignment which was 17.6 percentage points lower than the proportion for the ’89 sample. Approximately three-fourths of the defendants whose cases were adjourned at Criminal Court arraignment were released pretrial in 1998. Defendants classified as juvenile offenders by CJA were excluded from the analysis. More specifically, the analysis focused on 27,235 defendants whose cases were adjourned at Criminal Court arraignment and who were at risk of pretrial FTA in either Criminal Court or Supreme Court.
B. Dependent Variable The dependent variable, pretrial FTA, measured the issuance of a bench warrant at any appearance prior to the disposition of a defendant's case in Criminal or Supreme Court. C. Independent Variables The variables included in the final combined-court model (’89 sample) were community-ties items, criminal history indicators, and severity and type of the top arrest charge. The community-ties items contained information on whether the defendants had a working telephone in the residence, the length of time at their current address, whether they had a New York City area address, whether they expected someone at their Criminal Court arraignment, and whether they were either employed, in school, or in a training program full time at the time of their arrest. The criminal history variables provided data
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on pending cases and prior FTA. The top arrest charge "type" was based on its Uniform Crime Reports’ (UCR) category; the "severity" of the top arrest charge was derived from its New York State Penal Law offense class.
D. Statistical Methods In order to determine its predictive ability, the final combined-court model from the ’89 sample was applied to the ’98 sample. Due to the dichotomous nature of the dependent variable, multiple logistic regression analysis was employed. Multiple logistic regression is a statistical technique that is used to test the individual effect of a number of independent variables on a categorical dependent variable, while controlling for the other variables in the model. A logistic regression procedure predicts the log-odds (the logit coefficient) of an observation being in one category of the dependent variable versus another (in this case, FTA versus no FTA). When reporting the results from a logistic regression model, one may also wish to transform the log-odds into odds ratios. This is accomplished by taking the antilog of the logit coefficient. The result is then interpreted as how much the odds of an outcome change, given a specific category of an independent variable. In other words, the obtained logistic coefficient, transformed into an odds ratio, tells one how much the odds of an outcome change given a one unit change in an independent variable, controlling for the effects of the other variables. An odds ratio greater than one indicates an increase in the likelihood of an event occurring, and an odds ratio of less than one indicates a decrease in the likelihood of an event occurring. An odds ratio of one indicates the odds remain unchanged (no association between the independent and dependent variable). As an example, assume that a dichotomized independent variable is coded "1" if a defendant has a history of failure to appear, and "0" otherwise (prior FTA). Also assume that the dependent variable, indicating current FTA, is coded "1" if a defendant fails to
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appear for a court appearance on the present arrest, and "0" if they appear for all appearances. Estimating a univariate logistic regression with prior FTA as the only independent variable produces a logit coefficient (log-odds) of .676. This suggests that when the variable prior FTA changes from 0 to 1, there is an associated increase of .676 in the log-odds of failure to appear. Taking the antilog of the logit coefficient gives an odds ratio of 1.966. This indicates the odds of FTA for defendants with prior FTAs are about two times greater than that for defendants who do not have a history of failure to appear.
In the analyses presented in this report, if the independent variable is
categorical, the effect for each category except one is compared to the overall effect of that variable (deviation contrast technique). The effect for the excluded category is obtained by switching the reference category. Both in the ’89 and the present analyses, a .05 level (or less) was used to ascertain whether an observation had a statistically significant effect on the dependent variable. A .05 level of significance means that the observation could have occurred by chance alone five times in one hundred.
In this report, the interpretation of the effect for each
independent variable is based on the logit coefficient and the affiliated level of statistical significance. To reflect changes in the magnitude of the independent variables in the ’98 sample, the points were reassigned to all the significant variables in the model and a new scale was constructed. This readjusted point scale was compared with the original scale based on the ’89 research with respect to its predictive power, which was assessed by using the mean cost rating (MCR). MCR, introduced by Duncan, Ohlin, Reiss, and Stanton (1953), measures the predictive efficiency of an instrument over its base rate (see Siddiqi, 1999 for MCR computation). The values for this statistic range from 0 to 1, with 0 indicating no improvement in prediction and 1 suggesting perfect prediction. As a general rule, Fischer suggested that an MCR of .25 be attained to show utility for
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classification; a score of .35 or greater indicates significant improvement over existing clinical techniques (Fischer, 1985). At the next stage of the analysis, the point scale from the ’98 analysis was used to construct four alternative risk-classification schemes by dividing the sample of defendants at various points. The cutoff points applied were similar to those used in the ’89 analysis.
These risk-classification schemes were compared with the current
recommendation scheme with respect to the proportion of defendants in various risk categories and their corresponding FTA rates.
RESULTS A. Defendant Characteristics Table 2 presents characteristics of defendants who were at risk for FTA in either Criminal or Supreme Court. To facilitate comparison, the findings for the ’89 sample are also provided. As with the ’89 sample, the majority of the defendants in the 1998 combined-court sample were male. The distribution of the ethnicity of defendants in the 1998 sample was similar to the distribution of this variable in the 1989 sample. Almost half of the defendants were black, one-third were Hispanic, and the remainder were white or other ethnicity. The median age for the 1998 sample was slightly higher (30 years old) than the median age for the 1989 sample (27 years old). The average FTA rate for the ’98 sample was substantially lower than that for the ’89 sample (20% versus 35.2%). There was a shift in the proportion of defendants arrested in Brooklyn and Manhattan from 1989 to 1998. In 1989, 34.3 percent of the defendants in the sample were arrested in Manhattan and 26.6 percent of the defendants were arrested in Brooklyn, and in the 1998 sample the same proportion of defendants were arrested in Manhattan as in Brooklyn (30.2% and 29.7%, respectively). The two samples did not differ with
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respect to the proportion of defendants arrested in the remaining boroughs. Fewer cases were transferred to Supreme Court in 1998 than in 1989 (15% versus 23.3%). Examining the community ties items, approximately 90 percent of the defendants in both the 1989 and the 1998 combined-court samples reported living in the New York City area. Almost two-thirds of the defendants in both samples reported living at their current address for 18 months or longer, and approximately three-fifths of the defendants in both samples, reported living with someone at the time of their arrest. A slightly lower proportion of defendants in the 1998 sample expected someone at arraignment compared to the 1989 sample (39.9% versus 44.8%). There was a substantial increase in the proportion of defendants in the 1998 sample (70.6%) who reported having a telephone in their residence, compared to the 1989 sample (48.1%). In both the samples, almost onehalf of the defendants reported being employed, in school, or in a training program full time. With regard to differences between “yes” and “yes verified” categories for the community-ties variables, the proportion of defendants with affirmative, but unverified responses increased in 1998, ranging from 22.4 percentage points for having a telephone in the residence to 4.7 percentage points for the family-ties variable. In contrast, with the exception of the telephone variable, the proportion of defendants who were categorized as “yes verified” decreased slightly, ranging from 6.2 percentage points for the familyties variable to 2.5 percentage points for the full-time activity variable. The two samples did not differ with regard to the proportion of defendants who had been arrested previously (56.6% for the ’89 sample, 56.4 percent for the ’98 sample). However, in comparison to the ’89 sample, the proportion of defendants with respect to the other measures of criminal history decreased in the ’98 sample, ranging from 12.3 percentage points for open cases to 1.7 percentage points for prior violent felony convictions.
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The majority of the defendants in both the 1989 and 1998 samples were arrested for felony charges, primarily B and D felonies. The proportion of defendants charged with an A misdemeanor increased from 19.9 percent in 1989 to 31.1 percent in 1998. This may be attributed to an increase in arrests involving quality-of-life offenses. In 1998, there was a shift in the types of offenses defendants were charged with. For example, the proportion of defendants charged with violent crimes increased from 28.8 percent in 1989 to 37.1 percent in 1998. In contrast, relative to the ’89 sample, the proportion of defendants charged with property or drug offenses in the ’98 sample decreased by 8.7 percentage points and 4.7 percentage points, respectively.
B. Applying the Final Model from the ’89 Sample to the ’98 Sample For the validation analysis, the final combined-court model, which was used to construct a new point scale for the 1989 sample was applied to the 1998 sample. Table 3 presents the results for both samples.6 As shown by the table, all the variables that were significant in the ’89 sample remained significant in the ’98 sample, with the exception of the length of time at current address variable which was not a strong predictor in the former (logit coefficient= .207, p= .053). However, the interpretation for some of the variables changed. With regard to the community-ties variables, in both the samples, when controlling for the other variables in the model, the effect of having an affirmative, but unverified response to the telephone variable was significantly different than the overall effect of that variable; the odds of failure were lower among these defendants. In contrast, the odds of failure were higher among defendants who were verified as not having a telephone in the residence. Furthermore, when using the 1998 sample, the “no” category of this variable became a significant predictor of FTA--defendants with a
6
The effect for the reference category for a categorical independent variable was obtained by choosing an alternative reference category.
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negative, but unverified response were more likely to FTA than defendants with the overall effect of that variable. Expecting someone at Criminal Court arraignment was a significant predictor of lower FTA in both 1989 and 1998. In both the samples, the odds of failure were significantly lower for defendants who were verified as being employed, in school, or in a training program full time. In contrast, the likelihood of FTA was higher among defendants who gave a negative, but unverified response. In 1989, the “unresolved conflict” category of this variable was also significant; defendants in that category were more likely to FTA than defendants with the overall effect of that variable. This category did not attain statistical significance in the ’98 sample. Based on the number of statistically significant categories, the New York City area address variable was found to be a stronger predictor of FTA in 1998 than in 1989. More specifically, in the 1989 sample, defendants who did not report a New York City area address (the “no” category) were more likely to FTA than defendants with the overall effect of the variable. This was also true for the ’98 sample. In addition, defendants who reported a New York City area address (both yes and yes, verified responses) were less likely to FTA than defendants with the overall effect of this variable. These categories were not statistically significant in 1989. With regard to a defendant’s criminal history, the variables behaved in a similar fashion in both samples. In each sample, when controlling for the effects of the other variables in the model, defendants who had a history of prior FTA and defendants who had open cases at the time of the arrest were more likely to FTA than those who did not have a prior FTA or an open case. The type of top arrest charge was a significant predictor of FTA in both the samples and could be interpreted in the same fashion, with the exception of the violent and “other” offenses.7 To be more specific, in both 1989 and 1998, the likelihood of
7
The “other” category consists of all offenses not included in the violent, property, drug, gambling, DWI, weapon, VTL (excluding DWI), and criminal mischief categories.
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FTA was higher among defendants who were arrested for property and drug offenses. In contrast, defendants arrested for gambling offenses or driving while under the influence of drugs or alcohol, were less likely to FTA. The two other categories that significantly predicted FTA in the ’98 sample were the VTL offenses (Violation of Traffic Law, excluding DWI offenses) and offenses involving criminal mischief; the likelihood of FTA was higher among defendants charged with these offenses. In 1989, due to a small number of cases, these categories were collapsed with the “other” category for that variable which was a significant predictor of FTA in the 1989 model; defendants categorized as “other” were more likely to FTA than defendants with the overall effect of that variable. Defendants arrested for violent offenses significantly predicted FTA only for the 1989 sample where they were more likely to fail than the overall effect of that variable. The severity of the top arrest charge also proved to be statistically significant in both the ’89 and ’98 samples. However, its interpretation was different for the two samples. In 1989, when compared with the overall effect of the variable, the FTA rates were lower among defendants who were arrested for A misdemeanors.
In 1998,
defendants arrested for A or B felonies were less likely to FTA than the overall effect of that variable. In sum, all the variables that predicted FTA in ’89 sample attained statistical significance in the ’98 sample. However, the magnitude of the logit coefficients for some of the variables changed, suggesting a need to readjust the point scale.
C. Construction of the Point Scale The next step in the analysis was to construct a risk-classification instrument. To reflect changes in the magnitude of the some of the variables in the validation sample, Model 1 from the ’98 sample was selected for that purpose. However, before using this model for instrument construction, the length of time at current address variable was
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dropped and Model 1 was re-estimated (Table 4). The re-estimated model contained all the variables that were statistically significant in predicting pretrial FTA. The revised model was examined in terms of its predictive ability. Table 5 presents predicted versus observed behavior at a .25 cutpoint, a value close to the base rate.8 The cutpoint is a value, which determines how defendants will be classified by a prediction model. Defendants scoring higher than the cutpoint are classified as predicted to be “yes” on the dependent variable (pretrial FTA for this research), while those scoring lower than the cutpoint are predicted as “no.” The predicted values are compared with the actual scores on the dependent variable and rates of correct and false predictions are computed.9 As shown by Table 5, when using a .25 cutpoint, 74 percent of the defendants were predicted as not to fail. Approximately 63 percent of them were observed as not failing (true negatives) and 11 percent were observed as failing (false negatives). The table also shows that when applying a .25 cutpoint, 26 percent of the defendants were predicted to fail. Of this number, nine percent were observed as failing (true positive) and 17 percent were observed as not failing (false positive). Overall, the model correctly classified 72 percent of the at-risk sample. It should be noted that using a cutpoint for prediction has limitations--the predictive statistics are not consistent across all levels of a given model (Cuveliar, 1993).
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model’s predictive accuracy varies with differing cutpoints. Typically, a cutpoint close to the base rate minimizes the number of errors. 9The correct predictions include true positives and true negatives. In this research, a true positive resulted when a defendant was predicted to fail to appear for subsequent court appearances and was observed as failing. In comparison, a true negative was ensued when it was predicted that a defendant would not fail to appear and was observed as not failing. The errors in prediction can be false positives or false negatives. A false positive (also known as Type I or alpha error) resulted when failure to appear was predicted but nonfailure was observed. In contrast, the false negatives (Type II or beta error) denoted cases that were predicted as not failing but indeed failed. When using statistical models for prediction, false negatives, or Type II, errors result in the release of defendants who eventually fail to appear for scheduled court appearances, if all predicted non-failures were released. In contrast, false positives, or Type I, errors result in pretrial detention of defendants who would otherwise have not missed a court appearance, if all those predicted to fail were detained. Both types of errors have their costs, which should be considered carefully before selecting a statistical model to guide the construction of a prediction instrument.
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More specifically, a cutpoint method measures a model’s accuracy by classifying defendants into two groups; predicted failures and predicted successes. If a classification instrument, for example, categorizes defendants into three groups, low, moderate, and high risks, the proportions of predicted failures and non-failures will be determined either by classifying low and moderate risks into predicted non-failures and high risks as predicted failures, or categorizing low risks as predicted non-failures and moderate and high risks as predicted failures. The predictive accuracy of the model where low and moderate risks are treated as successes will be different from the model where only low risks are treated as successes. In sum, using different cutpoints would result in different distributions of predicted behavior. Because the objective of the point-scale research was to construct alternative riskclassification schemes, which would group defendants according to their likelihood of FTA, using a cutpoint to construct an instrument that predicts only failures and nonfailures did not seem to be practical. Thus, a point scale was constructed which was used to categorize defendants into different subgroups. These subgroups had different average FTA rates and as such represented different risk levels. The new point scale was examined in terms of its predictive power and comparisons were made with that for the point scale based on the ’89 sample. With regard to scale construction, as with the ’89 analysis, points were assigned to each of the independent variables based on their estimated coefficients and significance levels in the model. For purposes of standardization, the logit coefficients were divided by a constant (.15) and were then rounded to the nearest whole number.10 If the coefficient was negative and significant, a negative value was given, indicating that a defendant was less likely to FTA. Likewise, positive values were given for significant
10 The decision to divide by .15 was arbitrary, although consistent with that used in several studies (Goldkamp et al., 1981; Goodman, 1992).
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positive coefficients, meaning that the likelihood of FTA increased. The insignificant coefficients were assigned a value of zero. Table 6 shows the points assigned to each of the independent variables in Model 2.
As shown by the table, the prior FTA variable had the strongest effect on the
likelihood of FTA; defendants with prior FTA had five points added to their overall score. In the ’98 sample, one-fourth of the sample had a history of FTA. The type of top arrest charge was a strong contributor to a defendant’s total score; defendants arrested for gambling had eight points subtracted from their overall scores. However, only one percent of the defendants were arrested for gambling. Table 6 also shows the two variables that contributed least to the overall score, because they had the smallest effect on FTA--the severity of the top arrest charge and the expectation of someone at Criminal Court arraignment. With regard to the former, a defendant arrested for an A or B felony had one point subtracted from his or her total score. With regard to the latter, one point was also subtracted when a defendant expected someone at Criminal Court arraignment. A comparison of the point scale constructed for the ’98 sample with the point scale for the ’89 sample revealed that in both samples, prior FTA was the strongest contributor to a defendant’s score in both the samples (Table 7). The two samples differed somewhat with respect to the strength of the predictor variables. For example, the effect of the different categories comprising the charge type variable was slightly stronger in 1989 than in 1998, with the exception of gambling offenses. More specifically, in 1989, defendants arrested for property offenses and drug offenses scored four points each on the point scale. In 1998, each of these variables contributed two points to the total point-scale score.
Furthermore, in 1989, defendants arrested for
violent offenses had one point added to their score. In 1998, defendants arrested for violent offenses received zero points on the scale. Defendants arrested for gambling offenses scored slightly higher in 1998 than in 1989 (-8 points versus -6 points).
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With respect to the prior FTA variable, defendants arrested in the third quarter of 1998 scored one point higher than defendants arrested in 1989 (5 points versus 4 points). In both the samples, defendants with open cases added two points to their overall scores. The two samples also differed with respect to the points scored on the community-ties variables. For example, the effect of having a telephone and residing at a New York City area address was slightly stronger for the ’98 sample than for the ’89 sample; points were assigned to three of the five categories in 1998 versus two categories in 1989. Furthermore, the points scored on these variables were slightly higher for the ’98 sample than for the ’89 sample. Beginning with the telephone variable, in 1998, defendants categorized as “yes” had three points subtracted from their overall scores in comparison to two points in 1989. Furthermore, defendants with a “no” response to this variable had one point added in 1998. These defendants did not score any points in the ’89 sample. With regard to the New York City area address variable, in 1998, defendants with yes, yes verified, and no verified responses scored negative one, negative two, and positive three points, respectively. In comparison, in 1989, only one category of this variable was statistically significant--defendants with a “no” response scored two points on the scale. The effects of expecting someone at Criminal Court arraignment and being employed, in school, or in a training program full time were slightly weaker in the ’98 sample than in the ’89 sample.
In 1989, defendants who expected someone at
arraignment had two points subtracted from their overall scores whereas in 1998, they had only one point subtracted. With regard to being employed, in school, or in a training program full time, in 1989, defendants categorized as “yes verified” and “no” scored negative four and positive two points, respectively. The comparable figures for the ’98 sample were negative two and positive one points. In 1989, defendants classified as “unresolved conflict” on this variable had two points added to their scores. In 1998, because of their insignificance, such defendants did not score any points.
18
The two samples were also compared with respect to the predictive power of the point scales, which was measured by the mean cost rating (MCR tables not shown, see methodology P. 8 for a description of MCR). The MCR for the current recommendation system was .20, indicating that the current classification model had minimal predictive ability; it improved prediction by about 20 percent of the total possible improvement over the base rate.
The MCR for the point scale based on the ’98 model indicated a
substantial improvement over the base rate; the model improved prediction by about 34 percent over its base rate. The MCR statistic was also computed for the original point scale based on the ’89 sample. When applied to the ’98 sample, this scale was able to improve prediction by 28 percent. The comparable figure for the ’89 sample was 40 percent. Allowing for shrinkage when applied to a sample other than the one in which it was constructed, the point scale based on the ’89 model was still able to improve prediction over its base rate. However, its predictive power was improved by adjusting points to the ’98 model. To summarize, the two samples differed slightly with respect to the strength of the variables used to construct the point scale. However, the direction of relationship for all the significant variables remained the same.
Furthermore, the variables that
contributed strongly to a defendant’s total score in 1989 (prior FTA and top arrest charge type) continued to have a strong effect in 1998. Finally, as for the ’89 sample, the new point scale was able to improve prediction over the base rate. These findings suggested that the new scale could be used with confidence to construct alternative riskclassification schemes for the ’98 sample. All possible scores were calculated, ranging from -20 to 16 points.11 Defendants scoring lower points had lower FTA rates, whereas higher FTA rates were found for those scoring at the high end of the scale.
11
Theoretically, scores of -22 to 17 points were possible.
19
D. Alternative Risk-Classification Schemes Four risk-classification schemes were developed by dividing the sample of defendants at various points on the new scale. The range of points scored between those "cut-off points" and their corresponding FTA rates were examined. These varying FTA rates were then used to categorize defendants according to which represented the lowest risks for FTA and, thus, for ROR. These schemes were then compared with the current CJA ROR recommendation scheme, in terms of the proportion of defendants in various risk categories and their corresponding FTA rates. To facilitate these comparisons, the findings from the application of the current recommendation scheme to the combinedsample of at-risk defendants are also provided (Table 8). It should be noted that the current scheme is based on a risk of flight solely in Criminal Court, while the alternative schemes were designated with the intention of predicting FTA both in Criminal and Supreme Court. Current CJA ROR Recommendation Scheme As shown by Table 8, when applying the current scheme to the ’98 sample, 29.9 percent of the defendants at risk in either Criminal Court or Supreme Court had verified community ties, and were recommended for ROR release. Of this number, 14.1 percent failed to appear for at least one scheduled court appearance.
With regard to the
defendants with unverified community ties, 29.7 percent received a “qualified” recommendation. Their FTA rate was 15.5 percent. The remaining defendants (40.4%) were not recommended for ROR, with an FTA rate of 27.7 percent. A comparison of the ’98 sample with the ’89 sample revealed that the two samples did not differ with respect to the proportion of defendants classified as low risks for FTA. However, relative to the ’89 sample, 11.7 percentage points more defendants received a qualified rating in 1998.
This may be attributed to an increase in the
proportion of defendants with affirmative, but unverified community-ties in 1998. In
20
contrast, the proportion of defendants who were not recommended for release decreased from 50.5 percent in 1989 to 40.4 percent in 1998. When applying the current scheme, the FTA rates for all the categories were lower in 1998 than those in 1989. This was not surprising as the average FTA rate for the combined-sample of at-risk defendants dropped from 35.2 percent in 1989 to 20 percent in 1998.
In 1989, the CJA
recommendation scheme was able to classify defendants by their relative risk of flight. Its predictive ability declined somewhat in 1998 as it stopped distinguishing between low- and moderate-risk defendants.
However, it continued to distinguish high-risk
defendants from low-, and moderate-risk defendants.
Alternative Risk-Classification Scheme 1 Table 9 presents the alternative risk-classification schemes for the ’98 at-risk sample. As shown by table, the first alternative risk-classification scheme divided the sample into low and high risks, each containing approximately one-half of the defendants. The first group scored -7 points or less on the point scale, with an FTA rate of 12 percent. The defendants in the second group scored -6 points or higher on the scale. Their FTA rate was 27.8 percent. Because of their low FTA rates, the defendants in Group I would be considered good risks for an ROR recommendation. When compared with defendants classified as low risks under the current ROR scheme, this group would contain 20.3 percentage points more defendants. In addition, their FTA rate would decrease by 2.1 percentage points (Table 10). Group 1-II defendants had substantially higher FTA rates than Group 1-I defendants and were thus categorized as high risks for an ROR recommendation. If using alternative Scheme 1 rather than the current CJA ROR recommendation scheme, the proportion of defendants considered high risks would increase by 9.4 percentage points. However, their FTA rate would remain the same. In sum, relative to the current CJA ROR recommendation scheme, alternative risk-classification Scheme 1 would: 1)
21
categorize more defendants as good risks for ROR recommendation, while slightly decreasing their FTA rate; 2) eliminate a moderate-risk group of defendants; and 3) classify a higher proportion of defendants as high risks, without increasing their FTA rate. Alternative Risk-Classification Scheme 2 The second scheme classified defendants into three groups, each containing approximately one-third of the at risk sample (Table 9). The defendants in the first group had the lowest FTA rate (10.3%) and would be considered the best risks for an ROR recommendation consideration. These defendants scored -9 points or less on the point scale. When compared with the low-risk category of the current CJA ROR recommendation scheme, Scheme 2 would classify 5.1 percentage points more defendants as low risks. Furthermore, the FTA rate among these defendants would decrease by 3.8 percentage points (Table 10). Defendants in Group 2-II constituted a moderate-risk group, with an FTA rate falling approximately midway between that reported for Group 2-I and Group 2-III defendants (19%). These defendants had scores ranging from -8 to -4 points on the new scale (Table 9). Relative to the current scheme, Scheme 2 would increase both the proportion and FTA rate of moderate-risk defendants by approximately three percentage points (Table 10). Defendants in Group 2-III scored -3 points or higher on the new scale. These defendants had substantially higher FTA rate than Group 2-I and Group 2-II defendants; almost one-third of them did not appear for at least one scheduled court appearance in Criminal or Supreme Court. For this reason, they would be considered as high risks for an ROR recommendation. In comparison to the “not recommended� category of the current scheme, Scheme 2 would classify 7.8 percentage points fewer defendants as high
22
risks for an ROR recommendation. However, their FTA rate would increase by 3.4 percentage points. To summarize, the alternative risk-classification Scheme 2 would: 1) increase the number of low-risk defendants while decreasing their FTA rate, 2) increase both the proportion and FTA rate of moderate-risk defendants, and 3) decrease the proportion of defendants in the high risk category while increasing their FTA rate. Alternative Risk-Classification Scheme 3 As shown by Table 9, the third alternative risk-classification scheme divided the at-risk defendants into low, medium, high, and very high-risk groups. Each group contained approximately one-fourth of the sample. Defendants in the first group scored 10 points or less on the new scale, and had an FTA rate of 9.7 percent. The second group of the defendants scored -9 points to -7 points, and had an FTA rate of 14.9 percent. The FTA rates for defendants in Group 3-III and Group 3-IV were 22.4 percent and 33 percent, respectively. Defendants comprising the third group scored -6 to -1 points. Defendants in Group 3-IV scored zero point or higher on the point scale. A comparison of the third risk-classification scheme with the current CJA ROR recommendation scheme revealed that the former would: 1) classify slightly fewer defendants (1.9 percentage points) as good risks for ROR, and decrease the FTA rate (4.4 percentage points); 2) categorize fewer defendants (7.5 percentage points) as moderate risks, while keeping the FTA rate at the same level, and, 3) increase both the proportion (9.5 percentage points) and FTA rate (27.7 percentage points) of defendants in the combined high- and very high- risk category (Table 10). Taken together, Scheme 1 identified the largest proportion of defendants as low risks. However, the proportion of high-risk defendants was also greatest when using that scheme. Schemes 2 and 3 classified a smaller proportion of defendants as high risks, primarily because they both contained moderate-risk categories.
However, the
23
proportion of low-risk defendants in each was substantially lower than the proportion found for Scheme 1. Because that was the case, Schemes 2 and 3 may be too restrictive. Therefore, another scheme was created that combined Schemes 1 and 3 (Scheme 4). Alternative Risk-Classification Scheme 4 Scheme 4 consisted of three groups of defendants. As shown by Table 9, Group 4-I is precisely the same as Group 1-I. Group 4-II is the same as the third group from Scheme 3. The defendants in Group 4-III are the same as Group 3-IV defendants. If using alternative risk-classification Scheme 4, rather than the current CJA ROR recommendation scheme (Table 10), the proportion of good-risk defendants likely to be recommended for ROR would increase by 20.3 percentage points. Furthermore, the FTA rate for these defendants would decrease slightly (2.1 percentage points). Relative to those currently receiving a "qualified" recommendation, Scheme 4 would decrease the proportion of defendants by 5.4 percentage points. The FTA rate among these defendants would increase by 6.9 percentage-points. Defendants in Group 4-III had the highest FTA rate. In comparison to the current scheme, this alternative scheme would classify 14.8 percentage points fewer defendants as high risks, while increasing their FTA rate by 5.3 percentage points. A comparison of the alternative recommendation schemes constructed for combined-court at-risk defendants suggested that both Scheme 1 and Scheme 4 were able to classify greatest proportion of defendants as good risks for ROR recommendations without increasing the FTA rate over its current level. Scheme 1, however, did not have a moderate-risk category. Scheme 4, on the other hand, had the advantage of identifying moderate-risk defendants and, as such, decreasing the proportion of high-risk defendants.
24
E. Alternative Risk-Classification Schemes: A Comparison with the Current CJA ROR Recommendation Scheme The alternative risk-classification Scheme 4 based on the ’89 sample and the alternative risk-classification Scheme 4 devised for the ’98 sample were compared with the current CJA ROR recommendation scheme with respect to the proportion of defendants in different risk categories and their corresponding FTA rates (Table 11). The differences in the proportion and FTA rate of Scheme 4 for the ’89 sample are based on a comparison with the CJA scheme when applied to the ’89 sample. The objective was to determine whether the two schemes produced the similar results. As shown by Table 11, the two alternative risk-classification schemes produced very similar results when they were compared with the current CJA ROR recommendation scheme. Both were able to classify a larger proportion of defendants as good risks for ROR release (Groups 4-I), while slightly decreasing the FTA rate or keeping it at the same level. Furthermore, under both schemes, the proportion of highrisk defendants decreased. The two schemes, however, differed with respect to the proportion of the defendants classified as moderate risks (Group 4-II). In 1989, Scheme 4, when compared with the CJA ROR recommendation scheme, classified 6.2 percentage points more defendants as moderate risks. In contrast, Scheme 4 from the ’98 sample placed 5.4 percentage points fewer defendants in the moderate-risk category. Relative to the CJA scheme, the FTA rates for defendants classified as moderate risks increased in both samples. And, finally, both schemes decreased the number of high-risk defendants, but increased their FTA rate.
F. Conclusion The findings from the current analysis suggest that the point scale derived from the ’89 research is statistically valid and can be used with confidence to predict FTA in
25
both Criminal and Supreme Court.
The research further suggests that the use of
alternative risk-classification Scheme 4 would produce the most desirable results. Firstly, Scheme 4 would be able to identify at-risk defendants regardless of the court of disposition. The current CJA recommendation scheme, by contrast, is based on a risk of flight solely in Criminal Court. Secondly, Scheme 4 would be able to classify defendants by their relative risk of flight; defendants classified as good risks for an ROR recommendation would have the lowest FTA rate, the FTA rate for moderate-risk defendants would be approximately midway between the FTA rates for low-risk and high-risk defendants, and high-risk defendants would have the highest FTA rate. The current CJA recommendation scheme when applied to the ’98 sample did not differentiate between low- and moderate-risk defendants.
However, it was able to
distinguish high-risk defendants from low- and moderate-risk defendants in the ’89 sample.
Finally, Scheme 4 improves upon the current CJA ROR recommendation
scheme by substantially increasing the number of low-risk defendants while slightly decreasing their FTA rate. As such, it has the potential of reducing the jail population while awaiting trial, if followed by judges at arraignment. Furthermore, the identification of defendants at moderate risk for FTA would offer an opportunity to consider other release options, such as supervised- or conditional-release, aimed at reducing their risk of FTA. Similar suggestions could be made for defendants categorized as high risks--such defendants should be recommended for release under conditions that would improve the likelihood of their appearance for court.
26
BIBLIOGRAPHY
Copas, John B. and Roger Tarling (1986). “Some Methodological Issues in Making Predictions,” in Criminal Careers and Career Criminals. edited by Alfred Blumstein, Jacqueline Cohen, Jeffrey A. Roth, and Christy A. Visher. National Academy Press: Washington, D.C. Duncan, O., L. Ohlin, A. Jr. Reiss, and H. Stanton (1953). “Formal Devices for Making Selection Decisions,” American Journal of Sociology, 58(6): 574-584. Eckert, Mary A. and Mari Curbelo (2000). Alternative-to-Incarceration Information Services: First Half Fiscal Year 2000, Six-Month Report. New York: New York City Criminal Justice Agency. Fischer, D. (1985). Prediction and Incapacitation: Issues and Answers. Des Moines, IA: Statistical Analysis Center, Iowa Office for Planning and Programming. Goldkamp, John S., Michael R Gottfredson, and Susan Mitchell Herzfeld (1981). Bail Decisionmaking: A Study of Policy Guidelines. Washington, D.C.: U.S. Department of Justice, National Institute of Corrections. Goodman, Rebecca (1992). Hennepin County Bureau of Community Corrections Pretrial Release Study. Minneapolis, Minnesota: Planning and Evaluation Unit. Gottfredson, Stephen D. and Don M. Gottfredson (1986). “Accuracy of Prediction Models,” in Criminal Careers and Career Criminals, edited by Alfred Blumenstein, Jacqueline Cohen, Jeffrey A. Roth, and Christy A. Visher. National Academy Press: Washington, D.C. Loeber, R. and T. Dishion (1983). “Early Predictors of Male Delinquency: A Review,” Psychological Bulletin, 94 (1): 68-98. Siddiqi, Qudsia (1999). Assessing Risk of Pretrial Failure to Appear in New York City, A Research Summary and Implications for Developing Release Recommendation Schemes: New York. New York City Criminal Justice Agency.
NEW YORK CITY CRIMINAL JUSTICE AGENCY 3rd Quarter of 1998 Dataset Combined-Court Analysis Table 1: Arraignment Outcome (Defendant-based) 1989 Sample N= 14,333 ARRAIGNMENT OUTCOME
1998 Sample N= 68,281
N
%
N
%
NON-DISPOSED
10077
70.2
35900
52.6
PLED GUILTY
3316
23.1
20811
30.5
DISMISSED
571
4.0
11565
16.9
OTHER1
18
0.1
5
0.0
NOT PROSECUTED
373
2.6
NA
NA
14355
100.0
68281
100.0
TOTAL
1
OTHER includes transfer to other borough and family court. Note: "Not Prosecuted" cases were excluded from the 1998 Sample.
NEW YORK CITY CRIMINAL JUSTICE AGENCY 3rd Quarter 1998 Dataset Combined-Court Analysis Table 2: Characteristics of Defendants Released Pretrial Regardless of Court of Disposition: A Comparison of the 1998 Sample with the 1989 Sample
1989 Sample N=7,595
1998 Sample N=27,235
Defendant Characteristics SOCIO-DEMOGRAPHIC ATTRIBUTES Sex Male Female Total
N
%
N
%
6641 932 7573
87.7 12.3 100.0
22632 4575 27207
83.2 16.8 100.0
Ethnicity Black Hispanic White
3792 2649 927
49.9 34.9 12.2
12936 9089 3761
47.5 33.4 13.8
Other1 Total
227 7595
3.0 100.0
1412 27207
5.2 100.0
Age at Arrest 18 and under 19 - 20 years 21 - 24 years 25 - 29 years 30 - 34 years 35 - 39 years 40 - 78 years Total
962 732 1385 1574 1282 724 930 7589
12.7 9.6 18.3 20.7 16.9 9.5 12.3 100.0
3286 2237 3872 4135 4217 3692 5730 27169
12.1 8.2 14.3 15.2 15.5 13.6 21.1 100.0
Median: 27.0 years
Median: 30.0 years
CASE-PROCESSING CHARACTERISTICS Borough of Arrest Brooklyn Manhattan Queens Staten Istand Bronx Total
2021 2608 1244 176 1546 7595
26.6 34.3 16.4 2.3 20.4 100.0
8099 8230 4624 1287 4995 27235
29.7 30.2 17.0 4.7 18.3 100.0
Type of Court Criminal Court Supreme Court Total
5820 1771 7591
76.7 23.3 100.0
23159 4076 27235
85.0 15.0 100.0
6536 1055 7591
86.1 13.9 100.0
21419 2950 24369
87.9 12.1 100.0
Type of First Release in Criminal Court ROR Bail
Page 1 of 4
Defendant Characteristics CASE-PROCESSING CHARACTERISTICS FTA in Criminal Court (Defendants at risk in Criminal Court) Yes No
Table 2 (contd.) 1989 Sample N=7,595 N
1998 Sample N=27,235 %
N
%
2169 4935 7104
30.5 69.5 100.0
4764 21453 26217
18.2 81.8 100.0
554 1120 1674
33.1 66.9 100.0
749 2360 3109
24.1 75.9 100.0
FTA Regardless of Court of Disposition Yes No Total
2676 4916 7592
35.2 64.8 100.0
5446 21789 27235
20.0 80.0 100.0
COMMUNITY TIES ITEMS Verified NYC Area Address Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total
3727 2915 421 88 295 7446
50.1 39.1 5.6 1.2 4.0 100.0
15394 9092 1374 299 472 26631
57.8 34.1 5.2 1.1 1.8 100.0
Verified Length of Residence of at least 18 Months Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total
2612 2248 1618 533 465 7476
35.0 30.1 21.6 7.1 6.2 100.0
11068 6915 5806 2097 789 26675
41.5 25.9 21.8 7.9 3.0 100.0
Verified Family Ties Within the Residence Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total
2122 2429 2063 543 319 7476
28.3 32.5 27.6 7.3 4.3 100.0
8809 7020 7997 2249 592 26667
33.0 26.3 30.0 8.4 2.2 100.0
Expects Someone at Arraignment Yes No Total
3345 4114 7459
44.8 55.2 100.0
10595 15961 26556
39.9 60.1 100.0
FTA in Supreme Court (Defendants at risk in Supreme Court) Yes No
Page 2 of 4
Defendant Characteristics COMMUNITY TIES ITEMS Verified Telephone Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total
Table 2 (contd.) 1989 Sample N=7,595 N
1998 Sample N=27,235 %
N
%
1180 2415 2897 540 450 7482
15.8 32.3 38.7 7.2 6.0 100.0
10195 8649 6156 701 958 26659
38.2 32.4 23.1 2.6 3.6 100.0
Verified Full Time Employment/ School/Training Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total
1800 1573 2647 1118 334 7472
24.1 21.0 35.4 15.0 4.5 100.0
8152 4928 9090 3637 804 26611
30.6 18.5 34.2 13.7 3.0 100.0
Composite Item2 Yes No Total
2894 4549 7443
38.9 61.1 100.0
9012 17598 26610
33.9 66.1 100.0
CRIMINAL HISTORY First Arrest Yes No Total
3268 4268 7536
43.4 56.6 100.0
11745 15223 26968
43.6 56.4 100.0
Prior Violent Felony Convictions Yes No Total
695 6841 7536
9.2 90.8 100.0
2033 25202 27235
7.5 92.5 100.0
Prior Non-Violent Felony Convictions Yes No Total
1268 6268 7536
16.8 83.2 100.0
3526 23709 27235
12.9 87.1 100.0
Prior Misdemeanor Convictions Yes No Total
2371 5165 7536
31.5 68.5 100.0
7170 19657 26827
26.7 73.3 100.0 Page 3 of 4
Defendant Characteristics
Table 2 (contd.) 1989 Sample N=7,595 N
1998 Sample N=27,235 %
N
%
Open Cases Yes No Total
2883 4616 7499
38.4 61.6 100.0
7015 19812 26827
26.1 73.9 100.0
Type of Warrant Attached to Rap Sheet Bench Warrant No Bench Warrant Total
1047 6382 7429
14.1 85.9 100.0
1870 24901 26771
7.0 93.0 100.0
Prior FTA Yes No Total
2494 5042 7536
33.1 66.9 100.0
7002 20233 27235
25.7 74.3 100.0
TOP ARREST CHARGE SEVERITY A Felony B Felony C Felony D Felony E Felony A Misdemeanor B Misdemeanor
159 1890 813 2134 630 1510 85
2.1 24.9 10.7 28.1 8.3 19.9 1.1
335 5708 1781 6106 2525 8446 1329
1.2 21.0 6.6 22.5 9.3 31.1 4.9
Other3 Total
374 7595
4.9 100.0
919 27149
3.4 100.0
TOP ARREST CHARGE TYPE4 Violent Property Drug Weapon Gambling DWI (alcohol or drugs) Criminal Mischief VTL (excluding DWI ) Other Total
2185 1555 2372 417 107 294 na na 665 7595
28.8 20.5 31.2 5.5 1.4 3.8 na na 8.8 100.0
10063 3209 7204 977 298 799 731 445 3423 27149
37.1 11.8 26.5 3.6 1.1 2.9 2.7 1.6 12.6 100.0
1
OTHER includes Asian, American Indian, and others. COMPOSITE ITEM refers to whether the defendant had one or more verified point scale items in addition to having a verified New York City address. 3 OTHER includes Unclassified Misdemeanors, Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes). 4 VIOLENT CRIMES include: murder, negligent murder, non-negligent murder, forcible rape, robbery, aggravated assault, and kidnapping. PROPERTY CRIMES include: burglary, larceny-theft, forgery & counterfeiting, stolen property, and possession of burglary tools. DRUG OFFENSES include: A) controlled substances sale/manufacture; opium, cocaine, or derivatives, marijuana, synthetic narcotics, and other dangerous drugs, and B) use/possession; opium, cocaine, or derivatives, marijuana, synthetic narcotics, and other dangerous drugs. Dangerous weapons comprise the WEAPON category. The GAMBLING category consists of bookmaking, numbers, lottery, and other activities. The DWI category refers to driving while under the influence of alcohol or drugs. The CRIMINAL MISCHIEF category refers to vandalism. VTL offenses includes all traffic offenses excluding DWI. The OTHER category consists of all other offenses not included in the aforementioned categories. In the 1989 Sample, due to the small number of cases arrested for CRIMINAL MISCHIEF and VTL (excluding DWI) charges, these categories were collpased with OTHER category. 2
Page 4 of 4
NEW YORK CITY CRIMINAL JUSTICE AGENCY 3rd Quarter 1998 Dataset Combined-Court Analysis Table 3 (Model 1): Multiple Logistic Regression Analysis Predicting Pretrial FTA: The Final Model from the 1989 Sample Applied to the 1998 Sample, A Comparison of the Two Samples
1989 sample
1998 sample
N=6,826
N=25,863
- ** ns ns + ** ns
- ** ns +* +* ns
- **
- **
EMPL/SCHOOL/TRAINING Yes Yes Verified No No Verified Unresolved Conflict
ns - ** + ** ns + **
ns - ** + ** ns ns
LENGTH OF RESIDENCE Yes Yes Verified No No Verified Unresolved Conflict
ns ns ns +* ns
ns ns ns ns ns
NYC AREA ADDRESS Yes Yes Verified No No Verified Unresolved Conflict
ns ns +* ns ns
ns - ** + ** ns ns
Variable TELEPHONE Yes Yes Verified No No Verified Unresolved Conflict
1
EXPECTS AT ARRAIGNMENT
PRIOR FTA
+ **
+ **
OPEN CASES
+ **
+ **
TOP ARREST CHARGE SEVERITY 2
A or B Felony C Felony D Felony E Felony A Misdemeanor 3
B Misdemeanor U Misdemeanor/Other4
ns ns ns ns +*
-* ns ns ns ns
ns ns
ns ns Page 1 of 2
Table 3 (contd.) 1989 sample N=6,826
Variable
1998 sample N=25,863
5
TOP ARREST CHARGE TYPE Violent Property Drug Weapon Gambling DWI (alcohol or drug) Criminal Mischief VTL (excluding DWI) Other
+ + + ns na na +
* ** ** ** *
**
ns + + ns + + ns
** ** ** ** ** **
1
The effect of the reference category for a categorical independent variable was obtained by choosing an alternative reference category. 2 Due to the small number of cases with A or B felonies, the two categories were collapsed for the 1998 Sample. 3 In the 1989 sample, B misdemeanor, U misdemeanors, and other unknown types were collpased. 4 OTHER includes Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes). 5 VIOLENT CRIMES include: murder, negligent murder, non-negligent murder, forcible rape, robbery, aggravated assault, and kidnapping. PROPERTY CRIMES include: burglary, larceny-theft, forgery & counterfeiting, stolen property, and possession of burglary tools. DRUG OFFENSES include: A) controlled substances sale/manufacture; opium, cocaine, or derivatives, marijuana, synthetic narcotics, and other dangerous drugs, and B) use/possession; opium, cocaine, or derivatives, marijuana, synthetic narcotics, and other dangerous drugs. Dangerous weapons comprise the WEAPON category. The GAMBLING category consists of bookmaking, numbers, lottery, and other activities. The DWI category refers to driving while under the influence of alcohol or drugs. The CRIMINAL MISCHIEF category refers to vandalism. VTL includes all traffic offenses excluding DWI. Due to a small number of cases arrested for CRIMINAL MISCHIEF and VTL (excluding DWI) offenses in 1989, these cases were collpased with OTHER category for this variable.
Note: +: positive relationship with FTA. -: negative relationship with FTA. ns: Not significant. na: Not applicable. * : Significant at 0.05 or less. **: Significant at 0.01 or less.
Page 2 of 2
NEW YORK CITY CRIMINAL JUSTICE AGENCY 3rd Quarter 1998 Dataset Combined-Court Analysis Table 4 (Model 2): Multiple Logistic Regression Analysis Predicting Pretrial FTA Regardless of Court of Disposition: The Re-Estimated Model Used to Create the New Point Scale, Excludes Length of Time at Current Address N=25,888 Logit Significance Odds Variable Coefficient Level Ratio TELEPHONE Yes -0.382 0.000 0.682 Yes Verified -0.090 0.257 0.914 No 0.184 0.003 1.203 No Verified 0.240 0.015 1.272 Unresolved Conflict 0.048 0.608 1.049 EXPECTS SOMEONE AT ARRAIGNMENT
-0.203
0.000
0.816
EMPL/SCHOOL/TRAINING Yes Yes Verified No No Verified Unresolved Conflict
-0.002 -0.280 0.224 0.003 0.056
0.977 0.000 0.000 0.962 0.502
0.998 0.756 1.251 1.003 1.057
NYC AREA ADDRESS Yes Yes Verified No No Verified Unresolved Conflict
-0.179 -0.347 0.429 0.155 -0.058
0.007 0.000 0.000 0.220 0.663
0.837 0.707 1.536 1.168 0.944
PRIOR FTA
0.676
0.000
1.966
OPEN CASES
0.235
0.000
1.265
TOP ARREST CHARGE TYPE Violent Property Drug Weapon Gambling DWI (alcohol or drug) Criminal Mischief VTL (excluding DWI ) 1 Other
-0.009 0.327 0.359 0.089 -1.206 -0.476 0.367 0.479 0.069
0.877 0.000 0.000 0.346 0.000 0.004 0.000 0.000 0.267
0.991 1.387 1.432 1.093 0.300 0.621 1.444 1.614 1.072 Page 1 of 2
Table 4 (contd.) Logit Coefficient
Significance Level
Odds Ratio
-0.113 -0.049 -0.046 -0.023 0.059 0.001
0.029 0.459 0.334 0.679 0.161 0.985
0.893 0.952 0.955 0.977 1.061 1.001
2
0.171
0.267
1.186
CONSTANT
-1.478
0.000
Variable TOP ARREST CHARGE SEVERITY A or B Felony C Felony D Felony E Felony A Misdemeanor B Misdemeanor Other
______________________________________ 1
VIOLENT CRIMES include: murder, negligent murder, non-negligent murder, forcible rape, robbery, aggravated assault, and kidnapping. PROPERTY CRIMES include: burglary, larceny-theft, forgery & counterfeiting, stolen property, and possession of burglary tools. DRUG OFFENSES include: A) controlled substances sale/manufacture; opium, cocaine, or derivatives, marijuana, synthetic narcotics, and other dangerous drugs, and B) use/possession; opium, cocaine, or derivatives, marijuana, synthetic narcotics, and other dangerous drugs. Dangerous weapons comprise the WEAPON category. The GAMBLING category consists of bookmaking, numbers, lottery, and other activities. The DWI category refers to driving while under the influence of alcohol or drugs. The CRIMINAL MISCHIEF category refers to vandalism. VTL offenses includes all traffic offenses excluding DWI. The OTHER category consists of all other offenses not included in the aforementioned categories. 2 OTHER includes Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes).
Page 2 of 2
NEW YORK CITY CRIMINAL JUSTICE AGENCY 3rd Quarter 1998 Dataset Combined-Court Analysis Table 5: Multiple Logistic Regression Analysis Predicting Pretrial FTA Regardless of Court of Disposition: Classification Table For Model 2 at a .25 Cutpoint PREDICTED Non-Failure (No Warrant)
Failure (Warrant)
Total
Non-Failure (No Warrant)
True Negative N = 16357 63.2%
False Positive N = 4378 16.9%
N= 20735 80.1%
Failure (Warrant)
False Negative N = 2839 11.0%
True Positive N = 2314 8.9%
N= 5153 19.9%
Total
N= 19196 74.2%
OBSERVED
RIOC Score 25.7
N= 25888 N= 6692 100% 26.2% Total Correctly Classified = 72.1%
NEW YORK CITY CRIMINAL JUSTICE AGENCY 3rd Quarter of 1998 Dataset Combined-Court Analysis Table 6: Points Derived from Model 21 Logit Coefficient
Points
TELEPHONE Yes Yes Verified No No Verified Unresolved Conflict
Variable
-0.382 -0.090 0.184 0.240 0.048
-3 0 1 2 0
EXPECTS AT ARRAIGNMENT Yes No
-0.203 0.203
-1 1
EMPL/SCHOOL/TRAINING Yes Yes Verified No No Verified Unresolved Conflict
-0.002 -0.280 0.224 0.003 0.056
0 -2 1 0 0
NYC AREA ADDRESS Yes Yes Verified No No Verified Unresolved Conflict
-0.179 -0.347 0.429 0.155 -0.058
-1 -2 3 0 0
PRIOR FTA Yes No
0.676 -0.676
5 -5
OPEN CASES Yes No
0.235 -0.235
2 -2
-0.113 -0.049 -0.046 -0.023 0.059 0.001
-1 0 0 0 0 0
0.171
0
TOP ARREST CHARGE SEVERITY A or B Felony C Felony D Felony E Felony A Misdemeanor B Misdemeanor Other2
Page 1 of 2
Table 6 (contd.) Variable TOP ARREST CHARGE TYPE Violent Property Drug Weapon Gambling DWI (alcohol or drug) Criminal Mischief VTL (excluding DWI ) Other
3
Logit Coefficient
Points
-0.009 0.327 0.359 0.089 -1.206 -0.476 0.367 0.479
0 2 2 0 -8 -3 2 3
0.069
0
______________________________________ 1
For purposes of standardization, the significant coefficients were divided by .15 and were then rounded to the nearest whole number. 2 OTHER includes Unclassified Misdemeanors, Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes). 3 The OTHER category consists of all offenses not included in the categories of DRUG OFFENSES, VIOLENT CRIMES, PROPERTY CRIMES, DWI offenses, WEAPON offenses, CRIMINAL MISCHIEF, and VTL offenses excluding DWI.
Page 2 of 2
NEW YORK CITY CRIMINAL JUSTICE AGENCY 1998 Sample Combined-Court Analysis Table 7: New Point Scale: A Comparison of the 1998 Sample with the 1989 Sample
1989 Sample N= 7,294
1998 Sample N=25,888
TELEPHONE Yes Yes Verified No No Verified Unresolved Conflict
-2 0 0 2 0
-3 0 1 2 0
EXPECTS AT ARRAIGNMENT Yes No
-2 2
-1 1
EMPL/SCHOOL/TRAINING Yes Yes Verified No No Verified Unresolved Conflict
0 -4 2 0 2
0 -2 1 0 0
LENGTH OF RESIDENCE Yes Yes Verified No No Verified Unresolved Conflict
0 0 0 1 0
na na na na na
NYC AREA ADDRESS Yes Yes Verified No No Verified Unresolved Conflict
0 0 2 0 0
-1 -2 3 0 0
PRIOR FTA Yes No
4 -4
5 -5
OPEN CASES Yes No
2 -2
2 -2
TOP ARREST CHARGE SEVERITY 1 A or B Felony C Felony D Felony E Felony A Misdemeanor B Misdemeanor U Misdemeanor/Other
0 0 0 0 1 0 0
-1 0 0 0 0 0 0
Variable
Page 1 of 2
Table 7 (contd.) Variable TOP ARREST CHARGE TYPE2 Violent Property Drug Weapon Gambling DWI (alcohol or drug) Criminal Mischief VTL (excluding DWI) Other
1989 Sample
1998 Sample
1 4 4 0 -6 -5 na na
0 2 2 0 -8 -3 2 3
2
0
__________________________________ Due to a small number of defendants arrested for an A Felony, A Felonies were collpased with B Felonies in 1989. OTHER includes Unclassified Misdemeanors, Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes). 2 Due to a small number of defendants arrested for CRIMINAL MISCHIEF and VTL (excluding DWI) offenses in 1989, these categories were collpased with the OTHER category for this variable. The OTHER category consists of all offenses not included in the categories of DRUG OFFENSES, VIOLENT CRIMES, PROPERTY CRIMES, GAMBLING, DWI, WEAPON, CRIMINAL MISCHIEF 1
and VTL (excluding DWI). Note: Due to its insignificance, the length of residence variable was not included in the model.
Page 2 of 2
7590
3833
NOT RECOMMENDED (High Risk) TOTAL
1363
QUALIFIED (Moderate Risk)
100.0
50.5
18.0
Defendants N % 2394 31.5
Current CJA ROR Recommendation RECOMMENDED (Low Risk)
2676
1735
426
N 515
1989 Sample N=7,590 FTA
35.2
45.3
31.3
% 21.5
27235
11007
8083
100.0
40.4
29.7
5446
3044
1253
20.0
27.7
15.5
1998 Sample N=27,235 Defendants FTA N % N % 8145 29.9 1149 14.1
Table 8: Pretrial FTA by Current CJA ROR Recommendation Scheme
Combined-Court Analysis
3rd Quarter of 1998 Dataset
NEW YORK CITY CRIMINAL JUSTICE AGENCY
NEW YORK CITY CRIMINAL JUSTICE AGENCY 3rd Quarter 1998 Dataset Combined-Court Analysis Table 9: Alternative Risk-Classification Schemes for Defendants Regardless of Court of Disposition N=25,888 Alternative Total Defendants FTA Risk-Classification Scheme Points Scored N % N % Scheme 1 Group I (Low Risk) -7 or less 12989 50.2 1563 12.0 Group II (High Risk) -6 or greater 12899 49.8 3590 27.8
MCR
0.25
Scheme 2 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk)
-9 or less -8 to -4 -3 or greater
9048 8406 8434
35.0 32.5 32.6
930 1596 2627
10.3 19.0 31.1
0.29
Scheme 3 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk) Group IV (Very High Risk)
-10 or less -9 to -7 -6 to -1 0 or greater
7245 5744 6284 6615
28.0 22.2 24.3 25.6
706 857 1407 2183
9.7 14.9 22.4 33.0
0.31
Scheme 4 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk)
-7 or less -6 to -1 0 or greater
12989 6284 6615
50.2 24.3 25.6
1563 1407 2183
12.0 22.4 33.0
0.29
NEW YORK CITY CRIMINAL JUSTICE AGENCY 3rd Quarter of 1998 Dataset Combined-Court Analysis Table 10: A Comparison of the Current CJA ROR Recommendation Scheme with the Alternative Risk-Classification Schemes Suggested for Defendants Regardless of Court of Disposition: The Difference in the Proportion and FTA Rate of Low-, Moderate-, and High-Risk Defendants N=25,888 Difference in Total Number Difference in Total Number of Defendants Classified of Defendants with FTA Alternative Risk-Classification Scheme N % N % Scheme 1 Group I (Low Risk) + 4844 + 20.3 + 414 - 2.1 Group II (High Risk) + 1892 + 9.4 + 546 + 0.1 Scheme 2 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk) Scheme 3 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk and Very High Risk) Scheme 4 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk)
+ 903 + 323 - 2573
+ 5.1 + 2.8 - 7.8
- 219 + 343 - 417
- 3.8 + 3.5 + 3.4
- 900 - 2339 + 1892
- 1.9 - 7.5 + 9.5
- 443 - 396 + 546
- 4.4 - 0.6 + 27.7
+ 4844 - 1799 - 4392
+ 20.3 - 5.4 - 14.8
+ 414 + 154 - 861
- 2.1 + 6.9 + 5.3
Alternative Risk-Classification Scheme Scheme 4 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk) % + 20.9 + 6.2 - 27.1
N + 1429 + 402 - 2127
+ 314 + 346 - 791
N + 0.2 + 12.4 + 10
%
1989 Sample N=7,294 Difference in Total Number Difference in Total Number of Defendants Classified of Defendants with FTA
+ 4844 - 1799 - 4392
N + 20.3 - 5.4 - 14.8
%
+ 414 + 154 - 861
N
- 2.1 + 6.9 + 5.3
%
1998 Sample N= 25,888 Difference in Total Number Difference in Total Number of Defendants Classified of Defendants with FTA
Table 11:Alterntative Risk-Classification Scheme 4: A Comparison with the Current CJA Recommendation Scheme
Combined-Court Analysis
3rd Quarter of 1998 Dataset
NEW YORK CITY CRIMINAL JUSTICE AGENCY