NPRP Draft Part B - Supporting Statement 20220419

NPRP Draft Part B - Supporting Statement 20220419.docx

The National Pretrial Reporting Program

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SUPPORTING STATEMENT (PART B)

National Pretrial Reporting Program (NPRP)


B. COLLECTION OF INFORMATION EMPLOYING STATISTICAL METHODS


1. Universe and Respondent Selection


The purpose of the NPRP is to understand the pretrial release or detention ordered for defendants with at least one felony charge filed in state courts in the largest 200 counties in the United States. Describing pretrial release and detention may require information from courts (for the initial bond decision, any changes in release or detention status during the case, disposition, and sentencing), jails (for any period the defendant is incarcerated or re-incarcerated during the pendency of the case), and pretrial services agencies (for any supervision during periods of pretrial release).


The target population for the NPRP is all criminal cases filed with at least one felony charge in state courts in the largest 200 counties in calendar year 2019.1 We will ask the courts to provide case-level data for all cases filed with at least one felony charge in calendar year 2019 through disposition (and, if possible, sentencing). We will ask jails to provide information for all bookings, and all cases opened by pretrial release agencies for calendar year 2019. We will match case-level data across the jail, court, and pretrial services agency files, knowing that some records may not be able to be matched (e.g., cases filed in January 2019 may have been booked in December 2018, and cases filed in December 2019 may not be released to a pretrial services agency until 2020).


The NPRP is based on an earlier data collection series of the same name, later amended to be called the State Court Processing Statistics (SCPS) series. Beginning in 1988, that program used a sample of 40 of the largest 75 counties based on county population size, with a certainty stratum based on the relative number of case filings in previous SCPS collections. The SCPS data collection paused in 2009 to examine different sampling strategies and data collection methods; as a result, BJS does not have an estimate of the number of felony criminal cases filed in the largest counties. However, population size is highly correlated with case filings, and with this NPRP, BJS is selecting the largest 75 counties with certainty and drawing a sample of 50 counties from the next largest 125 counties, to be representative of the largest 200 counties.


NPRP will include felony case filings in state courts of general jurisdiction. Although some felonies may resolve in limited jurisdiction courts, most will be transferred to the court of general jurisdiction for resolution. BJS is not targeting limited jurisdiction courts in the NPRP effort; however, if statewide or centralized data providers are able to provide data from limited jurisdiction courts for cases, then BJS will accept it. BJS is excluding municipal courts since their involvement in felony case processing is highly unlikely.


Overall Study and Sample Design


The overall NPRP sample design will combine a census of the 75 largest counties with a sample-based data collection from 50 of the next largest 125 counties to estimate the pretrial characteristics for the largest 200 counties. Counties 76-200 will be stratified by population size, and the sample of 50 will be drawn proportionate to size. If counties in the sample decline to provide data, BJS will draw a replacement county from the same stratum.


BJS will minimize the number of states, counties, and agencies asked to report data to NPRP. Table 1 shows the sources that BJS anticipates engaging in the NPRP collection. NPRP is county-based, and some counties have centralized reporting repositories for court, jail, and pretrial services data, where most or all agencies report their data to a single source, such as the county court. In those counties, BJS will request data from that single source. For some counties, all counties in a state report data to a central repository, such as a state administrative office of the courts. In these instances, BJS will request the data for the specific counties from the centralized repository and combine the state-reported county data with the data requested from the remaining agencies within the counties.


Regardless of the data sources, BJS will merge the data at the case level to follow an individual’s path from case filing to pretrial release or detention, and to case outcome and sentencing. BJS will request that courts, jails, and pretrial services agencies provide common identifiers between the three agencies, such as a unique defendant identifier or a series of case-linking identifiers. If no common identifier exists, BJS will match individuals based on demographic factors, such as name, date of birth, race, and sex.


The data collection is a census of the 75 largest counties and a sample of 50 of the next largest 125 counties, based on the size of the population aged 18 and over in 2019. The rationale for using 2019 is to avoid most of the disruption of “typical” pretrial release and detention practice that occurred because of the COVID-19 pandemic.


Table 2 details the largest 75 counties. Table 3 lists the remaining counties, 76-200, from which the sample will be drawn.


Table 2. Largest 75 counties by population, 2019

County Size Rank

County

State

2019 Population 18+

1

Los Angeles County

California

7,894,558

2

Cook County

Illinois

4,037,516

3

Harris County

Texas

3,467,885

4

Maricopa County

Arizona

3,432,975

5

San Diego County

California

2,623,532

6

Orange County

California

2,486,016

7

Miami-Dade County

Florida

2,167,261

8

Dallas County

Texas

1,955,358

9

Kings County

New York

1,979,773

10

Riverside County

California

1,856,391

11

Queens County

New York

1,802,531

12

King County

Washington

1,801,166

13

Clark County

Nevada

1,745,918

14

San Bernardino County

California

1,610,447

15

Tarrant County

Texas

1,555,282

16

Bexar County

Texas

1,497,113

17

Santa Clara County

California

1,511,935

18

Broward County

Florida

1,542,840

19

Wayne County

Michigan

1,336,953

20

Alameda County

California

1,331,231

21

New York County

New York

1,396,835

22

Middlesex County

Massachusetts

1,296,600

23

Philadelphia County

Pennsylvania

1,241,810

24

Sacramento County

California

1,188,937

25

Suffolk County

New York

1,167,701

26

Palm Beach County

Florida

1,212,898

27

Bronx County

New York

1,070,144

28

Hillsborough County

Florida

1,146,545

29

Nassau County

New York

1,065,969

30

Orange County

Florida

1,087,438

31

Franklin County

Ohio

1,011,351

32

Oakland County

Michigan

997,704

33

Cuyahoga County

Ohio

980,916

34

Hennepin County

Minnesota

989,821

35

Travis County

Texas

1,004,012

36

Allegheny County

Pennsylvania

989,647

37

Fairfax County

Virginia

880,601

38

Contra Costa County

California

894,142

39

Salt Lake County

Utah

851,291

40

Mecklenburg County

North Carolina

852,208

41

Wake County

North Carolina

849,055

42

Montgomery County

Maryland

808,651

43

Fulton County

Georgia

836,143

44

Pima County

Arizona

831,673

45

St. Louis County

Missouri

776,516

46

Honolulu County

Hawaii

769,689

47

Fresno County

California

717,718

48

Collin County

Texas

769,439

49

Westchester County

New York

757,148

50

Pinellas County

Florida

819,558

51

Marion County

Indiana

727,973

52

Milwaukee County

Wisconsin

720,305

53

Fairfield County

Connecticut

733,670

54

Shelby County

Tennessee

704,794

55

Duval County

Florida

742,210

56

Bergen County

New Jersey

735,892

57

DuPage County

Illinois

715,343

58

Erie County

New York

733,429

59

Gwinnett County

Georgia

686,917

60

Prince George's County

Maryland

707,865

61

Hartford County

Connecticut

705,385

62

Kern County

California

641,082

63

Pierce County

Washington

694,525

64

San Francisco County

California

763,303

65

Macomb County

Michigan

692,117

66

New Haven County

Connecticut

684,132

67

Hidalgo County

Texas

590,120

68

Ventura County

California

655,715

69

El Paso County

Texas

614,939

70

Denton County

Texas

671,750

71

Baltimore County

Maryland

648,363

72

Middlesex County

New Jersey

646,614

73

Worcester County

Massachusetts

657,270

74

Montgomery County

Pennsylvania

652,573

75

Hamilton County

Ohio

630,440


Table 3. Largest 76-200 counties by population, 2019

County size rank

County

State

2019 Population

18+

76

Multnomah County

Oregon

663,188

77

Snohomish County

Washington

637,832

78

Suffolk County

Massachusetts

672,740

79

Essex County

New Jersey

609,597

80

Oklahoma County

Oklahoma

594,839

81

Essex County

Massachusetts

622,724

82

San Mateo County

California

611,781

83

Jefferson County

Kentucky

598,203

84

Fort Bend County

Texas

589,946

85

Cobb County

Georgia

583,597

86

DeKalb County

Georgia

585,187

87

Monroe County

New York

588,820

88

San Joaquin County

California

558,389

89

Lee County

Florida

636,679

90

Denver County

Colorado

588,587

91

Lake County

Illinois

530,410

92

Norfolk County

Massachusetts

559,627

93

El Paso County

Colorado

549,134

94

Jackson County

Missouri

538,783

95

District of Columbia

District of Columbia

577,848

96

Will County

Illinois

521,914

97

Davidson County

Tennessee

551,090

98

Polk County

Florida

565,638

99

Bernalillo County

New Mexico

534,056

100

Hudson County

New Jersey

535,864

101

Jefferson County

Alabama

509,191

102

Kent County

Michigan

499,889

103

Tulsa County

Oklahoma

487,873

104

Arapahoe County

Colorado

504,162

105

Providence County

Rhode Island

507,922

106

Bucks County

Pennsylvania

501,425

107

Monmouth County

New Jersey

489,192

108

Baltimore city

Maryland

473,923

109

Utah County

Utah

426,950

110

Ocean County

New Jersey

460,496

111

Johnson County

Kansas

457,474

112

Washington County

Oregon

466,438

113

Brevard County

Florida

492,569

114

Jefferson County

Colorado

469,684

115

Montgomery County

Texas

448,951

116

Anne Arundel County

Maryland

450,650

117

Delaware County

Pennsylvania

442,201

118

Bristol County

Massachusetts

449,495

119

Douglas County

Nebraska

425,639

120

New Castle County

Delaware

439,396

121

Union County

New Jersey

426,292

122

Williamson County

Texas

440,981

123

Ramsey County

Minnesota

422,367

124

Stanislaus County

California

402,887

125

Summit County

Ohio

428,863

126

Lancaster County

Pennsylvania

417,852

127

Volusia County

Florida

456,552

128

Dane County

Wisconsin

436,428

129

Montgomery County

Ohio

415,349

130

Kane County

Illinois

399,424

131

Guilford County

North Carolina

418,280

132

Pasco County

Florida

441,991

133

Chester County

Pennsylvania

407,023

134

Plymouth County

Massachusetts

410,783

135

Sedgwick County

Kansas

384,757

136

Greenville County

South Carolina

403,474

137

Camden County

New Jersey

392,466

138

Spokane County

Washington

407,948

139

Adams County

Colorado

382,294

140

Passaic County

New Jersey

382,808

141

Sonoma County

California

398,859

142

Morris County

New Jersey

389,366

143

Lake County

Indiana

373,045

144

Polk County

Iowa

369,064

145

Richmond County

New York

372,457

146

Clark County

Washington

373,556

147

Hampden County

Massachusetts

366,727

148

Onondaga County

New York

363,435

149

Tulare County

California

323,943

150

Prince William County

Virginia

344,025

151

Seminole County

Florida

372,855

152

Knox County

Tennessee

371,876

153

Washoe County

Nevada

370,990

154

Ada County

Idaho

369,859

155

Virginia Beach city

Virginia

350,926

156

Burlington County

New Jersey

353,190

157

York County

Pennsylvania

350,419

158

Santa Barbara County

California

348,215

159

East Baton Rouge Parish

Louisiana

339,986

160

Solano County

California

348,758

161

Jefferson Parish

Louisiana

337,196

162

Monterey County

California

320,870

163

Pinal County

Arizona

360,216

164

Lucas County

Ohio

330,356

165

Cameron County

Texas

296,542

166

Dakota County

Minnesota

325,107

167

Sarasota County

Florida

372,984

168

Berks County

Pennsylvania

327,545

169

Mobile County

Alabama

316,868

170

Hillsborough County

New Hampshire

332,756

171

Richland County

South Carolina

326,666

172

Clackamas County

Oregon

329,826

173

Genesee County

Michigan

315,245

174

Charleston County

South Carolina

330,609

175

Waukesha County

Wisconsin

318,146

176

Loudoun County

Virginia

298,272

177

St. Charles County

Missouri

309,611

178

Pulaski County

Arkansas

301,662

179

Orleans Parish

Louisiana

313,010

180

Placer County

California

310,171

181

Manatee County

Florida

330,933

182

Orange County

New York

287,134

183

Butler County

Ohio

293,990

184

Forsyth County

North Carolina

295,459

185

Lane County

Oregon

312,496

186

Allen County

Indiana

282,488

187

Stark County

Ohio

291,678

188

Collier County

Florida

319,864

189

Mercer County

New Jersey

289,368

190

Washtenaw County

Michigan

300,102

191

Lehigh County

Pennsylvania

286,118

192

Madison County

Alabama

292,193

193

Nueces County

Texas

274,352

194

Hamilton County

Tennessee

291,381

195

Brazoria County

Texas

276,764

196

Marion County

Florida

298,327

197

Westmoreland County

Pennsylvania

285,145

198

Osceola County

Florida

285,152

199

Anoka County

Minnesota

272,162

200

Bell County

Texas

263,178

Source: U.S. Census Bureau, Population Division. Table 1. Annual Estimates of the Resident Population for the United States, States, Counties and Puerto Rico Commonwealth and Municipios: April 1, 2010 to July 1, 2019


For the purposes of the overall design, BJS has assumed the following:

Class 1 – Collection from the largest 75 counties


During work under BJS’s generic clearance (OMB Control No.1121-0339), BJS contacted court data leaders, jails, and pretrial service agencies in the largest 75 counties to determine whether their electronic case-level records systems are capable of extracting data elements necessary to support the NPRP. Overall, the data systems vary in terms of geographic coverage (e.g., statewide data system, centralized county with all jail, court, and pretrial records, and county agency-specific data systems). The data systems are used largely for case management and include data elements related to general case information, defendants/inmates/clients, charges, filing/booking/intake, and disposition/release/termination of supervision. Sentencing data are sometimes maintained by the court or jail data systems, and sometimes by both. Some of these data are in free text fields or contained in scanned or paper documents, such as orders of release or orders of supervision.


Any agency or centralized data repository (e.g., centralized data for all agencies within the county or state) will be asked for an electronic file containing all criminal cases filed as felonies in calendar year 2019. Courts will be asked for cases filed with at least one felony charge, jails will be asked for bookings with at least one felony charge, and pretrial services agencies will be asked for cases opened with at least one felony charge. We will ask the agencies to include all information about each case until it is disposed. “Disposed” for courts is defined as a final finding by a judicial officer (typically a judge), and includes dismissal, nolle prosequi, placement on an inactive docket (stay of prosecution), placement in a diversion program, guilty, not guilty, acquittal, or other finding. “Disposed” for jails means that the person is released from custody as a release without a return prior to disposition (i.e., there is no rearrest for pretrial misconduct), sentenced by the courts to the jail or held pending transfer to another incarceration facility, or otherwise unable to be located before the end of the study (e.g., released pretrial, a bench warrant issued for some reason, but had not been rearrested). Often, jails assign unique booking identifiers each time a person is taken into jail, so BJS may need to provide an end date for the jail data extract. BJS will use March 15, 2020 for this purpose. For pretrial services agencies, “disposed” means that the pretrial agency is no longer responsible for monitoring the individual’s release, either because the release was revoked for misconduct or because the person completed pretrial release and was sentenced by the courts.


Courts, jails, and pretrial services agencies may provide data on all such cases in any format. BJS expects most will provide an unformatted data extract, where the data are extracted from the system “as-is” and BJS will work with the state to clean and standardize the data. Rarely, agencies may choose to provide a full system extract (“data dump”) of the entire case records system. In that case, BJS will extract the relevant cases.


Some courts, jails, or pretrial services agencies, or even entire counties in Class 1 may decline to provide data. These counties cannot be replaced, and BJS cannot substitute agency information from other counties (i.e., BJS cannot use data from a responding county as a substitute for a nonresponding county). BJS will use as much of the responding agencies’ data as possible and mark any elements not reported as missing. If the entire county fails to respond, BJS will either (a) have to adjust the coverage of the data; for example, to represent 73 counties rather than 75, or (b) use the participating largest 75 counties to represent those who do not participate. Once the nonparticipating counties are known (i.e., at the end of data collection), a determination will be made about each nonparticipating county as to whether any of the participating 75 can be used to represent it.


Class 2 - Sampling of Non-Certainty Counties


The goal of Class 2 of the NPRP is to develop representative estimates related to the pretrial release or detention ordered for defendants with at least one felony charge filed in state courts within one of the largest 200 counties not included in Class 1, or the largest 75 U.S. counties. As such, the Class 2 inferential population consists of the 76th to 200th largest counties in the country based on the 2019 American Community Survey 5-year population estimates (Table 3).


Sample Design. A random sample will be drawn such that the counties in which information is collected can be used to make inferences about all 125 counties. The sample size of Class 2 will be 50 counties in which all criminal cases filed with at least one felony charge will be collected.


Sample Stratification. While not much is known about the type and quantity of criminal cases filed with at least one felony charge in state courts in advance of data collection, a correlation with county population is assumed. Because county population size ranges from approximately 800,000 to 350,000 (Table 1), the sample will stratify the 125 counties by population size. Population size will be the only variable used to stratify the counties for two reasons. First, any other demographic information about the counties is likely to be highly correlated to population size and, therefore, will not add any additional information. Second, characteristics beyond county demographics are not known for all 125 counties.


Five strata will be created consisting of 25 counties each based on the rank ordering of the counties. In other words, the first stratum will consist of the 76th to 100th largest counties and the fifth stratum will consist of the 176th to 200th largest counties. These strata are designated strata 2 – 6 (Table 4) as stratum 1 is the Class 1 counties. Five strata were selected for two reasons. First, it kept the size differential between the largest and smallest county in a stratum relatively small. Second, five strata allow for an equal number of counties to be in each stratum (i.e., quintiles).


Table 4. Sample stratification

Stratum

Smallest County Population

Largest County Population

2

535,864

663,188

3

428,863

509,191

4

344,025

417,852

5

318,146

372,855

6

263,178

298,272


Sample Allocation. The sample will be allocated in a balanced fashion. This means an equal number of counties (i.e., 10) will be selected from each stratum. A balanced allocation is recommended to ensure there is representation from the smaller counties which may be different in terms of the outcomes of interest or characteristics of the pretrial population. Additionally, because the strata are of equal size, a balanced allocation of the sample produces an equal probability of selection for each sampled county.


Sample Selection. Within each stratum, a replicate/replacement design will be used for selected counties. Under a replicate design, the 25 counties within each stratum will be randomly assigned to a replicate. To form the replicates the 25 counties will be assigned a random number and ordered in descending fashion based on their random number. The initial replicate will consist of the first 10 randomly ordered counties. The remaining 15 counties will be assigned to a replicate of size one and used to replace one of the initial 10 counties if there is nonresponse (see next section).


Under this design, within each stratum, counties will be treated equally regardless of their population size. As such the probability of selection for each county in a stratum (h) will be


In other words, each county within a stratum will have an equal probability of selection.


An alternative to this design is a more traditional approach where a nonresponse rate is assumed and a larger than needed sample is selected. However, because the nonresponse rate is unknown and both a larger and smaller than desired sample size within each stratum is not desirable, this approach has too much uncertainty to be a viable option.


Accounting for Nonresponse. Nonresponse is likely to occur in both cycles of the study. Because the selection methods are different for each cycle, the method for addressing nonresponse will be tailored to the specific cycle.


Class 1. In Class 1, the largest 75 counties are treated as self-representing. That is, each county is selected with certainty and only represents itself. However, it is likely that some of these counties will not participate. This leaves two options: BJS will either (a) have to adjust the coverage of the data; for example, to represent 73 counties rather than 75, or (b) use the participating largest 75 counties to represent those who do not participate. Once the nonparticipating counties are known (i.e., at the end of data collection), a determination will be made about each nonparticipating county as to whether any of the participating 75 can be used to represent it.


For those with similarities to the participating counties, weighting classes (i.e., counties grouped together for the purpose of creating a weight adjustment) will be formed consisting of participating and nonparticipating counties. The weighting classes will be defined based on similar county-level characteristics such as population size, county demographic profile, and expected similarities in the types of felonies which occur. Within each weighting class, a ratio adjustment will be formed and applied to the sum of the base weights of each participating county (the base weight for each county is 1 since they are self-representing). In other words,


Where is the base weight for a responding county in weight class c and is an indicator of response for a given county.


Class 2. While it is anticipated that a high percentage of counties will participate, some counties – or a high number of agencies within the county – may not be able or willing to provide the requested information. Because a final sample of 50 counties is desired, a plan will be put in place to replace counties who cannot participate using the replicate design. The plan for accounting for nonresponse will be tied to the sample selection process. Specifically, because each county within a stratum has the same probability of selection and are considered similar to the nonparticipating county, the replicate counties in each stratum (i.e., counties 11 – 25 under the random ordering) will replace each nonparticipating county. The replacement counties will be selected in their random order (i.e., randomly ordered county 11 will be used first, county 12 second, etc.). Once 10 participating counties are identified, no further counties will be selected.


To adjust for nonresponse, a ratio adjustment of the participating counties over the total counties in the stratum (i.e., 25) will be applied. However, because each county has an equal probability of selection this adjustment will yield the same equal weights within each stratum.


2. Procedures for Collecting Information

In work done under BJS’s generic clearance (OMB Control No. 1121-0339), BJS interviewed county court, jail, and pretrial services agency leaders, many of whom reported that they would be able to provide most information in the form of data extracts from case management systems. A data extraction guide will be provided to all respondents (see Attachment 2).

At the start of the collection, BJS will email the state court, jail, and pretrial leaders in states with centralized statewide data. The letter will describe the purpose and importance of the collection, introduce the data collection agents (RTI International (RTI) and the National Center for State Courts (NCSC)), and invite the court, jail, and pretrial services agency to participate in the collection (Attachment 5). The following week, the same letter will be sent to county court, jail, and pretrial leaders in the counties without centralized court data systems. The same letters will be sent in staggered mailings to state and county leaders where some of the data are centralized at the state level and some of the data are maintained at the county level (e.g., the court data is held by a state agency, but pretrial and jail data are kept at the county agency level).

Once permission to collect data is obtained from the relevant contacts, RTI and NCSC will work with staff who manage the agency’s information system to obtain data files (Attachment 6). All data files will be submitted to RTI via a secure AWS GovCloud drive, RTI’s secure FTP, the agency’s secure FTP, or BJS’s secure BOX account. BJS is providing multiple options for submission to avoid difficulties in agency firewall or security issues. RTI will process the jail and pretrial services agency files, and NCSC will process the court data files on RTI’s secure AWS GovCloud drive, working with the respondent to evaluate data quality and completeness. NCSC is conducting the initial file processing because its analysts are more familiar with state court data from other NCSC projects, such as the Court Statistics Project. All identifiable files will be maintained on the AWS GovCloud drive during the data processing and merging. After NCSC conducts the preliminary processing of the court data, RTI will combine the court files with the pretrial and jail files.

After the files are processed, RTI will link the court, jail, and pretrial services agency data files using the personal identifiers provided. Once the files are linked, RTI will create a crosswalk of unique identifiers to replace any personally identifiable information (PII). The de-identified files will remain on the AWS GovCloud drive for further analysis, while the crosswalk will be moved to RTI’s secure project network. The de-identified file and crosswalk will not be stored in the same location unless it is necessary to update the de-identified file. In that event, a copy of the crosswalk will be moved to the AWS GovCloud, the data updated, and the crosswalk moved back to the RTI secure project drive.

As the data collection progresses, some courts, jails, and pretrial services agencies may decide not to participate. If this occurs, NCSC and RTI will continue the request from the remaining agencies in the county and will use as much of the data as possible to describe pretrial release from that county. The completeness of the data collection depends on how many agencies refuse in each county.

3. Methods to Maximize Response Rates

Every attempt will be made to collect complete information on felony criminal cases filed in state and county courts in 2019, to collect detention data from jails, and to collect pretrial release information from pretrial services agencies. BJS developed a project factsheet that has been circulated among court, jail, and pretrial services agencies in the largest 75 counties (Attachment 15). BJS also hosted a webinar, and provided links to the recorded webinar, available at RTI’s website (https://youtu.be/c1QFRxJldnA) and NCSC’s website (https://vimeo.com/604855587).


RTI and NCSC have already spoken with many of the data providers as part of the work done under BJS’s generic clearance. RTI and NCSC asked court, jail, and pretrial services agency leaders about their data systems and the policies that affect how they record the data. During these interviews, RTI and NCSC were able to explain the importance of the NPRP collection, and describe the products that may be published from the data collection.

The data extraction guides clearly articulate the data elements requested in the collection and the various acceptable data formats. RTI also maintains two main submission methods: AWS GovCloud and secure FTP. If agencies cannot access either, RTI can use the agency’s own FTP and move the data to the secure drive for processing. A final option is to allow the agency to submit data using BJS’s BOX account.

It is assumed that BJS will enter into data use agreements with some or all the state and county courts, jails, and pretrial services agencies. During the interviews, most of the agencies indicated that they would require both a data use agreement and some method of secure file transfer to participate in NPRP. Further, many agencies indicated that several personnel would need to review the data use agreements prior to agreeing to participate in the project and noted that time to review the agreement and data extract requests is important when considering participation in research projects.

A team of RTI and NCSC staff members will be assigned to act as the point of contact for each respondent. The data extraction guides for courts and for pretrial services agencies and jails include direct phone and email contact information for respondents. Additionally, RTI maintains a project email ([email protected]) monitored by the project director and data manager to respond to any technical questions.

4. Testing of Procedures

During the data interviews conducted under an earlier generic clearance, we asked whether the agency would be willing to provide a sample of their extracted data. Eight jurisdictions agreed, but BJS and RTI decided to follow up with seven.2 The sites varied in terms of the agencies that were requested to provide data and the size of the population covered.

Table 8. Pilot test sites

Pilot Test Site No

County

Data systems

State

Region

Population

1

Allegheny County

Court, Jail, Pretrial Services

Pennsylvania

Northeast

989,647

2

El Paso County

Jail, Pretrial

Texas

South

614,939

3

King County

Jail, Pretrial

Washington

West

1,801,166

4

Middlesex County

Jail

Massachusetts

Northeast

1,296,600

5

Bexar County

Jail

Texas

South

1,497,113

6

New York City Criminal Justice Agency (multiple counties – Bronx, Queens, Kings, New York)

Pretrial

New York

Northeast

6,621,740

7

Orange County

Jail

Florida

South

1,087,438


RTI sent a follow-up email to the seven sites that reminded them of their voluntary participation in the pilot, the purpose of the NPRP and the pilot project, the BJS template data use agreement, and the draft data extraction guide that contained the data elements discussed in the data capacity interviews in November 2021. RTI followed up with reminder emails rather than following a more aggressive plan, in case the counties failed to respond and RTI would have to reach out again for the data after completing the OMB review process. Two jurisdictions (Allegheny and King) requested phone conversations to discuss the DUA requirements and the data extraction guides.


As of the end of February, RTI adjusted the nonresponse contact to every two weeks, and then in March to every week. As summarized in Table 9 below, our approach to information gathering yielded varying outcomes.


Table 9: Summary of Pilot Results

Jurisdiction

No Response

Held Call

Reviewed Data Request

Completed DUA

Closed Reason

Allegheny County, PA

 

X

X


Still negotiating DUA

El Paso County, TX


 

 X


Still negotiating DUA

King County, WA

 

X

X


Experiencing backups due to Covid and IT emergencies (1/28/22)

Middlesex County, MA




X

Has login, has not submitted data

Bexar County, TX

 




Data received 4/1/2022

New York City Criminal Justice Agency (multiple counties – Bronx, Queens, Kings, New York), NY

 



X

Data received 3/10/2022

Orange County, FL



X


Still negotiating DUA.


As of April 4, 2022, New York City Criminal Justice Agency and Bexar County, TX submitted data. Middlesex County, MA completed the data use agreement with BJS and has the login information to submit data, but has not been responsive to email requests for a status update. Orange County, FL, Allegheny County, PA, and El Paso County, TX are still negotiating the data use agreement with BJS, but have agreed to submit data. King County, WA remains non-responsive to follow-up emails.


5. Contact for Statistical Aspects and Data Collection


The prosecution and judicial statistics unit staff at BJS are responsible for the overall design and management of the NPRP data collection, including the development of the data extraction guide and the analysis and publication of the data.


Erica Grasmick, Statistician

Judicial Statistics Unit

Bureau of Justice Statistics

810 7th Street, NW

Washington, D.C. 20531

(202) 307-1402


Attachments (from CCSC to be updated to NPRP by BJS)

  1. 34 USC § 10132

  2. Data extraction guide

  3. 60-day notice

  4. 30-day notice

  5. BJS introduction letter

5a. FAQs

  1. Request for data

  2. Initial follow-up script

  3. Second follow-up

  4. BJS final follow-up

  5. Confirm data script

  6. Thank you email

  7. Collection closing script

  8. Tyler Technologies Comments

  9. Legal Rights Center Comments

  10. Minnesota Freedom Fund Comments

  11. NPRP Factsheet

  12. Letter of Support


1 This definition excludes misdemeanors (other than those charged in addition to a felony charge), violations of probation and all civil cases, including traffic offenses (if charged civilly instead of criminally), municipal ordinance violations, infractions, fish and game commission charges, and habeas corpus petitions.

2 Harris County, Texas offered to be a pilot jurisdiction, but noted that pretrial data extracts would require court review and approval. BJS and RTI determined it would be burdensome to ask the court to review a data request for a pilot study and decided not to request data from Harris County until the final data collection.

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