U.S. Department of Justice

National Institute of Corrections

How to collect and analyze data: 

A Manual for Sheriffs and Jail Administrators
U.S. Department of Justice

National Institute of Corrections

320 First Street, NW

Washington, DC 20534

Morris L. Thigpen

Director

Thomas J. Beauclair

Deputy Director

Virginia Hutchinson

Chief, Jails Division

Vicci Persons

Project Manager

National Institute of Corrections

http://www.nicic.org
How to collect

and analyze data: 

A Manual for 

Sheriffs and Jail 

Administrators

3rd edition

Gail Elias

July 2007

NIC Accession Number 021826
This publication was funded by the National Institute of Corrections, U.S. Department of Justice. Points of view 

or opinions stated in this publication are those of the author and do not necessarily represent the official position 

or policies of the U.S. Department of Justice.

Copyright © 2007, Voorhis Associates, Inc. The National Institute of Corrections reserves the right to reproduce, 

publish, translate, or otherwise use and to authorize others to publish and use all or any part of the copyrighted 

material contained in this publication.
iii

T a b l e o f C o n t e n t s

FOREWORD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

PREFACE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

CHAPTER 1: INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

The Situation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3

Traditional Resources Have Failed To Help . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

Resource 1: Introductory Statistics Courses . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

Resource 2: Information Systems Seminars . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

Resource 3: Research Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5

Additional Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5

CHAPTER 2: GOOD MANAGEMENT REQUIRES GOOD INFORMATION . . . 2-1

What Is Management? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

Ways To Get the Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

What Can You Do With Data? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

Example 1: Better Budgeting and Allocation of Funds . . . . . . . . . . . . . . . . . . . 2-4

Example 2: More Effective Deployment of Staff . . . . . . . . . . . . . . . . . . . . . . . 2-5

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6

CHAPTER 3: INFORMATION THAT SHOULD BE COLLECTED . . . . . . . . . . . . . 3-1

Writing Good Problem Statements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3

Identifying Data Elements From Problem Statements . . . . . . . . . . . . . . . . . . . . . . 3-4

A Catalog of Correctional Data Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5

Inmate Population Data Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6

Inmate Profile Data Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7

Operational Data Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-10

Criminal Justice System Performance Data Elements . . . . . . . . . . . . . . . . . . . 3-12

A Word About Collecting Data in Other Agencies . . . . . . . . . . . . . . . . . . . . . . . 3-14

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-14
iv HOW TO COLLECT AND ANALYZE DATA

CHAPTER 4: PREPARING FOR THE DATA COLLECTION . . . . . . . . . . . . . . . . . . 4-1

Data Collection Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3

Where To Find the Skills You Need . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-4

Working With People From Outside the System . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6

CHAPTER 5: HOW TO LOCATE AND CAPTURE INFORMATION . . . . . . . . . . 5-1

Jail Information Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3

Logs and Forms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-7

The Problem With Data Sources—and Some Solutions . . . . . . . . . . . . . . . . . . . . 5-8

Problem 1: Data Elements Are Scattered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-8

Problem 2: Data Is Missing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-8

Problem 3: Information on the Form Is Wrong . . . . . . . . . . . . . . . . . . . . . . . . . 5-9

Problem 4: Data Elements Are Poorly Defined . . . . . . . . . . . . . . . . . . . . . . . . . 5-9

Problem 5: The Handwriting Is Illegible . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10

Problem 6: No One Keeps Data for the Whole System . . . . . . . . . . . . . . . . . 5-10

Setting Up an Information System for the Jail . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10

Elements of the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10

System Startup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10

Special Issue Data Collections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-18

Designing Special Data Collection Forms and Code Books . . . . . . . . . . . . . . . . 5-19

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-24

CHAPTER 6: HOW TO PUT IT ALL TOGETHER . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

Doing the Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3

Determine an Overall Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3

Determine How Much Data To Collect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4

Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7

Rules for Coding Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7

Coding in an Automated System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-8

Gather Your Data Collection Supplies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-10

Begin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-10

Automation Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-12

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-12

CHAPTER 7: HOW TO ANALYZE INFORMATION . . . . . . . . . . . . . . . . . . . . . . . 7-1

What is Statistics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-4

Statistically Significant Differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-4

Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-4

Normal (Bell-Shaped) Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-5

Statistical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6

Types of Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6

Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6

Statistics for Examining Differences Between Groups . . . . . . . . . . . . . . . . . . 7-11

Statistics To Examine the Relationship Between Data Elements . . . . . . . . . . 7-13

Statistical Sins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-15

Sin 1: How Could the Results Be Wrong if the Calculations 

Were Perfect? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-15
v

Sin 2: What Is the Real Average? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-16

Sin 3: What Is Missing From This Picture? . . . . . . . . . . . . . . . . . . . . . . . . . . 7-17

Sin 4: So What if It Is Statistically Significant? . . . . . . . . . . . . . . . . . . . . . . . 7-17

Sin 5: If A, Then B? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-17

Sin 6: What Is Wrong With This Picture? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-18

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-18

CHAPTER 8: HOW TO INTERPRET INFORMATION . . . . . . . . . . . . . . . . . . . . . . 8-1

Case Study 1: The Friday Night Blues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-4

Before Going on to Case Study 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-15

Case Study 2: The Alternative Answer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-15

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-27

CHAPTER 9: SHARING INFORMATION WITH OTHERS . . . . . . . . . . . . . . . . . . 9-1

Why Pictures Are Worth a Thousand Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-4

Methods for Displaying Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-4

Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-4

Bar Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-5

Pie Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-6

Line Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-6

Guidelines for Good Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-7

Rule 1: Know Your Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-7

Rule 2: Make the Graphic Illustrate the Most Important Information . . . . . . . 9-7

Rule 2a: Keep It Simple . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-7

Rule 3: Determine the Best Medium for Sharing the Information . . . . . . . . . . 9-9

Rule 4: Decide What Scale Will Best Display the Data . . . . . . . . . . . . . . . . . 9-10

Rule 5: Consider Who the Audience Is . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-10

Rule 6: Make the Graphic Pleasing to the Eye . . . . . . . . . . . . . . . . . . . . . . . . 9-11

Rule 7: Resist the Temptation to Save Money by Putting Everything 

on One Page, Transparency, or Slide . . . . . . . . . . . . . . . . . . . . . . . . . 9-11

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-11

CHAPTER 10: GETTING THE MOST FROM YOUR 

INFORMATION SYSTEM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-1

What This Chapter Is—and Is Not—About . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-3

A Short Course in Computers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-4

Overview of Computer Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-4

The Central Computer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-4

Peripherals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-5

Communication Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-5

Overview of Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-5

Applications Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-6

A Last Word on Hardware and Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-7

Getting the Most Out of a Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-7

What To Do Before Buying or Upgrading a Computer . . . . . . . . . . . . . . . . . . . . 10-8

Determine the Purpose of the Information System . . . . . . . . . . . . . . . . . . . . . 10-8

Define Your Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-9

System Expectations and Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-12

The User Manages the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-12

Contents
vi HOW TO COLLECT AND ANALYZE DATA

The System Avoids Duplication of Effort . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-12

The System Is Reliable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-12

The System Has a Safety Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-12

Match the Right Application to the Task . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-12

Know How the System Handles Updated Information . . . . . . . . . . . . . . . . . 10-12

Format Flexibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-13

Determine Internal Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-13

User Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-13

Explore the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-13

Practice Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-13

APPENDIX A: A GLOSSARY OF STATISTICAL TERMS FOR 

NON-STATISTICIANS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

APPENDIX B: ANNOTATED BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . B-1 

Resources for the Criminal Justice System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-3

Other Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-5

APPENDIX C: MANUAL DATA COLLECTION PROCEDURES AND 

SAMPLE FORMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

Elements of the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-3

System Startup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-4

APPENDIX D: INMATE PROFILE DATA COLLECTION . . . . . . . . . . . . . . . . . . . D-1

Data Collection Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-3

Inmate Profile Data Collection Code Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-4

APPENDIX E: INCIDENT DATA CODE BOOK . . . . . . . . . . . . . . . . . . . . . . . . . . E-1

APPENDIX F: TRANSPORT DATA COLLECTION . . . . . . . . . . . . . . . . . . . . . . . . F-1

APPENDIX G: TABLES FOR DETERMINING SAMPLE SIZE . . . . . . . . . . . . . . . G-1

APPENDIX H: SIMPLE RANDOM SAMPLING . . . . . . . . . . . . . . . . . . . . . . . . . . H-1

APPENDIX I: CALCULATING THE STANDARD DEVIATION . . . . . . . . . . . . . . . I-1

APPENDIX J: CALCULATING CHI-SQUARE . . . . . . . . . . . . . . . . . . . . . . . . . . . . J-1

APPENDIX K: MANUAL DATA DISPLAY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K-1

Bar Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K-3

Pie Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K-4

Line Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K-4

Graphic Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K-5
vii

F o r e w o r d

How To Collect and Analyze Data: A Manual for Sheriffs and Jail Administrators

provides step-by-step instructions for local corrections personnel who want to use statistical data to improve their organization’s efficiency and provide support for funding 

initiatives.

This book covers the entire process. It advises readers on what types of data they 

should regularly collect, the sources from which data can be obtained, how to store data 

and access it, and methods for interpreting it. Direction is given on performing these 

tasks both manually and electronically.

Along the way, readers will find explanations of management techniques, methods of 

displaying data, fundamental mathematics and statistics, and ways to maximize the 

potential of information systems.

The appendices include a glossary of technical terms, an annotated bibliography, sample forms for data collection, and tables for determining sample sizes and generating 

random numbers for use in sample selection.

How To Collect and Analyze Data: A Manual for Sheriffs and Jail Administrators has 

been available since 1982. We believe this updated and redesigned edition will provide 

readers with all the information they need to guide them through the complicated but 

important process of collecting and analyzing institutional data.

Morris Thigpen

Director

National Institute of Corrections
ix

P r e f a c e

Who Should Use This Document—and Who Will 

Not Need To

Although the title of this document, How To Collect and Analyze Data: A Manual for 

Sheriffs and Jail Administrators, indicates it was written for individuals who are in 

policymaking roles in corrections, it does not mean others in the chain of command—

from lieutenants to line officers—will not also find it helpful. Its concepts and content 

also apply to community corrections facilities, juvenile detention facilities, and other 

institutions. It will also be beneficial to people outside the jail who are involved in the 

collection and analysis of jail data. Actually, the manual should be helpful to anyone 

who has to gather information about jail problems, policies, and practices. How to 

Collect and Analyze Data addresses both ongoing and special issue data collections 

in local jails.

Those who have been involved in in-house data collections or who have crime analysts or statisticians on staff will find that many of the items covered in this manual are 

familiar. This manual is not intended for criminal justice policy analysts or planners 

who regularly work with statistics, information systems, or techniques like systems 

analysis. However, because of its focus on the issues facing jail administrators, it may 

be a useful resource for policy analysts or statisticians who are new to the criminal 

justice arena.

It is particularly difficult to address issues associated with automation in this document. 

Computer hardware and software are changing at an incredible rate. Whatever is said 

here today will probably be outdated tomorrow. While most jails and local law enforcement agencies have computers, what they have and how they use them vary tremendously. Some restrict computers to National Crime Information Center (NCIC) or office 

functions, like word processing. Many have “jail systems,” but their capabilities are 

so varied that it is impossible to generalize. As a result, unlike the first edition of this 

manual in which it was easy to assume that there were few automated jails, this manual 

assumes that you have some degree of automation, but makes no further assumptions 

about what kind. Rather, the manual focuses on what can be done with off-the-shelf 
x HOW TO COLLECT AND ANALYZE DATA

software. For many small jails, some combination of this software may be all that 

is needed to get into the “information system” business. For larger jails, some 

combination of this software may be the easiest way to begin the process of turning the jail information system into a management information system.

What This Manual Will—and Will Not—Do

This manual cannot be all things to all people. It will not teach you everything 

you ever needed to know about statistics, but it will make you a more informed 

consumer of the statistics you receive. It will also provide a basic explanation of 

common descriptive statistics written in understandable language, not statistical 

jargon. It will not teach you how to design the most up-to-date computerized management information system, but it will help you identify the elements that should 

be included in such a system and will provide some help in getting started. It will 

not make you a criminal justice policy analyst, but it will give you the opportunity 

to analyze some real data and show how that information was used in policy 

decisionmaking in other criminal justice systems.

This manual has several goals:

To identify reasons why sheriffs and jail administrators should collect data.

To “de-mystify” statistics and data collection procedures.

To provide tips on how to collect data in the simplest, easiest, most efficient 

way possible that allows policymakers to draw valid conclusions.

To provide an opportunity to practice analytical skills.

To provide guidelines for displaying the information collected clearly 

and effectively so that county officials, other agencies, and the public can 

understand it.

About Good Detective Work

I hope that you find this manual to be a useful tool that introduces you to a new 

way of understanding and solving problems. Statistics are tools that managers can 

use to “get the facts.” That is all analysis really is. Analysis is very much like good 

detective work. It involves looking at a series of facts and trying to piece together 

a picture of what has happened.

Those with a background in law enforcement actually have a head start on many 

individuals who are just beginning to try to analyze information. Many of the skills 

are the same. You will probably have a theory about the things you want to analyze. You will need to look for facts that support or refute that hypothesis. You will 

still need to “play your hunches.” The tools that you will be using may be strange 

at first, but once you are used to them, they will be as comfortable as the techniques you use to solve cases. I hope this manual makes you feel more at home 

with statistics and data and that it encourages you to apply these tools to solve the 

problems that your jail and criminal justice system face.

Gail Elias

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xi

A c k n o w l e d g m e n t s

In a sense, many people contributed to this manual, both academics (who tried to teach 

me something about statistics, policy analysis, and research methods) and practitioners 

(who taught me more than I ever wanted to know about the “nitty-gritty” of collecting 

data in jails). To all of you—you know who you are—thanks for your help and your 

patience.

Gail Elias
c h a p t e r o n e

Introduction
The Situation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3

Traditional Resources Have Failed to Help . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

Resource 1: Introductory Statistics Courses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

Resource 2: Information Systems Seminars . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

Resource 3: Research Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5

Additional Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5
1-3

I n t r o d u c t i o n

The Situation

At times, correctional organizations appear to be slowly sinking under the weight of 

their own paperwork. Like law enforcement and the courts, jails document tremendous 

amounts of information.

Law enforcement officers complete paperwork associated with arrests and 

complaints.

Court personnel document countless appearances and dispositions in addition to 

the text of every hearing.

Jail staff record details, such as descriptions of inmate property, operational procedures, head counts, incidents that occur while an individual is in custody, etc.

Considering the vast amounts of time involved in these activities, it is frustrating and 

disheartening to think that so little of the information captured can be easily retrieved 

later for use in management decisionmaking. However, this state of affairs is not too 

surprising when you think about the purposes of both manual and automated recordkeeping systems found in criminal justice agencies. These information systems are 

designed to store data about people or events; information is put together on a case-bycase or event-by-event basis. It is relatively easy to retrieve information about a specific 

person or event. However, it is often difficult to aggregate that information to answer a 

question for management.

The way in which data is identified for each information system often makes it impossible to look for specific data in any other way than by searching through the entire 

recordkeeping system, file by file. In the case of automated systems, often information 

is lost when staff “write over” one of the variables. For example, when an inmate’s 

bond is reduced, the original bond amount may be lost. Many older information systems used data structures that did not link data together in a way that made it easy (or 

in some cases possible) to combine data from multiple files or sources. These technical 

problems have frustrated many data collection efforts.

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c h a p t e r o n e
1-4 HOW TO COLLECT AND ANALYZE DATA

Traditional Resources Have Failed To 

Help

When sheriffs and jail administrators tried to learn 

how to collect jail data or set up a management 

information system in the jail, many found that the 

traditional resources available to them were not very 

helpful. These resources included:

Statistics course work.

Information systems seminars.

Research methods.

Resource 1: Introductory Statistics Courses

Most statistics courses focus on mathematical foundations of statistics, derivations, and computation, which 

are things most correctional managers do not need to 

know. Computers can perform these computations for 

you. And memorizing statistical formulas does not 

make a whole lot of sense, since they are readily available in texts. Yet frequently, that is what success in 

statistics course work requires.

In addition, most statistics courses require breaking 

through a statistical language barrier. Not only do you 

have to deal with mathematical formulas, but you also 

have to deal with phrases like “95-percent confidence 

intervals,” “significant at the .001 level,” and “nonparametric tests of significance,” which mean little 

at first exposure to statistics. And the explanations of 

these terms are often worse than the terms themselves!

In the long run, introductory statistics classes do not 

deal well with analysis, which is precisely what sheriffs and jail administrators hope to get from these 

courses. Analysis is the process of figuring out what 

something means and what may be done about it. 

Statistics are tools that can aid in that process, not the 

process itself. Sheriffs and jail administrators want to 

apply these tools to “real-life” problems. Instead, in 

most statistics classes, they get a series of instructions 

on how to make the tool. As a result, beginning statistics courses leave most sheriffs and jail administrators 

frustrated, confused, or yawning. This manual avoids 

statistical jargon, focuses on statistical assumptions, 

and exposes sheriffs and jail administrators to data 

analysis and its associated statistics.

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From time to time, this manual will explain the statistical term for the concept being discussed. This 

may be necessary so you are prepared when a serious student of statistics starts talking about “standard 

deviations” and “sampling schemes.” Any statistical 

terms introduced are defined in the text and then summarized in appendix A.

Resource 2: Information Systems Seminars

Information systems seminars often focus on computer hardware and software and forget about data. In 

the excitement of the computer “bells and whistles” 

and the complex terminology, it is too easy to assume 

that a particular type of equipment will solve all of 

management's information problems. Many sheriffs 

and jail administrators attend these seminars to determine what jail management software they should buy. 

That is, unfortunately, often putting the cart before the 

horse. To evaluate how good an automated system is 

and how well it will meet your needs, you first need to 

know what data you should capture.

Actually, the process for designing a manual system 

is a precursor for designing an automated system. 

However, many software companies and designers do 

not emphasize this point, because they have an investment in selling their computer hardware and software. 

This may turn out to be a sizable additional cost when 

programs have to be tailored to meet the needs of a 

specific department. Sheriffs and jail administrators 

who do not have the resources to invest in a specialized jail information system may hope they can buy 

“off-the-shelf' software and a personal computer and 

program it to meet their needs. However, problems in 

learning to use the applications, setting up the procedures to ensure that staff enter data consistently, and 

training people how to use the software frequently 

make these individuals reconsider. And then, for many 

small organizations, there is the sizable issue of making the commitment to actually enter the data that is 

needed.

Frequently, too, the language associated with information systems is even more confusing than that associated with statistics. Conversations are spiced with 

words like “database,” “LAN,” and others. None of 

this seems to help much when all you really wanted 

to know is how to set things up so you could find out 
1-5CHAPTER ONE

how many pretrial inmates charged with felonies were 

held for more than a month in 2003 by some easier 

method than looking at all the prisoner files and the 

Shift Activity Sheets for that year. In frustration, many 

sheriffs and jail administrators throw up their hands 

and continue gathering information like they had in 

the past, or they do not collect the information at all.

This manual provides assistance in designing a manageable manual information system and transferring 

key management information system elements to a 

personal computer using off-the-shelf software. There 

are many ways to develop this system. Two strategies 

discussed in this manual are:

1. Extracting data from an automated information 

system to a personal computer and manipulating 

the data using off-the-shelf software

2. Designing a manual information system to collect 

data that may not be available in an operational 

system and manipulating the data using off-theshelf software.

Resource 3: Research Methods

Sheriffs and jail administrators who try traditional 

research or program evaluation course work may 

actually have more of their specific questions 

answered than those who try the previously mentioned 

resources. This type of course work will provide some 

answers about how to define problems, design questionnaires, and organize data collections.

However, several problems attach to this resource as 

well. Research methods also have a language of their 

own, and the complexity of research designs that 

require control and experimental groups may seem 

unrealistic to sheriffs and jail administrators. The jail’s 

often competing missions of safety, security, and 

service do not mesh well with traditional research 

models. As a result, there is a tendency to “throw out 

the baby with the bath water” and disregard useful 

aspects of these courses because other aspects make 

very little sense to sheriffs and jail administrators 

faced with real decisions and real problems. This 

manual will provide “real-world” research methods 

that can help with jail management.

Additional Resources

Since this manual was first published in 1982, other 

materials that deal with data collection have been 

published. Many of these publications are both practical and pertinent to correctional settings. Although 

a number are written in technical jargon and others 

intimidate by their very size, there are more available 

resources now. Appendix B contains a bibliography of 

data collection manuals and materials with some comments regarding their contents. It also lists several statistics and evaluation texts, as well as other books on 

the subject. These should be helpful to those getting 

started collecting and analyzing data and those who 

are organizing an information system.

Despite the fact that a considerable amount of material 

has been developed over the last decade, there is still 

a surprising lack of materials and resources for those 

who need basic information about how to collect data 

in their correctional facilities. This manual will help 

fill that gap by identifying:

Why you should collect data.

How to identify the information that is needed.

How to organize a data collection.

How to locate and capture that information.

How to put the data collection together.

How to analyze the information.

How to interpret the results.

How to share that information most effectively.

This manual also includes the following resources you 

may find useful in collecting data:

A list of common data elements that jails should 

begin to collect.

A description of the skills needed to collect data.

A list of common places where data elements are 

found in jails.

A model manual information system.

A model information system constructed using 

off-the-shelf software.

A sample data collection sheet, code book, and a 

corresponding computer format in which to process this information.

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Introduction
1-6 HOW TO COLLECT AND ANALYZE DATA

A short introduction to descriptive statistics.

A series of programmed learning exercises to 

practice your analytical skills.

Examples of charts and graphs that can be used to 

display data.

These resources can be adapted to meet the needs of 

individual jails.

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c h a p t e r N u m b e r c h a p t e r t w o

Good Management 

Requires Good 

Information
What Is Management? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

Ways To Get the Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

What Can You Do With Data? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

Example 1: Better Budgeting and Allocation of Funds . . . . . . . . . . . . . . . . . . . 2-4

Example 2: More Effective Deployment of Staff . . . . . . . . . . . . . . . . . . . . . . . . 2-5

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6
2–

G o o d M a n a g e m e n t R e q u i r e s G o o d I n f o r m a t i o n

In the past, managing jails was considered to be so basic that there was nothing to it. 

However, with increased court intervention in correctional matters, demands for better 

management of correctional facilities increased in the 1970s. The courts discovered that 

often the difference between a “constitutional jail” and an “unconstitutional jail” was 

the way in which the facility was managed. Since good management relies strongly on 

good information, sheriffs and jail administrators found that their organizational world 

had become a much more complex place in which to work.

As a result, “professional management” arrived in correctional facilities. Sheriffs 

and jail administrators were introduced to a number of techniques, such as costbenefit analysis, Total Quality Management, and organizational development, that were 

designed to help them improve organizational performance. Sometimes these techniques were very helpful, and sometimes they were not. In analyzing their relative successes and failures, the ability of the organization to generate good, valid information 

about its problems emerged as a critical variable.

What Is Management?

Management is mostly about mobilizing an organization’s resources, in this case the 

jail’s, to solve or avoid problems. In many cases, problems are not solved in the sense 

that they go away, but the organization finds a way to manage them more effectively. 

Regardless of the complexity of the problem, a basic management formula applies to 

analyzing a situation. It consists of five steps:

1. Facts are gathered.

2. Facts are interpreted in light of the organization’s mission and values.

3. Alternative solutions are developed.

4. A decision is made.

5. Action is taken.

All of the steps in the formula are critical to its successful application. Facts are the 

foundation on which everything that follows depends. If the facts are wrong, you are 

c h a p t e r t w o
2– HOW TO COLLECT AND ANALYZE DATA

likely to reach the wrong conclusion, make the wrong 

decision, and take the wrong action. Good detectives 

do not bring a case to court unless they have gathered 

the evidence. Correctional managers are in precisely 

the same position. They should avoid making policy 

decisions without the best available information. This 

manual provides guidelines for getting the facts.

Ways To Get the Facts

Facts can be gathered in two major ways: by quantitative methods and by qualitative methods.

Quantitative methods rely on numbers to reveal something about an event or phenomenon. They usually 

focus on questions like, “How often?” or “How much?” 

Qualitative methods describe an event or phenomenon. 

Law enforcement professionals use qualitative methods 

all the time. Two of the most commonly used qualitative methods are interviews and observations.

Qualitative methods are excellent tools for problem 

solving. However, even in the hands of trained observers and interviewers, some problems attach to using 

them. First, as human beings, we have certain perceptual biases; we hold certain values and assumptions 

about the way the world operates. And we process all 

information in the light of those biases and assumptions. Qualitative tools provide “checks” on these 

biases.

Another problem associated with qualitative measures 

lies in the fact that “where we sit often determines 

where we stand.” All officials in the criminal justice 

system have specific sets of tasks that they perceive 

as their “mission.” Together with the expectations that 

other individuals have of them, these tasks comprise 

their roles. Because of their roles, different criminal 

justice officials often see the same event differently.

For example, someone charged with major narcotic 

sales may post a bond. The court has no problem with 

this as long as he comes to court. A crowded jail may 

see the individual as a relatively good risk for release 

since he is not violent. However, law enforcement sees 

things differently because they suspect this individual 

will probably disappear. If a researcher approached the 

judge, the jail administrator, and the chief of police 

regarding this case, with questions about changing 

bonding procedures, they would have decidedly 

different reactions. With qualitative methods, the 

researcher would get a great deal of rich, descriptive 

information, but there would be no way to evaluate 

how often such a situation actually occurs or how 

often the individual actually absconds.

Without quantitative data, there is no way of knowing 

how many people bond out of jail, the level at which 

bond is set, how many fail to appear, and many other 

questions that should be answered when bonding 

criteria are reviewed. Information gathered in numerical form is more objective and limits individuals’ 

perceptual biases in interpreting the information. This 

is not to say that statistics cannot be biased, because 

they can be. However, it is rarely the numbers that are 

biased. Most often, it is their interpretation or representation that is slanted. Chapter 7 addresses these 

issues and how to guard against them.

Perhaps the greatest advantage of gathering information in quantitative form is that you can determine 

how much error is involved. Not only can you estimate what the chances are of being wrong, but you 

can also estimate how wrong you are likely to be. In 

other words, you can identify how many times out of 

a hundred you would come up with a similar answer 

and how much the new answer would probably vary 

from the first one.

In addition to focusing on the facts portion of the 

management equation, this manual focuses specifically on quantitative ways of getting facts. This is the 

process we call collecting, analyzing, interpreting, and 

displaying data.

What Can You Do With Data?

Sometimes the hardest thing about collecting data is 

convincing yourself that it can be useful. Two examples of how jail data can be used to make better management decisions follow.

Example 1: Better Budgeting and Allocation 

of Funds

For many jails, budgeting is a somewhat mystical 

process, in which the next year’s decision is based on 

what was spent last year. This figure is then divided 

into four equal amounts for each of the four quarters. 

Sometimes this process “backfires” when additional 

funding without any unusual circumstances is required 
Good Management—Good Information 2–CHAPTER TWO

because critical changes were not considered. Let’s 

consider one area of the budget—food service—and 

apply the traditional approach as well as one that is 

somewhat different.

Last year, two small, nearly identical facilities with 

Average Daily Populations of 50 prisoners each spent 

$63,875 for food. Operating on the “let’s ask for 5 

percent over last year’s” approach, Sheriff #1 requests 

$67,069 for food in the next year’s budget. Sheriff #2 

decides to try a different approach.

The local Consumer Affairs Office advises Sheriff #2 

that inflation hit food products heavily last year, causing an increase of 5 percent in food costs, and that 

costs are projected to increase by at least 5 percent 

next year. Sheriff #2 has also been following increases 

in the jail population closely. The statistics on Average 

Daily Population, Length of Stay, and Jail Days1 have 

also increased regularly. Over the past 5 years, Jail 

Days have increased by about 5 percent each year. 

Furthermore, Sheriff #2 knows that the jail was fuller 

from July through December.

Last year, Sheriff #2 calculated that $3.50 was spent 

on food each day for each prisoner in the facility. At 

5 percent inflation, it is anticipated that $3.67 will be 

necessary to provide the same meals next year. And, 

if the population continues to increase, Jail Days will 

increase to approximately 19,175 for the whole year. 

As a result, rounding the totals off, Sheriff #2 asks for 

$70,375 for food. 

Let’s assume that Sheriff #2 is also a good politician 

and that the county commissioners provide the full 

amount requested. Instead of dividing the money into 

four equal amounts of $17,593 and change per quarter 

as the accountant recommends, Sheriff #2 calculates 

how much money will really be used each quarter if 

the jail population behaves as it has over the past 5 

years. Usually, 19 percent of the Jail Days fall during 

each of the first two quarters of the year; 29 percent, 

during the third quarter; and 33 percent, during the 

fourth quarter. So, by dividing the $70,375 by those 

percentages, Sheriff #2 knows that about $13,371.25 

should be spent during each of the first two quarters 

of the year, $20,408.75 during the third quarter, and 

$23,223.75 during the last quarter.

Based on this scenario, assuming Sheriff #2 can stick 

to the budget, the funds should last through the fiscal 

year without any difficulty. Sheriff #1 will have spent 

all the funds in the food budget somewhere around the 

first week of December.

Similar strategies can be used to calculate future costs 

of any items supplied to the inmate population. A 

little information and a relatively simple mathematical procedure helped Sheriff #2 make a more accurate 

estimate of future expenditures and monitor the rate at 

which dollars were being allocated for a specific function. It also got Sheriff #2 a slightly larger budget.

Example 2: More Effective Deployment of 

Staff

Jails have traditionally been understaffed. If personnel 

are among the jail’s most precious resources, it is critical that they are deployed in the most effective manner. Let’s assume that Sheriff #1 and Sheriff #2 are 

evaluating their staffing patterns. Both know that their 

Average Daily Populations are about 50 and that there 

are about 3,500 bookings a year (about 9.5 bookings 

per day).

Sheriff #1 looks at these statistics and decides to staff 

the booking room 365 days a year, 24 hours a day. 

After all, prisoners are brought in 7 days a week, 24 

hours a day, and personnel have to staff the booking 

room. If the shift relief factor (the number of people 

it takes to provide coverage on one 8-hour shift, seven 

days a week) is 1.75, then 5.25 officers (three times 

this number) will be needed to cover each post in the 

booking room 24 hours a day. Sheriff #1 decides that 

two officers should always be assigned to the booking 

room. This results in the need for 10.5 total officers. 

When presented with this information, the Board of 

Commissioners expresses great concern about the 

costs attached to this staffing pattern and refuses to 

give Sheriff #1 the money to hire additional staff.

Sheriff #2 decides that more information is needed 

before deciding how to deploy staff. Because Sheriff 

#2 suspects that more people are booked during the 

weekend, a search for data to validate this assumption 

begins. Sheriff #2 has the jail administrator go over 1Jail Days is the total number of days spent by all prisoners in jail during 

a fixed period of time, such as a month or year.
2– HOW TO COLLECT AND ANALYZE DATA

the Booking Log for the previous year and prepare 

a chart that shows the number of bookings on each 

shift for the last year by day of the week. All the jail 

administrator has to do is count the number of bookings that took place on each shift each day of the 

week. The results are displayed in table 2-1.

Sheriff #2 decides against full-time booking officers 

on all but the 16:00–24:00 shift, when the volume 

of activity is heavy enough to warrant a full-time 

position. During the week on the other shifts, jail 

staff assigned to other areas should be able to handle 

incoming prisoners. However, Friday nights and 

weekends are very busy in the booking room. Sheriff 

#2 elects to post an additional staff member in the 

booking area on Friday evenings and weekends with 

instructions that the “roving jail officer” assigned to 

each shift will assist in the booking room when it is 

busy. Ultimately, this results in the equivalent of 2.29 

officers posted in the booking room—one 7 days a 

week on the swing shift, and a second officer on all 

shifts from 16:00 on Friday through 8:00 on Monday. 

When the shift relief factor is considered, Sheriff #2 

needs a total of four officers for the booking room. 

And the sheriff believes that the area will be covered 

more effectively during peak periods of use than it had 

been in the past.

Conclusion

The examples given above are only two of many that 

illustrate how good information can be used to help 

sheriffs and jail administrators manage more effectively. Generating and using good data is a critical aspect 

of dealing with such jail issues as:

Crowding.

Facility planning.

Policy and procedure development.

Controlling violence in facilities.

Managing special inmates.

Transporting prisoners.

Cost-effective operations.

Staffing.

Determining appropriate programs and services.

These examples suggest that a lot of good can come 

from collecting, analyzing, and using good information. In addition, most standards for correctional 

facilities mandate that jails maintain records systems containing information that can (and should) 

be used for management purposes. In some cases, 

management information systems are even specified. 

Furthermore, compliance with standards requires a 

considerable amount of documentation. As discussed 

in chapter 5, much of the documentation you keep to 

defend against litigation will be part of the management information and data collection system.

Each sheriff or jail administrator may decide why to 

collect data and how to use the data in his/her own 

situation. Information is power. It is up to you to tap 

it, channel it, and put it to good use.

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Table 2-1 Yearly Bookings by Shift and Day of Week (2004)

Shift Mon Tues Weds Thurs Fri Sat Sun Total

0:00–8:00 115 75 75 90 45 350 350 1,100

8:00–16:00 95 85 55 115 85 225 150 810

16:00–24:00 115 125 100 175 450 425 200 1,590

Total 325 285 230 380 580 1,000 700 3,500

Average/day 6.25 5.48 4.42 7.30 11.15 19.23 13.46 9.50
c h a p t e r t h r e e

Information That Should 

Be Collected
Writing Good Problem Statements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3

Identifying Data Elements From Problem Statements . . . . . . . . . . . . . . . . . . . . . . 3-4

A Catalog of Correctional Data Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5

Inmate Population Data Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6

Inmate Profile Data Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7

Operational Data Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-10

Criminal Justice System Performance Data Elements . . . . . . . . . . . . . . . . . . . 3-12

A Word About Collecting Data in Other Agencies . . . . . . . . . . . . . . . . . . . . . . . . 3-14

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-14
3–

I n f o r m a t i o n T h a t S h o u l d B e C o l l e c t e d

Many people find it difficult to identify all the information (or data elements) needed 

to improve both the quantity and quality of information available for management 

decisionmaking. As a result, sheriffs and jail administrators may rely on professional 

analysts to define what will be collected. The problem is that the analysts will select 

data elements that they think jails could use, and most computer software designers or 

systems analysts know relatively little about jail operations.

This chapter will help you decide which data elements should be collected on a routine 

basis. It also provides guidelines for developing good problem statements, since they 

are the basis for identifying the data you will need to gather in special issue data collections. It suggests how organizational or program goals can be used to identify data 

elements. Finally, it categorizes and lists data elements commonly collected in jails 

and identifies other criminal justice data elements that should be of concern to the jail, 

although they may not be readily available in the jail itself. These data elements will 

probably become part of your management information system.

Writing Good Problem Statements

This section describes a process for identifying the data elements to be gathered in 

special issue data collections. Statements that describe problems in detail are actually 

composed of many basic data elements that should be collected; others may be suggested by the problem statement. Writing a good problem statement sounds elementary. 

However, most people find it difficult to describe a situation in its most basic terms, 

which is what a good problem statement does. Many people substitute solutions, obstacles, reasons, symptoms, and goals for good problem statements. As shown in table 3-1, 

each substitution, or imposter, has an associated cost.

c h a p t e r t h r e e
3– HOW TO COLLECT AND ANALYZE DATA

Table 3-1 The Costs of Poor Problem Statements

Imposters Costs

Solutions (ways of solving 

problems)

Other options that may be longer lasting or more cost effective may be glossed 

over, or you may fail to solve the “right” problem

Obstacles (conditions that make it 

difficult to solve problems)

Problems may seem insurmountable and no effort to change the status quo is 

made

Reasons (why things happen) It is possible to derail the whole process into a disagreement over who is right

Symptoms (visible signs that indicate a deeper problem is present)It is too easy to miss the factors that are really causing the problem

Goals (the end results to be 

achieved)

No description of the current situation is developed, making it difficult to identify 

the data elements

A good problem statement is a concrete description 

of the situation. It makes it possible to identify the 

data elements needed to determine causes and suggest 

solutions. For example, the following paragraph is a 

good statement of the crowding problem in a specific 

jail:

For the last year, the number of people who 

have been arrested and booked at the jail 

has steadily increased; their length of stay 

has increased, too. As a result, I have had 

prisoners sleeping on mattresses on the floor. 

Something has changed the way the criminal 

justice system has been using the jail.

This is a useful problem statement, because it concretely describes the crowding that is troubling the 

jail. In the next section, this problem statement is used 

to identify the pieces of information that should be 

collected to determine what is causing the problem.

Identifying Data Elements From 

Problem Statements

Turning a problem statement into the data elements 

related to it is usually not too difficult. The better the 

problem statement, the easier it will be. Generally, by 

asking yourself the questions for which you are trying 

to find answers, the pieces of information you need 

begin to emerge. It may help to think of possible questions like:

What has changed?

How much? 

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How often? 

Why?

What is related to this change?

The problem statement written above directs the 

analyst toward pieces of information that will help 

unravel the reason for crowding. These include data 

elements like:

The ones mentioned in the problem statement 

(i.e., number of people arrested and booked, and 

length of stay, this year compared with last year).

Charges on which persons are arrested (this year 

compared with last year).

Numbers of summonses and citations issued by 

local law enforcement agencies (this year compared with last year).

Length of time from first appearance to arraignment and trial dis­position (this year compared 

with last year).

Average length of sentences (this year compared 

with last year).

Charges for which sentences are given (this year 

compared with last year).

While these data elements will not identify the ultimate cause of the problem, they will identify the arena 

in which the investigation should proceed. A good 

followup to this analysis would include interviews 

with key actors in other law enforcement agencies, the 

courts, and the prosecutor’s office to identify any policy changes that would lead to crowding in the facility. 

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Information That Should Be Collected 3–CHAPTER

This problem statement is good

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for two other reasons:

It is based on knowledge of the criminal justice 

system and an understanding of the basic factors 

that lead to crowding (e.g., increased admissions 

and length of stay).

It includes an educated guess (a “hypothesis” in 

research terminology) about what may be causing 

the problem.

If the problem statement actually defines goals, the 

task of identifying the data elements becomes a bit 

more difficult, but it is still manageable. In fact, most 

program or performance evaluations start from a statement of the particular program’s goals. The trick with 

goals, which are usually abstract statements, is to find 

something measurable that will be present if the goal 

is achieved. To put this in research terminology, you 

must look for measurable objectives that lead to the 

goal or an operational definition of the goal.

For example, suppose that Sheriff #2 has established 

a goal of providing a safe environment for staff, 

inmates, and visitors to the facility. After making a 

number of policy and procedure changes, Sheriff #2 

decides to find out how well the organization is meeting that goal. Sheriff #2 and the jail administrator 

define the conditions which describe a safe environment. These include the following:

There are no assaults on staff, inmates, or visitors.

No contraband enters the facility.

No fires occur in the facility.

There are no accidental injuries to staff, inmates, 

or visitors.

The tension level in the facility is low.

Sheriff #2 and the jail administrator have no trouble 

identifying the data elements for most of these objectives. The data elements are:

The number of assaults on staff, inmates, and 

visitors.

The number of times that contraband was found 

either in the facility or before it entered the 

facility.

The number of fires that occurred in the jail.

The number of compensation claims.

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The number of accidental injuries recorded in the 

inmate medical records.

The number of disciplinary reports.

While the process of identifying data elements from 

goal statements is somewhat more complex than 

working from a problem statement, jails can identify 

the data elements needed to evaluate program performance. A number of good handbooks that can help 

sheriffs and jail administrators with this particular 

kind of data collection are listed in appendix B.

So, to determine the data elements you need to collect, 

you will generally follow these steps:

1. Define the problem and its component parts.

2. Determine the data elements that are represented 

in the problem.

3. Identify potential causes and contributing factors.

4. Write the questions for which you are trying to 

find answers.

5. Determine the data elements represented in the 

potential causes, contributing factors, and 

questions.

A Catalog of Correctional Data 

Elements

The process described in the preceding section is 

helpful when collecting data that deals with a specific 

issue or problem. However, it makes sense to collect 

some data on a routine basis so that it is readily available. This section categorizes, identifies, and describes 

frequent uses for commonly collected data elements. 

Some of these data elements should be routinely collected to be most useful; others will more likely be 

collected only when specific problems confront the 

jail.

Correctional data elements can be divided into four 

categories:

1. Inmate Population Data Elements.

2. Inmate Profile Data Elements.

3. Operational Data Elements.

4. Criminal Justice System Performance Data 

Elements.

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3– HOW TO COLLECT AND ANALYZE DATA

Inmate Population Data Elements

Inmate Population Data Elements are called “population” data elements because information is kept 

about each person booked at the jail (the whole 

population in a statistical sense). In the language 

of information systems, they are summary numbers 

or statistics assembled by time period, typically a 

month or a year. While they can be calculated or 

recreated from most databases, sometimes that may 

require more effort than you are willing or able to 

expend at the time you really need to know what the 

Average Daily Population of your jail was in March 

last year (after that data has been archived) or what 

the difference between the average population and 

the peak population was 2 years ago. These data elements should be kept regularly; they are the items 

that should be included in a jail information system! 

Inmate Population Data Elements include the following: Average Daily Population (ADP), Length of Stay 

(LOS), Jail Days, Total Bookings, and Net Bookings.

Average Daily Population is an average of the 

number of people housed at the jail (in statistical 

terms, the "mean"). ADP is usually expressed as a 

monthly or yearly average. It is a computed data 

element and is calculated for 1 year by dividing 

total Jail Days (see below) by the 365 days in the 

year. ADP is most often used as a way of expressing how full the facility is.

Length of Stay is the number of days between 

a prisoner’s admission and release. LOS is often 

expressed as an average. That way it refers to how 

long (on the average) each prisoner stays at the 

jail. LOS can also be expressed as a frequency 

distribution. In that case, the amount of time each 

prisoner stays is divided into groups (e.g., prisoners released within 24 hours, 48 hours, 72 hours, 

1 week, etc.), and then the number of prisoners in 

each group is counted and expressed as a percentage (e.g., 66 percent are released within 24 hours, 

another 14 percent are released within 48 hours, 

etc.). LOS is almost always expressed in days for 

jails and in months for prisons. For pretrial prisoners, it is used as a way of assessing the speed of 

the court process.

Jail Days is another measure of the amount of 

time spent in the jail. Jail Days are calculated 

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by adding the time each prisoner spends at the 

facility within a given period of time (usually a 

month). In many respects, this data element is 

one of the most useful because ADP and LOS can 

both be calculated from it if the number of bookings and the amount of time it spans are known. 

Jail Days can be translated directly into the percentage of capacity at which the jail operates and 

the number of beds that are needed to accommodate a certain jail population, and it is often used 

with other data elements to develop cost ratios 

(e.g., food cost per inmate per day). Jail Days are 

often called other names, including prisoner man 

days, inmate days, resident days, days of care, etc.

Total Bookings is the total number of persons 

arrested and brought to the jail and is a good measure of booking room activity and the number of 

people that enter the justice system through the 

jail. Together with Total Arrests, Total Bookings 

suggests something about law enforcement practices in the jurisdiction—namely, if and how 

often field citations are used. A word of caution 

is in order here, however. Sometimes persons are 

brought to the jail, detained there for some time, 

and not booked. These could include persons 

such as those held on mental health or alcohol 

detainers. Sometimes, inmates are boarded in 

other facilities and returned to your jail, typically 

through booking for court, and then returned to 

another facility. In both cases, from an operational 

perspective, these people should probably be 

booked. However, from a statistical point of view, 

they are different from people who are newly 

admitted to your facility. If these practices occur 

in your facility, you will need to keep separate 

statistics on these individuals. 

Net Bookings is the number of persons actually 

housed at the jail after booking. Net Bookings also 

may be called inmates, prisoners, residents, etc. 

While Total Bookings is the best measure of activity in the booking room, Net Bookings is the best 

measure of activity in the jail itself. If you subtract 

Net Bookings from Total Bookings, the result will 

be the number of persons released directly from 

the booking room without ever entering the general population. These individuals are released on 

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Information That Should Be Collected 3–CHAPTER

recognizance, bond, a summons or citation, or at 

THREE

the authorization of the arresting officer. 

Most jails maintain records on several subgroups of 

this data element as a way of describing the jail population. Common subsets of Net Bookings include:

Felony and misdemeanor inmates, traffic offenders, and holds.

Inmates who are pretrial, posttrial, or on a hold or 

detainer.

Male and female inmates.

Adult and juvenile inmates.

Jails frequently keep information on these subsets 

because of statutory requirements for separation. 

These statistics become particularly important when 

planning for a new facility because they are directly 

related to the number of beds that must be provided 

for certain types of prisoners.

These are the basic data elements that jails should 

routinely keep to describe the inmate population. They 

can be used for many purposes, including monitoring 

population levels in the facility, long-range facility 

planning, population forecasting to determine anticipated bed space needs, budgeting, deployment of staff, 

and program development.

Inmate Profile Data Elements

The following list represents many common inmate 

profile data elements. However, many additional data 

elements related to the unique needs of a specific 

jurisdiction deserve consideration. You will have to 

identify these for your jurisdiction on your own. You 

may elect to collect some of this information on a 

regular basis and other data elements only as some 

special issue or problem emerges. Your challenge is 

to determine which is which and how to quickly and 

easily retrieve those data elements that you do not routinely collect. Inmate profile data elements include:

Legal Status.

Charge Status.

Special Charge or Charges.

Date of Birth

Ethnicity.

Education.

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Vocational Skills.

Employment.

Family Ties.

Residence.

Physical Health Status.

Mental Health Status.

Substance Abuse Status.

Release Status.

Legal Status. This data element describes the legal 

reason the inmate is at the facility. General categories used with this data element are pretrial, posttrial, 

sentenced, and holds. This is one of the most basic 

descriptive data elements and is used in a variety of 

ways. Some uses relate to the kinds of programs in 

which the inmate may participate (e.g., only sentenced 

inmates may work); others relate to the development 

of alternatives to incarceration or changes in arrest or 

court practices to relieve crowding. If your classification system is based on objective criteria, legal status 

is probably one of them. Be aware that legal status 

changes over time. An inmate may enter the jail in 

pretrial status and leave in sentenced status. He/she 

may be pretrial on a local charge, but on a hold for 

another jurisdiction. As a result, jurisdictions need to 

consider how to record this information. Jurisdictions 

may:

Record legal status several times at the time of 

admission and the time of release)

Associate legal status with a specific charge

Track legal status as it relates to the most 

serious charge.

Note: Do not be surprised by what your 

automated jail management system does with 

this information. Some automated systems 

“lose” the initial data when information is 

updated or replaced. For example, if you have 

just one variable for Legal Status and you 

gathered information about people who left, 

you would not be able to tell how many people 

came in pretrial and left sentenced because 

only the last entry—which should have been 

updated when they were sentenced—will 

show up.

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Charge Status. This data element describes the level 

of charges on which the prisoner is detained or sentenced. General categories used with this data element 

are felon, misdemeanant, municipal ordinance violator, traffic offender, or hold (especially probation and 

parole violations). This is another very basic descriptive data element. Charge Status is frequently used 

as one of many criteria to decide if an individual is 

housed in maximum, medium, or minimum security.

Specific Charge or Charges. This is what the prisoner is charged with. This data element is often used 

to try to predict behavior within the facility. Charge 

at arrest is often a good indicator of who will remain 

in custody. Charge can be a particularly difficult data 

element because it changes over time. The charge at 

the time of arrest is often modified by the prosecutor. 

Plea negotiation may result in a third possible charge, 

for which the individual is sentenced. You may want 

to add a data element or elements that tell what the 

charges were at release and what the charges were on 

which the inmate was sentenced.

Inmates may be arrested on multiple charges. One 

strategy commonly used to deal with multiple charges 

is to select the “most serious offense” for the analysis. However, sometimes the “less serious” charges 

provide insight into the prisoner that may be very 

useful for other purposes, such as classification. For 

example, a prisoner charged with a felony residential 

burglary may also be charged with misdemeanor-level 

possession of a controlled substance. One strategy 

to deal with this issue is to develop one or two specialized charge data elements that consider all of 

the charges. These could include a data element that 

identifies whether any drug or alcohol offenses were 

included, a data element that identifies whether there 

were any failure to appear (FTA) or failure to comply 

(FTC) warrants or detainers, or any other category of 

charges that might be of specific local interest.

A word of caution in categorizing charges is 

in order. If you hope to pick out information 

about persons charged with specific offenses 

(e.g., all individuals charged with driving 

under the influence (DUI), you have to code 

your information at that level. You can group 

charges in a number of ways in later analyses 

(e.g., all traffic offenses or all alcohol-related 

offenses), but if you code the charges as traffic 

or alcohol-related offenses, you can never 

separate out the information for only DUIs.

Chapter 5 contains more information about 

this coding procedure.

While charge information has to be entered at this 

level of detail so that data about specific offenders, 

such as DUI or domestic violence offenders, can be 

used to identify specific cases, it is often useful to 

categorize charges by looking at groups, such as prisoners who are charged with crimes against persons, 

property offenders, public order offenders, etc.

Automated systems sometimes use a code, such as a 

statute or Uniform Crime Report (UCR) number, to 

record this information. Depending on the characteristics of the program, this can work well or create some 

interesting problems. The issue is whether the computer system checks the numbers that your staff enters. If 

it does not, the potential for human error can produce 

some bizarre or nonexistent charges on reports the 

computer generates. The computer system should have 

an internal validation process that alerts staff to the 

charge they enter or, better yet, have a system that is 

menu-driven that lets the operator pick the charge. 

Sometimes automated systems let staff freely enter 

information for a variable. In database terminology, 

these are “free-form” fields. The problem here is not 

visible until you try to analyze this data by computer.

Keep in mind that “Possession of a Controlled 

Substance,” “Controlled Substance—

Possession,” and “Drug Possession” are 

the same charge. However, the computer 

will not understand that they are the same 

thing. Computers are very literal. This makes 

for a long and tedious process to recode 

information. If your computer program has 

free-form fields, a great deal of time will be 

saved if you ensure that your staff always 

enters information the same way. Even better, 

use an alternative coding strategy, such as 

statute number, to avoid this problem.

Date of Birth. This is preferable to Age as a data element. Statistical studies have shown that people make 

more mistakes (or misrepresentations) when asked 
Information That Should Be Collected 3–CHAPTER THREE

their age. Many computer programs can calculate 

Age from Date of Birth. This data element is often 

expressed as the average (mean) age of jail inmates. 

It is also useful to use another statistic, the median, to 

explore age. See chapter 7 for more information about 

the mean and the median. Age is frequently more useful when displayed as a frequency distribution. This 

data element is often used to make decisions about the 

types of programs that would be appropriate for the 

inmate population (e.g., younger prisoners are more 

likely to use an active recreation program than older 

prisoners).

Ethnicity. This refers to the prisoners’ racial or 

ethnic backgrounds. Common general categories 

include Caucasian, African-American, Asian, Native 

American, and Other. Hispanic and Non-Hispanic 

are two other categories often included separately in 

this data element. The Ethnicity data element can be 

used in a number of ways. It may suggest cultural and 

ethnic groups from which detention personnel should 

be recruited, cultural or social customs or practices of 

which staff should know, and potential language barriers. It may also be useful in dealing with the assumptions the community holds about who is in jail.

It is worth noting that these categories may 

not always total 100 percent. For example, 

in a number of States as well as for the U.S.

Census and FBI requirements, respondents 

are characterized as Hispanic/Non-Hispanic 

separately from ethnic background.

Education. Education comprises several potential 

data elements. They include the last grade completed, 

actual level of educational attainment as indicated by 

testing, interest in obtaining a general equivalency 

diploma (GED), interest in a remedial program, or 

interest in college course work. Its main application is 

in deciding what type of education program is appropriate for the inmate population. 

Vocational Skills. Vocational Skills consists of several 

data elements, including skills attained and interest in 

skills building. This data element, in combination with 

LOS data, is useful in developing programs and services for the inmate population. It also provides information that is potentially useful for those designing a 

“bridge” or “reentry” program for inmates.

Employment. This comprises several data elements, 

including the individual’s employment status at time 

of arrest (i.e., employed full-time, part-time, unemployed), last date employed, whether the prisoner is or 

was receiving unemployment benefits or other public 

assistance, and occupation (categorized). This data 

element is frequently used as one criterion for release 

on recognizance, and may also suggest programs or 

services that might be helpful to the prisoner in preparation for release (e.g., employment counseling).

Family Ties. Family Ties consists of several data 

elements, which include marital status, next of kin, 

residence, and number of dependents. All of these are 

potentially useful in assessing whether the prisoner is 

a good candidate for release on recognizance.

Residence. This data element is related to family ties 

and employment. It is frequently examined in considering release on recognizance.

Physical Health Status. Physical Health Status consists of several data elements, including current illnesses or injuries, disabilities, and history of physical 

illnesses. These data elements define the most likely 

medical conditions the medical staff will have to treat. 

They are also useful in preparing training for staff in 

first aid, cardiopulmonary resuscitation, etc., and in 

preparing and defending the medical budget, including 

requests for medical staff.

Mental Health Status. Mental Health Status consists 

of several data elements, including past or present 

treatment for mental health problems, type of treatment (e.g., inpatient vs. outpatient), whether a mental 

health crisis worker had to see the prisoner on intake, 

and whether special housing is required because of a 

psychiatric condition. These data elements are becoming more important to sheriffs and jail administrators 

as increasing numbers of seriously disturbed individuals wind up in jails. This information is useful in making decisions about the level of programs and services 

required by the inmate population and in identifying 

the types of people staff should be trained to manage. 

It is also helpful in prearchitectural programming, as 

these individuals have special housing requirements. 

Other actors in the criminal justice system and the 

community are also interested in this information.
3–10 HOW TO COLLECT AND ANALYZE DATA

Substance Abuse Status. Substance Abuse Status 

consists of several discrete data elements, including 

the presence of past or present substance abuse problems, types of substances abused, whether there is a 

history of treatment for substance abuse, whether substance abuse is related to the reason the prisoner is in 

custody, whether the prisoner was under the influence 

when the crime was committed or under the influence 

when arrested and booked, and interest in treatment 

for substance abuse problems. These data elements are 

useful in making decisions about the kinds and levels 

of programs and services that should be provided for 

the inmate population and can also be used to identify 

areas in which staff will require training. They are also 

of interest to many other actors in the criminal justice 

system.

Release Status. Release Status is a series of data elements that describe how the individual was released 

from the facility, including the location to which the 

prisoner was released; his/her legal status at the time 

of release (i.e., pretrial vs. posttrial); if pretrial, the 

bond type and amount (if a bond release) or release 

on citation; and, if posttrial, whether the prisoner was 

released for “time served,” transferred to another facility, or charges were dismissed. These data elements 

can be particularly helpful in assessing how the local 

criminal justice system is working and in making 

choices about the appropriateness of alternatives to 

incarceration for some of the jail population.

Summary. Inmate Profile Data Elements contain 

some of the most useful information that can be 

gathered in the jail. They can be used to:

Assess the impact (or potential impact) on the 

jail population of alternatives to incarceration 

and additional release options.

Illustrate the impact of criminal justice policy 

and legislative decisions on the jail.

Determine the causes of crowding.

Design and implement a classification system.

Develop a prearchitectural program based on 

needs and describe the new facility’s functions 

and programs.

Many jails do not collect all Inmate Profile Data 

Elements on a routine basis, although some of the data 

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elements are almost certainly recorded in every jail. 

Data of this type is often collected at specific times 

to provide information about specific problems and 

is often collected for only a specially selected portion 

(a sample) of the inmates at the facility. There are 

a variety of ways in which samples can be selected 

in jails. Information about sampling is provided in 

chapter 7.

Particularly in smaller jails, Inmate Profile Data 

Elements are some of the most difficult data to gather 

because of the way most jail record systems are 

structured. This data is also some of the most powerful information you can have. But unless your record 

system is atypical, you will not find a single source 

for the information you need, and much information 

can be lost when an inmate is discharged. If you are to 

capture this information, most likely you will need to 

fill out a special data collection sheet while the individual is in custody.

Operational Data Elements

Operations are central to any correctional facility. 

Operational Data Elements are necessary to:

Complete a staffing analysis.

Change operational policies and practices.

Evaluate organizational efficiency and 

effectiveness.

Assess the “climate” in the facility.

Defend against litigation.

These data elements also can be grouped in “sets” that 

relate to specific operational areas. Some of the most 

common operational data elements are the following:

Transport Set.

Activity Set.

Incident Set.

Personnel Set.

Cost or Budget Set.

Transport Set. The Transport Set consists of data 

elements that provide information about transports 

to court and to other locations. The elements include 

the destinations of transport, reasons for transport, 

number of officers required for the transport, number 

of inmates transported, time in, time out, transport 

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Information That Should Be Collected 3–11CHAPTER THREE

expenditures (e.g., meals, gasoline), and miles traveled. These data elements can be combined to identify 

the staff hours used (from which personnel costs can 

be calculated), the most frequent destinations, and 

the number and cost of medical transports (useful in 

assessing whether providing a medical service inhouse would be more cost efficient than transporting 

to hospitals, doctor or dentist offices, etc.). This data 

is also critical in supporting requests for additional 

staff.

Activity Set. The Activity Set consists of data elements that provide information about routine activities, such as hours of recreation provided, the number 

of inmates who participated in recreational programming, the number and types of visits provided, the 

hours various types of programs are provided, the 

number of inmates served by various types of programs, etc. These data elements, like the Transport 

data elements, can be linked to staff time to identify 

the cost of programs and services. Elements in this 

data set may be useful in grant applications, at budget 

time, and in documenting compliance with specific 

standards.

Incident Set. The Incident Set consists of data elements that provide information about the crimes and 

incidents (significant events) that occur in the jail, 

including reason for incident; number of inmates 

involved; number of staff involved; location of incident; time of incident; date of incident; action(s), 

including disciplinary hearings, taken 

as a result of the incident; character of the incident 

(violent or nonviolent); and injuries and/or property 

damage resulting from the incident. 

You should already be documenting these data 

elements in a systematic way to use if legal action 

is taken against the jail. By analyzing these data 

elements, you may be able to identify problems 

that lead to incidents by identifying specific places, 

times, days, or staffing patterns when the incidents 

occur. Corrective action can be taken if patterns are 

identified.

Personnel Set. The Personnel Set summarizes all personnel changes and practices, including the number 

of sick days used per employee; vacation days; compensatory time authorized and taken; other leave time 

(e.g., military, maternity) authorized and used; rate of 

turnover, both by those leaving the department and by 

those vacating a position through transfer or promotion but remaining in the department; and demographic information (age, sex, race, etc.). This is another set 

of data elements that you already record in your personnel records. They are critical for establishing your 

net annual work hours or shift relief factor (how many 

people it actually takes to fill one post that operates 

24 hours a day, 365 days a year). They are also critical 

for addressing employee turnover, which is an ongoing problem in many jails.

Cost or Budget Set. The Cost or Budget Set consists 

of data elements associated with the cost of running 

the jail, including all budget information, particularly 

categorical expenditures. Your budget or accounting 

system should divide the dollars you are authorized 

into categories, such as personnel, fringe benefits, 

food, medical, inmate supplies, etc. Most budgets 

provide a considerable amount of detail about how 

the jail’s money is spent.2 These data elements are 

very useful when translated into ratios with Inmate 

Population Data. Examples of these ratios include 

the cost per inmate per day for meals, medical care, 

and staff, and the cost per inmate for photo processing (mug shots) and other functions. They are often 

used to evaluate the efficiency and effectiveness of jail 

operations.

Summary. Operational data elements are most frequently used for in-house purposes: to evaluate and 

monitor organizational performance, locate organizational trouble spots before they get out of hand, and 

document current problems that can then be rectified. 

However, these data elements have some obvious 

uses outside the organization, primarily in developing 

effective budget requests. In these days of limited and 

declining government resources, good budget justifications are critical. This type of data can help you prepare a well-documented rationale for funding.

2 For data analysis purposes, you need detail in the budget categories to 

track jail expenses separately from other costs. However, depending on the 

“rules” in your jurisdiction, it may be better to group expenses by categories. Make sure you can track internal costs in enough detail to make the 

information useful!
3–12 HOW TO COLLECT AND ANALYZE DATA

Criminal Justice System Performance Data 

Elements

The policy choices of the other elements of the criminal justice system (primarily law enforcement and 

the courts) determine the size and nature of the jail 

population. Law enforcement practices determine the 

number of people who come into the criminal justice 

system in general and the jail in particular. On the 

other hand, the court and other key officials in the 

justice system, including the prosecutor, probation, 

the defense, and judges determine the jail population 

by controlling the amount of time that inmates spend 

in the facility. This leaves the jail with little ability on 

its own to regulate the jail population.

The jail’s influence over the size of the inmate population is indirect at best. As a result, it is essential to 

delve into the practices of the other elements of the 

criminal justice system to understand what is happening with the jail population. This is particularly important when planning a new facility and when trying to 

reduce crowding. Many data elements reveal a great 

deal about the policy and practices of other components of the system.

Law enforcement data elements. Data elements that 

are helpful in assessing the activity of law enforcement agencies are Police Agency, Arrest Type, 

Summons and Citation in Lieu of Arrest, TrafficRelated Arrests, and Alcohol-Related Arrests.

Police Agency identifies the agency or police 

department responsible for the arrest. It is generally used to determine which agency accounts for 

what percentage of the bookings at the jail, and 

on what charges. Comparisons are often made 

from agency to agency or against a common set 

of arrest criteria.

Arrest Type attempts to document how the 

agency uses the jail. General categories of Arrest 

Type could include “book and releases” or station 

house bookings (in which prisoners are booked 

and prints and photographs done prior to immediate release from the jail upon signing a citation), 

warrant arrests, and on-view arrests. This data 

element is often used in conjunction with Charges 

and other Inmate Profile Data Elements to determine if there are individuals being jailed who 

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are candidates for release on citation or diversion 

alternatives.

Summons and Citation in Lieu of Arrest refers 

to the number of summonses and citations written 

by law enforcement agencies. It is most commonly used in conjunction with Charges to determine 

if there are individuals who might be diverted 

from the jail.

Traffic-Related Arrests is used to identify arrests 

associated with a traffic violation in which a summons is not used. It is generally used in conjunction with Charges and Arrest Type to determine if 

there are individuals who are candidates for diversion from the jail.

Alcohol-Related Arrests identifies whether 

alcohol abuse was related to the arrest. It is generally used in conjunction with Charges, Arrest 

Type, and Inmate Profile Data Elements to determine if placement in a detoxification facility is a 

viable alternative to arrest.

These data elements can help determine which agency 

is the chief “supplier” of the jail’s population and 

assess how each department uses the jail. They can 

also help assess the viability of diversion programs 

and the use of summonses and citations as ways of 

reducing the jail population. This is especially true 

when the data identify the amount of time that 

individuals who are potential diversion candidates 

actually spend in the jail. Such information will help 

you assess whether the increased use of summons and 

citation (or other options) will significantly reduce 

crowding or the need for additional bed space.

Court data elements. Court data elements are critical 

to managing jail populations. They are often the hardest to collect because they are kept in another record 

system, which, like the jail’s, is seldom organized to 

facilitate data collection. Since the court affects the 

amount of time that prisoners spend in the jail, Length 

of Stay and Jail Days become even more important 

when trying to assess the impact of changes in court 

policy and practice. Furthermore, it is critical to be 

able to identify the Length of Stay or the number of 

Jail Days of the group you are trying to divert. The 

average LOS for the whole jail will only give you 

misleading information. Thus, either LOS or Jail Days 

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Information That Should Be Collected 3–13CHAPTER THREE

information must be collected with the court information. The court data elements include Bonding Set; 

Failure To Appear (FTA) Rate; Offenses for Which 

Jail Sentences are Given; Type of Release; Court of 

Jurisdiction; Judge; Defense Attorney and Prosecutor; 

Offenses on Arrest, Filing, and Sentencing; Sentence; 

and Critical Events.

The Bonding Set constitutes data relating to the 

bonding practices of the criminal justice system, 

including the types of bonds used in the jurisdiction, offenses for which bond is granted, the level 

at which bond is set, and the number of bond 

reductions. These data elements are often used 

with the Inmate Profile Data Elements, especially 

Employment Status, Residence, and Marital 

Status. Frequently two other elements are added: 

Number of Prior Arrests and Number of Prior 

Failures to Appears. The primary application of 

these data elements is to determine if changes 

in bonding practices (e.g., the use of release on 

recognizance bonds or reduction in bond) would 

reduce the jail population,

Failure To Appear Rate measures the number 

of people who were granted bond and later failed 

to return for court (also potentially the number of 

bond revocations) in comparison to the total number of bonds and citations given. Local criminal 

justice systems should determine what an acceptable FTA Rate is for their jurisdiction. This data 

element can be used to compare FTA Rates for 

individuals released through different mechanisms 

(e.g., those released with a street summons vs. 

those released after screening at the jail).

Offenses for Which Jail Sentences Are Given

identifies the charges that resulted in sentences 

at the local jail. This data element is often used in 

conjunction with other data elements (e.g., Judge 

or Court) to assess differences in sentencing 

practices. If your jurisdiction has sentencing 

guidelines, this data element would be useful in 

assessing the degree to which they are followed.

Type of Release describes how inmates are 

released (e.g., cash bond, own recognizance, sentence served, etc.). Qualitative information about 

the system is needed to help interpret this data 

element.

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Court of Jurisdiction identifies the court with 

responsibility for the case. This data element can 

be useful in comparing the processing efficiency 

of various courts. Different jurisdictions do not 

use the jail in the same way. If changes in practices are needed, it is critical to identify who and 

what must change. Both Court of Jurisdiction 

and Judge require some “special handling” to be 

useful. The special handling requires analyzing 

these data elements in the context of charge, prior 

offenses, FTAs and other violations, and other 

related variables.

Judge identifies which judge in a court has 

responsibility for the case. This data element 

should be handled with great care, because it 

can raise very sensitive issues around judicial 

discretion.

Defense Attorney and Prosecutor identify the 

attorneys involved in the case. Defense Attorney 

can be categorized as public defender, other 

appointed counsel, retained counsel, or none. 

Attorneys frequently manage cases differently 

and require different amounts of processing time. 

Changes in processing speed can be critical to 

reducing crowding at the jail. This data element 

is needed to identify points at which extra manpower or a change in policy could influence the 

jail population.

Offenses on Arrest, Filing, and Sentencing are 

all charges. “Charge” is a tricky data element to 

interpret even though its definition seems relatively clear. It needs to be interpreted in the context of 

information you have about criminal justice policy 

and practice. Some of the issues that emerge 

around this data element include:

Does law enforcement incorrectly charge 

the persons they arrest?

Does law enforcement “overcharge” the 

persons they arrest?

Does the prosecutor engage in plea 

bargaining?

Do law enforcement and the prosecutor 

charge bargain?

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3–14 HOW TO COLLECT AND ANALYZE DATA

Often, the trickiest part of this data element is its 

political sensitivity. Use caution when you present 

information on this data element.

Sentence describes the sentence imposed by the 

court. It should include all alternative sentences 

used in the jurisdiction and the length of the 

sentence.

Critical Events are the dates of critical events 

in the court process and their outcomes. Since 

the length of time needed to process a case is 

the major way in which courts affect the jail 

population, it is critical to identify the amount of 

time each aspect of the process requires. The critical event data elements include the dates of arrest, 

first appearance, arraignment, trial, and sentencing. This data set should also include Number of 

Continuances (and on whose request). Outcomes 

of the major events should be categorized to identify what happened. Data elements should allow 

you to differentiate between cases in which a 

plea was entered, those in which a trial actually 

occurred, and those dismissed (with and without 

prejudice).

Both law enforcement and court data serve a critical 

purpose in planning. To establish an appropriate level 

of confidence in any planning or forecasting done 

based on jail statistics, it is important to consider how 

much the behavior of the other parts of the system has 

varied. Law Enforcement and Court data elements 

provide the best means of assessing the degree to 

which the behavior of other parts of the criminal 

justice system has fluctuated over the years and 

determining the actual variations in their practices.

A Word About Collecting Data in 

Other Agencies

Develop a strategy to make good use of any data you 

plan to collect in another criminal justice agency. 

First, the information you need to collect is not your 

information; the other agency owns it. The owner may 

view your interest in this information somewhat skeptically. Most sheriffs, for instance, would wonder why 

a chief judge was interested in average daily jail population data. Second, all record systems have their own 

quirks, and you are not likely to know what another 

agency’s are. As a result, you can easily misinterpret 

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the information. Third, significant technical issues are 

involved with getting different types of computers and 

computer programs to communicate with each other.

Many data collections that involve multiple agencies 

can benefit from a collaborative approach in which 

all of the agencies involved participate. In addition 

to addressing issues about the motivation for the data 

collection and problems of interpreting and accessing 

information, a collaborative approach tends to build 

commitment to both the results of the analysis and the 

solutions that will come out of the process. When key 

participants are not included in the data collection, the 

outcome is very predictable—they are likely to discount the data, the process by which it was gathered, 

or the analysis. In these situations, the participants 

tend to disagree about the data and not address the 

issue that is at the root of the problem.

Conclusion

Sometimes the hardest part of any activity is getting 

started. Knowing what data elements can be collected 

and how other jurisdictions have used this data should 

help sheriffs and jail administrators decide what data 

elements they need to collect to solve specific problems. In many cases, the data elements discussed will 

more than cover the spectrum. Some systems will 

not require more than the Inmate Population Data 

Elements, a few of the Inmate Profile Data Elements, 

and the Operational Data Elements. Other systems 

that are confronting specific problems will need to 

do a special data collection that includes both Inmate 

Profile Data elements and the Criminal Justice System 

Performance elements.

You may sometimes be confronted with specific issues 

or problems that were not addressed here. You should 

define the data elements needed, using the methods 

suggested in the earlier section, “Identifying Data 

Elements from Problem Statements.” The program 

evaluation and research texts cited in appendix B can 

provide some additional help. By now, you may be 

wondering when you will find the time to do a data 

collection and, if you hire someone, how much it will 

cost. For those who are not concerned about those 

issues, go directly to chapter 5. For those who are 

concerned about those issues, see chapter 4 for information on data collection strategies.
c h a p t e r f o u r

Preparing for the 

Data Collection
Data Collection Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3

Where To Find the Skills You Need . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-4

Working With People From Outside the System . . . . . . . . . . . . . . . . . . . . . . . . 4-5

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6
4–

P r e p a r i n g f o r t h e D a t a C o l l e c t i o n

When the data elements to be collected have been identified, the next big task is to 

decide how to collect them. This is not the same thing as the format in which they 

should be collected, nor does it mean identifying the sources of the information. This 

chapter offers suggestions about who might find the information and record it on your 

manual information system’s data collection sheet or enter it in the computer. What you 

are really looking for is a set of skills and people who possess them.

Data Collection Skills

At this point, it is important to differentiate between the routine information-gathering 

activities the jail will perform to maintain a modest but effective manual or automated 

information system and the special data collections that are done for specific purposes 

(e.g., planning for a new facility or reducing crowding in the jail).

Let’s start with maintaining the management information system. Assume that the data 

elements to be routinely captured have already been determined and that the mechanisms for recording this information have been designed. (Chapter 5 provides guidelines for sheriffs and jail administrators who are interested.) But if the organization 

does not have the skills to maintain the system, there is no point in setting it up in the 

first place.

The skills associated with maintaining the information system are good clerical and/or 

basic computer skills, including:

Ability to read and write legibly.

Ability to do simple math (mostly adding and subtracting) with the assistance 

of a calculator (if not using a computer).

Ability to organize work.

Working knowledge of a spreadsheet and/or database program (if using a 

computer).

Commitment to doing the work on a daily basis.

Once the system is set up, clerical personnel can maintain it.

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c h a p t e r f o u r
4– HOW TO COLLECT AND ANALYZE DATA

Collecting data for a specific study requires a few 

more skills, particularly when the data collection 

involves a lot of data and information must be collected from more than one source. Still, these skills 

are routinely found in jails, other agencies of county 

government, criminal justice planning agencies, consulting firms, and the community. The skills you will 

need are:

Data collecting and coding skills. Once again, 

these are mostly good clerical skills. It is very 

helpful if the collector or coder has some knowledge of jail operations and criminal justice system 

terminology. Data collectors and coders should 

also know what information is already being collected in the jail. They should be resourceful people who are not afraid to ask questions about the 

information they uncover. They will need access 

to people within the system who can answer their 

questions. Depending on the size, scope, and time 

constraints of the data collection, this person will 

probably need to work at least half-time on the 

data collection while tracking down information.

Coordinating skills. These are mostly administrative skills, enhanced by some technical expertise. 

The data collection coordinator needs to have an 

understanding of basic descriptive statistics, good 

writing skills, some experience in using data to 

convey information and in making presentations, 

and some understanding of the jail. This person 

will most likely present the information. He/she 

will help make some critical data collection 

decisions­—like how big the sample will be, what 

data elements will be included, and which sources 

of information will be used.

Analytical skills. These are largely interpretive 

skills. The person (or preferably persons) involved 

in analyzing the information should be able to 

think logically and see the whole situation in 

terms of the relevant components. These people 

will draw on their experience and knowledge of 

the jail and the criminal justice system. They will 

try to answer the question, “What does this data 

mean to us?”

Data processing skills. These skills could vary 

a great deal depending on the nature of the 

equipment being used for the data collection. 

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At a minimum, data entry skills (typing information into a computer terminal) will be necessary. 

In the case of the Inmate Profile Data Collection 

Forms included in this document, the services of 

a person skilled in use of spreadsheet or database 

programs would be very helpful.

Regardless of the software used, sheriffs and jail 

administrators should work closely with the person 

who will enter and process the data when designing 

the data collection sheet or the computer forms. This 

is to make sure that:

The data can be input as easily as possible.

The computer program being used can process the 

data collected.

Few things are more frustrating than to collect data 

you really want to use only to discover that the computer cannot handle it the way you collected it. This 

is rare, since most common statistical programs can 

process any data that is numerically coded, but it can 

happen. Make sure it does not happen to your data 

collection!

For those who are reluctant to try their hand at a computer, or who do not have access to one, this document 

includes a manual data collection system in appendix 

C. However, it is much faster and easier to use a computer to process data (particularly for a large number 

of cases) than it is to calculate averages on an adding 

machine and use tally sheets to figure out frequency 

distributions. When this manual was first published in 

1982, few sheriffs or jail administrators had personal 

computers powerful enough and with enough memory 

to do major data collections. Few of the automated 

systems allowed someone other than a computer programmer to examine and analyze data. Now, most 

sheriffs and jail administrators have access to computer hardware and software capable of handling even 

large data collections. However, if you do not have a 

computer, this chapter will give you some ideas about 

where to find one for free or for a low cost to pay for 

the amount of time you actually use it.

Where To Find the Skills You Need

Each jail, criminal justice system, county, and community is unique. As a result, it is not possible to tell 

sheriffs and jail administrators specifically where they 

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Preparing for the Data Collection 4–CHAPTER FOUR

can find people with the necessary skills in their communities. A few lucky ones may be able to find all 

the skills in one person, but most will find that two 

or more people are needed. While it may be easier to 

coordinate the data collection if only one person is 

doing the multiple tasks, a team approach to the data 

collection has distinct advantages. A number of strategies can be pursued to locate people with the skills 

needed. 

Community volunteers. Many communities have 

volunteer offices that coordinate placement of 

people who want to volunteer their skills for public service. This is a source of clerical help that 

cannot be overlooked by jails, particularly those 

with little or no clerical help of their own. If the 

county does not have a volunteer office, sheriffs 

and jail administrators should consider recruiting 

volunteers on their own. Churches often prove to 

be good sources of volunteer help. This chapter 

offers some suggestions to make working with 

volunteers in jails easier for everyone.

Law enforcement reserves. While most Reserves 

are interested in the more exciting aspects of 

working for the Sheriffs Department, they are a 

potential source of help for some special projects.

Student volunteers. Many community colleges 

have criminal justice course work, and students 

in these programs are ideal volunteers. Not only 

do they work for nothing, but they also are usually very motivated because they will be earning 

credits or completing a required internship, and 

their professor serves as an outside supervisor 

who can help ensure that they cooperate and participate fully. You also get the added benefit of 

prescreening individuals who may apply for jobs 

in the near future. The same colleges often have 

computer centers and computer classes, with students who need practical experience. A number of 

counties have found students to enter all the data 

and a professor or graduate student with the skills 

to complete the computer run to process the data. 

The only thing these counties had to pay for was 

the computer time, which, in several cases, cost 

the county less than $200.

Temporary clerical help. While temporary help 

does cost money, most temps do not cost as much 

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as a regular employee and may be needed for a 

relatively short period of time (e.g., 3 to 4 weeks).

Other county or city agencies. Other county 

agencies may be willing to lend clerical staff to 

your department for a short period of time, particularly if they have an interest in the outcome of 

the data collection. This may be very beneficial 

in the criminal justice system, since data collectors and coders who are familiar with the court 

or prosecutor’s record systems may collect data 

there more efficiently than individuals who do 

not know the “ins and outs” of the system. If the 

county has a data processing department, assisting 

the Sheriffs Department or the jail with this kind 

of an effort may be part of its mission.

Clerical staff. One of the most underused 

resources in criminal justice agencies is clerical personnel. Not only do they have the necessary skills, but they also have firsthand working 

knowledge of the criminal justice system, and 

they know where to find information. Training the 

clerical staff to collect and code data may have 

many benefits, both to the organization and to the 

personnel involved, especially when collecting 

data outside of your own agency.

Department personnel with technical skills.

More and more people in all professions, including in the sheriffs department, are computer literate and enjoy activities like “surfing the Net” in 

their spare time. Most people graduating from 

high school and junior college have more than a 

passing acquaintance with computer technology, 

and many have been introduced to statistics as 

well. Although these activities may not be in their 

job description, except in the phrase “other duties 

as assigned,” they can be a valuable resource. You 

will need to decide whether this should become 

part of their official job or done on an overtime 

basis.

Working With People From Outside 

the System

Many criminal justice organizations use volunteers 

from outside the criminal justice system to help in 

special data collection efforts, particularly those associated with planning new jails. Citizens’ advisory 

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4– HOW TO COLLECT AND ANALYZE DATA

boards often take an active role in this process. While 

this manual cannot provide all the information needed 

to successfully initiate a volunteer program, the 

National Institute of Corrections Information Center 

has information that could help you get started. In the 

meantime, here are a few tips that may help.

Speak English, not "jail." Criminal justice 

professionals have a language of their own. It is 

filled with abbreviations that are routinely used by 

people in the system, but are unfamiliar to outsiders. If you hope to collect valid information, make 

sure that everyone understands what you are talking about.

Find out who you are dealing with. Set up a 

mechanism to screen volunteers. Most criminal 

justice organizations that use volunteers conduct 

the same background investigation on them as for 

employees. This is not to suggest that you should 

require psychological evaluations of volunteers, 

but routine reference checks and clearances 

should be part of the volunteer selection process.

Provide orientation and training. Most people 

from outside the criminal justice system find 

jails very intimidating places to be. Volunteers 

will need an orientation to the jail, as well as a 

place to work in which they feel safe and relatively comfortable. Training must be provided 

in the tasks that the volunteers are to perform. 

For example, if volunteers collect and code data, 

training in good coding procedures and the codes 

being used is mandatory. If they will use the computer, training in the appropriate procedures is 

needed. If volunteers are going to work in the jail 

itself, they will need an explanation of the security requirements that apply to them and a clear 

explanation of the behavior expected of them 

while in the jail. They should also be clear about 

unacceptable behavior.

Check your confidentiality requirements.

Before recruiting volunteers, check your State’s 

statutes regarding the confidentiality of inmate 

records. If there is any question regarding the 

use of volunteers, an opinion from the County 

Attorney may be necessary. Generally, the laws 

regarding the confidentiality of records allow 

individuals to gather information from them for 

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research purposes. To ensure the confidentiality 

of the records once the information is gathered, 

remove any identifying information, such as a 

name, from the data collection sheet. When in 

doubt, use caution and good sense. 

Accept the volunteers. Law enforcement and 

correctional agencies sometimes find it very hard 

to accept “outsiders.” This is not very conducive 

to developing good working relationships with 

volunteer groups. Volunteers will be doing you 

and your jail a favor, and being supportive of 

them is critical.

Conclusion

Doing a good data collection in-house is hard work. 

It is an area in which counties frequently seek outside 

help. And there is nothing wrong with securing professional help in this area. Sometimes it may be much 

more practical to seek outside assistance, as when 

the jail lacks the necessary skills or when there is not 

enough time to do the study in-house.

However, there are a number of benefits to doing the 

data collection in-house:

Since you understand the criminal justice system 

better than outsiders, you may be able to identify 

better sources of information than they could. You 

may have access to information that outsiders do 

not and will never be privy to. You will not need 

help to understand either the language of corrections or the peculiarities of your organization.

Your familiarity with the criminal justice system 

may let you make better “educated guesses” that 

shape the research.

You understand the politics of your organization better than an outsider and will probably 

know more accurately the alternatives that will be 

acceptable to the criminal justice system.

Outside consultants cost money.

Most important, if you do your own data collection, you are far more likely to believe the findings and, as a result, it is more likely that some 

action will be taken.

As you might expect, there are also drawbacks to 

doing your own data collection:

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Preparing for the Data Collection 4–CHAPTER FOUR

Doing a data collection involves an investment of 

time and energy. And data collection is something 

that should be done right if you want to be sure 

that the results are accurate. 

Because you are so close to the situation, your 

interpretation of the data may be somewhat slanted. Having an outsider participate in the analysis 

and interpretation of the information can guard 

against bias.

If you are not familiar with what is happening in 

similar jurisdictions, it may be difficult to identify 

data that suggests something unusual is happening 

in your criminal justice system. To illustrate, if 

you had little knowledge of other jail populations, 

you might not think it unusual that 40 percent 

of your jail population was female. An outsider 

who has seen data from other jails can help guard 

against this problem.

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In deciding whether you are going to do your own 

data collection, you must realistically determine if 

you have the people with the right skills and the time 

to do the work. Chapter 5 identifies places to locate 

the necessary information and tips on useful data 

collection formats.
c h a p t e r f i v e

How To Locate and 

Capture Information
Jail Information Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3

Logs and Forms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-7

The Problem With Data Sources—and Some Solutions . . . . . . . . . . . . . . . . . . . . . . 5-8

Problem 1: Data Elements Are Scattered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-8

Problem 2: Data Is Missing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-8

Problem 3: Information on the Form Is Wrong . . . . . . . . . . . . . . . . . . . . . . . . . 5-9

Problem 4: Data Elements Are Poorly Defined . . . . . . . . . . . . . . . . . . . . . . . . . 5-9

Problem 5: The Handwriting Is Illegible . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10

Problem 6: No One Keeps Data for the Whole System . . . . . . . . . . . . . . . . . 5-10

Setting Up an Information System for the Jail . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10

Elements of the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10

System Startup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10

Special Issue Data Collections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-18

Designing Special Data Collection Forms and Code Books . . . . . . . . . . . . . . . . . . 5-19

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-24
5–

H o w T o L o c a t e a n d C a p t u r e I n f o r m a t i o n

In the past, people often began data collections by grabbing a pad and pencil, starting 

a tally sheet, and rummaging through files. Six pages later, unable to read their notes, 

they designed a form on which the data could be recorded. Halfway through the data 

collection, they discovered that they did not leave enough space for one of the data elements. At this point, they often changed their minds about doing the data collection.

On today’s information highway, people begin data collections by turning on a computer, bringing up their favorite database or spreadsheet program, entering some data 

element names, and rummaging through either computer or paper files. If, halfway 

through the data collection, they discover that they did not leave enough space for 

one of the data elements, it is no problem—just insert a new column or field and 

re-rummage through the files. But what if they cannot remember how they coded 

“Breaking and Entering” yesterday? Or, when they attempt their first sort, they discover—oops!—not enough memory. After trying to recode all the alpha characters as 

numeric, the data collection is ultimately abandoned. 

This chapter suggests ways to avoid these difficulties by:

Identifying documents, forms, and logs commonly found in local jails which 

are frequent sources for the data elements identified earlier.

Suggesting how to implement an automated or manual information system, 

providing samples of the formats or forms that might be used in such a system, 

and estimating the time involved in maintaining it.

Discussing guidelines for the development of data collection formats or sheets 

an code books. 

Jail Information Sources

The first step in locating information is to identify all the places where it is already 

being captured or recorded on a routine basis. One way to do this is to take blank 

copies of every form routinely used and list the data elements that are specified on 

them. Next, review all the activity logs (such as the Booking Log, Release Log, Daily 

Activity Log or Shift Activity Sheet, etc.) and do the same. Do not list items that are 

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c h a p t e r f i v e
5– HOW TO COLLECT AND ANALYZE DATA

normally recorded in any narrative sections, because 

they will probably not be recorded on all the forms 

(and certainly will not be found in the same place on 

the forms) and reading the entire narrative to extract 

one piece of information is far too time-consuming for 

most data collections.

Figure 5-1 is a traditional Incident Report form. The 

data elements that officers must fill out on this form are:

Inmate’s name.

Inmate’s booking number.

Inmate’s housing assignment at time of 

occurrence.

Date of incident.

Time of incident.

Date report is completed.

Time report is completed.

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Figure 5-1 Sample Incident Report

Any County Sheriff’s Department

Jail Division Incident Report

Inmate Name ____________________________ Person Identification # _______________________________________

Booking # _________________________ Housing/Cell Assignment ________________________________________

Incident Date _______________________ Time _________________ Reported by _____________________________

Report Date ________________________ Time _________________ Supervisor ______________________________

Description (who was involved, what happened, where the incident occurred, what caused it, etc.)

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

Disciplinary Action Taken _____________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

Rule Violation Major _________ Minor ___ Not applicable ____________________________________

New Offense Yes __________ No _______________________________________________________
How To Locate and Capture Information 5– CHAPTER FIVE

Reviewing the forms will almost certainly reveal that 

a number of data elements are already being collected. However, some essential data elements may 

be camouflaged in the narrative section of the form. 

If so, make a list of these items and consider revising 

the form so that it specifically asks for the essential 

missing items. For example, in the case of the Incident 

Report form in figure 5-1, a data element that is missing and highly desirable is Location of Incident. While 

this is normally included in the narrative section, it is 

often not very visible. Sometimes officers forget to 

record the location or list several locations. This data 

element should be placed on the list of missing data 

elements. Later examples of this form suggest several 

ways in which the form could be modified.

It may be that a number of common data elements are 

not collected in your facility. If few forms are used 

and the only log book is the Booking Log, your jail 

does not have a very developed recordkeeping system. 

While that is beyond the scope of this document, it 

should be a matter of great concern to sheriffs and jail 

administrators. With the prevalence of litigation against 

jails and prisons, the sheriff, jail administrator, county 

commissioners, and the county itself are in a very vulnerable position without adequate documentation.

Logs and Forms

The logs and forms described below capture the data 

elements commonly collected in jails and provide 

documentation in the event of litigation.

Booking Log. A Booking Log is a chronological 

record of all persons who are arrested and booked at 

the facility. It typically captures:

Date.

Time prisoner accepted.

Arresting agency and officer.

Name of prisoner.

Date of birth.

Place of birth.

Charges.

Court of jurisdiction.

Bond for each charge.

Booking number.

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Do not be surprised if the Booking Log used in your 

jail is not exactly like this. The information recorded 

in the Booking Log will vary from county to county, 

but it should contain most of these data elements. The 

Booking Log is an important source document for 

both the Inmate Population and Inmate Profile Data 

Elements.

Release Log. A Release Log is a chronological record 

of all individuals who are released from the facility. It 

typically includes:

Date and time of release.

Name of prisoner.

Booking number.

Means of release (including information about 

bond).

Release Logs are often combined with Booking Logs 

so that release information is recorded on the same 

line as each inmate’s booking information.

Arrest Report. The Arrest Report provides basic 

identifying and some demographic information about 

persons who have been arrested, a statement of the 

authority under which they are detained, and some 

information about the details of the arrest. Jails usually keep a copy of the Arrest Report (or a comparable 

summary) as their documentation of the reason for 

the arrest. This practice enables them to prove that 

the jail is not unlawfully detaining anyone. The Arrest 

Report is a good source of some Inmate Profile Data 

Elements.

A word of caution is in order. Although there 

is some evidence that information that comes 

directly from people without any additional 

documentation (“self-report data” in research 

terminology) is quite reliable, it is important 

to note that arresting officers get information 

at a time when prisoners have good reasons 

to conceal the truth and make themselves 

“look good.” As a result, data elements like 

employment status or education level may not 

be accurate.

Bond Log. A Bond Log is a chronological record of 

all bonds that are paid at the jail. It usually includes:

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5– HOW TO COLLECT AND ANALYZE DATA

Name.

Date.

Time.

Charge (and bond for each).

Type and amount of bond.

Return court date.

Jurisdiction.

Officer’s initials.

A Medical Screening Form. The results of the 

intake medical screening are recorded on the Medical 

Screening Form. It usually includes:

The inmate’s responses to a series of questions 

designed to reveal the presence of any medical conditions requiring immediate treatment or 

action by jail staff.

A brief description of the inmate’s physical state.

A brief medical history, including questions about 

substance abuse, mental health problems, and 

any treatment the inmate was receiving or had 

received in the past.

This form is one of the few places to find Inmate 

Profile Data Elements relating to medical, mental 

health, and substance abuse problems.

Classification Screening Form. This form is used to 

record much information about the prisoner, including:

Demographic information.

Criminal history.

Current legal status.

Medical, psychological, and substance abuse 

problems, and treatment recommendations.

Program and service needs.

Classification Screening Forms may be relatively 

simple forms or very detailed documents resulting 

from an indepth interview. They are excellent sources 

of Inmate Profile Data Elements.

Daily Activity Log (or Shift Activity Sheet).

This is a chronological record of all operational 

activities. Whether the jail uses a Daily Activity Log 

or Shift Activity Sheet will depend on the size of the 

facility. Small facilities (fewer than 50 beds) will 

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probably use a Daily Activity Log; larger facilities 

will probably use a Shift Activity Sheet, which will 

probably need to subdivide the data elements listed 

below into separate logs: 

Court activity (who went to court for what type 

of hearing).

Master control activity (results of routine head 

counts, inmate movement records, results of any 

routine tests done on jail equipment, notation of 

any unusual events—e.g., smoke detectors going 

off, etc.).

Physical plant checks (routine checks of all locks, 

windows, security equipment, life safety equipment, etc.).

Laundry exchange (dates of distribution of 

bedding and/or uniforms to prisoners).

Outside agency inspections (dates and results 

of routine inspections by state inspectors, fire 

marshals, public health officers, etc.).

Program activity records (date, time, type of 

program—e.g., recreation, religious services, 

etc.—and identity/number of inmates attending).

Other than the head counts, which would provide 

Average Daily Population, this information does not 

relate directly to any of the data sets identified in this 

manual. There is a great deal of variation in what 

these logs are like and how they are maintained. Some 

jurisdictions have automated this log in a way that 

allows them to extract information in summary form 

for management decisionmaking purposes. That is an 

ideal situation. Others have automated the information 

using word processing software, which makes it 

difficult to analyze without having a person extract 

and review the information. Others continue to use 

a manual log, which has the same drawbacks as a log 

in a word processing document. In both word processing and narrative formats, information is often 

inadvertently omitted or not recorded in the same way. 

These problems make logs of this type difficult to use 

for data collection and analysis.

Visitor’s Log. This is a chronological record of visitors to the facility. Both professional and family visitors should be entered in the Visitor’s Log. Data elements found here include:

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How To Locate and Capture Information 5–CHAPTER FIVE

Name of visitor.

Name of inmate visited.

Relationship to visitor.

Date.

Time.

This is a good source of information about the level 

of activities in the facility, and its review is a critical 

component of a staffing analysis.

Program or Activity Log. This log is a chronological 

record of each type of program or service provided in 

the facility. These logs are often kept for activities that 

are required by standards (to document compliance), 

that are associated with grant funding (such as some 

drug and alcohol programs), or for budget purposes. 

Data elements found here include:

Name or title of program.

Number of inmates participating.

Name or other identifier of service provider.

Date.

Time or duration of program.

Like the Visitor’s Log, these logs are good measures 

of activity in the facility. They are useful for budgetary purposes, defense against possible litigation, program development, and staffing analysis.

Transport Log. The Transport Log is a chronological 

record of prisoner transportation activities. It includes 

all the data elements identified in the Transport Set 

mentioned in chapter 3. It is often the only source of 

that information and is critical for staffing analysis. 

Facilities that do many transports may want to develop 

a Transport Form to be completed for each transport 

and then compiled in a monthly report. It should be 

possible to analyze a specific set of transports related 

to courts.

Incident Log or Incident Report Form. This is a 

chronological record or file of all “out-of-the-ordinary 

events” in the facility, including fires, assaults, injuries, contraband, major and minor rule infractions, suicide attempts, violent behavior, psychiatric problems, 

etc. It should include all the data elements noted in the 

Incident Data Set described in chapter 3. This is often 

the only source of information about these events.

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Personnel Records. These records document all personnel actions taken, including:

Hiring (date, salary, position).

Terminations (date, position, type, reason).

Demographic information.

These are normally kept in each individual employees’

files; they are an important source of information for 

the Personnel Set. One way to facilitate using these 

documents (without having to go through every file) 

is to develop a Personnel Notebook with a face sheet 

for each employee. Copies of all personnel action 

forms can be placed in the notebook behind each 

employee’s face sheet with a special section of the 

notebook for the sheets of all employees who are no 

longer with the agency.

Budget Records. These records document budget 

allocations and expenditures. The budget should be 

divided into categories so that costs attached to personnel and salaries, fringe benefits, food, medical 

care, inmate supplies, etc., can be used to determine 

jail costs per inmate.

Jail List or Roster. This is a daily list of:

All inmates detained at the jail, including their 

charge status, legal status: specific charge, any 

holds, housing location, gender, and age.

All bookings.

Releases and transfers (means of release; type 

and amount if a bond).

This list is primarily put together for others in the 

criminal justice system and should be sent to all 

courts, the public defender, the prosecutor, law 

enforcement agencies, etc. It is a source of both 

Inmate Population Data and Inmate Profile Data. 

Maintaining this document is not as time consuming 

as might be imagined, particularly considering database (or even word processing) software capabilities. 

For the unautomated jail, a little creative use of correction fluid and the copy machine makes it possible 

to type only one new copy a week.

Summary

The documents listed above are found in most jails. 

The forms and titles may vary, but you should be 

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5– HOW TO COLLECT AND ANALYZE DATA

able to identify them by their function. If you have 

questions about recordkeeping systems, the National 

Institute of Corrections (NIC) Information Center 

(800–877–1461) has additional resources that might 

help you.

In terms of data collection efforts, however, many 

of the forms and logs routinely kept by the jail are 

not particularly helpful. The reliance on narrative to 

record information is too time consuming and often 

results in omissions. The most useful information 

sources for data collections tend to be:

The Arrest Report.

The Booking Log.

The Incident Log or Report Form.

The Transport Log or Form.

The Classification Screening Form.

The Jail List or Roster.

The Problem With Data Sources—

and Some Solutions

The documents kept in the jail provide little information about overall performance of the criminal justice 

system. It is possible to find out something about 

arrest, bonding practices, and sentencing for which 

jail time is given, but the whole picture is not available. As the jail data collection begins, some problems 

or quirks will emerge, because no system is perfect. 

Common problems are discussed below, but because 

each jail and criminal justice system is unique, a particular problem may not be included. However, the 

problems listed here are shared by virtually every 

criminal justice system. Some of them can be managed; others can severely damage the integrity of the 

data collection and should be avoided at all costs.

Problem 1: Data Elements Are Scattered

Typically, when jail data is being collected, the data 

elements are scattered among information sources. 

Some elements are available only outside the jail. 

Not only does this increase the amount of time 

required to collect the data, but it sets other problems 

in motion.

Even within one criminal justice system, the same 

term can have different meanings. For example, the 

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term “charge” has distinctly different meanings to 

law enforcement, the prosecuting attorney, and the 

courts. To avoid comparing “apples and oranges,” 

sheriffs and jail administrators who use more than one 

source of information must make sure that everyone 

uses the same definition for the data elements they 

are collecting. Errors of this type can lead to mistaken 

conclusions in spite of the most sophisticated computer 

programs and statistical analysis.

Problem 2: Data Is Missing

Missing data is a common problem and one of the 

most frustrating. When the collecting and coding persons finally find the right Arrest Report in the thick 

file on one of the jail’s regular prisoners, they may 

discover that the one piece of information they are 

looking for is missing.

Missing data is frustrating from another perspective. 

Let’s assume that a good sample of 500 prisoners has 

been developed for an Inmate Profile Data collection. 

Unfortunately, because a lot of the data is missing, 

only 50 of the files have any information about one of 

the variables (e.g., employment status). Assuming that 

the information gathered about the 50 cases applies 

equally well to the other 450 people in the sample 

would be a serious mistake. Researchers consider this 

an “external validity” problem. While information is 

available about some of the prisoners, no evidence 

suggests that the results can be generalized to the 

other prisoners.

In fact there may be good reason to believe that the 

50 prisoners who provided that information might 

be different from the other 450. Maybe they were 

unemployed people, charged with serious offenses, 

who stayed in jail a long time. Maybe they were all 

students who were trying to convince the arresting 

officer to take pity on them, or maybe they had good 

employment histories and were trying to make themselves look like good candidates for release on bond.

In those three “maybes” are three significantly different pictures of the employment variable. A little missing data is manageable in a data collection, but a lot 

of missing data makes it very difficult to interpret the 

results of the data collection and analysis. Any more 

than 10 percent missing data is a real problem.
How To Locate and Capture Information 5–CHAPTER FIVE

However, there is some consolation. Missing data 

provides an extremely useful piece of management 

information. It means that staff are not filling out the 

forms completely. There may be a number of reasons 

for that:

They do not have the information when filling 

out the form.

They forget to ask for the information.

They forget to write down the information.

They do not think the information is important.

As a manager, you can excuse the first reason as long 

as the form is changed to reflect the information that 

is available when staff members complete the form. 

Then make arrangements to routinely capture the 

information when it does become available. However, 

you cannot consistently excuse the other reasons. 

They imply one of two things: Either your staff has to 

learn the importance of filling out the information, or 

you need to design better forms. Two more implications are hidden in that statement: Staff should not be 

asked to fill out information that is not important, and 

the information collected should be shared with staff 

and used to solve organizational problems.

Problem 3: Information on the Form Is Wrong

“Bad data” is a serious problem for any data collection. It is especially serious if you do not know that 

the information being gathered is wrong. Earlier in 

this chapter, we mentioned a problem associated with 

taking the Employment Status data element from an 

Arrest Report (i.e., the prisoner might have reasons to 

give inaccurate information). That problem belongs in 

this general category. One way to deal with this situation is to check the information given by the prisoner 

at one time with information given later or against 

information that has been verified.

Another kind of error should be mentioned. The 

people who fill out forms are human; they make clerical and typographical errors in recording information. 

While these errors may be obvious to you or to operational staff, they will be only a series of numbers to 

the person who enters data into the computer. Even 

worse, the computer will make no judgments at all 

about the quality of the information that it processes. 

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One of the oldest computer programmer sayings is . . .

Garbage in = Garbage out

. . . and it is still true today.

Sheriffs and jail administrators can take steps to avoid 

this problem by identifying a logical range of responses for each data element. For example, the likely range 

of responses for the Age variable might be from 18 to 

55. Checking anything above or below that is a common procedure statisticians use to make sure they 

do not have bad data. Most computer printouts will 

list the high and low scores for each variable, which 

makes it easy to perform this simple check.

If you note responses that are significantly above or 

below expectations, the person who processed the data 

will know how to find out whether there is a glitch in 

the program or problems with the data itself. There are 

statistical conventions to help counteract the effects of 

bad data.

Problem 4: Data Elements Are Poorly Defined

This can happen to the most skilled developers of data 

collection sheets. They design a question that asks the 

person filling out the form to make a value judgment 

and then fail to provide the criteria for making that 

decision. A common jail example follows:

Suppose the medical screening form asks 

the booking officer to answer the following 

question: “Does the prisoner exhibit any 

mental health problems?” This sounds fine 

until you think about what constitutes a 

mental health problem. Does it include:

Only people who are so “out of it” that they 

have to be seen by a crisis worker from the 

Mental Health Department?

People who are violent on intake after a bad 

night at a local bar?

People who smile benevolently and giggle 

at questions like “Name?” but are totally 

cooperative?

Only people who would be considered 

legally insane?

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5–10 HOW TO COLLECT AND ANALYZE DATA

There is nothing wrong with asking this question, provided that the criteria for answering “Yes” or “No” are 

defined. Make sure to get some help in defining these 

criteria and then train the jail staff in applying them.

Problem 5: The Handwriting Is Illegible

Sometimes the data is there, but the handwriting is 

indecipherable to the data collectors and coders. And 

“guesses” can lead to bad data. This is something a 

manager can remedy for future data collections. If you 

have people on staff whose handwriting is not legible, 

suggest they print. If that fails, suggest they type. And 

if that fails, consider whether that person should be in 

that particular position.

Problem 6: No One Keeps Data for the 

Whole System

This is really an extension of Problem 1. Each of the 

agencies in the criminal justice system keeps its own 

data, and some guard that data well. As a result, each 

agency has facts about only its own part of the system, 

which results in information being lost or fragmented. 

It may be extremely difficult for a data collector from 

one part of the system to get information from or 

access to records in another part of the system. This 

can sometimes lead to different agencies maintaining 

duplicate record systems.

All of this lends credibility to charges that the criminal justice system is uncoordinated. As policymakers 

in the system, sheriffs and jail administrators must 

take an active role in remedying this problem. At 

a minimum, the elements of the system must share 

information and work together on common problems. 

But developing an automated, integrated information 

system for the entire criminal justice system may not 

be a feasible goal for many systems. It may be more 

feasible to develop an automated information system 

for the jail using off-the-shelf software.3 Once the rest 

of the system sees the benefits of having management 

information on a regular basis, they might be more 

interested in working on a shared system.

Setting Up an Information System for 

the Jail

Information systems are structurally or functionally 

related elements or procedures that provide information to an organization, usually in summary form. 

They may be automated (computerized) or manual 

(on paper). They vary a great deal in their usefulness, 

their complexity, their “user-friendliness,” and the 

amount of time involved in maintaining them. The 

system described in this section is a very basic management information system that generates monthly 

statistical reports about the Inmate Population Data 

Elements and some Inmate Profile Data Elements, 

as well as some operational data. If you will not be 

automating the system, now would be a good time to 

turn to appendix C.

Elements of the System

This system assumes that you have no other automated jail functions. If you do, your system should be 

providing you with much of this information already. 

However, even if you already have an automated 

system, do not skip this section of the manual. It will 

help you get some ideas about what your system 

should be doing for you. Automated information systems are discussed further in chapter 9. To set up this 

management information system, the jail will need:

A computer capable of running your selected 

off-the-shelf software.

Either spreadsheet software (such as Lotus 1–2–3,

Quattro Pro, or Excel) or database software (such 

as dBase, Paradox, Access, or Approach).

A regular source of the data elements selected.

System Startup

As discussed in chapter 3, the first thing to be considered is to define the problem and associated data 

elements. A second critical decision is to determine 

which types of software will be used. Generally 

speaking, spreadsheets are very good at computing 

and calculating math; they were developed to add, 

subtract, multiply, and divide. Databases are very 

good for storing and sorting information. While most 

spreadsheets software can do some database functions, 

most spreadsheet software has limited ability to sort 

data by a set of criteria. If you rely on a spreadsheet, 

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3 For those using a manual information system, refer to appendix C.
How To Locate and Capture Information 5–11CHAPTER FIVE

you will need to do many sorts with many data ranges 

to get the information that a good database program 

could easily provide with one query. On the other 

hand, databases (particularly some older databases and 

some that were specifically written for one user agency) find doing math awkward, even if they can sort by 

multiple criteria. As a result there may be a tendency 

to use spreadsheet software for Inmate Population 

Data Elements, which record summary information 

like counts on a daily basis, and database software for 

most other data collections, such as an Inmate Profile 

Data, especially large data collections.

It is also worth noting that most current off-the-shelf 

software has been written in a way that allows users 

to import or export data from one application to 

another. For example, you could store information in 

a database, produce your frequency distributions in 

the database program, export the data to a spreadsheet 

application to “crunch numbers,” and then export your 

tables to a word processing program for inclusion 

in a report and to make the graphics. If you have 

an existing jail information system, you should be 

encouraged to know that it is almost always possible 

to retrieve the information that your staff have entered 

to perform a special analysis. The issue will be how 

easy or difficult it is to do that. Take the time to learn 

how to develop special reports, query the system, and 

export data.

Step 1: Identify the information the system should 

collect. It is hard to decide what information is routinely needed if you have not worked much with data 

and information systems. Sometimes sheriffs and jail 

administrators find it difficult to identify what they 

should be asking their systems to do for them. As a 

result, they may hesitate to ask. That puts them in the 

position of letting system designers decide what they 

need. Since most system designers do not have operational experience, they tend to miss a number of the 

Operational Data Elements. The purposes of the rudimentary information system in this manual are:

To collect the Inmate Population Data Elements 

(Average Daily Population; Length of Stay; Jail 

Days; Total Bookings; Net Bookings; number 

of felons, misdemeanants, traffic offenders, and 

holds; number of pretrial, posttrial, and sentenced 

offenders; number of male and female inmates; 

and adult and juvenile inmates).

To collect some basic Inmate Profile Data 

Elements (Charge, Age, Ethnicity, Residence, 

and Release status).

To collect some Operational Data Elements 

(number and location of transports; number of 

incidents; and number of contact, noncontact, 

professional, and family visits).

Step 2: Determine how frequently summary 

reports are needed. Most sheriffs and jail administrators will probably want monthly statistical reports. 

The Inmate Population Data Elements are most 

understandable statistically when grouped by month; 

weekly statistics are too much to absorb when looking 

for jail population trends and patterns. An exception 

occurs when the jail is trying to control crowding. 

Then monthly statistical reports would not be frequent 

enough; weekly summaries would be helpful.

Step 3: Design the format. Tips on how to design 

good data collection sheets and forms are given 

later in this chapter. Figure 5-2 provides a sample 

Inmate Information Format spreadsheet. A sample 

card is included in appendix C. If you use a database 

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Figure 5-2 Sample Inmate Information Format

ID 

# Name

BookIn 

Date

BookIn

Time

BookOut 

Date

BookOut

Time

Length

of 

Stay

Birth 

Date Race Sex Employment Charge Residence Release To

Bond 

Type $
5–12 HOW TO COLLECT AND ANALYZE DATA

application to record this information, the entry screen 

can be designed to your specifications.

Step 4: Decide who will maintain the system. 

Sheriffs and jail administrators should make one person responsible for maintaining the information system. And remember, it is a clerical function. If there 

are no clerical staff in the jail, officers who work the 

graveyard shift or dispatchers might be able to maintain the system during slow periods. One of the benefits of having clerical staff maintain the information 

system is that they are not tied to a radio like dispatchers. They can leave their work area to get information 

if need be.

Step 5: Determine where the jail routinely captures 

information and decide how to get it from that 

source. It is hard to be very specific about this step 

because jails capture the information in so many places. It would be helpful to review the list of documents 

included in this chapter. The Daily Activity Log or 

a Shift Activity Sheet, Booking Log, and the Arrest 

Report will probably provide most of the Inmate 

Population and Inmate Profile Data Elements. The 

Operational Data Elements will come from the 

Transport, Incident, and Visitor Logs. Another potential resource for the Inmate Population and Inmate 

Profile Data Elements would be the status board found 

in most booking or central control rooms that records 

the inmates who are in custody, some descriptive 

information, and their housing assignments. For jails 

that have jail or booking information systems, review 

each of the screens on which data is entered to identify 

the data fields needed for the analysis.

Let’s assume, for this example, that the inmate information is already being collected on a Daily Activity 

Log. Figure 5-3 provides an example. Appendix C 

includes an 81⁄2- by 11-inch copy.

By adding a few formulas to a spreadsheet (such as 

figure 5-4), you can easily create your own internal 

check to guard against entry mistakes. The totals of 

columns 6 through 8, columns 9 through 11, and columns 12 and 13 should each equal column 2.

Figure 5-3 Sample Daily Activity Log

Any County Sheriff’s Department

Jail Division

Daily Activity Log

Statistical Summary: Beginning Count End Count 

# Felons _____________ # Misdemeanants _____________ # Traffic _____________ # Other ____________ 

# Pretrial ___________ # Sentenced _________________ # Females _____________ # Males ____________

OUT:

Time: _________________________________________

Name: ________________________________________

Release to: _____________________________________

Bond: _________________________________________

Time: _________________________________________

Name: ________________________________________

Release to: _____________________________________

Bond: _________________________________________

IN:

Time: _________________________________________

Name: ________________________________________

Charge: _______________________________________

P.D.: _________________________________________

Time: _________________________________________

Name: ________________________________________

Charge: _______________________________________

P.D.: __________________________________________
How To Locate and Capture Information 5–13CHAPTER FIVE

Let’s also assume that the formats needed to collect 

the operating information about transports, incidents, 

and visitors from the logs are not available. For each, 

repeat steps 1 through 4 to decide:

What information should be included in the 

format.

How frequently the information will be needed.

The design of the Transport, Incident, and Visitor 

Log Sheets.

Who will maintain the system.

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Figures 5-5 through 5-7 provide sample spreadsheet 

formats for Visitor, Transport, and Incident Logs; 

appendix C provides full-sized forms from which 

these spreadsheets can be compiled. 

If you use a database program, you will be able to 

design your own data entry screen. However, if the jail 

is not automated, before you can use these formats, 

you have to gather the information on the forms. The 

easiest way to get this information is to:

Figure 5-4 Sample Monthly Statistical Summary Format

Monthly Statistical Report

1 2 3 4 5 6 7 8 9 10 11 12 13

Date Begin 

Count

End 

Count Bookings Releases # Felons

#

Misdemeanants

#

Traffic

#

Pretrial

# 

Sentenced

# 

Holds

# 

Males

#

Females

1/1/2004

to

1/31/2004

January 1 – January 31

Figure 5-5 Sample Visitor Log Format

Visitor Log

Date Time In Time Out Inmate Name Visitor name Relationship Contact Noncontact

Figure 5-6 Sample Transport Log Format

Transport Log

Date #

Officers

# 

Officers 

on OT/

Comp

Reason #

Inmates

Location 

1

Location 

2

Time 

Out

Time 

Back

Meal 

Cost

Miles 

Start

Miles

End

Vehicle 

No.

Gas 

$

Figure 5-7 Sample Incident Log Format

Incident Log

Inmate Cell Date Time Officer Location Reason Rule Action Restraint Review 

Board

RB 

Date

Date 

Served Grieved
5–14 HOW TO COLLECT AND ANALYZE DATA

Require that all incidents that occur in the facility 

be documented in the Incident Log. The sheriff or 

jail administrator must define for staff what kinds 

of events must be documented. He/she should 

review each form and then give it to the person 

maintaining the information system, to be placed 

in the Incident Log file.

Require that all officers transporting inmates 

outside the facility begin a transport form prior 

to their departure from the jail and that they complete it as soon as they return. Have them give the 

completed form to the person maintaining the system, to be placed in the Transport Log file. 

Require that all persons who visit the facility are 

entered in the Visitor’s Log. The forms should be 

placed in the Visitor Log file.

If the jail has more than one source for a particular 

data element, a decision must be made about which 

source should be used. One source may be more 

accurate than another, and the fewer sources needed 

to get the information, the better. When confronted 

by a choice between one source that has only one data 

element and another source that has more than one 

data element, unless there is a compelling reason to 

use the source with just one element, use the source 

that provides the most data elements.

Step 6: Train the person who will maintain the 

system in how to use it. Insist that it be done daily! 

Automated systems, which require staff to manually 

enter information into the computer, are very manageable if they are maintained on a daily basis. But if 

either manual or automated information systems are 

neglected, updating them when it is time to complete 

the monthly report is extremely tedious. Worse, if 

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some of the information is missing, it may be impossible to get it once the inmate is gone and staff members’ memories have faded. Occasionally, it is also a 

good idea to make sure that the information entered 

in the Inmate Information Format is accurate. Before 

beginning, make sure that the staff person who will 

enter the information has been trained on the software. 

Most software has a tutorial that can get people started, and other training resources, such as adult education courses, can help with common software. You will 

also need to explain how each data element is to be 

entered. If you use codes (e.g., 1 = male, 2 = female), 

you should have a list of these codes available.

Step 7: Pick a day to start. Ideally, the information 

system should start on the first day of the month. It 

is wise to allow a few days of practice to be sure that 

the system is working properly. On Day 1, open the 

Monthly Statistical Summary, and make the first entry. 

Figures 5-8 and 5-9 show two sample spreadsheet formats with entries.

A review of figure 5-8 shows what happens when 

you add formulas to the format. Counts are averaged, 

which will give the Average Daily Population within 

the categories defined on the spreadsheet. Sums will 

provide total bookings and releases. If you add the 

counts rather than averaging them, the result is the 

statistic Jail Days discussed in chapter 4. One final 

computer note: be sure to save this computer file with 

a new name; that leaves the original file to use as a 

template, or model, each time you want to start a new 

monthly summary. It is a good idea to use a simple 

file-naming convention to identify the new file. Many 

people use the month and year (e.g., 012004SUM.

XLS) to make it easy to identify the file.

Figure 5-8 Sample Monthly Statistical Summary Format with Entries

Monthly Statistical Report

Date Begin 

Count

End 

Count Bookings Releases #Felons

#

Misdemeanants

#

Traffic

#

Pre-trial

# 

Sentenced

#

Holds

# 

Males

#

Females

1/1/04 67 73 8 2 44 19 4 33 26 8 60 7

1/2/04

Average 67.00 73.00 44.00 19.00 4.00 33.00 26.00 8.00 60.00 7.00

Sum 67 73 8 2
How To Locate and Capture Information 5–15CHAPTER FIVE

Figures 5-10 and 5-11 show examples of statistical 

summaries that might be developed from Visitor Logs. 

Completing this statistical summary involves counting 

the numbers of visits and visitors in each category. 

It can help document the number of visits that 

occurred during each visiting period. Put this together 

with information about the number of visiting areas 

Enter each inmate who was released on the date 

you selected. In the figure 5-9 example, two inmates 

were released. Save this file with a new name (e.g., 

022004INM.XLS) and save the original file as a template from which you can build later months in your 

information system.

Figure 5-10 Visitor Log Statistical Summary, Version 1

Visitor Log

Date Time In Time Out Inmate Name Visitor Name Relationship Contact Noncontact

1/1/04 13:00 14:00 Jones, Paul Jones, James Brother X

1/1/04 13:30 13:45 Smith, Paul Smith, Vanessa Wife X

1/1/04 14:15 15:00 Williams, Richard Williams, Susan and Gregory Mother, son X

1/1/04 14:15 15:00 Anderson, Jeffrey Richards, Marilyn Friend X

Number of inmate visits 4

Number of visitors 5

Number of contact visits 2

Number of noncontact visits 2

Figure 5-11 Visitor Log Statistical Summary, Version 2

Visitor Log

Date Time In Time 

Out Time Inmate Name Visitor Name Relationship Contact Noncontact

1/1/04 13:00 14:00 1 Jones, Paul Jones, James Brother X

1/1/04 13:30 13:45 0.25 Smith, Paul Smith, Vanessa Wife X

1/1/04 14:15 15:00 0.75 Williams, Richard Williams, Susan and 

Gregory Mother, son X

1/1/04 14:15 15:00 0.75 Anderson, Jeffrey Richards, Marilyn Friend X

Number of inmate visits 4

Number of visitors 5

Number of contact visits 2

Number of noncontact visits 2

Hours of visiting provided 2.75

Average hours per visit 41.25 mins.

Figure 5-9 Sample Inmate Information Format with Entries

ID # Name Book-in 

Date

Book-in 

Time

Book-out 

Date Book-out Time Length of Stay Birth date Race Sex Charge Residence

04–001 Doe, John 01/01/04 01:30 am 01/01/04 06:30 am 0.21 07/01/53 White M Criminal Trespass State

04–002 Smith, John 12/14/03 18:05 pm 01/01/04 07:00 am 18.00 12/14/62 Black M DUI County
5–16 HOW TO COLLECT AND ANALYZE DATA

that are available and it can be helpful in determining 

if the scheduled hours are adequate. However, version 

2 (figure 5-11) of the Visitor Log Statistical Summary 

takes this process a little further and, in doing so, 

becomes more valuable to the jail operation.

Version 2 takes advantage of the math capabilities of 

the spreadsheet program to calculate the number of 

hours of visiting provided. Think about how this information might be useful. It could:

Document compliance with a standard that 

requires a specific amount of visiting.

Be useful ammunition if litigation occurs around 

visiting issues.

Together with staffing information, identify the 

impact of visiting in terms of staff hours involved.

Together with budget information, identify the 

cost of visiting.

Observation: Sheriffs and jail administrators 

typically collect statistical information like that 

shown in the Monthly Statistical Summary.

Automated information systems, particularly 

booking systems, often collect information like 

that shown in the Inmate Information Format.

However, even if jail operational functions, 

like visiting, are automated, the information 

contained in them is seldom summarized, and 

yet it is potentially some of the most useful in 

practical terms.

Review the other two operational formats provided, 

the Transport Log and the Incident Log, and automate 

those as part of your information system.

Step 8: Begin a daily maintenance routine. Each 

day, make the entries on the appropriate format. Be 

sure to save and back up your work! The time that is 

needed will depend on your decisions about the information needed. A reasonable time target is 30 to 60 

minutes per day.

Step 9: Prepare the first monthly report. One of the 

conveniences of automation is that it makes completing monthly reports almost automatic. As you make 

the last entries for the month in the Monthly Statistical 

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Summary, and the Visitor, Transport, and Incident 

Logs in your information system, you are actually 

completing your Monthly Report. You might use some 

of the techniques identified in chapter 9 to improve 

their appearance.

Summarizing the information from the Inmate 

Information Format is only a little more complicated. 

Without access to a good database program, you can 

use the database properties of the spreadsheet to categorize and sort information.

Figure 5-12 shows a completed Inmate Information 

Format. This format could be a table in either your 

database or your spreadsheet. To begin to summarize 

this information, use your spreadsheet’s commands to 

identify the data range or your database’s table to be 

sorted. Then identify the variable, column, or field by 

which you want to sort. For example, you may want 

to sort by residence. In database and spreadsheet terminology, residence is the primary key. After sorting, the 

result will resemble figure 5-13. Most databases and 

spreadsheets have another function that allows you to 

create tables. Continuing to explore “residence,” look 

at the six different responses listed in figure 5-13:

1. Unincorporated Boulder County.

2. City of Boulder.

3. City of Longmont.

4. Other municipality in Boulder County.

5. Out of state.

6. Other Colorado county.

Assign a code number to each.

1. Unincorporated Boulder County = (1).

2. City of Boulder = (2).

3. City of Longmont = (3).

4. Other municipality in Boulder County = (4).

5. Out of state = (5).

6. Other Colorado county = (6).

Complete the data distribution to develop the table 

shown in figure 5-13.
How To Locate and Capture Information 5–17CHAPTER FIVE

Figure 5-12 Sample Inmate Information Format ID# Name Book-in Date Book-in Time Book-out Date Book-out Time Length of Stay Birth Date Race Sex Charge Residence Release Bond Type Amount 04-001 Doe, John 01/0l/04 01:30am 01/01/04 06:30 am 0.21 07/01/53 White

M Criminal Trespass City of Boulder Acme Bail Bonds Surety $1,000

04-002 Smith, John 12/14/03 18:05 pm 01/01/04 07:00 am 18.00 12/14/62 Black

M DUI City of Boulder Self

04-003 Henry, James 12/15/03 06:30 am 01/02/04 01:30 am 18.00 09/24/64 White

M Probation Violation Other Boulder City Self Cash $500

04-004 Milton, Fred 12/30/03 07:00 am 01/04/04 18:05 pm 5.00 07/06/66 White

M DUI Other Boulder City Sentence Served

04-005 Brown, Patricia 01/04/04 19:30 pm 01/05/04 06:30 am 1.00 04/16/68 White F Simple Assault City of Longmont Self OR $50004-006 Jones, Paul 12/l9/03 18:05 pm 01/06/04 07:00 am 18.00 01/26/70 White

M Assault and Battery City of Longmont Sentence Served

04-007 Black, Richard 01/09/04 15:54 pm 01/10/04 01:30 am 1.00 11/07/71 White

M

Possession Controlled Substance

Boulder County Self OR $2,500

04-008 White, Carolyn 01/10/04 03:41am 01/11/04 18:05 pm 1.00 10/03/68 White F Petty Theft City of Longmont Self Cash $50004-009 Sanders, Robert 01/12/04 03:50am 01/12/04 12:30 pm 0.36 07/15/70 White

M Resisting Arrest City of Boulder Self Cash $500

04-010 Johnson, Michael 12/30/03 15:41pm 01/13/04 18:05 pm 14.00 04/25/72 White

M DUI Boulder County Sentence Served

04-011 Patterson, Ralph 01/05/04 01:30am 0l/15/04 01:30 am 10.00 02/20/72 Black

M FTA City of Boulder Other agency

04-012 Richards, Kevin 01/13/04 l9:05 pm 01/18/04 18:05 pm 5.00 06/13/58 Black

M Probation Violation City of Boulder State DOC

04-013 McDonald, Alfred 01/18/04 12:30 pm 01/19/04 01:30 am 1.00 03/24/60 Black

M Hold City of Longmont Other agency

04-014 Jefferson, Donald 01/11/04 18:45 pm 01/20/04 18:05 pm 9.00 04/29/38 Black

M FTA City of Longmont Other agency

04-015 Ray, William 01/22/04 16:30 pm 01/22/04 21:30 pm 0.21 06/04/52 White

M DUI Boulder County Spouse OR $500

04-016 Larson, Oscar 01/23/04 14:25 pm 01/23/04 18:05 pm 0.15 03/16/54 Black

M

Possession Controlled Substance

City of Boulder Self Cash $1,000

04-017 Ford, George 12/19/03 22:30 pm 01/24/04 01:30am 36.00 12/26/55 Black

M Robbery City of Boulder Self Property $10,000

04-018 Ellis, Edward 01/24/04 13:05 pm 01/27/04 18:05 pm 3.00 07/02/60 White

M DUI Adams County Sentence Served

04-019 Kraft, Mark 01/27/04 00:30am 01/28/04 01:30 am 1.00 04/13/62 White

M Hold Other Boulder City Other agency

04-020 Patton, Gloria 01/24/04 21:05 pm 01/29/04 18:05 pm 5.00 01/23/64 White F

Receiving Stolen Property

Los Angeles, 

California Self Cash $2,500
5–18 HOW TO COLLECT AND ANALYZE DATA

Each data element collected can be coded and analyzed. Some of the data elements, such as Length of 

Stay, can be averaged and may be very useful on a 

monthly basis. Other data elements may be more useful on an annual basis for either budgeting or for a 

section of the department’s annual report. This chapter 

provides more information about coding data elements 

later.

Step 10: Evaluate the information system in 6 

months and make any necessary changes. Every 

jail has its own peculiarities, and this system may not 

fit your facility. After 6 months, however, you should 

have enough experience with it to know what portions should be changed or adapted to fit your needs. 

You may want to add some data elements that are 

not included in the samples provided in this manual. 

Most spreadsheet and database software allows you to 

do just that. Set a time to do this: and be sure to talk 

about it with the person who is maintaining the system. Evaluations that are not planned have a way of 

never happening. This information system, when 

tailored to meet the needs of the jail, can be a powerful management tool. Take the time to improve on it!

A word to those of you who may already 

have some degree of automation. Review 

the preceding section and ask yourself, “Does 

my system do this for me?” If it does, great! 

That’s one less thing you have to do. If it does 

not, you may need to determine if the system 

can do some of the things discussed. Begin 

by talking with your programmer or software 

vendor to determine your system’s capabilities.

For more on this topic, see chapter 10.

Congratulations! You have just set up an automated 

management information system. You have a mechanism by which you can collect all the information you 

routinely need. Now, let’s deal with those special issue 

data collections.

Special Issue Data Collections

Although your automated system may routinely 

collect a great deal of the information needed for jail 

management, sooner or later a special data collection 

will be needed. You may decide to:

Build a new jail.

Design a new inmate classification system or 

evaluate the current one.

Evaluate inmate programs provided in the jail.

Change operational policies and procedures.

Conduct an analysis to determine appropriate 

staffing levels in the jail.

Take a proactive approach to crowding.

To begin, some historical data may be needed. 

Depending on how the information is stored (manually 

or by automation) and how accessible it is, this may be 

as simple as downloading information from the jail’s 

automated information system to a personal computer 

in a database or spreadsheet format that allows you to 

manipulate the data without risking the integrity of the 

records. Or, it may be as tedious and time-consuming 

as searching through hard-copy records.

Either way, although the data available through the 

information system will be invaluable in these projects, 

additional information that the system does not provide will often be required. The next section describes 

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Figure 5-13 Sample Sorted Inmate Information Data Element

Residence Code Number Percentage

Unincorporated Boulder County 1 3 15%

City of Boulder 2 7 35%

City of Longmont 3 5 25%

Other municipality in Boulder County 4 3 15%

Out of state 5 1 5%

Other Colorado county 6 1 5%

Total 20 100%
How To Locate and Capture Information 5–19CHAPTER FIVE

a process for designing these special data collection 

forms, including tips for designing easy-to-use forms. 

The appendixes provide copies of data collection 

sheets and code books for:

Inmate Profile Data Collection (appendix D).

Incident Data Collection (appendix E).

Transport Data Collection (appendix F).

Designing Special Data Collection 

Forms and Code Books

The process for designing forms for special data collections is similar to the process for designing the format for the automated information system.

Step 1: Identify the information to be collected. 

If questions arise regarding this step, refer to earlier 

sections of this chapter.

Step 2: Determine where the jail routinely captures 

the information. Designing the forms for special data 

collections is similar to the process for designing the 

form for the manual information system. Furthermore, 

if good design procedures were followed to design the 

Log Sheets or forms on which you routinely collect 

information, this should be simple. The forms routinely used will double as data collection sheets. If the 

jail’s record system does not capture the information at 

all, there are only two choices to remedy the situation:

1. Redesign a form that staff already completes at 

a time when the information needed is likely to 

be available. For example, the special data collection may be concerned with finding out how 

many people in the jail have been hospitalized for 

mental illness. The jail’s Medical Screening Form 

already asks whether the prisoner exhibits signs 

of mental illness. This would be an ideal time to 

ask about hospitalization for mental illness; the 

Medical Screening Form would be a likely target 

for redesign. This strategy limits the proliferation 

of additional forms, and it does not increase officer paperwork significantly.

2. Design a form to collect the information. To 

use the previous example, if you did not have a 

Medical Screening Form, this would be the time 

to adopt one. It is important to design good forms 

that capture all the needed information and reduce 

the amount of duplication.

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If the Log Sheets and other routinely used forms 

are well designed, they will double as data collection sheets with no modification. A good example of 

a form that doubles as a data collection sheet is the 

Transport Log. However, if the information needed 

is located in several different documents, a separate 

data collection sheet will be needed on which all the 

information for each case is recorded. An example of 

this kind of form is the Inmate Profile Data Collection 

Form included in appendix D.

Step 3: Analyze the data elements. Once all the data 

elements are identified, the next step to developing 

a special data collection sheet is to think about what 

information will be recorded for each data element. 

This is another time that firsthand working knowledge 

of the jail and its operations will be invaluable. To 

explain this step, let’s work with the Transport Log 

Form. Figure 5-14 provides a sample of a completed 

Transport Log Form.

This step identifies the different ways in which data 

elements are measured. On the Transport Log, the 

responses to some of the data elements easily fall into 

different categories, such as:

Reason for Transport—The items listed in the 

checklist define the number of categories that are 

possible for this data element.

Location From and To—All the locations that 

are possible places to which prisoners would be 

transported define the categories possible for this 

data element.

Vehicle Used—There are three possible 

categories for this data element.

Badge Number—The number of commissioned 

employees defines the number of possible 

categories in this data element.

Date of Transport—There are 365 possible 

categories for this data element (which would 

probably be grouped into months for a study).

Return and Departure Times—There are as 

many possible categories as there are minutes in 

the day (which would probably be grouped into 

hours for a study).

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5–20 HOW TO COLLECT AND ANALYZE DATA

Figure 5-14 Sample Completed Transport Log Form

Any County Sheriff’s Department

Jail Division

Transport Log

Officer Name and Badge Number:

1. George B. Good #1617 Regular Hours__Comp/O.T. __ 

2. Regular Hours__Comp/O.T. __ 

Reason for Transport: 

__ Emergency Room Treatment __ Transport to State Penal Institution

__ Scheduled Medical Appointment __ Transport to State Hospital

__ Court Appearance __ Transport to Treatment Facility

__ Warrant Pick-up __ Court Ordered

__ Return to Other Jurisdiction __ Other: 

Name: From To

1. Frank E. Felon Jail DOC

2. 

3. 

4. 

Time of Departure: 09:00 Meal Cost:

Time of Return: 19:00 1. $ 3.99

 2. $ 3.99

Vehicle: Total $ 7.98

Starting Mileage: 32,675 Vehicle Used: CO-17

Ending Mileage: 33,115 Mechanical Condition: 0 1 2 3 4 X 5

Total Mileage: 440 Cleanliness: 0 1 2 3 4 X 5

Gas Cost: $21.80 Gallons Purchased 20

Related Transport Problems None 

4

4
How To Locate and Capture Information 5–21CHAPTER FIVE

Other data elements have responses that fall into some 

kind of order from better to worse. The data elements 

like that on the Transport Log relate to:

Mechanical Condition—Asks the officer to rank 

the functionality of the transport vehicle from 

good to poor.

Cleanliness—Asks the officer to rank the cleanliness of the vehicle from good to poor.

The rest of the data elements have possible answers 

recorded in equal units. These data elements could 

have a great many possible answers, such as:

Regular and O.T. Hours—Answers could vary 

from less than an hour to several days, but they 

will all be expressed in hours (or minutes that can 

be turned into parts of hours).

Total Time—Answers could vary from less than 

an hour to more than a day, but they will all be 

expressed in hours or parts of hours.

Total Mileage—Answers could vary from less 

than a mile to many miles, but they will all be 

expressed in miles.

Gas Cost—Answers will vary from $0.00 to a 

specific dollar amount, but they will all be 

expressed in dollars and cents.

Number of Gallons Purchased—Answers will 

vary, but they will all be expressed in gallons.

Number of Gallons Required to Fill—Answers 

will vary from close to zero to the entire capacity 

of the gas tank, hut they will all be expressed in 

gallons.

Meal Cost—Answers will vary from $0.00 to the 

total amount the Department authorizes for food 

on transports, but they will always be expressed in 

dollars and cents.

Why is so much time and space spent on describing 

the different kinds of data elements? To statisticians, 

this elementary information is a big deal. These are 

the levels of measurement associated with all data 

elements. The first group of data elements (whose 

answers can be divided into a series of categories) 

are called nominal data elements. The second group 

(whose answers can be ranked in some kind of order) 

are called ordinal data elements. The third group of 

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data elements (whose answers were expressed in equal 

units) are called internal and/or ratio data elements. 

Statisticians divide the third group into two subgroups, 

depending on whether zero is a possible response. For 

the purposes of this manual, they can be treated as one 

group.

Levels of measurement are critical in calculating statistics, because some statistical procedures are valid 

only with data of certain levels of measurement. So, 

if the statistical procedure applied is not appropriate 

for the level of measurement, the resulting analysis of 

the data may be meaningless. Chapter 7 spends more 

time on problems of this nature. However, this fact 

has one very obvious application for developing data 

collection sheets: nominal data elements with a relatively short list of possible categories (like Reason for 

Transport) lend themselves to checklists that make the 

forms easier to fill out.

The final task in analyzing the data elements is to 

decide the level of detail to be reported. For example, 

for Total Mileage, it is necessary to decide if the 

answer must be written out to the tenth of a mile or if 

responses can be rounded off. Either write this instruction on the sheet or design the sheet in such a way 

that officers must write the answer in the correct form. 

This is a critical task because these categories of variables will become the finest amount of detail that your 

data collection sheet will measure. It is always possible to regroup or redefine the categories into larger 

topic areas, but it is impossible to get finer detail once 

the information is coded. The data element Charge is 

an excellent illustration of this problem. If, for example, charges are coded as “Crimes Against Persons,” 

“Traffic Offenses,” etc., it would be impossible to 

find out how many inmates are charged with specific 

types of offenses or to investigate the characteristics 

of inmates charged with DUI, assault, etc. Take the 

time to make sure the information is gathered in the 

finest level of detail that could possibly be important 

to the jail.

Step 4: Design a draft data collection sheet or format. Once all the data elements have been defined and 

the ones requiring special treatment (e.g., checklists, 

decimal places, etc.) have been determined, design of 

the data collection sheet itself can begin. Data collection sheets are just paper forms or computer formats 
5–22 HOW TO COLLECT AND ANALYZE DATA

on which people record the information collected. 

Statisticians call them “Data Collection Instruments.”

Good data collection sheets have the following 

characteristics:

They reduce the amount of writing to a minimum 

by preprinting the names of the data elements.

They are clear, easy to understand, and easy to fill 

out.

They group related data elements in sections so 

that the person filling out the sheet has a logical 

sequence to follow. 

They are as short as possible (preferably one sheet 

of paper) without being crowded.

The first data element on the data collection sheet 

(and in the code book) should be a Case Identification 

Number (a unique number assigned to only one case). 

Remember, a “case” in the statistical sense can be 

a person, event, or object about which information 

is gathered. This is important in jail data collections 

because:

The same person may be booked at the jail on 

more than one occasion.

The same booking may involve several incarcerations (e.g., an inmate sentenced to serve weekends).

The same booking may involve several charges.

Depending on what you are analyzing, the “case” 

could be the person, the booking, or the charge. 

Thinking in terms of “cases” is so important it is also 

addressed in the next chapter. But for now, either fill 

in the Case Identification Number (such as 001, 002, 

003, etc., through the last case) on each of the data 

collection sheets before you start using them, or you 

can number the data collection sheets after the data 

has been collected but before it is entered into the 

computer. But do number the sheets. Otherwise, there 

is no good way to check and correct entry errors, 

which, unfortunately, happen in both manual and automated information systems.

Begin by looking at the data collection sheets that are 

included in this manual. Think about the format of the 

data collection sheet. Once the results are satisfactory, 

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type the form either on paper or on the computer (the 

Master Format). As nice as it may look, this will probably not be the final data collection sheet. It needs to 

be tested first. This will require a code book.

Step 5: Draft a code book. Code books are documents that translate the data element categories (which 

are usually written words) into a numeric coding 

system so they can be entered more rapidly and computers can process them more quickly and efficiently. 

Code books identify:

The data elements.

The categories for each data element.

The codes for each data element.

The name of the computer field or column 

location for each data element (if the data 

will be computerized).

It is generally good practice to have the data analyst 

help develop the code book. He/she will probably 

have suggestions about ways to format the data collection sheet that will make it easier to enter the information into the computer.

It is not very efficient to type words into the computer, 

and some computer programs can handle only numerical data. To deal with this problem, people designing 

data collection sheets develop codes (numbers or short 

abbreviations like “M” for male and “F” for female) 

to represent the different categories (or values) of each 

of the different data elements (or variables). So, for 

example, the values of Resident Status developed earlier in this chapter would become:

1. Unincorporated Boulder County = (1),

2. City of Boulder = (2),

3. City of Longmont = (3),

4. Other municipality in Boulder County = (4),

5. Out of state = (5),

6. Other Colorado county = (6),

7. Other = (7),

8. Not applicable = (8), and

9. Unknown = (9).

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How To Locate and Capture Information 5–23CHAPTER FIVE

The code for the data collection is in some respects 

like Morse code or the 10-codes often used in radio 

transmissions: it makes communication with the 

computer more efficient. Three major challenges 

lie ahead. The first concerns the Categorical Data 

Elements (which statisticians call nominal variables). 

It is critical to identify all of the possible categories 

(or as many as possible). This is another area in which 

knowledge of corrections and the criminal justice 

system is very helpful.

There are a number of ways to get started. First decide 

what level of detail is needed. For example, continuing with the data element Resident Status, decide if 

the responses should be rather general (like the values 

identified above) or more specific (the specific city 

or town in which the prisoner resides). For some data 

elements (e.g., Charge Status), this will be very easy. 

The criminal code will define some of the categories 

for you (e.g., felony, misdemeanor, etc.). For other 

data elements, it will be much more difficult.

Next do a little more research to identify all the 

possible categories. It is often a good idea to ask 

an associate to review a final list of categories. For 

added insurance, include a category “Other,” with a 

space to write in information. If it is possible that the 

people who will fill out the form may not have the 

information, add two categories “Not Applicable” and 

“Unknown.” These will be a big help in finalizing the 

data collection sheet.

The second major challenge is particularly important 

if database computer software will be used to process 

the data. It concerns the interval and ratio data elements, which are measured in some type of numerical 

unit. These are almost always expressed in numbers 

already. The challenge is to identify the number of 

digits that will be in the largest possible answer to that 

question. Meal Cost, one of the data elements on the 

Transport Log Form, is a good example. Assuming 

that both dollars and cents are necessary, the code 

book must identify how many spaces should be 

reserved for that data element. It could be three digits 

($9.99) or four digits ($09.99).

If it is likely that the cost for a single meal would 

exceed $9.99, then a four-digit code is necessary, or 

information about the more expensive meals will be 

lost. It is better to use an extra digit if the size of the 

largest response is uncertain or if the form will be in 

use for a long time.4

The third and final challenge is to review every question to ensure that only one question at a time is 

asked.

Let’s take an innocent-looking question, 

which might be found on a Medical Screening 

Form: “Has the prisoner been hospitalized 

for treatment of mental health or substance 

abuse problems?” This kind of question may 

sneak through the coding stages of the data 

collection without causing many problems.

However, when it is time to interpret the 

information, there will be no way to identify 

how many prisoners who were hospitalized 

were treated for which problem(s).

Review the sample code books in the appendixes and 

begin to develop your own.

Step 6: Revise the data collection sheet or format. 

Based on changes made while developing the code 

book, revise the data collection sheet or format. This 

time make sure that the space next to each data element reflects the exact number of digits that will be 

included in that code. So, for Meal Cost, a four-digit 

code, the entry on the data collection sheet would look 

like this:

Meal Cost q q . q q

Step 7: Test it. No matter how thorough the job of 

designing the data collection sheet has been, no one 

has ever designed a perfect form on the first try. It 

is important to find out how well the data collection 

sheet works. Statisticians call this process “pretesting 

the survey instrument.” It is a good idea to ask someone who works in the jail to fill out the form and give 

you feedback about how well it works before the form 

is finalized. When you are confident that the data collection sheet or format is right, decide on a time period 

for the pretest.

As the data collection sheet is tested, problems will 

quickly surface. Information may suddenly appear that 

4 This is a much greater issue with data collections that use 

statistical programs and some database programs.
5–24 HOW TO COLLECT AND ANALYZE DATA

is not included in the coding system; data may not fit 

into the categories that seemed so clear. As decisions 

are made about how to handle each instance, write 

them down in the code book.

Step 8: Finalize the code book and the data collection sheet or format. When the test period is over, 

there are several items to consider. First, review the 

code book for changes made to respond to coding 

problems. Second, review each data collection sheet 

and look for places where testers have had to use the 

“Other” category. If the same things are frequently 

written in, it is likely that one or more categories have 

been omitted. Third, look for data elements that have 

a lot of “Do Not Know” responses. This can mean a 

couple of things: 

Staff should be directed to find the missing 

information if it is important.

The data element should be removed from the 

data collection sheet if it is not important.

The question is confusing.

Most often, it is the latter. The only way to find out 

is to ask the people who completed the form. Once 

the nature of the problem has been determined, after 

a final check with the data analyst, make the final 

copies of the data collection sheet or format and the 

code book.

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Conclusion

That is about all there is to it. The data collection 

sheet and the code book have been developed. Once 

the data collection sheet or format has been finalized 

and the data collection begun, your attitude toward 

changing it should be like your attitude toward using 

force: If there are compelling reasons, by all means 

go ahead, but if you can avoid it, do so at all costs! 

By keeping the data collection essentially the same 

from year to year, it is possible to compare the data 

elements over time. Statisticians call this a “longitudinal” data collection. If you do the data collection 

just once, statisticians call it a “cross-sectional’’ data 

collection. While a cross section can help you describe 

something, longitudinal data can document change 

and even begin to explain it.

So, without further delay, let’s take care of a few final 

tips about putting the actual data collection together.
c h a p t e r s i x

How To Put It 

All Together
Doing the Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3

Determine an Overall Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3

Determine How Much Data To Collect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4

Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7

Rules for Coding Data............................................. 6-7

Coding in an Automated System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-8

Gather Your Data Collection Supplies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-10

Begin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-10

Automation Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-12

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-12
6–

H o w T o P u t I t A l l T o g e t h e r

Now that the data collection sheet or format and the code book have been developed, 

only a few more details need to be cleared up before starting the data collection. Now 

is a good time to think about the details of really doing the data collection. That is what 

this chapter is about.

Doing the Data Collection

Doing the data collection is just a matter of good planning, organization, and some 

hard work. Collecting data in jails can be tedious and time consuming. This manual 

has done its best, however, to make it as easy as possible to collect jail data by insisting that the proper preparations are made and by providing a number of guidelines and 

resources to save you extra time and energy. Now it is time to go to work. As you start 

the data collection, follow these steps to get organized.

Determine an Overall Strategy

First, determine where your jail routinely captures information and (if necessary) 

decide how to get it from there to a data collection sheet or format. This manual dedicated a considerable amount of time to this topic in the previous chapter. Documents 

that are common sources of jail information were identified. If you are operating the 

jail with a totally manual system, you will need to follow each step in the manual, 

using a handwritten data collection sheet (see appendix C for instructions on doing a 

manual data collection). If you have automated your information system as suggested 

in the previous chapter, you may have some or all of the information you need already 

collected. If you already had an automated booking system, the information you need 

may be available there.

If your information is already automated, you still need to determine how to copy it to 

the file in which you will do the analysis. And you do want to copy it, to avoid the possibility of accidentally damaging or losing the data. Depending on your software, you 

may need to copy the files you want to analyze or extract the records you want from 

your database. This may involve translating the data from one computer application that 

stores your information to another that will do the statistical analysis.

c h a p t e r s i x
6– HOW TO COLLECT AND ANALYZE DATA

Determine How Much Data To Collect

For some of the data elements (Inmate Population 

Data Elements and the Operational Data Elements), 

determining how much data to collect is not an issue. 

Information will be collected about every inmate or 

event. But for other types of data collections, determining how many and which cases to collect is a 

major issue. Statisticians refer to this process as 

"sampling."

Although it may be desirable from a statistical point 

of view to have a large amount of data (e.g., Inmate 

Profile Data for an entire year), in reality that is often 

not possible. Sometimes jail records have not captured 

the necessary information, or the record system may 

be unreliable. If the record system is in “good shape,” 

historical cases can be used, but, in many situations, 

the data collection has to move forward in time to 

gather information about people who are currently 

passing through the jail. However, most systems 

cannot afford to wait for an entire year to get the 

information they need. Data collections need to be 

timely as well as accurate. Furthermore, collecting 

some kinds of data about every case may be too 

time consuming. This is particularly true when the 

data collection involves getting data from other parts 

of the criminal justice system.

Note: Automated systems vary considerably 

in terms of what they will do with the data 

when someone is released. In the worst 

case, the information is simply discarded 

or overwritten. In the best case, the most 

important information is retained in a master 

index file. Most automated systems ultimately 

have to deal with disk storage limitations. Old 

information may have to be backed up and 

removed from the disk.

As a result, if you are using historical data, you 

may find that the information you need is not 

as accessible as you might wish. Ask your data 

processing department or computer vendor 

to help determine the best way to access the 

information you need.

Fortunately, statistical sampling allows analysts to 

gather information from a segment of the entire population (a statistical sample) in such a way that it is safe 

to apply what you have found in the sample to the 

whole population. You can even determine how safe 

you are in making this assumption. Earlier, in chapter 

2, this manual indicated that statistics enables analysts to estimate what the chances of being wrong are. 

Statistics uses the laws of probability and applies math 

formulas to what is known about the data. The laws of 

probability should be familiar to individuals who have 

ever placed a bet; the laws of probability are essentially the same thing as “the odds.”

In sampling, statisticians use probability to determine 

what the odds are of selecting a statistically similar 

sample. Samples are statistically similar if they are 

drawn from the same population (all of the cases 

from which the sample is drawn) and the differences 

between them can be explained by the sampling process itself (sampling error). Statistics can determine 

how many times out of a hundred a sampling scheme 

will result in a statistically similar sample. Most statisticians want to be at least 95 percent certain that they 

would get similar results if they analyzed a different 

sample from the same population, and some want to 

be 99 percent certain.

The similarity of samples taken from the same population is measured by a statistic called the standard 

error of the mean. Statisticians can also estimate the 

accuracy of this statistic, which indicates how reliable 

the sample is, as are the statistics that result from it. 

Researchers have to decide how big a difference they 

can tolerate in the results of their studies when they 

use a sample. This concept, “reliability,” is meaningful when scientists are determining appropriate dosage 

levels for new medications; it is less a “life and death” 

issue for social research.

One primary factor which causes the difference 

between samples drawn from the same population is 

something that statisticians call variability. Variability 

examines how different the individual cases that make 

up the entire population are from one another. The 

more the cases differ, the more likely that samples 

drawn from the population will also differ. 

In other words, the statistical reliability will be lower.

So, to construct a sample, you first need to answer 

three basic questions:
How To Put It All Together 6–CHAPTER SIX

1. What is the population to be sampled?

2. How many cases should go into the sample?

3. Which cases should go into the sample?

In most jail data collections, defining the population 

is relatively easy. For example, a “typical population” would include “all persons booked at the Metro 

County Jail during calendar year 2004.” To determine 

exactly how many cases should go into a sample, 

statisticians and mathematicians have developed a 

formula. It is:

SEx = The standard error of the mean

s = The standard deviation of the sample

n = The number of cases in the sample

This manual introduces “standard deviation” in the 

next chapter. For now, think about the concept rather 

than the specifics. This formula forces the user to 

decide two things:

1. How much variation can exist in the sample? In 

other words, how sure of the results do you want 

to be? 99 percent certain? 95 percent certain? 

Less?

2. How reliable must the results be? How much error 

can be tolerated? (This gets interesting when estimating dollar variables.)

Most researchers do not follow this process every 

time they put together a good sample but use a table 

developed by statisticians to save time. The tables give 

the actual number of cases that must be included in 

the sample to meet the specifications determined by 

the answers to the two questions above. The tables 

included in appendix G are for 95-percent and 99-

percent confidence intervals. Variations in the standard 

error of the mean of 2 percent, 3 percent, and 5 percent are calculated for both the 95-percent and 99-

percent confidence intervals. All you need to know is:

The level of confidence the analysts must have in 

the results (most jail data collections can live with 

95-percent confidence intervals).

How reliable must the data be (most jails can get 

along with 2-percent or 3-percent sampling error, 

which is the same as 98-percent or 97-percent 

reliability).

The size of the population from which you are 

sampling.

What proportion of the population from which the 

sample is drawn has the characteristic being measured. For example, if the Inmate Population Data 

shows that 20 percent of 1,000 annual admissions 

are women, the table will tell you that a sample of 

198 cases should result in a sample in which 20 

percent of the cases are women (see table 6-1).

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Table 6-1 Sample Size Example

Population Size 5% or 95% 10% or 90% 20% or 80% 30% or 70% 40% or 60% 50%

Expected Rate of Occurrence in the Population

500 64 108 165 196 213 217

1,000 68 121 198 244 262 278

1,500 70 126 211 269 297 306

2,000 70 130 219 278 312 322

2,500 71 131 224 286 322 333

10,000 72 136 240 313 356 370

50,000 72 137 245 321 367 380

100,000 73 138 246 322 369 384

s 

 n

SEx =
6– HOW TO COLLECT AND ANALYZE DATA

The selection of sample size is particularly important 

when you are looking at a subset (segment) of the 

population. For example, pretrial felons often have 

a disproportionate impact on the jail because of their 

long lengths of stay. Sometimes in the pretrial process, 

felony charges may be dismissed or reduced prior to 

bind-over to the upper court. Using the percentage of 

arrests that are bound over to the higher court is an 

effective way to develop a statistically reliable sample 

size of pretrial felony bookings.

Once the number of cases to go into the sample has 

been determined, the next problem is to decide which 

cases to include. This is important because the laws 

of probability (and all the statistics that are based on 

them) assume that the cases in the sample are selected 

randomly. This is a “random sample,” which means 

that each case has an equal chance of being selected 

for the sample. Samples that are structured this way 

have the least bias (built-in error).

There are a number of ways to determine which cases 

go into the sample (sampling schemes). They include:

Simple Random Sample, in which cases 

are selected randomly from a homogeneous 

population.

Stratified Random Sample, in which the population is divided into groups, such as felons and 

misdemeanants, and cases are selected randomly 

from within those groups so that equal numbers 

are selected from each group. This kind of a sample would include an equal number of felons and 

misdemeanants.

Proportional Random Sample, in which the 

population is divided into groups and cases are 

selected randomly from within the groups until 

the groups are in the same proportion in the 

sample as they are in the population (e.g., if the 

population had 70 percent felons and 30 percent 

misdemeanants, so would the sample).

Cluster Sample, in which the population is 

clustered into specific areas or units (e.g., housing units). Then the housing units to be sampled 

would be selected randomly, and information 

collected about every individual in those housing 

units.

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Cohort Sample is essentially the same as a cluster 

sample, but is based on time periods rather than 

physical or geographic areas (e.g., all prisoners 

arrested on a randomly selected date).

Systematic Sample, in which information is 

collected about cases in a certain sequence until 

the number of cases required for the sample is 

reached. For example, data is collected about 

every third, fifth, or tenth, etc., person booked 

(every “Nth” person in statistical terms) until 

enough cases are reached, with the number on 

which you begin being randomly selected.

Some of these sampling schemes seem to work 

better than others. Appendix H discusses methods 

for constructing a simple random sample and provides 

the random number tables (charts of numbers without 

any logical sequence) often used to develop random 

samples. Constructing random samples from random 

number tables can be extremely tedious and timeconsuming. Statistically, they are “the best,” but other 

sampling methods may be preferable.

Because of the nature of jail recordkeeping practices, 

a Systematic Random Sample based on the Booking 

or Release Log works well for Inmate Profile Data 

Collections. Once the number of cases to be included 

in the sample has been determined, divide the number of cases in the population by the number of cases 

wanted in the sample. The result tells you how frequently to put a case in the sample (e.g., every third, 

fourth, or Nth case). For example, if the jail books 

10,000 people per year and a 20 percent sample is 

wanted, information should be collected on every fifth 

case in the Booking or Release Log. Do not simply 

start with the first case in the year. Randomly pick a 

number from (in this case) one to five and start with 

that case. This method will also work well if most of 

the information is gathered from records.

If possible, it is preferable to use a Release Log 

(rather than a Booking Log) as the basis of the sample. This helps to eliminate a particular type of bias 

with regard to Length of Stay data. Even over periods as long as a year, if information is collected on 

intake, at the end of the year some individuals in the 

sample may still be in custody, with no release data. 

In addressing this problem, the two most common 

■

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How To Put It All Together 6–CHAPTER SIX

approaches are either to leave Length of Stay as 

missing data (omit this data element on the coding 

sheet or format) or to use the last date of the period 

for which data is being collected as the release date 

(e.g., if the sample period were 2004, the release date 

would be 12/31/2004). This biases the sample by making the Length of Stay shorter than it really is. This 

problem is particularly acute if data is being collected 

for only a short period of time.

Another type of sample that works well is a Cohort 

Sample. Randomly select dates throughout the year 

and collect information on all individuals in custody 

on those dates until the necessary sample size is 

reached. These samples are frequently called “snapshots” of the jail population.

Edward Lakner of the National Clearinghouse for 

Criminal Justice Planning and Architecture has 

developed a manual, Statistical Sampling Methods for 

Correctional Planners (see appendix B), which provides much information about sampling procedures. If 

determining sample size is difficult, perhaps a professional viewpoint will help. In general, samples smaller 

than 10 percent of the total population and less than 

500 in absolute size make most statisticians uncomfortable. When in doubt, err in the direction of including too many people in the sample rather than too few.

Coding

The person who will collect and code the data must 

be thoroughly trained to fill out the data collection 

sheet or format. The rules that follow are good training points, but the data entry person also needs basic 

computer operation and software training. Many 

people underrate the importance of good data collection and coding practices, but this makes the difference between a data collection that provides accurate 

information and one filled with errors. A number of 

coding practices will make the task easier for anyone 

who enters the data into the computer. The degree of 

automation and the type of software being used can 

make the coding process much easier.

If a totally manual system is being used, you 

should follow the steps in the manual to code 

the data. 

If the system is automated, at least some of the 

data may already be coded.

■

■

If you are gathering data from multiple sources 

and are not part of an integrated criminal justice 

information system, you will probably need to 

use either a manual or automated data collection 

sheet.

Rules for Coding Data

The following are guidelines for coding data on the 

data collection sheet or format:

Rule 1: Use only one meaning for each code. 

Never use more than one meaning for a single 

code. For example, the Inmate Profile Data 

Collection Form and code book include several 

blank spaces to list city police agencies that 

bring prisoners to the local jail. Assign one city 

to each value of this variable (e.g., 6 = City of 

Metropolis). This is just as true for automated systems as it is for manual data collections.

Rule 2: Always write legibly. Be sure to write 

large and legibly if the data must be entered from 

a data collection sheet rather than directly into the 

computer. Data entry personnel who cannot read 

the information may enter the wrong codes, which 

will result in wrong analysis from the computer.

Rule 3: Limit the number of people who code 

the data. If possible, have only one person do all 

the coding to ensure that the information is coded 

consistently. If that is not possible, it is essential 

that all those who code information understand 

each data element and code it in exactly the same 

way. The person who coordinates the data collection should verify that this is happening.

Rule 4: Be consistent. Once codes have been 

established, do not change them! It will bias your 

information. If questions arise during the data collection, make a decision, record the answer in the 

Code Book, and make sure that all coders know 

how to handle this coding irregularity.

Rule 5: Make a copy of the last version of the 

code book. Without the code book, it is extremely 

difficult to analyze the data collection. If the same 

study is to be repeated at a later date for purposes 

of comparison, information must be collected in 

exactly the same format. Without the code book, 

that is next to impossible.

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6– HOW TO COLLECT AND ANALYZE DATA

Rule 6: When in doubt, consider it as missing data. When in doubt about information to 

be coded (e.g., the handwriting is so bad that 

you cannot tell if the officer meant “Burglary” 

or “Battery”), treat it as missing data (leave the 

coding sheet blank or use your code for missing 

data).

Before the data collection begins, train the coders in 

good coding practices. Go over the code book item by 

item so that all the coders understand each one of the 

data elements. This avoids a situation such as having 

a coder ask something like, “Oh, you mean robbery 

is a crime against persons, not a property crime?”

Coding always goes slowly at first. There are questions to be answered, and coders need to become 

familiar with the code book and the information 

sources. Be patient. The more time people spend 

coding, the faster they get. The time spent up front to 

learn the coding scheme and to develop good coding 

habits is worth it!

Coding in an Automated System

This section of the manual is for those who have 

automated their information system as outlined in the 

previous chapter. Everyone else can turn to the next 

section, “Gather Your Data Collection Supplies.” 

If you are using spreadsheet software, you should be 

able to automate coding. If you are using one of the 

■ more “user friendly” database programs, you should 

not need to actually code the data. Figure 6-1 shows 

a section of a typical Inmate Information Format. The 

advantage of both spreadsheets and databases is that 

they generally allow you to enter information in alphabetic characters. However, to analyze the information, 

in the case of spreadsheets, you will need to do some 

coding, and in the case of databases, you may have to 

“refresh” some of the information entered. For example, assume you want information about the charges 

on which an inmate was arrested. The following steps 

illustrate how to move from a list of charges to a table 

(or, in statistical terms, a frequency distribution).

Step 1: Define the data range. In some spreadsheet 

programs, you will define the data range by selecting 

the entire spreadsheet except for the column headings. 

In figure 6-1, the data range consists of all the cells that 

are not shaded. In a database program, select the field 

you want to sort by placing your cursor there. In figure 

6-1, each of the column headings represents a field.

Step 2: Sort by charge. In a spreadsheet, define the 

primary key for the sort and specify alphabetical order 

(ascending). If you are using a database program, 

select the field (or fields) you want to sort, define 

whether you want ascending (A–Z) or descending 

(Z–A) order, and proceed with the sort. Figure 6-2 

shows the result of a sort in ascending alphabetical 

order by charge.

Figure 6-1 Sample Inmate Information Format

ID # Name Book-in

Date

Book-in

Time

Book-out

Date

Book-out

Time

Length of Stay in 

Days Charge

04–001 Doe, John 01/01/2004 01:30 01/01/2004 06:30 0.21 Criminal Trespass

04–002 Smith, John 12/31/2003 18:05 01/01/2004 07:00 18.00 DUI

04–003 Henry, James 12/15/2003 06:30 01/02/2004 01:30 18.00 Probation Violation

04–004 Milton, Fred 12/30/2003 07:00 01/04/2004 18:05 5.00 DUS

04–005 Brown, Patricia 01/04/2004 19:30 01/05/2004 06:30 1.00 Simple Assault

04–006 Black, Richard 12/19/2003 18:05 01/06/2004 07:00 18.00 Battery

04–007 White, Carolyn 01/09/2004 15:54 01/10/2004 01:30 1.00 Possession Controlled

Substance

04–008 Sanders, Robert 01/10/2004 03:41 01/11/2004 18:05 1.00 Petty Theft

04–009 Jones, Paul 01/12/2004 03:50 01/12/2004 12:30 0.036 Resisting Arrest

04–010 Johnson, May 12/30/2003 15:41 01/13/2004 18:05 14.00 DUI
How To Put It All Together 6–CHAPTER SIX

Figure 6-2 Inmate Information Format Sorted by Charge

ID # Name Book-in

Date

Book-in

Time

Book-out

Date

Book-out

Time

Length of Stay in

Days Charge

04–006 Black, Richard 12/19/2003 18:05 01/06/2004 07:00 18.00 Battery

04–001 Doe, John 01/01/2004 01:30 01/01/2004 06:30 0.21 Criminal Trespass

04–002 Smith, John 12/31/2003 18:05 01/01/2004 07:00 18.00 DUI

04–010 Johnson, May 12/30/2003 15:41 01/13/2004 18:05 14.00 DUI

04–004 Milton, Fred 12/30/2003 07:00 01/04/2004 18:05 5.00 DUS

04–008 Sanders, Robert 01/10/2004 03:41 01/11/2004 18:05 1.00 Petty Theft

04–007 White, Carolyn 01/09/2004 15:54 01/10/2004 01:30 1.00 Possession Controlled 

Substance

04–003 Henry, James 12/15/2003 06:30 01/02/2004 01:30 18.00 Probation Violation

04–009 Jones, Paul 01/12/2004 03:50 01/12/2004 12:30 0.036 Resisting Arrest

04–005 Brown, Patricia 01/04/2004 19:30 01/05/2004 06:30 1.00 Simple Assault

Step 3: Determine how often each charge appears.

Examples do not usually include many cases. In the 

example, for instance, there are few enough cases 

to allow the responses in each type to be counted. 

However, in the real world of data collections, there 

will be too many cases to count efficiently. The software package you are using should be able to count 

the number of times each charge appears. Begin by 

reviewing the charges to make sure that the same 

charge is listed the same way each time. Note that 

the computer may treat capitals and small letters 

as different symbols and that simple assault, assault 

3, and assault III will not be listed together. This is 

equally true of both databases and spreadsheets. Once 

you review the charges that have been sorted, you may 

need to edit them so that they will be grouped together 

for easier coding and then resort them. When you are 

satisfied with the list of charges, assign a code to each 

unique charge. Figure 6-3 shows what the spreadsheet 

looks like when the codes have been added.

Figure 6-3 Sorted Inmate Information Format With Codes

ID # Name Book-in

Date

Bookin

Time

Book-out

Date

Bookout

Time

Length

of Stay

in Days

Charge Code

04–006 Black, Richard 12/19/2003 18:05 01/06/2004 07:00 18.00 Battery 1

04–001 Doe, John 01/01/2004 01:30 01/01/2004 06:30 0.21 Criminal Trespass 2

04–002 Smith, John 12/31/2003 18:05 01/01/2004 07:00 18.00 DUI 3

04–010 Johnson, May 12/30/2003 15:41 01/13/2004 18:05 14.00 DUI 3

04–004 Milton, Fred 12/30/2003 07:00 01/04/2004 18:05 5.00 DUS 4

04–008 Sanders, Robert 01/10/2004 03:41 01/11/2004 18:05 1.00 Petty Theft 5

04–007 White, Carolyn 01/09/2004 15:54 01/10/2004 01:30 1.00 Possession Controlled 

Substance 6

04–003 Henry, James 12/15/2003 06:30 01/02/2004 01:30 18.00 Probation Violation 7

04–009 Jones, Paul 01/12/2004 03:50 01/12/2004 12:30 .036 Resisting Arrest 8

04–005 Brown, Patricia 01/04/2004 19:30 01/05/2004 06:30 1.00 Simple Assault 9
6–10 HOW TO COLLECT AND ANALYZE DATA

After sorting the data, be certain that there are no variations within the same category (i.e., the same charge 

is always written the same way). If you find duplicate 

entries, edit the data and correct this problem.

Step 4: Distribute the data. If you are using a 

spreadsheet, you can start to distribute the data by 

listing all of the codes on your spreadsheet as shown 

in figure 6-4. Many database programs will perform 

a count without having the data coded, which eliminates this step.

Refer to the manual for your software for the exact 

commands and steps it uses to distribute the data 

and perform mathematic calculations, such as sums, 

averages, counts, standard deviations, etc. Figure 6-5 

shows the results of a spreadsheet with distributed 

charge data, and figure 6-6 shows the completed 

version with percentages.

Congratulations! Not only did you code all your data, 

but you also put it into a format that can be used in the 

document developed as a result of the data collection.

Gather Your Data Collection Supplies

Step 1: Copy the data collection sheets and code 

books. Make as many copies of the data collection 

sheet and code book as you will need to complete 

the data collection. Each coder should have a copy of 

the code book and several copies should be filed in 

reserve.

Step 2: Develop working files for the collection 

sheets. If this is a completely manual process, purchase several large file folders or manila envelopes to 

hold the data collection sheets. These will be useful if 

coders have to go to several sources for the information or if the information is collected over time. Label 

them “Completed Data Collection Sheets,” “Blank 

Data Collection Sheets,” and “Pending Information.” 

If the system is automated, create the computer data 

entry formats. These may be a single file (for one 

coder and one machine) or multiple files (for multiple 

coders) that may be combined at a later time.

Begin

For some data collections (e.g., those that use historical data from already completed records), beginning 

the data collection will be a matter of pulling information out of paper files or from an existing database. 

If the data collection is manual, the information is 

recorded on the data collection sheet, which is then 

placed in the “Completed Data Collection Sheets” 

folder. If the data collection is automated, the information is entered in the data collection format and saved 

when the information is entered. Remember, always 

work with a copy of data from your automated information system. This protects you against unintentionally changing information in your records.

Other data collections, ones in which the information 

will be gathered over time or from multiple sources, 

are more complex. An Inmate Profile Data Collection, 

which collects information from a variety of sources 

about people currently in custody, is probably the 

most difficult logistically. In that case, follow the 

steps below.

Step 1: Begin a data collection sheet or format 

for each person in the sample. Start a data collection sheet or format for each person selected for the 

sample.

Step 2: Organize them. Usually, the identifier on the 

coding sheet will be Inmate Name. In that case, file 

the data collection sheets in alphabetical order or sort 

the file alphabetically. Often Inmate Name is the only

identifier that all of the elements of the system may 

have in common. If a numerical identifier (e.g., booking number) is used, file or sort the data collection 

sheets in numerical order.

Step 3: Secure information from the source documents. Go to the information source that provides the 

most information about each individual and complete 

as much as possible of the data collection sheet or format. Then go to the next information source. Continue 

until each information source has been exhausted and 

no more information is available about any of the 

cases.

Step 4: File partially completed forms. Place all the 

partially completed forms for persons who are still in 

custody in one file folder. If you are entering information into an automated format, you may elect to keep 

complete records in one file and incomplete records 

in another to conserve computer memory or to make 

it quicker to enter new information.
How To Put It All Together 6–11CHAPTER SIX

Figure 6-4 Table Prepared for Data Distribution

Charge Code Number Percentage

Assault and Battery 1

Criminal Trespass 2

DUI 3

DUS 4

Petty Theft 5

Possession Controlled Substance 6

Probation Violation 7

Resisting Arrest 8

Simple Assault 9

Total

Figure 6-5 Distributed Charge Data

Charge Code Number Percentage

Assault and Battery 1 1

Criminal Trespass 2 1

DUI 3 2

DUS 4 1

Petty Theft 5 1

Possession Controlled Substance 6 1

Probation Violation 7 1

Resisting Arrest 8 1

Simple Assault 9 1

Total 10

Figure 6-6 Completed Charge Table

Charge Code Number Percentage

Assault and Battery 1 1 10%

Criminal Trespass 2 1 10%

DUI 3 2 20%

DUS 4 1 10%

Petty Theft 5 1 10%

Possession Controlled Substance 6 1 10%

Probation Violation 7 1 10%

Resisting Arrest 8 1 10%

Simple Assault 9 1 10%

Total 10 100%
6–12 HOW TO COLLECT AND ANALYZE DATA

Step 5: Update release information daily. Each day, 

as people are released from the jail, pull their data 

collection sheets, add the release information, and 

review the forms to see if any additional information can be obtained. If so, file them in a “Pending 

Information” folder or keep them in your entry file. 

If not, place them in the “Completed Inmate Data 

Collection Sheet” folder or combine them with the 

new file that you created in Step 4.

Step 6: Add cases to the sample daily. Each day, 

as people are booked into the jail, begin a Data 

Collection Sheet for new bookings or add them to 

the entry file following the sampling scheme. 

Step 7: Stop sampling when the necessary number 

of cases is reached. When the required number of 

cases is in the “Completed Inmate Data Collection 

Sheet” folder, the data collection is done.

Automation Issues

Several problems occur when you automate a data 

collection that involves multiple sources. The most 

significant is how to appropriately match cases. The 

criminal justice system tends to track people, events, 

and charges. One person can be involved in multiple 

events (such as bookings or arrests), and each event 

can include multiple charges, which makes it difficult 

to match information about one event. This is particularly true when one organization keeps information 

based on charges or events and another organizes its 

record system by people. One strategy that can be 

effective in dealing with multiple charges is to collect information about the most serious charge. If that 

charge is later dismissed by the court, then the data 

collector gathers information about the next most 

serious charge. This strategy works well for most 

planning processes.

To address this case-matching problem, an integrated 

criminal justice information system assigns person, 

event, and charge identifiers linked across agencies. 

This often means that a portion of the data collection 

may be manual. For example, the data collection may 

include analyzing case flow in the courts. The primary 

source of information is, however, the jail’s record system. It may be more accurate to develop a data collection sheet or format from the court records than to try 

to electronically match the records and manually enter 

the new information on the paper or electronic form.

Conclusion

Now that you have the information, the worst of the 

data collection is over. The next stages are exciting. 

Chapter 7 suggests some ways to analyze the information and introduces some statistics that are found in 

almost all statistical studies. Chapter 8 provides the 

opportunity to figure out what those statistics really 

mean.
c h a p t e r s e v e n

How To Analyze 

Information
What is Statistics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-4

Statistically Significant Differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-4

Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-4

Normal (Bell-Shaped) Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-5

Statistical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6

Types of Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6

Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6

Statistics for Examining Differences Between Groups . . . . . . . . . . . . . . . . . . 7-11

Statistics To Examine the Relationship Between Data Elements . . . . . . . . . . 7-13

Statistical Sins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-15

Sin 1: How Could the Results Be Wrong if the Calculations 

Were Perfect? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-15

Sin 2: What Is the Real Average?................................... 7-16

Sin 3: What Is Missing From This Picture? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-17

Sin 4: So What if It Is Statistically Significant? . . . . . . . . . . . . . . . . . . . . . . . . 7-17

Sin 5: If A, Then B? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-17

Sin 6: What Is Wrong With This Picture? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-18

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-18
7–

H o w T o A n a l y z e I n f o r m a t i o n

Many people may develop an aversion to statistics, believing that:

Only someone with a Ph.D. can understand them.

Understanding statistics requires expertise in calculus.

Statistics can be made to say whatever analysts want.

These popular misconceptions are reinforced by statistical jargon. No one can deny that 

at the level of proving and discovering theoretical relationships, the field of statistics 

is complex. But it is no more essential to understand the theoretical proofs than it is 

to understand why the internal combustion engine works to be able to drive! What is 

important is to apply the right statistical tools to the data being analyzed, understand 

some statistical assumptions, and be aware of the limitations of these techniques.

You should now put aside your assumptions and preconceptions and pretend you have 

never heard of statistics. This chapter presents a discussion of statistics that differs from 

most statistics courses. It will not spend much time explaining how to compute the statistics, and it will not require the memorization of any formulas. A computer can provide a wealth of statistics—so many, in fact, that some may have to be ignored.5

This chapter:

Defines statistics and identifies the purposes for which statistics can be used 

in jails.

Explains the differences between statistics calculated for an entire population and 

statistics calculated for a sample (a specially selected subgroup) of that population.

Defines the types of results of statistical computations.

Introduces a number of common statistics.

Describes a number of statistical “sins.”

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■

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■

■

c h a p t e r s e v e n

5 For those who need to compute more complex statistics than those explained in this chapter, a good resource for nonstatisticians is a book cited in appendix B, How to Calculate Statistics, by Carol Taylor Fitz-Gibbon and Lynn Lyons 

Morris. Another basic statistics textbook is Understanding Statistics, by William Mendenhall and Lyman Ott. Both of 

these sources have been used throughout this chapter.
7– HOW TO COLLECT AND ANALYZE DATA

What Is Statistics?

Statistics, as a field, is an area of mathematics that 

uses numbers to make large or diverse amounts of 

information more understandable. Most people can 

remember only limited amounts of information at one 

time. For example, when asked to summarize the findings about the entire inmate population of a jail for a 

year, most people cannot do so without using numbers 

to represent facts.

The term “statistic” is also frequently used to mean 

the information that has been summarized by 

statistics (e.g., “jail statistics”). The potential for 

confusion is great enough without using the same 

word for two terms. For that reason, this manual uses 

the term “data element” for what many would call 

a jail statistic.

Basic statistics have three main purposes (How to 

Calculate Statistics, see appendix B):

1. To summarize information.

2. To indicate how seriously to take differences 

between groups of people or events.

3. To help determine how strongly pieces of 

information are related to each other.

Certain kinds of statistics are used for each of these 

purposes. This chapter deals with which statistics go 

with which purposes and which kind of data elements 

later. In the meantime, let’s focus on one of the most 

important dividing lines in statistics—the difference 

between statistics based on entire populations and 

those that come from samples (parts of a whole 

population).

Statistically Significant Differences

In chapter 3, this manual introduced an important 

term, “population,” which, in statistics, means all the 

cases (persons, events, objects, times, etc.) about a 

data element that could be collected. In chapter 6, 

another important term, “sample,” was introduced. 

A sample is a specially selected part of a population. 

The difference between “population” and “sample” is 

so great that statisticians actually use different terms 

to refer to the statistics associated with populations 

(“parameters”) and those that relate to samples 

(“statistics”).

When data is collected about a population and a 

parameter (such as a frequency distribution) is computed, analysts can be certain that the information is 

correct. For example, if an analyst collected Legal 

Status data for all the people booked at the jail during 

a given year and discovered that 80 percent of them 

were pretrial and 20 percent were sentenced, those 

numbers are absolute.

However, if this information was collected from a 

sample of people booked at the jail, there is some 

uncertainty. A skeptic might ask, “How do you know 

that is true for everyone? You might have selected 

people for the sample who are somehow different 

from the ‘real’ jail population.”

In statistics, when information from a sample is 

applied to the entire population, an “inference” is 

made. Analysts infer that what is discovered about a 

sample is equally true for the population. If the sample 

is collected randomly (by a sampling scheme like the 

ones identified in chapter 6) and if it is large enough 

to adequately represent the population, making inferences is acceptable statistical practice, provided that 

the analyst identifies how reliable the inference is. The 

mechanism that allows analysts to make inferences 

about a whole population from a sample is probability.

Probability

Chapter 6 introduced the term “probability” as “the 

odds.” An example will explain the concept more 

completely.

Let’s revisit Sheriff #2 (from chapter 2). The Inmate 

Population Data at Sheriff #2’s jail indicates that 60 

percent of the people booked are pretrial and 40 percent are sentenced. Sheriff #2 collected the Inmate 

Population Data over an entire year, when there were 

2,500 bookings.

Sheriff #2 and the county commissioners are working 

together to build a new jail to replace the 60-year-old 

facility the county has outgrown. Inmate Profile Data 

is needed to help the architect with programming the 

facility, but Sheriff #2 is disturbed by the prospect of 

collecting Inmate Profile Data for 2,500 cases. A statistical solution allows Sheriff #2 to randomly select 

a sample of about 750 cases to represent the whole 

population, which is preferable to collecting data on 

2,500 cases.
How To Analyze Information 7–CHAPTER

However, to be on the safe side, Sheriff #2 decides to 

SEVEN

experiment with the sampling procedures. He heard 

that some people questioned numbers reached by statistics in other counties building new jails. Because 

of the manual information system, Sheriff #2 knows 

what some of the actual population parameters are. 

Randomly selecting 100 of the last year’s Inmate 

Information Cards, Sheriff #2 asks the jail administrator to count how many pretrial and sentenced inmates 

are in the sample. Sheriff #2 repeats this process three 

times. The jail administrator tabulates the results, 

which are displayed in table 7-1.

Table 7-1 Results of Sheriff #2’s Experiment

Sample Pretrial Sentenced

Sample #1 59 41

Sample #2 57 43

Sample #3 62 38

Sheriff #2 was convinced that sampling would work 

and that he could defend using it in the Inmate Profile 

Data collection. Furthermore, he was convinced that 

once the probability of getting a specific sample was 

known, it would be relatively easy to decide whether 

to agree on the statistics. Sheriff #2 had grasped an 

important point about probability. Probability measures a belief in a particular outcome. Often, in statistics, probability is defined as the number of times a 

particular outcome will occur if a test or experiment is 

repeated many times. This is a criterion for deciding 

whether the results are credible. Probability theory is 

one of the foundations of statistics.

Unfortunately, life is not always as easy as it was for 

Sheriff #2 in this example. Often, the true population 

parameter is unknown. Statisticians have provided 

tools that can be used to determine how sure we can 

be that statistics derived from a sample represent the 

whole population. However, they work only when the 

sample has been randomly selected. All other samples 

have unknown probabilities and are, as a result, useless for statistical purposes. Randomly selected samples have what is usually called normal distribution 

(or bell-shaped distribution), which is the other statistical foundation covered in this manual.

Normal (Bell-Shaped) Distribution

Normal or bell-shaped distribution is not just a statistical or mathematical phenomenon—it is real. Once 

it is understood, many aspects of statistics that were 

confusing may suddenly start to make sense. An 

example will help to illustrate this phenomenon. The 

jail administrator who tabulated the results in the last 

example was surprised by Sheriff #2’s ability to pull 

100 cards out of the information system that represented the whole inmate population so well.

To make sure Sheriff #2 had not used some kind 

of trick, the jail administrator tried an experiment. 

Thinking that Legal Status might be too easy to 

manipulate (after all, 60 percent of the inmates were 

pretrial), the jail administrator chose a data element 

that would be much harder to “fix”: Length of Stay 

(LOS). The information system revealed that the average LOS was 20 days. Thinking that the sheriff’s 

three attempts to test the sampling procedure were not 

enough, the jail administrator repeated the sampling 

procedure 100 times. Using the sheriff’s procedure, 

the jail administrator randomly selected 100 cards 

from the information system, calculated the average 

LOS for those 100 cards, and recorded the result on 

a piece of paper. The jail administrator then put those 

cards back and randomly selected another 100 cards. 

This continued 98 more times. When the results were 

posted, they looked like figure 7-1.

Figure 7-1 The Jail Administrator’s Experiment

0-13 14-15 16-18 19-20 21-22 23-24 25-?

34

26

22

18

14

10

 0

Length of Stay in Days
7– HOW TO COLLECT AND ANALYZE DATA

“0” is a possible response). The level of measurement 

of each data element determines the types of statistics 

used to analyze it. These data element categories actually fall on a kind of scale themselves. This scale (or 

continuum) ranges from the simplest (nominal) to the 

most complex (interval/ratio).

The jail administrator discovered something it took 

statisticians some time to figure out: A more complex 

data element can be graphed or expressed like a simpler one. In the example, LOS (which is an interval/

ratio data element) was grouped by ranges of scores 

(cases with a LOS of 0–14 days, 14–16 days, 16–18 

days, etc.). In doing so, internal/ratio data was turned 

into ordinal data. That, in turn, allowed the data to be 

displayed on a bar chart, which was easier to understand. It also would have allowed a statistician 

to use a different statistical procedure (a frequency 

distribution)—one that is much easier to use without 

a computer—to analyze the data.

Unfortunately, it is impossible to turn simpler data 

into more complex data. This is just one more compelling reason to collect data carefully.

Statisticians refer to the process of grouping data 

into various ranges as “aggregating the data” and the 

reverse process as “disaggregation.” Experimenting 

with different ways to group data is one way analysis 

takes place. Generally, analysis begins with the data 

grouped in the largest general categories and proceeds 

by disaggregating it to show progressively finer levels 

of detail.

Types of Statistics

The rest of this chapter focuses mainly on the statistics that summarize information (descriptive statistics). Some time is also spent on the other statistical types (those that examine differences between 

groups or relationships between data elements). 

These probably will be included in reports prepared 

for you, and some may be encountered in special 

issue data collections analyzed on a computer.

Descriptive Statistics

There are three basic types of descriptive statistics: 

(1) Frequency distributions; (2) measures of 

central tendency; and (3) measures of variability. 

Together, these three types of descriptive statistics 

Sixty-eight out of the 100 trials resulted in samples 

with an LOS between 18 and 22 days, and 95 out of 

the 100 trials resulted in samples between 16 and 24 

days. By now, the jail administrator was a believer. 

What the jail administrator discovered is a phenomenon that characterizes nearly all data elements. If 

selected randomly and graphed like figure 7-1, data 

elements are distributed to form a very distinctive 

curve called a normal or bell-shaped curve.

What is most important in the jail administrator’s 

experiment is the fact that randomly selected sample 

averages (“means”) also have a normal distribution. 

This is a basic statistical assumption. It is what makes 

statistics work. Statisticians and mathematicians discovered that this “normal” curve has some special 

mathematical properties, which are discussed later 

in this chapter.

Right now, you deserve some credit, because in the 

last three and a half pages, you have absorbed two 

topics that often take half a semester in a basic statistics course. Undeniably, more detail is provided 

in basic statistics courses, but once you understand 

probability and normal distribution, you understand 

two major statistical concepts.

Statistical Results

Almost incidentally, the jail administrator’s experiment illustrates two other important statistical facts. 

First, statistics can be expressed by using graphs and 

charts as well as numbers. In fact, reports in which 

statistics are used benefit by a good display of the data. 

Using graphs and charts effectively is so important that 

this manual devotes much of chapter 9 to this topic.

The second concept the jail administrator discovered, Levels of Measurement, was discussed in some 

detail in chapter 5. This is another basic concept that 

organizes the field of statistics. It refers to how different types of data elements can be measured. Data 

elements are divided into three basic categories. The 

first group (whose answers can be divided into a series 

of categories) is called nominal data elements. The 

second group (whose answers can be ranked in some 

kind of order, such as from good to bad or high to 

low) is called ordinal data elements. The third group 

(whose answers are expressed in equal units) is called 

interval or ratio data elements (depending on whether 
How To Analyze Information 7– CHAPTER

can provide much information about the data with a 

SEVEN

minimum of math. With the exception of frequency 

distributions, which work with data elements of all 

levels of measurement, these statistics make sense 

only with interval and ratio level variables.

Type 1: Frequency distributions. Frequency distributions were discussed earlier in this manual, 

but they are worth a short review because they 

are among the most useful basic statistics. They 

are actually where all descriptive statistics begin. 

Frequency distributions are calculated by dividing 

all the data points (the scores or individual measurements for one data element or variable) into a 

series of categories (either because they fall into 

categories naturally or because the data was divided 

into groups). The number of cases that fall into each 

group are counted and then expressed graphically 

(e.g., figure 7-2) or in numbers (including both a 

percentage and the number of cases in each group). 

Table 7-2 is a frequency distribution displayed in 

percentages.

An analyst can learn a lot about the jail population from 

just this frequency distribution. It is possible to tell:

The percentages and numbers of people in various 

age groups in the jail.

How many people were included in the sample.

That the population distribution leans toward the 

younger end of the age scale (statisticians would 

say that the distribution is skewed).

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■

Sometimes, the data is neatly divided into categories, 

and the frequency distribution is obvious. Nominal 

and ordinal variables, like Legal Status or Charge 

Status, which have values that fall into distinct categories, are examples. However, interval/ratio variables 

have a wide range of possible values. To express these 

variables in a frequency distribution, the possible 

values must be divided into groups. Several rules can 

help:

Rule 1: Divide the possible values into groups 

that “make sense.” Statisticians call these groups 

classes. In the previous example, you should not 

divide the data about age into classes that consisted of 13 months.

Rule 2: Make the classes small enough so that 

the data is not bunched into one group. In the 

previous example, you should not divide the jail 

population into groups of 18 and younger, 19–50, 

and over 50.

Rule 3: The classes should generally be equal 

in size. For example, the same number of years 

should be included in each class.

Rule 4: No value should fall into two of the 

classes. For example, if you had two classes, 

18–21 and 21–24, it would be impossible to know 

where to put someone who was exactly 21 years 

old. Pick boundaries for each of the classes so that 

no cases fall on the boundary line between two 

classes.

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■

Age Number Percentage

Under 18 0 0%

18-21 103 16%

22-25 104 16%

26-29 182 28%

30-33 78 12%

34-37 85 13%

38-41 33 5%

42-45 52 8%

46 or older 13 2%

Figure 7-2 Frequency Distribution of Age Chart Table 7-2 Frequency Distribution of Age Table

22–25 30–33 38–41 46 or older 

18–21 26–29 34–37 42–45

200

150

100

 50

 0

Age
7– HOW TO COLLECT AND ANALYZE DATA

Type 2: Measures of central tendency. While frequency distributions are helpful, sometimes they are 

not very convenient and can take up a lot of room 

in a report. Descriptive statistics has another way to 

counteract these problems. Measures of central tendency locate the center of a frequency distribution. 

Central Tendency Measure 1—The Mean. The mean 

is an arithmetic average. To calculate the mean, add 

all the measurements for a particular data element 

and divide the result by the total number of measurements in the set. If Sheriff #2 wanted to calculate 

the Average Daily Population of the jail for a given 

month, he would add the counts for each day and 

divide by the number of days in the month (table 7-3).

When most people talk about an “average,” this is 

what they mean. However, two other measures of central tendency can be used in addition to (or instead of) 

the mean. (That “instead of’ will receive a little extra 

attention in the section of this chapter on statistical 

sins.) Sheriff #2 did not stop with just an average, but 

continued to explore several other measures of central 

tendency.

Central Tendency Measure 2—The Median. The 

median is the middle measurement when all of the 

measurements in the set are arranged according to 

size. Statisticians call this process “rank ordering.” If 

there are an odd number of measurements in the set, 

Day 

Number Count Day Number Count Day Number Count

1 26 11 36 21 34

2 32 12 31 22 29

3 37 13 32 23 27

4 33 14 29 24 28

5 29 15 25 25 31

6 28 16 28 26 33

7 29 17 30 27 30

8 30 18 33 28 35

9 37 19 34 29 31

10 35 20 31 30 29

Total = 932

Mean = 932/30 = 31.07

Table 7-3 Calculating the Mean Jail 

Population for One Month

Day

Number Count RankOrder

3 37 1

9 37 2

11 36 3

10 35 4

28 35 5

21 34 6

19 34 7

26 33 8

4 33 9

18 33 10

2 32 11

13 32 12

29 31 13

12 31 14

20 31 15 The

25 31 16 Median

17 30 17

27 30 18

8 30 19

30 29 20

14 29 21

22 29 22

7 29 23

5 29 24

6 28 25

24 28 26

16 28 27

23 27 28

1 26 29

15 25 30

Table 7-4 Calculating the Median

the median falls exactly on the middle measurement. 

If there is an even number in the set, the median falls 

exactly halfway between the middle two measurements. Using the same data, Sheriff #2 put all the 

measurements in order from lowest to highest. Table 

7-4 is the result.
How To Analyze Information 7–CHAPTER

The table clearly shows why it is important to look at 

SEVEN

more than one measure of central tendency. Extreme 

cases can distort the mean, but the median and the 

mode are less likely to be affected by extreme cases. 

As you can see, the three jails have very different 

patterns of use:

Sheriff #1 has a problem with weekenders. The 

numbers in Jail #1’s column regularly peak every 

seven days.

Sheriff #2 has an evenly distributed population. 

It appears that the judge who sentences most 

people supports the sheriffs work program and 

does not use weekend sentences.

Sheriff #3’s county is generally idyllic, rural, and 

heavily forested, with a low population. There 

were few major law enforcement problems until 

a motorcycle gang camped there during a long 

holiday weekend. Sheriff #3 had a hectic weekend, and the jail statistics for the month reflect it.

This is an example of a major problem with the measures of central tendency. Without another statistic that 

measures how much variation exists in the data, all of 

the measures of central tendency are incomplete.

Type 3: Measures of variability. Measures of 

variability tell how widely the data is distributed. 

If the measures of central tendency tell where the 

middle of the bell-shaped curve is, the measures of 

variability describe how wide the curve is from side 

to side. There are four important statistical measures 

of variability.

Measure of Variability 1—Range. Range is the simplest measure of variability to calculate and use. In a 

series of data points (individual scores or values), the 

range is the difference between the highest and lowest 

data points. In the case of the three jails, the range is 

quite different for all three. Table 7-7 illustrates the 

difference.

Range is also the fastest measure of variability to 

calculate. By looking at the high score, the low score, 

and a measure of central tendency, it is possible to tell 

if all the scores are grouped close together or if the 

scores are widely scattered. The more the scores are 

scattered, the less confidence should be placed in the 

average.

■

■

■

Count Number of 

Occurrences

25 1

26 1

27 1

28 3

29 5 The Mode

30 3

31 4

32 2

33 3

34 2

35 2

36 1

37 2

Table 7-5 Calculating the Mode

Sheriff #2 noticed that although the median (31) and 

the mean (31.07) were not very different, neither were 

they exactly the same. He thought that the difference 

was not caused by a math error, because the two measures of central tendency actually measure different 

things, so he calculated one more measure of central 

tendency.

Central Tendency Measure 3—The Mode. The mode 

is the measurement that occurs most frequently in a 

set of measurements. To calculate the mode, Sheriff 

#2 arranged the scores like a frequency distribution. 

Table 7-5 shows the results.

As might be expected, the mode (29) is different from 

both the median (31) and the mean (31.07). In this 

case, all three measures of central tendency are very 

close, which would lead an analyst to believe that the 

data is both normally distributed and clustered closely 

together. However, this is not always the case. Sheriff 

#1’s jail was about the same size, but the jail population had greater fluctuations. And in a nearby county, 

Sheriff #3 had a small jail with some unusual population problems. Table 7-6 compares the Daily Counts 

for the three jails during the same month.
7–10 HOW TO COLLECT AND ANALYZE DATA

Table 7-6 Measures of Central Tendency at 

Three Small Jails

Day No. Jail #1 Jail #2 Jail #3

1 47 26 14

2 44 32 15

3 30 37 16

4 27 33 18

5 15 29 17

6 21 28 16

7 27 29 13

8 48 30 19

9 50 37 15

10 24 35 17

11 19 36 16

12 10 31 13

13 12 32 19

14 23 29 15

15 38 25 17

16 43 28 16

17 46 30 15

18 36 33 17

19 33 34 18

20 31 31 14

21 26 34 16

22 43 29 172

23 40 27 167

24 23 28 161

25 28 31 16

26 25 33 18

27 21 30 14

28 20 35 17

29 43 31 15

30 39 29 16

Resident 

Days 932 932 932

Mean 31.07 31.07 31.07

Median 29 31 16

Mode 43 29 16

Table 7-7 Range at the Three Jails

Jail No. High Count Low Count Range

1 50 10 40

2 37 25 12

3 172 13 159

6 This will be useful if you do not have access to a computer. With a 

computer, you can use the statistical functions of your software to do the 

calculation.

Measure of Variability 2—Percentiles or Quartiles.

Percentiles refine the median (the middle measurement when all of the measurements in the set are 

arranged according to size). They are often used 

to divide all the scores into 10 groups (i.e., so that 

scores are located in the 80th, 90th, etc., percentile). 

Quartiles divide all the scores into four equal groups. 

The quarter that is the lowest is below the 25th percentile; scores that are below the median are below 

the 50th percentile, etc. The score exactly between 

the median and the highest score becomes the upper 

quartile (75 percent of all the scores are below it). The 

score exactly between the lowest score and the median 

(25 percent of all scores are below it) becomes the 

lower quartile.

Percentiles and quartiles provide a good view of how 

the data is distributed. They are frequently used to 

report scores for tests such as the Scholastic Aptitude 

Test. Although they are seldom used in jail data, they 

are useful in beginning to analyze the data. A disadvantage is that quartiles and percentiles do not define 

the highest and lowest scores.

Measure of Variability 3—Variance. Variance measures how far individual data points lie from the mean. 

It is rarely mentioned in anything but technical papers, 

but is essential for calculating probably the most 

famous measure of variation, the standard deviation.

Measure of Variability 4—Standard Deviation. The 

standard deviation is the square root of the variance. 

A formula for calculating the standard deviation is 

included in appendix I.6 The most important thing to 

learn is how to use and interpret a standard deviation.

What is important about the standard deviation? If 

distribution is normal (the data is distributed in a 
How To Analyze Information 7–11CHAPTER

normal or bell-shaped curve) and one standard deviaSEVEN

tion is added to and subtracted from the mean, 68 percent of all the cases will be found in the intervening 

interval. If approximately two standard deviations—

1.96 standard deviations to be exact—are added to and 

subtracted from the mean, 95 percent of all cases will 

be found in the interval—this represents a 95-percent 

confidence interval. If three standard deviations are 

added to and subtracted from the mean, then nearly 

all (99 percent) of the cases will be contained in that 

interval. Statisticians call this the “Empirical Rule.” 

This phenomenon provides a great deal of information 

to an analyst from just two statistics, the mean and 

the standard deviation. Not only can the central point 

in the data be identified, but an interval can be placed 

around it that defines where 95 percent of all the cases 

in the data set are found.

Before closing this section on descriptive statistics, 

let’s calculate the standard deviation for the Daily 

Counts of the three jails we have been studying (table 

7-8). Remember—the mean for all three jails is 31.07.

With the standard deviation calculated, the differences 

between the three jails are obvious when the Empirical 

Rule is applied. The Empirical Rule is that 68 percent 

of all cases lie within plus and minus one standard 

deviation, and 95 percent of all cases lie within plus 

and minus approximately two standard deviations. 

Sheriff #2’s jail has the smallest variation in its inmate 

population. Sheriff #l’s jail has a much broader range 

of daily populations, and, for that size, the standard 

deviation is large. Sheriff #3 has what seems to be the 

strangest situation of all. The standard deviation for 

the jail is larger than its Average Daily Population. 

However, this is relatively common in some kinds of 

jail data—especially Length of Stay data. To the experienced analyst, Sheriff #3’s strange standard deviation 

means one of two things:

1. Two groups are represented in the data (one near 

the low end of the range and one near the high 

end) and few, if any, cases are in the middle. 

Statisticians call this bimodal distribution, which 

means that there are two normal curves (and two 

populations) instead of one.

2. Several extreme cases throw off the calculations. 

On some days (when the motorcycle gang was in 

town), Sheriff #3 had over 150 inmates in the jail. 

Table 7-8 Standard Deviations of the 

Three Jails

Jail No. High Count Low Count Range Std Dev

1 50 10 40 11.12

2 37 25 12 3.10

3 172 103 159 45.25

The standard deviation is a great help to statisticians 

because it is one way to determine whether the distribution of the data is normal (and that is the basis for a 

great many statistical assumptions). The standard deviation is a kind of check on many basic assumptions.

This is about as complicated as basic jail statistics get, 

and if sheriffs and jail administrators use these tools 

to make good management decisions and to monitor 

the jail population, this manual will have achieved one 

of its primary goals. However, reports, articles, newspaper stories, etc., often cite other kinds of statistics. 

It is important to understand something about more 

advanced statistics and to know how to interpret them.

This manual also provides some information below 

about statistics that serve other purposes, but does not 

give much detail or discuss how to calculate them. 

A resource listed in appendix B, How to Calculate 

Statistics, will do precisely that.

Statistics for Examining Differences Between 

Groups

Statistics that examine the differences between two or 

more groups are called Tests of Significance. Sheriffs 

and jail administrators are most likely to encounter 

significance tests in journal or magazine articles 

about inmate programs and services that claim to 

make a difference in inmate behavior either before 

or after release. Significance tests typically are used 

to determine differences in the number of arrests after 

release between two groups, one that participated in 

a prerelease program and one that did not.

The significance of statistical significance. Many 

tests of significance work by using two of the basic 

statistical concepts, normal distribution and the 

Empirical Rule. Statisticians apply the Empirical 

Rule in a situation in which interval/ratio-level scores 

of two groups are compared. One group’s score is 
7–12 HOW TO COLLECT AND ANALYZE DATA

allowed to represent the mean. Using logic, statisticians deduce that if the other group’s score falls 

within the intervals defined by the Empirical Rule, 

the difference might be attributed to the sample, and 

therefore the difference might not be “real.” Therefore, 

these differences are not “statistically significant.”

The opposite is also true. Using one group’s score as 

the mean, if the other group’s score falls outside the 

interval defined by the Empirical Rule, the difference 

might be “real” and not merely a sampling difference. 

If the other score is outside the two standard deviations in either direction from the mean that contains 

95 percent of all the cases, it has to be in the other 5 

percent of the cases. This kind of difference is statistically significant at the .05 level. For those who are not 

satisfied with significance at the .05 level, mathematicians determined that to be significant at the .01 level, 

the interval around the mean has to be plus and minus 

2.58 standard deviations. That increases the odds to 1 

out of 100 times that the difference might be real and 

not just a statistical quirk.

Errors in statistics. Notice that the previous discussion is full of “mights.” Statisticians recognize the 

possibility that the difference might be a “statistical 

long shot” and their logic might be wrong. Statistical 

logic can be wrong two ways, called Type I and Type 

II Errors.

Type I Error occurs when researchers say there has 

been a change when there has not been. The difference that was detected is just one of those long-shot 

sampling flukes. The sample that was drawn was one 

out of the 5 percent or even 1 percent of all the possible samples that is different from the real population. 

Type I Error can be controlled by using small significance levels. Type II Error occurs when researchers 

say there has not been a change when there really has 

been. Program evaluation results might suggest that 

the difference between two groups is significant only 

at the .15 level. Traditional researchers would say that 

this difference is “not statistically significant” and 

might discontinue the project.

Generally, the best prevention for Type I and Type II

Errors is good sampling procedures and large enough 

samples. But this problem has led to a major debate 

in social research. Since our understanding of what 

makes people change is imperfect, it is possible that 

the measurements used to evaluate programs that are 

supposed to change people are not able to pick up true 

differences. Researchers might say that a program 

has not made a difference simply because they cannot 

measure accurately what has happened.

Analysts must use some judgment in this area. 

Significance levels are only tools that sometimes may 

be helpful in making management decisions. For 

example, consider this situation. One group of inmates 

who participated in a prerelease program at the jail 

had an average of 4.3 arrests after release; another 

group who did not participate had 4.4 arrests after 

release. If the groups were very large, these results 

could be statistically significant at the .001 level. 

But the real difference between the scores is so small 

that the results are not significant from a management 

perspective. However, if the situation were changed 

so that the differences were significant only at the .15 

level, but the difference in arrests was much greater 

(e.g., 1.3 arrests for the group that participated in the 

prerelease program compared with 4.4 for the group 

that did not), sheriffs and jail administrators would 

likely have a better opinion of the prerelease program.

Deciding which kind of significance test. There 

are many different tests of statistical significance. 

Variations can be used with different numbers of 

groups, different sizes of groups, different types 

of groups, and different types of data. Other tests 

include:

t test.

Mann-Whitney U test.

Sign test.

Analysis of variance.

A technique that researchers call a “decision tree” 

helps you to decide which test to use. This involves 

working through a series of questions that ultimately 

lead to the only possible answer. Figure 7-3 illustrates 

a decision tree to determine which significance test 

should be used.7

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7 Figure 7-3 is based on a decision tree developed in How to Calculate 

Statistics (see appendix B), which also provides the instructions for actually doing the significant tests mentioned here and develops worksheets 

that will help in their computation.
How To Analyze Information 7–13CHAPTER SEVEN

Question 1: How many groups are there? If three 

or more groups are included, an advanced statistical 

technique called analysis of variance will be needed 

to measure the differences in scores among groups. If 

analysis of variance is required, expert help probably 

will be necessary, as it is a complex statistical procedure. If only two groups are included, other statistics 

mentioned here can be used.

Question 2: Are the groups matched? Researchers 

consider groups “matched” if each case in Group 1 

is paired with a similar case in Group 2. A match can 

be a person with similar characteristics, or it may be 

the same person who has been tested at another time. 

A likely instance of “matched groups” in correctional 

research is when one group is compared before and 

after participation in a program.

All other groups have to be considered “unmatched.” 

When working with unmatched groups, the relative 

sizes of the two groups are important, as is the amount 

of variation within each group. These two factors will 

help to determine how different their scores are likely 

to be. The smaller the groups or the more variation in 

the scores, the more likely that one group’s mean will 

be different from the mean for both groups together.

Question 3: What level of measurement are the

scores? Significance tests that are used on the wrong 

kind of data are worthless. Differences between nominal level variables (data elements that are divided into 

discrete categories, e.g., the Pretrial and Sentenced 

categories of Legal Status) show the relationship 

between the two categories that are being measured. 

This chapter discusses this kind of relationship later. 

Differences between ordinal-level variables (data 

elements that are rank-ordered, e.g., arranged from 

lowest to highest) are measured by two tests. If the 

groups are not matched, the Mann-Whitney U test 

should be used; if they are matched, the sign test can 

be used. Differences in interval/ratio-level variables 

(data elements like age, number of miles, dollar costs, 

etc., that have equal units on a scale) are measured by 

t tests; t tests have been developed for both matched 

or unmatched groups.

Most of the statistical tests of difference are not difficult to calculate, but are merely tedious and involve 

a great many repetitions of similar mathematical 

functions. If you must calculate these tests by hand, 

a good programmable calculator (or, better yet, a 

statistical calculator that allows you to enter the data 

and then does the test for you) would be a great asset. 

Most special data collections are automated, and the 

computer can print out the results of these tests with 

the significance levels that are attached to them. With 

the understanding of statistical significance gained in 

this chapter, you should be ready to handle most of 

the common statistics of this type that you encounter.

Statistics To Examine the Relationship 

Between Data Elements

Many statistics test the relationships, or “correlations,” 

between data elements. Correlations are statistical 

shorthand which describes the nature and strength of 

relationships between data elements.

Types of relationships between data elements. 

Correlations operate in a variety of ways:

1. The more one variable increases, the more another 

variable increases (e.g., the greater the number of 

miles driven on a transport, the greater the number of gallons of gasoline used).

2. The more one variable decreases, the more another variable decreases (e.g., the fewer the number 

of prior arrests, the lower the level at which bond 

is set).

Figure 7-3 Test of Significance Decision Tree
7–14 HOW TO COLLECT AND ANALYZE DATA

3. The more one variable increases, the more another 

variable decreases (e.g., the older the prisoner, the 

fewer the number of incidents in which he/she is 

involved at the jail).

Correlations like those described in items 1 and 

2 above are called positive correlations because a 

change in one direction in one variable is accompanied by a change in the same direction in another variable. The variables have a direct relationship; statisticians abbreviate correlations like these by the symbol 

“+r.” Correlations like the one described in item 3 are 

called negative correlations because a change in one 

direction in one variable is accompanied by a change 

in the other direction in another variable. The variables have an inverse relationship; statisticians abbreviate correlations like these by “–r.” 

All correlations are expressed in numbers between 

+1 and –1. The “+” sign denotes positive correlations while the “–” sign denotes negative correlations. 

Numbers that are close to either +1 or –1 (i.e., +/– .7 

and above) indicate very strong relationships.

If a graph showing all the data points for a strong 

positive or negative correlation is plotted, the data 

points “cluster” together. A straight line can be drawn 

through the cluster that “summarizes” the cluster. 

When it is relatively easy to draw this straight line, 

the relationship between the data elements is said to 

be “linear.” This is called the trend line, which is used 

by statisticians for a special kind of correlation called 

population forecasting or trend analysis.8 In population forecasting, statisticians attempt to determine if 

there is a relationship between a data element, such 

as Average Daily Population, and time. Figure 7-4 

provides examples of data that show different types 

of correlation.

Actually seeing a plot of the data points is important 

because there are times when correlations should not 

be used to measure the relationship between variables. 

The correlation itself cannot reveal this problem. In 

fact, some very strong relationships that are not linear 

exhibit weak correlations. For example, when taking tests, people who have either very low or very 

high anxiety levels tend to get low scores, but people 

who have moderate levels of anxiety get high scores. 

Relationships like this are called curvilinear. While 

there are few in basic jail data, they are possible. The 

best way to detect them is to graph the data. If such a 

relationship does emerge from the data, use a statistic 

called a chi-square or phi coefficient to find out if 

there is a relationship between the data elements.

Statistical tests for correlation. Most statistical tests 

for correlation are mathematically descended from a 

test called the Pearson’s product moment correlation. 

Common statistical tests of correlation include:

Phi coefficient.

Rank biserial correlation coefficient.

Point biserial correlation coefficient.

Spearman’s rank order correlation coefficient.

Pearson’s product moment correlation coefficient.

Another statistic used to assess a relationship that is 

not a true correlation is the chi-square. A series of 

questions will help to decide which test to use (How 

to Analyze Data, see appendix B).

Question 1: What kind of measurement is used for 

the first data element measured? Is the case classified (put into categories), ranked (put in some order), 

or rated (given a score)? There are no problems if 

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Positive Linear Correlation Negative Linear Correlation

No Linear Correlation

Figure 7-4 Data Clusters 

8 This advanced statistical technique is beyond the scope of this manual. 

Most jurisdictions require technical help from someone experienced in this 

method.
How To Analyze Information 7–15CHAPTER

the measurement is ordinal or interval. However, if 

SEVEN

the case is classified (nominal), the researcher must 

answer question la before going to question 2.

Question 1a: Does the measurement of the data 

element put the case into two categories or more 

than two? If the answer is more than two (as it will be 

for many jail data elements), a correlation cannot be 

used to define the relationship between the variables. 

However, a chi-square can be used to test for 

a relationship.

Question 2: What kind of measurement is used for 

the second data element measured? See question 1. 

If the case is classified, the researcher also must ask 

Question la for the second data element.

Table 7-9 shows which test of correlation should 

be used, depending on the results of the answers to 

these questions.

Another statistical relationship test: The chisquare. Correlations cannot be used when either of 

the variables is classified and put into more than two 

groups. The statistical test that allows analysts to identify whether being a member of a particular category 

of one data element has anything to do with being a 

member of a particular category of another data element is the chi-square.

This is a useful statistical test for sheriffs and jail 

administrators because most of the basic jail data elements can be categorized and their values usually fall 

into more than one category. It is also easy to calculate. The chi-square begins with what statisticians call 

a “contingency table” or a cross-tabulation, which 

is a chart that displays the relationship between two 

frequency distributions. The chi-square test indicates 

whether the results of the contingency table are different from the results that would be expected if the two 

data elements were not related. Appendix J provides 

step-by-step instructions for calculating the chi-square.

Interpreting measures of relationship. It is important to be cautious when interpreting correlations and 

chi-squares. One common problem in interpreting 

these statistics is the fact that people assume that one 

data element has caused the other because a relationship exists between them. Correlation is not the same 

thing as causation. Think about height and weight, 

which have a strong positive correlation. But does 

height cause weight?

The technical aspects of this section have been included as resources, but are not part of the basic descriptive statistics that are tools for better management. 

If sheriffs and jail administrators can begin to keep 

good descriptive statistics on the jail population, then 

a major goal of this manual will have been achieved. 

The discussions about the statistics themselves should 

make sheriffs and jail administrators much more 

informed consumers of the statistics they receive.

Statistical Sins

Statistical sins can be sins of either commission or 

omission, and both can leave the statistical consumer 

in a very uncomfortable position. Part of the problem 

is that sometimes the most carefully calculated statistics are used carelessly; they may be interpreted by 

people who really do not understand statistical foundations. An additional purpose for this manual is to 

make sheriffs and jail administrators more knowledgeable consumers of the statistics that they encounter on 

a daily basis, both on and off the job.

One of the best resources in this area is the book How 

To Lie With Statistics, written by Darrell Huff in 1954 

(see appendix B). Although the examples this book 

used to illustrate the “statistical sins” are dated, the 

sins Huff identifies are just as prevalent today as they 

were then. This book is the source from which the following section was drawn.

Sin 1: How Could the Results Be Wrong if the 

Calculations Were Perfect?

Chapter 6 emphasized random sampling, and this 

chapter showed why it is important. But sometimes, 

in spite of the best intentions, samples can acquire a 

“built-in bias.” This can happen in a number of ways:

Researchers may report as fact what respondents 

say they do. People have a tendency to either 

embellish or “play down” some aspects of their 

behavior. For example, they may report earning 

more money than they actually do.

Researchers may fail to mention how many 

people did not respond to their survey. Frequently, 

people with certain characteristics (perhaps some 

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7–16 HOW TO COLLECT AND ANALYZE DATA

of those being measured) will fail to respond to a 

survey. College surveys that report how successful their alumni are may be guilty of this kind of 

built-in bias. Possibly alumni who are not very 

successful did not respond.

Researchers may ask questions about behavior 

that is not socially acceptable. Surveys that ask 

“Do you cheat on your taxes?” will probably discover that no one cheats on their taxes.

The source on which the sample is based does 

not represent the whole population. A frequent 

source of names for many surveys is the telephone 

book or a list of registered voters. While more 

people have telephones now than in the era when 

social research was beginning, some people still 

do not have telephones. This biases the sample. 

Even though the people who were surveyed were 

selected randomly, they were selected from a 

biased source.

If information is collected by interview, the time 

of day and location of the interviews can bias the 

sample. For instance, house-to-house, daytime 

interviews can bias the sample by filling it with 

people who do not work outside the home, and 

“man in the street” interviews miss the people 

who stay home.

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Classification of First Measure

Classification of Second Measure

Dichotomous Ordinal Interval

Dichotomous Examples:

Yes-No

Agree-Disagree

US Citizen-Alien

Sentenced-Not Sentenced

Phi Coefficient Rank Biserial Point Biserial

Ordinal Examples:

Rank in Class

Low, Moderate, High

Never, Sometimes, Often

Negative, Neutral, Positive

Rank Biserial Spearman’s Rank Order Spearman’s Rank Order

Interval Examples:

Arithmetic Score

Length of Stay

Years of Education

Point Biserial Spearman’s Rank Order Pearson’s Product Moment

Table 7-9 Appropriate Correlation Coefficients for Use With Different Types of Measurements

The person asking the question can bias the 

response. Gender or verbal intonation of the interviewer may elicit responses that the respondent 

believes the interviewer is seeking.

In developing a sample, not only must the cases be 

randomly selected, but the sample must represent the 

whole population. This can be a problem in jail surveys. For example, a systematic random sample of all 

people booked at the jail was developed for an Inmate 

Profile Data Collection. A major concern was the 

number of prisoners who were management problems 

during booking (e.g., they were violent, incapacitated 

by alcohol, mentally ill, etc.). When the statistics were 

analyzed, the results seemed low to all who worked in 

the system. The problem was soon discovered. Some 

prisoners who were the greatest management problems in the booking room were never booked. They 

were held on mental health detainers and not charged 

with a crime. No crime . . . no booking; it resulted in 

a biased sample.

Sin 2: What Is the Real Average?

In the discussion of measures of central tendency, 

three different measures (mean, median, and mode) 

were presented. These are not always the same 

thing, as illustrated by table 6-6. It is fine that these 

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Source: Carol Taylor Fitz-Gibbon and Lynn Lyons Morris, How to Calculate Statistics (Beverly Hills, CA: Sage Publications, 1978).
How To Analyze Information 7–17CHAPTER

measures are not the same because they measure difSEVEN

ferent things. The problem comes when researchers do 

not say which average they are using. Data elements 

that are expressed in dollars, like bond amounts, are 

most vulnerable to this kind of statistical misrepresentation. Figure 7-5 illustrates why.

Imagine how these differences could be used during a political campaign with a “get tough on crime” 

political platform. The incumbent says, “The average 

amount at which I set bond was $750.” The challenger 

says, “The incumbent is soft on crime. Why, the 

average amount at which bond was set was only 

$200!” The different averages present different pictures of bonding practices. Watch out for “averages” 

that do not say which kind of average they are.

Sin 3: What Is Missing From This Picture?

This statistical sin is illustrated by the data from the 

three jails. There are two aspects of descriptive statistics: measures of central tendency and measures of 

variability. The two of them should be inseparable. 

Frequently, however, the “average” is presented without any measure of variability, such as range or standard deviation. Recall Sheriff #3’s weekend when the 

jail (with a usual Average Daily Population [ADP] of 

16) was disrupted by 172 bikers, resulting in an ADP 

of 31, twice what the ADP was the other 27 days in 

that month. Mistaken conclusions could easily be 

drawn by using an “average” without something that 

tells how representative that number is. That is what 

the measure of variability does. Be a little skeptical 

of averages that stop before giving a measure of 

variability.

Another statistic that is frequently missing—the 

number of cases—is somewhat less obvious. And it 

may be more devious because it misrepresents one of 

the basics, the frequency distribution. Sometimes the 

data is presented as a percentage without the number 

of cases in each group. 

Compare table 7-10, which shows the number of cases 

in the whole sample (1,000) and how many cases were 

in each group, with table 7-11, which gives only the 

percentage. In table 7-11, there is no way to know 

if there are 100 or 1,000 inmates in the sample. Just 

remember that “50 percent of the cases” can be just 

one case, and be suspicious of results reported only in 

percentages.

Sin 4: So What if It Is Statistically Significant?

With a very large sample, very small differences 

between groups can be statistically significant simply 

because there are many cases. From a manager’s 

perspective, what difference does a prerelease program 

make if program participants average 4.21 subsequent 

arrests versus nonparticipants who average 4.23 subsequent arrests? This distinction may be statistically 

significant, but it would be difficult to use as justification for program funding.

Informed consumers look not only at whether the 

difference is statistically significant but also at what 

the real difference is. It is important to remember that 

some differences that are not statistically significant 

may be important for management decisions.

Sin 5: If A, Then B?

This is so important that, although it was mentioned 

earlier, it is worth repeating: Correlation is not the 

same as causation. Just because one thing happens 

before another does, it does not imply that one causes 

the other. Strong correlations can result from:

Both factors being related to a third (and 

possibly unknown factor) that causes both of 

them.

Chance or “spurious correlations.” An example 

of this is finding a strong positive correlation 

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Figure 7-5 Measures of Central Tendency

$100 $200 $300 $370 $500 $570 $750 $1,000 $1,500

15

14

13

12

11

10

 9

 8

 7

 6

 5

 4

 3

 2

 1

 0

Bond Amount

Number fo Inmates

Mode

Median Mean
7–18 HOW TO COLLECT AND ANALYZE DATA

between a county’s jail bookings and the price of 

hog futures on a local commodity exchange.

Situations in which an actual relationship exists 

between two data elements, but it is not known 

which data element causes the other. One example 

would be a relationship between Length of Stay 

and the number of incidents committed by prisoners. Do prisoners commit more incidents because 

they stay longer and thus have more opportunity? 

Or does the number of incidents committed by 

prisoners predispose the court to keep bond high 

in the case of pretrial inmates and reduce the 

amount of good time earned or increase the length 

of the sentence in the case of sentenced prisoners? 

Or are long-term prisoners more likely to commit 

incidents than prisoners who are released within 

shorter periods?

Sin 6: What Is Wrong With This Picture?

Statistics can result in both a numerical summary of 

information and a graphic representation. However, 

some of the worst statistical misrepresentations occur 

in how information is displayed. By carefully choosing the scale on which information is graphed, a 

strong (and sometimes misleading) message of what 

the data means can be depicted. Figures 7-6, 7-7, and 

7-8 display the same data on three scales.

Most people react differently to the three graphs but 

do not look closely enough at graphs and charts to see 

the “tricks” that were played. Look at the numbers at 

■

the left of figures 7-6 and 7-7. The scale in figure 7-6 

begins at zero; the scale in figure 7-7 begins at 1900. 

Now compare the lines in figures 7-6 and 7-7 with 

the line in figure 7-8. Additional data points were 

plotted in figure 7-8, so the line shows the upward 

trend with more specific detail. Displaying data effectively and responsibly is a major part of any data collection. Sometimes there is a fine line between effective and ethical data display.

Conclusion

This chapter covered a great deal—from some of the 

foundations on which the entire field rests, to how to 

use the different types of statistics (and how to calculate some of the more basic ones), and, finally, to the 

statistical sins that have given the field a bad reputation. This manual should encourage sheriffs and jail 

administrators to keep jail statistics and use them to 

make more informed decisions about situations that 

affect the jail. But, learning how to understand and 

calculate statistics is only a part of this manual’s mission. If sheriffs and jail administrators are to benefit 

from this knowledge, they need to learn how to apply 

it in their own situations and be able to interpret statistics. That is the purpose of chapter 8.

Table 7-10 Frequency Distribution of Age, 

Version 1 

Age Number Percentage

Under 18 0 0%

18–21 160 16%

22–25 160 16%

26–29 280 28%

30–33 120 12%

34–37 130 13%

38–41 50 5%

42–45 80 8%

46 or older 20 2%

1000 100%

Age Percentage

Under 18 0%

18–21 16%

22–25 16%

26–29 28%

30–33 12%

34–37 13%

38–41 5%

42–45 8%

46 or older 2%

100%

Table 7-11 Frequency Distribution of Age, 

Version 2
How To Analyze Information 7–19CHAPTER

Figure 7-6 Scale, Version 1 

SEVEN

2500

2000

1500

1000

 500

 

 0

Figure 7-7 Scale, Version 2

2400

2300

2200

2100

2000

1900 

Figure 7-8 Scale, Version 3

2400

2300

2200

2100

2000

1900 
c h a p t e r e i g h t

How To Interpret 

Information
Case Study 1: The Friday Night Blues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-4

Before Going on to Case Study 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-15

Case Study 2: The Alternative Answer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-15

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-27
8–

H o w T o I n t e r p r e t I n f o r m a t i o n

When the information has been collected and processed, the exciting part of the data 

collection—the analysis itself—begins. Analysis is the process of interpreting what 

the information means in the context of the environment in which it was collected. 

Explaining how to begin this process is like telling someone how to ride a bicycle. 

It is possible to explain about balance, velocity, and the laws of motion, but until he/she 

actually starts pedaling, none of it makes much sense.

Ideally, sheriffs and jail administrators who want to learn how to interpret statistical 

information should work with someone who can help to analyze the data. The next 

best alternative is to use a “programmed text” that teaches analytical skills. In a programmed text, the reader is presented with a problem, given an opportunity to respond 

to it, and then given instructions to see the preferred response. Although you may be 

tempted to gloss over them, take the time to write down your responses. Writing helps 

many people think things through.

This chapter is structured as a programmed text so that you can work through the 

examples that are provided in this chapter. Two case studies provide real-life examples 

of how basic jail data can be used for management decisionmaking. What the key 

actors chose to do in each case may not be exactly what you would do in the same 

position. Although you may not agree with their choices, these case studies make it 

clear how people in these actual situations used the information that was available to 

make critical policy decisions for their jails.

c h a p t e r e i g h t
8– HOW TO COLLECT AND ANALYZE DATA

Case Study 1: The Friday Night Blues

The Metro County Jail holds people who are intoxicated but not charged with an offense under two circumstances: (1) if they are violent or (2) if the local 

detoxification center is full.

When they are brought to the jail, they are held in the 

booking room for 8 hours and then released. Although 

the detox center has been operating at only about 

50 percent of capacity, the number of detox holds at 

Metro County Jail has steadily increased over the 3 

years that this practice has been followed. The booking area is frequently crowded and congested. If the 

situation gets worse, the booking area may need to be 

expanded.

You are the jail administrator. A special task force 

has been established by the sheriff and county commissioners to decide whether Metro County should 

expand the present booking area. The sheriff told 

you to have data available for the next meeting of 

the task force.

What should the jail administrator do first? (Write your answer below.)

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page to see what happened.
How To Interpret Information 8–CHAPTER EIGHT

The jail administrator and the sheriff’s administrative assistant developed a problem statement below 

that described the situation. If you had a different 

response, you might want to review chapter 3.

The Metro County Jail has the authority to 

detain intoxicated people without charges in 

two circumstances:

1. If the person is violent.

2. If the detox center is full.

We hold these people in the booking room 

for 8 hours and then release them. The detox 

center has been operating consistently at 

about 50 percent of capacity, but the number 

of detox holds at Metro County Jail has 

steadily increased over the 3 years that this 

practice has been followed. The booking area 

is frequently crowded. If the situation gets 

worse, it is likely that the booking area will 

need to be expanded.

Does that sound familiar? It is the description of the 

situation that you read earlier. It is quite a good problem statement. The jail administrator was lucky: The 

first task was already completed.

Now that the jail administrator has a good problem statement, what is the next step?

 

When you have completed your answer, go to the next page.
8– HOW TO COLLECT AND ANALYZE DATA

The jail administrator identified the questions that had 

to be answered in the analysis and the data elements 

needed. The jail administrator identified the following 

questions:

1.	 How much have detox holds increased over the

last 3 years?

2.	 Are the detox holds brought to the jail violent?

3.	 Are detox holds brought to the jail when there is

space for them at the detox center?

4.	 Will the number of detox holds stabilize,

decrease, or increase?

The jail administrator identified the following data 

elements that were needed:

The number of detox holds detained at the jail by 

month as far back as possible.

The number of detox holds who commit incidents 

in the booking room.

The number of times that the detox center was 

full during each year.

■

■

■

What should the jail administrator do next?

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.
How To Interpret Information 8–CHAPTER EIGHT

The jail administrator was concerned about whether 

the jail kept information about detox holds since they 

were never booked. However, the jail clerk who maintained the information system indicated that a special 

category for detox holds had been established, records 

had been kept about detox holds for about 5 years, 

and the information system could provide the required 

historical data.

The jail administrator was also worried about identifying the number of detox holds who were violent. 

Jail records could provide the number of detox holds 

involved in incidents at the jail. It was the jail’s 

policy that whenever someone had to be restrained or 

behaved in a violent way, the officer(s) involved wrote 

an Incident Report. The reports were filed in both an 

Incident Log and the inmate’s record. The jail administrator would be able to review the Incident Log to 

identify how many detox holds were involved in incidents in the facility.

The question that was the hardest for the jail administrator to answer was how often people were brought to 

the jail because the detox center was full. The minutes 

of the task force meeting indicated that the detox center had been operating at about 50 percent of capacity, 

but the jail administrator was suspicious of averages. 

Worse, the jail had no way to gather that data.

The jail administrator did not know whether detox 

holds were likely to increase, decrease, or remain 

stable. He hoped that the available information would 

provide the answer.

Assume that the jail administrator got all the data 

together. He thought it might be a good idea to share 

this information with the sheriff first. This is wise. No 

one likes to be surprised about data from his/her own 

organization.

 

What information should the jail administrator prepare for the meeting with the sheriff?

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.
8– HOW TO COLLECT AND ANALYZE DATA

The jail administrator used both tables and charts to 

summarize the information he gathered. Table 8-1 and 

figure 8-1 display the number of detox holds detained 

at the jail for the past 5 years.

Figure 8-1 Historical Trend in Detox Holds

Table 8-1 Historical Trend in Detox Holds

Year Detox Holds

2000 237

2001 332

2002 564

2003 790

2004 1,027

What do the table and figure suggest?

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.

1200

1000

 800

 600

 400

 200

 0 

2000 2001 2002 2003 2004 

Year

Detox Holds

Detox Holds
How To Interpret Information 8–CHAPTER EIGHT

Both the sheriff and jail administrator were surprised 

to find that detox holds had increased 333 percent 

from 2000 to 2004. This increase made them nervous. 

Even if additional booking space was built, it would 

be at least 3 years until that space was available. And 

if detox holds increased so much in 4 years, things 

could get worse before new space was built.

The sheriff knew of a technique called population 

forecasting, which allows statisticians to predict what 

a future population was likely to be. Recognizing that 

this was an advanced statistical technique, the sheriff 

asked a staff member, who was a graduate student at 

a local university, for assistance.

The jail administrator next showed the sheriff table 

8-2 and figure 8-2, which displayed the relationship 

between detox holds and incidents committed in the 

jail in 2004.

Figure 8-2 Detox Holds With Incidents

Table 8-2 Detox Holds With Incidents 

Detox Holds Number Percentage

With Incidents 185 18%

Without Incidents 842 82%

Total 1,027 100%

What do the table and figure suggest?

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.

82%

Detox Holds with Incidents

Incident

18%

No

Yes
8–10 HOW TO COLLECT AND ANALYZE DATA

Both the sheriff and jail administrator were surprised 

by how few of the detox holds were involved in incidents while in the booking room. A review of the 

Incident Log Book revealed that, although some of 

the incidents that did involve detox holds were violent 

(usually because the person was violent when brought 

in), a number involved medical or health-related problems. Only 185 of the 1,027 detox holds that were 

brought in that year were violent.

The jail administrator thought that the odds were 

against the detox center being full the other 842 times 

that the jail had detained people. The jail administrator 

had talked with the detox center administrator, who 

indicated that the detox center was at capacity only 

about one day a month. Even if this provided a reason 

for some of the detox holds, it left a great many not 

accounted for.

 What does this information suggest? 

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.
How To Interpret Information 8–11CHAPTER EIGHT

Both the sheriff and jail administrator concluded that a 

number of detox holds coming to the jail did not meet 

the criteria for detention. Remember . . .

The Metro County Jail may detain persons for 

the local detox center:

1 If they are violent.

2. If the detox center is full.

While the sheriff and the jail administrator, both former patrol officers, were beginning to suspect what 

might be occurring, they did not want to jump to 

conclusions. They agreed that some additional information about detox holds and why they were brought 

to the jail was necessary. The task of obtaining that 

information was assigned to the jail administrator. The 

sheriff and jail administrator agreed to meet 1 week 

later to decide what action (if any) the jail should take 

and what they would recommend to the task force.

What did the jail administrator do next?

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.
8–12 HOW TO COLLECT AND ANALYZE DATA

MEMO

To: All Booking Officers

From: Jail Administrator

Subject: Detox Holds

Date: September 27, 2004

Effective immediately, when a patrol officer brings in a detox hold, booking officers will:

1. Ask the patrol officer why the detox hold is being brought to the jail rather than the detox center and 

make sure that this reason is recorded on the Booking Form.

2. Accept the detox hold.

3. If the detox hold is violent at the time of admission, attach a copy of the Incident Report to the Booking 

Form and forward to the jail administrator.

4. If the patrol officer indicates that the detox hold was violent at the detox center, call the detox center for 

verification after the patrol officer has left, record this information on the Booking Form, and forward to 

the jail administrator.

5. If the patrol officer indicates that the detox center was full, call the detox center for verification after the 

patrol officer has left, record this information on the Booking Form, and forward to the jail administrator.

Figure 8-3 Memo from the Jail Administrator

The jail administrator considered a number of different strategies, including asking patrol officers about 

their past use of the detox center for intoxicated 

people and identifying several dates when there were 

a number of detox holds, pulling the complaint reports 

for them, and calling the detox center to find out 

whether it was full on those dates.

The jail administrator, however, rejected both strategies. The patrol officers might not remember, or there 

would not be any documentation about why the detox 

holds had been brought to the jail. In addition, the 

thought of going through all that paperwork was discouraging. Instead, the jail administrator developed a 

third strategy. The following directive (figure 8-3) for 

booking officers was written and read at all shift briefings that day.

While the jail administrator was concerned about how 

patrol officers might view this strategy, he thought it 

was more important to avoid disagreements between 

the booking and patrol officers (particularly in front of 

the detox hold) while the information was being gathered. He was also somewhat concerned about the size 

of his sample. The current week had only 25 detox 

holds.

Nevertheless, the jail administrator, thinking that some 

information is better than none, compiled the available 

information for the meeting with the sheriff (table 8-3 

and figures 8-4 and 8-5).
How To Interpret Information 8–13CHAPTER EIGHT

Table 8-3 Patrol Officers’ Reasons for Detox Holds

Patrol Officer Said: Jail Finding Number Percentage

Violent at Detox Center Yes 1 4%

No 15 60%

Detox Center Full Yes 2 8%

No 7 28%

Total Detox Holds 25 100%

Figure 8-4 Detox Holds, Violent at 

Detox Center 

What conclusion would you draw from this sample?

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.

Figure 8-5 Detox Holds, When Detox 

Center Full

93.75%

Reason for Detox Holds

Violent at Intake

6.25%

No

Yes

Reason for Detox Holds

Detox Center Full

No

Yes

22.2%

77.8%
8–14 HOW TO COLLECT AND ANALYZE DATA

Both the sheriff and jail administrator concluded that 

patrol officers were bringing detox holds to the jail 

that should have gone to the detox center. The jail 

administrator was able to verify this—several of the 

booking officers had noted on the Booking Form that 

patrol officers who said they first took people to the 

detox center had never been there.

The sheriff then showed his projected increases in 

the number of detox holds that would be held at the 

jail (table 8-4 and figure 8-6). The analysis began by 

summarizing the number of detox holds for 5 years, 

although the forecast was actually made by using data 

grouped by month. The numbers indicated that if the 

present trend in detox holds detained at the jail continued, they could expect to have 1,231 (about 3.5 a day) 

in 2005 and 1,435 (about 4 a day) in 2006. 

Figure 8-6 Trend in Detox Holds

Table 8-4 Trend in Detox Holds 

Year Detox Holds

2000 237

2001 332

2002 564

2003 790

2004 1,027

2005 1,231

2006 1,435

Based on this information, what should the sheriff and jail administrator recommend to the task force?

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.

2000 2001 2002 2003 2004 2005 2006 

1600

1400

1200

1000

 800

 600

 400

 200

 0

Trend in Detox Holds

Year

Detox Holds
How To Interpret Information 8–15CHAPTER EIGHT

A wide range of actions could be taken to deal with 

the large number of detox holds brought to the jail 

instead of the detox center. The sheriff knew that a 

city police department was the primary user of the 

jail (with about 75 percent of all inmates and nearly 

all detox holds). The first approach to solving the 

problem was to discuss it with the local police chief. 

Unfortunately, they were not able to come to an 

agreement.

Not successful in his attempt at collaboration, the 

sheriff decided that acting to preserve the jail’s interest in this situation was consistent with local criminal 

justice system policy. The detox center was developed 

as an alternative to jail after public intoxication was 

decriminalized, to avoid having to increase the jail’s 

capacity.

In his response to the task force, the sheriff indicated 

that the jail had detained detox holds as a favor to the 

local criminal justice system and the detox center, 

but that this favor had been somewhat abused. Detox 

holds had become a serious problem for the jail on 

weekends as they used bed space the jail needed for 

persons who had been arrested. While the current situation was bad enough, detox holds were projected to 

increase at such a rate that, if no changes were made, 

the booking room would require expansion within the 

next 2 years. The sheriff concluded that expanding the 

booking room to accommodate the detox holds would 

represent a major shift in criminal justice system 

policy.

The sheriff established a policy that the jail would no 

longer accept detox holds. Those holds who were violent could be charged with a crime. The detox center 

indicated that, although it was seldom full, it had the 

space to expand if necessary. This was not an easy 

decision to make—nor a very popular one with some 

of the local law enforcement community.

Six months later, the detox center reported that it was 

operating at close to 95 percent of capacity and was 

adding 10 more beds. It changed its weekend staffing 

pattern to more adequately manage the people placed 

in its custody. The jail reported that space problems in 

the booking area were much improved. Even though 

Friday and Saturday nights were still busy, the jail no 

longer had people sleeping it off for 8 hours in the 

three available holding cells while officers tried to 

book others charged with crimes.

Before Going on to Case Study 2

Clearly, statistics are used with both qualitative information (the sheriff’s meeting with the police chief) 

and criminal justice system policy preferences (the 

system’s choice to use the detox center as a way of 

dealing with alcohol problems) to make policy decisions. In the first case study, the sheriff made a policy 

decision that was both proactive and supportive of the 

jail’s position. Other sheriffs might have made different but equally effective decisions. Without the information gathered in the study, the sheriff might have 

recommended to the task force that the booking room 

be expanded immediately, resulting in the expenditure of county funds to house people in the jail that 

the criminal justice system had determined should be 

housed elsewhere.

By now, the process of analysis should be clearer. 

Take a look at the second case study and see what 

decisions you make to resolve a problem many counties face when they plan new jails: their needs cannot 

be accommodated by their resources.

Case Study 2: The Alternative Answer

Forest County encompasses a relatively small, rugged 

area and has a population of about 65,000. The 60-bed 

jail, constructed in 1935, is crowded, with an Average 

Daily Population (ADP) of 81.25 inmates. Because 

of a Federal District Court consent decree, the county 

has been preparing to build a new jail and sheriff’s 

office for about a year. A task force9 has been working with a criminal justice planning firm to complete 

a needs assessment. Population projections indicate 

that a jail of approximately 250 beds will be needed to 

meet anticipated needs until 2020. Unfortunately, the 

county can afford a facility of only 150 beds.

After the initial budgeting session, the task force 

decided to rethink a number of criminal justice 

policies. Task force members asked their planning 

9 The Jail Task Force includes the sheriff, the chief of police of the primary city, the presiding judges of the county and district courts, the prosecutor, the chief probation officer, and two representatives of the five-person 

county commission.
8–16 HOW TO COLLECT AND ANALYZE DATA

consultant to help them determine potential segments 

of the jail population that might be appropriate for 

alternatives to incarceration. They identified a number 

of alternatives that would be appropriate for the community, such as house arrest with electronic monitoring, day reporting, boot camp, pretrial case processing 

modifications, and intensive supervision. 

The criminal justice planner provided task force 

members with an Inmate Profile Data Collection 

Report based on a sample of more than 600 cases 

collected over a 6-month period in 2003. The task 

force was asked to help identify the alternatives that 

would have the greatest impact on the jail population. 

You are the sheriff. You begin to review the Inmate 

Profile Data Collection Report. Table 8-5 and figure 

8-7 catch your attention.

 

Figure 8-7 Resident Status

Table 8-5 Resident Status 

Resident Status Number Percentage

County 574 85%

State 78 11%

Out of State 26 4%

Total 678 100%

How would you analyze and use this information?

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.

Residence

Out of State

State

County

11.5%

84.7%

3.8%
How To Interpret Information 8–17CHAPTER EIGHT

The sheriff found that most of the people detained in 

the jail were county residents. It would be easier to 

decide if they would be reasonable candidates for 

a sentencing alternative and they would be easier to 

find later.

The sheriff looked a little further. Table 8-6 and figure 

8-8 were next in the Inmate Profile Data Collection 

Report.

Figure 8-8 Gender of Jail Residents

Table 8-6 Gender of Jail Residents 

Gender Number Percentage

Male 513 76%

Female 165 24%

Total 678 100%

Gender

Female

Male

24.3%

75.7%

What does this information suggest?

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.
8–18 HOW TO COLLECT AND ANALYZE DATA

Table 8-6 troubled the sheriff because it seemed 

inconsistent with national trends. The sheriff went 

to a copy of the Bureau of Justice Statistics (BJS) 

Bulletin “Prison and Jail Inmates at Midyear 2003.” 

This report indicated that women are about 11 percent 

of the national jail population. About 24 percent of 

the inmate admissions to the Forest County jail were 

Table 8-7 Age of Jail Residents Figure 8-9 Age of Jail Residents

Age Number Percentage

18–21 98 14%

22–25 186 27%

26–29 86 13%

30–33 52 8%

34–37 36 5%

38–41 88 13%

42–45 44 6%

46–49 48 7%

50 or older 40 6%

Total 678 100%

What does this information suggest?

	 	 	 	 	 	 	 	 	 	 	 	

	 	 	

	 	 	 	 	 	 	 	 	

	 	 	 	 	 	 	 	 	 	 	 	

	 	 	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.

women. Even if the BJS statistics dealt with inmates 

in custody and the sheriff was counting bookings, the 

difference still seemed too great. The sheriff wondered 

if this was an important clue to something unusual in 

the system’s practices. Table 8-7 and figure 8-9 were 

next in the study.

 

22–25 30–33 38–41 46-49

18–21 26–29 34–37 42–45 50 or Older

200

150

100

 50

 0

Age

Age Group

Number of Inmates
How To Interpret Information 8–19CHAPTER EIGHT

Table 8-7 did not seem out of the ordinary. While 45 

percent of the jail population was over 30 (which may 

make it slightly older than the population in many 

jails), this was not very different from what the sheriff 

had expected. The sheriff suspected that it might not 

make much difference in this situation, because few 

alternatives or programs specifically target a particular 

age of offender.

This table represents one of the problems in analyzing information: All the information available does 

not pertain to the problem. One of the critical tasks in 

interpreting the results of data collections is to sort out 

the information that does make a difference from all 

the information generated. Computers, in conjunction 

with poorly focused analysis, frequently cause “information overload,” in which too much information is 

processed and presented for the analysts to comprehend. The sheriff had learned to sort out the things 

that were unusual or unexpected.

The next table and figure in the Inmate Profile Data 

Collection were about Legal Status (table 8-8 and 

figure 8-10). 

Figure 8-10 Legal Status of Jail Residents 

Table 8-8 Legal Status of Jail Residents

Legal Status Number Percentage

Pretrial 347 51%

Sentenced 301 44%

Hold 30 4%

Total 678

What does this information suggest?

	 	 	 	 	 	 	 	 	 	 	 	

	 	 	 	

	 	 	

	 	 	 	 	

	 	 	 	 	 	 	 	 	 	 	 	 	 	

	 	 	 	 	 	 	 	 	 	 	 	

	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.

Pre-Trial Sentenced Hold

 Legal Status

350

300

250

200

150

100

 50

 0

Legal Status

Number of Inmates
8–20 HOW TO COLLECT AND ANALYZE DATA

Table 8-8 indicated that 51 percent of the people 

booked at the jail were pretrial detainees and 44 

percent were sentenced. The rest were held only for 

another jurisdiction on a warrant. Any alternatives 

that the jail would use to manage the jail population 

needed to work with both populations. The next table 

and chart (table 8-9 and figure 8-11) provided information about charge level.

Charge level

Other

Traffic

Felony

Misdemeanor

44.4%

53.1%

2.2%

0.3%

Figure 8-11 Charge Level of Jail Residents

Table 8-9 Charge Level of Jail Residents

Charge Level Number Percentage

Felony 301 44.40%

Misdemeanor 360 53.10%

Traffic 15 2.21%

Other 2 0.29%

Total 678 100.00%

In deciding which alternatives will be most effective, how do you interpret this information and what else does it 

make you want to know?

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.
How To Interpret Information 8–21CHAPTER EIGHT

What are the implications of this information for alternatives to incarceration?

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.

The sheriff was not surprised by the data shown in 

table 8-9. Just under half of the inmates were charged 

with felonies—the hardest inmate group to divert 

from jail. Fifty-five percent were charged with either 

misdemeanors or traffic offenses carrying the weight 

of a misdemeanor. Charge certainly was a major consideration in matching inmates with alternatives. The 

sheriff knew that placing certain types of inmates in 

alternative programs would be like waving a red flag 

in the faces of a relatively conservative constituency. 

And it was clear that alternatives appropriate for a 

pretrial misdemeanant would not work for a sentenced 

misdemeanant. The sheriff decided two additional 

questions must be answered:

1.	 What were these inmates charged with?

2.	 What was the relationship between charge level

and legal status?

Fortunately, the table 8-10 and figure 8-12 provided 

information about the type of offense for which each 

person had been booked.

Offense Category Number Percentage

Persons 97 14%

Property 239 35%

Children & Family 10 1%

Sex 17 3%

Forgery & Fraud 67 10%

Weapons 21 3%

Drugs & Alcohol 207 31%

Traffic 13 2%

All Other 7 1%

Total 678 100%

Table 8-10 Most Serious Charge of Jail 

Residents

Figure 8-12 Most Serious Charge of Residents

Offense Type

Drugs/Alcohol

Traffic

Other

Persons

Weapons Forgery

Sex

Child/Family

Property

30.5%

1.9%

14.3%

35.3%

3.1% 9.9%

2.5%

1.5% 1%
8–22 HOW TO COLLECT AND ANALYZE DATA

The sheriff noted that 14 percent of those who were 

booked in the jail were charged with offenses against 

persons. These were among the least likely candidates 

for diversion. Also, very few traffic offenders were 

booked, suggesting that the arrest standards calling for 

summons and citation of traffic offenders were being 

followed. The largest groups of inmates were booked 

for property and drug and alcohol offenses. The sheriff thought the task force might need some additional 

information about these inmates to determine how 

alternatives might be used with this part of the jail 

population.

What other things would you want to know about the property and drug and alcohol offenders?

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.
How To Interpret Information 8–23CHAPTER EIGHT

The sheriff then examined the report for information 

about the relationship between legal status and charge 

level. The sheriff found a cross-tabulation of the two 

data elements in table 8-11 and figure 8-13.

Table 8-11 Legal Status, by Charge Level

Charge Level

Legal Status

Pretrial Sentenced Hold Total

Felony 222 33% 64 9% 15 2% 301

Misdemeanor 125 18% 230 34% 5 1% 360

Traffic 0 0% 7 1% 8 1% 15

Other 0 0% 0 0% 2 0% 2

Total 347 51% 301 44% 30 4% 678

Figure 8-13 Legal Status, By Charge Level

What are the implications of the cross-tabulation of charge level and legal status?

	 	 	 	 	 	 	 	 	 	 	 	

	 	 	 	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.

Felony Misdemeanor Hold

 Legal Status

250

200

150

100

 50

 0

Legal Status by Charge Level

Pre-Trial

Traffic

Sentenced

Other

Number of Bookings
8–24 HOW TO COLLECT AND ANALYZE DATA

The sheriff was feeling a certain amount of frustration with this business of “analysis.” It seemed that 

the more that you learn, the more you need (or want) 

to know. The sheriff decided that the criminal justice 

planner should have answers to the following questions at the next task force meeting:

1.	 What were the specific charges in the property

and drug and alcohol categories?

2.	 How many people had been booked for each?

3.	 If this was the most serious charge, how many

other charges were involved?

4.	 Were any of those charges crimes against

persons?

5.	 Did the person have any outstanding warrants

from other jurisdictions?

6.	 Was there a probation or parole violation in

addition to this charge?

The implications of the cross-tabulation seemed 

clear to the sheriff. Three large groups (subpopulations in statistical language) were present in the jail. 

They were:

1. Pretrial felons.

2. Pretrial misdemeanants.

3. Sentenced misdemeanants.

The sheriff talked with the jail administrator about 

these three groups. Developing alternatives for pretrial 

felons was likely to be challenging, and alternatives for 

sentenced misdemeanants had complications of their 

own. However, pretrial misdemeanants seemed to be 

a relatively easy category to find alternatives for. A 

good review of the bond schedule or a pretrial release 

program should do the trick. The jail administrator, 

however, was puzzled by this data, which seemed 

inconsistent with the faces that she saw every week 

during the facility inspection. And yet, the sheriff and 

jail administrator knew that the information had come 

directly from their automated booking system. They 

contacted the criminal justice planner who had analyzed the data to see if this apparent discrepancy could 

be resolved.

What could cause this discrepancy?

	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.
How To Interpret Information 8–25CHAPTER EIGHT

The criminal justice planner suggested to the sheriff 

and jail administrator that it was possible they were 

dealing with a common jail phenomenon: the difference between the number of people booked and 

the number of people in custody on any given day. 

The easiest way to illustrate this was by tables 8-12 

through 8-15, which were to be part of the final report 

to the task force.

The jail administrator and sheriff looked at tables 8-12 

and 8-13 and said, “So what?” Tables 8-14 and 8-15 

made the criminal justice planner’s point clearer.

Table 8-12 Persons Booked for Property and Drug and Alcohol Offenses

Bookings Pretrial Sentenced

Offense Category Felony Misdemeanor Felony Misdemeanor Total

Burglary 33 0 9 0 42

Criminal Mischief 3 11 6 35 55

Theft 37 47 14 28 126

All Other Property 0 0 3 13 16

Subtotal Property 73 58 32 76 239

Possession 34 14 6 37 91

Sales 21 0 12 0 33

DUI 1 8 7 67 83

Subtotal Drugs & Alcohol 56 22 25 104 207

Total 129 80 57 180 446

Table 8-13 Average Daily Population of Property and Drug and Alcohol Inmates

Average Daily Population Pretrial Sentenced

Offense Category Felony Misdemeanor Felony Misdemeanor Total

Burglary 3.35 0.00 4.44 0.00 7.78

Criminal Mischief 0.11 0.06 1.48 2.88 4.52

Theft 1 0.39 3.45 2.30 7.86

All Other Property 1.72 0.00 0.37 1.07 1.44

Subtotal Property 5.18 0.45 9.74 6.25 21.61

Possession 1.30 0.08 2.96 4.05 8.39

Sales 1.21 0.00 6.12 0.00 7.32

DUI 0.01 0.02 3.45 5.51 8.99

Subtotal Drugs & Alcohol 7.69 0.55 22.27 15.81 46.31

Total 12.87 .99 32.01 22.05 67.92
8–26 HOW TO COLLECT AND ANALYZE DATA

Table 8-14 Percentage of Persons Booked for Property and Drug and Alcohol Offenses

Bookings Pretrial Sentenced

Offense Category Felony Misdemeanor Felony Misdemeanor Total

Burglary 7% 0% 2% 0% 9%

Criminal Mischief 1% 2% 1% 8% 12%

Theft 8% 11% 3% 6% 28%

All Other Property 0% 0% 1% 3% 4%

Subtotal Property 16% 13% 7% 17% 54%

Possession 8% 3% 1% 8% 20%

Sales 5% 0% 3% 0% 7%

DUI 0% 2% 2% 15% 19%

Subtotal Drugs & Alcohol 13% 5% 6% 23% 46%

Total 29% 18% 13% 40% 100%

Table 8-15 Percentage of Average Daily Population of Property and Drug and Alcohol Inmates

Average Daily Population Pretrial Sentenced

Offense Category Felony Misdemeanor Felony Misdemeanor Total

Burglary 5% 0% 7% 0% 11%

Criminal Mischief 0% 0% 2% 4% 7%

Theft 3% 1% 5% 3% 12%

All Other Property 0% 0% 1% 2% 2%

Subtotal Property 8% 1% 14% 9% 32%

Possession 2% 0% 4% 6% 12%

Sales 2% 0% 9% 0% 11%

DUI 0% 0% 5% 8% 13%

Subtotal Drugs & Alcohol 11% 1% 33% 23% 68%

Total 19% 1% 47% 32% 100%

What conclusions can be drawn from these tables?

	 	 	 	 	 	 	 	 	 	 	 	

	 	 	 	 	 	 	 	 	 	 	 	 	 	

When you have completed your answer, go to the next page.
How To Interpret Information 8–27CHAPTER EIGHT

The sheriff and jail administrator noticed substantial 

differences between the first two tables when they 

were expressed as percentages in the second two 

tables.

1. Although pretrial felons in the two categories 

reviewed were 29 percent of the bookings, they 

accounted for only 19 percent of the ADP of the 

facility.

2. Pretrial misdemeanants were 18 percent of the 

bookings in the two categories, but only 1 percent 

of the ADP.

3. Sentenced felons were 13 percent of bookings, but 

47 percent of the ADP.

4. Felons sentenced for drug and alcohol offenses 

were 6 percent of bookings, but 33 percent of the 

ADP.

5. Sentenced misdemeanants were 40 percent of 

bookings in the two categories, but 32 percent of 

the ADP.

Figure 8-14 made the point even clearer. In examining 

the groups that populated the jail, it was critical that 

the task force members think in terms of ADP, Inmate 

Days, or another measure of how much of the jail bed 

space a group of inmates actually uses. Clearly, different groups of inmates have very different Lengths of 

Stay. In making decisions about alternatives and how 

they might be used to “save” or “defer” jail bed space, 

Figure 8-14 Comparison of Bookings and ADP 

of Property and Drug and Alcohol

Pre. Fel. Pre. Misd. Sent. Fel. Sent. Misd.

Offense Category

50%

40%

30%

20%

10%

 0%

Comparison of Bookings and ADP

Bookings ADP

Percent

it is a matter of both how many people come into the 

jail and how long they stay. Understanding this up 

front helped the task force members avoid a common problem—focusing on the wrong target group. 

For example, 18 percent of the bookings were pretrial 

misdemeanants. The sheriff and jail administrator had 

considered that this might be a relatively easy group 

to divert from the facility. However, even if all of them 

disappeared, the ADP of the jail would have decreased 

only about 1 percent. However, while sentenced felons 

are only 13 percent of the bookings, they use nearly 

half of the jail bed space. The average Length of Stay 

for sentenced felons in these categories was 142.5 

days. If their average Length of Stay could be reduced 

to 90 days, the ADP of sentenced felons would be 

reduced from 32 to 14.

The sheriff and jail administrator saw a way through 

what the task force feared would be a difficult situation politically. No one was going to divert sentenced 

felons from the jail. Part of their sentence would be 

served in the jail, but the remainder of it could be 

served in any of the nonresidential alternatives. The 

sheriff and jail administrator developed a proposal for 

electronic home detention, day reporting, and intensive supervision as part of the sentence. The task force 

considered this proposal an appropriate alternative for 

the county and recommended its implementation.

Conclusion

These case studies represent what it is really like to 

analyze data. The process is not as straightforward as 

statisticians may tell you and requires a measure of 

qualitative as well as quantitative information. Some 

areas are open to interpretation. Data collections often 

raise as many questions as they answer. You analyze 

information, discover you want to know something 

else, track down that information, reanalyze, and then 

reinterpret. Data analysis is much like good detective 

work. The “tools of the trade” may be different, but 

the thought processes are similar. Finally, analysis is 

very much about putting humans, not computers, in 

the role of interpreting what data means.

Interpreting jail or criminal justice system data is best 

done by people who know something about how the 

system works. Good analysts can present the data, 

identify things that seem unusual, locate targets for 
8–28 HOW TO COLLECT AND ANALYZE DATA

change, and perhaps point out potential alternatives, 

but they cannot make decisions. Sheriffs and jail 

administrators (and their counterparts in other parts of 

the criminal justice system) must take an active role in 

deciding what the information gathered means. Unless 

the managers in the system take part in the analysis, 

reports are shelved no matter how accurate or well 

done they are.

Sometimes, while collecting the data or calculating the statistics, it is possible to forget why they are 

being done. The data collections discussed here were 

not research projects or academic excursions into a 

practitioner’s world. They were tools that real managers used to make better decisions about their organizations. They also provided an opportunity to improve 

the functioning of the criminal justice system.

There is one more critical part to data collection that 

has been “glossed over.” The best information can still 

be overlooked if it is not displayed effectively. That is 

what chapter 9 discusses.
c h a p t e r n i n e

Sharing Information 

With Others
Why Pictures Are Worth a Thousand Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-4

Methods for Displaying Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-4

Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-4

Bar Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-5

Pie Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-6

Line Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-6

Guidelines for Good Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-7

Rule 1: Know Your Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-7

Rule 2: Make the Graphic Illustrate the Most Important Information . . . . . . 9-7

Rule 2a: Keep It Simple . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-7

Rule 3: Determine the Best Medium for Sharing the Information . . . . . . . . . 9-9

Rule 4: Decide What Scale Will Best Display the Data.................. 9-10

Rule 5: Consider Who the Audience Is . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-10

Rule 6: Make the Graphic Pleasing to the Eye . . . . . . . . . . . . . . . . . . . . . . . . 9-11

Rule 7: Resist the Temptation to Save Money by Putting Everything 

on One Page, Transparency, or Slide . . . . . . . . . . . . . . . . . . . . . . . . . 9-11

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-11
9–

S h a r i n g I n f o r m a t i o n W i t h O t h e r s

The final step in collecting and analyzing data is to share the results with others. The 

true test of a good analysis is if the data can be understood and used by someone else 

to solve problems or make decisions. Sometimes, analysts make it difficult for even the 

most determined people to use the data because information may be buried in too many 

figures or displayed ineffectively.

Much of the material in this chapter will be second nature to those who have been 

exposed to any of the common computer graphics programs or used the graphic functions of off-the-shelf spreadsheet and word processing software. However, good information often is displayed so poorly that it is discounted or disregarded because it is 

too difficult to interpret. This manual has provided methods by which sheriffs and jail 

administrators can improve their data collection and analysis. The data generated after 

such an effort merits the time and attention required to display it clearly and effectively. 

This chapter provides help in this area by:

1. Explaining why displaying data is important.

2. Describing and illustrating methods that can be used to display data.

3. Offering guidelines for displaying data.

4. Showing “good” and “bad” examples of data that has been displayed.

c h a p t e r N i n e
9– HOW TO COLLECT AND ANALYZE DATA

Why Pictures Are Worth a Thousand 

Numbers

As chapter 7 suggested, statistics have two results: (1) 

a number or numbers that summarize(s) the information and (2) a graphic representation of the data.

Next, chapter 8 focused on interpreting the numbers, 

as if the “pictures” in the data were not an important tool for interpretation. That may be backwards, 

because displaying the data is often an essential part 

of the analysis. Sometimes, the only thing that makes 

the data clear is to plot it, point by point. Displaying 

data effectively is critical for a number of other 

reasons.

Most people do not think in numbers. The nonmathematically minded person cannot conceptualize what “an increase of 937 net bookings over 

the previous year” really means. Pictures provide 

a way of making that point very clear.

When people try to remember things, they 

often attempt to visualize what it is they want 

to remember. Well-displayed data provides just 

such an image.

Carefully displayed data can highlight the critical 

points of the analysis. A well-constructed graphic 

will draw the eye of the viewer to exactly what 

analysts want him/her to remember.

Rows and rows of numbers are very boring, 

unless the audience is an atypical group that finds 

numbers more interesting than words or images. 

Well-displayed data can make reading about data 

more interesting.

Methods for Displaying Data

Statistical jargon sometimes makes it difficult to figure 

out what is being said about displaying data. Analysts 

talk about frequency polygons, histograms, cumulative frequency graphs, and other complicated terms for 

some very simple concepts.

There are four basic tools for displaying virtually all 

data. Computer graphics technology can enhance them 

and apply a variety of different “styles” to each. 

■

■

■

■

1. Tables, in which the numbers are placed in columns and rows that have titles.

2. Bar charts, in which the height, order, and sometimes the width of the bars represent the size of 

one value or group in the data; bar charts can 

show one series, i.e., bookings by year, or multiple series, i.e., male and female bookings by year.

3. Pie charts, in which the size of a segment represents the number of cases in one of the categories 

of the data element.

4. Line graphs, in which a line connects all related 

data points in a series.

These descriptions actually make a good case for 

using graphic examples, because the words are not as 

clear as a picture. An illustration of each method of 

display is given in each of the following discussions. 

In most situations, unless it is possible to provide the 

“numbers” shown in the graphic by adding data labels, 

it is appropriate to provide both a graphic image of the 

data and a table of the actual numbers.

Tables

Tables display data by placing the numbers in titled 

columns and rows. Table 9-1 is an example of a “tabular display of data.” Tables are used to list numerical 

information and to store data so that it will be easy to 

find and use again. Generally, tables clearly show what 

the actual numbers are. Although other figures may 

better show the relationships between categories of 

the data or the patterns in two data elements, they are 

harder to read. As a result, most other figures require 

tables to clarify and present the actual numbers.

Table 9-1 Transports

Reason for Transport Number Percentage

Court 152 64%

Medical 40 17%

Warrant Pick-Up 26 11%

Mental Health 12 5%

All Other 7 3%

Total 237 100%
Sharing Information With Others 9–CHAPTER NINE

The following steps improve the appearance of most 

tables:

Label the columns and the rows. The data displayed may be clear to you but a mystery to anyone else.

Make the table easy to read. Most word processing software has a “tables” function that puts data 

(either directly from a spreadsheet or manually 

entered) in boxes or “cells,” like the examples in 

the tables in this document. The “tables” function has the advantage of separating each piece of 

information. The lines help your eye follow information across the table. If your word processing 

software does not have a tables function, be sure 

to separate columns and rows by manually drawing in lines.

Do not put all the data into one table if there 

is a lot of data. The results look overwhelming. 

Most people tend to “skim” the tables in a report. 

This tendency can be overcome by making the 

tables short, neat, and to the point.

Provide totals. If the table displays all the categories of a data element (like table 8-1), total 

the columns. If the table is a cross-tabulation (a 

frequency distribution of two data elements), total 

both the columns and the rows.

Both spreadsheets and databases are based on tables. 

Most word processing programs have the ability to 

display tables that are created in either spreadsheets 

or databases. Tables help to organize data, but they do 

not create an image of the data. The following techniques do that.

Bar Charts

In bar charts, the height, order, and sometimes the 

width of the bars represent the size of one value or 

group in the data. Figure 9-1 takes the same data from 

table 9-1 and displays it in a bar chart.

Bar charts are one of the most commonly used methods to display data because:

They are easy to read and understand.

They can make the major point of the data clear.

They are easy to construct.

■

■

■

■

■

■

■

Figure 9-1 Reason for Transport

Bar charts can be used with data at any level of measurement by following a few simple rules:

With nominal data (data elements that are divided 

into categories, like the data displayed in figure 

9-1), the bars should not touch. Only the relative 

heights of the bars are important.

With ordinal data (data elements that have groups 

ranked in some kind of order, but not measured 

in equal units) the bars should be in order. The 

heights have meaning.

With interval/ratio data (data elements that are 

measured in equal units), the area inside the bars 

should be proportional to the number of cases 

in each group (which affects the height and the 

width of the bar), and the bars should touch. This 

bar chart actually resembles the real-life frequency 

distribution of the data.

With practice, either with or without a computer 

graphics program, making a bar chart is easy. Charts 

and graphs are oriented around two straight lines 

(each one called an axis), one horizontal (the X axis) 

and one vertical (the Y axis). Each line represents 

something. In figure 9-1, the X axis tells what the 

categories of the data element are and the Y axis 

tells how many cases, or what percent, are in each 

category. The Y axis must be divided into equal units, 

i.e., a “scale.” Most automated spreadsheet and word 

processing programs have a “chart feature” that automatically calculates the best scale to display the data. 

■

■

■

Medical Mental Health

Court Warrant Pick-up All Other

160

140

120

100

 80

 60

 40

 20

 0

Transports

Type of Transport

Number of Trips
9– HOW TO COLLECT AND ANALYZE DATA

If you are not using a computer, appendix K provides 

advice and hints.

Pie Charts

In pie charts, the size of the “piece of the pie” represents the number of cases in one of the categories 

of the data element. Pie charts can be used only with 

data grouped into categories and totaling 100 percent. 

Figure 9-2 is based on the same transport data displayed in table 9-1 and figure 9-1.

Transports

All Other

Mental Health

Warrant Pick-up

Medical

Court

64%

3%

11%

17%

5%

Figure 9-2 Reason for Transport

Table 9-2 Comparison of Transports in 2003 

and 2004

2003 

Medical

Warrant Pick-up

All Other

Mental Health

Court

Medical

Warrant Pick-up

Mental Health All Other

Court

2004 

Figure 9-3 Comparison of Transports in 2003 

and 2004

Reason for Transport 2003 2004

Court 102 152

Medical 54 40

Warrant Pick-Up 22 26

Mental Health 2 12

All Other 4 7

Total 184 237

Line Graphs

Line graphs display data with a single line connecting 

all related data points in a series. Table 9-3 represents 

male and female felony admissions from 1999 to 

2004. Figure 9-4 illustrates these elements displayed 

on a line graph.

Line graphs can reveal a great deal about the distribution of the data if they are done properly. They should 

only be used for interval/ratio level data. They are 

easy to make with contemporary computer technology. 

Those who will make these graphs by hand should see 

appendix K for helpful hints.

The ease of making pie charts with computer graphics programs can be a two-edged sword. Just because 

you can make a pie chart that has 15 categories does 

not mean that people will be able to understand it. A 

general rule is to limit the number of slices to five. 

With more categories, combine the smallest ones into 

a category called “Other,” then do a separate pie chart 

for the “Other” category.

Computer programs can allow you to use pie charts 

for comparisons by linking one or more. If you want 

their size to mean something, you can make them proportional. Table 9-2 and figure 9-3 compare the transport data used previously with transport data from 

another year. Those not using automation should see 

appendix K for hints on making pie charts.
Sharing Information With Others 9–CHAPTER NINE

Table 9-3 Male and Female Felony Admissions 

(1999–2004)

Figure 9-4 Male and Female Felony Admissions 

(1999–2004)

Year Male Female

1999 255 41

2000 375 67

2001 458 79

2002 517 93

2003 593 126

2004 639 147

With this much information, you have a number of 

options. If the primary interest is how much admissions have increased, a simpler display shown in figure 9-6 would make the point more effectively.

Sometimes less is more. Figure 9-6 is easier to read 

and shows the overall trend. You may show more 

detail about this data by focusing on a segment of the 

information. Figure 9-7 compares the trend in male 

and female admissions. Figure 9-8 takes the same 

information and examines the pattern of male and 

female admissions. (See page 9–9.)

The difference between this figure and figure 9-7 

is that figure 9-8 displays the data on two “Y axes” 

instead of one, as if the numbers were expressed as 

percentages of the total in each category. That lets the 

viewer examine the pattern within each category. For 

example, figure 9-8 shows that the pattern of male and 

female admissions is quite similar for 2001 through 

2004. However, female admissions increased more 

dramatically than male admissions in 2001. The difference is in the scale. 

If you really are interested in more detail in each of 

the categories, consider an option like figures 9-9 and 

9-10 (see page 9–9). Even these, which separate male 

and female, are very busy. So, before you turn your 

graphics program loose, think about the message in 

the data and plan a graphic to make that message most 

clear.

Guidelines for Good Graphics

The art of displaying data has “tricks of the trade.” 

After all, an entire profession is based on the effective 

display of information.

Rule 1: Know Your Limitations 

Some people have more talent in this area than others. They know what looks good, what is “balanced.” 

If you do not have these skills, delegate this task to 

someone who does. Individuals with good clerical 

skills and an “eye for detail” may be talented in these 

areas. Automated chart features included with many 

software programs are often best for the “graphically 

challenged.” 

Rule 2: Make the Graphic Illustrate the Most 

Important Information 

Analysis and interpretation of the data should make 

the most important point(s) clear. Display that information clearly. If the point is that since 1999 the 

number of felony admissions has risen steadily, use 

an example like figure 9-4 to make that point clearly. 

Modem computer technology has given us a corollary 

to this rule.

Rule 2a: Keep It Simple 

Tables are convenient because they let you store much 

information compactly (such as table 9-4, which contains a lot of information). Computer-generated graphics have little trouble handling this much information. 

Figure 9-5 is the graphic depiction of table 9-4.

The computer may not have trouble with this, but the 

human eye does, because there are too many categories to differentiate. And this figure must be larger to 

make it possible to even see the lines.

Felony Admissions

1999 2000 2001 2002 2003 2004

Year

Number

0

100

200

300

400

500

600

700

Male Female

233

375

458

517

593 639

41

67 79 93

126 147
9– HOW TO COLLECT AND ANALYZE DATA

Table 9-4 Male and Female Admissions, by Charge Type and Legal Status by Year

Category 1999 2000 2001 2002 2003 2004

Sentenced Males

Felons 433 655 458 405 503 469

Misdemeanants 1,317 2,304 1,751 1,782 1,694 1,826

Other 979 84 1,166 1,290 1,037 1,077

Unsentenced Males

Felons 2,961 3,799 3,105 3,319 3,270 3,355

Misdemeanants 2,912 2,939 2,461 3,133 3,033 3,382

Other 1,246 160 1,637 1,496 1,743 1,582

Sentenced Females

Felons 41 91 57 43 46 47

Misdemeanants 308 472 548 521 482 489

Other 106 11 177 171 127 180

Unsentenced Females

Felons 344 327 324 287 337 400

Misdemeanants 943 982 1,118 1,236 1,077 1,282

Other 177 14 317 290 291 293

Total 11,767 11,838 13,119 13,973 13,640 14,382

Figure 9-5 Male and Female Admissions, by Charge Type and Legal Status by Year

1999 2000 2001 2002 2003 2004

4,000

3,500

3,000

2,500

2,000

1,500

1,000

 500

 0

Male and Female Admissions

by Charge Type and Legal Status

Sent. M Felon

Sent. M Other

Unsent. M Misdemeanant

Sent. F Felon

Sent. F Other

Unsent. F Misdemeanant

Year

Sent. M Misdemeanant

Unsent. M Felon

Unsent. M Other

Sent. F Misdemeanant

Unsent. F Felon

Unsent. F Other

Number
Sharing Information With Others 9–

CHAPTER NINE

Figure 9-6 Admissions Figure 9-7 Male and Female Admissions, 

Version 1 

Figure 9-8 Male and Female Admissions, 

Version 2

Handouts. Written handouts can detract from a verbal 

presentation if they are given out first. People tend 

to read ahead; this results in rustling papers, which 

distracts those who are listening. Generally, if people 

should follow along, give them the handout first. 

If people should listen and use the handout for review, 

give them the handout last. If a summary of the presentation will be provided, tell the audience. People 

are often unhappy when a summary is handed out 

after they have gone to the trouble of taking notes.

Easel charts. Easel charts, large pads of either lined 

or unlined paper (usually 27 by 34 inches), can be 

prepared before a presentation or done on the spot. 

Both have their advantages. Easel charts done “on the 

Rule 3: Determine the Best Medium for 

Sharing the Information 

Most sheriffs and jail administrators tell other people 

about data in one of three ways:

1. A report or other written document.

2. A small working meeting.

3. A large public meeting.

Information can be shared by:

A verbal presentation.

A written handout.

Easel charts.

Transparencies or overheads.

Slides.

A computerized presentation.

Verbal presentations. Verbal presentations are probably the most informal way to share information. Points 

to consider about verbal presentations are:

The speaker should be accustomed to speaking in 

front of groups.

The speaker has to be able to clearly and concisely 

make the points that he/she wants the audience to 

remember.

People have difficulty concentrating on a verbal 

presentation over an extended period of time, and, 

as a result, do not remember much, particularly 

specifics.

■

■

■

■

■

■

■

■

■

16,000

14,000

12,000

10,000

 8,000

 6,000

 4,000

 2,000

 0

Admissions

1999 2000 2001 2002 2003 2004

Year

Number

12,000

10,000

 8,000

 6,000

 4,000

 2,000

 0

Male and Female Admissions

1999 2000 2001 2002 2003 2004

Year

Number

Male Female

12,000

10,000

 8,000

 6,000

 4,000

 2,000

 0

3,000

2,500

2,000

1,500

1,000

500

0

Male and Female Admissions

1999 2000 2001 2002 2003 2004

Year

Number

Male (Y1) Female (Y2)
9–10 HOW TO COLLECT AND ANALYZE DATA

spot” can make a presentation more informal, and the 

major points can be written on the chart ahead of time 

in pencil (light enough to be invisible to the audience 

but visible to the presenter). Easel charts can introduce 

color into a presentation. On the other hand, they can 

take quite a while to prepare, and they tend to winkle 

easily, which detracts from their appearance.

Transparencies or overheads. Transparencies are 

clear 81⁄2- by 11-inch acetate panels that are shown 

by an overhead projector. Many duplicating machines 

can copy onto the acetate from a white paper original. 

Varied type styles can make these images interesting, 

but remember that the audience must be able to read 

the transparencies. Transparencies make a presentation seem more professional, which can be a plus or 

a minus depending on the audience. However, using 

transparencies requires equipment (e.g., an overhead 

projector and a screen), which carries the possibility 

of such technical difficulties as a bulb burning out.

Computerized presentations. Computer technology makes it possible to produce a wide variety of 

images using computer software, such as PowerPoint 

or Presentations. These are particularly effective with 

larger audiences and spaces. With the right video 

and computer hardware and software interface, this 

software can include the integration of digital photographs and video. These presentations will require an 

LCD projector (or large screen monitor). Consult the 

price list before you buy the software and equipment. 

These presentations have a more finished appearance, 

especially if you use the provided design templates, 

and they have the added advantage of providing for 

automated generation of speaker notes and handouts. 

However, this approach may require that the room be 

darkened, which can result in losing contact with the 

audience. If the presentation happens to follow a meal, 

the majority of the audience may doze through the 

presentation. Equipment failures with this approach 

are even more disastrous than similar failures with 

other approaches. To avoid difficulty, practice setting 

up and operating the computer and projector. 

Rule 4: Decide What Scale Will Best Display 

the Data

Chapter 7 discussed the potential “statistical sins” 

associated with scale, and automation of computer 

graphics has eliminated much of the potential for 

an “unintentional” sin. However, it is important to 

remember that it is possible to adjust scales on the 

computer. Figures 9-11 and 9-12 show the same data 

with only one minor modification: the starting point 

of the Y axis is “0” in figure 9-11 and “900” in figure 

9-12.

Rule 5: Consider Who the Audience Is

Display the data and make the presentation for the 

audience you hope to reach. Like statisticians and 

researchers, people who work in corrections and 

law enforcement have a language of their own that 

Figure 9-9 Male Admissions by Charge Level 

and Legal Status

Figure 9-10 Female Admissions by Charge 

Level and Legal Status

4,000

3,500

3,000

2,500 

2,000

1,000

 500

 0

Male Admissions

1999 2000 2001 2002 2003 2004

Year

Number

Unsent. Felon

Sent. Misd.

Sent. Felon

Unsent. Misd.

Sent. Other

1,400

1,200

1,000

 800

 600

 400

 200

 0

Female Admissions

1999 2000 2001 2002 2003 2004

Year

Number

Unsent. Felon

Sent. Misd.

Sent. Other

Unsent. Misd.

Sent. Other

Sent. Felon
Sharing Information With Others 9–11CHAPTER NINE

Figure 9-12 Admission Data on Scale, Version 2

about a 70 percent increase in bookings in the last 

year? If so, use a pastel blue for that particular series 

on the bar chart. Primary colors are better than pastels; they stand out more and make a stronger point. 

Some colors, like pink and blue, have connotations to 

be avoided. Also avoid “unusual” colors, like puce or 

fuchsia.

Rule 7: Resist the Temptation To Save 

Money by Putting Everything on One Page, 

Transparency, or Slide

Crowding makes a graphic image look messy and 

reduces its effectiveness. People process information 

only in limited amounts. Do not display all the data in 

a single table and expect the audience to read it. Make 

one key point per graphic. This is particularly true 

with transparencies and slides. Limit the number of 

words or numbers to a minimum.

Perhaps the best advice to keep in mind when displaying data is simply: experiment. Try different ways of 

displaying the data until you find a style that works 

for you. Sometimes, it is helpful to see what other 

people have done. Use the “galleries” of charts that 

have been created in your computer graphics program 

or the graphics module of your spreadsheet. Those 

who need to create graphics manually should see 

appendix K for information about graphic tools.

Conclusion

With a little time, a little talent, a computer program, 

and a few dollars, most people can turn out very 

credible graphics. Graphics not only enhance the 

is incomprehensible to outsiders, with many “pet 

terms” and even more abbreviations (ROR, FTA, 

Part I Offenses, etc.). If the audience includes people 

who are not familiar with such jargon, make sure that 

all terms and abbreviations are explained. Show the 

abbreviation the first time a term is used—e.g., Length 

of Stay (LOS)—and thereafter use the abbreviation. It 

is also important to gear the presentation to the interests and abilities of the audience. A presentation for 

the commissioners will be markedly different from 

one done for the high school civics class, and both 

will differ from a presentation to a professional corrections association.

Rule 6: Make the Graphic Pleasing to the Eye

At a minimum, the tables, charts, and graphs should 

be neat and positioned nicely on the page. Beyond 

that, the following suggestions may improve their 

overall quality.

Draw the eye where you want it to go.

Centering something on a page does this. 

Using symbols like can have the same 

effect. So can using a slightly bolder style of type. 

Type comes in various sizes.

Find a way to send a message. If possible, use color 

to send a message. While using color in graphics can 

take a bit more time and money, it can send a powerful (and often subliminal) statement. Generally, “hot” 

colors like reds and oranges send warning messages; 

“cool” colors like blues and greens send calming messages. Do you want the commissioners to feel calm 

Figure 9-11 Admission Data on Scale, Version 1 

1,000

 800

 600

 400

 200

 0

Admissions

1999 2000 2001 2002 2003 2004

Year

Number

1,000

 800

 600

 400

 200

 0

Admissions

1999 2000 2001 2002 2003 2004

Year

Number
9–12 HOW TO COLLECT AND ANALYZE DATA

appearance of any report, but they also make it easier 

to understand the data that has been gathered. And that 

really is the bottom line. If people do not understand 

the information, they cannot use it effectively.

This manual has spent so much time and space on the 

technicalities of collecting, analyzing, and sharing 

data that a brief reminder about why we are doing this 

might be useful. Sheriffs and jail administrators must 

take strong leadership roles in using the information 

that is hidden in local jails to manage their organizations more effectively, plan for change, and work with 

other officials to improve the criminal justice system.

Sheriffs and jail administrators must take proactive 

roles in criminal justice affairs. For too long, jails have 

served as passive receptacles for the rest of the justice system. Only by using the information that often 

already exists can the jail become a fuller partner in 

the criminal justice system. This will not happen by 

itself—sheriffs and jail administrators have to make it 

happen.

In the past, sheriffs and jail administrators have been 

made to feel that they could not gather data themselves. This manual should have convinced you that 

you can. It may feel strange at first and may take 

time, but it can be a very powerful experience. When 

done collaboratively, it can help criminal justice 

organizations respond more effectively as a system. 

Information, in the right hands, is power. Systems that 

have gathered their own data have been surprised by 

how much they were able to find out about their system and were excited about how they could use the 

information.
c h a p t e r t e n

Getting the Most From 

Your Information System
What This Chapter Is—and Is Not—About . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-3

A Short Course in Computers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-4

Overview of Computer Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-4

The Central Computer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-4

Peripherals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-5

Communication Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-5

Overview of Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-5

Applications Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-6

A Last Word on Hardware and Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-7

Getting the Most Out of a Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-7

What To Do Before Buying or Upgrading a Computer . . . . . . . . . . . . . . . . . . . . . 10-8

Determine the Purpose of the Information System . . . . . . . . . . . . . . . . . . . . 10-8

Define Your Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-9

System Expectations and Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-12

The User Manages the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-12

The System Avoids Duplication of Effort . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-12

The System Is Reliable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-12

The System Has a Safety Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-12

Match the Right Application to the Task . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-12

Know How the System Handles Updated Information . . . . . . . . . . . . . . . . 10-12

Format Flexibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-13

Determine Internal Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-13

User Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-13

Explore the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-13

Practice Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-13
10–

G e t t i n g t h e M o s t F r o m Y o u r I n f o r m a t i o n S y s t e m

What This Chapter Is—and Is Not—About

What do you think about when you hear “information system”? If you are like most 

people, what comes into your mind is probably “computer.” This chapter is not really 

about computers any more than your policy on the use of restraints is about the specifications for handcuffs. However, people often want to start automating by deciding 

which computer to buy.

Computers are the equipment or hardware that may be used with an information system. The software used to operate the computer is the vehicle used to store and process 

the information. As a result, this chapter does not place a great deal of emphasis on 

computer hardware and software, and it will not identify products or vendors. This 

approach would quickly become outdated because of the rapid changes in this technology. To use computers effectively in an information system, particularly a management 

information system (MIS), it is important to start at the beginning—with the information. That is one focus of this chapter.

A second focus of this chapter is about making an informed decision about buying 

computer hardware and software. Often the key tasks are asking the right questions 

and having appropriate and realistic expectations. Buying the “wrong” equipment or 

software can quickly sabotage your efforts to automate. But when you begin the task of 

automating, how do you know that you are buying the “right stuff”? New hardware and 

software are a major investment for any jail, and sheriffs and jail administrators need to 

do it right the first time.

It is important to understand that computer technology changes rapidly. State-of-the-art 

hardware and software become outdated almost before you can figure out how to use 

them. It is also important to understand that the information systems currently in use in 

jails and their users vary greatly. With these caveats, this chapter gives a general overview of the current technology.

c h a p t e r t e n
10– HOW TO COLLECT AND ANALYZE DATA

A Short Course in Computers

Computers come in many shapes and sizes, with 

widely varying attributes. This section provides some 

basics about computer hardware and software.10 In 

the 1990s, the amount of information about this topic 

exploded to the degree that it is difficult for computer 

professionals to know all there is to know on the topic. 

Understanding some computer basics should help 

sheriffs and jail administrators determine the area(s) in 

which they need information to make an appropriate 

selection.

Overview of Computer Elements

All computers, regardless of size, are made up of four 

basic components:

1. Central computer (the box), which contains the 

central processing unit (CPU), the computer 

chip that contains the memory or “brain” of the 

computer.

2 Input devices, the equipment or peripherals used 

to communicate with the CPU.

3. Output devices, the equipment or peripherals used 

to show the results of the processes that happened 

in the CPU.

4. Storage devices—media, such as tapes, fixed or 

floppy disks, CD’s etc.—used to store programs 

or data. 

The computer may be:

A mainframe, which is a large, central computer, 

typically shared by multiple user groups (such as 

agencies in a county).

A departmental computer, which may be as large 

as a mainframe or as small as a local area network 

(LAN), shared by multiple users within the 

department.

A personal computer, which is used by only one 

person.

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The Central Computer

Three concepts are important:

1. The CPU.

2. Memory.

3. Input/output interfaces.

Central processing unit. The CPU or “brain” is a 

silicon chip. It translates the user’s instructions into a 

language (machine language or binary) that other parts 

of the computer can understand. In the course of doing 

so, the CPU puts data in memory, identifies where it 

is stored, and determines the sequence of events inside 

the computer.

When computer people talk about a CPU, they often 

use some of the following terms:

Bits, the basic element of machine language, 

which is either a 1 or a 0.11

Bytes, the number of bits needed to encode one 

character (e.g., an “a”). 

Bus, the electronic pathway in which the CPU 

moves bytes.

Megahertz (millions of cycles per second), a measurement of how fast the CPU can get data to and 

from memory.

These factors can make a difference in how the computer functions. In general, the more bits a computer 

can process at one time, the more work it can do; 

and the more megahertz the computer has, the faster 

it can go.

Memory. There are two kinds of memory:

1. Random access memory (RAM) is where the 

computer temporarily stores data while working on it. The amount of RAM determines the 

total number of locations in the computer where 

information can be temporarily stored. Many current applications use a lot of RAM. A shortage of 

RAM restricts how the computer can be used. As 

a result, RAM expandability is a significant issue. 

Unless your software has an automatic backup 

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10 This section summarizes material that is provided in greater detail in a 

Bureau of Justice Assistance monograph, A Guide to Selecting Criminal 

Justice Microcomputers, (see appendix B).

11 In the beginning, computers were a series of switches. Each switch had 

only two positions, “open” or “closed.” The 1 and 0 relate to whether a 

switch in the computer was open or closed.
Getting the Most From Your Information System 10–CHAPTER TEN

function, data in RAM is lost after a power failure 

and everything you entered since the last time you 

saved your file will be gone.

2. Read only memory (ROM) is permanent memory 

and comes installed on the computer by its manufacturer. ROM stores things like the operating 

system.

All memory is measured in bytes. Memory is referred 

to in terms of kilobytes (1,000 bytes or 1 KB), 

megabytes (1,000,000 bytes or 1 MB), or gigabytes 

(1,000,000,000,000 bytes or 1 GB).

Input/output functions (I/O). If the CPU is like the 

brain, the I/O functions are like the body’s nerves. 

They link the CPU with its memory and both the CPU 

and memory with the computer’s peripherals. Think 

about these devices as a switchboard.

Peripherals

If the CPU, “the brain,” were the only part of the computer, then humans would be unable to communicate 

with it. The peripherals are the “eyes and ears” of the 

computer, providing input to the brain. They are also 

the “muscles” which voluntarily or involuntarily follow the brain’s directions. Peripherals are divided into 

several categories:

Input Devices, which put data into the computer. 

The most common kinds are keyboards, a mouse 

or a pen (pointing devices), and scanners (bar 

code readers and optical scanners).

Output Devices, which display data in a way that 

either a person or another machine can read it. 

The most common kinds are monitors (cathode 

ray tube [CRT] or video display terminal [VDT]) 

and printers.

Storage Devices, which store data (remember that 

RAM is temporary). The most common kinds are 

disks (floppy diskettes, hard disks, and compact 

disk read only memory [CD–ROM] devices) and 

magnetic tape. The most significant issue is how 

much of this is needed.

Communication Devices

Communication devices are the mechanisms that let 

the computer communicate with the peripherals and 

other computer systems. There are four types:

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1. Input/output ports, the interfaces used to 

connect the wires between the central computer, 

the peripherals, and other communication devices. 

Ports can be either serial or parallel, depending 

on the number of bits that can be sent between 

a computer and peripheral at one time. Parallel 

ports, usually used for short distances, can send 

multiple bits at one time using a wire for each 

bit. Serial ports take the bytes apart, send them 

one at a time in a stream, and then put them back 

together at the other end. Serial ports require 

some type of checking process to be sure that the 

bits are reassembled correctly. They are used over 

long distances.

2. Modems allow the computer to send data over a 

telephone line. They are measured by the rate at 

which they can send data (baud), faster is better. 

Modems require special communications software.

3. Facsimile cards or fax modems allow the computer to function as a fax machine.

4. Networks or local area networks (LANs) allow a 

number of independent devices to communicate 

with each other. This, in turn, allows computer 

users to share software, peripherals like printers, 

and data. In a LAN, the computer on which the 

shared data is stored is called the server.

Overview of Software

If the hardware described above is the equivalent of 

the body’s nervous system, then software is thought 

itself. It consists of electronic or magnetic signals that 

tell the elements of the computer what to do. Software 

can be divided into two basic types:

1. System software, including the computer’s 

operating system (such as DOS or Windows) 

and utilities, tools that support the operating 

system. Utilities help a user perform routine file 

management functions.

2. Applications software, which are the instructions 

that allow the user to work on the computer. There 

are many applications, including word processing, 

spreadsheets, and databases.
10– HOW TO COLLECT AND ANALYZE DATA

Applications Software

Software may be proprietary (owned by a corporation or individual and protected by copyright), public 

domain (not owned by anyone and available at no 

charge, for example through the Internet), or shared 

(copyrighted but able to be shared, although often 

without printed documentation). Of the three, proprietary software is the most significant.

Proprietary software comes in two basic types: (1) 

software designed to be used by different disciplines 

(e.g., word processing, spreadsheet, database, and 

communications packages) and (2) software designed 

to be used for a specific function, such as fleet management, commissary, or jail booking.

Database applications. Database software is the most 

commonly used type for information systems. It stores 

and retrieves information in a structure and provides 

a way to examine relationships among the data elements. It is important to understand that there are different kinds of databases:

File management/report generator/flat file databases allow the development of various reports 

from each case in the file.

Relational databases allow the development of 

reports from multiple files.

Hierarchical databases store the data in several 

levels rather than in a “flat” file.

File management databases, like address files, are easy 

to create and use but cannot be linked to other databases or files and therefore support limited reporting. 

Relational and hierarchical databases combine and 

compare data from various records. They are complicated to create, but provide much more powerful and 

flexible reports.

Database software organizes data elements into 

“fields,” in which related information about a single 

case (or “record”) is stored. The fields can be summarized in standard or specialized reports. Fields are 

organized into database files that logically belong 

together, such as booking, classification, housing, 

commissary, etc. Databases routinely allow users to:

Browse—Scan records in the database for 

information.

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Search or query—Find records based on specific 

criteria (such as pretrial felons who were in custody for more than 6 months).

Sort—List records in a specified order (such as 

an alphabetical listing of inmates who had more 

than five disciplinary violations in 2004).

Update—Add, change, or delete records in the 

database (usually a function restricted to a limited 

list of specified users).

At first, databases were not particularly “user friendly.” 

However, the “kinder and gentler” databases of the 

current software generation can:

Easily develop the contingency tables discussed 

in this document.

Allow a moderately experienced user to develop 

special reports, in addition to the standard reports, 

in response to the need for additional information.

Perform basic math operations, such as totals, 

counts, minimums and maximums, and standard 

deviations.

Create graphics.

Import and export data to and from other 

applications, such as spreadsheets and some 

word processing programs.

A “user friendly” off-the-shelf database software 

package is preferred for specialized data collections 

such as the Inmate Profile Data Collection.

Spreadsheet applications. If database software is 

the “file cabinet” of the computer world, spreadsheet 

software is the ledger. Spreadsheets are useful in organizing data that requires statistical analysis or a lot of 

computation. Spreadsheets were originally developed 

as a way of dealing with accounting information, but 

now most also have some database qualities, although 

they are limited (for example, in the number of criteria you can use to sort the file and identify particular 

cases). Most spreadsheets have a graphics component 

that allows the development of bar charts, pie charts, 

and the other graphics discussed earlier. Most newer 

versions of spreadsheets have the ability to import 

and export (or “translate”) spreadsheet data into other 

formats, such as those that could be read by a data

base or word processing program. A “user friendly” 

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Getting the Most From Your Information System 10–CHAPTER TEN

off-the-shelf spreadsheet package is the preferred 

option for Inmate Population Data Collections.

Communications software. Communications software is not typically used to collect or analyze data, 

and yet it is some of the most common software found 

in criminal justice systems’ computers. The way in 

which agencies communicate with the National Crime 

Information Center is a form of communications software. Communications software is used to exchange 

data electronically on an electronic bulletin board or 

via the Internet.

Word processing software. While word processing 

software is probably the most common application 

found in most organizations, it has little use in collecting and analyzing data, except in developing the 

report. It should not be used in a major data collection or as a record keeper, even with the math functions that some word processing packages provide. 

It is very useful, however, to have a word processing 

program that can link to a database or spreadsheet 

through its table function or create graphics through 

an internal drawing or chart-making program.

A last word on using applications. Particularly at 

the management level, most people find they use a 

combination of software packages for different aspects 

of their information systems, data collection, analysis, 

and reporting functions. Software has become more 

integrated and more flexible in its ability to move data 

from one type of application to another. If your information system and other software do not allow this, it 

will likely impede your analytical efforts. These limitations may be an indication that it is time to upgrade 

your applications software.

A Last Word on Hardware and Software

After this discussion, it should be obvious that many 

choices must be made in selecting hardware and software. How do you know which hardware and software 

to buy? The answer is both very simple and very complex. It depends on what information you need, and 

answering that question thoroughly and effectively 

will make the difference between a system that supports your operation and one that handicaps it.

Getting the Most Out of a Database

If you already have a jail information system that 

was designed for your operation, regardless of whether it operates on a mainframe, a midsize computer 

dedicated to your department, a server, or a personal 

computer, it is probably a database of some type. If 

you do not know what it is and what it can do, make 

the effort to find out!

A good way to begin is by finding the data dictionary 

for your database. It is similar to the code books that 

were described in chapter 5. A data dictionary is:

An organized reference to the content of 

a database.

A tool that helps to manage data and convert it 

into information.

A tool that defines the characteristics of each data 

element in the database by providing the name 

of the data element, its definition or aliases (or 

other similar terms), a physical description of the 

data element (i.e., its length, type, format, and 

a rationale for any rules for entry, etc.), the edit 

rules (acceptable range or reference values for 

the data element), its function (a description of 

how this data can be used), responsibility for the 

data element (i.e., who creates and maintains this 

data element), and access authority (who has the 

authority to have access to this data element).

If you do not have a data dictionary, consider developing one as a way of first learning how the system 

works and then improving its usefulness. Several 

guidelines can make this process (and the resulting 

data entries in the information system) more useful.

Avoid ambiguities. You may recall the discussion 

in chapter 3 of the variable Charge and the problems associated with defining it. “Capacity” is 

another term like “charge.” It may mean a courtordered capacity, a rated capacity (established 

by the state), a design capacity (the number of 

beds provided in the design), actual capacity (the 

number of beds normally available), or working 

capacity (actual beds less any temporarily unavailable beds). Be specific.

Document aliases. A number of terms can refer 

to the same thing. In a direct-supervision jail, we 

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10– HOW TO COLLECT AND ANALYZE DATA

might call the areas in which inmates live modules, housing units, or pods. Identify which terms 

mean the same thing and decide on the preferred 

term.

Use naming conventions. Name your data elements consistently (e.g., BookNum and FBINum, 

not Booking# and FBINo).

What To Do Before Buying or 

Upgrading a Computer

Sheriffs and jail administrators who want information 

systems to help in decisionmaking, not just as an automated recordkeeping system, have a number of tasks 

to do before collecting vendor information. These 

tasks are described in detail in the Bureau of Justice 

Assistance monograph, A Guide to Selecting Criminal 

Justice Microcomputers, and are reviewed in chapter 3 

of this manual.

Before buying the computer and software, your information system needs should be defined in more detail. 

There are three steps in the needs assessment process:

1. Define the problem.

2. Review the existing systems or functions.

3. Document the relationship of this system and 

information.

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Determine the Purpose of the Information 

System

One difficulty in discussing jail information systems 

is that they mean different things to different people. 

Figure 10-1 illustrates three types of information systems (Executive Information, Decision Support, and 

Traditional Operational), the types of activities they 

support, and examples of functions typically automated in each.

Many jail information systems currently fall into the 

category of Traditional Operational Systems. They 

record and store information about bookings, inmate 

accounts, commissary transactions, etc. This manual 

suggests that sheriffs and jail administrators need an 

executive information system (EIS) to provide timely, 

accurate, and relevant information to determine the 

status of various activities and assist them in both 

long- and short-term planning. A good EIS can:

Determine the status of individual programs.

Monitor organizational performance.

Analyze the cost of delivering various programs 

and services.

Measure the impact of legislative and policy 

changes.

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Figure 10-1 Jail Information Systems

 Policy Analysis, Population 

Management, Program Evaluation

Classification Criminal History

Accounts, Store, Clinic,

 Inmate Tracking

Executive Information Systems (EIS)

Decision Support Systems (DSS)

Traditional

Operational Systems

Inmate-Specific

Decisionmaking

Management Decisionmaking

Operational Support

Process Management
Getting the Most From Your Information System 10–CHAPTER TEN

The EIS grew in popularity in the 1980s as the correctional environment became more complex and 

computer technology became more accessible. Early 

EISs were tied to mainframe computers by expensive 

software written for that system and accessed by programmers. These systems extracted information from 

an already existing database and made it accessible 

to managers, often in summary form. However, with 

better, quicker, and more powerful personal computers 

(PCs) and LANs, EIS has come into the mainstream. 

The tools are currently available to build an EIS using 

a good, contemporary database program and other 

software tools. Once the statistical data needed is 

defined, the basic reports can be set up in an EIS. The 

capabilities of the database software selected should 

allow a query of the database to find answers to specific questions not covered in the routine reports as 

well as a user-friendly report generator. The database 

also can be structured to alert you to information that 

requires your attention, such as any housing unit in 

your facility that is over capacity.

Define Your Needs

If these functions are wanted or needed, a system will 

need to be capable of moving beyond the traditional 

jail information system to an EIS. Begin with a good 

definition of the problem.

Step 1: Define the problem. Chapter 2 discussed 

defining a problem. That is where the needs assessment begins. At this point, it is productive to involve 

people who have hands-on experience with the problem. They can bring to the problem statement the 

“nitty-gritty” details that administrators may not know. 

Developing the problem statement helps to focus the 

assessment on specific areas or functions. Consider 

the following example.

The administrator of a 100-bed jail learned that Frank 

E. Felon, an inmate back from the state system, had 

filed yet another lawsuit, alleging that he had been 

denied recreation for more than a month. The jail 

administrator knew that there had not been a disciplinary action against the inmate that resulted in the loss 

of recreation. The jail could prove that Felon’s unit 

had been scheduled for and received recreation during 

the month in question, and the shift log documented 

that a number of inmates had gone to recreation. 

However, no one could identify which inmates, nor 

could the recreation officer remember if Frank Felon 

had been to the gym. The department, then, could not 

prove that the allegations were untrue. The jail administrator, shift supervisor, and recreation officer met to 

discuss the situation.

The existing system for logging inmate 

movement does not identify inmates who are 

moved to activities like recreation, and the 

current system of manually listing inmates is 

too labor intensive. As the recreation officer 

said, “I can either do recreation or write that 

I’ve done it.” But the jail administrator wanted 

to avoid any further trips to federal district 

court, so something had to be done.

Step 2: Examine the existing system. Examining 

the existing system (defined here as the Program 

Participation System) could be a very poorly defined 

task. But jails have an “edge” on a number of other 

kinds of organizations because they have policies and 

procedures. Reviewing these should provide both the 

purpose of the system and the “flow” (the step-by-step 

listing of how the procedure is carried out). By using 

a computer programming technique called systems 

analysis, you can relate the steps in the procedure to 

informational needs.

Systems analysis looks at all processes in terms of 

inputs, throughput, and output. In trying to track 

information, it is important to think about activities 

in terms of the information they generate, modify, or 

capture. Inputs start the system. In this case, the inputs 

are the recreation schedule and an inmate’s request 

to go to recreation. Outputs result from the steps in 

the process. In this case, the outputs are entries in the 

daily log that a number of inmates went to recreation 

and documentation of the inmate’s participation in 

the recreation program. Throughput is the steps or 

processes in the program participation process. In this 

case, they are:

1. Log the inmate out of the housing unit.

2. Log the inmate into the program area.

3. Program participation.

4. Log the inmate out of the program area.
10–10 HOW TO COLLECT AND ANALYZE DATA

Figure 10-2 Programs Flowchart

Figure 10-3 Programs Flowchart with 

Documents

Schedule

Log inmate 

out of unit

Log inmate 

into rec

Program

participation

Log inmate 

out of rec

Log inmate

into unit

Summarize

rec hours

Inmate

Request

Schedule

Log inmate 

out of unit

Log inmate 

into rec

Program

participation

Log inmate

out of rec

Log inmate

into unit

Summarize

rec hours

Inmate

Request

Movement

Log 1

Movement

Log 2

Movement

Log 2

Movement

Log 1

Program

Summary

5. Log the inmate back into housing.

6. Summarize the hours of recreation provided.

Systems analysts use graphic shorthand technique 

called flowcharting, shown in figure 10-2. Each of the 

rectangles represents a step in the process. The parallelograms represent inputs. Figure 10-3 shows how 

to relate this to information gathering by showing the 

documents that are generated as a result of the process.

Once the flow is described, several other things should 

be considered:

How many people use or modify the information 

recorded on the documents?

What is the volume of recorded information?

How is the information processed?

In this system, three basic documents store information 

about program participation:

Inmate Movement Log 1, completed at the 

housing unit.

Inmate Movement Log 2, completed in the 

Program area.

Program Summary, completed by the person 

responsible for each program

As the jail administrator and staff continue to examine 

the issue, additional issues surface:

It takes too long to manually record the name of 

each inmate who leaves or enters the housing unit 

in Movement Log 1 and each inmate who enters 

or leaves the program area in Movement Log 2. 

There are too many inmates, and they move too 

often.

The recreation officer has to manually calculate 

the number of hours of recreation delivered each 

month.

The existing forms still cannot meet the jail 

administrator’s need to prove exactly who 

participated in what program.

Step 3: Prepare a needs assessment statement. The 

jail administrator can now prepare a needs assessment 

statement that provides more information to make a 

better choice about how automation could be used to 

solve this problem and what approach might be most 

effective. For example, it is clear that:

Just putting the existing inmate movement logs 

on the computer will not solve the problem if the 

housing and recreation officers still have to type 

the inmates’ names. As a result, the system must 

minimize manual entry of inmate names through 

the use of such technology as bar-coded inmate 

ID bracelets or ID cards.

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Getting the Most From Your Information System 10–11CHAPTER TEN

Unless Movement Log 2 is linked to the Program 

Summary, the recreation officer will still be forced 

to manually calculate the hours the program was 

delivered.

Unless the software used for the movement logs 

can allow the jail administrator to query the system to identify each time Frank E. Felon went to 

recreation, all the movement logs will have to be 

reviewed for the period in question.

Unless the system can keep the information for an 

unspecified time period, all of the documentation 

about inmate movement will not help in court.

While this is just the beginning of the process to follow to determine what kind of system best meets 

your needs, it should be clear that much of the work 

involves understanding the flow and volume of information in the facility.

Step 4: Determine the data elements. Chapter 5 

suggested a review of forms and logs be done to see 

what information is collected where. For our Program 

Participation System, one report and two logs provided the information. The next step in the process 

involves looking at each of these forms to identify:

The data elements that are currently included.

The data elements that are not currently included 

but should be.

The information that is included in both the report 

and the log.

Step 5: Estimate the volume and storage requirements. Once all the data elements have been identified, you will need to estimate the volume of your 

information—how much data must be handled and the 

speed at which the computer must operate. In the case 

of the movement log, it would be important to know 

how many inmates move at one time and how often 

during a shift, day, or other time period movement 

occurs.

One of the most significant aspects of the information system is its storage devices. A way to begin to 

understand storage requirements is to go to the paper 

files where the information is stored to determine how 

much paper is generated over a time period. While 

recognizing that it is really the number of characters 

on each piece of paper that will help with storage 

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size, a quick look in the files should give you an idea 

of how much will need to be stored. Related to storage is the question of whether you will need access to 

this information immediately or if it can be stored on 

external storage devices, such as floppy disks or tape.

Step 6: Determine the reporting requirements.

The next step is to determine what the system should 

tell you both routinely and on an “as-needed” basis. 

For example, a standard report this jail administrator 

would want is the Program Summary, which should 

be designed to identify how many hours of recreation 

were provided, the number of inmates who went to 

that service, and perhaps what proportion of inmates 

do not use the service. On an “as-needed” basis, the 

jail administrator would want to be able to generate a 

report that listed all inmates who went to recreation 

during a specific period.

This is a very difficult task, because information needs 

change over time. As a result, systems that are very 

flexible, that allow links to information from multiple 

tables or files, and that put together information in 

varied ways on an “ad hoc” basis are likely to work 

best for management decisionmaking.

Step 7: Explore compatibility issues. Avoid creating 

“information islands,” which are isolated and cannot 

communicate with each other. The jail administrator will need to consider whether the system to track 

inmate movement to recreation should interface with 

any others and whether these issues are applicable to 

any other systems. A review of other program functions and logs indicated that the problems associated 

with movement logging were much broader than just 

recreation. Any solution created for recreation movement would apply to any other programs and also 

to transfers within the facility and cell assignments. 

Because cell assignments and transfers were recorded 

on the daily roster, currently created by the jail’s 

booking system, the issue of compatibility with the 

jail’s existing software was raised. And by broadening 

the purpose of the system, it was possible to avoid a 

very common automation phenomenon—an underestimation of its use or applicability.

Step 8: Explore computer options. This is where 

most organizations start in the process of developing an automated information system, and it is where 
10–12 HOW TO COLLECT AND ANALYZE DATA

this section of the chapter will stop. For additional 

information about automated systems, contact the NIC 

Information Center (800–877–1461). 

System Expectations and Guidelines

When the first edition of this manual was published in 

1982, automation was uncommon in jails. Computers 

seemed mysterious and the expectations of automation were high. By this time, the computer has lost its 

mystique, and many law enforcement and corrections 

agencies routinely use computers for a broad spectrum 

of administrative and operational functions. However, 

relatively few agencies have completely integrated 

computers into their daily operations to capitalize on 

the vast information resources in their systems. Many 

agencies continue to maintain dual automated and 

paper systems. If there is any blame to attribute in 

this situation, it is shared. Automated systems are not 

always user friendly, and many agencies continue in 

the unfortunate belief that computer work is clerical. 

This section offers some guidelines for automated systems and their users.

The User Manages the System

A good system does not require programmers to 

reprogram the computer to modify a report or to 

obtain anything other than standard reports. In older 

“unfriendly” systems, the user is so dependent on the 

programmer that he/she becomes a receiver, not an 

analyzer, of information. Particularly in network environments, users need an in-house network manager, 

who, as a “high-level” user, can generate and develop 

special reports, make minor modifications to reports, 

and in general use the system.

The System Avoids Duplication of Effort

A good system can help to avoid duplication of 

effort. First, once a variable, such as Inmate Name, is 

entered, it does not need to be reentered on subsequent 

reports. For example, once the initial booking screen is 

completed, the inmate name and other pertinent identifying information should automatically appear in later 

forms, like health screening. Second, the information 

is entered directly into the computer, not written on 

paper and subsequently entered into the computer. 

Think about it! By setting up a separate antiparallel 

paper system, we increase the level of effort.

The System Is Reliable

System reliability has a direct relationship to the 

tendency of many organizations to maintain a parallel paper system. If the system is often down and 

requires someone from data processing or another 

agency to reset it, people begin to mistrust it. After all, 

jails never sleep—and neither should the computer. 

Reliability in large computer systems can be absolute; 

“double fault tolerant” systems, which never go down 

for service and which have multiple redundant parts, 

exist. In small computer systems, reliability means 

good and regular backup procedures.

The System Has a Safety Net

Many new users fear that they will somehow “lose or 

modify” all the data. And if the automated system is 

your records system, you need to be concerned about 

the integrity of the records. A “safety net” can take 

many forms, such as:

Automatic back-up features, which preserve data 

in its previous form.

Data that is modified or replaced never replaces 

or overwrites the initial entry.

The operator works with or manipulates a copy 

of the data, not the original.

It is critical to understand the “safety net(s)” your 

system provides.

Match the Right Application to the Task

As the section on software in this chapter suggests, each kind of software has a specific purpose. 

However, people often use the software that is available, regardless of whether it is appropriate for the 

task. This can lead to user frustration. For example, 

one jurisdiction entered inmate count statistics into 

a word processing document and then was unable 

to perform any math functions. Another jurisdiction 

entered a tool and toxic materials inventory into a 

word processing document that could not be sorted 

as needed.

Know How the System Handles Updated 

Information

Many automated systems do not handle changes to 

original information well. In many cases, the original 

information is written over and lost. This can present 

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Getting the Most From Your Information System 10–13CHAPTER TEN

a number of operational problems. For example, one 

of the things that often changes is Bond Amount. 

At the initial appearance, the amount is set, but bond 

is commonly lowered at some point in the process. 

Imagine trying to determine if the initially set bond 

amounts are unrealistically high if the information is 

written over. Without knowing that the amount in the 

computer record is a revised amount, things might 

look normal. However, the user can require that the 

computer store both initial and revised amounts.

Format Flexibility

As managers, most sheriffs and jail administrators 

want summary information on a regular basis with the 

ability to view the detail if needed. Some rigid information systems have to churn out all of the detail to 

generate a summary. In one jurisdiction, this resulted 

in printing a 400-page report daily to get to the 1-page 

summary the jail administrator needed. In addition to 

the waste of paper and resources, this process took 

approximately 1 hour of computer time, a significant 

issue on a county mainframe. 

Determine Internal Checks

It is easy to make entry errors. Good computer software has one or more of the following characteristics:

It minimizes the amount of direct entry by using 

things like “pop-up” menus for the most common 

answers.

It asks the operator to confirm an entry.

If the operator enters a numerical code, it defines 

in words what was entered.

It allows only numerical characters in numerical 

fields.

It designates critical variables as “forced” fields 

(which must have an entry before the program 

will continue) to ensure that they are entered.

Such internal checks are necessary to ensure that the 

information is entered correctly.

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User Guidelines

Regardless of the type of hardware and software 

used, the most important component in the automated 

information system is the user. Without a creative and 

curious user, the best system will never live up to its 

capabilities. As a result, the last word in this manual is 

about people, not machines.

Explore the System

One of the best ways to learn about your system is to 

see what it can do. To do this safely, make sure that 

you have a safety net to protect the integrity of your 

data before you start. Try to generate special reports. 

Try to analyze information on your computer. Try 

different applications before you start. Try to create 

reports on the computer that you now do manually. 

Explore each of the functions and applications available on your computer. If you do not try these things, 

the computer will continue to generate only the standard reports.

Practice Analysis

In any information system, just as in statistics, the 

user’s responsibility is analysis. It is up to users to:

Determine what the information implies.

Search out relationships among pieces of 

information.

Identify missing information.

Use the available information to manage and 

direct the organization.

Analysis gives us the tools to be detectives. In information systems and statistics, we may use different 

tools, but the thought processes are similar. Too often, 

our feelings about computers or statistics cause us 

to overlook these resources that are rich in potential 

clues to organizational problems.

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APPENDIX A

A Glossary of 

Statistical Terms for 

Non-Statisticians
A G l o s s a r y o f S t a t i s t i c a l T e r m s f o r 

Non-Statisticians

Aggregation is a process of grouping data together. The reverse is called disaggregation. Experimenting with different ways to group data is one way that analysis takes 

place.

Analysis of Variance is an advanced statistical technique used to determine whether 

significant differences exist between three or more groups.

Audit is a form of process evaluation to determine whether resources are being used for 

the intended purpose and/or the intended target population. It may include the elements 

of process evaluation, program monitoring, and effectiveness enumeration. It includes 

internal controls designed to ensure that program goals and objectives are defined and 

achieved and that the program is in compliance with statutes and regulations and is 

economically effective.

Axes are the vertical and horizontal lines that orient many charts and graphs. 

Statisticians call each line an “axis.” The horizontal line is the “X axis,” and the vertical 

line is the “Y axis.”

Barriers are conditions or obstacles that must be overcome to solve problems.

Bias is built-in error. It usually comes from picking a bad sample or from the perceptual bias of the person designing the study.

Case can be a person, event, or object about which information is being gathered.

Case Identification Number is a unique number assigned to each case.

Cell is the name given to each “box” in a contingency table.

Chi-Square is a statistical test that determines whether being a member of a particular 

category of one data element has anything to do with being a member of a particular 

category of another.

Classes are the groups into which the data is divided to calculate and display frequency 

distributions.

APPENDIX A

A–3
HOW TO COLLECT AND ANALYZE DATA

Cluster Sample is a sample in which the population 

is clustered into specific areas or units (e.g., housing 

units). Then the units to be sampled are selected randomly, and information is collected about each case 

in the unit.

Code Books translate the data elements and their categories (which are usually written words) into numbers so that computers can process them more quickly. 

Code books identify the data elements, the categories 

for each data element, the number of digits in the code 

for each data element, and (if the data collection is 

computerized) the column name/number or field name 

of each data element.

Cohort Sample is essentially the same as a cluster 

sample, but is based on time periods rather than physical or geographic areas (e.g., all prisoners arrested on 

a specific, randomly selected date).

Columns are the vertical lines in statistical tables.

Confidence Intervals are statistical limits. In sampling, statisticians use probability theory to determine 

what the odds are of developing a statistically similar sample. Most statisticians want to be at least 95 

percent certain that they would get similar results if 

they analyzed another sample, but some want to be 99 

percent certain. In other words, they want either 95- 

percent or 99-percent confidence intervals around their 

samples.

Contingency Table is a chart that displays the relationship between two frequency distributions. They 

are used in calculating the chi-square statistic and are 

often called cross-tabulations.

Correlation measures the degree and direction of the 

relationship between two data elements. Correlations 

are positive if change in one direction in one variable 

is associated with change in the same direction in the 

other variable. These variables have a direct relationship. Correlations are negative if change in one direction in one variable is associated with change in the 

other direction in the other variable. These variables 

have an inverse relationship.

Cross-Sectional Data Collections are done only 

once; most typically they provide information about 

one time frame. They can only describe something.

Curvilinear Relationships are not uncovered by tests 

of correlation. A curvilinear relationship is found in 

a situation like the following example: When taking 

tests, people who have either very low or very high 

anxiety levels tend to get low scores, but people who 

have moderate levels of anxiety get high scores.

Data is information.

Data Collection Sheets are forms on which people 

record the information being collected. Statisticians 

call them instruments or survey instruments.

Data Element is a piece of information. Statisticians 

and scientists call data elements variables.

Data Points or Scores are the individual measurements for a single data element or variable.

Decision Tree is a series of questions that funnel to 

the only possible answer or conclusion.

Degrees of Freedom is a mathematical concept that 

measures the amount of information available in normally distributed data.

Dependent Variables are data elements that researchers are trying to explain (indicated by X) by analyzing 

their relationship to a known variable.

Descriptive Statistics summarize information about 

a population or a sample.

Design is the strategy of an evaluation, specifying 

those individuals or units to be studied (sampling 

procedure to ensure representativeness), the measures 

to be used in the collection of data, and the comparison groups to be contrasted to determine the effect 

of an intervention and the outcome needed to indicate 

program impact.

Empirical Rule is a statistical law or theorem that 

specifies that if the data is distributed in a normal 

or bell-shaped curve, 68 percent of all cases will be 

found in the interval created by adding and subtracting 

one standard deviation to the mean. If two standard 

deviations are added to and subtracted from the mean, 

95 percent of all cases will be found. If three standard 

deviations are added to and subtracted from the mean, 

then all (or nearly all) of the cases will be contained in 

that interval.

A–4
GLOSSARY OF STATISTICAL TERMS

Evaluation is the process used to address the effectiveness of a program. An evaluation can be of the 

process used to implement the program or of its 

outcome.

External Validity is a research problem stemming 

from having information about part of a population. 

The question that arises is whether the information 

discovered in the research about part of the population 

is equally applicable to the whole population. This is 

often phrased in terms of the relative ability (or inability) to generalize the results to the whole population.

Frequency Distribution is a statistic in which the 

responses to a data element are categorized and the 

number of responses in each category is expressed 

in percentage form.

Goals are end results to be achieved.

Hypothesis is an educated guess about what may be 

involved with or cause a problem. It is often expressed 

as a question and is what researchers try to prove (or 

disprove). Researchers complicate this by a series of 

complex logical moves that allow acceptance or rejection of something called the null hypothesis, which 

is the opposite of the statement just defined. For our 

purposes, just think of the hypothesis as an educated 

guess.

Impact usually refers to the difference in outcome 

between an intervention and no intervention. It can 

also refer to the contrasting differences between 

several alternative interventions.

Independent Variables are variables that researchers 

think may cause or have some sort of relationship with 

X (the dependent variable). Independent variables are 

indicated by Y.

Inference, in statistics, means that what has been discovered about the sample is assumed to be true for the 

population. If the sample was collected randomly and 

is large enough to adequately represent the population, 

making an inference is acceptable statistical practice if 

the reliability of the inference is identified.

Information Systems are structurally or functionally 

related mechanisms and procedures that provide information to an organization, usually in summary form. 

They may be automated (computerized) or manual 

(on paper).

Internal Validity determines whether there is any 

proof that what was done to change a situation actually caused the change.

Intervening Variables act as a link between or interfere with the interaction of dependent and independent 

variables.

Level of Measurement refers to how different types 

of data elements can be measured. Data elements are 

divided into three basic categories. The first group 

(whose answers can be divided into a series of categories) is called nominal data elements. The second 

group (whose answers can be ranked in some kind of 

order) is called ordinal data elements. The third group 

(whose answers are expressed in equal units) is called 

interval and ratio data elements; this group is further 

divided into two subgroups, depending on whether 

zero is a possible response. The level of measurement 

of each data element determines the types of statistics 

that may be used to analyze it.

Linear Relationships usually occur when a strong 

correlation exists between two variables. If a graph is 

plotted to show all the data points, a strong positive 

or negative correlation will result in data that “clusters” together. A straight line can be drawn through 

the cluster to summarize how the data points lie in 

relationship to each other. When it is easy to draw 

such a straight line, the relationship between the data 

elements is said to be “linear.” This line is called the 

trend line and is used by statisticians for a special 

kind of correlation called population forecasting or 

trend analysis. In population forecasting, statisticians 

attempt to determine whether a relationship exists 

between a data element, such as a jail’s Average Daily 

Population, and time.

Log Book is a chronological list of events or activities.

Longitudinal Data Collections are those in which 

the same data is collected from year to year, allowing 

comparisons, documentation of change, and (possibly) 

identification of reasons for change.

Marginals are the numbers written around the outside 

of a contingency table. 

Matched Groups exist if each case in Group I is 

paired with a case in Group II with common characteristics. The most likely instance of matched groups 

A–5APPENDIX A
HOW TO COLLECT AND ANALYZE DATA

in correctional research is when one group is compared before and after participation in a program.

Mean is a measure of central tendency in which all 

the measurements for a particular data element are 

added and the result divided by the total number of 

measurements in the set (the “mathematical average”).

Median is the middle measurement when all of the 

measurements in the set are arranged according to 

size. If there are an odd number of measurements in 

the set, the median falls exactly on the middle measurement. If there is an even number in the set, the 

median falls exactly halfway between the middle two 

measurements.

Methodology is the method used to implement a 

design. Methods usually can be divided into several 

categories: descriptive; process and outcome, or naturalistic observation; correlation; quasi-experimental; 

or experimental. No one methodology is correct, but 

the choice of methodology depends on the nature of 

the research question and the time and resources available for the research project.

Mode is the measurement that occurs most frequently.

Model is a suggested approach to address a problem 

that is considered appropriate or ideal.

Normal or Bell-Shaped Curve is the most common 

or usual probability distribution. If the total area under 

the curve is set equal to 1, then the area within one 

standard deviation of the mean is approximately 0.68; 

within two standard deviations, 0.95. Normal probability distributions are symmetrical around the mean. 

The location and spread of the normal distribution 

will depend on the values of the mean and standard 

deviation.

Objectives are concrete items that can be measured to 

determine if a goal is achieved. Researchers often link 

a series of objectives into an “operational definition” 

of a goal or concept. 

Population, in a statistical sense, means all the units 

(people, items, events, etc.) from which a sample can 

be drawn.

Population Data Elements are variables for which 

information is kept on each person, event, or object 

being studied (the whole population). These are elements that should be kept routinely.

Pre-Test is a process in which a survey instrument is 

given to a small group, similar to the targeted population, before the main data collection to determine if 

there are problems with the instrument.

Probability allows inferences about a whole population to be made from a sample.

Probability Laws (or Probability Theorem) allow 

analysts to calculate the chances of drawing a particular sample.

Problem Statement is a description of the situation 

clear enough to allow analysts to identify the pieces of 

information needed to determine causes and suggest 

solutions.

Proportional Random Sample divides a population 

into groups, and cases are selected randomly from the 

population until the groups are in the same proportion 

in the sample as they are in the population. If a population is composed of 40 percent felons and 60 percent 

misdemeanants, the sample would be also.

Qualitative Research Methods (most commonly 

interviews and observation) describe an event or phenomenon in words.

Quantitative Research Methods rely on numbers 

(most commonly statistics) to reveal something about 

an event or phenomenon.

Random means that each case has an equal chance 

of being selected for a sample.

Random Number Tables are charts of numbers without any logical sequence.

Rank Ordering is a procedure to arrange data elements from lowest to highest.

Reliability is a statistical concept that addresses how 

precise the sample results must be or whether the 

same results could be achieved if the measurements 

were done again.

Research is a systematic method of addressing an 

empirical question. Experimental research is considered the most sophisticated method.

Rows are the horizontal lines in statistical tables.

Sample is a specially selected part of a population.

A–6
GLOSSARY OF STATISTICAL TERMS

Sampling Schemes (or Sampling Frames or 

Sampling Designs) are ways to determine which 

cases go into the sample.

Scales for graphs are like the scales on maps, where 

one inch represents so many miles. In most cases, the 

distance on the scale represents a fixed number of 

cases.

Scores or Data Points are the individual measurements for a single data element or variable.

Simple Random Sample is one in which cases are 

selected randomly from a homogeneous population 

(usually by using a random number table or similar 

tool).

Skewed Distribution is a normal or bell-shaped 

distribution that “leans” more toward one end of the 

scale than the other.

Solutions are ways to fix a problem. Every problem 

has more than one solution. If there is no solution or 

only one solution, the result is a constraint that limits 

how operations or choices can occur.

Spurious Correlations are caused by chance. For 

example, a very strong positive correlation may be 

found between a county’s jail population and the price 

of hog futures on a commodity exchange, but it is 

unlikely that the two situations are related.

Square is a mathematical function in which a number 

is multiplied by itself.

Standard Deviation is the square root of the variance. 

It measures the degree of variation found in the data. 

See also Empirical Rule.

Standard Error of the Mean measures the similarity of the samples (how much variability exists in the 

data).

Statistical Sampling allows information to be gathered from a segment of the entire population in such 

a way that the information can be applied to the entire 

population.

Statistical Significance describes whether a difference between the measures of a single data element 

in two groups is likely to result from sampling differences or if it is a true difference. If a score is outside 

two standard deviations in either direction from the 

mean, it is “statistically significant at the .05 level.” To 

be “significant at the –.01 level,” the interval has to be 

plus or minus 2.68 standard deviations from the mean.

Statistics, as a field, is an area of mathematics with 

strong ties to logic. It uses numbers to make large or 

diverse amounts of information more understandable. 

Statistics are used for four main purposes:

To summarize information.

To determine how seriously to take differences 

between groups of people or events.

To determine how strongly pieces of information 

are related to each other.

To predict future trends.

Stratified Random Sample is one in which the population is divided into groups, such as felons and misdemeanants, and equal numbers of cases are selected 

randomly from within each group.

Survey Instruments are the forms or formats in 

which people record the information being collected.

Symptoms are visible indications that indicate a problem is present.

Systematic Sample is one in which information is 

collected about cases in a certain sequence (e.g., 

every third person booked) until the number of cases 

required for the sample is reached.

Tally Sheet is a form listing all the categories for 

each data element. A check mark is placed next to the 

appropriate category for each case.

Tests of Significance are statistics that examine the 

differences between two or more groups.

Variability is a statistical concept that determines how 

different the individual cases that comprise an entire 

population are from one another.

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A–7APPENDIX A
APPENDIX B

Annotated Bibliography
Resources for the Criminal Justice System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-3

Other Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-5
APPENDIX B

Annotated Bibliography

This bibliography is divided into two sections:

Data collection or analysis documents developed specifically for the criminal justice system in general and jails in particular.

Documents found to be useful both in preparing this manual and in learning about 

research methods and statistics. Also identified are documents that will be particularly helpful to people who are not planners, researchers, or statisticians.

Many of the criminal justice documents can be obtained from the NIC Information 

Center, 1860 Industrial Circle, Suite A, Longmont, Colorado 80501, telephone 800–

877–1461. Citations for resources not available there include the name of publishers, 

which local bookstores may need to order them.

Some of the documents are older, but statistics and research have not changed much 

since this manual was originally published in 1982. However, because information 

systems technology has changed greatly and rapidly, most of those resources are dated. 

As a result, the resources listed here focus exclusively on data collection and analysis, 

statistics, and research methods.

Resources for the Criminal Justice System

Assessing the Effectiveness of Criminal Justice Programs. Robert A. Kirchener, Roger 

K. Przybylski, Ruth A. Cardella. Published by the Justice Research and Statistics 

Association and the US. Bureau of Justice Assistance in Washington, D.C., 1994.

This handbook aids criminal justice policymakers and program managers in assessing 

the effectiveness of their programs. It identifies general criteria for program effectiveness and develops questions to be answered during an evaluation.

Corrections Planning Handbooks, Book 3 (Collect Data) (2nd edition.) Prepared by the 

California State Board of Corrections. June 1999.

California developed a needs assessment process that its counties had to follow 

when applying for funding for local jail construction. One step in the needs assessment process was to collect data about local jail operations. Book 3, developed to 

assist counties with data collection, is an excellent resource for counties that will 

■

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B–3
HOW TO COLLECT AND ANALYZE DATA

undertake a major inmate profile data collection. 

It gives detailed, step-by-step instructions about 

how to profile the jail population, existing programs, and current criminal justice system functioning; how to document trends in the justice system and jail population; and how to convert these 

projections to capacity and program needs. This 

approach provides for the integration of alternatives to incarceration in the planning process and 

provides excellent guidance in assessing criminal 

justice system functioning. This document is written in understandable terms and is geared toward 

counties that do not have access to computers for 

processing data. Its one possible drawback is that 

it is based on a given set of data elements. Systems 

that want to collect other items may experience 

a little difficulty integrating them into the study. 

A word of caution about this manual. If you are 

going to use this approach, follow it step by step. 

Do not skip around. All in all, this is probably the 

most comprehensive resource that has been written 

for jails. It is based on a solid planning methodology, and its potential for use in other states is high.

Criminal Justice Analysis: An Introductory Course. 

Henry S. Dogin, Perry A. Rivkind, and Richard N. 

Ulrich. Prepared for a special seminar of the Law 

Enforcement Assistance Administration in 1978.

This document consists of fact sheets and exercises 

put together to train seminar participants in using 

statistical information for criminal justice policymaking and planning. It will not tell you how to 

analyze the information, however, or give you the 

answers to the exercises that are included.

Guide to Data Collection and Analysis: Jail 

Overcrowding/Pretrial Detainee Program. Jerome 

R. Bush. Prepared for the National Institute of 

Corrections in 1980.

This document was developed specifically for 

data collections arising out of a need to reduce jail 

crowding. It identifies the data elements that are 

of particular importance in understanding crowding. The section that describes charting the flow 

of the criminal justice system will be a useful tool 

for everyone who needs to clarify who has the 

authority to make which decisions in the system. 

This is a critical part of understanding how alternatives to incarceration “fit” into the criminal justice 

system. Data collection sheets and code books are 

included.

How to Handle Seasonality: Introduction to the 

Detection and Analysis of Seasonal Fluctuation 

in Criminal Justice Time Series Analysis. Carolyn 

Rebecca Block. Published by the Illinois Criminal 

Justice Information Authority in 1983.

Seasonality normally becomes an issue only when 

time series analysis or population forecasting is 

being done. However, it is something that every 

jail administrator experiences. Sometimes the jail 

is fuller than average. Typically, there are seasonal effects (the jail is fuller certain times of the 

year) and also weekly effects. This article offers 

approaches to dealing with this problem statistically. Understanding seasonality can help to determine 

how much over the average capacity to plan for.

The Intermediate Sanctions Handbook: Experiences 

and Tools for Policy Makers. Peggy McGarry and 

Madeline M. Carter, Center for Effective Public 

Policy. Published by the National Institute of 

Corrections and the State Justice Institute in 1993.

This document focuses on alternatives to incarceration. Two chapters are particularly applicable. 

Chapter 11 discusses the process used in building an information system to monitor sentencing, including variables that should be collected. 

Chapter 13 discusses an analytical approach to 

targeting offenders for specific sanctions and how 

data is used in this process. For those who think 

of alternatives as being “outside” the jail, these 

chapters help make clear just how tied together 

jails and alternatives are.

Jail Overcrowding Project Information Requirements.

Duane Baker and Associates. Published by the 

American Justice Institute, Sacramento, California, 

in 1979.

This manual provides advice on how to plan a data 

collection that deals with incarceration practices. 

It includes sample forms, procedures, and data 

display techniques.

B–4
ANNOTATED BIBLIOGRAPHY

Policy Making in Criminal Justice: The Use of Hard 

Data at Each Stage of the Policy/Planning Process.

Tim Brennan and David Wells. Published by the 

Northpointe Institute of Public Management, Traverse 

City, Michigan, in 1991.

This paper shows how data can be used at each 

stage of the planning and policymaking process. 

The examples used to illustrate each stage come 

from varied areas of the criminal justice system 

and from actual clients. This is a good “real-world” 

example of how data can be used.

Research Methods in Criminal Justice and 

Criminology. Frank E. Hagan. Published by 

Macmillan Publishing Company, New York, in 1982.

This document includes sections on the use of 

nationally available crime statistics, various survey 

methods (including victim surveys), the use of 

unobtrusive measures, and data collection strategies. It is a valuable resource.

Statistical Applications in Criminal Justice. Gennaro 

F. Vito and Edward J. Latessa. Published by Sage 

Publications, Newbury Park, California, 1989. This 

is Volume 10 of the Law and Criminal Justice Series.

This basic statistics text has been used in a variety 

of criminal justice educational settings. Its advantage over other basic statistical texts is that all of 

the examples used are from criminal justice.

Statistical Sampling Methods for Correctional 

Planners. Edward Lakner. Prepared by the National 

Clearinghouse for Criminal Justice Planning and 

Architecture. Published by the University of Illinois 

at Urbana-Champaign in 1976.

This book was written for correctional planners 

and uses very technical language, which makes it 

extremely difficult reading for anyone without a 

background in statistics. Despite this difficulty, it 

is the only resource found that deals with problems 

specific to using samples to estimate jail population statistics. For those who will be working with 

professional planners on data collections, it will be 

very helpful. Those who need more help to develop 

a sample than is available through this manual 

might be better off consulting one of the basic 

statistics books mentioned below.

Other Resources

Evaluator’s Handbook. Joan L. Herman, Lynn Lyons 

Morris, and Carol Taylor Fitz-Gibbon. Published by 

Sage Publications, Beverly Hills, California, in 1987.

This handbook is one of a series of eight books called 

the Program Evaluation Kit. It is very practical; takes 

a systematic, step-by-step approach to evaluation; and 

is written in everyday language. This volume explains 

what program evaluation is, differentiates between 

several kinds of evaluations, and identifies the steps 

in each. It provides many worksheets and similar tools 

that can be used in an evaluation. This is an excellent 

resource for those interested in evaluating jail programs and services.

Handbook in Research and Evaluation. Stephen 

Isaac and William B. Michael. Published by Edits 

Publishers, San Diego, California, in 1971.

This handbook is one of the most frequently used 

texts in research methods courses. It is divided into 

short sections about specific research issues, which 

makes it easy to find help in a specific area. The book 

is not as helpful as a guide on how to do research and, 

because it is written in rather technical language, a lay 

person may find it hard to understand. Other resources 

in this area may be more helpful for people not very 

familiar with research methods.

How to Analyze Data. Carol Taylor Fitz-Gibbon and 

Lynn Lyons Morris. Published by Sage Publications, 

Newbury Park, California, in 1987.

This book, part of the Program Evaluation Kit,

was used in NIC’s program evaluation seminar as 

resource material. It is a basic introduction to a 

variety of elementary statistical techniques, including those for summarizing data, for examining differences between groups, and for examining relationships between two measures. Analysis of Effect 

Size—a relatively recent and simple approach for 

examining differences between groups and for 

conducting meta-analysis—is shown. Only the 

most basic and useful statistical techniques that 

are appropriate for answering essential evaluation questions have been included. Worksheets 

and practical examples are given throughout the 

volume to support the use of each technique. 

B–5APPENDIX B
HOW TO COLLECT AND ANALYZE DATA

Guidance is provided on the use of statistical techniques for constructing tests and questionnaires, 

on methods for choosing appropriate statistics, on 

using meta-analytic techniques, and on using statistical packages—particularly SPSSX (a statistical 

package for microcomputers) giving readers all the 

information needed to properly analyze their data.

How to Calculate Statistics. Carol Taylor FitzGibbon and Lynn Lyons Morris. Published by Sage 

Publications, Beverly Hills, California, in 1978.

This book is also part of the Program Evaluation 

Kit. Plainly written, it takes the reader through 

calculating and displaying most of the statistics 

needed to analyze jail data. Worksheets are provided to use in calculations, which make the calculations more understandable. The accompanying 

text addresses most of the questions that statistical 

beginners might have.

How to Lie with Statistics. Darrell Huff. Published by 

W.W. Norton Company, New York, in 1954.

Although this book is more than four decades old, 

it still does an excellent job of identifying common 

statistical misuses, abuses, and misrepresentations. 

It identifies actions that statistical consumers can 

take to make sure they are not “duped” by statistics. This book is easy to read and informative.

Practical Research: Planning and Design. Paul D. 

Leedy. Published by Macmillan Publishing Company, 

New York, in 1974.

For those working with research methods for the 

first time, this manual would be a good investment. 

It is relatively jargon free and addresses research 

planning (which is an often underplayed but critical element), different research designs, and ways 

to present the results. This manual does a good 

job in providing guidelines for writing a research 

report. Illustrations and examples are used to show 

what research materials look like.

Social Statistics. (2nd edition.) Hubert M. Blalock, Jr. 

Published by the McGraw-Hill Book Company, New 

York, in 1979.

One of the most frequently used statistics texts, 

this book is sometimes difficult to read, primarily 

because of the highly technical language. If the 

reader is already familiar with statistics, it would 

be an excellent resource because of the exceptional 

detail included.

Statistics: The Essentials for Research. (3rd edition.) 

Henry E. Klugh. Published by Lawrence Erlbaum 

Associates, Hillsdale, New Jersey, in 1986.

This is another useful statistical text, with a good 

section on displaying data.

“Uncertainty, Policy Analysis and Statistics.” James S. 

Hodges. Printed in Statistical Science, Volume 2, No. 

3, August 1987, pp. 259–291.

This very technical paper deals with an important 

issue—uncertainty. Each of the statistical techniques has uncertainty attached to it. The academic 

world learns to live with uncertainty, but uncertainty presents problems in the politicized arena of 

criminal justice policy. This paper argues for constructing systems to eliminate that bias.

Understanding Statistics. (2nd edition.) William 

Mendenhall and Lyman Ott. Published by Duxbury 

Press, North Scituate, Massachusetts, in 1976.

For someone inexperienced in statistics, this book 

would be a good resource. It is readable, uses 

many examples, and is less confusing than many 

traditional statistics texts. Because it is basic, it 

does not have the detail that other texts might offer, 

which may be a bonus for beginners.

B–6
APPENDIX C

Manual Data Collection 

Procedures and 

Sample Forms
Elements of the System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-3

System Startup. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-4
M a n u a l D a t a C o l l e c t i o n P r o c e d u r e s a n d 

Sample Forms

The manual information system presented in this appendix was used in a 100-bed facility which booked about 6,000 people per year for 6 years. It provides basic information 

to management, is relatively easy to maintain, and does not demand much time on the 

part of clerical staff. And it works!

Elements of the System

To set up this management information system, a jail needs:

1. A supply of 5” x 8” index cards, preferably preprinted with the names of the data 

elements to be collected and spaces for information to be recorded.

2. Two sets of alphabetic file dividers.

3. Two card files.

4. A pen.

5. About 70 hours of a clerk’s time for every 1,000 bookings. This translates to about 

4 minutes for each card filled out.

So, if the jail booked 1,000 people in a year, the cost of maintaining this manual information system would be less than $100 for equipment (assuming you chose the least 

expensive equipment) and $560 in direct labor costs (if the person maintaining the 

system makes $8 an hour). At less than $700 for the annual operating cost, this manual 

information system is within reach of any facility.

APPENDIX C

C–3
HOW TO COLLECT AND ANALYZE DATA

System Startup

For the discussion on identifying information that 

should be collected, refer to chapter 3.

Step 1: Identify the information to be 

collected.

The purposes of this information system are to:

Collect the Inmate Population Data Elements 

(Average Daily Population; Length of Stay; Jail 

Days; Total Bookings; Net Bookings; Number 

of Felons, Misdemeanants, Traffic Offenders, 

and Holds; Number of Pretrial, Posttrial, and 

Sentenced Inmates; Number of Male and Female 

Inmates; and Adult and Juvenile Inmates).

Collect some basic Inmate Profile Data Elements 

(Charge, Age, Ethnicity, Residence, and Release 

Status).

Collect some Operational Data Elements (Number 

and Location of Transports; Number of Incidents; 

and Number of Contact, Noncontact, Professional, 

and Family Visits).

Step 2: Determine how frequently summary 

reports are needed.

Most sheriffs and jail administrators need monthly statistical reports. The Inmate Population Data Elements 

are most understandable statistically when grouped 

by month—weekly statistics are too much to absorb 

when looking for jail population trends and patterns. 

An exception is when the jail is trying to control 

crowding—monthly statistical reports would not be 

frequent enough.

Step 3: Design an inmate information card.

Figure C-1 provides a sample Inmate Information 

Card

Step 4: Decide who will maintain the system.

One person, preferably a clerk, should be responsible 

for maintaining the information system. If the jail 

has no clerical staff, officers on the graveyard shift or 

dispatchers might be able to maintain the system during slow periods. One benefit to having clerical staff 

maintain the information system is that they are not 

tied to radios as dispatchers are and can leave their 

work area to get the information.

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Step 5: Determine where the jail captures 

the information and how to get it from that 

source to the card.

It is hard to be specific about this step because jails 

capture the information in so many places. The Daily 

Activity Log or a Shift Activity Sheet, Booking Log, 

and the Arrest Report probably provide most of the 

Inmate Population and Inmate Profile Data Elements. 

The Operational Data Elements may come from the 

Transport, Incident, and Visitor Logs. Another potential resource is the status board found in the booking 

room or in Central Control, which records who is in 

custody, some descriptive information, and their housing assignments.

Assume, for this example, that the inmate information 

can be collected from an Inmate Information Card 

(figure C-1) and a Daily Activity Log or Shift Activity 

Sheet (figure C-2). (Legal size paper could be used if 

activity warrants.)

Assume also that none of the logs needed to gather the 

operating information about transports, incidents, and 

visitors is available. This will require some additional 

items: three looseleaf binders and a ream of copy 

paper.

Repeat Steps 1 through 4 to decide:

What information should be included in these 

logs.

How frequently the information will be needed.

The design of the Visitor, Transport, and Incident 

Log Sheets.

Who will maintain these documents.

Figures C-3, C-4, and C-5 provide sample Visitor, 

Transport, and Incident Logs.

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C–4
MANUAL DATA COLLECTION

Figure C-1 Sample Inmate Information Card

Inmate Information Card

Name: _________________ Book-in Date: _________________________ Book-out Date: ____________________

Birth date: ______________ Race: ____ Gender: ____________________ Length of Stay: ____________________

Charges: __________________________________________________________________________________________

________ Felony ________ Pretrial ________ Released at Booking

_______ Misdemeanor ________ Posttrial ________ Housed

_______ Traffic ________ Sentenced ______________________ Hold for: __________________________________ 

_______ Other: __________________________________________________________________________

_______ County Resident Released to: ___________________

_______ State Resident Bond Type: ___________________

_______ Out of State Resident Bond Amount: __________________

_______ Undocumented Alien

Employed:____ Full-time ____ Part-time ____ Unemployed ____ Student 

Figure C-2 Daily Activity Log

Any County Sheriff’s Department

Jail Division

Daily Activity Log

IN: ____________________________________ OUT: _______________________________ 

Time: _______________________________ Time: _______________________________ 

Name: _______________________________ Name: _______________________________ 

Charge: _______________________________ Release to: ___________________________ 

P.D.: _______________________________ Bond: ______________________________ 

Time: _______________________________ Time: ______________________________ 

Name: _______________________________ Name: ______________________________ 

Charge: _______________________________ Release to: ___________________________ 

P.D.: _______________________________ Bond: ______________________________ 

Time: _______________________________ Time: ______________________________ 

Name: _______________________________ Name: ______________________________ 

Charge: _______________________________ Release to: ___________________________ 

P.D.: _______________________________ Bond: ______________________________ 

Time: _______________________________ Time: ______________________________ 

Name: _______________________________ Name: ______________________________ 

Charge: _______________________________ Release to: ___________________________ 

P.D.: _______________________________ Bond: ______________________________ 

Statistical Summary: Beginning Count __________ End Count __________

# Felons _____ # Misdemeanants _____ # Traffic Offenders _____ # Other _____

# Pretrial _____ # Sentenced _____ # Females _____ # Juveniles (Male) _____ # Juveniles (Female) _____

Date: _____

Shift: _____

C–5APPENDIX C
HOW TO COLLECT AND ANALYZE DATA

Figure C-3 Sample Visitor Log

Any County Jail Visitor Log

Date Time 

In

Time 

Out Inmate Name Visitor Name Relationship Noncontact Contact

C–6
MANUAL DATA COLLECTION

Figure C-4 Sample Transport Log

Any County Sheriff’s Department

Jail Division

Transport Log

Officer Name:

1. __________________________________________________ Regular Hours _ Comp/O.T. _____________ 

2. __________________________________________________ Regular Hours _ Comp/O.T. _____________ 

3. __________________________________________________ Regular Hours _ Comp/O.T. _____________ 

4. __________________________________________________ Regular Hours _ Comp/O.T. _____________ 

5. __________________________________________________ Regular Hours _ Comp/O.T. _____________ 

Date of Transport:

Reason for Transport: ______________________________________________________________________________

_________________________________________________________________________________________________

Inmate Name: From: To: 

1. __________________________________________________ ________________ ________________ 

2. __________________________________________________ ________________ ________________ 

3. __________________________________________________ ________________ ________________ 

4. __________________________________________________ ________________ ________________ 

5. __________________________________________________ ________________ ________________ 

6. __________________________________________________ ________________ _____________ ___

7. __________________________________________________ ________________ _____________ ___

8. __________________________________________________ ________________ _____________ ___

Time of Departure: Meal Cost:

Time of Return: 1. $____________________________

 2. $____________________________

 Total $__________________________

Vehicle:

Starting Mileage: __________________________ Vehicle Used: 

Ending Mileage: ___________________________ Condition: 

Total Mileage: _____________________________ 

Gas Cost: _________________________________ Gallons Purchased: 

Related Transport Problems:__________________________________________________________________________

_________________________________________________________________________________________________

_________________________________________________________________________________________________

_________________________________________________________________________________________________

_________________________________________________________________________________________________

Submitted: ____________________________________________ Date: _________________________________ 

C–7APPENDIX C
HOW TO COLLECT AND ANALYZE DATA

Figure C-5 Sample Incident Log

Any County Sheriff’s Department

Jail Division

Facility Incident Log

Background:

Inmate Name: _______________________________________ Booking Number: _____________________ 

Housing Assignment: _________________________________ Cell Assignment: ______________________ 

Incident Date: _________________________ Time: ________ Reporting Employee: ___________________ 

Report Date: __________________________ Time: ________ Supervisor: _______________________________ 

Location:

__________ Holding Cell __________ Corridor __________ Security Visiting

__________ Booking Room __________ Multi-Use Room __________ Contact Visiting

__________ Vehicle Sallyport __________ Indoor Exercise __________ Housing Unit #: __________

__________ Isolation Cell __________ Outdoor Exercise __________ Cell #: __________________

__________ Other: _________________________________________________________________________________

Reason:

__________ Suicidal __________ Causing a Disturbance ___________ Contraband

__________ Assault on Staff __________ Threatening Staff ___________ Escape Attempt

__________ Assault on Inmate __________ Threatening Inmate ___________ Escape

__________ Illness __________ Arson ___________ Destroy Jail Property

__________ Injury __________ Protective Custody ___________ Refuse an Order

__________ Other: ______________________________________________________________________________

Description of Incident: ______________________________________________________________________________

Rule Violation: ____ Yes ____ Major ____ Minor ____ No New Offense: ____ Yes ____ No

Disciplinary Action:

Major Violation: Minor Violation:

__________ Immediate segregation __________ No action

__________ Move to more restrictive housing __________ Verbal reprimand

__________ Short-term lockdown __________ 48-hour loss of privilege: 

 __________ 24-hour lockdown

 __________ Other: __________________________

Use of Restraints/Use of Force: Injury or Property Damage to:

__________ None __________ Staff __________ Inmate

__________ Physical force/restraint __________ Facility Property

__________ Mechanical restraint __________ Inmate Property __________ Staff Property

__________ Intermediate weapon

Disciplinary Hearing Required: __________ Yes __________ No

Hearing Date: _______________ Date Served: _______________

Grievance Filed: __________ Yes __________ No Additional Comment: ___________________

Administrative Review and Approval:

Detention Officer: ________________________________________ Date: _______________________________

Supervisor: ______________________________________________ Date: _______________________________

Jail Administrator: ________________________________________ Date: _______________________________

C–8
MANUAL DATA COLLECTION

The easiest ways to get this information are to:

Require that all incidents in the facility be documented on the Incident Log. To do that, the sheriff 

or jail administrator must define for staff what 

kind of events must be documented. Before the 

form is finalized, they should review each form to 

be sure it provides the information that is needed 

and then give it to the person who is maintaining 

the information system to be placed in the proper 

binder. 

Require that all officers transporting inmates outside the facility begin a Transport Log prior to 

departing the jail and complete it as soon as they 

return. Have them give the completed form to the 

person who is maintaining the system to be placed 

in the proper binder.

Require that all persons who visit the facility 

complete a Visitor Log entry. The forms should be 

prepunched and placed in a three-ring binder kept 

at the visitors’ entrance.

If the jail has more than one source for a particular 

data element, a decision must be made about which 

source to use. Two conditions might help with that 

choice:

1. One source may be more accurate than another.

2. The fewer sources providing the information, the 

better. If one source has only one data element 

and another source has more than one, unless 

there is a compelling reason to use the former, use 

the source that provides the most data elements.

Step 6: Train the person who will maintain 

the system in how to fill out the inmate 

information card. Insist that it be done daily.

Information systems are manageable if they are maintained daily, but are extremely tedious to update. 

Worse, any missing information may be impossible 

to get once the inmate is gone and the staff’s memories have faded. Occasionally, it is also a good idea 

to make sure the information filled out on each of 

the Inmate Information Cards is accurate. Be sure to 

explain how each data element is to be entered and 

review the rules for coding data in chapter 6.

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Step 7: Pick a day to start.

Ideally, the information system should start on the first 

of the month. Allow a few days of practice to be sure 

that the system is working properly. On Day I, make 

a card for each inmate who was in custody at 8:00 

a.m. Label two card files: “Current Inmates” and “Past 

Inmates.” File the Inmate Information Cards by inmate 

last name after the appropriate letter in the Current 

Inmate Card File. Get a calendar small enough to fit 

inside one of the card files. At the same time each 

day, record the number of inmates in custody. Be sure 

to pick a time when the jail’s population is not artificially low (like right after court). Usually 8:00 a.m. is 

a good time.

Step 8: Begin daily maintenance.

Each day, complete an Inmate Information Card for 

every person booked at the jail. Pull out the Inmate 

Information Card of every person who is released, 

enter the release information, and file it in the Past 

Inmate Card file.

Step 9: Prepare the first monthly report.

Begin preparing a monthly report by averaging the 

daily head counts to calculate the jail’s Average Daily 

Population. Before starting the monthly report, make 

“tally sheets” for each data element. Figure C-6 provides a sample of a monthly tally sheet for the data 

element “Inmate Age.”

Take out the Past Inmate Card file and remove all 

the Inmate Information Cards. Be careful to use only 

the Past Inmate Card file. The system actually counts 

inmates as they are released from the jail rather than 

counting them as they are booked. Statistically this 

is better, because it captures information sooner about 

people who have long lengths of stay. Since inmate 

turnover in jails is relatively rapid, it will take only 

about a month for the system to include people who 

have been in jail for periods longer than a month. 

If information is gathered on admission, it will take 

many months, perhaps more than a year, to include 

people who stay very long periods of time. This 

avoids sample bias.

When going through the Inmate Information Cards, 

make a mark on the section of a tally sheet that represents the value noted on the card. For instance, put a 

C–9APPENDIX C
HOW TO COLLECT AND ANALYZE DATA

check mark next to “Felony” if the prisoner is charged 

with a felony. If there is more than one charge, always 

code the most serious charge. When finished with the 

inmate information, pull the Transport, Incident, and 

Visitor Logs for the month and follow the same process. Summarize the information in report form. For 

some help in doing this, refer to chapter 9 for some 

tips on how to display data.

Next, make an accounting sheet for each of the data 

elements so when each monthly report is completed, a 

“Year-to-Date” total can be calculated. This will make 

doing an annual report easier. Put these cards together 

and label them by month. When the annual report is 

completed, store the cards in a dry, secure place. They 

may be useful in later data collections.

Step 10. Evaluate the information system in 

six months and make any necessary changes.

Every jail has its own peculiarities, and this system 

may not exactly fit your facility. After 6 months, however, you should have enough experience with it to 

know what portions should be changed or adapted to 

better fit your needs. Set a time to do this. Be sure to 

talk with the person who is maintaining the system. 

This information system, when tailored to meet the 

needs of the jail, can be a powerful management tool. 

Take the time to improve on it!

Age Male Female Age Male Female

18 34

19 35

20 36

21 37

22 38

23 39

24 40

25 41

26 42

27 43

28 44

29 45

30 46

31 47

32 48

33 49 or older

# of Males

# of Females

Figure C-6 Sample Monthly Tally Sheet for Inmate Age

Any County Sheriff’s Department

Jail Division

Inmate Information System Monthly Tally Sheet

C–10
a p p e n d i x D

INMATE PROFILE 

DATA COLLECTION
Data Collection Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-3

Inmate Profile Data Collection Code Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-4
D–

Data Collection Form

Inmate Name: ______________________________________________________________________________________

ID # __ __ __ Arresting Agency __

Booking # __ __ __ __ __ __ Occupation __ __

Residence __ Employment Status __

Sex __ Marital Status __

Date of Birth __ __ / __ __ / __ __ __ __ Legal Status __

Race __ Release Status __ __

Hold Type __ Bond Type __ __

Charge Type __ Last Grade Attended __ __

Charge 1 __ __ Military Status __

Charge 2 __ __ Security Status __

Charge 3 __ __

Charge 4 __ __

Total # Charges __ __

Priors (Felony) __ __

Priors (Misdemeanor) __ __ 

Convictions __ __

Prior Violent __ __

Prior Drug __ __

Prior Alcohol __ __

Days Served __ __

Disposition Date __ __ / __ __ / __ __ __ __ 

Sentencing Date __ __ / __ __ / __ __ __ __

Continuances __ __

Court __

Judge __

Booking Date __ __ / __ __ / __ __ __ __

Booking Time __ __ : __ __

Release Date __ __ / __ __ / __ __ __ __

Release Time __ __ : __ __

These are often “yes/no” answers.

Does the subject have health problems that require immediate treatment? __

Is the subject under the influence of drugs or alcohol? __

Does the subject exhibit psychological problems that require immediate treatment? __

Is the subject currently under a doctor’s care? __

Is the subject currently receiving psychiatric care? __

Has the subject ever been in treatment for mental health issues? __

Treatment type. __

Does the subject have a history of substance abuse? __

Primary substance. __ 

Has the subject ever been in treatment for substance abuse? __

Treatment type. __

APPENDIX

D
D– HOW TO COLLECT AND ANALYZE DATA

Inmate Profile Data Collection Code Book

Variable Name Code Explanation

ID Number Three-digit number assigned to each case on which data is collected (e.g., 

001, 002, 003, etc.)

Booking Number A number assigned to each case booking. Note: The number of digits will 

vary based on the booking number protocol.

Residence 1

2

3

4

5

6

8

9

City of ____________________________________

City of ____________________________________

City of ____________________________________

Other in-county

In-state, out of county

Out of state

Out of U.S.

Unknown

Sex 1

2

Male

Female

Date of Birth Eight-digit number (e.g., 01/04/1976) 

Race 1

2

3

4

5

6

Caucasian

African-American

Hispanic

Native American

Asian

Other

Legal Status 1

2

3

4

Pretrial

Sentenced

Hold Only

Other

Hold Type 1

2

3

4

5

Hold for other county or state

Hold for probation/parole

Hold for immigration

Hold for AWOL

Hold for ______________________________________________________

Charge Type 1

2

3

4

Felony

Misdemeanor

Traffic

Other

Continued on next page.
INMATE PROFILE DATA COLLECTION D–

Variable Name Code Explanation

Charge 1–4 (Person 

Offense Codes)

10

11

12

13

14

15

16

17

18

19

Murder

Attempted murder

Manslaughter

Aggravated assault

Robbery, armed

Robbery, unarmed

Minor assault

Kidnapping

Menacing

Other offenses against persons

Charge 1–4 (Property 

Offense Codes)

20

21

22

23

24

25

26

27

28

29

Larceny

Auto theft

Burglary

Breaking and entering

Arson

Theft

Shoplifting

Criminal mischief

Trespassing

Other offenses against property

Charge 1–4 (Offenses 

Against Family or 

Children Codes)

30

31

32

33

34

35

36

37

38

39

Nonsupport

Failure to provide

Desertion

Neglect

Bigamy

Adultery

Contributing to the delinquency of a minor

Violation of compulsory school law

Paternity offenses

Other offenses against family or children

Continued on next page.

Inmate Profile Data Collection Code Book (continued)

APPENDIX

D
D– HOW TO COLLECT AND ANALYZE DATA

Variable Name Code Explanation

Charge 1–4 (Sex Offenses 

Codes)

40

41

42

43

44

45

46

47

48

49

Forcible sex acts

Unnatural sex acts

Prostitution

Immoral acts

Promiscuity

Illegal sexual relations

Pornography

Soliciting

Pandering

Other sex offenses

Charge 1–4 (Crimes 

of Forgery, Fraud, and 

Conspiracy Codes)

50

51

52

53

54

55

56

57

58

59

Forgery

Fraud

Deception/embezzlement

Uttering fraudulent instrument

Issuing fraudulent instrument

Conspiracy

Blackmail, extortion

Receiving/concealing stolen property

Impersonation

Other crimes of forgery, fraud, and conspiracy

Charge 1–4 (Drug, 

Alcohol, and Weapon 

Offenses Codes)

60

61

62

63

64

65

66

69

Violation of weapons laws

Violation of liquor laws

Violation of narcotics laws

Violation of gambling laws

Drunk or drinking

City ordinance violations (except disorderly conduct)

Disorderly conduct

Other weapon, drug, or alcohol offenses

Charge 1–4 (Traffic 

Violations Codes)

70

71

72

73

74

79

Moving violation

Standing violation

Operating a motor vehicle without license or registration

Operating a motor vehicle under the influence of drugs or alcohol

Careless or reckless driving

Other traffic or motor vehicle offenses

Inmate Profile Data Collection Code Book (continued)

Continued on next page.
INMATE PROFILE DATA COLLECTION D–

Variable Name Code Explanation

Charge 1–4 

(Miscellaneous Offenses 

Codes)

80

81

82

83

84

85

86

87

88

89

90

91

92

93

99

Escape from custody

Obstructing

Habitual criminal

Harboring a fugitive

Cruelty to animals

Violation of court order

FTA/FTP/bond revocations

Appeals

Writs

Temporary holds

Probation violation

Parole violation

Protective custody

Other: ____________________________________

Unknown

Total Number of Charges Total number of charges this booking

Arresting Agency 1

2

3

4

5

6

7

8

9

County of ________________________________

City of ___________________________________

City of ___________________________________

City of ___________________________________

State police or patrol

Other state agency

Federal

Other county

Unknown

Continued on next page.

Inmate Profile Data Collection Code Book (continued)

APPENDIX

D
D– HOW TO COLLECT AND ANALYZE DATA

Variable Code Explanation

Occupation 1

2

3

4

5

6

7

8

9

10

11

12

99

Professional, technical

Managerial, administrative

Sales

Clerical

Craftsman

Operator (e.g., machine operator or factory worker)

Transportation equipment operator

Laborer (except farm)

Farm laborer

Service worker

Student

No employment history

Unknown

Employment Status 1

2

3

4

Full-time employed

Part-time employed

Unemployed

Never employed

Marital Status 1

2

3

4

5

6

9

Married

Single

Divorced

Separated

Common-law

Widow(er)

Unknown

Booking Date Eight-digit date of booking (e.g., 04/19/2004) 

Booking Time Time of booking in military time (e.g., 04:25, 17:25)

Release Date Eight-digit date of release (e.g., 09/30/2004) 

Release Time Time of release in military time (e.g., 23:45)

Inmate Profile Data Collection Code Book (continued)

Continued on next page.
INMATE PROFILE DATA COLLECTION D–

Variable Name Code Explanation

Release Status 01

02

03

04

05

06

07

08

09

10

11

12

13

14

Jail sentence completed

Acquitted, charges dropped

Transferred to state or federal institution

Placed on probation, probation reinstated

Paroled, parole reinstated

Bail or bond

Release to other jurisdiction

Release to mental health facility

Release to medical facility

Release to treatment program/facility

Deferred sentence or prosecution

Escape or walk-away

Fine paid

Other

Bond Type 01

02

03

04

05

06

07

08

09

10

11

12

13

14

Personal recognizance—pretrial services

Personal recognizance—court ordered

Cash

Surety

Property

10% bond

Personal recognizance with cosigner(s)

Personal recognizance with conditions

Personal recognizance and surety

Personal recognizance and cash

Personal recognizance and property

Cash and surety

Bond reinstated

Other: ________________________________________________________

Last Grade Two-digit number indicating the last grade completed (e.g., 13 = one year of 

college)

Military Status 1

2

Veteran

Nonveteran

Continued on next page.

Inmate Profile Data Collection Code Book (continued)

APPENDIX

D
D–10 HOW TO COLLECT AND ANALYZE DATA

Variable Name Code Explanation

Security Classification 1

2

3

4

8

Maximum

Medium

Minimum

Special housing

Not applicable—another data numbering convention

Prior Arrests (Felony) Two-digit number indicating the number of prior felony arrests in this 

county

Prior Arrests 

(Misdemeanor)

Two-digit number indicating the number of prior misdemeanor arrests in this 

county

Number of Convictions Two-digit number indicating the number of convictions in this county

Number of Violent Arrests Two-digit number indicating the number of arrests in this county for offenses 

defined as violent. Note: Each jurisdiction needs to identify the charges it 

includes in this category

Number of Drug Arrests Two-digit number indicating the number of arrests in this county for offenses 

involving drugs

Number of Alcohol 

Arrests

Two-digit number indicating the number of arrests in this county for offenses 

involving alcohol

Days Served A computed variable, the number of days between date of booking and date 

of release. Note: If less than 24 hours, express as a decimal (portion of a 

day), e.g., 12 hours = .5

Date of Disposition Eight-digit date showing the date trial was held, plea was entered, or case 

was dismissed

Date of Sentencing Eight-digit date showing the date of sentencing

Number of Continuances Two-digit number showing the number of continuances granted prior to trial 

on this charge

Court 1

2

3

4

5

Superior court

Lower court

Municipal court

Appeals court

Other ________________________________________________________

Judge 1

2

3

_____________________________________________________________

_____________________________________________________________

_____________________________________________________________

Health Problems 1

2

Yes

No

Influence of Drugs, 

Alcohol

1

2

Yes

No

Inmate Profile Data Collection Code Book (continued)

Continued on next page.
INMATE PROFILE DATA COLLECTION D–11

Variable Name Code Explanation

Mental Health Problems 1

2

Yes

No

Doctor’s Care 1

2

Yes

No

Current Mental Health 

Care

1

2

Yes

No

Previous Mental Health 

Care

1

2

Yes

No

Treatment Type 1

2

3

Inpatient

Outpatient

Other _________________________________________________________

Substance Abuse History 1

2

Yes

No

Primary Substance 1

2

3

4

5

6

7

8

9

Alcohol

Marijuana

Hallucinogens

Amphetamines

Cocaine

Heroin, other opiates

Crack or crank

PCP

Other _________________________________________________________ 

Substance Abuse 

Treatment 

1

2

Yes

No

Treatment Type 1

2

3

Inpatient

Outpatient

Other _________________________________________________________

Inmate Profile Data Collection Code Book (continued)

APPENDIX

D
a p p e n d i x E

Incident Data 

Code Book
E–APPENDIX

E

Variable Code Explanation

Housing Assignment 11

12

13

14

15

99

Housing Unit # _________________________________________________

Housing Unit # _________________________________________________

Housing Unit # _________________________________________________

Housing Unit # _________________________________________________

Housing Unit # _________________________________________________

Not yet assigned to housing _______________________________________

Location of Incident 01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

88

99

Holding

Booking

Intake sallyport

Property room, search/shower

Isolation

Corridor

Library or classroom

Exercise

Visiting

Kitchen

Housing Unit # same as above

Housing Unit # same as above

Housing Unit # same as above

Housing Unit # same as above

Housing Unit # same as above

Other (in facility)

Outside facility

Not applicable

Unknown

Incident Data Code Book

Continued on next page.
HOW TO COLLECT AND ANALYZE DATA

Variable Code Explanation

Reason 01

02

03

04

05

06

07

08

09

10

11

12

13

14

88

99

Suicidal

Violent

Illness

Accidental injury

Causing a disturbance

Harming/threatening staff

Harming/threatening inmate

Arson

Protective custody

Contraband violation

Escape attempt/risk

Refusal to obey an order

Destruction of jail property

Other _________________________________________________________

Not applicable

Unknown

Rule Violation 1

2

3

Major

Minor

None

Crime Report Required 1

2

Yes

No

Major Violation 1

2

3

4

8

9

Move to isolation

Move to maximum security

Move to special management

Move to other

Not applicable

Unknown

Minor Violation 1

2

3

4

5

6

8

9

No action

Verbal reprimand

48-hour loss of privilege

24-hour lockdown

Over 48-hour loss of privilege

Other _________________________________________________________

Not applicable

Unknown

E–4

Incident Data Code Book (continued)

Continued on next page.
Incident Data Code Book

Variable Code Explanation

Privilege Loss 1

2

3

4

5

6

7

8

9

Contact visit

Exercise

Library

Program participation ____________________________________________

Television

Nonattorney telephone

Other

Not applicable

Unknown

Use of Restraints 1

2

3

8

9

None needed

Physical

Mechanical

Not applicable

Unknown

Injury to 1

2

3

8

9

Staff

Inmate

Other

Not applicable

Unknown

Review Board 1

2

8

9

Yes

No

Not applicable

Unknown

Date of Review Eight-digit date documenting the date of the review board hearing

Date Notice Served Eight-digit date documenting the date the inmate was served notice of the 

review board hearing

Grievance Filed Yes

No

Not applicable

Unknown

E–5APPENDIX

E

Incident Data Code Book (continued)
a p p e n d i x F

Transport Data 

Collection
F–

Transport Data Collection

Variable Code Explanation

Reason for Transport 01

02

03

04

05

06

07

08

09

10

88

99

Emergency room treatment

Scheduled medical, dental, or other treatment appointment

Court appearance

Pick-up in another county on warrant

Return from this county to another county on its warrant

Transport to state Department of Corrections

Transport to state hospital

Transport to treatment facility

Court ordered

Other _________________________________________________________

Not applicable

Unknown

Location 01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

50

51

52

53

54

Hospital

Doctor’s office

Dental office

Psychiatrist/psychologist's office

Mental health clinic

Public health department

Other health-related transport

State prison

State reformatory

State hospital

Veteran’s hospital

Halfway house

Other treatment placement

County ____________________________________

County ____________________________________

Other county

Civil court

District court

County court

Juvenile court

Municipal court

Continued on next page.

APPENDIX F
HOW TO COLLECT AND ANALYZE DATA

Variable Code Explanation

Location (continued) 55

88

98

99

Other court

Not applicable

County jail

Unknown

Departure Time Four-digit number on the 24-hour (military) clock designating the time officers left the jail for the transport.

Return Time Four-digit number on the 24-hour (military) clock designating the time officers returned to the jail.

OT/Comp 1

2

3

4

Yes

No

Not applicable

Unknown

Meals Four-digit number indicating in dollars and cents the amount of money spent 

for food during the transport.

Odometer Start Six-digit number recording the odometer reading on the vehicle at 

departure

Odometer End Six-digit number recording the odometer reading on the vehicle on return 

form the transport

Type of Vehicle 1

2

3

8

9

Van

Car

Other

Not Applicable

Unknown

Vehicle Number Two-digit number documenting the fleet vehicle number of the vehicle used 

on the transport

Cleanliness of Vehicle 1

2

3

Poor

Fair

Good

Mechanical Condition of 

Vehicle

1

2

3

Poor

Fair

Good

Gas Cost Four-digit number documenting in dollars and cents the amount of money 

spent to purchase gasoline on the transport

Number of Gallons Two-digit number documenting the number of gallons of gasoline purchased 

for the transport.

F–4

Transport Data Collection (continued)
APPENDIX G

Tables for 

Determining 

Sample Size
Tables for Determining Sample Size

This section is reprinted with permission from Statistical Sample Methods for 

Correctional Planners, by Edward Lakner, prepared at the National Clearinghouse for 

Criminal Justice Planning and Architecture and published by the University of Illinois 

at Urbana-Champaign in 1976, pp. 68–71.

Tables G-l through G-5 give the sample sizes needed to estimate proportions in 

selected populations at the 95-percent and 99-percent confidence levels, with allowable 

errors of ±2 percent, ±3 percent, and ±5 percent. By interpolating between the marginal* values of the table (the expected rate of occurrence and the size of the population), 

approximate intermediate sample sizes may be found. Exact sample sizes for proportions and population sizes not listed in the tables can be found by using the formulas 

provided in chapter 6.

APPENDIX G

* Marginals in this sense mean the numbers around the outside of the table.

G–3
HOW TO COLLECT AND ANALYZE DATA

Table G-l Sample Size for Reliability of ±2% at the 95% Confidence Interval

Population 

Size 5% or 95% 10% or 90% 20% or 80% 30% or 70% 40% or 60% 50%

Expected Rate of Occurrence in the Population

500 239 316 377 400 410 413

1,000 314 464 606 669 697 706

1,500 350 549 758 859 907 922

2,000 372 604 869 1,004 1,071 1,091

2,500 386 642 952 1,117 1,200 1,225

10,000 436 796 1,332 1,678 1,873 1,936

50,000 452 850 1,491 1,939 2,203 2,291

100,000 456 863 1,532 2,009 2,294 2,390

Table G-2 Sample Size for Reliability of ±3% at the 95% Confidence Interval

Population 

Size 5% or 95% 10% or 90% 20% or 80% 30% or 70% 40% or 60% 50%

Expected Rate of Occurrence in the Population

500 144 217 288 320 336 340

1,000 169 278 406 473 506 551

1,500 179 306 470 561 609 624

2,000 184 322 509 619 678 696

2,500 188 333 537 660 727 748

10,000 199 370 639 823 929 964

50,000 202 381 674 881 1,004 1,045

100,000 203 384 682 895 1,023 1,065

Table G-3 Sample Size for Reliability of ±5% at the 95% Confidence Interval

Population 

Size 5% or 95% 10% or 90% 20% or 80% 30% or 70% 40% or 60% 50%

Expected Rate of Occurrence in the Population

500 64 108 165 196 213 217

1,000 68 121 198 244 262 278

1,500 70 126 211 269 297 306

2,000 70 130 219 278 312 322

2,500 71 131 224 286 322 333

10,000 72 136 240 313 356 370

50,000 72 137 245 321 367 380

100,000 73 138 246 322 369 384

G–4
TABLES FOR DETERMINING SAMPLE SIZE

Table G-4 Sample Size for Reliability of ±2% at the 99% Confidence Interval

Table G-5 Sample Size for Reliability of ±3% at the 99% Confidence Interval

Population 

Size 5% or 95% 10% or 90% 20% or 80% 30% or 70% 40% or 60% 50%

Expected Rate of Occurrence in the Population

500 305 374 420 437 444 446

1,000 441 598 725 776 798 804

1,500 517 748 957 1,047 1,087 1,099

2,000 565 855 1,138 1,267 1,328 1,346

2,500 599 935 1,284 1,451 1,531 1,901

10,000 731 1,299 2,098 2,584 2,848 2,932

50,000 776 1,450 2,520 3,257 3,688 3,830

100,000 787 1,489 2,640 3,460 3,950 4,113

Population 

Size 5% or 95% 10% or 90% 20% or 80% 30% or 70% 40% or 60% 50%

Expected Rate of Occurrence in the Population

500 206 285 351 378 390 393

1,000 260 399 541 607 638 647

1,500 284 460 661 761 788 826

2,000 298 498 742 873 939 959

2,500 307 525 801 955 1,036 1,061

10,000 338 622 1,055 1,341 1,504 1,556

50,000 348 655 1,152 1,502 1,709 1,778

100,000 350 663 1,177 1,544 1,763 1,836

G–5APPENDIX G
a p p e n d i x H

Simple Random 

Sampling
S i m p l e R a n d o m S a m p l i n g

This appendix is taken directly from Practical Research: Planning and Design, by Paul 

D. Leedy, published by Macmillan Publishers, 1974, pp. 93–99. Simple random sampling theory and practice is described in considerable detail.

Types, Methods, and Procedures of Sampling

One basic rule holds whenever a researcher is considering methodology in relation to 

data—it matters not whether this methodology concerns sampling, a statistical procedure, or any other type of operation—and that one basic rule is: Look carefully at the 

nature, the characteristics, the quality of the data. After the researcher sees this clearly, 

he/she can then more intelligently select the proper methodology for the treatment of 

those data.

The composition of the sample is derived by selecting the sample units from those of 

a much larger population. In survey studies the manner in which the sample units are 

selected is very important. Generally, the components of the sample are chosen from the 

population universe by a process known as randomization. Such a sample is a random 

sample. The two elements that are more important than any others in survey research 

are randomization and bias. We shall discuss randomization here.

Randomization means selecting apart of the whole population in such a way that the 

characteristics of each of the units of the sample approximate the broad characteristics 

inherent in the total population. Let’s explain that. I have a beaker which contains 100 

cc. of water. I have in another container a concentrated solution of 10 cc. of acid. I combine the water and the acid in proportions of 10 to 1. After thoroughly mixing the water 

and the acid, I should be able to extract 1 cc. from any part of the solution and find in 

that 1 cc. sample a mixture of water and acid in precisely a 10:1 proportion. Just so, if 

we have a conglomerate population with variables such as differences of race, wealth, 

education, and other factors, and if I have a perfectly selected random sample

(a situation that is more theoretical than practical), I will find in the sampling those 

same characteristics of race, wealth, education, and so forth that exist in the larger 

population universe.

a p p e n d i x h

H–3
HOW TO COLLECT AND ANALYZE DATA

A sample is no more representative of the total population, therefore, than the validity of the method of 

randomization employed in selecting it. There are, of 

course, many methods of random selection. We shall 

look at merely a few of the more common ones.

The Roulette Wheel Method

If the population is small—50 to 75 individuals—each 

individual may be assigned a number in some orderly 

sequence: alphabetically by surname, by birth date 

(youngest to oldest or the reverse), by the respective 

weight of each individual, or by any other systematic 

arrangement. Corresponding numbers are on a roulette 

wheel. A spin of the wheel and its fortuitous stopping 

at a particular number selects the individual assigned 

to that number as a unit of the sample. The process of 

spinning the wheel and selecting the sample unit goes 

on until all the individuals needed to compose the 

sample have been chosen.

The Lottery Method

In the lottery method the population again is arranged 

sequentially and assigned a numerical identification. 

Corresponding numbers are marked on separate tabs 

and put into a revolving drum or closed container. 

The numbers are tossed so that they are thoroughly 

intermixed. Then one tab bearing a number is selected 

from the total number of tabs in the container without 

the selector seeing the pool. The number is recorded 

and the tab is then tossed back into the tab pool. 

This is an important feature of the lottery method. It 

ensures that every individual has the same chance of 

being chosen as every other individual. If, for example, we are selecting 50 units out of a population of 

100 and we do not cast each tab back after it has been 

selected, we will have an ever-diminishing population 

from which to make the choices. And whereas the first 

choice would have 1 in 100 chances of being selected, 

the last unit chosen would have 1 in 50 chances of 

being selected or, in other words, his chances of being 

selected would be twice as great as those of the first 

individual.

In the event of the same number being drawn twice, 

the second drawing is ignored; the number is returned 

to the pool; the entire mass of numbers is tumbled 

again, and another drawing is made. Drawing and 

tumbling go on until 50 tabs have been selected purely 

by chance.

The Table of Random Numbers Method

The table of random numbers method is perhaps the 

most frequently used method for the random selection 

of a sample. Following is a part of a table of random 

numbers. We may employ this table in any manner in 

which we choose to use it. Generally the researcher 

enters the table according to some predetermined 

method.

Entrance into the table may, in fact, be accomplished 

in many ways. One fundamental principle must be 

kept in mind, however: The purpose of randomness is 

to permit blind chance to determine the outcomes of 

the selection process to as great a degree as possible. 

Hence, in determining a starting point for the selection 

of random numbers, chance must be given free reign.

Consider the following tables of random numbers 

(tables H-1 through H-4). Considering the second 

table (table H-2) as a continuation of the first table 

(table H-1), and counting from left to right, there 

are five blocks of random numbers in the horizontal 

arrangement. Vertically there are 10 blocks from top 

to bottom of the table.

In a copy of the first table (table H-3), for illustrative 

purposes, we have numbered the horizontal blocks 

1–5 and the vertical blocks from the top downward 

as 1–10.

The horizontal and vertical numbers of the columns 

give us a way to identify the intersecting axes and, 

thus, a starting point within the table. To determine 

the point of axial intersection and to get a starting 

point, you might engage in any one of several activities. The purpose of any of these is to select two digits 

purely by chance. Here are some suggestions.

Use a telephone directory. Open a telephone directory at random. Take the last two digits of the first 

number in the first column on either the left-hand 

or right-hand page. Let the first digit of those chosen 

be the designating digit of the horizontal column of 

the table of random numbers; the second digit will, 

then, indicate the number of the vertical column. 

The intersection of the two columns will indicate the 

H–4
Simple Random Sampling

Table H-1 Random Number Table (#1)

97 74 24 67 62 42 81 14 57 20 42 53 32 37 32 27 07 36 07 51 24 51 79 89 73

16 76 62 27 66 56 50 26 71 07 32 90 79 78 53 13 55 38 58 59 88 97 54 14 10

12 56 85 99 26 96 96 68 27 31 05 03 72 93 15 57 12 10 14 21 88 26 49 81 76

03 47 43 73 86 36 96 47 36 61 46 98 63 71 62 33 28 16 80 45 60 11 14 10 95

55 59 56 35 64 38 54 82 46 22 31 62 43 09 90 08 18 44 32 53 23 83 01 30 30

16 22 77 94 39 49 54 43 54 82 17 37 93 23 78 87 35 20 96 43 84 26 34 91 64

84 42 17 53 31 57 24 55 06 88 77 04 74 47 67 21 76 33 50 25 83 92 12 06 16

63 01 63 78 59 18 95 55 67 19 98 10 50 71 75 12 86 73 58 07 44 39 52 38 79

33 21 12 34 29 78 64 58 07 82 52 42 07 44 38 15 51 00 13 42 99 66 02 79 54 

57 60 86 32 44 09 47 27 96 54 49 17 48 09 62 90 52 84 77 27 08 02 73 43 28

18 18 07 92 46 44 17 16 58 09 79 83 86 19 62 06 76 50 03 10 55 23 64 05 05

28 62 38 97 75 84 16 07 44 99 83 11 46 32 24 20 14 85 88 45 10 93 72 88 71

23 42 40 64 74 82 97 77 77 81 07 45 32 14 08 32 98 94 07 72 93 85 79 10 75

62 38 28 19 95 50 92 26 11 97 00 56 76 31 38 80 22 02 53 53 88 60 42 04 53

37 85 94 35 12 83 39 50 08 30 42 34 07 96 88 54 42 06 87 98 35 85 29 48 39

70 29 17 12 13 40 33 20 38 26 13 89 51 03 74 17 76 37 13 04 17 74 21 19 30

56 62 18 37 35 96 83 50 87 75 97 12 25 93 47 70 33 24 03 54 97 77 46 44 80

99 49 57 22 77 88 42 95 45 72 16 64 36 18 00 04 43 18 66 79 94 77 24 21 90

18 08 15 04 72 33 27 14 34 09 45 59 34 68 49 12 72 07 34 45 99 27 72 95 14

31 16 93 32 43 50 27 89 87 19 20 15 37 00 49 52 85 66 60 44 38 68 88 11 80

68 34 30 13 70 55 74 30 77 40 44 22 78 84 26 04 33 46 09 52 68 07 97 06 57

74 57 25 65 76 59 29 97 68 60 71 91 38 67 54 13 58 18 24 76 15 54 55 95 52

27 42 37 86 53 48 55 90 65 72 96 57 69 36 10 96 46 92 42 45 97 60 49 04 91

39 68 29 61 00 66 37 32 20 30 77 84 57 03 29 10 45 66 04 26 11 04 96 67 24

29 94 98 94 24 68 49 69 10 82 53 75 91 93 30 34 25 20 57 27 40 48 73 51 92

18 90 82 66 59 83 62 64 11 12 67 19 00 71 74 60 47 21 29 68 02 02 37 03 31

11 27 94 75 06 06 09 19 74 66 02 94 37 34 02 76 70 90 30 86 38 45 94 30 38

35 24 10 16 20 33 32 51 26 38 79 78 45 04 91 16 92 53 56 16 02 75 50 95 98

38 23 16 86 38 42 38 97 01 50 87 75 66 81 41 40 01 74 91 62 48 51 84 08 32

31 96 25 91 47 96 44 33 49 13 34 86 82 53 91 00 52 43 48 85 27 55 26 89 62

66 67 40 67 14 64 05 71 95 86 11 05 65 09 68 76 83 20 37 90 57 16 00 11 66

14 90 84 45 11 75 73 88 05 90 52 27 41 14 86 22 98 12 22 08 07 52 74 95 80

68 05 51 18 00 33 96 02 75 19 07 60 62 93 55 59 33 82 43 90 49 37 38 44 59

20 46 78 73 90 97 51 40 14 02 04 02 33 31 08 39 54 16 49 36 47 95 93 13 30

64 19 58 97 79 15 06 15 93 20 01 90 10 75 06 40 78 78 89 62 02 67 74 17 33

05 26 93 70 60 22 35 86 15 13 92 03 51 59 77 59 56 78 06 83 52 91 05 70 74

70 97 10 88 23 09 98 42 99 64 61 71 62 99 15 06 51 29 16 93 58 05 77 09 51

68 71 86 85 85 54 87 66 47 54 73 32 08 11 12 44 95 92 63 16 29 56 24 29 48

26 99 61 65 53 58 37 78 80 70 42 10 50 67 42 32 17 55 85 74 94 44 67 16 94

14 65 52 68 75 87 59 36 22 41 26 78 63 06 55 13 08 27 01 50 15 29 39 39 43

17 53 77 58 71 71 41 61 50 72 12 41 94 96 26 44 95 27 36 99 02 96 74 30 83

90 26 59 21 19 23 52 23 33 12 96 93 02 18 39 07 02 18 36 07 25 99 32 70 23

41 23 52 55 99 31 04 49 69 98 10 47 48 45 88 13 41 43 89 20 97 17 14 49 17

60 20 50 81 69 31 99 73 68 68 35 81 33 03 76 24 30 12 48 60 18 89 10 72 34

91 25 38 05 90 94 58 28 41 36 45 37 59 03 09 90 35 57 29 12 82 62 54 65 60

34 50 57 74 37 98 80 33 00 91 09 77 93 19 82 74 94 80 04 04 45 07 31 66 49

85 22 04 39 43 73 81 53 94 79 33 62 46 86 28 08 31 54 46 31 53 94 13 38 47

09 79 13 77 48 73 82 97 22 21 05 03 27 24 83 72 89 44 05 60 35 80 39 84 88

88 75 80 18 14 22 95 75 42 49 39 32 82 22 49 02 48 07 70 37 16 04 81 67 87

90 98 23 70 00 39 90 03 08 90 55 85 78 38 36 94 37 30 69 32 90 89 00 76 33

H–5APPENDIX H
HOW TO COLLECT AND ANALYZE DATA

Table H-2 Random Number Table (#2)

63 38 06 86 54 99 00 65 26 94 09 82 90 23 07 79 62 67 80 60 75 91 12 81 19

35 30 58 21 46 06 72 17 10 94 25 21 31 75 96 49 28 24 00 49 55 65 79 78 07

63 43 36 8269 65 51 18 37 88 61 38 44 12 45 32 92 85 88 65 54 34 81 85 35

98 25 37 55 26 01 91 82 81 46 74 71 12 94 97 24 02 71 37 07 03 82 18 66 75

53 74 23 89 67 61 32 28 69 84 94 62 67 86 24 98 33 41 19 95 47 53 53 38 09

02 83 21 17 69 71 50 80 89 56 38 15 70 11 48 43 40 45 86 98 00 83 26 91 03

64 55 22 21 82 48 22 28 06 00 61 54 13 43 91 82 78 12 23 29 06 66 24 12 27

85 07 26 13 89 01 10 07 82 04 59 63 69 36 03 69 11 15 83 80 13 29 54 19 28

58 54 16 24 15 51 54 44 82 00 62 61 85 04 69 38 18 65 18 97 85 72 13 49 21

34 85 27 84 87 61 48 84 56 26 90 18 48 13 26 37 70 15 42 57 65 65 80 39 07

03 82 18 27 46 57 99 16 96 56 30 33 72 85 22 84 64 38 56 98 99 01 30 98 64

62 95 30 27 59 37 75 41 66 48 86 87 80 61 45 23 53 04 01 63 45 76 08 64 27

08 45 93 15 22 60 21 75 46 91 98 77 27 85 42 28 88 61 08 84 69 62 03 42 73

70 08 55 18 40 45 44 75 13 90 24 84 96 61 02 57 55 66 83 15 73 42 37 11 61

01 85 89 95 86 51 10 19 34 88 15 84 97 18 75 12 76 39 43 78 64 63 91 08 25

72 84 71 14 35 19 11 58 49 26 50 11 17 17 76 86 31 57 20 18 95 60 78 46 75

88 78 28 16 84 13 52 53 94 53 75 45 69 30 96 73 89 65 70 31 99 17 43 48 76

45 17 75 65 57 28 40 19 72 12 25 12 74 75 67 60 40 60 81 19 24 62 01 61 16

96 76 28 12 54 22 01 11 94 25 71 96 16 16 88 68 64 36 74 45 19 59 50 88 92

43 31 67 72 30 24 02 84 08 63 38 32 36 66 02 69 36 38 25 39 48 03 45 15 22

50 44 86 44 21 66 06 58 05 62 68 15 54 35 02 42 35 48 96 32 14 52 41 52 48

22 66 22 15 86 26 63 75 41 89 58 42 36 72 24 58 37 52 18 51 03 37 18 39 11

96 24 40 14 51 23 22 30 88 57 95 67 47 29 83 94 69 40 06 07 18 16 36 78 86

31 73 81 61 19 60 20 72 83 48 98 57 07 23 69 65 95 39 69 58 56 80 30 19 44

78 60 73 89 84 43 89 84 36 45 56 69 47 07 41 90 22 91 07 12 78 35 34 08 72

84 37 80 61 56 70 10 23 98 05 85 11 34 76 60 76 48 45 34 60 01 64 18 39 96

36 67 10 08 23 98 93 35 08 86 89 28 76 29 81 33 34 91 58 93 63 14 52 32 52

07 28 59 07 48 89 64 58 89 75 83 85 62 27 89 30 14 78 56 27 86 63 59 80 02

10 15 83 87 60 78 24 31 66 56 21 48 24 06 93 91 98 94 05 49 01 47 59 38 00

55 19 68 97 65 03 73 52 16 56 00 53 55 90 27 33 42 29 38 87 22 13 88 83 34

53 81 29 13 39 35 01 20 71 34 62 33 74 82 14 53 73 19 09 03 56 54 29 56 93

51 86 32 68 92 33 98 74 66 99 40 14 71 94 58 45 94 19 38 81 14 44 99 81 07

35 81 70 29 13 80 03 54 07 27 86 84 78 32 66 50 85 52 74 33 13 80 55 62 54

37 71 67 95 13 20 02 44 95 84 64 85 04 05 72 01 32 90 76 14 53 89 74 60 41

93 66 13 83 27 82 78 64 64 72 28 54 86 53 84 48 14 52 88 84 56 07 93 89 30

02 96 08 45 65 13 05 00 41 84 83 07 54 72 59 21 45 57 09 77 19 48 56 27 44

49 83 43 48 35 82 88 33 69 96 72 36 04 19 96 47 45 15 18 60 82 11 08 95 97

84 60 71 62 46 40 80 81 30 37 34 39 23 05 38 25 15 35 71 30 88 12 57 21 77

18 17 30 88 71 44 81 14 88 47 89 23 30 63 15 56 34 20 47 88 89 82 93 24 98

79 69 10 61 78 71 32 76 95 62 87 00 22 58 62 82 54 01 75 26 43 11 71 89 31

75 93 36 57 83 56 20 14 82 11 74 21 97 90 65 86 42 68 63 86 74 54 13 26 84

38 30 92 29 03 06 28 81 39 38 62 25 06 84 63 61 28 08 83 67 04 32 82 08 09

51 28 60 10 34 31 67 75 85 80 51 87 02 74 77 76 15 48 48 44 18 55 63 77 09

21 31 38 86 24 37 78 38 86 24 37 78 81 53 74 73 24 16 10 33 70 47 14 54 36

29 01 23 87 88 58 02 39 37 67 42 10 14 20 92 16 55 23 42 46 64 96 08 11 06

H–6
Simple Random Sampling

Table H-3 Random Number Table (#1) Designates

H–7APPENDIX H

1 2 3 4 5

1

97 74 24 67 62 42 81 14 57 20 42 53 32 37 32 27 07 36 07 51 24 51 79 89 73

16 76 62 27 66 56 50 26 71 07 32 90 79 78 53 13 55 38 58 59 88 97 54 14 10

12 56 85 99 26 96 96 68 27 31 05 03 72 93 15 57 12 10 14 21 88 26 49 81 76

03 47 43 73 86 36 96 47 36 61 46 98 63 71 62 33 28 16 80 45 60 11 14 10 95

55 59 56 35 64 38 54 82 46 22 31 62 43 09 90 08 18 44 32 53 23 83 01 30 30

2

16 22 77 94 39 49 54 43 54 82 17 37 93 23 78 87 35 20 96 43 84 26 34 91 64

84 42 17 53 31 57 24 55 06 88 77 04 74 47 67 21 76 33 50 25 83 92 12 06 16

63 01 63 78 59 18 95 55 67 19 98 10 50 71 75 12 86 73 58 07 44 39 52 38 79

33 21 12 34 29 78 64 58 07 82 52 42 07 44 38 15 51 00 13 42 99 66 02 79 54 

57 60 86 32 44 09 47 27 96 54 49 17 48 09 62 90 52 84 77 27 08 02 73 43 28

3

18 18 07 92 46 44 17 16 58 09 79 83 86 19 62 06 76 50 03 10 55 23 64 05 05

28 62 38 97 75 84 16 07 44 99 83 11 46 32 24 20 14 85 88 45 10 93 72 88 71

23 42 40 64 74 82 97 77 77 81 07 45 32 14 08 32 98 94 07 72 93 85 79 10 75

62 38 28 19 95 50 92 26 11 97 00 56 76 31 38 80 22 02 53 53 88 60 42 04 53

37 85 94 35 12 83 39 50 08 30 42 34 07 96 88 54 42 06 87 98 35 85 29 48 39

4

70 29 17 12 13 40 33 20 38 26 13 89 51 03 74 17 76 37 13 04 17 74 21 19 30

56 62 18 37 35 96 83 50 87 75 97 12 25 93 47 70 33 24 03 54 97 77 46 44 80

99 49 57 22 77 88 42 95 45 72 16 64 36 18 00 04 43 18 66 79 94 77 24 21 90

18 08 15 04 72 33 27 14 34 09 45 59 34 68 49 12 72 07 34 45 99 27 72 95 14

31 16 93 32 43 50 27 89 87 19 20 15 37 00 49 52 85 66 60 44 38 68 88 11 80

5

68 34 30 13 70 55 74 30 77 40 44 22 78 84 26 04 33 46 09 52 68 07 97 06 57

74 57 25 65 76 59 29 97 68 60 71 91 38 67 54 13 58 18 24 76 15 54 55 95 52

27 42 37 86 53 48 55 90 65 72 96 57 69 36 10 96 46 92 42 45 97 60 49 04 91

39 68 29 61 00 66 37 32 20 30 77 84 57 03 29 10 45 66 04 26 11 04 96 67 24

29 94 98 94 24 68 49 69 10 82 53 75 91 93 30 34 25 20 57 27 40 48 73 51 92

6

18 90 82 66 59 83 62 64 11 12 67 19 00 71 74 60 47 21 29 68 02 02 37 03 31

11 27 94 75 06 06 09 19 74 66 02 94 37 34 02 76 70 90 30 86 38 45 94 30 38

35 24 10 16 20 33 32 51 26 38 79 78 45 04 91 16 92 53 56 16 02 75 50 95 98

38 23 16 86 38 42 38 97 01 50 87 75 66 81 41 40 01 74 91 62 48 51 84 08 32

31 96 25 91 47 96 44 33 49 13 34 86 82 53 91 00 52 43 48 85 27 55 26 89 62

7

66 67 40 67 14 64 05 71 95 86 11 05 65 09 68 76 83 20 37 90 57 16 00 11 66

14 90 84 45 11 75 73 88 05 90 52 27 41 14 86 22 98 12 22 08 07 52 74 95 80

68 05 51 18 00 33 96 02 75 19 07 60 62 93 55 59 33 82 43 90 49 37 38 44 59

20 46 78 73 90 97 51 40 14 02 04 02 33 31 08 39 54 16 49 36 47 95 93 13 30

64 19 58 97 79 15 06 15 93 20 01 90 10 75 06 40 78 78 89 62 02 67 74 17 33

8

05 26 93 70 60 22 35 86 15 13 92 03 51 59 77 59 56 78 06 83 52 91 05 70 74

70 97 10 88 23 09 98 42 99 64 61 71 62 99 15 06 51 29 16 93 58 05 77 09 51

68 71 86 85 85 54 87 66 47 54 73 32 08 11 12 44 95 92 63 16 29 56 24 29 48

26 99 61 65 53 58 37 78 80 70 42 10 50 67 42 32 17 55 85 74 94 44 67 16 94

14 65 52 68 75 87 59 36 22 41 26 78 63 06 55 13 08 27 01 50 15 29 39 39 43

9

17 53 77 58 71 71 41 61 50 72 12 41 94 96 26 44 95 27 36 99 02 96 74 30 83

90 26 59 21 19 23 52 23 33 12 96 93 02 18 39 07 02 18 36 07 25 99 32 70 23

41 23 52 55 99 31 04 49 69 98 10 47 48 45 88 13 41 43 89 20 97 17 14 49 17

60 20 50 81 69 31 99 73 68 68 35 81 33 03 76 24 30 12 48 60 18 89 10 72 34

91 25 38 05 90 94 58 28 41 36 45 37 59 03 09 90 35 57 29 12 82 62 54 65 60

10

34 50 57 74 37 98 80 33 00 91 09 77 93 19 82 74 94 80 04 04 45 07 31 66 49

85 22 04 39 43 73 81 53 94 79 33 62 46 86 28 08 31 54 46 31 53 94 13 38 47

09 79 13 77 48 73 82 97 22 21 05 03 27 24 83 72 89 44 05 60 35 80 39 84 88

88 75 80 18 14 22 95 75 42 49 39 32 82 22 49 02 48 07 70 37 16 04 81 67 87

90 98 23 70 00 39 90 03 08 90 55 85 78 38 36 94 37 30 69 32 90 89 00 76 33
HOW TO COLLECT AND ANALYZE DATA

block where you will begin within the table to select 

your random numbers, either in an upward or in a 

downward succession along the vertical column.

Note a vehicle license plate. Step outside and observe 

the first vehicle that passes. Note the last (or first) two 

digits of the license plate. The first digit will give you 

the horizontal column designation; the second digit, 

the vertical column. Where they intersect is your place 

of beginning within the table.

Look at a dollar bill. Note the first two digits of the 

serial number in the lower left or upper right corner 

to determine the axial locations.

Check the stock quotations. Take any newspaper and 

turn to the stock quotation page. Take the first letter of 

your surname. The first stock which begins with that 

letter will be your predetermined stock; note its quotation for high and low. Disregard the fractional quotations. Take the first two digits in either the high or low 

quotation column; or if only one digit appears in each 

column, take the two digits together.

Having arrived at the digital block location, the next 

step is to determine the size of the proposed sample. 

If it is to be less than 100 individuals, we shall select 

only two-digit numbers; if it is to be less than 1,000, 

we shall need three digits to accommodate the sample 

size.

Let us go back to the total population for a moment 

and consider the total group from which the sample 

is to be drawn. It will be necessary to designate these 

individuals in some specific manner. It is, therefore, 

advantageous to arrange the individuals within the 

population in some systematic order (alphabetically, 

for example, by surname) and assign to each a serial 

number for identification purposes.

Now we are ready for the random selection. We shall 

start with the upper left-hand digits in the designated 

block and work first downward in the column; if there 

are not enough digits for the total sample demand 

in that direction, we will return to the starting digits 

and proceed upward. Having exhausted all 50 digits 

in any one column, move to the adjoining columns 

and proceed as before until the sample requirement is 

filled. As each digit designation comes up, select the 

individual in the population who has been assigned 

that random number. Keep so selecting until the entire 

sample total is reached.

The following illustration recapitulates what we have 

been describing. We have pulled a dollar bill from 

our wallet and note that the first two digits are 4 and 

5. These we shall use in locating the beginning block 

from which we will select the first random number 

for the sample. For purposes of illustration, we shall 

assume that the total population consists of 90 individuals from which we shall select a sample group of 40. 

We will need random numbers of two digits each.

Here again is a reduction of the first table of random 

numbers (table H-4), from which we have extracted 

the beginning block of digits that is at the intersection 

of the fourth horizontal block column and the fifth 

vertical block column.

Beginning now in the upper left-hand comer of the 

designated block, and remembering that there are only 

90 individuals in the total population, we see that by 

going down the two leftmost columns of digits in the 

block we will choose from the total population individual number 4, and individual number 13. The next 

two digits (96) do not apply, because there are only 90 

individuals in the population, and so we will ignore 

this number. Our next choices will be 10, 34, 60, 76, 

16, 40, and again we will ignore the 00 as well as 

the next number, 76 (the first set of digits in the third 

block), because we already have selected that number.

We have perhaps said enough with respect to the use 

of a table of random numbers, but because randomization is so often effected by the use of just such a table, 

the foregoing discussion is probably justified.

H–8
Simple Random Sampling H–9APPENDIX H

Table H-4 Random Number Table (#1) with Designated Block

1 2 3 4 5

1

97 74 24 67 62 42 81 14 57 20 42 53 32 37 32 27 07 36 07 51 24 51 79 89 73

16 76 62 27 66 56 50 26 71 07 32 90 79 78 53 13 55 38 58 59 88 97 54 14 10

12 56 85 99 26 96 96 68 27 31 05 03 72 93 15 57 12 10 14 21 88 26 49 81 76

03 47 43 73 86 36 96 47 36 61 46 98 63 71 62 33 28 16 80 45 60 11 14 10 95

55 59 56 35 64 38 54 82 46 22 31 62 43 09 90 08 18 44 32 53 23 83 01 30 30

2

16 22 77 94 39 49 54 43 54 82 17 37 93 23 78 87 35 20 96 43 84 26 34 91 64

84 42 17 53 31 57 24 55 06 88 77 04 74 47 67 21 76 33 50 25 83 92 12 06 16

63 01 63 78 59 18 95 55 67 19 98 10 50 71 75 12 86 73 58 07 44 39 52 38 79

33 21 12 34 29 78 64 58 07 82 52 42 07 44 38 15 51 00 13 42 99 66 02 79 54 

57 60 86 32 44 09 47 27 96 54 49 17 48 09 62 90 52 84 77 27 08 02 73 43 28

3

18 18 07 92 46 44 17 16 58 09 79 83 86 19 62 06 76 50 03 10 55 23 64 05 05

28 62 38 97 75 84 16 07 44 99 83 11 46 32 24 20 14 85 88 45 10 93 72 88 71

23 42 40 64 74 82 97 77 77 81 07 45 32 14 08 32 98 94 07 72 93 85 79 10 75

62 38 28 19 95 50 92 26 11 97 00 56 76 31 38 80 22 02 53 53 88 60 42 04 53

37 85 94 35 12 83 39 50 08 30 42 34 07 96 88 54 42 06 87 98 35 85 29 48 39

4

70 29 17 12 13 40 33 20 38 26 13 89 51 03 74 17 76 37 13 04 17 74 21 19 30

56 62 18 37 35 96 83 50 87 75 97 12 25 93 47 70 33 24 03 54 97 77 46 44 80

99 49 57 22 77 88 42 95 45 72 16 64 36 18 00 04 43 18 66 79 94 77 24 21 90

18 08 15 04 72 33 27 14 34 09 45 59 34 68 49 12 72 07 34 45 99 27 72 95 14

31 16 93 32 43 50 27 89 87 19 20 15 37 00 49 52 85 66 60 44 38 68 88 11 80

5

68 34 30 13 70 55 74 30 77 40 44 22 78 84 26 04 33 46 09 52 68 07 97 06 57

74 57 25 65 76 59 29 97 68 60 71 91 38 67 54 13 58 18 24 76 15 54 55 95 52

27 42 37 86 53 48 55 90 65 72 96 57 69 36 10 96 46 92 42 45 97 60 49 04 91

39 68 29 61 00 66 37 32 20 30 77 84 57 03 29 10 45 66 04 26 11 04 96 67 24

29 94 98 94 24 68 49 69 10 82 53 75 91 93 30 34 25 20 57 27 40 48 73 51 92

6

18 90 82 66 59 83 62 64 11 12 67 19 00 71 74 60 47 21 29 68 02 02 37 03 31

11 27 94 75 06 06 09 19 74 66 02 94 37 34 02 76 70 90 30 86 38 45 94 30 38

35 24 10 16 20 33 32 51 26 38 79 78 45 04 91 16 92 53 56 16 02 75 50 95 98

38 23 16 86 38 42 38 97 01 50 87 75 66 81 41 40 01 74 91 62 48 51 84 08 32

31 96 25 91 47 96 44 33 49 13 34 86 82 53 91 00 52 43 48 85 27 55 26 89 62

7

66 67 40 67 14 64 05 71 95 86 11 05 65 09 68 76 83 20 37 90 57 16 00 11 66

14 90 84 45 11 75 73 88 05 90 52 27 41 14 86 22 98 12 22 08 07 52 74 95 80

68 05 51 18 00 33 96 02 75 19 07 60 62 93 55 59 33 82 43 90 49 37 38 44 59

20 46 78 73 90 97 51 40 14 02 04 02 33 31 08 39 54 16 49 36 47 95 93 13 30

64 19 58 97 79 15 06 15 93 20 01 90 10 75 06 40 78 78 89 62 02 67 74 17 33

8

05 26 93 70 60 22 35 86 15 13 92 03 51 59 77 59 56 78 06 83 52 91 05 70 74

70 97 10 88 23 09 98 42 99 64 61 71 62 99 15 06 51 29 16 93 58 05 77 09 51

68 71 86 85 85 54 87 66 47 54 73 32 08 11 12 44 95 92 63 16 29 56 24 29 48

26 99 61 65 53 58 37 78 80 70 42 10 50 67 42 32 17 55 85 74 94 44 67 16 94

14 65 52 68 75 87 59 36 22 41 26 78 63 06 55 13 08 27 01 50 15 29 39 39 43

9

17 53 77 58 71 71 41 61 50 72 12 41 94 96 26 44 95 27 36 99 02 96 74 30 83

90 26 59 21 19 23 52 23 33 12 96 93 02 18 39 07 02 18 36 07 25 99 32 70 23

41 23 52 55 99 31 04 49 69 98 10 47 48 45 88 13 41 43 89 20 97 17 14 49 17

60 20 50 81 69 31 99 73 68 68 35 81 33 03 76 24 30 12 48 60 18 89 10 72 34

91 25 38 05 90 94 58 28 41 36 45 37 59 03 09 90 35 57 29 12 82 62 54 65 60

10

34 50 57 74 37 98 80 33 00 91 09 77 93 19 82 74 94 80 04 04 45 07 31 66 49

85 22 04 39 43 73 81 53 94 79 33 62 46 86 28 08 31 54 46 31 53 94 13 38 47

09 79 13 77 48 73 82 97 22 21 05 03 27 24 83 72 89 44 05 60 35 80 39 84 88

88 75 80 18 14 22 95 75 42 49 39 32 82 22 49 02 48 07 70 37 16 04 81 67 87

90 98 23 70 00 39 90 03 08 90 55 85 78 38 36 94 37 30 69 32 90 89 00 76 33
a p p e n d i x I

Calculating the 

Standard Deviation
C a l c u l a t i n g t h e S t a n d a r d D e v i a t i o n

The following are formulas used for calculating the standard deviation. (Spreadsheet 

software will calculate a standard deviation for you.) The formula on the right 

(described in Steps 1 through 5 below) is a shortcut for solving the numerator in the 

standard deviation formula on the left.

Assume that the average (mean) number of prior arrests of a sample of the jail population has been calculated. The standard deviation is needed to identify how much 

variation exists in the number of priors. Also assume that there are only five cases in 

the sample (although, in reality, a much bigger sample would be necessary).

Statisticians identify variables as X or Y. They use X for variables they are trying to 

explain (called dependent variables) and Y for variables (called independent variables) 

that they think may cause or have some sort of relationship with X. In this case, Prior 

Arrests is a Y. That means that it is believed that prior arrests cause something, perhaps longer Length of Stay. To calculate the standard deviation of Y (Prior Arrests), 

follow these steps.

Step 1: List each measurement of Y (Prior Arrests).

a p p e n d i x I

Table I-1 Measures of Y (Prior Arrests)

Case Number Y

Prisoner #1 5

Prisoner #2 7

Prisoner #3 1

Prisoner #4 2

Prisoner #5 4

Total 19

I–3

s2 = ∑(y - y)

2

 n - 1

∑(y - y)

2 = ∑y 2

 - (∑y)

2

 n

- -
HOW TO COLLECT AND ANALYZE DATA

Step 2: Square each measurement of 

Y and record it in a second column.

Step 3: Square the total of Y.

19 * 19 = 361

Step 4: Divide the result of Step 3 by 

the number of cases in the sample.

361/5 = 72.2

Step 5: Subtract the result of Step 4 

from the Y2 total.

95 – 72.2 = 22.8

Table I-2 Measures of Y and Y-Squared

Case Number Y Y2

Prisoner #1 5 25

Prisoner #2 7 49

Prisoner #3 1 1

Prisoner #4 2 4

Prisoner #5 4 16

Total 19 95

Step 6: Subtract 1 from the number of 

cases in the sample. 

5 – 1 = 4

Step 7: Divide the result of Step 5 by 

the result of Step 6.

22.8/4 = 5.7

Step 8: Using a calculator or square 

root table, find the square root of the 

result of Step 7. 

√5.7 = 2.39 

Step 7 produces the variance, and Step 8 produces the 

standard deviation.

In this analysis, the “average” prisoner has 3.4 prior 

arrests. However, the standard deviation is 2.39, which 

suggests there are major differences between the prisoners in the sample. Now, for the shortcut! If the standard deviation is not provided, and if there is not sufficient time to calculate it, the standard deviation can be 

estimated by dividing the range by 4. Even if all the 

calculations are done, this is a good check on them.

I–4
a p p e n d i x J

Calculating Chi-Square
C a l c u l a t i n g C h i - S q u a r e

Let’s use Sheriff #2, introduced earlier, to illustrate another example. The jail has 

experienced an increase in the number of incidents and the sheriff wants to explore the 

relationship between the number of incidents prisoners commit and the number of prior 

arrests they have. Sheriff #2 suspects that some more serious offenders are spending 

longer periods of time in the facility. He calculates a chi-square to discover the nature 

of the relationship between these two data elements.

Calculating a chi-square takes some time, but is straightforward. Follow these steps to 

determine if there is a relationship between membership in a particular category of one 

data element and membership in another category of a different data element.

Step 1: Construct a contingency table for the two data 

elements.

Sheriff #2 constructs a contingency table (table J-l). In this process, the prisoners in the 

sample are divided into two groups: those who were involved in incidents and those 

who were not. Then for each inmate, Sheriff #2 determines their number of previous 

arrests.

This is a contingency table that shows the observed frequencies.

APPENDIX J

Table J-1 Contingency Table of Prior Arrests and Incident Involvement

Prior Arrests Involved in Incidents Not Involved in Incidents

More than 5 30 15

3–5 25 20

0–2 25 65

J–3
HOW TO COLLECT AND ANALYZE DATA

Step 2: Total the columns and the rows and write the totals around the outside 

of the contingency table.

Table J-2 Contingency Table of Prior Arrests and Incident Involvement With Marginals

Prior Arrests Involved in Incidents Not Involved in Incidents

More than 5 30 15 45

3–5 25 20 45

0–2 25 65 90

80 100 100

Lines of numbers are called columns if they are vertical and rows if they are horizontal. The numbers written 

around the outside of the contingency tables are called marginals, because they are written in the margins (table 

J-2).

Step 3: Calculate the expected distribution as if there were no relationship 

between the two data elements.

Of the 180 inmates in Sheriff #2’s sample, half of the inmates (90) had from 0 to 2 prior arrests, while one quarter of them (45) had from 3 to 5 prior arrests, and a final quarter (45) had more than 5 prior arrests. Of the 180 

inmates, 80 were involved in incidents at the jail and 100 were not. If there were no relationship between the 

number of prior arrests and committing incidents at the jail, the arrest pattern within the two groups based on 

incident status should be the same as the pattern for all the inmates. The expected distribution then would look 

something like table J-3.

Table J-3 Contingency Table of Prior Arrests and Incident Involvement With Expected 

Distribution

Prior Arrests Involved in Incidents Not Involved in Incidents

More than 5 20 25 45 = A

3–5 20 25 45 = B

0–2 40 50 90 = C

Table J-3 shows the Expected Frequencies. Sometimes, the patterns are not quite so clear as in this example. 

When that is the case, a formula can be used to calculate the expected frequency of each “box” in the contingency table. Statisticians call each box a cell.

Expected frequencies of the group who were involved in incidents would be:

 M * (A/T) or 80 * 45/180 = 80 * .25 = 20

 M * (B/T) or 80 * 45/180 = 80 * .25 = 20

 M * (C/T) or 80 * 90/180 = 80 * .5 = 40

J–4
Calculating Chi-Square

Expected frequencies of the group who were not involved in incidents would be:

 N * (A/T) or 100 * 45/180 = 100 * .25 = 25

 N * (B/T) or 100 * 45/180 = 100 * .25 = 25

 N * (C/T) or 100 * 90/180 = 100 * .5 = 50

Step 4: Assign a number to each cell in the contingency table.

When this is done, the contingency table will look something like table J-4.

This step is to make sure that you identify each cell and avoid confusion.

Step 5: For each cell, compute the formula.

So, for Cell #1, after using the formula, the results are:

In How to Calculate Statistics, Taylor Fitz-Gibbon and Morris (1978, see appendix B) suggest making a worksheet like table J-5 for each of the items in the formula to make sure that nothing is overlooked.

Table J-4 Contingency Table With Assigned Cell Numbers

Prior Arrests Involved in Incidents Not Involved in Incidents

More than 5 Cell 1 20 Cell 4 25 45 = A

3–5 Cell 2 20 Cell 5 25 45 = B

0–2 Cell 3 40 Cell 6 50 90 = C

80 = M 100 = N 180 = T

(O – E)2/E

(Observed Frequency – Expected Frequency)2/Expected Frequency

(30 – 20)2/20

or

(10)2/20

or

100/20 = 5

J–5APPENDIX J
a p p e n d i x K

Manual Data Display
Bar Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K-1

Pie Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K-2

Line Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K-2

Graphic Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K-3
M a n u a l D a t a D i s p l a y

This appendix is provided for the truly desperate who do not have access to a computer. 

If you need to display your data this way, you will seriously need to consider investing 

in software with graphics capability. 

Bar Charts

Hint 1: Use graph paper.

Graph paper, gridded to 10 x 10, on which the graph is built in units of 10, seems easiest to use for most jail data. Each small square can represent 1, 10, 100, or whatever 

the most appropriate number is of the items being counted. Graph paper also comes in 

what is called nonreproducible blue” so that the graph lines on the paper do not show 

up when it is copied.

Hint 2: Calculate the height of the tallest bar first.

The height of the tallest “bar” will determine the height of the Y axis. The Y axis 

should be at least a bit taller than the highest bar.

Hint 3: Consider different scales.

Each small square on the graph paper represents a fixed number of cases. The easiest 

way to do this is to determine how many squares are on the paper from top to bottom 

or side to side. Then look at the largest category. Make sure that the scale selected will 

accommodate the bar representing the largest category. The scale that is used often 

determines how much impact the graph has.

Hint 4: Determine the width of the bars.

Decide both how wide and how far apart the bars will be. Mark them on the X axis. 

It is wise to avoid making tall, thin bars because they look like they are going to fall 

down. If so many categories are included that the width of the paper dictates that the 

bars be thin, consider turning the paper around so that the Y axis is longer than the X 

axis. If the bars are now too tall for the paper, use another scale, or use larger paper and 

reduce the finished chart.

a p p e n d i x k

K–3
HOW TO COLLECT AND ANALYZE DATA

Hint 5: Draw each bar in pencil.

Using the scale on the Y axis, make a pencil line 

where the top of each bar will be, then draw in the rest 

of each bar in pencil. Check your work against the 

scale and the data. If everything appears to be right 

use a pen or graphic tape to finish the bars.

Hint 6: Label the chart.

Put a title on the chart, label each of the categories, 

and mark the scale on the Y axis.

Hint 7: Consider using color or shading the 

bars.

Either shading or coloring the bars will make the chart 

easier to interpret.

Pie Charts

Hint 1: Invest in a drafting compass.

Use a compass to make a circle sized properly for 

your paper. If you have no compass, you can make a 

pie chart with a glass or other round object.

Hint 2: Use a photocopy.

Make a copy of the circle and work on the copy. That 

way, when mistakes are made, it is not necessary to 

start again from the beginning.

Hint 3: Calculate the size of each piece of 

the pie.

Sometimes, it is easy to calculate the size of the segments. If the category is expressed as a percentage 

and the values are 50 percent, 25 percent, and 25 

percent, it is obvious that half a circle will represent 

50 percent, etc. Often, however, it is not so obvious. 

When in doubt, since there are 360 degrees in a circle 

and the total number of cases in each category is also 

known, it is always possible to resort to algebra and 

geometry. To figure out how many degrees to include 

in the piece of pie that represents a particular category, 

let X stand for the number of degrees in the piece of 

the pie and substitute that number in the following 

formula:

X	 	 n (category)

	 	 :	

	360	 	 N (total)

Do this for each piece of the pie. Use a protractor to 

mark off the degrees on the circle. Do not just estimate what 51 degrees looks like.

Hint 4: Label the chart.

Label both the whole pie chart and each piece of the 

pie. Usually labels should be placed around the outside of the pie, with neatly drawn arrows leading to 

the section the label represents. Another approach is 

to develop a key beneath the pie chart to show what 

the shading or color on the pie represents.

Line Graphs

Hint 1: Use graph paper.

Once again, 10 x 10 graph paper is easiest to use for 

most jail data.

Hint 2: Draw in the X and Y axes.

Both the axes represent a data element in a line graph.

Hint 3: Determine the range of values to be 

displayed.

Identify the highest and lowest value of the data element to be displayed on the Y axis. This determines 

how tall the Y axis has to be, since it should be at least 

a bit taller than the highest value.

Hint 4: Repeat the process for the X axis.

For much jail data, all that is necessary is to divide the 

X axis into equal sections to represent the number of 

years, months, days, etc., to be included on the graph. 

If there are actually two data elements, like height 

and weight, a scale also will be needed for the X axis. 

Data like this is quite rare in jails, however.

Hint 5: Determine the scale.

This process is identical to developing a scale for a 

bar chart.

Hint 6: Plot each data point.

Using the scale on the Y axis, make a dot for each 

data point to be displayed on the graph. This is called 

plotting the data. Be sure to use a pencil and a ruler to 

place the points in the right spots. The farther from the 

Y axis, the easier it is to make mistakes. Once the data 

points are plotted, connect the dots in pencil. Check 

K–4
Manual Data Display

your work against the scale and the data. If there are 

no errors, use a pen or graphic tape to finish the graph.

Hint 7: Title the line graph.

Put a title on the line graph, label each of the categories, and mark the scale on both the X and Y axes.

Graphic Tools

A first stop for purchasing drafting equipment and 

graphics supplies should be a local store that sells 

office and/or art supplies and equipment. If there is 

not one nearby, this material can be ordered from a 

catalog. Following are some basic supplies and equipment that make working on graphics much easier. 

While some sheriffs and jail administrators may consider these items a substantial investment, many are 

quite inexpensive, particularly when compared to the 

cost of professionally done graphics.

Basic 1: A Place to Work

A good place to work is essential. While any flat 

surface like a desk or table will suffice, all drafting 

students know the value of a drafting table. Many 

graphic artists work on a “light board,” a box with 

a translucent glass top that is slanted at about 25 

degrees. Inside the box is a fluorescent light. The light 

illuminates anything (like a grid or lined paper) under 

the paper being worked on. This makes it much easier 

to line things up. Commercial light boards are rather 

expensive, but they are quite easy and relatively inexpensive to build.

Basic 2: Good Writing Equipment

There are many writing tools on the market. Most 

expensive—and most effective—are technical or drafting pens, which come in different sizes and make lines 

of different widths. They can cost more than $10. 

Almost as good as drafting pens are fine-point markers, and they are much less expensive. Do not use a 

ballpoint pen as it does not copy well and tends to 

leave little “blobs” of ink. Several good possibilities 

exist for making lines that do not show on the final 

copy. The best is a nonreproducible blue pencil, priced 

about the same as a regular colored pencil. However, 

it will leave an obvious mark on the original copy.

Basic 3: A Good Straightedge

Do not use the edge of a yellow pad. If a drafting 

table is available, a T square is the best alternative, 

but a good ruler with a metal indented edge is equally 

effective. The indented edge prevents smearing when 

the ruler is picked up. Drafting tape is another excellent alternative. It comes in a variety of widths (from 

1/64 inch to 1/2 inch in width) and colors and makes 

sharp lines. Dotted lines, dashes, and other variations 

are also available. Drafting tapes vary in cost by width 

and will last quite some time.

Basic 4: A Good Compass and Protractor

A good compass and protractor are even more essential than a good straightedge for doing pie charts. 

Another alternative would be to use a template (a 

sheet of plastic or metal with geometric shapes 

punched out) to trace circles, but these are not big 

enough for most pie charts.

Basic 5: The Right Kind of Paper

There are differences in paper. White, relatively 

smooth paper is preferable because it copies better and 

is easier to write on. Two special kinds of paper are 

worth mentioning here:

1. Nonreproducible blue graph paper has lines that 

do not show when photocopied (however, it may 

be necessary to lighten up the copy a bit).

2. Drafting paper makes smearing ink difficult 

(although not impossible).

Basic 6: Something to Make Letters and 

Symbols

With few exceptions, most people’s handwriting is not 

good enough to go into a document or on a graphic. 

Print from a good typewriter looks clean and sharp 

and will work well in most report graphics However, 

when projected, typewriter type is not large enough 

for an audience to read, and the slightly larger or bolder titles and headings that improve the appearance of a 

graphic cannot be done on a typewriter.

The most reasonable alterative for doing small 

amounts of graphic work is rub-on lettering. Over a 

hundred different styles of rub-on lettering are sold, 

and each style comes in a variety of sizes. It gets its 

name because it is literally rubbed off the sheet it 

K–5APPENDIX K
HOW TO COLLECT AND ANALYZE DATA

comes on and on to another sheet. If you try this, get 

a small wooden or metal tool called a “burnisher,” 

which gets the letter off the sheet more smoothly than 

a pencil. One problem with rub-on lettering is that it is 

difficult and time consuming to evenly space the letters and get them all aligned. Rub-ons also come in a 

variety of symbols: circles, squares, triangles, arrows, 

check marks, etc.

Basic 7: Something to Add Color

A wide variety of colored pens and pencils can be 

used on paper. In addition, special marking pens are 

made to use on acetate (transparencies). They are certainly the most reasonable in cost, but all fail to provide an “even” color and as a result may detract from 

the finished product. Color will probably be added to 

only the most important graphics. The best alternative 

is “graphic film.” This is a thin sheet of colored, translucent material that has adhesive on the back. It can 

be positioned over something that already has writing 

on it—for example, a piece of a pie chart—and it will 

add the color or texture of your choice. Film is made 

for use on both paper and transparencies.

Basic 8: A Good Adhesive

The days of rubber cement and cellophane tape have 

gone by. Rubber cement is too messy, and tape makes 

lines when copied. Mounting adhesive, essentially 

rubber cement in aerosol form, is a better alternative. 

It allows repositioning of what has been sprayed.

K–6
National Institute of Corrections

Advisory Board

Collene Thompson Campbell

San Juan Capistrano, CA

Norman A. Carlson

Chisago City, MN

Michael S. Carona

Sheriff, Orange County

Santa Ana, CA

Jack Cowley

Alpha for Prison and Reentry

Tulsa, OK

J. Robert Flores

Administrator

Office of Juvenile Justice and

 Delinquency Prevention

U.S. Department of Justice

Washington, DC

Stanley Glanz

Sheriff, Tulsa County

Tulsa, OK

Wade F. Horn, Ph.D.

Assistant Secretary for Children and Families

U.S. Department of Health and 

 Human Services

Washington, DC

Byron Johnson, Ph.D.

Department of Sociology and Anthropology

Baylor University

Waco, TX

Harley G. Lappin

Director

Federal Bureau of Prisons

U.S. Department of Justice

Washington, DC

Colonel David M. Parrish

Hillsborough County Sheriff’s Office

Tampa, FL

Judge Sheryl A. Ramstad

Minnesota Tax Court

St. Paul, MN

Edward F. Reilly, Jr.

Chairman

U.S. Parole Commission

Chevy Chase, MD

Judge Barbara J. Rothstein

Director

Federal Judicial Center

Washington, DC

Regina B. Schofield

Assistant Attorney General

Office of Justice Programs 

U.S. Department of Justice

Washington, DC

Reginald A. Wilkinson, Ed.D.

Executive Director

Ohio Business Alliance

Columbus, OH

B. Diane Williams

President

The Safer Foundation

Chicago, IL
MEDIA MAIL

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U.S. Department of Justice

National Institute of Corrections

Washington, DC 20534

Official Business

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