Improving the Efficiency of Data Editing and Imputation for 

a Large-Scale British Annual Business Survey 

Alaa Al-Hamad(Office for National Statistics) 

Pedro Luis do Nascimento Silva(Southampton University) 

Gary Brown(Office for National Statistics) 

Abstract 

This paper reports results from a project to evaluate and improve the editing and 

imputation approach adopted in the Annual Business Inquiry – Part 2 (ABI/2). This is 

joint work carried out by the UK Office for National Statistics (ONS) and 

Southampton University’s Statistical Sciences Research Institute. The ABI/2 is a large 

scale annual survey covering most sectors of the British economy, with an annual 

sample of around 60,000 businesses. We examined detailed specifications for the 

current editing and imputation processes, and their connections to data collection 

instruments, data capture, and estimation methods. A variety of quality indicators, 

impact measures, and statistical editing and imputation techniques were tested on 

three years of pre- and post-edited data. A number of alternative approaches to the 

overall editing and imputation process were investigated to maximise efficiency 

without impacting negatively on quality. Preliminary results suggest that these will 

yield increased benefits to the survey. 

Introduction 

In recent years the ONS has been undergoing a large efficiency programme to its 

business statistical surveys with a vision mainly concerned with increasing the 

efficiency of data processing. This programme has three aims: 

1. to cope with the ever increasing demand for statistical information within a 

shorter time period, at lower cost and at a much higher level of detail and 

quality than before; 

2. to meet financial pressures; and 

3. to generate savings for reinvestment into improved outputs. 

Currently one of the most costly components in the survey processing procedures in 

ONS is data cleaning. This is because to fulfil our role as a National statistical agency 

successfully, the organisation is always under pressure to produce high-quality data in 

order to maintain public confidence in official statistics. This leads to ONS business 

surveys having high cleaning costs – on average consuming around 40% of the total 

survey budget. Furthermore, most of the resources within data cleaning are consumed 

by data editing. In practice, editing consists mainly of two stages: 

• Error localisation/detection; 

• Error verification / correction. 

The very high costs are generally due to use of labour intensive processes for error 

verification / correction, which often involve re-contacting respondents. The 

requirement for manual intervention is introduced through the nature of the errors 
 

 

 

 

 

 

 

 

 

commonly found in business surveys. Some errors can be corrected through the 

application of logical editing rules, and some through recalculation of totals, but a 

large proportion are often simply “suspect data” that need to be queried directly with 

the businesses that provided the responses. These suspect data are rarely found to be 

in error - studies have found the “hit rate” to be as low as 20% (Rivière 2002) and this 

is found to be true for ONS surveys. Therefore, these suspect data are often simply 

confirmed by the respondents and hence pass through the data editing process 

unchanged. The benefits from querying these confirmed data are often intended to be 

informative rather than statistical, i.e., they help explain movements in the data. 

In the ONS a number of methods have been used over the years to cut costs and 

achieve more efficient editing processes. Some of these methods prioritise suspect 

data by their importance, i.e. selective editing and (to a degree) (Hidiroglou and 

Berthelot 1986). Others correct these suspect data automatically through recalculation 

and the application of logic. However, although such methods are employed for some 

surveys it was thought they are not fully utilised to achieve the maximum efficiency 

across all business surveys. As a matter of fact, some surveys have not being reviewed 

for a number of years which resulted in them costing a considerable amount of 

resources and not achieving the optimum results required. 

Therefore the aim of the study on which this paper is based is to review the current 

data editing process for a large ONS business survey, ABI/2, and to suggest ways in 

which efficiency can be improved. The approach adopted considers a holistic view of 

the editing process, rather than only some of its individual stages. Detailed 

specifications for the current editing processes will be examined, as well as their 

connections to data collection mode, data capture and the current editing 

methodology. The broader aim is mainly to learn from this exercise, and assess what 

possible improvements could be made to other ONS business surveys. However this 

paper will focus only on one aspect of this study, the consideration and use of 

selective editing procedures in an attempt to recommend more efficient alternative to 

the current editing procedure. 

Basic Characteristics of the ABI/2 

ABI/2 is the survey providing structural financial information about UK businesses on 

a yearly basis. This survey was formed in 1998 by pulling together several annual 

surveys previously carried out for specific sectors of the economy by ONS or its 

predecessors (Smith, Pont and Jones 2003). It provides estimates of totals for each 

section of the economy, with a coverage that includes sections A to O – see (ONS 

2007c) – of the Standard Industrial Classification (SIC) of economic activities, for the 

variables listed below 

Variable name / description 

• Number of enterprises 

• Total turnover 

• Approximate gross value added (GVA) at basic prices 

• Total purchases of goods, materials and services 

• Total employment, point in time 

• Total employment, average over the year 

• Total employment costs 
 

 

 

 

 

 

 

 

 

• Total net capital expenditure 

• Total net capital expenditure, acquisitions 

• Total net capital expenditure, disposals 

• Total stocks and work in progress – value at end of year 

• Total stocks and work in progress – value at beginning of year 

• Total stocks and work in progress – increase during year. 

These results are published on the internet, using tables that present time series for 

each section of the economy and each selected SIC, for all the above variables. 

To collect the data, the ABI/2 survey in 2006 used 42 different questionnaires. These 

questionnaires are different for each section of the economy, and for several sections 

(like catering, retail, etc.) there are two questionnaires: a long and more detailed 

questionnaire, used to obtain data from all the large businesses and from a sample of 

the smaller ones; and a short questionnaire, which asks for less detailed information 

from the sampled businesses not getting the long questionnaire. 

The sample for the ABI/2 is a stratified simple random sample. The stratification uses 

a combination of three variables: 

• Region (England, Scotland and Wales); 

• SIC activity codes (3 or 4 digit, depending on the region); 

• Employment size band. 

The sample allocation is highly disproportional, with all the large businesses (those 

with 250+ employees according to the survey frame extracted from the InterDepartmental Business Register - IDBR) included in the sample with certainty, and 

sampling fractions that increase with the size of the business. The overall sample size 

for the 2005 survey edition was 73,955 businesses, with 54,123 respondents. Hence 

the total nonresponse rate was 26.8%. The main mode of data collection is through 

paper questionnaires. 

For the purpose of the analysis in this paper, data will be used from only one sector of 

the economy and one questionnaire type: catering sector short form. 

Assessing Options for Selective Editing 

In this section we discuss the options being considered for tackling the efficiency of 

the editing on ABI/2 using an approach based on selective editing ideas (Hedlin 

2003), (Lawrence and McKenzie 2000) (Latouche and Berthelot 1992). 

In selective (or significance) editing, the key idea is that only a subset of the survey 

data are fully verified, or are verified using costly methods, while the editing of the 

remaining data is dealt with using cheap methods. Hence the survey records must be 

split between critical and non-critical sets. The critical records (those having an 

important expected impact on the final estimates) are submitted to all edit rules, and 

referred to reviewers whenever they fail any edits. The non-critical records are 

submitted to a smaller set of edit rules (or even no edit rules), and any edit failures are 

dealt with by automatic imputation. 
 

 

 

 

 

 

 

 

 

The goals of using selective editing are to reduce survey costs, survey processing time 

respondent burden (by limiting re-contacts), and to avoid or reduce over-editing. It 

should also help staff focus their attention on cases with the highest likely impact on 

survey estimates. 

To implement selective editing, a key decision is the choice of a score function, 

namely a function that is going to be used to split records between the critical and 

non-critical streams. There are two alternative approaches to setting up score 

functions: 

• Estimate related – score functions computed taking account of target survey 

estimates; 

• Edit related – score functions which depend on a specified set of edits. 

For the ABI/2 we will test a number of alternatives developed under both approaches. 

For the estimate related score functions, there are two main options under 

consideration. The first is simply 

dk = wk ∑i y − yˆ (1) ki ki 

where yki is the a raw value of variable i for unit k, yˆ

ki is a predicted or anticipated 

value for the same variable and observation, and wk is the survey weight attached to 

unit k. Note that yki may or may not be suspect at this stage. Only records with values 

of dk larger than a specified threshold c would be submitted to the current manual 

revision and possible respondent re-contact procedures. Some alternative versions of 

this score function would consider: 

a) All variables for which total estimates are published; 

b) A subset of variables for which estimates of totals are published. 

In addition, the edit-related score functions RATIO, FLAG and DIFF proposed by 

(Latouche, et al. 1992) would also be considered. 

Evidence from analysing edit failure rates for one sector using the current ABI/2 data 

suggest that there is good potential for savings by using selective editing. We 

examined the frequency distribution of the number of edit failures per record, 

considering both raw (unedited) data and final (clean, edited) data for two years. In 

both cases the responding businesses are the units of analysis. Tables 1 and 2 present 

the frequency distributions of number of edit failures per record for the catering short 

questionnaire in the two years considered. These tables provide a very brief summary 

of the editing process, but are useful because they allow us to assess how much 

editing takes place. The same set of edits applied in both years, and it contained 18 

edit rules. 

First, consider the proportion of records which are ‘clean’ on arrival, i.e., failed no 

edits – see the columns referring to unedited data sets. These proportions were 35-

36% in 2003-2004. The corresponding complementary proportions were around 64-

65% for 2003-2004, and represent the fraction of the ABI/2 records which were 

reviewed manually as part of the validation operation. These are large proportions, 

implying a substantial workload for the ONS validation unit. Examining the 

corresponding proportions computed from the final edited data, we observed that 

around 45-47% of the records would still be flagged by some edits, meaning that they 
 

 

had been verified but either did not change, or if they did, the changes applied could 

not clear all edit failures initially detected. 

Table 1 – Frequency distribution of number of edit failures per record – 

catering short questionnaire - 2003 

Failures per 

record 

Unedited dataset 

# of % of # of 

records records failures 

Edited dataset 

# of % of # of 

records records failures 

0 659 34.8 0 1,004 53.1 0 

1 458 24.2 458 506 26.8 506 

2 416 22.0 832 273 14.4 546 

3 157 8.3 471 59 3.1 177 

4 97 5.1 388 41 2.2 164 

5 57 3.0 285 6 0.3 30 

6 28 1.5 168 2 0.1 12 

7 15 0.8 105 - - -

8 3 0.2 24 - - -

9 1 0.1 9 - - -

Total 1,891 100.0 2,740 1,891 100.0 1,435 

Table 2 – Frequency distribution of number of edit failures per record – 

catering short questionnaire - 2004 

Failures per 

record 

Unedited dataset 

# of % of # of 

records records failures 

Edited dataset 

# of % of # of 

records records failures 

0 624 35.9 0 946 54.5 0 

1 420 24.2 420 464 26.7 464 

2 375 21.6 750 259 14.9 518 

3 163 9.4 489 56 3.2 168 

4 80 4.6 320 9 0.5 36 

5 46 2.6 230 1 0.1 5 

6 18 1.0 108 1 0.1 6 

7 8 0.5 56 - - -

8 2 0.1 16 - - -

Total 1,736 100.0 2,389 1,736 100.0 1,197 

A synthetic measure of the size of the editing operation can be obtained as the total 

number of edit failures each year, namely the sum of the cross products of the 

frequencies of records by the numbers of edit failures per record in tables 1 and 2. The 

validation unit handled 2,740 edit failures in 2003, and 2,389 in 2004, a reduction of 

12.8%. 
 

 

 

 

 

Another interesting analysis which can be derived from the calculations of total 

numbers of edit failures is the proportion of “false alarms”, namely the ratio between 

the total number of failures in the edited data divided by the number of failures in the

unedited data. Assuming that the data are “error free” at the end of the editing 

process, this indicator summarizes the degree to which the edits flag false error 

situations. The values of this proportion were 52.4% for 2003, and 50.1% for 2004. 

This demonstrates that there is substantial potential to reduce the editing effort by 

revising the edits and/or the editing approach, because half of the editing effort did 

not result in changes that would remove the edit failure markers in 2003-2004. 

So the apparent false hit rate of the editing operation was quite high (@ 50%). 

In addition to testing some alternative score functions, a thorough review of the edits

currently applied to the survey is under way. This review starts with assessing hit 

rates for each individual edit, and attempts to locate potential factors causing the 

highest hit rates, looking at questionnaire design, instructions for filling in 

questionnaires, methods used for data capture, etc. This review shall provide the basis 

for redesigning the edits applied to the data, also paving the way for use of automatic 

editing and imputation systems to handle the non-critical cases as indicated by the 

selective editing. 

Concluding Remarks 

The analysis in the previous section demonstrated that there is potential to improve 

efficiency of the editing without perhaps affecting the quality of the resulting output. 

At this stage, these findings are restricted to a single sector of the economy, and to the 

short form only. Our work proceeds in extending the analysis to other sectors, as well 

as to the testing of alternative score functions, using the raw and final datasets from 

the ABI/2 for 2003-2005. The final paper shall contain the results of this project. 

References 

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Hidiroglou, M. A., and Berthelot, J.-M. (1986), "Statistical Editing and Imputation for 

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2. 

Latouche, M., and Berthelot, J.-M. (1992), "Use of a Score Function to Prioritize and 

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400. 

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