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MEMBERS OF THE FEDERAL COMMITTEE ON
STATISTICAL METHODOLOGY
                            (April 1990)
Maria E. Gonzalez (Chair)
Office of Management and Budget
Yvonne M. Bishop                   Daniel Kasprzyk
Energy Information                 Bureau of the Census
Administration
Warren L. Buckler                  Daniel Melnick
Social Security Administration     National Science Foundation
Charles E. Caudill                 Robert P. Parker
National Agricultural              Bureau of Economic Analysis
Statistical Service
John E. Cremeans                   David A. Pierce
office of Business Analysis        Federal Reserve Board
Zahava D. Doering                  Thomas J. Plewes
Smithsonian Institution            Bureau of Labor Statistics
Joseph K. Garrett                  Fritz J. Scheuren
Bureau of the Census               Internal Revenue Service
Robert M. Groves                   Monroe G. Sirken
Bureau of the Census               National Center for Health
                                     Statistics
C. Terry Ireland
National Computer Security         Robert D. Tortora
Center                             Bureau of the Census
Charles D. Jones
Bureau of the Census
                               Preface
The Federal Committee on Statistical Methodology was organized by OMB
in 1975 to investigate issues in Federal statistics.  Members of the
committee, selected by OMB on the basis of their individual expertise
and interest in statistical methods, serve in their personal capacity
rather than as agency representatives.  The committee conducts its
work through subcommittees that are organized to study particular
issues and that are open to any Federal employee who wishes to
participate in the studies. working papers are prepared by the
subcommittee members and reflect only their individual and collective
ideas.
The subcommittee on Data Editing in Federal Statistical Agencies was
formed in 1988 to document, profile, and discuss the topics of data
editing in Federal surveys.  In preparing this report, the
subcommittee walked in uncharted territory.  Unlike many other survey
process topics, such as design and estimators, where there is
substantial literature, textbooks, and documentation, the formal
literature pertaining to data editing is quite limited.  It is hoped
that this report will further the awareness within agencies of each
other's data editing practices, as well as of the state of the art of
data editing, and thus lead to improvements in data quality throughout
Federal statistical agencies.  A key ingredient in this effort is a
profile of current data editing practices constructed from an editing
questionnaire designed by the subcommittee and covering 117 Federal
surveys.  The report also describes current and recent research
developments that may aid agencies in evaluating their current data
editing practices, as well as in planning for future data editing
systems.
The subcommittee report is presented in a format and style that aims
to increase awareness of Federal survey managers and subject matter
specialists (statisticians, economists, computer programmers,
statistical assistants, and clerks, etc.) on survey data editing
practices.  When possible, observations are made in this report that
may aid in the evaluation of current editing practices and in the
planning of future editing systems.  In fact, this goal provided the
subcommittee with the incentive to also investigate the methodology
for software, technology, and research developments beyond the profile
of current editing practices.
This subcommittee on Data Editing in Federal Statistical Agencies was
chaired by George Hanuschak of the National Agricultural Statistics
Service, U.S. Department of Agriculture.
                                  i
MEMBERS OF THE SUBCOMMITTEE ON DATA EDITING IN
FEDERAL STATISTICAL AGENCIES
George Hanuschak, (Chair)
National Agricultural Statistics Service
Yahia Ahmed
Internal Revenue Service
Laura Bauer
Federal Reserve Board
Charles Day
Internal Revenue Service
Maria Gonzalez
Office of Management and Budget
Brian Greenberg
Bureau of the Census
Anne Hafner
National Center for Education Statistics
Gerry Hendershot
National Center for Health Statistics
Rita Hohenbrink
National Agricultural Statistics Service
Renee Miller
Energy Information Administration
Tom Petkunas
Bureau of the Census
David Pierce
Federal Reserve Board
Mark Pierzchala
National Agricultural Statistics Service
Marybeth Tschetter
Bureau of Labor Statistics
Paula Weir
Energy Information Administration
                                 ii
                           ACKNOWLEDGMENTS
This report represents an intensive voluntary effort on the part of
dedicated subcommittee members and outside reviewers over an eighteen
month period.  It is truly a collective effort on the part of the
subcommittee members, who worked very well as a team.  While
maintaining their full time Federal job responsibilities, the fifteen
subcommittee members worked diligently on this challenging mission.
The subcommittee expresses its appreciation to Cathy Mazur of the
National Agricultural Statistics Service, U.S. Department of
Agriculture for her timely summarization of the current editing
practices survey, to Dale Bodzer and Howard Magnus of the Energy
Information Administration, and Cathy Cotton of Statistics Canada and
John Monaco of the U.S. Bureau of the Census for demonstrating editing
software packages to the subcommittee.  Jelke Bethlehem of the
Netherlands Central Bureau of Statistics and John Kovar of Statistics
Canada provided substantial aid to the subcommittee by answering
numerous questions about editing systems and providing software
systems for subcommittee review.
The subcommittee extends its thanks to David Pierce, Federal Reserve
Board, Terry Ireland, National Security Agency, and Fritz Scheuren,
Internal Revenue Service of the Federal Committee on Statistical
Methodology for their reviews of the report.  The subcommittee extends
its appreciation to Maria Gonzalez, Chair of the Federal Committee on
Statistical Methodology, for her guidance, encouragement and advice
throughout the eighteen months.
Last but not least. the subcommittee extends its appreciation to
Sherri Finks, Amy Curkendall, and Jennifer Kotch of the National
Agricultural Statistical Service who diligently and patiently did the
fine word processing using a desktop publishing package under rather
severe time constraints.
                                 iii
                          TABLE OF CONTENTS
                                                                Page
Chapter 1. EXECUTIVE SUMMARY  1
     A. Introduction                                               1
     B. Key Findings                                               2
     C. Recommendations                                            3
     D. Implementation of Recommendations                          4
     E. Structure of Report                                        4
Chapter II. BACKGROUND                                             5
     A. Scope, Audience and Objectives                             5
     B. Subcommittee Approach to Accomplishing Mission             5
     C. Subcommittee Work Groups                                   7
     D. Practices and Issues in Editing                            7
Chapter III. CURRENT EDITING PRACTICES IN
     FEDERAL STATISTICAL AGENCIES                                 11
     A. Profile on Editing Practices                              11
     B. Case Studies                                              17
Chapter IV. EDITING SOFTWARE                                      21
     A. Introduction                                              21
     B. Software Improving Quality and Productivity               22
     C. Descriptions of Editing Software                          26
Chapter V. RESEARCH ON EDITING                                    31
     A.  Introduction                                             31
     B.  Areas of Edit Research                                   31
     C.  Editing Research in Other Countries                      33
     D.  Case Studies                                             34
     E.  Summary                                                  36
     F.  Bibliography                                             38
APPENDIX A:    Results of Editing Practices Profile From
                Questionnaire Responses                           39
APPENDIX B:    Cast Studies                                       47
APPENDIX C:    Checklist of Functions of Editing Software
               Systems                                            65
APPENDIX D:    Annotated Bibliography of Articles on Editing      77
APPENDIX E:    Glossary of Terms                                  87
                                 iv
                                                           CHAPTER I
                          EXECUTIVE SUMMARY
A. INTRODUCTION
The Subcommittee on Data Editing in Federal Statistical Agencies was
established by the Federal Committee on Statistical Methodology in
November 1988 to document, profile and discuss data editing practices
in Federal surveys.  The subcommittee had the following mission
statement:
     The objective is to determine how data editing is currently being
     done in Federal statistical agencies, recognize areas that may
     need attention and, if appropriate, to recommend any potential
     improvements for the editing process.
To accomplish its mission, the subcommittee first addressed the
definition of data editing - what was it? No universal definition of
survey data editing exists.  The following working definition of
editing was developed and adopted by the subcommittee:
     Procedure(s) designed and used for detecting erroneous and/or
     questionable survey data (survey response data or identification
     type data) with the goal of correcting (manually and/or via
     electronic means) as much of the erroneous data (not necessarily
     all of the questioned data) as possible, usually prior to data
     imputation and summary procedures.
Data editing can be seen as a data quality improvement tool by which
erroneous or highly suspect data are found, and if necessary
corrected.  The subcommittee members realize that the boundaries of
editing (where it begins and ends) is not absolute.  The subcommittee
was instructed by the Federal Committee on Statistical Methodology to
concentrate on the front end of the editing process and not to
duplicate the extensive work on imputation done by the Panel on
Incomplete Data, Incomplete Data in Sample Surveys, Volumes I, II and
III, Academic Press, 1983. Therefore, the rest of the document is
based on the subcommittee's working definition of editing.
In order to gather the necessary information related to Federal survey
editing practices, the subcommittee used a combination of information
gathering techniques: a profile on editing practices using a
subcommittee prepared questionnaire (6 pages and 41 questions) for 117
Federal surveys in 14 Agencies, an extensive literature search and
review, case studies of 8 Federal surveys, editing system software
evaluation for several recently developed generalized editing systems,
and a search and review of current research efforts, including a few
case studies. on the editing process.  These information-gathering
techniques contributed to the development of an extensive editing
information base for this report.
In summary, data editing is considered to be an important component
of Federal statistical agencies.  Key findings from the survey on
editing practices conducted by the subcommittee follow, along with
recommendations.  In some cases. detailed discussions of
recommendations are handled in the text of the document.  A glossary
of terms used in this report is in Appendix E.
                                  1
B. KEY FINDINGS
Key findings from the profile on editing practices follow.
     -    About 60 percent of Federal survey managers reported that
          they refer all data that fad edit checks (not only critical
          errors or severe outliers) to subject matter specialists or
          editors (economists, statisticians, clerks, etc.) for review
          and resolution.  The role of the subject matter specialist
          is often valued as somewhat indispensable, as their expert
          knowledge and judgment are key ingredients in the survey
          editing process.  Two key questions are:
          1.   at is the cost/benefit relationship of this extensive
               manual review?
          2.   How consistent are the actions of different subject
               matter specialists?
     -    Editing costs are reported to have a median value of about
          35 percent of the total survey cost; however, the mode is
          10 percent and the distribution is quite skewed to the
          right.  Administrative records systems, such as those used
          by the Federal Reserve Board and the Internal Revenue
          Service, are on the skewed right-hand tail of the cost
          distribution.  The reason is that compared to censuses or
          sample surveys, data collection costs are a much smaller
          portion of total costs. This finding points to the
          importance of improvements in the cost efficiency of the
          editing process as a target for all Agencies in the next
          decade.
     -    Federal survey managers report that in over 80 percent of
          their surveys there is a good internal documentation of the
          editing system.  Federal survey managers appear to take the
          editing process very seriously and recognize its importance
          in the overall survey performance.  Under tight resource
          constraints, the level of documentation on the editing
          process is impressive.
     -    There is a strong desire by many of those involved in the
          editing process to combine or replace "batch-oriented"
          systems with "on-line" or quick-turnaround systems.
     -    Another desire expressed by respondents is for continued
          research and development and implementation of more
          efficient, well targeted, consistent, and accurate methods
          to detect potentially erroneous survey data.
     -    Integration of survey tasks (e.g., computer-assisted data
          collection, data entry, data editing, imputation, and
          summary) is important for improving data quality and
          productivity in survey processing.
     -    Several major developments in generalized editing software
          are taking place in domestic and international statistics
          agencies.  Three major ones covered in some detail in this
          report (Chapter IV) are: Statistics Canada's Generalized
          Edit and Imputation System (GEIS): the Netherlands Central
          Bureau of Statistics Blaise system (named after Blaise
          Pascal, the well known mathematician of the 1600's); and the
          U.S. Census Bureau's Structured Program for Economic Edit
          and Referral (SPEER).
     -    Some agencies are currently conducting research on the
          editing process, and several case
                                  2
          studies are presented in Chapter V and Appendix B.
     -    There is substantial potential for several related
          technology and data systems developments to contribute to
          more efficient and consistent editing systems in the next
          decade.  These include data base systems, expert systems,
          electronic data collection such as computer assisted
          telephone interviewing (CATI), computer-assisted personal
          interviewing, (CAPI) and touchtone surveys, major
          generalized edit systems, and artificial intelligence
          systems.
     -    If the cost of data processing continues to drop at its
          current rapid pace, the analysis of multi-variate
          statistical relationships among survey variables can be more
          widely used for editing (and imputation) if appropriate.
     -    The major challenge in software development lies in the
          reconciliation of two goals: the increased use of computers
          for certain tasks and the more intelligent use of human
          expertise.
C.   RECOMMENDATIONS
Based upon the findings in the Subcommittee's editing information
base, we present the following recommendations.
Federal survey managers and administrators should:
     -    Evaluate and examine the cost efficiency, timeliness,
          productivity, repeatability, statistical defensibility, and
          accuracy of their current editing practices versus
          alternative systems.  The checklist of editing software
          features provided in Appendix C and the remainder of this
          report is an aid in such an effort.  Such an effort can also
          be part of a Total Quality Management system for surveys and
          agencies.
     -    Review and examine the implications, for their editing
          situation, of important developments in data processing such
          as powerful microcomputers and scientific workstations,
          local area networks (LAN's) and data base software that
          provide electronic communication links from microcomputers
          and LAN's to mainframe computers.
     -    Follow the research and applications developments in the use
          of CATI, CAPI, touchtone, and other electronic means of data
          capture with potential for improving the editing and/or data
          processing flow.
     -    Continue to share information on research and development
          and software systems efforts in the editing process with
          other Federal and international statistical agencies.
     -    Stay attuned to research and developments in the use of
          expert systems and/or artificial intelligence software for
          survey data editing.
     -    Evaluate both the role and the effectiveness of editing in
          reducing nonsampling errors for their surveys.
                                  3
     -    Evaluate the extensive relationship of extensive manual
          review on resulting estimates.
     -    Explore development of a catalog of situations in which
          various techniques work well or not; e.g., research has
          indicated that exponential smoothing does not work well when
          data are erratic.
     -    Recognize the value of editing research and place a high
          priority on devoting resources to their own research, to
          monitoring developments in data editing at other agencies
          and to implementing improvements when they are found to be
          desirable.
     -    Explore integration of functions in a survey; e.g., data
          entry, data editing and computer assisted data collection.
     -    Give attention to the future roles of the subject matter
          specialist and the methodologist and to the tools and
          consistency with which they perform their jobs.
D.  IMPLEMENTATION OF RECOMMENDATIONS
An interagency working group should be formed to continue the mission
of the subcommittee and work on the implementation of the subcomm-
ittee's recommendations.
E. STRUCTURE OF REPORT
The structure for this report is executive summary, followed by
Chapter II which is introductory, Chapter III on the editing profile
and the case studies, Chapter IV on the role of software in editing,
and Chapter V on the role and status of research in editing.
Supporting appendices include:
     A.   Results of Editing Practices Profile From Questionnaire
          Responses
     B.   Case Studies
     C.   Checklist of Functions of Editing Software Systems
     D.   Annotated Bibliography of Articles on Editing
     E.   Glossary of Terms
                                  4
                                                          CHAPTER II
                             BACKGROUND
A. SCOPE, AUDIENCE, AND OBJECTIVES
The Subcommittee on Data Editing in Federal Statistical Agencies was
established by the Federal Committee on Statistical Methodology in
November 1988 to document, profile and discuss data editing practices
for Federal surveys.  The Subcommittee had the following mission
statement:
     The objective is to determine how data editing is currently being
     done in Federal statistical agencies, recognize areas that may
     need attention and, if appropriate, to recommend any potential
     improvements for the editing process.
     The project will obtain information on current data editing
     practices.  The information on editing will include the role of
     subject matter specialists; hardware, software, and data base
     environment; new technologies of data collection (and editing)
     such as CATI and CAPI. and current research efforts in the
     Agencies and some recent developments, in generalized editing
     systems, from the U.S. Census Bureau, Statistics Canada, and the
     Netherlands Central Bureau of Statistics.
B. SUBCOMMITTEE APPROACH TO ACCOMPLISHING MISSION
A number of paths were followed by the subcommittee in accomplishing
its goals as set forth in the preceding mission statement, including
developing a questionnaire on survey editing practices. assembling
several detailed case studies, investigating alternative editing
systems and software, exploring research needs and practices, and
compiling an annotated bibliography of literature on editing.
The editing profile questionnaire (6 pages and 41 questions) was
developed and administered to 117 Federal surveys covering 14
different agencies.  The 117 surveys were selected by Subcommittee
members and thus were not a scientific sample of all Federal surveys.
The subcommittee members felt that the 117 surveys represented a broad
coverage of agencies and types of surveys or censuses that might have
different editing circumstances or situations.
The two major purposes of the editing questionnaire were to provide
an adequate profile of current editing practices and to aid in
developing a typology of surveys to be used for selecting case
studies.  The typology is a classification of surveys according to a
number of criteria such as frequency of the survey, number of
respondents. degree of automation and judgmental review of the edits,
whether respondents are contacted regarding questionable items,
whether historic data are used in the editing of current data, and so
forth.  This information is of general interest, and was useful to the
subcommittee in selecting a representative group of surveys to serve
as case studies
                                  5
of editing practices.  The questionnaire and a tabular summary of the
results are presented for the reader in Appendix A.
Chapter III of this report contains the analysis of the questionnaire
and a description of the case studies.  For each different editing
environment, a case study was conducted.  The case studies provide
more detailed information for the selected cases than just the editing
questionnaire . The case studies are published in two forms (long and
short) in Appendix B to give descriptions of the varied editing
practices and situations.
Another important area of the subcommittee's work was the
investigation and evaluation of some recently developed generalized
editing systems and software packages.  Several major editing systems
were studied and a profile of features was developed and is presented
in Chapter IV.  The editing systems reviewed were the U.S. Census
Bureau's Structured Program for Economic Edit and Referral (SPEER),
the Netherlands Central Bureau of Statistics Blaise system, and
Statistics Canada's Generalized Edit and Imputation System (GEIS).
Also, several recent application specific editing systems at the U.S.
Department of Energy and the Bureau of Labor Statistics were reviewed.
These systems were developed under different conditions and
applications, so direct comparisons are not feasible.  However, the
subcommittee believes that a description of these systems features and
capabilities is of substantial value to Federal statistical agencies.
Appendix C gives the reader a detailed checklist of editing software
system features.  This checklist will be a valuable tool to editing
system developers.
The remaining major activity of the subcommittee was a review of
historic and ongoing research.  This review consisted of a literature
search that enabled the subcommittee to develop an annotated
bibliography, presented in Appendix D. This appendix provides a
valuable source of information on editing literature.  In addition,
case studies of ongoing or recent editing research were conducted.
Also information about editing research and research needs on the
editing process were gleaned from the editing profile.  A more
detailed description of editing research is provided in Chapter V. A
short glossary of editing terms is given in Appendix E.
                                  6
C. SUBCOMMITTEE WORK GROUPS
To effectively accomplish its mission, the subcommittee was divided
into four major groups.
I.   Editing Profile Group
          Charles Day, Leader
          Yahia Ahmed
          George Hanuschak
          Rita Hohenbrink
          Renee Miller
II.  Case Studies Group
          Anne Hafner, Leader
          Yahia Ahmed
III. Editing Software Group
          Mark Pierzchala, Leader
          Charles Day
          Gerry Hendershot
          Rita Hohenbrink
          Tom Petkunas
          Marybeth Tschener
IV.  Editing Research Group
          Brian Greenberg, Leader
          Yahia Ahmed
          Laura Bauer
          Renee Miller
          David Pierce
          Paula Weir
D. PRACTICES AND ISSUES IN EDITING
Description of the Process
Pre-survey editing tasks include the writing and evaluation of editing
programs, evaluation of tile edits themselves, and writing
instructions for the inspection of questionnaires by interviewers.
field supervisors, clerks, and subject matter specialists.  These
activities influence how well editing is done, as well as how many
resources will be expended on editing once data are collected.  During
the survey itself, editing may occur in many ways and at many stages,
from data collection to publication, and even after publication in
some cases.
In paper and pencil interviewing, the interviewer is the first to
inspect the questionnaire for errors.  Optimally, this should be done
immediately after the interview so that the respondent can easily be
contacted to clarify responses.  If questionnaires are channeled
through a supervisor, then a second
                                  7
inspection can be done.  Not only can recontacts be made shortly after
the interview, but the supervisor can provide feedback to the
interviewers on how they are doing.  Once the questionnaires reach an
office, they may be edited manually by clerks, subject matter
specialists, or both.  In some organizations, this manual edit may
include a significant amount of coding.  It can also include a visual
check that answers are given in correct units, that routing
instructions have been followed correctly, and consideration of notes
written by either the respondent or the enumerator.  In most cases a
computer edit is then performed.  Error signals (flags) and messages
are presented to a reviewer either on printouts or a screen in an
interactive session.  If the program output is printed, then the
review tends to be cyclical as the computer must then re-edit, in
batch, all of the changes.  If the output is on a screen,
(microcomputers or terminals hooked to a larger computer), then
questionnaires are usually processed one at a time until they pass
review.
All of the above editing activities relate to reviewing data at the
record (or questionnaire) level.  This is often referred to as micro-
editing. Editing of data at an aggregate level will then take place
even if it is not explicitly recognized as such.  This macro-editing
may be by cells in an economic table, or by some other aggregation
such as a stratum.  The cells in a table may be edited against
themselves (one can visualize some sort of super-questionnaire) or
against similarly defined cells from previous surveys.  This macro-
editing may be done by hand or through specially designed software.
Depending on the degree of automation, it may or may not be possible
to trace inconsistencies at the aggregate level to the offending
questionnaires.  If the macro-editing program can trace
inconsistencies back to the micro-level, then macro-editing can in
theory be used to direct the micro-editing.
If Computer Assisted Data Collection is used, then much of the editing
process is formally introduced and enforced at the time of data
collection.  Not only are most major errors corrected at the time of
the interview. but the subject matter specialists may have greater
confidence in the data after collection and be more likely to let the
data pass without change.  Thus, Computer Assisted Data Collection has
enormous potential for reducing the costs of data editing after data
collection.  By introducing edits into data collection, it will also
improve the data themselves. Currently, Computer Assisted Data
Collection is becoming more common in the survey world.  However, for
the foreseeable future, many surveys will still be collected by mail
or by paper and pencil interviewing. In any case, the need for editing
after data collection will never be totally eliminated.
Issues in Editing
Costs and Benefits
The importance of data editing in reducing non-sampling errors has
been questioned.  Granquist (1984) questions whether the editing
process can essentially improve data quality after data are collected.
He states that there are three purposes of editing: to give more
detailed information about the quality of the survey, to provide basic
data for the improvement of the survey. and to tidy up the data so
that further processing can be done.  Further, Granquist considers the
sources and types of survey errors, and questions the ability of most
generalized editing systems to address all kinds of errors including
systematic response errors.  If data are considered to have a timely
quality. that is, the value of data deteriorate as time goes along,
then editing can reduce die value of the data.  Pullum, Harpham, and
Ozsever (1986) describe a situation where the editing of survey data
                                  8
had no discernible effect on the estimates other than to delay their
release by about one year.  One common question that many
organizations have is when to declare that editing is finished.
"Over-editing" of data has long been a concern.  In order to make sure
that all errors are flagged, often many unimportant error signals
(flags) are generated.  These extra signals not only take time to
examine but also distract the reviewer from important problems.  These
extra signals are generated because of the way that error limits are
set.  One way that researchers are trying to reduce the number of
error signals, while at the same time ensuring that the important
cases are flagged, is through the development of statistical editing
techniques.  For example, time series techniques can be used in
repetitive surveys on a record-by-record basis.  Alternatively, cross
record statistical editing can be done on either a univariate or
multivariate basis.  This may include the graphical inspection of
data.
Data editing often requires considerable resources in federally
conducted surveys both in terms of staff time and dollars.  These
expenditures are themselves reason enough to re-evaluate the editing
process.  In addition, there are often external economic incentives
in the form of reduced budgets for statistical agencies.  The
combination of rapidly decreasing computing costs, rapidly increasing
computing capabilities, and steady or increasing staff costs, is
changing the economics of the process vis-a-vis the proper mix of
human and computer processing.  Another cost that is not considered
much is the increase in respondent burden.  In some surveys, edits are
so tightly set that few if any records pass the edits.  As a result
respondents are called back, some many times, in order to clear up
suspicious situations.  There is also an opportunity cost to editing.
Any time spent in editing is time that is not being used for other
activities, some of which may have greater potential for reducing
nonsampling errors.
Statistical and Quality Concerns
Statistical considerations will impact the development of new editing
systems and may even lead to their development.  Defensibility of the
process is a concern because data are changed after data collection
and before summary.  The ability of an agency to defend itself from
criticism is enhanced by implementing methodologically sound
procedures, by capturing data electronically as they are reported, and
by auditing all changes made during the edit.  The effect of editing
can then in principle be known, and feedback for the improvement of
the survey can be given.  Conceptually, the edit process should be
repeatable (or reproducible).  This means that the same data run
through a system twice should lead to the same results.  Editing
should not change survey results in an unpredictable manner.
Integration of Survey Tasks
Integration of survey tasks is important for improving both data
quality and productivity in survey processing.  Consider the functions
of Computer Assisted Data Collection, data entry, and data editing.
By integrating these functions, data quality can be improved by
injecting the editing function into collection, and also by reducing
transcription errors by eliminating the need for in office data entry.
Given the proper software, pre-survey activities may be done more
productively by reducing the need for multiple specification of the
data.  For example, if a particular variable can take only the values
of 1, 2, and 3, then the program for each of the three functions
should have
                                  9
specified this limitation.  Time is saved, and potential for
inconsistencies are reduced, if all three programs derive from one
specification.  Usually, routing instructions and edits are common
between a data collection instrument and an editing program.  If both
functions derive from the same program, then double programming can
be eliminated.  Also it is easier to make more explicit and purposeful
the differences between the collection and editing instruments.
Constraints
Constraints (other than economic constraints already considered) on
the organization or on the survey itself often adversely affect the
quality of editing.  Some large federal surveys (e. g., in the
National Agricultural Statistics Service or in the Bureau of Labor
Statistics) are conducted under extremely tight deadlines.  Data are
collected, processed, analyzed, and published under a rigid schedule.
Detecting all of the major problems in such a large data set under the
time allowed becomes enormously difficult.  Computer hardware and
software have their own constraints.  For example, access to a
mainframe may be limited and editors may have to review error signals
on paper printouts because of costs.  Software, may not be easy to
use, and it may be extremely difficult to coordinate disparate
programs.  Data editors may not have sufficient knowledge of the
subject matter or survey procedures nor sufficient training. High
turnover of editors may be a problem in some surveys.  The challenge
then is in providing the inadequate staff with enough, effectively
presented information to allow the job to be done correctly.
There may be resistance to change or a questioning of its need in the
implementation of new editing systems and methodologies.  People may
wonder how their job will be changed or if it will be eliminated.
Some problems may be easy to identify (e.g., the amount of resources
consumed is too large) but others may require special studies (e. g.,
how much is spent on each task and how much do we get from it?).  In
considering either the development of a new editing system or the
purchase of one, it is often difficult to know which editing system
features are necessary, and their relative performance.  Evaluation
of editing software is difficult and time consuming.  Another
consideration is who should be on the evaluation team.
                                 10
                                                         CHAPTER III
                      CURRENT EDITING PRACTICES
A. PROFILE ON EDITING PRACTICES
To obtain an adequate profile of current editing practices, the
subcommittee developed an editing profile questionnaire which was
administered to 117 Federal surveys covering 14 different if agencies.
The 117 surveys were selected by Subcommittee members and thus were
not a scientific sample of all Federal surveys; however, the
subcommittee members felt that the 117 surveys represented a broad
coverage of agencies and types of surveys or censuses that might have
different editing situations.
This section describes how the questionnaire was designed and
administered, and summarizes the findings.  While this section focuses
on the highlights of the profile, tallies of responses to all of the
questions appear in Appendix A.
Designing and Administering the Questionnaire
The subcommittee designed a six-page questionnaire containing general
descriptive questions about a particular survey as well as specific
questions on editing practices. (See Appendix A for a copy of the
questionnaire) Each subcommittee member pretested the editing
questionnaire by answering the questions for a survey with which each
was familiar.  Although a scientific sample was not drawn, the goal
was to select a group of surveys that would be representative of the
surveys conducted by Federal statistical agencies.  Each subcommittee
member sought to obtain information for ten to twenty surveys that
represented their agencies surveys.  In addition, they obtained
information from several agencies not represented on the subcommittee.
Some subcommittee members reviewed the completed questionnaires for
consistency by contacting the agency respondents prior to submitting
them.  A small number of consistency edit checks were performed for
the questionnaires; however, the editing was limited.
                                 11
Characteristics of Surveys in Editing Practices Study
Illustrating the wide range of surveys in the study, large and small
surveys were represented.  The smallest survey in the study had 22
units, while the largest had about 1 million units.  As shown in
Figure 1:
     About three-fourths of the surveys in the study are sample
     surveys.
     Various frequencies of collection are represented (annual,
     quarterly, monthly, and weekly).
     About three-quarters of the surveys are filed by establishments,
     institutions, farms and other entities, and the remaining quarter
     by households or individuals.
     Traditional means of data collection such as mail, personal, and
     telephone interviews were the most common.  Only a small
     proportion used computer assisted telephone interview (CATI); and
     no survey respondents reported using computer-assisted personal
     interview (CAPI) as their primary method of data collection,
     although a few did report using CAPI as a secondary method.
     About sixteen percent of the surveys in the study use
     administrative records.
[GRAPHIC] \WP18F1.GIF
                                 12
The remainder of this section discusses editing practices.  As part
of the analysis, data on editing practices were cross-classified by
the characteristics just discussed (sample versus census, frequency
of collection, type of unit surveyed and mode of collection) to
determine whether editing practices varied by these characteristics.
If these characteristics do in fact affect editing practices, and the
surveys in the study are not representative of all surveys on these
characteristics, then the aggregated results of this study would not
be applicable to all surveys.  Results are presented for all of the
surveys in this study, but situations in which editing practices
differed greatly are highlighted.
Editing Practices
The questionnaire covered the following areas with respect to editing
practices: cost of editing, when and how editing occurs. type of edits
used, degree of satisfaction with current system, and future
applications.
Cost of Editing
The survey respondents were instructed to include all aspects of
editing in their cost figures, such as edits made at data entry.
clerical work, computer time, design, testing, monitoring, analyst
review, call-backs, and summary table review.  However, in all of the
following information on editing costs, about two-fifths of the
respondents reported that information on the cost of data editing was
available.  The subcommittee does not claim that this data is totally
free of nonsampling errors.  Therefore. all conclusions are subject
to this constraint.  Editing costs representing at least 20 percent
of the total cost of the survey were reported for four-fifths of these
surveys.  A similar pattern was observed for the surveys for which
cost information was not available.  Of the 73 surveys where no cost
data were available, cost estimates were provided for about two-thirds
(49 surveys).  About three-quarters of these surveys had editing costs
representing at least 20 percent of the total cost of the survey.
The median editing cost as a percentage of the total survey cost was
35 percent.  While an attempt was made in the instructions to the
survey to standardize the activities to be included in the cost of
editing (see question 20 in Appendix A), record-keeping practices
vary.  As a result, estimates may not represent the same activities
from survey to survey.  However, the data still proved useful in
determining the survey characteristics that most effect the cost of
editing.
Editing costs as a percentage of the total survey cost varied greatly
by the type of survey.  Demographic surveys (surveys of individuals
and households) had a far lower median than economic surveys (surveys
of farms, establishments or firms, institutions, and others).  The
median for demographic surveys was 20 percent compared with 40 percent
for economic surveys.  Among the economic surveys, the surveys that
used administrative records had the highest median of percentage of
cost, 60 percent. This high percentage does not necessarily indicate
a high absolute editing cost, it could indicate a low total survey
cost because no new survey data are collected.  As discussed in the
next section, these surveys have more extensive involvement of subject
matter analysts than demographic surveys have.
                                 13
Overall, surveys in which all error correction was done by clerks or
analysts were more likely to have editing costs that represent over
40 percent of the total survey cost than were surveys in which only
unusual cases were referred to analysts . Almost one-half of the
former group had editing costs in the category "40 percent or greater"
compared with one-third of the latter group.  Reversing the
perspective, only 6 percent of the former group (all error correction
by clerks or analysts) had editing costs in the category, "under 10
percent" compared with about one-third of the latter group (unusual
cases by analysts).
When and How Editing and Follow-up Occur
For about two-thirds of the 117 surveys studied, the majority of data
editing takes place after data entry.  Subject matter analysts play
a large role in almost all of the surveys.  In about three-quarters
of the surveys, subject matter analysts review all unusual or large
cases after automated or clerical editing.  Only seven surveys have
little or no intervention from subject matter specialists.  Of these,
only four are completely automated (i.e., edit checking and error
correction are done without referral to analysts).
Surveys of farms, establishments and institutions tend to have heavier
involvement from subject matter analysts than surveys of individuals
and households (i.e., higher proportions of the study respondents
report that all data editing is done by subject matter analysts for
these surveys than for the others). This could explain the relatively
higher editing costs as a percentage of the total survey cost reported
for the surveys of farms, establishments and institutions.
The degree of automation varies considerably among the surveys in the
study.  About three-fifths of the survey managers note that automated
edit checking is done, but error correction is performed by clerks or
analysts (Figure 2).  In about 62 percent of the cases, there is no
analysis of the effect of editing practices on the estimates produced.
[GRAPHIC] \WP18F2.GIF
                                 14
Types of Edits
Almost all the surveys in the study use validation editing which
detects inconsistent data within a record.  A large proportion (83
percent) also use macro-editing where aggregated data are examined to
detect inconsistencies.  In addition to these two types of edits, 57
percent of the survey managers report using other edits.
[GRAPHIC] \WP18F3.GIF
In response to an open-ended request to describe "other" edits, "range
edits" were mentioned most frequently. followed by procedures that
used historical data.  "Ratio edits" were another common response.
These three groups may not be distinct.  Because responses we re not
detailed, it was difficult to determine exactly what these edits
involved.  Other types of edits and analyses mentioned include:
comparisons with other surveys. comparing the current value to a value
estimated by regression analysis, using interquartile measures, and
listing the ten highest and ten lowest values before and after
expansion factors were applied.
                                 15
Satisfaction With Current Edit System
The study respondents were split in the level of satisfaction with
their current system.  About 47% were satisfied, while about half
thought that at least minor changes were required.  A small proportion
said it was not possible to determine what changes were required at
this point (Figure 4).
[GRAPHIC] \WP18F4.GIF
Future Applications
Among those expressing a need for change, an on-line system topped the
list of desired improvements.  Other changes that were mentioned
frequently (as a result of an open-ended question) included:
     -    The use of prior years' data to test the current year,
     -    More statistical edits, and
     -    More sophisticated and more extensive macro and validation
          editing.
An audit trail, more automation in general. and a user-friendly system
were also mentioned several times.  In addition, the following
enhancements were mentioned: automated error correction, incorporation
of imputation into the editing package. evaluation of the effect of
data editing, reduction of the number of edit flags to follow-up,
incorporation of information on auxiliary variables, multivariate
editing. use of an expert system approach for criteria which require
judgment, and editing using micro-computers.
In summary our questionnaire revealed wide diversity in current
editing practices and in user satisfaction with them.  To present more
of an in-depth picture, we now describe the development of the case
studies.
                                 16
B. CASE STUDIES
Federal government surveys, censuses, and administrative records
systems create a broad range of data editing situations.  In addition
to the statistical profile on editing practices, it was felt that a
further description of several of the surveys in case study form would
reveal in greater detail the complexity of the different editing
practice situations in operation.
A typology of editing situations was developed by the subcommittee to
be used for selecting case studies (Figure 5).  The typology was
developed through extensive subcommittee discussion and from analysis
of responses to the editing practices questionnaire.
The grouping variables included in Figure 5 are:
1.    Census or sample survey approach
2.   Longitudinal or cross sectional approach
3.   Frequency of census or sample survey
4.   Size of census or sample survey
5.   Continuous and/or categorical data
6.   Administrative records used (Yes or No)
7.   Mode(s) of data collection used (mail, telephone, CATI, CAPI,
     touchtone, personal, etc.)
8.   Use of historical data in the edit process (Yes or No)
There were also other grouping variables that were considered and then
discarded, for example, the level of clerical knowledge of subject
matter when editing.  The major reason for elimination was
subjectivity involved in measuring those variables.
In order to represent the range of different editing situations, the
subcommittee picked eight case, studies that covered the different
values of the eight grouping variables.  Four were chosen to develop
brief case studies which represent different survey situations and are
presented in short abstract form in Appendix B. These are:
     -    BLS:CPI: Commodities and Services
     -    IRS: US Corporation Income Tax Returns
     -    NCES: National Education Longitudinal Study of 1988
     -    Federal Reserve Board: Edited Deposits Data System
                                 17
The first paragraph of each abstract describes the environment in
which the survey takes place, including type of survey and size.  The
second paragraph includes a brief description of editing practices
used.
Four additional surveys are described in greater detail (in Appendix
B) to give the reader a flavor of the range of editing practices and
situations.  Surveys covered are:
         NCHS:  National Health Interview Survey;
         Census: Enterprise Summary Report;
         NASS: Quarterly Agricultural Survey; and
        EIA: Monthly and Weekly Refinery Reports.
The first section of the in-depth case studies describes the
environment in which the survey takes place.  The second section
describes editing practices - used, including data processing
environment, audit trail, micro, macro and statistical editing,
prioritizing of edits, imputation process, standards, costs, role of
subject matter specialists, measures of variation, and current and
future research.
The wide variation in editing situations makes it impossible to
recommend any one editing system or methodology for all Federal
statistical agencies, surveys, administrative records systems, or
censuses.
                                 18
[GRAPHIC] \WP18F5.GIF
                                 19
                                                          CHAPTER IV
                          EDITING SOFTWARE
A. INTRODUCTION
For most surveys, large parts of the editing process are carried out
through the use of computer systems.  The task of the Software
Subgroup has been to investigate software that in some way
incorporates new methodologies, has new ways of presenting data,
operates in recently developed hardware environments, or integrates
editing with other functions.  In order to fulfill this charge, the
Subgroup has evaluated or been given demonstrations of new editing
software.  In addition, the Subgroup has developed an editing software
evaluation checklist that appears in Appendix C. This checklist
contains possible functions and attributes of editing software, which
would be useful for an organization to use when evaluating editing
software.
Extremely technical jargon can be associated with new editing systems;
and new approaches to editing may not be familiar to the reader.  The
purpose of section B is to explain these approaches and their
associated terminology as well as to discuss briefly the role of
editing in assuring data quality.
A distinction must be made between generated systems and software
meant for one or a few surveys.  The former is meant to be used for
a variety of surveys.  Usually there is an institutional commitment
to spend staff time and money over several years to develop the
system.  It is hoped that the investment will be more than recaptured
after the system is developed through the reduction in resources spent
on editing itself and in the elimination of duplication of effort in
preparing editing programs.  Some software programs have been
developed that address specific problems in a particular survey.
While the ideas inherent in this software may be of general interest,
it may not be possible to apply the software directly to.other
surveys.  Section C describes three generalized systems in some
detail, and then briefly describes other systems and software.  These
three systems have been used or evaluated by Subgroup members in their
own surveys.
New and exciting statistical methodology is also improving the editing
process.  This includes developments in detecting outliers, aggregate
level data editing, imputation strategy, and statistical quality
control of the process itself.  These activities are covered more
fully in Chapter V. The Implementation of these activities, however,
requires that the techniques be encoded into a computer program or
system.
                                 21
B. SOFTWARE IMPROVING QUALITY AND PRODUCTIVITY
Reasons for the Development of New Editing Software
Traditional editing systems do not fully utilize the talents or
expertise of subject matter specialists.  Much of their time may be
spent in dealing with unimportant or spurious error signals and in
coping with system shortcomings.  As a result, the specialist has less
time to deal with important problems.  In addition, editing systems
may be able to give feedback on the survey itself.  For example, a
pattern of edit failures may suggest misunderstandings by the
respondent or interviewer.  If this is recognized, then the expertise
of the specialist may then be used to improve the survey itself.
Labor costs are a large part of the editing costs and are either
steady or increasing, whereas the cost of computing is decreasing.
In order to justify the heavy reliance on people in editing, their
productivity will have to be improved through the use of more powerful
tools.  However, even if productivity is improved, different people
may do different things in similar situations.  If so, this makes the
process less repeatable (reproducible) and more subject to criticism.
When work is done on paper, it is hard to track, and it is impossible
to estimate the effect of editing actions on estimates.  Finally, some
tasks are beyond the capability of human editors.  For example, it may
be impossible for a person to maintain the multivariate frequency
structure of the data when making changes.
These reasons and several others are commonly given as explanations
for the increased use of computer software to improve the editing
process.  It is in the reconciliation of these two goals, (the
increased use of computers for some tasks and the more intelligent use
of human expertise), that the major challenge in software development
lies.  There will always be a role for people, but it will be
modified.  One positive feature of new editing software is that it can
often improve the quality of the editing process and productivity at
the same time.
Ways that Productivity Can Be Improved
One way to improve productivity is to break the constraints imposed
by computer systems themselves.  The use of mainframe systems for
editing data is widespread.  In some cases, however, an editor may not
use the system directly.  For example, error signals may be presented
on paper printouts, and changes entered by data typists.  Processing
costs may dictate that editing jobs are run at low priority,
overnight. or even less frequently.  The effect of the changes made
by the editor may not be immediately known: thus, paper forms may be
filed, taken from files, and refiled several times.
The proliferation of microcomputers promises to eliminate many of
these bottlenecks. while at the same time it creates some challenges
in the process.  The editor will have direct access to the computer,
and will be able to prioritize its use.  Once the microcomputer is
acquired. user fees are eliminated, thus resource-intensive programs
such as interactive editing can be employed, provided the
microcomputers are fast enough.  Moving from a centralized environment
(i. e., the main frame) to a decentralized environment (i. e.,
microcomputers) will present challenges of control and consistency.
In processing a large survey on two or more microcomputers,
                                 22
communications will be necessary.  This will best be done by
connecting them into a Local Area Network (LAN).
New systems may reduce or eliminate some editing tasks.  For example,
where data are edited in batch and error signals are presented on
printouts, a manual edit of the questionnaires before the machine edit
may be a practical necessity.  Editing data and error messages on a
printout can be a hard, unsatisfactory chore because of the volume of
paper and the static and sometimes incomplete presentation of data.
The purpose of the manual edit in this situation is to reduce the
number of machine-generated error signals.  In an interactive
environment, information can be efficiently presented and immediately
processed.  The penalty associated with machine-generated signals is
greatly reduced.  As a result, the preliminary manual edit may be
eliminated.  In addition, questionnaires are handled only once,
further reducing filing and data entry tasks.
Productivity may be increased by reducing the need for editing after
data are collected.  Instruments for Computer Assisted Telephone
Interviewing (CATI), Computer Assisted Personal Interviewing (CAPI),
and on-site data entry and editing programs are gaining wider use.
Routing instructions are automatically followed, and other edit
failures are verified at the time of the interview.  There may still
be many error signals from suspicious edits, however, the analyst has
more confidence in the data and is more likely to let them pass.
There are two major ways that productivity can be improved in the
programming of the editing instruments.  First is to provide a system
that will handle all, or an important class, of the agency's editing
needs.  In this way the applications programmer need not worry about
systems details.  For example, in an interactive system, the
programmer does not have to worry about how and where to flag edit
failures as it is already provided.  The programmer only codes the
edit specification itself.  In addition, the end-user has to learn
only one system when editing different surveys.  Second is the
elimination of multiple specification and programming of variables and
edits.  For example, if data are collected by CATI, and edited with
another system, then essentially the same edits will be programmed
twice, possibly by two sets of people.  If the system integrates
several functions, e. g., data entry, data editing, and computer
assisted data collection, then one program may be able to handle all
of these tasks.  This integration would also reduce time spent on data
conversion from one system to another.
Systems that Take Editing and Imputation Actions
Some edit and imputation systems take actions usually reserved for
people.  They choose fields to be changed and then change them.  The
human element is not removed. rather this expertise is incorporated
into the system.  One way to incorporate expertise is to use the edits
themselves to define a feasible region.  This is the approach outlined
in a famous article by Fellegi and Holt ( 1 976).  Edits that are
explicitly written are used to generate implied edits.  For example
if 100 < x/y < 200, and 3 < y/z < 4, are explicit edits. then an
implied edit obtained algebraically is 300 < x/z < 800.  Once all
implied edits are generated the set of complete edits is defined as
the union of the explicit and implied edits.  This complete set of
edits is then used to determine a set of fields to be changed for
every possible edit failure.  This is called error localization.  An
essential aspect to this method is that changes are made to as few
fields as possible, or alternatively, to the least reliable set of
fields which are determined by weights given to each field.
                                 23
The analyst is given an opportunity to evaluate the explicit edits.
This is done through the inspection of the implied edits and external
records (the most extreme records that can pass through the edits
without causing an edit failure).  In inspecting the implied edits,
it may be determined if the data are being constrained in an
unintended way.  In inspecting external records, the analyst is
presented with combinations of the most extreme values possible that
can pass the edits.  The human editor has several ways to inject
expertise into this kind of a system: (1) the specification of the
edits; (2) the action of implied edits and external records and then
the respecification of edits; (3) the weighting of variables according
to their relative reliability.
There are some constraints in systems that allow the computer to take
editing actions.  Fellegi and Holt systems cannot handle certain kinds
of edits, notably nonlinear and conditional edits.  Also algorithms
that can handle categorical data cannot handle continuous data and
vice versa.  Within these constraints (and others), most edits can be
handled.  For surveys with continuous data, a considerable amount of
human attention may still be necessary, either before the system is
applied to data or after.
Another way that computers can take editing actions is by modeling
human behavior.  This is the expert system" approach.  For example,
if typically maize yields average 100 bushels per acre, and the value
1.000 is entered. then the most likely correction is to assume that
an extra zero was typed.  The computer can be programmed to substitute
100 for 1,000 directly and then to re-edit the data.
Ways that Data Quality can be Improved or Maintained
It is not clear that editing done after data collection can always
improve the quality of data by reducing non-sampling errors.  An
organization may not have the time or budget to recontact many of the
respondents or may refrain from recontacts in order to reduce
respondent burden.  Additionally, there may be cognitive errors or
systematic errors that an edit system cannot detect.  Often, all that
can be done is to maintain the quality of the data as they are
collected.  To use the maize yield example again, if the edit program
detects 1,000 bushels per acre, and sets the value to 100 bushels per
acre, then the edit program has only prevented the data from getting
worse.  Suppose the true value was really 103 bushels per acre.  The
edit and imputation program could not get the value closer to the
truth in this case.  Detecting outliers is usually not the only
problem.  The proper action to take after detection is the more
difficult problem.  One of the main reasons that Computer Assisted
Data Collection is employed is that data are corrected at the time of
collection.
There are a few ways that an editing system may be able to improve
data quality.  A system that captures raw data, keeps track of
changes, and provides well conceived reports, may provide feedback on
the performance of the survey.  This information can be used to
improve the survey in the future.  To take another agricultural
example, farmers often harvest corn for silage (the whole plant is
harvested, chopped into small pieces, and blown into a silo).
Production of silage is requested in tons.  Farmers often do not know
their silage production in tons.  Instead, the farmer will give the
size (diameter and height) of all silos containing silage.  In the
office, silo sizes are converted into tons of production.  If this
conversion takes place before data are entered, then there is no
indication from the machine edit of the extent of this reporting
problem.
                                 24
Another way that editing software can improve the quality of the data
is to reduce the opportunity cost of editing.  The time spent on
editing leaves less time for other tasks, such as persuading people
to participate, checking overlap of respondents between multiple
frames, and research on cognitive errors.
Ways that Quality of the Editing Process can be Defended or Confirmed
There is a difference between data quality and the quality of the
editing process itself.  To refer once again to the maize yield
example, a good quality process will have detected the transcription
error.  A-poor quality process might have let it pass.  Although
neither process will have improved data quality, the good quality
process would have prevented their deterioration from the
transcription error.  Editing and imputation have the potential to
distort data as well as to maintain their quality.  This distortion
may affect the levels of estimates and the univariate and multivariate
distributions.  A high quality process will attempt to e distortions.
For example, in Fellegi and Holt systems, changes to the data will be
made to the fewest fields possible and in a way such that
distributions are maintained.
A survey organization should be able to show that the editing process
is not abusing the data.  For editing after data collection, this may
be done by capturing raw (unedited) data and keeping track of changes
and the reasons for change. This is called an audit trail.  Given this
record keeping, it will be possible to estimate the impact of editing
and imputation on expansions and on distributions.  It will also be
possible to determine the editor effect on the estimates.  In
traditional batch mode editing on paper printouts, it is not unusual
for two or more specialists to edit the same record.  For example, one
may edit the questionnaire before data entry while another may edit
the record after the machine edit.. In this case, it is impossible to
assign responsibility for an editing action.  In an on-line mode one
person handles a record until it is done.  Thus all changes can be
traced to a person.  For editing at the time of data collection,
(e.g., in CATI), it may be necessary to conduct an experiment to see
if either the mode of collection, or the edits employed, will lead to
changes in the data.
A high quality editing process will have other features as well.  For
example, the process should be repeatable, in time and in space. This
means that the same data passed through the same process in two
different locations, or twice in one location, will look (nearly) the
same.  The process will have recognizable criteria for determining
when editing is done.  It will detect real errors without generating
too many spurious error signals.  The system should be easy to program
in and have an easy user interface.  It should promote the integration
of survey functions such as micro- and macro-editing.  Changes made
by people should be on-line (interactive) and traceable.  Database
connections will allow for quick and easy access to historical and
sampling frame data.  An editing system should be able to take actions
of minor impact without human intervention.  It should be able to
accommodate new advances in statistical editing methodology.  Finally,
quality can be promoted by providing statistically defensible methods
and software modules to the user.
                                 25
C. DESCRIPTIONS OF EDITING SOFTWARE
Three Generalized Editing Software Systems
Blaise
The Blaise system has been developed by the Netherlands Central Bureau
of Statistics and is its standard data processing system.  It is
intended for use on microcomputer Local Area Networks (LANS) but can
work on stand-alone machines as well.  The required operating system
for the microcomputers is MS DOS. The preferred LAN protocol is
Novell, though Blaise will work with others as well.  Turbo Pascal is
required to compile applications programs; however, it is not needed
by the end user.  Development of applications in Blaise can be done
in Dutch, English, Spanish, and French.
Blaise can handle categorical, continuous, and character data.  It has
been used for economic, agricultural, social, and demographic surveys.
It handles edits of all types.  In Blaise, the human editor is not
replaced as the primary reviewer of data.  Rather, the individual is
given a more powerful, interactive tool with which to work.
Blaise is used to perform CATI, CAPI, and data entry as well as
editing.  Herein lies the strength of the system.  Since it can
perform these related functions, it can also integrate them.  This
integration is done through the creation of a "Blaise Questionnaire".
This questionnaire is not a survey instrument itself, rather it is a
"specifications generator".  In it, data are defined, routes are
described. and edits are written.  From this one specification, the
Blaise system can generate two related modules.  The first, for data
collection, can be used for both CATI and CAPI applications.  The
second is used for data entry and data editing.  Since Blaise
integrates these related survey tasks, multiple specification of the
data and edits is avoided.
Blaise does not perform data analysis because there are already many
packages that can perform this Job.  Blaise does generate dataset
specifications for SPSS and Stata statistical packages and for the
Paradox database system.  Users can also specify their own specialized
setups.  Blaise can read in data from other sources as long as they
are in (virtually any) ASCII format.  A related tabulation module,
which is part of the Blaise system, is called ABACUS.  It can generate
tables from Blaise data sets.  These can be used for, among other
things, some survey management functions.  Weighted data can be
tabulated in ABACUS.
Interactive editing in Blaise can be approached in several different
ways.  For example, data can be entered either by the analyst or by
high speed data entry operators.  In the first case. data are edited
as they are entered.  In the second case, the editor has several
different ways of approaching the task.  A batch edit can be
performed.  In the batch edit, records are marked as clean.
suspicious. or dirty.  The editor can retrieve the records based on
their status.  Also, the editor can access any record by its
identification number or call up records based on certain criteria
such as stratum designation, or the value or range of values of
designated variables.
                                 26
Generalized Edit and Imputation System (GEIS)
The GEIS system, developed by Statistics Canada, is based on the work
of Fellegi and Holt.  A predecessor, the Numerical Edit and Imputation
System (based on the ideas of Sande), has been used as a prototype for
GEIS.  GEIS has been developed as part of the Business Survey Redesign
Project.
GEIS is intended to be applied to continuous data in economic surveys.
Editing and imputation are considered to be part of the same process.
In GEIS, data review and change are performed primarily in batch.
GEIS performs edit analysis, error localization, and imputation.  The
system can be used on mainframes as well as on microcomputers.  The
database system ORACLE is required for all stages of processing, (GEIS
is not part of the ORACLE system).
GEIS handles linear edits and variables that take positive values.
Within these constraints, most situations can be handled.  Non-linear
edits can often be transformed to linear edits or can be restated
keeping in mind the intent of the original edit.
It is intended that the system be used by a subject matter specialist
working with a methodologist.  Edits are specified interactively
through specially designed screens.  After specification, feedback is
provided in the form of implied edits, extremal records, and edit
analysis such as checks for consistency and redundancy.  Data are
edited in batch.  Fields are automatically selected for change under
the principle that the smallest (weighted) set of fields is changed.
Next, imputation is performed in a manner that the edits are
satisfied.  The primary method of imputation is hot-deck imputation
where good records are used to donate values for incomplete records.
Other model-based methods can also be specified.
Since GEIS is embedded in the ORACLE system, the edit and imputation
process can be easily monitored.  Many different kinds of reports can
be generated.  For example, the frequency of imputation by field, and
the number of times each donor record has been used in imputation are
two reports that can be generated.  Through these reports, it is
possible to measure the impact of the process on the estimates.
Defensibility of the edit and imputation process is a priority in
GEIS.  This is done not only through the tracking of records as they
proceed through the system, but also by providing the user with
statistically defensible methods.
Data are held in an ORACLE database.  Before they are edited in GEIS,
they are treated in a preliminary edit.  For example, all coding and
respondent follow-up would be done in this preliminary edit.
Unresolved records from the preliminary stage are sent to GEIS.
Structured Program for Economic Editing and Referrals (SPEER)
SPEER is intended primarily for continuous data under ratio edits for
economic surveys conducted by the various divisions of the U. S.
Bureau of the Census.  SPEER applies the Fellegi and Holt methodology
to ratio edits.  Within that realm, SPEER performs edit generation and
analysis, error localization. and imputation.  Additivity edits can
also be handled in SPEER.  Other edits are handled either in satellite
routines within SPEER or in a program outside of SPEER.  Data are
edited and imputed in batch mode first.  On-line (interactive) review
of referral records is an
                                 27
essential part of SPEER.  Records can be designated as referrals based
on criteria such as size of firm or on specific editing actions.
SPEER runs on mainframes as well as on microcomputer LANs.  All of the
SPEER modules are programmed in FORTRAN.  A FORTRAN compiler is
required to program new applications.  The use of FORTRAN as the base
language has the advantage of flexibility.  The limits of SPEER
regarding imputation routines, screen design, etc. are the same as
those of FORTRAN (there are very few limits).  When using the system,
the services of a programmer are required to incorporate survey
specific expert information.
In SPEER, both the machine and the human editor play major roles.
Subject matter expertise is incorporated into SPEER through the
programming of flexible modules.  A hierarchy of imputation procedures
for each variable is set; that is, imputation is on a field-by-field
basis.  The procedures are tried one at a time until a value within
the feasible region is found.  If desired, human editing actions can
also be modeled in SPEER, through the use of IF-THEN statements.
Since SPEER can handle most problems, the analyst is spared the task
of reviewing minor problems and can concentrate on unusual or large
cases.  When necessary, however, the analyst can review records
interactively.
In the interactive review, the screen display includes reported data,
corrected data, a status indicator. and the lower and upper limits of
the feasible region for each variable.  This allows the editor to see
the effect of the editing actions vis-a-vis the SPEER limits.  Also
incorporated into SPEER is an audit trail, which keeps track of
changes and reasons for them.  The analyst requests a specific record
and reviews the processing done by the automated system.  The human
expert can override the decision rules residing in the automated
system and replace them based on alternative information about the
case under review.  The analyst typically has access to one or more
of the following: the original response form, auxiliary information
about the establishment under review, or the respondent by a telephone
call.  Based on this additional information and personal experience.
an analyst may alter the decision rules built into the automated
system.  If there is reason to believe that the most appropriate
imputation value lies outside the acceptable region, the analyst can
select an imputed value outside the range.  This system has also been
used as a data entry vehicle for late arrival forms.  The late form
are entered into the data file by subject matter specificants using
SPEER and they are edited as they are being entered.
Brief Description of Other Systems or Programs
An Example of an Expert System Application.
An expert system application has been developed by the Quality
Assurance Division of the Energy Information Administration.  The
program has been written for the Monthly Power Plant Survey (EIA-759).
It was written to assist in the process of disposing of items that
fail computer edit routines and to compensate for insufficient
expertise and training of editors manually performing the process of
disposing of edit failures.  It was thought that the expert system
could guide and assist the data editors through the more difficult
dispositions of items that have failed edits thus
                                 28
allowing the data to be edited according to the standard required.
Though the system is ready for its first use, it has yet to be
implemented operationally.
PEDRO, a System for the On-Site Entering and Editing of Data.
The Petroleum Electronic Data Reporting Option (PEDRO), developed at
the Energy Information Administration (EIA), is an on-line system for
data entry in which the respondents are involved in the data editing
(Swann 1988).  The respondents can use a personal computer for data
entry or import data from the mainframe or another microcomputer
system using a predefined format.  The PEDRO system software then
provides them with an image of a printed form which they proceed to
"fill-out".  The PEDRO data entry programs include a wide variety of
edit checks to detect data errors at the time of entry.  Users can
enter and exit the PEDRO data entry function as often as they want
while working to resolve any errors in the data.  After data are
entered, checked by PEDRO, and reviewed by the respondent, the data
are transmitted to EIA.
Examples of the edits include a check to determine whether a total
equals the sum of its parts and whether current month beginning stocks
are equal to previous month ending stocks.  Range edits that use
historical data are included among the other system edits.  Sometimes
error messages will be generated for values that are actually correct.
In that situation, the respondent is asked to provide an explanation
for the anomalous value in the comments screen.  This information is
also transmitted to EIA making it unnecessary for an analyst to
contact the respondent to explain the anomaly.  Currently PEDRO is
used by approximately 61 respondents to the "Monthly Refinery Report"
and 10 respondents to the "Monthly Imports Report."  Other offices in
EIA are currently in the testing phase of using PEDRO for their
surveys.
DIA, a System for the Automatic Editing of Qualitative Data
DIA is the name of a system developed by the National Statistical
Institute of Spain (Garcia-Rubio and Villan, 1990).  It applies the
Fellegi and Holt methodology to qualitative data.  Only the minimum
number of fields necessary are changed in order to satisfy the edits.
The only specification necessary for imputation is that of the
conflict (edit) rules.  Each record is edited once and distributions
are maintained.  Random errors are distinguished from systematic
errors, however, a rules analyzer ensures that both types of errors
are treated consistently.  Detailed information is provided by DIA on
the whole editing and imputation process.
Micro-Macro Statistical Analysis System
The Micro-Macro Statistical Analysis System system is a graphics-on-
screen, interactive, macro-editing system developed by the Bureau of
Labor Statistics for use on the Current Employment Survey (CES).  It
is meant to replace the current batch system that generates thousands
of computer printout pages.  First, a table of industry identification
codes for industries with suspicious estimates is presented.  The
analyst chooses one industry to work with.  At this point. the analyst
will try to find suspicious sample data which might have caused the
problem.  This can be done in either of two modes of operation: query
or graphics.  In the query mode, tables of estimates for specific
cells are displayed.  The analyst can ask logical questions about a
set of sample members
                                 29
in order to select suspicious members.  For a particular record, the
analyst can reduce its weight so that it represents. orgy itself, can
reject it, or can change entries.  The effect of these micro changes
can be seen at the cell (macro) level.  In the graphics mode, current
versus previous data points are displayed in a scatter plot for each
variable.  Outliers are easily seen and can be marked for further
inspection in the query facility.  Records that are changed in the
query mode are marked when displayed in the graphics mode.  A full
audit trail is generated as changes are made in order to facilitate
supervisory oversight of the process.
Other Software
Paul Cotton (1988), reviewed four systems in a paper entitled "A
Comparison of Software for Editing Survey and Census Data".  The paper
is in two parts.  A set of criteria for evaluating Editing Software
is discussed followed by a review of the -four systems.  In addition
to the GEIS system, the paper describes three systems used primarily
in the Third World.  They are the Integrated System for Survey
Analysis (ISSA), PCEDIT, and the Integrated Microcomputer Processing
System (IMPS).  ISSA was developed by the Institute for Resource
Development Westinghouse, to process demographic and health surveys
in Third World countries on IBM personal computers.  It can perform
data entry, data editing, and tabulation.  ISSA is described in
Cushing (1988).  PCEDIT is available from the Department of Technical
Co-operation for Development of the United Nations.  It is meant to
be used to process population (demographic) data. IMPS, developed by
the International Statistical Programs Center of the U. S. Bureau of
the Census, consists of six major components, one each for data
definition, data entry, editing, tabulation, census management, and
census planning.  The name of the editing package is CONCOR.  IMPS was
developed to process census data in developing countries.  The U.S.
Census Bureau is using CONCOR to edit and impute data for the 1990
Decennial Census for the U. S. Pacific Islands, (Guam, American Samoa,
Northern Marianas, and Paluau).  CONCOR is also being used to test
edit specifications for population and housing characteristics for the
basic 1990 United States Census long-form questionnaire.  IMPS is
described in Toro and Chamberlain (1988).
                                 30
                                                           CHAPTER V
                         RESEARCH ON EDITING
A. INTRODUCTION
All survey or census data must go through some level of editing.  In
the absence of correction activities, errors could introduce serious
distortions into the data and derived statistics.  Surveys, survey
staff, and processing capabilities all change over time, and
procedures for editing change as well.  Redesign or improvement for
edit systems can be minor to correct for slight problems, or there can
be large research efforts to introduce major changes in methodology.
These investigations can be carried out by specialists for a specific
survey, programmers focusing on computer enhancements, or
methodologists working on edit research.
Three related goals of the Research Subgroup of the Subcommittee have
been to identify areas in which improvements to edit systems will
prove most useful, describe recent and current research activities to
enhance edit capabilities, and make recommendations for future
research.  The Edit System Questionnaire discussed in preceding
chapters included questions about edit improvements.  One question
asked was "For future applications, what would you like your edit
system to do that it doesn't do now?" Another source of information
was discussions with those responsible for edit tasks within a number
of Federal agencies.  Two areas emerged as priorities: (1) on-line,
human interaction with a computer edit system and (2) better ways to
detect potentially erroneous survey responses.
Section B of this chapter provides examples of research in the two
areas mentioned above.  Section C briefly describes editing research
in other countries.  Section D presents case studies of editing
research in United States Federal Statistical Agencies.  A summary is
provided in Section E. In Appendix D an annotated bibliography
describes research efforts over the past years and we discuss this
bibliography in section F. The bibliography is particularly important
because it is difficult to locate and identify research on edit
development.  Sometimes the research is part of a quality assurance
project.  Often, research findings are not written up as such, but
they are implemented and evolve into practical and useful software.
The chapter is limited to research on editing as opposed to
imputation.
B. AREAS OF EDIT RESEARCH
One area of current research interest is that of on-line edit
capabilities in which survey takers interact with editing software to
edit responses at the time of data collection.  This occurs in a CATI
(Computer Assisted Telephone Interviewing) or CAPI (Computer Assisted
Personal Interviewing) setting.  The BLAISE system discussed earlier
is an example of edit software used in support of a CAPI and CATI
program.  Computer Assisted Self Interviews (CASI), is an innovative
extension of these ideas which is to provide respondents with software
to allow them to edit their own responses before transmission to the
collecting agency.  One software system and supporting
                                 31
hardware for this purpose in use in Federal agencies is the PEDRO
system which is described in Chapter IV.  The topic of computer
assisted data collection activities has been investigated in detail
by the Computer Assisted Survey Information Collection (CASIC) Working
Group.
Another use of on-line, interactive edit programs is in the review of
edit referral documents.  Most survey editing, especially of economic
data, is a combination of automated batch computer runs and a follow-
up review of selected cases by subject matter staff.  The reason for
targeting a record may be changes to a large case, large or unusual
changes, or the need for an analyst to supply an imputation.  An on-
line referral system should allow an analyst to make changes in a
record, enter the change to the data file, and have the edit system
validate the change or indicate that further. adjustments may be
necessary.  After an analyst completes the review of a record using
an on-line system, the record should require no further action.  This
is in contrast to procedures currently in place in which an analyst
will make "paper and pencil" changes to a referral document, changes
will then be entered through some data entry process, the revised
record will be run through an automated batch system, and the record
may be targeted for further review.  With an on-line, interactive
referral system for analyst/clerical review of individual cases, the
review process should be more efficient, less error prone, and less
tenuous.  Research into this area has a major system design
orientation with the primary focus on software development rather than
on new editing methodologies.
Several of the systems described in Chapter IV, EDITING SOFTWARE,
incorporate interactive review.  Blaise is a system in which
interactive review is the primary method of data editing and which
integrates editing with computer assisted data collection.  SPEER is
a system where interactive review is tied in with Fellegi and Holt
editing principles.  In PEDRO, the respondent fills an electronic form
that is edited at the same time.  The Micro-Macro Statistical Analysis
System incorporates interactive tabular and graphical review in order
to perform macro-editing.  The systems ISSA, PC EDIT, and CONCOR also
have interactive capability.
A second area of active research is in the detection of potentially
erroneous responses.  The method for error detection most commonly
used in Federal agencies is to employ explicit edit rules.  For
example, edit rules may require that:
     (a)  the ratio of two fields lie between prescribed bounds,
     (b)  the current response be within some range of a predicted
          value based on a time series or other models, or
     (c)  various linear inequalities and/or equalities hold.
Edit rules and parameters are highly survey-specific.  A related
editing research area is the design of edit rules and the development
of methods for obtaining sensitive parameters.  For some automated
edit systems the primary activity is to screen records which fail some
combination of edit rules, after which data correction or verification
is completed by subject specialists.  This is especially true for
questionnaires having, in part, a regulatory purpose or having only
a small number of cases.  For such edit systems, research will focus
on selecting the appropriate edit rules. deriving sensitive bounds,
and setting up flagging procedures.  A related area of interest
focuses on optimal methods to target cases for review as one does not
want to burden the review process with an excessive number of referral
cases nor does one wish to let many errors escape detection.
                                 32
Several research studies are described in Section D in which the
editing objective is to detect potentially erroneous responses.  The
first case study on methods to develop edit rules and tolerances was
conducted at the Federal Reserve Board to derive set rules and
parameters for editing bank deposit data.  One objective of this study
was to determine procedures to group reporting units into clusters and
form edit parameters by cluster.  A related study at the Federal
Reserve Board (FRB) to investigate the use of more model based range
limits is described as well.  Three case studies follow on the use of
time series data on a firm's performance to predict current reporting
and then edit actual reported values against those predicted.  The
first two studies describe research at Energy Information
Administration (EIA) and are followed by a description of work at the
National Agricultural Statistics Service (NASS).  These studies
illustrate the type of research being conducted at various Federal
agencies and should prove useful as a source of ideas, directions, and
considerations in edit system design.
In contrast to the rule-driven method for the detection of potentially
erroneous response combinations within a record, one alternative
procedure is to analyze the distribution of questionnaire responses.
Records which do not conform to the observed distribution are then
targeted as outliers and are selected for review and examined further
for potential errors (Little and Smith, 1984 for example).  Although
there has been research interest in this topic, no application of
these multivariate methods was found.  In addition, an investigation
of the joint use of outlier detection procedures and rule-driven edits
to detect potentially erroneous responses may prove valuable.
C. EDITING RESEARCH IN OTHER COUNTRIES
Much editing research has been conducted in national statistical
offices around the world.  It is these organizations, which conduct
huge and complicated surveys, that have the most to be gained from
developing new systems and techniques.  They also have the resources
upon which to draw for this development.  The following are citations
of people and organizations about which the members of this
Subcommittee have knowledge.
Leopold Granquist of Statistics Sweden has presented papers on both
the purposes of editing (Granquist 1984), and on macro-editing
(Granquist, 1987).  Granquist has also developed a typology of survey
errors with which to judge the effectiveness of editing systems.
Members of the Australian Bureau of Statistics have given editing
papers at two recent Annual Research Conferences of the U. S. Bureau
of the Census.  The first by Linacre and Trewin (1989) addresses the
optimal allocation of resources to various survey functions (including
editing) in order to reduce non-sampling errors.  The second by
Hughes, McDermid, and Linacre (1990) concerns the use of graphical
techniques to find outliers at both the micro and macro level.
The National Statistical Institute of Spain has developed a Fellegi
and Holt system for edit and imputation of categorical data.  In a
recent paper, Garcia-Rubio and Villan (1990) discuss the applicability
of the Fellegi and Holt methodology to randomly generated and
systematic errors.  They have made modifications in the methodology
in order to better handle errors of the latter type.  The Netherlands
Central Bureau of Statistics (CBS) is the world leader in the use of
microcomputer
                                 33
Local Area Networks for the processing of survey data.  Keller and
Bethlehem (1990) describe the systems, organizational issues and their
resolution related to this new technology.  Currently, the CBS has
2,000 microcomputers installed in 60 LANS.  All the day-to-day
processing of survey data is now carried out on these LANs using
standardized software tools.  The CBS has also carried out a "Data
Editing Research Project" to determine the need for an interactive
computer assisted procedure (Bethlehem, 1987).  In Statistics Canada,
Hidiroglou and Berthelot (1986) have developed a method of statistical
editing for periodic business surveys.
An international group called the Data Editing Joint Group, has been
meeting for a few years under the auspices of the Statistical
Computing Project Phase 2 of the United Nations Development Program's
Economic Commission for Europe.  Countries represented include Sweden,
Netherlands, Soviet Union, Yugoslavia, Hungary, Spain, France, Canada,
and the United States. (The National Agricultural Statistics Service
is the U. S. representative.) This group concentrates more on the
systems aspects of editing and will be making recommendations about
systems specifications both for their own use and for systems
development in the third world.  Phase 2 will be finished in the au
of 1990.  The group intends to continue its work under the auspices
of the European Statisticians Association with a focus on cooperating
for their own benefit.
D. CASE STUDIES
Respondents to the questionnaire on editing practices expressed
interest in deriving sensitive tolerance edits and using more
sophisticated and extensive validation editing.  They also mentioned
that they would like to employ historic data to test the current data.
An important aspect in the development of edits is determination of
bounds or tolerance limits to use in identifying potentially erroneous
data.  Several recent research studies have focused on various ways
of setting the bounds and on the limitations of the approaches.
Determining Optimal Clusters for Model Specification
If a large number of separate clusters or groupings are used to
determine tolerances for edit rules, the procedure for providing
ranges can become unwieldy.  On the other hand, if too few groups are
used. erroneous items may not be flagged as ranges may become
insensitive.  Research to reduce the number of cells used to set
tolerance limits has been carried out at the Federal Reserve Board
(Pierce and Bauer, 1989).
To edit data that banks and other depository financial institutions
submit to the Federal Reserve System, tolerance bands are constructed
for groupings of institutions felt to be homogeneous by size.
location, and type of institution.  However, an objective measure of
this homogeneity was not available.  Since the edits were designed to
flag or identify observations falling into the tails of the
distribution of week-to-week changes. the measure proposed to assess
the degree of homogeneity of different institutions was the variances
of the changes for those institutions.  As a result of performing an
analysis of variance, with these variances as the cell observations,
it was determined that an unnecessarily fine subdivision of groups was
being used since the sample variances were not significantly different
between many of the groups.  Based on the results of multiple
comparisons, new ways of combining the groups were suggested.
                                 34
The Federal Reserve Board is also exploring an approach to editing
referred to as "model-based edit design".  The basic ideas behind this
are that information in addition to the previous value of the item
being edited is relevant in tolerance-band construction for edit
checks (for example, last month's or year's values of the item, values
of related variables, calendar or tax-date information), and such
information is best incorporated into the editing procedure through
a model which can then be used in determined the edit tolerances and
executing the edits.  Moreover, tolerance widths can be determined
from the model's standard error estimate and given a probabilistic
interpretation.  The Federal Reserve Board will experiment with such
models and model determined tolerances for pilot items and for
selected sources of additional information and then move toward a more
systematic development of this modeling approach.  One topic to be
investigated concerns the prospect for having common models for
classes of similar financial institutions.  This would avoid the
necessity for building an unwieldy number of models, while still
having each model provide a sufficiently accurate description of the
relevant behavior of the variable being edited for each institution.
Intermediate possibilities include having a common model specification
within a class but with different parameter values permitted for
individual banks, or fixing those values but allowing different
standard deviations (tolerance widths).
Use of Time Series Methods to Set Bounds
Another approach when historical data are available is to use past
data values for a particular respondent to predict the current value
and to then use the predicted value to construct tolerance limits for
the new data.  The Energy Information Administration (EIA) uses this
approach for its weekly surveys on petroleum supply and its monthly
surveys on petroleum marketing.  Exponential smoothing is the
particular technique used to obtain the predicted value (Burns, 1983).
This technique has worked well during periods when the data are
relatively stable; for example, on the weekly series on petroleum
supply.  However, it has not performed well when the data are erratic
such as when there are sharp price changes or seasonality.
To address the problem of this price change, the Petroleum Marketing
Division of EIA has looked into the possibility of introducing a
market shift into the edits that would account for real time market
changes.  The market shift is calculated from partial data from the
current period.  Before this was actually fully implemented, it was
employed using an externally calculated market shift based on
'industry information.  Later, these ideas were implemented by
calculating the shift using as much current month data as available
at the time of editing. This allowed not only full automation, but
also targeted market shifts for varying populations and products as
the data are received on a daily basis.  To address the problem of
seasonality in the monthly series on petroleum supply, the Petroleum
Supply Division implemented tests on month-to-month differences rather
than using exponential smoothing.
Research has also been conducted on the Kalman filter implementation
of exponential smoothing (Kirkendall, 1988).  The EIA used this
procedure to obtain preliminary estimates of crude oil purchases and
production.  The procedure provided a method to both estimate and edit
state data.  In some states the difference between the data on
purchase volumes and production has remained relatively constant since
1983.  In other states abrupt changes in the relationship or the
presence of outliers were observed.  Actually both transfer function
models and ARIMA models were tried.
                                 35
However, these procedures were not satisfactory in states in which
large outliers or abrupt level shifts appeared.
Use of Robust Estimators to Set Bounds
The National Agricultural Statistics Service (NASS) of the Department
of Agriculture has performed research on using Tukey's biweight to
develop bounds for their statistical edit of data on livestock
slaughter at the plant level (Mazur, 1990).
In searching for a statistical estimator to determine edit boundaries,
two desired properties are that tolerances quickly stabilize to new
levels if true changes occur and that they return to old levels in the
presence of outliers.  Therefore. robust methods were considered
because they are more resistant to outliers than the standard
statistical methods and work well on many distributions as compared
with the standard methods which work best when the distribution is
normal.  Initially, four measures of central tendency were considered:
the mean, the median, the an (sum of the upper and lower quarides plus
twice the median, entire quantity divided by four), and the 20 percent
ed mean (the lowest n*0.20 values where n is the sample size and the
highest n*0.20 values are dropped and the mean of the remaining values
is computed).  Four measures of spread were also considered the
standard deviation, the inter-quartile range, median absolute
difference, and the 20 percent trimmed standard deviation.
The mean and standard deviation were greatly affected by outliers.
The other estimators seemed inadequate because they excluded good
values.  There was also the concern that they may underestimate the
measure of spread.  Because of these limitations, further research was
conducted using the biweight.  The biweight differs from other
estimators in that the weights are dependent on the data set.
Therefore. it tends to include good values and excludes unreasonable
ones.  If the data are normal, the biweight is like the mean, but if
the data are not normal, it is more like the median.  The edit limits
will be calculated for each plant, using the plant's 13 previous
week's data.  Research was also conducted on identifying inliers, that
is, values that do not change much over time and are suspicious for
that reason.
A key feature of the process is the use of stratification to provide
edit limits for slaughter plants with insufficient data to edit small
plants, or to impute for missing data.  Also, a journal provides an
audit trail.  The analyst resolves error signals interactively on a
microcomputer.  Future research is being considered to extend the
biweight approach to other data series which collect data from the
same reporting units over time and to develop a system to plot
historic data.  Other possibilities include research to determine
whether seasonality could be incorporated into the biweight (mainly
for large plants) and whether a capability to identify plant holidays
could be added.
E. SUMMARY
The survey on editing practices indicated that there was little
analysis of the effect of editing on the estimates that were produced.
Considering that the cost of editing is significant for most surveys,
this is clearly an area in which more work is required.  A related
issue is to attempt to determine when to edit and not to edit.
Clearly, all the errors are not going to be found and we should not
                                 36
attempt to find them all at the risk of over-editing.  An interesting
task is in designing guidelines for determining what is an acceptable
level of editing.
Another neglected research area in this country concerns the editing
of data at the time they are keyed from maid responses.  Data entry
systems typically have some keying error detection capabilities of a
univariate nature, typically range checks and checks to detect when
an unacceptable character has been keyed.  The primary focus of checks
at this stage is to detect data entry errors.  This area is usually
discussed in the setting of quality control; however, it is an area
that can benefit from further research from the perspective of data
editing.  A number of surveys have reduced this sort of error through
the use of double keying.  In the Netherlands Central Bureau of
Statistics, subject-matter specialists enter data and edit them
interactively as they are entered.
The advancement of computer and peripheral technology is playing a
dual role in affecting survey editing.  On the one hand, some
developments have helped to eliminate or reduce the need for some
edits.  Computer Assisted Data Collection systems (e.g., CATI, CAPI)
not only reduce data entry errors but reduce other errors as well.
The use of machine-readable forms and bar codes will eliminate keying
errors.  On the other hand. the increased speed, memory, and storage
of computers, and networking have allowed statisticians to consider
computationally-intensive techniques for editing that previously would
have been possible, particularly considering survey deadlines, and to
utilize other databases.
The questionnaire respondents expressed interest in the use of expert
systems to improve survey specific sensitivity in editing.  The term
"expert system" is not really well defined and different analysts
attach different meanings to it.  With respect to data editing, it
refers to the treatment of survey specific information in a structured
way.  In that regard, the computer is simulating, to some degree, the
role of the subject matter specialist.  Two systems already in use
that have expert system components are SPEER and PEDRO. (These systems
are described in Chapter IV.) In these systems, decisions that may
have been made by subject matter specialists are now made by using
rules that have been programmed 'into an automated system.
In this chapter, examples of research on methods to detect erroneous
values were discussed.  With improved technology, the techniques have
become more sophisticated and undoubtedly will continue to become more
so.  The question then becomes how effective are the techniques in
actually detecting errors.  Two related areas for further research are
monitoring the effectiveness of the edits and determining guidelines
for when to use each technique.  To address these issues, it is
necessary to track the proportion of flagged items that are actually
errors (often referred to as the "hit" rate). This, of course, only
gives one side of the picture; it does not address the issue of errors
that are not detected by a specific procedure.  Despite this
limitation, tracking the "hit" rate is useful and ways of
automatically alerting the analyst that it has gone out of control
would be helpful.
As more techniques become computationally feasible, the analyst is
confronted with more choices in designing an edit system.  It would
be useful to know when the techniques work well.  For example,
research has already indicated that exponential smoothing does not
work well when the data are erratic.  If findings could be made
available about other techniques, time could be saved in developing
new edits.
                                 37
In conclusion, there are several recommendations for research in data
editing that are contained in the preceding paragraphs.  However, the
most important recommendation we can make is that agencies recognize
the value of editing research and place high priority on devoting
resources to their own research, to monitoring developments in data
editing at other agencies and elsewhere, and to implementing
improvements.
F. BIBLIOGRAPHY
It is quite difficult to provide a complete assessment of current
research activities in the area of editing because so much of the
research, progress, and innovations are described only in survey-
specific documentation.  The difficulty is even more fundamental.
Innovations in editing methods made by survey staff are often viewed
as enhancements to processing for that particular survey, and little
thought is given to the broader applicability of methods developed.
Accordingly, survey staff do not typically prepare a discussion of new
methods for publication or for other forms of wide dissemination.  A
description of editing methods and system design might be found in
survey processing specifications. instructions to programming staff,
or in survey processing code.  Innovations that are computer intensive
often are regarded not as method changes, but rather as computer
enhancements.  In other cases, edit activities may be included in the
general area of "quality assurance" with little thought of the subject
of editing per se.
For these reasons, any bibliography on editing will undoubtedly miss
important areas of research and innovations.  Fortunately, a number
of researchers did see editing as distinct from other processing tasks
and have taken the time to describe their experiences.  Some of the
papers in the bibliography can be viewed as case studies for a
particular editing strategy employed on a particular survey.  To some
extent, authors of such papers wanted to record their activities,
subject them to public scrutiny, and offer up their techniques to
others who may be working under similar conditions and who may find
their suggestions useful.  It is often in such articles that methods
which may be applicable to more than one survey are first introduced
and described.
There are features of the editing process that cut across surveys, and
this realization has encouraged the development of general
methodologies and multiuser systems.  Much recent research in the area
of editing has focused on the development of multipurpose edit
systems, and a number of papers in this bibliography discuss
multipurpose edit systems.  Some of these systems have imputation
components while others do not. nm preceding chapter on Editing
Software described three multipurpose software packages: GEIS, BLAISE,
and SPEER.  In the respective specialized bibliographies, ([A], [B],
and [C)), we include papers which describe underlying methods, the
software, proposed uses, and possible advantages of the respective
systems.  The bibliographic citations provide the theoretical and
research background for these systems and constitute a link between
the software chapter and this research chapter.
                                 38
                                                          APPENDIX A
              RESULTS OF EDITING PRACTICES PROFILE FROM
                       QUESTIONNAIRE RESPONSES
                                             Frequency      Percent
1.   What type of survey are you engaged in?
     a. Sample                                    90             77%
     b. Census                                    27             23%
2.   What is the purpose of the survey?
     a. Statistical                               98             84%
     b. Regulatory                                 0              0%
     c. Both                                      19             16%
3.   How would you classify your survey?
     a. Single-time survey                         6              5%
     b. Repeated survey (cross-sectional)         50             43%
     c. Panel Survey (longitudinal)               39             34%
     d. Rotating panel survey                     11             10%
     e. Split panel survey                         6              5%
     f. Other                                      3              3%
     Not answered                                  2
4.   What is the frequency of your survey?
     a. Weekly                                     4              4%
     b. Monthly                                   23             20%
     c. Quarterly                                 12             10%
     d. Annual                                    39             33%
     e. Other                                     39             33%
5.   What is your sampling unit?
     a. Individual                                21             18%
     b. Household                                 11              9%
     c. Farm                                       6              5%
     d. Economic Establishment or firm            58             50%
     c. Institution                                8              7%
     f. Other                                     13             11%
                                 39
                                                  Frequency  Percent
6. How many units are included in your survey?
     22 through 1,000,000
7. Is response to your survey mandatory?
     a. Yes                                            43        37%
     b. No                                             74        63%
8.   Averaging across all items, what level
     of item nonresponse does your survey
     experience?
     a. None                                           18        16%
     b. Less than 5%                                   43        38%
     c. 5% or greater, but less than 10%               20        18%
     d. 10% or greater, but less than 20%              19        17%
     e. 20% or greater                                 12        11%
     Not answered                                      5
9.   What is your primary data collection method?
     a. Computer-assisted telephone interview (CATI)   4         3%
     b. Computer-assisted personal interview (CAPI)    0         0%
     c. Telephone interview                            9         8%
     d. Personal interview                             25        21%
     e. Mailed questionnaire                           49        42%
     f. Administrative records                         18        16%
     g. Other (please specify)                         12        10%
10.  What secondary data collection method(s) do you use?
     (Circle all that apply)
     a. Computer-assisted telephone interview (CATI)   19
     b. Computer-assisted personal interview (CAPI)     2
     c. Telephone interview                            62
     d. Personal interview                             24
     e. Mailed questionnaire                           24
     f. Administrative records                         16
     g. Other (please specify)                          5
                                 40
                                                  Frequency  Percent
11.   What type of computer do you use for data processing?
     (Circle all that apply)
     a. Mainframe                                 107
     b. Minicomputer                               20
     c. Microcomputer                              40
     d. None                                        0
12.  What is your data processing environment?
     a. Batch mode                                49             42%
     b. On-line                                   9               8%
     c. Both                                      58             50%
     Not answered                                 1
13.  If your survey is computerized. what sort of file structure
     do you use? (Circle all that apply)
     a. Sequential                                71
     b. Database using ORACLE software             7
     c. Database using ADABAS software             5
     d. Database using DBASE software             10
     e. Database using other software
          (please specify)                        35
14. Are you limited in your ability to disseminate data by
     confidentiality (privacy) restrictions?
     a. Yes                                       104            89%
     b. No                                         13            11%
15.  Do you release microdata (respondent-level data)?
     a. Yes, and imputed data items are
          identified (flagged)                    36             31%
     b. Yes, and imputed data items are not
          identified                              19             16%
     c. No                                        62             53%
16.  When you release aggregated data, do you provide information as
     to the percentage of a particular data item which has been
     imputed?
     a. Yes                                       24             21%
     b. No                                        89             79%
     Not answered                                  4
                                 41
     Frequency Percent
17.  Are there minimum standards for reliability for the data you
     disseminate; e.g., do you require that an estimate have less than
     an established maximum variance or be based on more than an
     established number of observations before the estimate can be
     released?
     a. Yes                                       79             71%
     b. No                                        33             29%
     Not answered                                  4
18.  What documentation exists for your survey?
     a. All aspects of the survey are well
          documented                              88             76%
     b.   The data editing system is well
          documented, but some of other
          aspects are not                         10               9%
     c.   Some aspects are documented, but not the
          data editing system                      1%
     d.   Some documentation exists, but it is
          neither complete nor current throughout
          the system                              16             14%
     e.   No documentation exists for this survey  0              0%
     Not answered                                 2
19. Is information available on the . cost of data editing in your
survey?
     a.   Yes                                     44             38%
     b.   No                                      73             62%
20.  Please estimate the percentage of the total survey cost spent on
     data editing.
     (Please include all of the aspects of editing, such as any edits
     made at the time of data entry, clerical work, computer time,
     design, testing, monitoring, analyst review, call-backs, and
     review of summary tables.)
     Range                              5% through 90%
     Mean                                         41.4%
     Median                                       35%
     Mode                                         10%
     Standard Deviation                           27.2%
21.  Who designed your data editing system? (Circle all that apply)
     a. Subject matter analysts                   108
     b. Methodologists or mathematical
          statisticians                            70
     c. Computer systems analysts                  88
                                 42
                                             Frequency Percent
26.  Are there any procedures in place to monitor actions of editors
     (clerks or analysts) to detect a pattern of undesired actions in
     order to remedy the cause(s)?
     a. Yes                                       76             66%
     b. No                                        39             34%
     Not answered                                 2
27.  Are there any procedures in place to monitor actions of the
     automated part of your editing procedure to detect a pattern
     of undesired actions in order to remedy the cause(s)?
     a. Yes                                       84             74%
     b. No                                        29             26%
     Not answered                                  4
28. Is there an audit trail in your data editing system, i.e., is a
     record kept for all data editing transactions?
     a. Yes                                       70             61%
     b. No                                        45             39%
     Not answered                                  2
29.  Do you regularly collect performance statistics in order
     to evaluate your data editing system?
     a. Yes                                       69             61%
     b. No                                        45             39%
     Not answered                                  3
30.  Has any analysis been done on the effect of data editing
     practices on the estimates produced?
     a. Yes                                       42             38%
     b. No                                        69             62%
     Not answered                                  6
31.  Do you release data editing information on your survey?
     a. Yes, with the data                        20             18%
     b. Yes, in a different publication           12             10%
     c. No                                        83             72%
     Not answered                                  2
                                 44
                                                  Frequency Percent
32.  Validation editing is defined as a procedure which detects
     inconsistent data within a record.  One example might be a test
     which verifies that the sum of detail items equals a total field.
     Another example might be a test to detect an individual who
     reports that he is single, but also reports the name of a current
     spouse.  Do you do validation editing?
     a. Yes                                       115            98%
     b. No                                          2             2%
33.  Macro-editing is defined as a procedure which tests aggregated
     data to detect inconsistencies. An example might be the
     comparison of a data table from the current period's survey to
     one from a previous period to look for gross changes. Do you use
     macro-editing?
     a. Yes                                       97             83%
     b. No                                        20             17%
34.  Do you do any data editing techniques other then validation or
     macro editing?
     a. Yes                                       66             57%
     b. No                                        50             43%
     Not answered                                  1
35.  If you use other types of edits (for example, edits based on the
     statistical distribution of the data, using time-series
     techniques, or employing ratios between data items), briefly
     describe them below.
36.  May a record which fails some edits be accepted as a part of the
     final file (to be used for tabulation and/or dissemination)?
     a. Yes                                       102            87%
     b. No                                         15            13%
                                 45
                                                  Frequency Percent
37.  What method do you use of impute for data edit failures?
     (Circle all that apply)
     a. Imputing zeroes                           11
     b. Adjusting sample weights                  24
     c. Cold-deck imputation procedure            17
     d. Hot-deck imputation procedure             29
     e. Model-based imputation (regression, etc.) 19
     f. Multiple imputation procedure              8
     g. Imputing last period's value              31
     h. Other (Please specify)                    21
     i. None                                      37
38.  Does your data editing system perform those tasks which you
     intend it to?
     a. Yes                                      112        96%
     b. No                                         5         4%
39.  If you are not completely satisfied with your data editing
     system, do you feel the changes that need to be made are
     considered minor, or would a complete overhaul of the system be
     needed?
     a. Satisfied with our system at this point   53        47%
     b. Complete overhaul of the system needed     6         5%
     c. Minor changes needed                      28        25%
     d. Depending on system, some changes are
        minor, some major                         23        20%
     e. Not possible to determine at this point    4         3%
          Not answered                             3
40.  For future applications, what would you like your data editing
     system to do that it doesn't do now?
41.  Please describe any research efforts which you are currently
     engaged in on the topic of data editing?
                                 46
                                                          APPENDIX B
                            CASE STUDIES
I.   ABSTRACTS
Bureau of Labor Statistics (BLS):
CPI: Commodities and Services
This monthly survey is an important statistical series which is a
longitudinal sample survey with about 90,000 quotations per month.
Data are continuous and are collected by BLS professional data
collectors in 88 primary sampling units.  Response rates are very
high.  Historical data and trends are used.  CPI uses mainframe and
a micro-based PC network.  Micro/PC is used for data capture and
microdata editing processes.  The mainframe is the official database
for all historical data.
Data collection forms are pre-edited manually for completeness and
then a machine edit is done.  Some records require review by subject
matter specialists who review about 30% of the price quotations for
exception edits.  Extensive use is made of these specialists.  About
10% of the data are changed by specialists.  Data are used to compute
indexes, and are again reviewed in this form.  CPI approach to data
editing is "bottom up" in that subject matter specialists concentrate
first on individual price quotations referred to them for review.  A
new approach to editing is being studied using computer assisted
technologies, in which manual pre-editing is eliminated.  Changes in
the current process are recommended, so that the first review step is
summary level data, along with detailed analyses of outliers.
Internal Revenue Service (IRS):
US Corporation Income Tax Returns
This survey collects information from a split-panel sample of
approximately 85,OW corporation income tax returns to produce
population estimates from financial aggregates and to provide a
microdata file for revenue estimation.  Returns representing a given
fiscal year are selected over a two calendar-year period.  Both
continuous and categorical data are collected.  Data are processed in
a in environment in batch mode.
Skeleton records are drawn from IRS's revenue processing system. which
processes some, but not all of the information on each return.  This
information has been tested in revenue processing.  Additional
information is manually abstracted.  Some information for small firms
is imputed rather than abstracted as a cost-saving measure.  All data
are subjected to an iterative process of machine testing and manual
correction.  After all microdata edits have been passed, returns are
restratified based on edited information (about 3,750 returns were
reclassified in TY 1986) and population totals are adjusted.  Data are
then tabulated and the tabulated data are examined for suspicious or
inconsistent values by industry experts.  New methods are being
studied to incorporate on-line data entry and editing, ratio edits and
time-series edits.
                                 47
National Center for Education Statistics (NCES):
National Education Longitudinal Study of 1988 (NELS:88)
This survey is the third in a series of longitudinal sample studies
which take place about every 10 years.  NELS:88 began in 1988 with
25,000 eighth grade students, who will be re-surveyed every two years.
Both continuous and categorical data are collected in large group
administration sessions by survey personnel in schools.  Some data
from parents and teachers are collected by mail.  Microdata is
released to the public.  Item response rates are generally very high
(over 95 percent in most cases).  Estimated cost of editing is about
10-12% of entire cost of survey.
Some on-site edits are done for critical items.  Computers are used
to perform critical item edits, and missing data are retrieved by
phone.  Interview validation is carried out on 10% of instruments to
detect discrepancies.  The mainframe is used to put data through
"forced cleaning" and item specific issues.  Very little imputation
is done for missing values.  Range checks, inter-item consistency
check and some statistical editing are carried out.  In addition,
subject matter specialists perform various types of analyses.
Federal Reserve Board (FRB):
Edited Deposits Data System (EDDS)
The EDDS data are the primary source of information for money supply
estimates, and are collected daily and weekly by mail from 9900
depository financial institutions (DFIS) which meet minimum size
criteria.  DFIs complete a, standard form and data are continuous.
There is no imputation done and no problem with non-response.
Historical data are used to impute if a DFI is late in reporting.
Microdata are confidential and are not released.
When data are received at the 12 Federal Reserve Banks (FRBS) from
DFIS, they are transcribed into machine readable form.  They are
edited for both validity and quality (statistical editing) by the
FRBS.  If edit failures occur, FRB personnel investigate.  Data are
transmitted to the Board and are again edited for validity and
quality.  Editing is conducted on mainframe processing system.
Validity edits (for macro and micro data) and quality edits (for macro
and micro data) are carried out, including use of stochastic edit
checks.  Historical data, including changes in observed figures, are
widely used.  If quality edits are violated, FRBs call back to
institutions to verify.  Quality edits are prioritized, based on the
size of the DFI, total deposits, and degree of fluctuation from past
numbers seen.  Research is now underway to improve editing procedures
by modelling and by incorporating expert system technology into the
editing process.
                                 48
II.  IN-DEPTH CASE STUDIES
National Center for Health Statistics (NCHS):
National Health Interview Survey (NFHS)
Environment In Which Survey Takes Place
The National Health Interview Survey (NHIS) is a household sample
survey which has been operated by the National Center for Health
Statistics (NCHS), Centers for Disease Control, since 1957.  Its
purpose is to produce a wide range of general purpose statistics on
the health of the U.S. population.  It consists )f a continuing
standard health and demographic questionnaire, questionnaires on
special health topics, and ad hoc longitudinal follow-up studies (not
discussed further in this document).  The survey is designed,
processed, and analyzed by the Division of Health Interview Statistics
and other NCHS staff, numbering about 60 FTES.  Under an interagency
agreement with NCHS, field work for the survey is done by the Field
Division, Bureau of the Census.
To avoid seasonal bias, data are collected continuously throughout the
year, each week of data collection comprising a nationally
representative sample.  Although reports can be produced for
individual weeks or quarters of data collection (and have been), most
reports aggregate data for a full calendar year.  A standardized
annual summary report is published each October for data collected in
the previous calendar year.
Each year, interviews are completed in 47,000 households, yielding
information on 123,000 sample persons.  The amount of information
collected on each sample person varies, but averages several hundred
items.
Before 1988, all data were collected in face-to-face interviews in
sample households using paper and pencil (PAPI) (although interviewers
were allowed to administer questionnaires over the telephone after
repeated call backs failed to produce a face-to-face interview).
Beginning in 1988, computer assisted personal interviewing is being
phased in: one special health topic questionnaire (on AIDS knowledge)
is entirely on CAPI. and by 1990 all special topic questionnaires will
be on CAPI.
For the basic health and demographic questionnaire, interviews are
completed in about 95% of eligible households; for the special topic
questionnaires, which usually require self-response by one adult
subsampled in the household. interviews are completed with about 85-
90% of eligible respondents, yielding an overall response rate of 81-
90%.  Item response rates are in the range of 97-99% for nearly all
items, although nonresponse on income questions is about 15%.
Editing Practices
Data Processing Environment and Dispersion of the Work
CAPI and PAPI are done in the field by about 100 interviewers working
in about 200 first-stage sampling areas in all parts of the country.
Preliminary hand edits are done by field supervisors in 10 regional
offices of the Census Bureau.
                                 49
Editing, coding, and data entry (with electronic range edits) are done
at the NCHS processing facility in Research Triangle Park, North
Carolina.  Machine edit specifications and edit programs for
consistency edits and code changes are written at NCHS headquarters
in Hyattsville, Maryland.  These edit programs are the basic
components of a well-defined processing system for each special health
topic questionnaire.  Data processing is run on a mainframe computer
at the North Carolina facility, and edit failures are adjudicated
between the analysis and programming staffs at Hyattsville and the
coding staff at North Carolina.
Editing of questionnaires on special health topics, which change at
least annually, uses the same general approach as the editing of the
basic questionnaire, but because subject matter and personnel vary,
there are more inconsistencies in methods within and between these
questionnaires.  Editing procedures for CAPI are different than those
for PAPI.  Editing occurs in the field by the interviewer, at regional
offices by clerks, during coding and keying at the processing
facility, during machine processing, and at the data analysis and
reporting stage.
Audit Trail
A complete audit trail for machine edits of the basic health and
demographic questionnaire is produced, printed, and made available to
in-house users for each data year.  These documents are not available
to public users.
Micro-,  Macro-, and Statistical Editing
Extensive micro-computing is done by machine.  Some macro editing is
done by hand examination of the results of machine edit reports, such
as ex g counts of the number of times certain types of errors occurred
and examining distributions for outliers.  Standard sets of estimates
are produced quarterly and annually and visually compared with the
tables for earlier years to search for anomalies.  For in-house users,
a complete audit trail is made available as part of file
documentation; public use data tape documentation summarizes edit and
imputation information.
Prioritizing of Edits
Priority is given to identifiers needed to link data files. and to
questionnaire items used to weight the data to national estimates
(age, race. sex).  Otherwise. no formal system is used to give higher
priority to some edits rather than others. Informally, through the
allocation of staff skills, staff time and staff interest, priority
may be given to some questionnaire topics or questions.
Imputation Procedures
Unit nonresponse (missing sample cases) is imputed by inflating the
sample case weight by the reciprocal of the response rate at the final
stage of sample selection, or by a poststratification adjustment based
on independent estimates of the population size in 60 age-race-sex
categories.
Item non-response (missing question answers) is imputed, where
possible, by inferring a certain or probable value from existing
information for the respondent.  Most imputation is done by machine,
although some otherwise unresolved edit failures may be imputed by
hand.  No mathematical, hot deck. or cold deck imputation is done.
                                 50
Editing and imputation Standards.
Error tolerance standards are established for interviewer performance,
and enforced by editing and telephone reinterviews conducted by
regional supervisors.  Error tolerance standards are established for
keying of data, and are enforced by re-keying of a sample of
questionnaires.  No formal standards are established for editing at
other stages of the survey process.
Costs of Editing
The costs of editing include approximately 20 FTE's per year and about
$ 1,000,PW per year.
Role of Subject Matter Specialists
The primary role of subject matter specialists is to write edit
specifications, from which edit programs are prepared, to review
results of edit runs and adjudicate failures in collaboration with
programmers. Their secondary role is to compare standard sets of
estimates with historical series to identify anomalies.  In addition,
they also consult with survey design staff on field edits.
Measures of Variation
Estimates of sampling errors are produced and published for each
component survey.  No estimates of nonsampling error are produced.
Current and Future Research
The Division of Health Interview Statistics recently implemented a
program of methodological research which will include measurement of
data quality as a function.  In a current project, respondent reports
of health conditions are being compared with medical records of the
respondents.
                                 51
National Agricultural Statistics Service (NASS):
Agricultural Survey Program: Quarterly Agricultural Surveys
Environment In Which The Survey Takes Place
The Quarterly Agricultural Surveys are the main vehicles for
collecting current agricultural production data.  One of the essential
aspects of data quality is its timeliness.  In every quarter, data are
collected starting on the first of the month.  Data must be collected
and editing must be finished by about the 15th.  Results of the
surveys are released starting at the end of the month and continuing
into the first few weeks of the next month.  That is, all data
analysis is carried out and all summaries are executed within a month
of the end of data collection.  Estimates are released according to
a very rigid schedule that is published before each calendar year.
The estimates of current agricultural supply, when combined with
estimates of demand by economics, form the basis for price forecasts
in agricultural commodity markets.
Nationally, data arc collected on about 500 agricultural items.  Each
state collects data on about 70 variables.  In order to customize
questionnaires to each state's culture, about 35 versions of the
master questionnaire must be produced in each quarter.
Any farm, ranch, individual or partnership that has land on which any
crops have been grown, grains or oilseeds have been stored. or any
livestock has been raised in the calendar year, in any of the 48
contiguous states is 'in the survey universe.
The sample size varies between quarters, from about 75,000 to 86,000.
In 1988, unit nonresponse was 12%.  The number of forms to be
processed in a quarter ranges from about 65,000 to 80,000.  NASS also
requires that certain refusals be hand-estimated by statisticians in
the state offices.  This is done for very large operations from the
list frame and for all livestock data in the area frame.  This hand
estimation is considered to be part of the editing process.  A
rotating panel design is used for an annual cycle.  A new panel,
however, is created every June quarter.  Large operations are
interviewed every quarter, and others are rotated in and out during
the year to reduce burden.
The primary mode of collection is by phone onto paper questionnaires.
There are 14 states in which data are collected by Computer Assisted
Telephone Interviewing (CATI).  Data are also collected by face-to-
face interview for difficult respondents or for those who do not own
a phone.  Mail is also used heavily in a few states, but this mode
makes up only a small percent of the national total.  NASS is
procuring a microcomputer based LAN for each of its field offices.
When all of these LANs are installed, it is expected that CATI will
be the major mode of collection for this survey.
Editing Practices
Data Processing Environment and Dispersion of the Work
Data are collected through 42 field offices.  There is one field
office for each state, with the exception of New England (one office
for 6 states) and one office for Maryland and Delaware.  Hawaii and
Alaska are excluded from the survey.  Each field office is responsible
for the collection, capture, editing and initial analysis of its data.
The Agricultural Statistics Board for
                                 52
NASS releases and publishes the official statistics along with the
state level data.  The processing is coordinated in headquarters in
Washington DC.  All programs are written at headquarters.  All data
processing is carried out on one leased mainframe located in Florida.
The exception is CATI which requires the use of minicomputer servers
in the field offices for collection.  Data are sent between each field
office and mainframe through dedicated telecommunication lines.  The
results of each stage of processing are sent back to the field offices
and are printed out.  Editing occurs at data collection, especially
for CATI, and prior to data entry and during the analysis stage.
Audit Trail
There is no automated audit trail except with the CASES software used
in CATI.  In theory, it is possible to trace the editing actions for
an operation by working through a paper trail.  For example, if the
editor changes data during the hand edit, the changes are to be marked
on the questionnaire in red pencil.  If the editor makes a change in
data on a computer printout (the change is then keyed into the data
file), the editor is supposed to mark the change on the questionnaire
in green pencil.  In current procedures, there is no way to know the
effect of editing on the final estimates.  It is not possible to
summarize the editing actions for more than a few questionnaires.  It
would be a very tedious, hand intensive job.
Micro-, Macro-, and Statistical Editing
In the 14 states that employ CATI, much of the micro-editing is done
at the time of data collection.  The CATI instrument avoids some
errors such as routing errors because the program controls the flow
of the interview.  Some errors, such as consistency errors, are caught
by the program immediately.  For example, if a farmer has more
harvested acres of a crop than planted acres, the program will require
that one cell or the other is changed.  Noncritical, or suspicious
errors, are also caught in CATI.  If a farmer has an unusually high
or low yield, he can be asked to confirm that he understood the
question correctly.  In a 1983 cattle survey study, CATI reduced the
number of signals generated by 77% and 3% for critical and noncritical
errors respectively.  While the noncritical error signals were not
greatly reduced, more of these were allowed to pass to summary as they
had been verified with the farmer.
After CATI collection, data are transferred to the normal batch
editing system.  This system is called the Survey Processing System
(SPS), which is written in SAS.  When errors are flagged in this
system the editors do not have a form to refer to when evaluating the
error signals.  With a special effort, the data may be displayed on
the screen of the CATI computer.  However, the CATI software is not
used to do any post-codection editing.
In non-CATI data collection, micro-computing can be considered to be
a two stage process.  First is a hand edit.  This may be done by
clerks or by statisticians or both.  Second is a machine edit.  This
is performed in batch using the Survey Processing System (SPS).  The
SPS was developed by and is used exclusively in NASS.
In the hand edit, the editor inspects the form for notes, performs
required calculations, checks for reasonableness, and will rewrite
unclear numbers.  Changes may be made to the data at this stage.  If
so changes are to be noted on the form with a red pencil.  Also at
this stage, essential coding will
                                 53
be done. This coding is used to trigger certain edits and informs the
imputation routine how to handle the questionnaires.
After the hand edit is performed, the data are entered by high-speed
data entry operators - The data are transferred to the leased
mainframe computer and are edited in batch.  Error signals and
messages along with selected data are printed out.  The statistician
reviews the printouts and marks on both the printout and the form (in
blue pencil) the adjustment to be made.  These changes in data are
then re-keyed and re-edited in batch.
After the data are declared clean in the machine edit, and after the
data are run through the imputation routine, an "analysis package" is
run for each state.  This analysis package contains elements of both
macro- and statistical editing.  Any errors detected at this stage can
still be updated in the data file.
Macro-editing, defined as applying edits to aggregated data, is
carried out at the stratum level.  Expansions for selected items are
generated by stratum.  These expansions are compared to expansions
from the previous period.  This kind of inspection helps to trace
suspicious data that have an impact at an aggregated level.
The statistical editing for this survey consists of reviewing the
highest 20 and lowest 20 values of an item or ratios of items before
and after expansion factors are applied.  This may be done across all
records or within certain strata.
Imputation Procedures
Imputation occurs at two stages in the processing.  Imputation may
occur during the hand edit or an automated imputation routine which
is run after data are declared clean in the batch edit.
Hand imputation is used when the data editor feels that there is
enough information on the form to enable an accurate determination for
cells that are not filled in.  The most common example of this is when
an enumerator writes notes that enable a quantity to be calculated.
For example, NASS asks for hay stocks in tons of hay.  Many farmers
can give a number of bales and the average weight per bale, but not
an overall figure.  The tons are derived by hand and the figure is
imputed into the cell.  For production items (e. g., number of bushels
of corn produced) or for stock items (e. g., number of bushels of corn
stored), the statistician may write a "missing value" code in the
cell.  This figure will invoke the automated imputation routine for
that cell.
For list and area frame questionnaires, when crops or stocks sections
are totally blank, the statistician is to fill in a "completion code"
during the hand edit.  This code informs the imputation routine that
the operation has the items of interest does not have the items of
interest or it is unknown whether the operation has the items of
interest.  The level of the amputations are then based on the value
in the completion code.  For example, if the farmer is known to have
the items, then the imputations will be higher than if the operation's
status is unknown.  If livestock sections are blank for list frame
questionnaires. and the questionnaire is otherwise usable, the
statistician is required to impute the livestock data by hand using
whatever historical data are
                                 54
available.  For area frame questionnaires that have not been filled
in, the statistician is required to make the form usable through hand
imputation.  Any auxiliary data that are available are used.
The automated imputation procedure attempts to make use of
administrative data or data from previous surveys.  If these data are
unavailable, then the routine imputes appropriate averages for the
blank cells.  For a particular operation, averages are calculated from
as fine a sample as possible.  For example, averages may be based on
farms reporting an item within the operation's stratum and district
in the state.  If there are not enough reports, then a higher level
of aggregation is used.  This customizing of imputation has the effect
of making the imputed values very sensitive to the coding that is
done.  In other words, if the coding is done incorrectly, then the
level of the estimates may be affected.
Editing, and Imputation Standards
Quite detailed guidelines are published in a Supervisory and Editing
Manual.  Pre-survey practice, and editing sessions at National survey
training schools try to ensure that editing is done consistently
across the country.
Costs of Editing
Complete survey process management data are not currently generated
by NASS which would summarize the costs of editing in detail.
However, it is estimated that about 15 percent of the total survey
cost can be attributed to editing.
Role of Subject Matter Specialists
Subject matter specialists are called agricultural statisticians in
NASS and their expertise is considerable.  Agricultural and
mathematical statisticians are responsible for reconciling problems
at the state level.  Few actions are entrusted to a machine.  The
agricultural statistician is held to be fully competent in making
decisions.  The edit programmers in DC usually have had experience as
agricultural statisticians in state offices.
Measures of Variation
Standard errors are calculated along with the estimates but are not
published.  The standard errors do not take into consideration the
effects of editing.
Current and Future Research
An interactive editing research project has just been carried out in
Ohio and Wisconsin for the 1989 December Agricultural Survey using the
Blaise system.  This project investigated moving away from the
centralized batch environment to a microcomputer-based interactive
environment.  It also investigated the possibility of capturing and
editing "raw" data (data that has not been hand edited prior to data
entry).  The new processes took 50-80% of the time necessary for the
conventional process.
                                 55
The Interactive Editing Working Group concluded that interactive
microcomputer-based editing should become the standard process for
NASS.  As NASS is currently procuring microcomputers corrected in
LAN's for each office, there is an opportunity to collect this survey
in CATI and then to edit it interactively.  The wider implementation
of CATI would reduce considerably the need for editing, and the
editing that remains would be done very efficiently.
More sophisticated macro- and statistical edits, possibly on an
interactive basis, are beginning to be researched.  These include the
editing of data after it has been entered (i.e. no previous hand edit)
and outlier checks based on expanded, censored or robust techniques,
statistical graphics, and multivariate relationship data have all been
identified as ways editing could be improved.  Also, research on more
automated imputation (to replace the judgmentally based imputation)
for the large operations and livestock data could be done.
                                 56
Bureau of the Census: The Enterprise Summary Report
Environment in Which The Census Takes Place
The Enterprise Summary Report (ES-9100) and the Auxiliary
Establishment Report (ES-9200) are part of the Census Bureau's
Economic Censuses.  The economic censuses are conducted at five year
intervals in the years ending with a 2 or a 7. The ES-9100 and ES-9200
censuses target the following industries: Mineral, Construction,
Manufacturing, Wholesale, Retail, Selected Services and Selected
Transportation.
Respondents to the ES-9100 are all companies in the target population
with 500 or more employees, and the universe for the ES-9200 consists
of all auxiliary establishments of those firms.  An auxiliary
establishment is an establishment, typically a non-revenue producing
establishment, that provides support activities to other
establishments of a company.  Examples of auxiliaries are research and
development centers, warehouses and administrative offices.There were
8.811 companies in the ES-9100 universe and 39,461 auxiliary
establishments in the ES-9200 universe for the year 1987.  The primary
mode of data collection was through questionnaire mail-out with a
follow-up for large operations that did not respond using telephone
call backs.  A unit response rate of approximately 85% was achieved.
Editing Practices
Data Processing Environment and Dispersion of the Work
The batch version of the complex edit for these censuses was
implemented on the SPERRY mainframe.  It was then adapted to micro-
computers without any difficulty.  The micro-computer version is an
interactive, on-line edit used by analysts in their review of referral
cases.  The microcomputers are connected through a LAN sharing a
single database access.  The programs have also been adapted and
tested for the VAX/VMS system.
Editing and quality control activities take place throughout the
survey process.  Processing begins at the Census Bureau's data center
in Jeffersonville, Indiana.  It is here that data entry, following up
with correspondence to establishment, microfilming forms, and
clarifying types of business are done.  The editing done at
Jeffersonville is basically manual and is a standardized procedure
across the economic areas.  The clerks follow specific procedures
documented by subject matter specialists at Census headquarters.
Audit Trail
A complete audit trail is kept for all actions of the Structured
Programs for Economic Editing and Referrals (SPEER) complex edit.
During the complex edit, referral flags as well as informative flags
are set.  A referral flag is a flag that targets a record for analyst
review.  This flag also has priorities attached to it.  For example,
a flag indicating a large change to a basic item will cause a record
to be referred even though this may be the only flag set during the
entire survey process.  An informative flag is set simply to convey
information.  For example, each imputation option has a flag connected
with it to let the analyst know how a specific field was imputed.
                                 57
Micro-, Macro-, and Statistical Editing
For the most part, complex editing is very survey-specific.  Most of
the more involved editing is done at Census headquarters in-
Washington.  One of the most important aspects of the entire survey
process is the Standard Industrial Classification (SIC) coding.  Each
establishment must be coded into a single category, and each category
has its own set of ratio limits related to that type of 'industry
activity.
The SPEER system was used for inter-item editing for the 1987
Enterprise Summary Report and the 1987 Auxiliary Establishment Report.
Most of the editing that takes place in the complex edit deals with
ratio edits.  There is a front end program which takes the explicit
ratios and calculates implicit ratios.  This front end program is run
only once unless the explicit ratios need to be changed or updated.
Explicit ratios are user-supplied and each basic item must be
contained in at least one ratio.  The original ratios may be modified
or the program may find them to be inconsistent.  If the ratios are
found to be inconsistent, the explicit ratios must be revised before
editing begins.  This process of correcting inconsistent explicit
ratios typically takes only one iteration.  These implicit ratios are
then used as input for the SPEER program and are used for editing
purposes as well as at the time of imputation.
Below is a brief description of the general flow for the batch portion
of the SPEER system.  Basic items are edited against each other
simultaneously to check for inconsistencies (that is, ratios not
within SIC-based limits).  In the event of an edit failure or
failures, SPEER determines which basic item(s) will be deleted and
marked for imputation depending on how many times a field is involved
in an edit failure and the reliability weight of that field.  Fields
not deleted are mutually consistent.  An imputation range for the
deleted field(s) is then calculated using all other basic items that
are reported and unchanged or imputed earlier, and the implicit
ratios.  This imputation range will ensure a value will be imputed for
the missing field that is consistent with all other basic items
present.  Methods of imputation are determined by the subject matter
specialists.
Satellite items are related to one or two basic items, but not all
basic items.  Satellite items are edited against one other field not
in the satellite group, typically a basic item, and always a field
that has already been edited.  The field which is used to edit the
satellite item is called a primary indicator.  If a satellite item
fails the ratio edit with its primary indicator, it is targeted for
change and then imputed.  Complex editing for satellite items follows
along the same lines as complex editing for basic items, but in
general is a little less complicated.
Micro-editing is not the only type of editing that goes into the
survey process.  Prior to release of data. tables go through an
editing routine of their own which relates current summary values to
those in the prior reporting period.  After corrections are made to
the data, each industry is then tabulated again and sent through a
disclosure analysis system before data is released to the public.
Prioritizing of the Edits
The fields on the questionnaire are divided into basic items and non-
basic items, also called satellite items.  Basic items are typically
a small number of fields that are fundamental to the establishment's
operations and, for the purpose of editing, are related to one
another.  Items in each
                                 58
satellite are related to one another and are grouped together and for
that reason are typically not related to items in other satellites.
Imputation Procedures
Missing fields or inconsistent fields that have been deleted are
imputed on an item-by-item basis.  Any value imputed for a specific
field must fall within the imputation range, as mentioned above.
There are four major imputation methods used: 1) rounding of dollar
values, 2) administrative data substitution, 3) sum of the details
substitution and 4) industry average tolerances.  Dollar values are
typically reported in thousands of dollars, but at times a respondent
will report in actual dollar figures.  A first impute would then be
to divide a particular dollar field by 1000.  Administrative data is
available for the major fields on the questionnaire, such as Number
of Employees (EMP), Annual Payroll (APR) and 1st Quarter Payroll
(QPR).  An example of sum of the details substitution would be
substituting Rental Payments for Buildings (RPB) + Rental Payments for
Machinery (RPM) for the missing field Total Rental Payments (RPT).
Imputation through industry average tolerances is simply calculating
an average value for a certain ratio.  For example, setting EMP = APR
* the industry average of the ratio EMP/APR.
Interactive, On-line Processing
All records are edited using the batch version of the SPEER program
on the SPERRY mainframe.  Referral records, approximately 20% - 30%
of the universe, are then flagged for analyst review.  Analysts access
these referrals, one at a time using micro-computers, through an
interactive, online version of the same batch program.
Using the previous method of reviewing referral records, it would
literally take weeks to correct a record.  Using the SPEER programs
for the 1987 edit, analysts review and correct a referral record at
one sitting.  They can enter a value for one field, or an entire
record, and are able to edit that record within a manner of minutes
and need no further editing.
The on-line version of the SPEER edit is a menu-driven program which
starts out with a generic menu and is then tailored to a user's
specific needs.  Options a user may incorporate are those which make
correcting referral records easier and quicker for the survey under
review.  For example, an option exists that enables the analyst to
insert administrative data for those fields it is available for, and
then impute an entire record from that data. is useful if an analyst
determines a record to be completely unreliable.
Editing and Imputation Standards
The frequencies of failed edit ratios are tabulated to determine if
the ranges for the ratios are either too wide or too tight.  Analysts
use this information to adjust the ratios, if needed. Role of Subject
Matter Specialists
Subject matter specialists are essential throughout the editing
process.  They determine the applicable edit rules and the initial
explicit ratio parameters, which in turn, drive the entire SPEER edit.
They determine the imputation methods needed for missing and deleted
fields.  The
                                 59
interactive, on-line system was created with the idea that subject
matter specialists would be the end users.
Current and Future Research
One area of research involves adapting the SPEER system for macro-
editing, that is, editing summary data at various geographic levels.
Another area of research focuses on methods to assist subject matter
staff in setting ratio edit bounds.  In addition, Census is
considering ways to incorporate additional imputation options, such
as the hot-deck procedure, into SPEER.
                                 60
Energy Information Administration
The Weekly and Monthly Refinery Reports
Environment in Which the Survey Takes Place
The Petroleum Supply Division (PSD) of the Energy Information
Administration (EIA) operates an information collection and
dissemination system, called the Petroleum Supply Reporting System
(PSRS).  The PSRS includes one annual, seven monthly, and five weekly
surveys.  These surveys track the supply and disposition of crude oil,
petroleum products, and natural gas liquids in the United States.  Two
surveys, the Monthly Refinery Report (Form EIA-810) and the Weekly
Refinery Report (Form ElA-800), will be used as prototypes to describe
editing practices.
The monthly survey is a complete census, while the weekly survey is
a sample (151 sampling units) from the universe of 255 petroleum
refineries and blending plants that report to the monthly survey.  The
refinery universe is relatively small, but it is ever-changing due to
company births, deaths, mergers, and splits.  In order to maintain a
survey frame that is current and complete, the frame is updated
continuously, and a comprehensive investigation of the adequacy of the
frame, an exhaustive research activity to identify new eligible
respondents, is conducted triennially.
Unlike the monthly survey which collects data on as many as 50
variables, the weekly survey collects data only on crude oil, motor
gasoline, jet fuel, distillate fuel oil, and residual fuel oil.  Most
of the data reported in the weekly survey are estimated by the
reporting companies, while data reported in the monthly survey are
based on company accounting records.  Inventory data are reponed as
of the end of the reference period.  Data on inputs, production,
receipts, and shipments show the total volume of activity for the
reference period.  All quantities are reported in thousand barrels (42
U.S. gallons per barrel).  Zeros often dominate the response, i.e.,
not all of the units produce and/or store all products.  The
distribution of the petroleum supply variables is highly skewed, i.e.,
there are many small units and few large ones.
Response rates for both surveys are very high, often above 95 percent
for the weekly survey and range from 99 to 100 percent for the monthly
survey.
Editing Practices
Data Processing Environment and Dispersion of the Work
The reference period for the weekly survey extends from 7 a.m. Friday
to 7 a.m. the following Friday.  Weekly estimates are published on
Thursday following the close of the reference week.  They are also
used to calculate early preliminary monthly estimates.  The reference
period for the monthly survey begins 12 a.m. of the first day of the
month and ends midnight of the last day of the month.  Monthly
aggregates are published in preliminary form 60 days after the close
of the reference month.  Final aggregates, reflecting any necessary
corrections. are published six months after the close of the calendar
year.
When the monthly survey forms are received at ElA. they are reviewed,
primarily for identification information (ID number, company name,
date, etc.). The data on the forms are then key entered.
                                 61
As part of key entry, data are checked for errors.  In 1989, the
mainframe technology previously used to process survey data was
augmented with personal computers configured in a local area network
(LAN).  The LAN/mainframe system facilitates the on-line processing
of surveys and publication of survey statistics.
Weekly data are processed via a Computer Assisted Telephone
Interviewing (CATI) system.  This is a menu-driven system where data
are entered, edited, and updated on-line.  It also automatically
schedules and logs telephone calls to respondents and collects
performance statistics during data collection and processing
activities.  About one-half of the responses to the weekly surveys are
received by telephone; the remaining responses are submitted by
telefax or mail.
Audit Trail
For both surveys, a complete audit trail is kept for all machine
edits.  Reports which show in tabular forms different types of
performance statistics, are available to in-house reviewers.
Micro-, Macro-, and Statistical Editing
Two types of automated edit checks are performed in the monthly
survey:
1.   Consistency checks: these are designed to detect arithmetic
errors, e. g., row (column) total is not equal to sum of components.
They are also designed to detect data indicating the occurrence of
impossible events.
2.   Range checks: these are designed to detect reported values that
are theoretically possible but significantly different from the
company's historical reporting pattern.  Specifically, these detect
if the current reported value is significantly different from (1) the
average of the non-zero values in the past 12 months, (2) the previous
month's value, and (3) the previous month's value adjusted for the
difference between the same two months in the previous year.
There are three types of data edit checks performed in the weekly
survey:
1.   Consistency checks: these are designed to verify the internal
consistency of data on a form.
2.   Frequency checks: these are designed to flag items reported as
zero if the company has usually reported nonzero quantities and to
flag nonzero items which have usually been reported as zero.
3.   Outlier checks: These are designed to flag nonzero values that
are unusually large or small for a given company.
Frequency and Outlier checks are developed based on the simple
exponential smoothing technique.
For both surveys, each item failing a check is assigned a flag, which
is coded to indicate the severity of the failure.  These flags remain
associated with the item until the questionable value has been
verified or otherwise corrected.  The most severe failures (as
indicated by the flag codes) are
                                 62
corrected first.  Resolution of an edit flag takes one of two forms.
The suspicious datum may be verified as correct, despite its
differences with that company's reporting history and the edit flag
is overridden, or it may be identified as erroneous, and replaced with
the correct value.  Verification can be accomplished by calling the
reporting company, or by employing information from other sources that
verifies the value as correct.
Monthly aggregates (weekly estimates) are visually compared with the
previous month's aggregates (week's estimates) to search for
anomalies.  This kind of inspection helps to trace suspicious data
that have an impact on the aggregates.  Subject matter specialists
review aggregate level data and determine where further checks are
required.
Both monthly and weekly surveys are designed to measure the same
phenomena, only for different time intervals.  Therefore. an on-going
comparison of data submitted by individual companies on the weekly and
monthly forms is routinely conducted.  Historical reporting patterns
for both monthly and weekly surveys are compared and facilities that
systematically report different values are identified and contacted.
In addition, to monitor values of key products reported in the weekly
and monthly surveys. graphical comparisons are drawn between monthly
aggregates for a particular variable and the monthly value derived
from the weekly estimates for that product.
Every year, a "Quality Control Notebook" is prepared to document the
steps being taken to improve data quality. evaluate current data
quality, summarize current research, and establish an agenda for
future enhancements of studies.
Imputation Procedures
Imputation in the weekly system takes place during estimation
procedures.  The estimation program imputes a value if it encounters
datum with a flag indicating nonresponse or a critical error.  The
imputed value used is the exponentially smoothed mean weighted average
if the frequency of a nonzero response (probability of nonzero
response) is equal to or greater than 0.5, and zero otherwise.
Imputation in the monthly system takes place as an independent
processing step before summary tables are produced.  The program
computes only for nonrespondents.  Imputed data are kept on the data
files.  There is a code associated with each data element to indicate
whether the value is actually reported or imputed.  The imputation
procedure used is to insert the previous month's values (whether they
are actually reported or imputed) for missing data.
Costs of Editing
The day to day operational cost of the edit system, including follow-
up phone calls. is roughly estimated at 10 percent of the survey
costs.
                                 63
Current and Future Research
In 1988, PSD developed an electronic data communications software
package, called the Petroleum Electronic Data Reporting Option
(PEDRO), that allows respondents to transmit edited data to EIA's
mainframe computer.  PEDRO uses a personal computer to display the
image of a printed survey form; the user (respondent) can then enter
the data via the keyboard or import them from another computer system.
PEDRO can perform the automated edit checks described above and flag
errors.  PEDRO also automatically dials EIA's central computer and
uploads the data.  Currently, PEDRO is used by 61 respondents to the
Monthly Refinery Report.  PEDRO has increased the timeliness and
accuracy of the data submitted in addition to reducing respondent
burden by eliminating paperwork, providing immediate on-site
correction of data errors, and reducing the need for follow-up calls
and data resubmissions.
Currently, the PSD is working on improving the efficiency of PEDRO and
expanding it to cover more of the petroleum monthly and weekly
surveys.
                                 64
                                                          APPENDIX C
                      CHECKLIST OF FUNCTIONS OF
                      EDITING SOFTWARE SYSTEMS
A.   USING THIS APPENDIX
This is a list of possible functions and attributes of editing
software systems.  It can be used as an aid in evaluating editing
software.  Explanations of some of the more technical terms are given
in brief notes in the checklist and in the glossary.  In addition,
Chapter IV on editing software discusses the main areas of editing
systems' development and the capabilities and limitations of types of
editing systems.  While the evaluation of editing systems is a time
consuming procedure, the effort should pay off by reducing costs in
the future.  An existing system developed by another organization may
satisfy most requirements.  The cost of adapting an existing system
may be a fraction of the cost of developing of a new one.  Even if
existing systems cannot be used, an inspection of their capabilities
would broaden the organization's perspective when consider development
of its own system.
Evaluation of systems should be done by a team of people drawn from
various parts of the organization.  At a minimum, one person each from
the data processing, methodology, research, and end-user departments
should be represented.  They should start with an evaluation of the
checklist itself and determine which of the over 200 items apply to
their organization.  Some systems can be quickly eliminated based on
the answers to the GENERAL FEATURES section.  For example, an editing
system may not work on all operating systems. (This may eliminate
procurement of the system, but it may still be worthwhile to review
it for its other features.) Evaluation may proceed by reading
descriptions of the systems such as presented in Chapter IV of this
report, reviews of systems (e. g., Cotton, 1988), and systems
documentation provided by the developers.  Other good software review
techniques include: attending a demonstration of a system, and running
an application provided by the developers on diskette.
For the few systems that show promise, a complete evaluation will
require that the software be acquired and that edits from a
questionnaire (or section(s) of a questionnaire) be programmed.  Data
should be run through the system.  The system should be evaluated on
the items that have been deemed important.  It may be necessary to
make notes for most features.  That is, it is often not enough to
merely check yes or no for a function.  The way the system carries out
a function may be as important as whether it carries it out.  If the
system still looks promising, the evaluators should contact system
users in the originating organization, not those who have developed
it, and get the users' opinions about the system.  Do they like it?
If yes or no, why? Is it easy to use? How is the survey flow managed
with the system? At this point, a trip to the developing organization
may be necessary.  It should be determined whether the system will be
supported and if future updates are planned.
If you have evaluated a system by obtaining the software and trying
it out, take the time to write to the developers with comments on the
system, both good and bad.  This will repay the developers for
spending time on your requests.  This feedback will generally be used
to improve the systems.
                                 65
Even if your organization rejects the system, keep an eye on its
developments.  Your comments may well have been taken into account by
the developers and incorporated into the system in a subsequent
release.  Likewise, do not reject a system just because it is missing
a few key items.  First ask the developers ff they plan to add the
necessary features.  If not, try to determine what it would take to
add them yourselves or to have them added.  The cost of developing
your own system may exceed one million dollars and take years to do.
1.   GENERAL FEATURES
1.1  Can the system handle:
     Categorical data? (A number represents a category in a group.
     For example, 1 = female, 2 = male.)
     Continuous data? (A number represents a quantity.)
     Decimal values are allowed?
     Character data? (Text is accepted as data.  For example, a
     comment may be allowed, or the words "female" or "male" may be
     used directly.)
1.2  Can the system be used for Economic surveys?
     Agricultural production surveys? Social surveys?
     Demographic surveys?
1.3  Is the system a(n):
     Editing system only?
     Editing and imputation system?
     CATI (Computer Assisted Telephone Interviewing) system?
     CAPI (Computer Assisted Personal Interviewing) system?
     Data entry system?
     Tabulation system?
     Data analysis system?
     Summary system, (estimates, tables, matrix, manipulation, etc.)?
1.4  One part of a larger system?
1.5  Can the system be used on the following computers:
     Mainframes?
     Mini-computer?
     Super-mini (e. g., Sun workstation)?
     Stand-alone microcomputers?
     Microcomputers in a Local Area Network (LAN)?
1.6  Can the system be used on the following operating systems:
     MS DOS?
     OS/2?
     UNIX?
     MVS?
     CMS?
     VMS?
     Other (please specify)?
                                 66
1.7  Does the system require other software for the developer or end
     user.
     ORACLE?
     Turbo Pascal?
     SAS?
     Fortran Compiler?
     Expert System Shell?
     Other software (please specify)?
1.8  For a microcomputer:
     Does the system require a numeric co-processor?
     Can the system use a numeric co-processor?
     Does the system require more than 640 kilobytes of Random Access
     Memory (RAM)?
     Are there other special requirements such as:
     Extended memory?
     Graphics monitor?
1.9  Are changes in the data made primarily by:
     Human data editors?
     Computer?
     Both have important roles in correcting data?
1.10 Is the system available to others? (Is the organization willing
     to sell it or give it to other organizations?)
1.11 Is the system in the public domain?
2.   SURVEY MANAGEMENT
2.1  Can the system check-in records against a sample master?
2.2  Does the system have a "missing reports" capability?
2.3  Can the system provide data on the cost of the editing process?
2.4  Can the system indicate that records are in need of a recall or
     re-check?
2.5  Can the system determine:
     which individual records belong to a particular questionnaire?
     That all records of a particular questionnaire are present?
     That extra records are present?
2.6  Does the system have an audit trail? That is, can it track
     changes made (and possibly the reasons for them)?
2.7  Is information provided on the impact of the system on estimates?
2.8  Is information provided on contributions from nonresponse?
2.9  Regarding reports:
     Can the system provide reports?
     Can the specialist select which reports are to be generated?
     Can the specialist select geographic regions or other breakdowns?
     Can the specialist select records by values of variables?
                                 67
     Can the specialist request a dump (listing of values) of records?
     Can the system determine the number of times each edit rule
     failed?
     Per record?
     Per file?
     Can the system determine the edit rules that failed for each
     questionnaire? Can the system determine the frequency with which
     each field is involved in an edit failure? Can the system
     determine the frequency with which each field is identified for
     change? Can the system determine the number of times particular
     donor values were assigned to a variable? (This is important in
     hot- or cold-deck imputation where there are few donors and many
     recipients.)
     Can the system provide information on enumerator performance?
3.   SYSTEMS ITEMS
3.1  Are corrections made to the original file?
3.2  Are corrections made to a copy of the original file?
3.3  Are original data values part of the final record?
3.4  Is the audit trail part of the final record?
3.5  Can the following items be extracted from the file: Any
     combination of data from the original input file? Any generated
     variable?
3.6  Can the system generate setups in the following formats:
     ASCII?
     ORACLE?
     DBase?
     SAS?
     SPSS?
     Spreadsheet program (please specify)?
     Other (please specify)?
3.7  Can the user program a customized format (e. g., to produce a
     data set for statistical or
     database software not specifically provided for by the system)?
3.8  Does the input file description include: Record layout?
     Field specification?
     Length, type, and other attributes of each variable?
     Field that determines the record type?
     Fields that identify the record?
     Fields that identify geographic regions or other breakdowns?
3.9  Is there a coding module (e. g., the system has a catalogue of
     codes for occupations)?
     Is there a step-wise coding module (the coder negotiates through
     a hierarchy of codes until
     the proper code is obtained)?
                                 68
     Is there a dictionary coding module (the open-ended answer is
     used to search for a similarly spelled coded answer)?
     If both a step-wise coding module and a dictionary coding module
     are available, can they be used in combination?
3.10 Is the system a:
     Compiled system? (The programming code is translated into low-
     level machine language for fast execution.  The user does not
     have to have a copy of the language in order to use the editing
     system.  Thus the execution of the program is faster and more
     flexible than in an interpreted system.  However, changes to the
     code must be re-compiled in order to be put into effect.)
     Interpreted system? (The translation of the programming code into
     a low-level machine language takes place on the fly, (as the code
     is used).  Execution in this mode is slower than in a compiled
     language.  In addition, the user must have a copy of the software
     in order to use the editing system.  However, updates to the code
     do not have to be re-compiled to be put into effect.)
3.11 Is the system constructed in modules? (This allows parts of the
     system to be updated without affecting the other modules.  This
     eases the development of updates and allows prototypes to be
     tried for specific functions.)
3.12 Does the system have a "specifications generator"? (This allows
     data, routes, edits, and other information to be specified once
     and used in other parts of the processing system.)
3.13 Does the system have a menu for:
     End users?
     Developers?
3.14 Is the system secure? (Does it require passwords or other
     measures in order to use it?)
3.15 Is the system embedded in a database?
     Is it linked to data from a sampling frame?
     Is it linked to historical data from a previous survey?
     Is it linked to previous summary level data?
3.16 Can the system accept input data from the following sources:
     CATI
     CAPI
     Data entry software
     Data files generated from other software
     ASCII formatted data
     User specified format
     Other (please specify)
3.17 Does the system generate a log file? (The system keeps track of
     every key-stroke.)
3.18 Does the system have specialized storage structures optimized for
     retrieval functional
3.19 Does the system promote statistical defensibility by:
     Supplying the user with defensible modules and methods?
     Relying on the integrity and expertise of the staff
     Providing an audit trail?
3.20 Does the system allow "calls" to some user programmed sub-
     routine?
                                 69
4.   EDIT WRITING
4.1  Do specialists enter edits directly into system (perhaps using
     an integrated text editor or screens that have been provided)?
     Do specialists specify edits that others enter into the system?
     What programming language is used for edit specification:
     Fortran
     SAS
     System specific language
     Other (please specify)
4.2  Can edits be tested:
     Interactively or on-line (i.e., easily and without waiting for
     paper output)? In batch (i.e., must wait for paper output)?
4.3  Are acceptable values specified (e. g., if 2 < X then OK)?
4.4  Are unacceptable values specified (e. g., if X  2 then fail)?
4.5  Does the specialist design the layout of a computer screen?
     Can the specialist choose which variables to flag for each edit
     failure? Can historical variables appear on the screen?
     Can calculated variables appear on the screen?
     Can variables be protected from change?
     Can the specialist control where the variables appear?
     Can colors, fonts. and highlighting be specified?
4.6  Is a table format available?
     Can the analyst move freely within the table?
     Is it possible to specify one line of a table (edits, routes,
     etc.,) then specify the number of times the line is to be
     repeated (this feature will save much programming time)?
4.7  When messages appear on a computer Screen, are:
     Error signals flagged at failing variables? (An error signal is
     an indication that an edit has failed. This may be a flag or a
     number next to the variable on the screen, or it may be a status
     code associated with the variable.)
     Error signals given at the bottom (or top) of the screen?
     Error messages viewed through pop-up windows?
     Error messages viewed at the bottom (top) of the screen?
     Can labels be programmed that make the screen more readable?
4.8  Does the specialist design the layout of a computer printout?
4.9  When messages appear on a computer printout:
     Do messages appear with failing variables?
     Can the specialist choose variables to be printed out for each
     error signal? For a variable involved in X error signals, will
     the value be printed out X times?
     Once?
4.10 Are messages in coded (E.g.,error 333)?
4.11 Are messages in a spoken language (e. g., in English as opposed
     to a code)?
                                 70
4.12 Can calculations of auxiliary variables appear in messages?
     On screen?
     In messages?
5.   TYPES OF EDITS
5.1  Can the system handle edits with the following mathematical
     properties:
     Linear edits, (Their graphical representations are straight
     lines.  Linear edits are more amenable to edit analysis than
     nonlinear edits.)
     Nonlinear edits
     Conditional edits (They are not triggered until a condition is
     met.  These are difficult to handle in edit analysis.)
     Ratio edits (e. g.. 1  a / b  2)? (These can be restated
     linearly or ratio edits can be analyzed within themselves.)
     Variables that can accept negative values?
     Quantitative edits?
     Qualitative edits?
5.2  Can the system handle edits with the following functions: Valid
     values checks. (univariate range checks)? Consistency checks,
     (multivariate. record-level checks):
     Consistency "balance" checks (i.e., parts (or details) add up to
     total)?
     Other consistency checks (e. g., marital status vs. age)?
     Route checks and skip patterns (i.e., the proper path was
     followed through the form,
     including skips based on certain values)?
     Generated values against external standards (e. g., average wage
      minimum wage)?
5.3  Can the system accept logical operators, e. g., "and", "or", and
     "not".
5.4  Is there just one level of edit failure or priority?
5.5  Are there two or more levels of edit failure or priority (e. g.,
     critical vs. noncritical)?
5.6  Can the system perform non-statistical cross record checks? For
     example, can it check values in subfiles against values in the
     master file?
5.7  Can states, provinces, other regions, or strata have customized
     error limits?
5.8  Can the system perform statistical editing: Based on historical
     data?
     Based on cross-record checks:
     univariate outlier detection?
     multivariate outlier detection?
5.9  Can the system handle record-level edits:
     Based on frame data?
     Based on previous reports (nonstatistical historical editing)?
5.10 Can the system perform macro editing (editing at an aggregate
     level): At the summary level?
     At the stratum level?
     Using historical data?
                                 71
     And allow tracing back to individual records?
     And perform on-line correction of records and recalculation of
     aggregate level statistics?
5.11 Can the system perform graphical inspection of the data?
     On paper printouts?
     Interactively on computer screens?
     At an aggregated (macro) level?
     And allow tracing back to individual reports?
6.   EDIT RULE ANALYSIS
6.1  Can the system check edits for redundancy? (A redundant edit does
     not further restrict the set of acceptable values.)
6.2  Can the system check that edits are not contradictory? (If two
     edits are contradictory, then no record will pass the edits.)
6.3  Can the system generate implied edits? (Implied edits are derived
     by implication from explicitly written edits, e. g., if 1 < a/b
     < 2 and 2 < b/c < 4 are explicit edits, then 2 < a/c < 8 is an
     implied edit.  Implied edits allow the specialist to check that
     the data is not being restricted in an unintended way.  They are
     also important for determining which variables to correct.)
     For linear edits?
     For ratio edits?
     For other nonlinear edits (e. g., conditional edits)?
6.4  Can the system generate external records? (The vertices of the
     acceptable region are generated for inspection.  For example, A
     = 0, B = 1000, and C = 2 may be the vertices of an acceptable
     region.  This analysis will allow the specialist to examine the
     worst possible records that can pass through the edits without
     an error signal being generated.)
6.5  Can the system analyze groups of edits separately (some edits may
     be applied to only certain questionnaires depending on coded
     values or reported data).
7.   DATA REVIEW AND COMMON
7.1  Does the end-user access the system through menus?
7.2  In the computer processing flow, does the system:
     Require substantial cleaning up of the data before it is applied
     (either manually or by another
     system module)?
     Require substantial cleaning up of the data after it is applied
     (either manually or ty another system module)?
     Make (almost) all corrections automatically in batch?
     Require manual correction of machine generated signals?
     Have batch capability with manual resolution of records in error?
                                 72
7.3  Does the system allow manual review and changes: Before data
     entry?
     During data entry?
     After data entry?
7.4  Are manual changes made:,
     Interactively through terminals or work stations?
     Is there full screen mobility?
     Is there full between-screen mobility?
     Are changes made through prompts that appear at the bottom (top)
     of the screen?
     In a batch only environment using paper printouts?
     Are error messages generated on paper but changes made on a
     terminal?
7.5  Does the system determine which variables are to be changed? This
     is called error localization.)
     For all variables and edits?
     For subsets of variables and edits?
7.6  Does the system allow automatic correction (imputation)?
     Does it allow simultaneous multivariate imputation?
     Does it allow several imputation options per variable?
     Does it do deterministic imputation (where only one value will
     work)?
     Does it do hot-deck donor imputation? (Values of records from the
     current survey are used to impute for missing values in recipient
     records.)
     Does it do cold-deck donor imputation? (Values of records from
     a previous survey are used to impute for missing values in
     current recipient records.)
     Does it perform administrative (frame) imputation?
7.7  Does the system have other integration features such as:
     Consistent menu formats between modules?
     Historical integration, (the ability to update questionnaires
     easily from one period to the next)?
     Can edit specification for different parts of the questionnaire
     be done independently by two or more people?
     Are associated software packages available that can be used with
     the editing system for related functions such as tabulation or
     survey management?
7.8  In the data entry program, can:
     Data be entered and edited at the same time?
     Data be entered without correction? (High speed, heads down.)
     Full screen data entry be done?
     Item code data entry be done?
     Verification be done? (Data are entered twice.)
     Implied decimals be typed? (e. g., type 23 for 2.3.)
     For certain variables that have a predetermined length, can the
     typist go on to the next value
     without pressing < Enter >.
     Are data entry statistics generated?
73
7.9  Can the system handle complex data structures such as:
     Hierachical data (e. g., individual questions asked within a
     household)?
     Complicated skip patterns?
     Can the system handle subfiles (the variables are split up
     between different files, making further processing easier in some
     cases, allowing data to be stored more efficiently in other
     cases)?
     Is cross record checking allowed (e. g., between lines of a
     subfile from a table of questions and the master record)?
7.10 Does the system allow respecification of edits without having to
     manually redo other parts of the processing software? (For
     example, can changes in edits be automatically reflected in data
     entry, data editing, and data collection modules without
     respecifying changes in each module?)
7.11 Does the system allow respecification of data without having to
     manually redo other parts of the processing software? (Same note
     as in 7.10.)
8.   SUPPORT, UPDATES, AND TRAINING
8.1  Is the system updated systematically (e. g., future updates
     planned)?
8.2  Is there a cost for the update?
8.3  Is the system supported with training?
8.4  Is there a cost for the training?
8.5  Is the development environment of the system available in:
     English?
     French?
     Dutch?
     Spanish?
     Can it be convened to other languages?
8.6  Does the system developer offer support?
8.7  Is there a. cost for the support?
8.8  Is the system comprehensible to the end user? (That is, can the
     end user understand what the system is doing methodologically
     speaking? It is not just a black box.)
8.9  Could one person use all aspects of the system for a small one-
     time survey without much support (this is an indication of the
     ease of development and use)?
8.10 Is there on-line documentation?
     A help screen of keys only?
     Is the on-line documentation "context sensitive"?
8.11 Do the written documentation items include:
     Installation instructions and "getting started" information? A
     tutorial for novice applications programmers?
                                 74
     A user's guide?
     A reference manual?
     A manual of examples?
     A discussion of the overall approach used by the system in
     editing a survey?
8.12 What are there capacity and performance limits for The number of
     edits?
     The number of variables?
     The number of questionnaire versions?
     The number of executable statements?
9.   References
     A Comparison of Software for Editing Survey and Census Data.
Paul Cotton.  Presented October 1988, at the Statistics Canada
Symposium, The Impact of High Technology on Survey Taking. (The
topical division of this checklist and about half (perhaps more) of
the items are taken from this report.)
     An Evaluation of Edit and Imputation Procedures Used in the 1982
Economic Censuses in Business Division.  Brian Greenberg and Thomas
Petkunas, Statistical Research Division, Bureau of the Census, 1986.
A Review of the State of the Art in Automated Data Editing and
Imputation.  Mark Pierzchala, National Agricultural Statistics
Service, United States Department of Agriculture, 1988.
     CAI Software, An Evaluation of Software for Computer Assisted
Interviewing.  Steven E. de Bie, Ineke A. L. Stoop, and Katrinus L.
M. de Vries.  VOI, Association of Social Research Institutes, Data
Collection Group.  Amsterdam, the Netherlands, March 1989.
The checklist was written in its present form by Mark Pierzchala based
first on Cotton's work and then modified by reference to the other
papers.  It was revised with the help of the members of the Software
Subgroup.  Many other people have reviewed it including other members
of the Subcommittee on Editing in Federal Statistical Agencies and
members of the Data Editing Joint Group, of the Statistical Computing
Project Phase 2, of the United Nations.  A version of this checklist
was provided to latter group.  Various other reviewers of this report
have also added some comments.
                                 75
                                                          APPENDIX D
ANNOTATED BIBLIOGRAPHY ON EDITING
A.   LISTING OF PAPERS BY TOPIC FOR:
[A]  GEIS
[G]  BLAISE
[C]  SPEER
[D] TIME SERIES AND OUTLIER DETECTION APPLICATIONS
[E] ERROR LOCALIZATION METHODS
In the annotated bibliography each paper belonging to a special topic
[A]-[E] will be so indicated.  The annotations are brief and are only
intended to give a very general idea of paper content.  If the content
is clear from the paper title. no annotation is provided.
Specialized Bibliography
[A] GEIS PAPERS
Fellegi and Holt (1976); Giles (1986,1987,1988); Giles and Patrick
(1986); Greenberg (1987b); Hidiroglou and Berthelot (1986); Kovar
(1991); Kovar, MacMillian and Whitridge (1988); Nesich (1980); Sande,
G. (1976,1978,1981), Sande, I. (1988); Schiopu-Kratina and Kovar
(1988).
[B] BLAISE PAPERS
Bethlehem (1987); Bethlehem, Denteneer, Hundepool. and Keller (1987);
Bethlehem, Hundepool, Schuerhoff and Vermeuler (1987); Denteneer,
Bethlehem, Hundepool, and Schuerhoff (1987a,1987b); Greenberg (1987b);
Keller and Bethlehem (1990).
[C] SPEER PAPERS
Greenberg (1981,1982,1987a,1987b); Greenberg and Petkunas (1987);
Greenberg and Surdi (1984).
[D] TIME SERIES AND OUTLIER DETECTION APPLICATIONS
Bell (1983); Burns (1980,1983); Cherriick (1982,1983); Chemick,
Downing and Pike (1982); Chemick and Murthy (1983); Dinh (1987); Hill
and French (1981); Kirkendall (1988);  Little and Smith (1983,1987);
Mazur (1990); Miller, Meroney and Titus (1987); Passe,  Carpenter and
Passe (1987); Pierce and Bauer (1989).
[E] ERROR LOCALIZATION METHODOLOGIES
Fellegi and Holt (1976); Garcia-Rubio and Villan (1990); Garfinkel
(1979); Garfinkel, Kuruiathur, and Liepins (1986); Greenberg (1981);
Liepins,  Garfinkel and Kunnathur (1982); Liepins and Pack
(1980,1981); Little and Smith (1983,1987); McKeown (1984); Sande
(1978); Schiopu-Kratina and Kovar (1988).
                                 77
B. ANNOTATED BIBLIOGRAPHY
ABRAHAM, B. and YATAWARA N. (1988), "A Score Test for Detection of
Time Series Outliers," Journal of Time Series Analysis, 9, 109-119.
[D] Outlier detection in time series; not typically employed in survey
editing.
ASHRAF, A. and MACREDIE, I. (1978), "Edit and Imputation in the Labour
Force Survey," in Proceedings of the Section on Survey Research
Methods, American Statistical Association, 425-430.  Describes edit
and imputation system used in Canadian Labor Force Survey.
BAIR, R. B. (1981), "CONCOR:  An Edit and Automatic Correction
Package," incomputer Science and Statistics: Proceedings of the 13th
symposium on the Interface, 340-343.  Automated System developed at
the Census Bureau primarily focusing on validity edits and their
correction.
BANISTER, J. (1980), "Use and Abuse of Census Editing and Imputation,"
Asian and Pacific Census Forum, 6. Raises concerns about excessive
editing and imputation in censuses.
BELL, W.R. (1983), "A Computer Program for Detecting Outliers in Time
Series," in Proceedings of the Section of Business and Economic
Statistics, American Statistical Association, 634-639. [D] Categorizes
three major causes of outliers in a time series framework and presents
tests sensitive to each.
BETHLEHEM, J. G. (1987), "The Data Editing Research Project of the
Netherlands Central Bureau of Statistics," in Proceedings of the Third
Annual Research Conference of the Bureau of the Census, Washington,
D.C.: U. S. Bureau of the Census, 194-203. [B] Discusses problems of
editing at CBS and need for Blaise.
BETHLEHEM, J. G., DENTENEER, D., HUNDEPOOL, A. J., and KELLER, W. J.
(1987).  "The Blaise System for Computer-Assisted Survey Processing."
in Proceedings of the Third Annual Research Conference of the Bureau
of the Census, Washington, D.C.: U. S. Bureau of the Census, 194-203.
[B] Report on Blaise.
BETHLEHEM, J. G., HUNDEPOOL, A. J., SCHUERHOFF, M. H. and VERMEULER,
L. F. M. (1989).  "Blaise 2.0 An Introduction." System Documentation,
Netherlands Central Bureau of Statistics. [B] Introduction to the
Blaise system for Computer Assisted Editing, Data Collection. and Data
Entry.
BIEMER, P. P. (1980), "A Survey Error Model Which Includes Edit and
Imputation Error," in Proceedings of the Section on Survey Research
Methods.  American Statistical Association, 610-615.  Presents a
survey error model which includes edit error, imputation error,
response error and sampling error.
BILOCQ, F. (1989), "Analysis on Grouping of Variables and or Detection
of Questionable Units." Business Surveys Methods Division, Statistics
Canada.  Groups of variables are identified to optimize editing.
Questionable units identified by statistical techniques for change or
follow-up.
                                 78
BILOCQ, F., and BERTHELOT, J. M. (1989) "An Edit Scheme Based on
Multivariate Data Analysis," Business Surveys Methods Division,
Statistics Canada.  Multivariate data analysis techniques for grouping
items for editing.
BRANT, J. D. and CHALK, S. M. (1985), "The Use of Automatic Editing
in the 1981 Census," Journal of the Royal Statistical Society (Series
A), 148, 126-146.  Describes the automatic editing and imputation
system used in the. 1981 census in England and Wales.
BURNS.  E.M. (1980).  "Procedures for the Detection of Outliers in
Weekly Time Series," in Proceedings of the Section of Business and
Economic Statistics, American Statistical Association, 560-563. [D]
Compares five outlier detection procedures for repeated surveys.  Each
uses a different set of features from historical data to test incoming
data.
BURNS, E.M. (1983), "Editing and Imputation for the EIA Weekly-
Petroleum Surveys," in Proceedings of the Section on Survey Research
Methods, American Statistical Association, 539-543. [D] Discusses use
of exponential smoothing in editing EIA's weekly petroleum surveys.
CHERNICK, M. R. (1982), "The Influence Function and its Application
to Data Validation," American Journal of Mathematical and Management
Sciences, 263-288. [D] Hampel's Influence Function used to detect
bivariate observations, which have unusual influence on estimates of
correlation.
CHERNICK, M. R. (1983), "Influence Functions, Outlier Detection, and
Data Editing," in Statistical Methods and the Improvement of Data
Quality, ed.  T. Wright, New York: Academic Press, 167-176. [D] Review
article on influence functions to detect outliers.
CHERNICK, M. R., DOWNING.  D. J., and PIKE, D. H. (1982), "Detecting
Outliers in Time Series Data," Journal of the American Statistical
Association, 77,743-747. [D] Effect of outliers on time series by
considering the influence function for the auto correlations of a
stationary time series.
CHERNICK.  M. R., and MURTHY, V. K. (1983), "The Use of Influence
Functions for Outlier Detection and Data Editing," American Journal
of Mathematical and Management Sciences. 3,47-61. [D].
COTTON, P. (1988), "A Comparison of Software for Editing Survey and
Census Data," in Proceedings of Symposium 88, The Impact of high
Technology on Survey Taking, Ottawa, Ontario, Canada: Statistics
Canada, 211-241.  Criteria to evaluate editing software.  Four systems
that operate on MS-DOS microcomputers are reviewed.
CUSHING, J. (1988), "A Report on Recent Survey Processing in
Developing Countries: The Demographic and Health Surveys Microcomputer
Approach." in Proceedings of Symposium 88, The Impact of High
Technology on Survey Taking, Ottawa, Ontario, Canada: Statistics
Canada: 201-210.  Describes the Integrated System for Survey Analysis
(ISSA), a microcomputer survey processing system.
                                 79
DENTENEER, D., BETHLEHEM, J. G., HUNDEPOOL, A. J., and SCHUERHOFF, M.
S. (1987a), "Blaise, A New Approach to Computer Assisted Survey
Processing," Staff Report, Netherlands Central Bureau of Statistics.
[B] Discusses Blaise and contains Blaise related bibliography.
DENTENEER, D., BETHLEHEM, J. G., HUNDEPOOL, A. J., and SCHUERHOFF, M.
S. (1987b), "The Blaise System for Computer-Assisted Survey
Processing," in Proceedings of the Third Annual Research Conference
of the Bureau of the Census, Washington, D.C.: U. S. Bureau of the
Census, 112-127. [B] Discusses Blaise.
DINH, K. (1987), "Application of Spectral Analysis to Editing a Large
Data    Base,"Journal of Office Statistics, 3, 431- 483. [D] Simple
statistical procedure to minimize reporting and data entry errors in
repeated surveys.
FELLEGI, I. P. and HOLT, D. (1976), "A Systematic Approach to
Automatic Edit and Imputation," Journal of the American Statistical
Association, 71, 17-35. [A],[E] Introduces "Fellegi-Holt" procedure
for automated data edit and imputation; corner-stone paper in this
area.
FREUND, R. J. and HARTLEY.  H. O. (1967), "A Procedure for Automatic
Data Editing," A procedure for the American Statistical Association,
62,341-353. Proposes an automated edit scheme for a large variety of
surveys.
GARCIA-RUBIO, E., and VILLAN.  I. (1990), "DlA System: Software for
the Automatic Editing of Qualitative Data," in Proceedings of the
Sixth Annual Research Conference of the Bureau of the of Census,
Washington, D.C.: U. S. Bureau of the Census, (to appear). [E]
Describes the DIA system for data editing and imputation based in part
on Fellegi-Holt methodology.
GARFINKEL. R. S. (1979), "An Algorithm for Optimal Imputation of
Erroneous Data," College of Business Administration Working Paper
Series, The University of Tennessee, Knoxville. [E] Algorithm based
on Fellegi-Holt for error localization.
GARFINKEL R. S., KUNNATHLTR, A. S., and LIEPINS, G. E. (1986),
"Optimal Imputation of Erroneous Data: Categorical Data, General
Edits," Operations Research. 34, 744-751. [E] Algorithm for error
localization.
GILES, P. (1986).  "Methodological Specifications for a Generalized
Edit and Imputation System." Business Survey Methods Division.
Statistics Canada. [A] Specifications for development of GEIS, with
proposed requirements and priority for development.
GILES.  P. (1987).  "Towards the Development of a Generalized Edit and
Imputation System." in Proceedings of the Census Bureau Third Annual
Research Conference of the Bureau of the Census Washington, D.C.: 185-
193. [A] Methodological issues related to development and
implementation of GEIS.
GILES, P. (1988), "A Model for Generalized Edit and Imputation of
Survey Data." The Canadian Journal of Statistics, 16, Supplement, 57-
73. [A] Focuses on methodology of GEIS.
                                 80
GRANQLIST, L. (1982), "On Generalized Editing Programs and the
Solution of the Data Quality Problems," UNDP Statistical Computing
Project, Data Editing Joint Group.  Discusses impact of editing, need
to concentrate on systematic errors and resource allocation.
GRANQLTIST, L. (1994), "On the Role of Editing," Statistisk Tidskrift,
2, 105-118.  Guidelines on editing surveys of official statistical
agencies.
GRANQUIST L. (1987).  "A Report of the Main Features of a Macro-
editing Procedure which is used in Statistics Sweden for Detecting
Errors in Individual Observations." Report presented at the Data
Editing Joint Group, Statistics Sweden.
GRAVES, R. B. (1976), "A Generalized Edit and Imputation System in a
Data Base Environment," presented to the Electronic Data Processing
Working Party of the Conference of European Statisticians, Geneva.
Describes CAN-EDIT, an implementation of Fellegi-Holt methodology for
categorical data.
GREENBERG, B. (1981), "Developing an Edit System for Industry
Statistics," Computer Science and Statistics: in Proceedings of the
13th Symposium on the Interface New York: Springer-Verlag, 11-16. [C]
First paper describing what has evolved into the SPEER system.
GREENBERG, B. (1982), "Using an Edit System to Develop Editing
Specifications," in Proceedings of the Section on Survey Research
Methods, American Statistical Association, 366371. [C],[E] Discussion
of how edit system diagnostics used to develop edit criteria.
GREENBERG, B. (1987a), "Edit and Imputation as an Expert System," in
Statistical Policy Working Paper Number 14: Statistical Uses of
Microcomputers in Federal Agencies, Washington, D.C.: Office of
Management and Budget, 85-92. [C] Describes the Expert System features
in SPEER.
GREENBERG, B. (1987b), Discussion, "Session on Designing Automated
Data Editing Systems," in Proceedings of the Third Annual Research
Conference of the Bureau of the Census, Washington, D.C.: U. S. Bureau
of the Census, 204-212. [A],[B],[C] Discusses paper by Giles on GEIS
and by Bethlehem on Blaise.
GREENBERG, B., and PETKUNAS, T. (1987), "An Evaluation of Edit and
Imputation Procedures Used in the 1982 Economic Censuses in Business
Division," in 1982 Economic Censuses and Census of Governments
Evaluation Studies, U. S. , Department of Commerce.  Bureau of the
Census. [C] Work completed as part of Evaluation Studies Task Force
for 1982 Economic Censuses.
GREENBERG, B., and SURDI, R. (1984).  "A Flexible and Interactive Edit
and Imputation System for Ratio Edits," in Proceedings of the Section
on Survey Research Methods.  American Statistical Association, 421-
426. [C] Discusses actual uses of SPEER system and illustrates options
based on user needs.
                                 81
HIDIROGLOU, M. A. and BERTHELOT, J. M. (1986), "Statistical Editing
and Imputation for Periodic Business Surveys,"  Survey Methodology,
12, 73-83. [A] Methods for editing and imputing data for.units
contacted on a periodic basis.
HILL, C.J. (1978), "A Report on the Application of a Systematic Method
of Automatic Edit and Imputation to the 1976, Canadian Census," in
Proceedings of the Section on Survey Research Methods, American
Statistical Association, 474-479.  Rationale of edit and imputation
system for the Canadian Census and a non-technical description of
methodology.
HILL, E., Jr. and FRENCH, C. (1981), "Editing Very Large Data Bases,"
in Proceedings of Conference on Information, Science, and Systems, 70-
78. [D] Algorithm which employs spectral analysis to classify
historical data series and to edit data according to classifications.
HODGES, B.S. M (1983), "Using the Computer to Edit and Code Survey
Data," in Proceedings of the Section on Statistical Computing,
American Statistical Association, 238-240.  Computer edits and codes
single word or short phrase responses to open-ended questions.
HUGHES, P. J., McDERMID, I., and LINACRE, S. J. (1990), "The Use of
Graphical Methods in Editing," in Proceedings of the Sixth Annual
Research Conference of the Bureau of the Census, Washington, D.C.:
U.S. Bureau of the Census (to appear). [D] Examines the potential for
graphical methods in the different stages of editing, such as setting
edit bounds and in output (aggregate) editing.
KELLER, W.J. and BETHLEHEM, J.G. (1990), 'The Impact of Microcomputers
on Survey Processing at the Netherlands Central Bureau of Statistics,"
in Proceedings of the Sixth Annual Research Conference of the Bureau
of the Census, Washington, D.C.: U. S. Bureau of the Census (to
appear). [B] Discussion of Blaise implementation activities.
KIRKENDALL, NJ. (1988), "An Application of a Kalman Filter for
Estimation and Edit". in Proceedings of the Business and Economic
Statistics Section, American Statistical Association, 510-515. [D)
Application of exponential smoothing, and Kalman filter models; for
series with shifts in levels.
KOVAR, J. (1981), "Edit and Imputation Package for the Integrated
Agriculture Survey," Statistics Canada. [A] Summarizes IAS
Edit/Imputation package and flexibility it may provide.
KOVAR.  J. G. (1990), Discussion, "Session on Editing," in Proceedings
of sixth Annual Research Conference of the Bureau of the Census,
Washington, D.C.: U. S. Bureau of the Census (to appear).  Discusses
papers by Garcia-Rubio and Villan on DIA and by Hughes, McDermid and
Linacre on graphical methods.
KOVAR, J. G., MacMILLAN J. H., and GE, P. (1988).  "Overview and
Strategy for the Generalized Edit and Imputation System." Working
Paper No. BSMD-88-007E.  Methodology Branch, Statistics Canada. [A]
Overview of GEIS.
LIEPINS, G.E. (1983), "Can Automatic Data Editing Be Justified? One
Person's Opinion," in Statistical Methods and the Improvement of Data
Quality, ed.  T. Wright, New York: Academic Press, 205-213.  Issues
concerning application of automatic data editing.
                                 82
LIEPINS, G. E., GARFINKEL, R. S. and KUNNATHUR, A. S. (1982), "Error
Localization for Erroneous Data: A Survey," TIMS Studies in Management
Sciences, 19, 205-219.[E]Survey of Error Localization Techniques.
LIEPINS, G.E. and PACK, DJ. (1980), "An Integrated Approach to Data
Editing," in Proceedings of the Section on Survey Research Methods,
American Statistical Association, 777-781. [E] Simulation study on
error localization for categorical data from large data sets.
LIEPINS, G.E. and PACK, DJ. (1981), "Maximal Posterior Probability
Error Localization," in Proceedings of the Section on Survey Research
Methods, American Statistical Association, 192-195. [E] Simulations
suggest that MPPEL is not superior to minimal fields to impute.
LINACRE, S. J. and TREWIN, D. J. (1989), "Evaluation of Errors and
Appropriate Resource Allocation in Economic Collections." in
Proceedings of the Fifth Annual Research Conference of the Bureau of
the Census, Washington, D. C.: U. S. Bureau of the Census, 197-209.
A study by Australian Bureau of Statistics of a near optimal
allocation of resources to reduce nonsampling errors.
LITTLE, R.J. A., and SMITH, P.J. (1983).  "Multivariate Edit and
Imputation for Economic Data," in Proceedings of the Section on Survey
Research Methods, American Statistical Association, 518-522. [D],[E]
Edit and Imputation procedure based on outlying cases.
LITTLE, R J. A., and SMITH, P. J. (1987), "Editing and Imputation for
Quantitative Survey Data," Journal of the American Statistical
Association, 82, 58-67. [D],[E] Methodological tools include distance
measures, graphical procedures, maximum likelihood and robust
estimation for incomplete multivariate data.
MADOW, W.G., NISSELSON, H., and OLKIN, I. (eds.) (1983).  Incomplete
Data in Sample Surveys, Volume 1, Report and Case Studies. New York:
Academic Press. (See below).
MADOW, W.G., and OLKIN, I. (eds.) (1983).  Incomplete Data in Sample
Surveys, Volume 3, Proceedings of the Symposium.  New York:  Academic
Press. (See below).
MADOW, W.G., OLKIN, I., and RUBIN, D.B. (eds.) (1983).  Incomplete
Data in Sample Survives, Volume 2. Theory and Bibliographies. New
York: Academic Press.  Broad treatment of incomplete data with
extensive, partially annotated, bibliography in Volume 2.
MAGNAS, H.L. (1989), "An Expert System to Assist in the Disposition
of Computer Edit Error Flags." Presented at American Statistical
Association Committee on Energy Statistics spring meeting.  An expert
system designed to standardize the follow-up procedure for flagged
items.
MAZUR.  C. (1990).  "Statistical.  Edit System for Livestock Slaughter
Data," NASS Staff Report.' SRB-90-01, National Agricultural Statistics
Service., U. S. Department of Agriculture.  [D] Statistical edit in
which a plant's historical data define edit limits.  Classical and
simple robust estimates and Tukey's biweight estimator used to
calculate edit limits.
                                 83
MCKEOWN, P.G. (1984), "A Mathematical Programming Approach to Editing
of Continuous Survey Data," SIAM Journal. of Scientific and
Statistical Computing, 5, 784-797. [E] Mathematical programming
approach to error locations
MILLER, R., MERONEY, W. and TITUS, E. (1987), "Identification of
Anomalous Values,in Energy Data," Proceedings of the Business and
Economic Statistics Section, American Statistical Association, 241-
246. [D] Presents results of using Box-Whisker techniques to identify
outliers in refinery production data.
NAUS, J. I. (1975), Data Quality Control and Editing, New York: Marcel
Dekker.  Variety of techniques for probabilistic edits and quality
control.
NAUS, J.I. (1982), "Editing Statistical Data," in Encyclopedia of
statistical Sciences (Vol. 2), eds.  S. Kotz, N. L. Johnson, and C.
B. Read, New York: Wiley, 455-461.  Review article.
NAUS, J.I., JOHNSON.  T.G. and MONTALVO.  R. (1972), "A Probabilistic
Model for Identifying Errors in Data Editing," Journal of the American
Statistical Association, 67, 943-950.  Procedures for assigning
likelihood of error given simultaneous failure of deterministic edits.
NESICH, R. (1980), "General Methodological Approach to Edit and
Imputation in the 1981 Census of Agriculture." Statistics Canada. [A]
Approach to be used in editing and imputing the 1981 Census of
Agriculture data.
NORDBOTTON, S. (1965), "The Efficiency of Automatic Detection and
Correction of Errors in Individual Observations as Compared with other
Means for Improving the Quality of Statistics," in Bulletin of the
International Statistical Institute, Proceedings of the 35th Session,
Belgrade 41, 417-441.  Compares effect on survey results of editing
to other methods to improve the quality.
PASSE, R. M., CARPENTER, M. J., and PASSE, H. A. (1987), "Operational
Outlier Detection, Communications in Statistics (Theory & Methods) 16,
3379-91. [D] Outlier detection techniques.
PIERCE, D. A. and BAUER, L. L. (1989), "Tolerance-Width Groupings for
Editing Banking Deposits Data: An Analysis of Variance of Variances,"
Finance and Economics Discussion Series, No. 72. [D] Determination of
optimal clusters for setting edit tolerances.
PIERZCHALA, M. (1988), "A Review of the State of the Art in Automated
Data Editing and Imputation," NASS Staff Report, SRB-88-10, National
Agricultural Statistics Service, U. S. Department of Agriculture.
Explores approaches to automation of data editing and imputation with
focus on state of the art.  Includes bibliography.
PODEHL, W. M. (1974), "Introduction to the Generalized Editing and
Imputation System using Hot-Deck-Approach." General Survey Statistics
Division, Statistics Canda.  Application of Fellegi-Holt methods to
1971 Census of Population and Housing.
PRITZKER, L., OGUS, J., and HANSEN, M. H. (1965), "Computer Editing
Methods-Some Applications and Results," in Bulletin of the
International Statistical Institute, Proceedings of the 35th Session,
Belgrade, 41, 442-465.  Summarizes philosophy, problems, and practice
in editing and imputation at the Census Bureau.
                                 84
PULLUM, T. W., HARPHAM, T., and OZSEVER, N. (1986), "The Machine
Editing of Large-Sample Surveys: The Experience of the World Fertility
Survey," International Statistical Review, 54, 311-326.  Addresses
trade-offs between improvements in data quality and costs of editing.
SANDE, G. (1976), "Numerical Edit and Imputation," invited paper
presented at the International Association for Statistical Computing,
42nd session of the International Statistical Institute, Manila,
Philippines. [A] Precursor to GEIS.
SANDE, G. (1978), "An algorithm for the fields to impute problems of
numerical and coded data." Statistics Canada Report. [A],[E]
Introduces linear programming methods for error localization.
SANDE, G. (1981), "Descriptive Statistics Used in Monitoring Edit and
Imputation Processes," presented at Workshop on Automated Edit and
Imputation, Computer Science and Statistics; Proceedings of the 13th
Symposium on the Interface, Springer-Verlag, New York. [A) Discusses
descriptive statistics in precursor to GEIS.
SANDE, I. G. (1982), "Imputation in Surveys: Coping with Reality," The
American Statistician, 36, 145-152.  A variety of practical and
methodological considerations in edit and imputation.
SANDE, I. G. (1988).  "A Statistics Canada Perspective on Numerical
Edit and Imputation in Business Surveys," presented at the Conference
of European Statisticians in Geneva. [A] Discusses some ideas behind
GEIS.
SCHIOPU-KRATINA, I. and KOVAR, J. G. (1988) "Use of Chernikova's
Algorithm in the Generalized Edit and Imputation System," Working
Paper 89-001, Methodology Branch, Statistics Canada. [A],[E] Presents
methodology in GEIS which uses linear programming to find fields to
impute.
SIMINOFF, J. S. (1984), "The Calculation of Outlier Detection
Statistics," Communications in Statistics, Simulation & Computation,
13, 275-285.  Provides a program to calculate four outlier detection
statistics.
STUART, WJ. (1966).  "Computer Editing Survey Data - Five Years of
Experience in BLS Manpower Surveys," Journal of the American
Statistical Association, 375-383.  A review of steps at BLS to
computerize the screening process.
SZAMEITAT, K. and ZINDLER.  H. J. (1965), "'The Reduction of Errors
in Statistics by Automatic Corrections," in Bulletin of the
International Statistical Institute, Proceedings of the 35th Session,
Belgrade, 41, 395-417.  Discusses practical and theoretical
considerations of reducing errors in statistical processing.
TORO, V. and CHAMBERLAIN, K. (1988).  "The Use of Microcomputers for
Census Processing in Developing Countries: An Update,"' in Proceedings
of Symposium 88, The Impact of High Technology on Survey Training,
Ottawa, Ontario, Canada: Statistics Canada, 181-199.  Describes the
Integrated Microcomputer Processing System (IMPS).
                                 85
                                                          APPENDIX E
GLOSSARY OF TERMS
Acceptance region - The set of acceptable values defined by the edits
for each record.  For categorical data the acceptance region can be
represented as a set of lattice points in N-Space.  For numerical data
it is a set of convex regions in N-space.  Also called a feasible
region.
Audit trail - An accounting of changes to values in a field and the
reasons for the changes.
Balance edit - An edit which checks that a total equals the sum of its
parts.  Also called an accounting edit.  Example: Closing inventory
= Opening Inventory + Purchases - Sales. (Example from Kovar, 1988)
Complete set of edits - The union of explicit edits and implied edits.
Sufficient for the generation of feasible (acceptance) regions for
imputation (that is if the amputations are to satisfy the edits).
Conditional edit - An edit where the value of one field determines the
editing relationship between other fields and possibly itself.  For
example, suppose there are three fields A, B, and C. A conditional
edit would exist ff the relationship between fields B and C as
expressed through the edits depended on the value in A.
Consistency edit - A check for determinant relationships, such as
parts adding to a total or harvested acres are less than or equal to
planted acres.
Consistent edits - A set of edits which do not contradict each other
is considered to be consistent.  If edits are not consistent then no
record can pass the edits without invoking an error signal.
Deterministic edit - An edit, which if violated, points to an error
in the data with a probability of one.  Example: Age 5 and Status =
mother.  Contrast with stochastic edit.
Deterministic imputation - The situation when only one value of a
field will cause the record to satisfy all of the edits.  Occurs in
some situations (such as the parts of a total not adding to the
total).  This is the first solution to be checked for in the automated
editing and imputation of survey data.
Error localization - The automatic identification of the fields to
impute.  That is, the determination of the al set of fields to impute
for.
Explicit edit - An edit explicitly written by a subject matter
specialist. (Contrast explicit edits with implied edits.)
                                 87
Expert system - computer system that solves complex problems in a
given field using knowledge and inference procedures, similar to a
human with specialized knowledge in that field.
Failed edit graph - As used by the U.S. Bureau of the Census, a graph
containing nodes (corresponding to fields) which are connected by arcs
(an arc between two nodes indicates that the two fields are involved
in an edit failure.) Deleting a node is equivalent to choosing that
field to be imputed.  A minimal set of deleted nodes is equivalent to
a minimal set as defined by Fellegi and Holt.
Hot-deck imputation - A method of imputation whereby values of
variables for good records in the current (hot) survey file are used
to impute for blank values of incomplete records.
Implied edit - A unstated edit derived logically from explicit edits
that were written by a subject matter specialist.
Imputation - A procedure for entering a value for a specific data item
where the response is missing or unusable.
Integrated Survey Processing - The concept that all parts of the
survey process be integrated in a coherent manner, the results of one
part of the process automatically giving information, to the next part
of the process.  The Blaise system is an example of integrated
software in which the specification of the Blaise Questionnaire gives
rise to a data entry module as well as CATI and CAPI instruments.  The
goals of Integrated Survey Processing include the one-time
specification of the data, which in turn would reduce duplication of
effort and reduce the numbers of errors introduced into the system due
to multiple specifications.
Linear edits - Edits arising from linear constraints.  For example:
     a. a<= F <=b.
     b. a+b=c+d.
Local area network (LAN) - A group of microcomputers hooked together
and which share memory and processing resources.  Important to editing
in that a LAN may be able to handle some editing tasks that might
overwhelm one microcomputer while at the same time avoiding expensive
processing on a mainframe.
Macro-edit - Detection of individual errors by: 1) checks on
aggregated data, or 2) checks applied to the whole body of records.
The checks are based on the impact on the estimates, (Granquist, 1987)
Micro-edit - editing done at the record, or questionnaire level.
Minimal set of fields to impute - The smallest set of fields requiring
imputation that will guarantee that all edits are passed.  See also
"Weighted minimal set".
Multivariate edit - A type of statistical edit where multivariate
distributions are used to evaluate the data and to find outliers.
88
Nonlinear edits - Edits from nonlinear constraints.  For example:
     a.   Ratio edits
     b.   Conditional edits
     The importance of nonlinear edits is that they occur often but
     are not amendable to theory in the determination of a al set.
     Some nonlinear edits, such as ratio edits, can be cast in a
     linear form.
Ratio edit - An edit in which the value of a ratio of two fields lies
between specified bounds.  The U.S. Bureau of the Census has
implemented an automated editing and imputation system in the special
case where all edits are ratio checks.
Repeatability - The concept that survey procedures should be
repeatable from survey to survey and from location to location; the
same data processed twice should yield the same results. (Also called
reproducibility.)
Specifications generator - A module in an editing system from which
files for paper questionnaires, data entry modules, editing software,
CATI, CAPI, and summary software are generated.  The specifications
generator is the unifying feature in Integrated Survey Processing
software.  In the Blaise system, the Blaise Questionnaire can be
considered to be a specifications generator.  The specifications
generator contains information relating to the data to be collected
as well as to the edits and routes to applied to the data.
Statistical edit - A set of checks based on statistical analysis of
respondent data, e. g., the ratio of two fields lies between limits
determined by a statistical analysis of that ratio for presumed valid
reporters (Greenberg and Surdi, 1984).  A statistical edit may
incorporate cross-record checks, e. g., the comparison of the value
of an item in one record against a frequency distribution for that
item for all records.  A statistical edit may also use historical data
on a firm by firm basis in a time series modeling procedure.
Stochastic edit - An edit which ff violated points to an error in the
data with probability less than one.  Example: 80 < yield < 120.
Contrast with deterministic edit.
Subject-based edit - Checks incorporating real-world structures. which
are neither statistical nor structural.  Example: wages paid / hours
worked > minimum wage.
Validation edits - Edits checks which are made between fields in a
particular record.  This includes the checking of every field of every
record to ascertain whether it contains a valid entry and the checking
that entries are consistent with each other.
Weighted minimal set - A set in which fields are weighted according
to reliability in generating amputations.  All other things being
equal, a choice of two or more minimal sets with the same number of
elements is made by choosing the minimal set with the higher weight.
                                 89
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