1

A Generalized Edit and Analysis System for Agricultural

Data

Dale Atkinson and Carol House

__________________________________________________________________________________

The transfer of the census of agriculture from the U.S. Bureau of the Census to the National

Agricultural Statistics service provided an opportunity for the Agency to improve both the census and

its ongoing survey and estimation program through effective integration of the two. This paper

addresses the re-engineering of the census processing system into a generalized edit and analysis

system for use on the broad spectrum of agricultural data. The paper discusses issues such as edit

philosophy, data capture, macro and micro graphical analysis.

KEY WORDS: edit, analysis, integrated, interactive, seamless, modular design.

__________________________________________________________________________________

1 BACKGROUND1

 In 1997 the responsibility for the

quinquennial census of agriculture was

transferred from the U.S. Bureau of the

Census (BOC) to the National

Agricultural Statistics Service (NASS) in

the U.S. Department of Agriculture. This

fulfilled a goal of NASS to become the

national source of all essential statistics

related to U.S. agriculture. It also

provided an opportunity for the Agency

to improve both the census and its

ongoing survey and estimation program

through effective integration of the two.

 The timing of the transfer, however,

severely limited the changes NASS could

make for the 1997 Census of Agriculture.

To complete this census NASS formed a

Census Division that had primary

responsibility for managing the day-today operations of the census activities.

This Division included former BOC

employees who transferred to NASS with

the census. Much of the data collection,

data capture and editing was contracted

out to the BOC’s National Processing

Center (NPC) in Jeffersonville, Indiana,

which had also assumed these functions

in prior censuses.

 NASS was able to make significant

changes in some of the census processes.

Specifically, the Agency was able to

utilize its 45 State Statistical Offices

(SSOs) in coordinating census data

collection with that of its ongoing survey

program. The SSOs also played a key

role in the processes from macro-level

editing through the final review of the

data for publication. In previous censuses

these processes had been centralized and

the States’ data were reviewed

sequentially, in a pre-determined order.

1This paper was presented at the Conference

on Agricultural and Environmental Statistical

Applications in Rome (CAESAR), June 5-7, 2001.

Both authors are with the National Agricultural Statistics

Service, Research and Development Division. Dale

Atkinson is the Chief, Census and Survey Research

Branch. Carol House is the Division Director.
2

By decentralizing the review process, the

States’ data were reviewed concurrently -

- significantly reducing the time from

initial data aggregation to publication.

This allowed the publication of 1997

census data a year earlier than those of

previous censuses.

 However, some of the main benefits of

NASS acquiring the census of agriculture

have yet to be realized. In particular, a

proper integration of the census program

with NASS’ traditional program figures

to improve the quality and efficiency of

each. These are benefits that Agency

management has targeted for 2002 and

beyond. To begin the process of

integrating the programs NASS took two

major steps. The first of these was the

creation in late 1998 of the Project to

Reengineer and Integrate Statistical

Methods (PRISM). The team named to

manage this project was charged with

conducting a comprehensive review of all

aspects of the NASS statistical program

and recommending any needed changes.

The second step was a major structural

reorganization of the Agency. This

reorganization essentially absorbed the

staff and functions of the Census

Division, as formed for the 1997 census,

into an enhanced survey/census

functional structure. The reorganization

was designed to increase efficiency and

eliminate duplication of effort by

integrating census responsibilities

throughout the structure. 

2 INTRODUCTION

 The census processing system needed to

be reengineered prior to 2002. With the

transfer of census responsibility in 1997,

NASS had inherited an aging system that

had been used, largely unmodified, since

1982. It was out-of-date technologywise and, to a lesser extent, methodologywise. The system was relatively

inflexible in that decision logic tables

(DLTs) were “hard coded” in Fortran. It

was programmed to run on aging DEC

VAX machines running the VMS

operating system. While manual review

and correction could be performed on

standard PC screens, some functionality

was lost when the system was used with

display terminals other than the amberscreened DEC terminals for which it was

designed. In general, the record review

and correction process at both the microand macro-levels involved navigating an

often-frustrating combination of function

and control keys. The system had served

its purpose through the processing of the

1997 census, but it was time for a more

up-to-date system.

 In September 1999 the Processing

Methodology Sub-Team of PRISM was

chartered to specify a new edit,

imputation and analysis system for the

2002 Census of Agriculture and

subsequent, large NASS surveys. This

group reviewed editing literature and

processing systems used in NASS and

other organizations (U.S. Bureau of the

Census, 1996 and Weir, 1996) to

synthesize the best of what was available

into its recommendations for the new

system. In February it published its

findings and recommendations in an

internal Agency research report. The

report highlighted the team’s guiding

principles, as follows:
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 1) Automate as much as possible,

minimizing re q u ired manual

intervention – Having dealt exclusively

with much smaller sample surveys in the

past, the NASS culture has been to touch

every questionnaire and have statisticians

manually specify needed data changes in

response to automated edit flags. The

sheer volume of data precludes this

option for the census and necessitates a

s y stem that make s mo r e

edi ting/imputation decisi o n s

automatically, without manua l

intervention 

 2) Adopt a “less is more” philosophy to

editing – There’s a tendency in many

organizations to over-edit data --

automatically and/or manually. A leaner

edit that focuses on critical data problems

is less resource intensive and often more

effective than a more complex one.

 3) Identify real data and edit problems

as early as possible -- One of the

concerns about the edit used for the 1997

census was that SSO analysts had nothing

to review from the highly automated

process for several months after editing

started. Except for a few who were

temporarily detailed to NPC to correct

edit failures, SSO statisticians were

unable to see the data until they were

weighted for nonresponse and

aggregated. This was often six months

after initial data collection. The delay

caused problems that could have been

more effectively handled earlier in the

process and imposed additional stress on

the SSOs by compli cating and

compressing their data review time

 4) Design a system that works

seamlessly – While ‘seamless’ means

different things to different people, what

is needed is a system in which all the

components interrelate smoothly such

that the analyst can quickly and easily

navigate to any screen and get any

auxiliary data needed to identify and

resolve a data problem. A system is

definitely not seamless if the user has to

log into various computer systems

separately to obtain needed auxiliary data

or run an ad hoc query. Lack of

‘seamlessness’ was a problem that

reduced the effectiveness of the 1997

census processing system.

 5) Use the best features of existing

products in developing the new system --

By the time the 1997 Census of

Agriculture was completely put to rest,

the 2002 Census of Agriculture was

uncomfortably close at hand. The short

developmental time would preclude “reinventing the wheel.” It was imperative

that NASS incorporate the best aspects of

what had already been done researchwise and developmentally in NASS and

other organizations to expedite the

process as much as possible. 

 In view of the above guiding principles

the sub-team documented the features it

felt the new system should include

(Processing Methodology Sub-Team,

2000). Considerable emphasis was

placed on minimizing unnecessary review

and on the visual display of data. The

sub-team discussed display attributes and

methodologies that could be used to

identify problematic data with high

potential impact on published estimates.

The ‘features’ section of their paper
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discussed the issue of refreshing the

review screens as error corrections are

made and stressed the need for the system

to help manage the review process (i.e., to

identify records that had already been

reviewed, through color and/or special

characters). The sub-team concluded its

paper with the f ollowin g

recommendations:

i) To the extent possible, use

Fellegi-Holt methodology in the new

system.

ii) Have the computer

automatically correct everything with

imputation at the micro-level (i.e.,

eliminate the requirement for manual

review).

iii) Utilize the NASS data

warehouse as the primary repository of

historical data and ensure that it is

directly accessible by all modules of the

new system. 

iv) Design the system with

tracking and diagnostic capabilities to

enable the monitoring of the effect of

editing and imputation. Develop

analytics for a quality assurance program

to ensure edited/imputed data are trusted. 

v) Incorporate a score function to

prioritize manual review.

vi) Provide universal access to

data and program execution within the

Agency.

vii) Ensure that the system is

integrated into the Agency’s overall

information technology architecture.

viii) Make the system generalized

enough, through modular design, to work

over the entire scope of the Agency’s

survey and census programs.

ix) Enable users to enter and

access comments anywhere in the system.

x) Present as much pertinent

information as possible on each screen of

the system and provide on-screen help for

system navigation.

xi) Consider the use of browser

and Java programming technology to

assist in integrating parts of the system

across software, hardware, and functions.

xii) Designate a developmental

team to take this report, develop detailed

specifications and begin programming the

system.

3 THE SYSTEM DEVELOPMENT

In response to recommendation xii, a

number of working groups were formed

to focus on various aspects of the

processing system development. These

included groups addressing check-in, data

capture, edit specifications, interactive

data review (IDR) screens, imputation,

analysis, and census coverage evaluation.

In order to ensure consistency of

decisions across the working groups in

assembling the system an oversight and

technical decision-making body, the

Processing Sub-Team, was formed of the

leaders of the individual working groups.

This sub-team was charged with

considering the overall system flow and

ensuring that the individual modules

work together effectively. The sub-team
5

members keep each other informed about

the activities of their individual groups,

thus ensuring consistency and that

required connectivity is addressed. The

sub-team also serves as the technical

decision-making body for crosscutting

decisions that can’t be made by the

individual working groups. The following

sections describe plans for selected

modules of the system, the progress made

to date and some key issues that the

working groups are grappling with. 

3.1 Data Capture

 As was the case in 1997, NASS will

contract the printing, mailing and checkin of questionnaires and the data capture

activities to NPC. While all data capture

for the 1997 Census of Agriculture was

accomplished through key-entry, NASS’

early discussions in preparing for 2002

indicated that scanning could be used to

capture both an image of the

questionnaire for interactive data review

and the data itself, through

optical/intelligent character recognition

(OCR/ICR). Preliminary testing done

with the Agency’s Retail Seed Survey

supported the practicality of using

scanning for data capture. Testing of the

OCR/ICR process for this survey was

conducted at three different confidence

levels (65, 75 and 85%). The outcome of

this small test was that at 65%, 4% of the

characters were questionable; at 75%, 5-

7%; and at 85%, 13%. 

 NASS will utilize scanning with

OCR/ICR as the primary mode of data

capture for 2002. Current plans are to

start with the industry standard

confidence level of 85%, but this might

be adjusted with further experience in

using the system with agricultural census

data. Results from the recently

completed census of agriculture content

test should help fine-tune the process.

Questionable returns will be reviewed,

with erroneous data re-entered by correctfrom-image (CFI) key-entry operators.

The scanning process will produce data

and image files, which will be sent to the

Agency’s leased mainframe computers at

the National Information Technology

Center (NITC) in Kansas City, Missouri

for further processing. The data will pass

into the editing system and the images

will be brought into the interactive data

review screens that will be activated from

the analysis system to review and correct

problematic data.

3.2 Edit 

 As the edit groups began to meet on a

regular basis the magnitude of the task of

developing a new editing system became

obvious. The machine edit/imputation

used for the 1997 census was enormous.

It had consisted of 54 sequentially run

modules of approximately 50,000 lines of

Fortran code, and the sheer volume of the

input decision logic tables (DLTs) was

staggering. Through 1997, the census

questionnaires had changed very little

from one census to the next, so the DLTs

and Fortran code had required little

modification. For 2002, however, an

entirely new processing system would be

built on a questionnaire that was also

undergoing radical changes. Some of the

questionnaire changes were necessitated

by recent structural changes in

agricultural production and marketing,

while others were due to the planned use
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of OCR/ICR for data capture. In any

case, the group members were saddled

with the onerous task of working through

the mountainous DLTs from 1997 to

determine what routines were still

applicable and, of these, which should be

included in the ne w s yst em

specifications. 

 One of the key edit issues is reducing

manual review without damaging data

quality. In processing the 1997 census

data, the complex edit corrected all

critical errors and the staff at

Jeffersonville manually reviewed ALL

"warning" errors. The approximate

workload and time invested in this

activity follows:

C Approximately 1,800,000 records

passed through the edit at least

once. Of these, 470,000 (26%)

were flagged with warning errors.

About 200,000 (47%) of the

flagged records required updates.

C About 4,000 staff days were spent

performing the review in

Jeffersonville.

 For 2002, the edit review (and analysis)

will be performed in NASS’ SSOs.

Considering the expected staff shortages

in 2002 relative to 1997, the above

figures would represent an intolerable

commitment of staff resources.

Furthermore, indications are that this

amount of manual review is not

altogether needed or (in some cases)

desirable. Table 1 shows the relative

impact of 1) the automatic (computer)

edit changes with no manual review; 2)

edit changes with/from manual review

and 3) changes made during analytic

review. Due to deficiencies in the edit

coding, some changes made totally by

computer could not be cleanly broken out

from those with manual intervention,

resulting in an overstatement of the

manual edit effect. All changes made

during analytic review resulted from

human interaction and are considered part

of the impact of manual review.

 Table 1 shows that the overall effect of

the edit/imputation/analysis process was

relatively small for most items, especially

crop acreages. Considerably larger

adjustments are required for both

nonresponse and undercoverage. While

admittedly these numbers only reflect the

impact on high-level aggregates (U.S.

level) and the processing can often be

more beneficial at lower levels (e.g.,

county totals), the size of the adjustments

still raises questions about the efficacy of

the extremely resource-intensive data

editing and review process. Such

considerations underpinned our two

guiding principles of 1) adopting a “less

is more” philosophy to editing and 2)

automating as much as possible.

3.3 Imputation

 Certainly one of the key considerations

in moving to an automated system is

determining how to impute for missing

and erroneous data. The imputation

group is currently working through the

census questionnaire “question by

question” and “section by section” to
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Table1: Relative Impact of the Editing/Imputation/Analysis Processing of the

1997 Census of Agriculture Data (U.S. Level)

Characteristic

Net Effect of

Automated Edit

Changes

(%)

Net Effect of

Edit Manual 

 Review

(%)

Net Effect of

Analytic

Review

(%)

Total

Effect

(%)

Total

Manual

Effect

(%)

Corn Acres (0.24) (3.97) 0.26 (3.94) (3.71)

Soybean Acres (0.20) (2.33) 0.31 (2.22) (2.02)

Wheat Acres (0.69) (4.18) (0.01) (4.88) (4.19)

Cotton Acres (0.10) (0.29) (0.27) (0.66) (0.56)

Cranberry Acres 0.13 1.72 (4.04) (2.18) (2.32)

No. of Cattle 0.74 4.75 (0.74) 4.74 4.01

No. of Hogs 0.17 (4.23) (3.92) (7.98) (8.15)

determine the most effective routines to

use. Nearest-neighbor donor imputation

will play a strong role in filling data gaps.

The group is currently developing a

SASÒ-based donor imputation module,

which will provide near optimal

imputations in certain situations where

high quality matching variables are

available. The group will be leveraging

the Agency’s relatively new data

warehouse capabilities of providing

previously reported survey data. The

data warehouse was populated with the

1997 census data and contains the data

from most of the Agency’s surveys since

1997. As such, it serves as a valuable

input into the imputation process, since

many of the respondents in the current

survey will have responded to one or

more prior surveys. The warehouse data

can provide direct imputations in some

cases and identify items requiring

imputation in many others. 

 A review of the imputation done for the

1997 Census of Agriculture and the

current plans for 2002 indicates the vast

majority of the imputation performed will

be deterministic (e.g., forcing subparts to

equal a total). Deterministic imputation

could amount to 70-80% of all imputation

for the 2002 Census of Agriculture.

Nearest neighbor donor imputation will

likely account for 10-20%, while direct

imputation of historical data, perhaps 5-

10%. 

3.4 Analysis

3.4.1 General Description. The analysis

system is perhaps the module of interest

to the broadest audience in NASS. This

module will provide the tools and

functionality through which analysts in

Headquarters and our SSOs will interact

with the data. All processes prior to this

point are ones with no manual

intervention or, in the case of data

capture, one in which only a few will

touch the data. As one of our senior

executives aptly put it, “All this other

stuff – data capture, edit and imputation

will happen while I’m sleeping. I’m

interested in what will go on when my

eyes are open.” That’s analysis!

 Because of the broad interest in and the

expected large number of users of the
8

analysis system, the development team

has made a special effort to solicit user

input into its specification. The working

group chartered to design and program

this module circulated a hard-copy

prototype of the proposed system to staff

throughout the Agency early this year.

This exercise resulted in very useful

feedback from potential users. The

feedback received has been subsequently

worked into the module specifications.

3.4.2 Micro-Analysis. After the data

have been processed through the edit and

imputation steps, during which

essentially all critical errors have been

computer corrected, they are ready for

SSO review in the Analysis System. The

first of two analysis phases, microanalysis, begins immediately. During

micro-analysis SSOs will review (and

update, if necessary) all records for which

imputation was unsuccessful, all records

failing consistency checks, and all those

with specific items that were flagged for

mandatory review. Such records are said

to contain critical errors and must be

corrected. This work will be done while

data collection is ongoing, and will allow

ample time for any follow-up deemed

necessary. As review time permits the

system will also provide the capability to

review records that have no critical

errors, but may be nonetheless of

concern. These would include those

identified by the computer as influential

or high scoring or with potential

problems identified through graphical

analytic views. Unlike the 1997 edit,

warning errors will NOT be

automatically corrected nor require

manual intervention

A score function is being developed for

2002 Census of Agriculture to ensure that

the records manually reviewed are those

that are expected to have a substantial

impact on aggregate totals. The quality

of county aggregates is of particular

concern with the census of agriculture.

Therefore, the score function used for

2002 will be one that assigns high scores

to records whose current report for

selected characteristics represents a large

percentage of the previous census’ county

total for that characteristic.

 

 Micro-level graphics are simply a

collection of record level information

shown together for all records for specific

item(s) of interest. The user will have the

option of sub-setting the graph by

selecting a group of points or by

specifying a sub-setting condition. For

some plots, the option of additional

grouping and/or sub-grouping of a

variable(s) through the use of colors and

symbols will be available (e.g., by size of

farm, type of operation, race, total value

of production, other size groups). Scatter

plots, box-plots and frequency bar charts

of various types will be provided. All

graphics will provide drill-down

capability to data values and the IDR

screens to review and update problematic

records.

 Finally, the system will track IDs that

have been previously reviewed, compare

current values to historic data, allow for

canned and ad hoc queries and have a

comments feature to document actions.

Micro-analysis will also include tables to

review previously reported data for nonresponding units. This will allow SSOs
9

to focus nonresponse follow-up efforts on

the most “important” records.

3.4.3 Macro-Analysis. The second phase

of analysis, macro analysis, begins

immediately after preliminary weighting

(adjusting for under-coverage and nonresponse). Macro-analysis uses tables

and graphs to review data totals and farm

counts by item, county and state. While

the macro-analysis tools will retain the

key objectives of the analytical review

system used for the 1997 census, it will

be much more interactive and userfriendly. The focal point of the macroanalysis will be a collection of graphics

showing aggregate data at state and

county levels. These graphics will

include dot plots or bar charts of county

rankings with historic comparisons, state

maps with counties color-coded by

various statistics and scatter plots of

current vs. previous data.

 The new macro-analysis tool will also be

integrated more effectively with the

Agency’s data warehouse and its

associated standard tools for user-defined

ad-hoc queries. Graphics or tables will

be used to compare current census

weighted totals and farm counts against

previous census values and other

published estimates. There will be a

prepared library of database queries, in

addition to the ability to build your own.

Analysts will drill down to the IDR

screens to verify/update records. If

micro-analysis is done effectively, the

number of issues to be dealt with in this

phase will be fewer than in 1997, when

no micro-analysis module was available.

 

 The macro-edit can be run as soon as

data collection is complete, the last

records are run through edit and

imputation, and preliminary weights are

available. The objective of the macro

review will be the same as for 1997. That

is, an analyst will be responsible for the

complete review of all the state and

county totals. According to a state’s

particular needs and characteristics the

SSO’s managers can elect to either 1)

assign an analyst to a county for the

review of all items, 2) have a commodity

specialist review items by state and

county, or 3) use a combination of both.

In any case, every item in each county

must be reviewed, and a check-off system

will be provided in the analysis system to

ensure this is achieved.

4 DEVELOPMENT STATUS

 Timelines have been developed for

specification and development of the

various modules and the groups are

working hard to stick to them. Due to a

number of factors beyond their control

the developmental work started at least a

year later than it should have, considering

the magnitude of the system overhaul. In

spite of the delays and overall staff

shortages as compared to what was

available for past censuses, the groups

have done a fantastic job of moving

ahead with the developmental work.

5 ISSUES

 One of the key issues in edit

development is determining what edits

are essential to ensure the integrity of the

data without over-editing. This is

something that the edit group and
10

Processing Sub-team have struggled with.

The team members represent an

interesting blend of cultures. The longerterm, pre-census NASS staff developed

within a culture of processing the returns

from its sample surveys, where every

questionnaire is hand-reviewed and

corrected as necessary. While there is a

need for some of this extra attention for

sample surveys since survey weights can

be high, this type of approach is

nonetheless prone to manual over-editing.

The NASS staff that came over with the

census are much more comfortable with

automatic editing/imputation and are

perhaps overly interested in having the

system account for every possible data

anomaly. This approach can lead to an

excessively complex system that

automatically over-edits data.

 The combination of these two cultures

has resulted in some interesting

discussions and decisions relative to the

guiding principles of automating as much

as possible and adopting a ‘less is more’

philosophy of editing. Everyone has his

or her own pet anecdote indicating a

“crucial” situation that a reduced edit

would not identify and correct. Such

concerns have resulted in some

compromises in the editing approach

taken for 2002. The new processing

system currently being developed will

look more like a SAS version of the 1997

edit than the greatly reduced,

predominantly error-localization driven

system envisioned by the Processing

Methodology Sub-Team. The bottom

line for 2002 is that there will be 49 DLT

edit modules, which will consist of much

of the same type of intensive, sequential

“if-then” edit conditions that existed in

1997. There are some notable differences

in the processes, however. There will be

a presence of GEIS-type errorlocalization (Statistics Canada, 1998) in

the new system in addition to the 1997

style editing. Imputation has been

moved out of the edit DLTs to a separate

module of the system. This module will

make strong use of nearest-neighbor

donor imputation, enhanced by

previously reported data from the

Agency’s data warehouse. The errorlocalization presence will help ensure that

the imputations will pass all edits. The

approach to be used in 2002 will serve as

a foothold for the approach (Fellegi-Holt,

1976) initially endorsed by the Processing

Methodology Sub-Team. For 2007 there

will be a strong push to simplify the edit

and increase the error-localization

presence or move to the NIM-type

approach of Statistics Canada (Bankier,

1999).

 Another key issue in assembling the

system lies in how muc h

modularity/generality is possible. All

efforts currently are, and need to be,

directed at having a system in place and

tested for the 2002 Census of Agriculture.

Due to the tight time frame the

developmental team is working with,

some compromise on the goal of

generality is inevitable. The evolving

system is being developed modularly,

however; so some retrofitting of

generality should be possible. 

 One of the questions that have yet to be

answered is whether runtimes and

response times will be adequate? The

current plans for the processing system

are complex, requiring considerable
11

cycling through the various sections of

the questionnaire. Whether or not the

runtimes on batch aspects of the system

and response times on the interactive

portions will be within workable

tolerances will not be fully known until

more of the system is built. If the answer

in either case turns out to be negative,

short- cuts will need to be taken to make

the system workable.

 Exactly what combination of processing

platforms will be used in the final system

is another issue that has yet to be fully

decided. It will be comprised of some

combination of the Agency’s leased

mainframe, its UNIX boxes and its

Windows 98 machines on a Novell widearea network. Since the system is being

written in SAS, which will run on any of

the three platforms, the processing

platform decision has been delayed up to

now. However, in the interest of

seamless interoperability it will need to

be made soon. 

REFERENCES

Bankier, M. (1999), Experience with the

New Imputation Methodology Used in

the 1996 Canadian Census with

Extensions for Future Censuses,

Conference of European Statisticians,

June 2-4, 1999, Rome, Italy.

Fellegi, I.P. and Holt, D. (1976), A

Systematic Approach to Automatic

Edit and Imputation, Journal of the

American Statistical Association,

March 1976, Vol. 71, No. 353, pp. 17-

35.

Granquist, L. and Kovar, J. (1997),

Editing of Survey Data: How Much is

Enough? in L. Lyberg, P. Biemer, M.

Collins, E. DeLeeuw, C. Dippo, N.

Schwarz, and D. Trewin (eds.), Survey

Measurement and Process Quality.

New York: John Wiley and Sons.

Luzi, O. and Pallara, A. (1999),

Combining Macroediting and

Selective Editing to Detect Influential

Observations in a Cross-Sectional

Survey Data, Conference of European

Statisticians, June 2-4, 1999, Rome,

Italy.

Processing Methodology Sub-Team

(2000), Developing a State of the Art

Editing, Imputation and Analysis

System for the 2002 Agricultural

Census and Beyond, NASS Staff

Report, February 2000.

Statistics Canada (1998), Functional

Description of the Generalized Edit

and Imputation System, Technical

report from Statistics Canada.

U.S. Census Bureau (1996), StEPS:

Concepts and Overview, Technical

report from the U.S. Census Bureau.

Todaro, T.A. (1999), Overview and

Evaluation of the AGGIES Automated

Edit and Imputation System,

Conference of European Statisticians,

June 2-4, 1999, Rome, Italy.
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Weir, P. (1996), Graphical Editing

Analysis Query System (GEAQS),

Data Editing Workshop and

Exposition, Statistical Policy Working

Paper 25, pp. 126-136, Statistical

Policy Office, Office of Management

and Budget.