The use of linked administrative data to tackle non response and 

attrition in longitudinal studies 

Andrew Ledger & James Halse

Department for Children, Schools & Families (UK) 

Andrew.Ledger@dcsf.gsi.gov.uk

James.Halse@dcsf.gsi.gov.uk

Abstract 

Most social surveys encounter problems through non response. In longitudinal studies, this problem is compounded by 

attrition between waves and the combined non response across the course of a study can lead to significant bias and 

inflation of errors. It is often possible to deal with the effects of attrition in later waves through use of data collected in 

earlier waves before a subject dropped out of the study. Techniques like CHAID analysis or logistic regression can then 

be used to weight the data back to be representative of the data from the first wave. However, most often we know 

nothing about those who do not respond at the first wave and this is generally where non response is highest. Although it 

is possible to calibrate data to take some account of this, it can be a serious problem if non response patterns contain 

significant bias. 

This paper considers the improvements that can be made to longitudinal studies through the availability of individualised 

administrative records. In particular, administrative data offers information about those who do not respond to surveys. 

This enables a more sophisticated approach towards analysing non response and attrition and also enables better planning 

for future studies. With reference to two large scale longitudinal studies of young people carried out in England, this 

paper shows how the availability of administrative data can be used to make significant methodological improvements. 

Sources of data used in this work

This paper focuses on two surveys: the Longitudinal Study of Young People in England (LSYPE) and the Youth Cohort 

Study (YCS). YCS, which has run for more than 20 years, has mainly been a postal survey with telephone follow up 

where possible for non-respondents while LSYPE has used face to face interviewing so far over 4 waves. Both studies try 

to track young people around the time when they finish compulsory education (YCS starts at age 16/17, LSYPE starts at 

13/14) and to follow them to further education or into the labour market. YCS has just embarked on its 13th cohort while 

LSYPE has only had one. Both are fairly large exercises. YCS has tended to have an issued sample of around 30 

thousand for the first wave, while LSYPE had an issued sample of 21 thousand at the first wave. 

In addition to the survey data, in recent years, it has become possible to use the National Pupil Database (NPD); an 

administrative record of all pupils in maintained (not independent) schools in England. This record contains limited 

demographic information as well as individual level examination results and other indicators – such as whether a child is 

entitled to Free School Meals (FSM - a proxy for poverty) or whether a child has Special Educational Needs (SEN). The 

main interest as regards academic attainment is in a young person’s attainment in examinations known as GCSEs. These 

are taken at age 15/16 and whether or not a young person attains 5 ‘good’ GCSEs (meaning at grades A*, A, B or C) is 

often used as a threshold measure for success. Other academic attainment is recorded at younger ages, but is used less 

often. 

NPD provides a reasonably complete record and can provide the basis for a sampling frame, or can be linked in to survey 

responses at a later stage. Around 7 per cent of young people attend independent schools and will not have a full record, 

although many of these will have been at a maintained school at some point and will have some details recorded. For the 

rest, some information is still available in the form of results for the main compulsory school age examinations (GCSEs), 

but little else is recorded. 

Problems of non-response and attrition

Much attention is paid to response rates at the first wave of a longitudinal study. However, while this is an important 

measure, also important are the patterns of response and the levels of attrition between waves. YCS offers a good 

illustration of the difficulties faced in longitudinal studies. For many years the response rate has been falling and serious 

biases are evident from the data. At wave 1, there is a large discrepancy between response rates between higher and lower 

attainers, but this is exacerbated further as attrition affects lower attainers disproportionately. Clearly with only some 3 

per cent remaining after wave 4 of those in the lowest attainment bands, results have to be treated with caution and this is 

especially unfortunate as these people are often of greatest policy interest. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 1: Cumulative response rates in YCS 12, by wave and by attainment of sample 

Wave 1 Wave 2 Wave 3 Wave 4 

GCSE attainment 

None 19 9 5 3 

1-4 D-G 21 10 6 3 

5+ D-G 30 18 11 6 

1-4 A*-C 38 23 15 9 

5-7 A*-C 47 32 22 14 

8+ A*-C 63 48 36 24 

All 46 32 22 14 

Source Youth Cohort Study, Cohort 12, waves 1-4, 2004-2007 

It is reasonably clear how we might apply weighting to adjust for the problems caused by non-response. By calibrating 

the data so that, for example, the level of attainment of the responding sample members is equal to the level of attainment 

of the overall cohort, we can improve the usefulness of the data. This technique can be improved further by use of other 

variables. The weighting procedure for the first wave of Cohort 12 of YCS involved the construction of 3 sets of weights: 

selection weights; ethnic boost weights and non-response weights. The first two are readily calculable before the start of 

fieldwork, but clearly the final component part of the overall weights (the product of the three detailed above) depends on 

what happens during fieldwork. The technique used was cell weighting in which a population matrix is compared against 

a sample matrix and response rates calculated for each cell. Choosing the variables that define the matrix is not always 

straightforward, but as with the previous cohort weighting, the variables used were region, school type, gender and level 

of attainment. 

To carry out an exercise like this, we may use administrative data for those individuals who did not respond or we may 

not – because we know what national figures are, the aggregated administrative data are just about good enough on their 

own. However, where individual level non response is especially important is in enabling us to look in detail at the 

characteristics of non-responders. If, early on in a survey, we can tell that the response rate from certain groups of young 

people is low (perhaps those of a certain ethnic background, for example), then we can ensure that special measures are 

taken to compensate. This might include boosting the survey for this group, attempting to convert early refusals, moving 

senior interviewers onto such cases and anything else appropriate. In this way we can mitigate against some of the 

problems about which we would otherwise find out only when we have finished the survey. 

The following table shows how response rates can vary across groups, especially in a postal survey such as YCS Cohort 

12, wave 1. This analysis uses administrative data and covers only young people in maintained schools in England (the 

full sample for YCS includes young people in independent schools and also a sample of young people from Wales – 

another of the constituent countries of the UK). The differences are not as marked as in table 1 since it is attainment that

tends to have the strongest influence on response. However, there are still some marked differences – even by gender, we 

have more than a 10 percentage point difference in response at the first wave. Much of these differences can be explained 

by reference to the differences present by attainment. Boys tend to have lower attainment than girls and those eligible for 

Free School Meals tend to have lower attainment. The situation by ethnicity is less clear. Some ethnic groups have higher 

attainment that the white majority – in particular those from Indian and Chinese backgrounds, although here these are 

mixed with groups such as Pakistani and Bangladeshi who tend to have lower attainment and lower response rates. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 2: Cumulative response rates in YCS 12, by wave and by characteristics

Wave 1 Wave 2 Wave 3 Wave 4 

Female 52 37 27 18 

Male 40 27 18 11 

Non FSM 48 34 24 16 

FSM 31 18 12 7 

Asian 45 32 23 16 

Black 31 21 14 8 

Other ethnic group 42 28 20 14 

Unclassified 43 30 20 11 

White 47 33 23 15 

Total 46 32 22 14 

Source Youth Cohort Study, Cohort 12, waves 1-4, 2004-2007 

Using administrative data for planning surveys 

As well as early intervention, the use of administrative data is fundamental in enabling better planning for future studies. 

In the planning stages for YCS 13, it was possible to use the data from Cohort 12 to simulate the changing composition 

of the sample given the likely levels of response at each wave of the survey. An initial aim of this developmental work 

was to ensure a nationally representative sample by the end of the fourth wave of Cohort 13 on the assumption that 

Cohort 13 was to be carried out in much the same way as previous cohorts. However, based on the patterns of response 

that we observed in Cohort 12 and the characteristics of the non-responders at each wave, it became clear that in order to 

have a good number of very low attainers in the sample at wave 4, we would need to include a large number at the first 

wave. This would actually represent something approaching a third of the total national number of pupils with no 

qualifications since, thankfully, this is a rare outcome. 

Table 3: Optimum sample method required for Cohort 13 if it had been face to face

Actual attainment Initial sample needed to ensure a 

distribution for 2003 representative sample at 4th wave 

percentages percentages 

None 3.5 14.7 

1-4 D-G 3.4 8.1 

5+ D-G 16.9 25.5 

1-4 A*-C 24.1 26.0 

5-7 A*-C 15.8 11.2 

8+ A*-C 36.2 14.5 

Source: Calculations based on Youth Cohort Study, Cohort 12, waves 1-4, 2004-2007 

A further consideration in the planning for Cohort 13 of YCS was that LSYPE appeared to have been successful in 

avoiding the usual problems of differential non-response. The table below shows responses at wave 1, by some of the 

main categories across which response often varies considerably. The intention was to lift response rates to 70 per cent 

and to reduce the differential non-response which had been so clear in YCS. Despite the planned approach, we still 

expected to see significant levels of differential non-response – varying according to ethnicity and to geographical area, 

for example. However, the wave 1 patterns of response were remarkably consistent. It is unusual for surveys in the UK to 

have such consistent response rates; often response is poor amongst certain ethnic groups and in London, for example. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 4: Response rates for wave 1 of LSYPE, by characteristics 

full response partial response Non response 

White 67 9 25 

Black 56 12 33 

Asian 62 15 23 

Other 58 10 32 

Male 65 10 25 

Female 64 10 26 

FSM 59 13 28 

Non FSM 66 9 25 

Independent 56 11 32 

Maintained 64 10 26 

London 60 9 31 

Other England 65 10 25 

Overall 64 10 26 

Note: partial response usually means we could not interview both parents 

Source: LSYPE, wave 1, 2004 

Putting these two pieces of information together (the difficulties of ensuring a representative sample from our existing 

method and the potential benefits of moving to a face to face methodology), it was a natural consequence to move YCS 

to a face to face survey for the first wave of Cohort 13. This has been successful with a much improved response rate 

overall and much less evidence of differential non response. The final response rate was around 70 per cent, compared to 

below 50 per cent for the postal methodology of the previous cohort. There was some difference according to 

characteristics such as attainment, but much less than previously. Although cost considerations meant that our sample 

had to be much smaller, the higher response and more even non-response make this a price worth paying. The issued 

sample under face to face interviewing was only a fraction of what it would have been with postal interviewing and 

although even the achieved sample was also significantly smaller, the removal of serious bias is a major benefit. 

Another immediate benefit of administrative data is that sample design is a much more exact science. The issued sample 

can include precise numbers in any given category included in the administrative data and precise boosts can be 

implemented. Most such surveys in England contain boosts for young people in Minority Ethnic Groups since ethnicity is 

recorded as standard on the administrative datasets. For LSYPE, the initial design included boosts for the 6 main 

Minority Ethnic Groups (Indian, Pakistani, Bangladeshi, Black African, Black Caribbean and Mixed). 

Using administrative data during surveys 

While the above improvements are concerned with the early stages of planning for a study, administrative data also have 

their uses as a study progresses. One obvious example is that the availability of administrative data can reduce the need 

for questions. In both LSYPE and YCS, we no longer have to ask respondents about standard academic qualifications 

they have achieved. This may seem like a minor point, but in fact the questions on qualifications were some of the most 

complex and, in some cases, monotonous for respondents to complete. It is thought that being able to remove this section 

of questions from our questionnaires had a significant effect on response. 

In the case of LSYPE, another way in which administrative data can be used emerged later on in the study. On analysing 

wave 1 data, although patterns of individual level response were generally even across groups of interest, it became clear 

that there was something of a problem in a different form of non-response. In order to contact young people and their 

parents, the study team had to contact schools to request personal details for the chosen sample members since, at that 

time, address details were not held on the administrative record. The level of school response in this first stage of 

sampling was satisfactory overall (over 74 per cent). However, school response was somewhat biased in that nonresponse was greater amongst schools in inner city, deprived areas; 67 per cent in deprived areas compared to 78 per cent 

in non deprived areas. These deprived areas tend to have larger concentrations of certain groups and, in particular, it 

became clear that we would not be able to meet our targets for those of Black origin (both Black African and Black 

Caribbean). By the time that this problem came to light, address details had been added to the schools census 

administrative record (one of the main reasons being to make it easier to draw samples). So, we were able to reissue those 

sample members from these particular ethnic groups in order to restore numbers from Wave 4 onwards. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The differences between non-response and attrition 

In most, if not all, longitudinal studies the drop out between waves will be less than the initial levels of non-response. 

This raises the possibility that the characteristics of those dropping out at different times may be different. As an example 

of what we might see, the following table shows drop out at different stages of YCS Cohort 12. This presents data based 

on that already seen in earlier tables, but looks specifically at the percentage of each group that drops out at each wave. 

This highlights the differences between initial non response and attrition in the later waves. There are a couple of patterns 

that hold for most disaggregations. Firstly, drop out is always less at wave 2 than wave 1, and much the same at wave 3 

as it is at wave 2. However, drop out at wave 4 is generally higher than at wave 3, though interestingly this is not true for 

the very low attaining group. Secondly, not surprisingly, high non response at wave 1 is associated with higher rates of 

drop out in later waves. However, some gaps close considerably – compare the difference between drop out of Asian and 

Black young people in waves 2 and 3 for example. 

Table 5: Drop out rates at each wave of YCS cohort 12, by characteristics 

Wave 1 Wave 2 Wave 3 Wave 4 

GCSE attainment 

None 81 52 47 42 

1-4 D-G 79 50 41 44 

5+ D-G 70 41 39 46 

1-4 A*-C 62 39 34 39 

5-7 A*-C 53 32 33 36 

8+ A*-C 37 23 25 33 

Female 48 28 29 33 

Male 60 34 31 40 

Non FSM 52 29 29 35 

FSM 69 41 35 43 

Asian 55 29 29 32 

Black 69 32 32 41 

Other ethnic group 58 33 28 33 

Unclassified 57 31 34 41 

White 53 30 30 36 

Total 54 30 30 36 

Source Youth Cohort Study, Cohort 12, waves 1-4, 2004-2007 

It is important to consider what we would know about such cases in the absence of administrative data. Without such 

knowledge, the table would look like the Table 6 below. The key difference is that we know very little about nonresponse at wave 1. It is generally possible to collect equivalent data in subsequent waves (though, in this case, it would 

not be possible to collect information on whether or not the young person was eligible for Free School Meals), but 

information on those not responding at wave 1 is gone for good. While Table 5 offers some reassurance that, in the case 

of YCS, patterns of attrition are reasonably well matched to patterns of non-response, this may not always be the case. 

Under other circumstances, such as a change in mode between waves 1 and 2, it would be more likely that important 

differences would be hidden in the absence of administrative data. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 6: Table 5 as it would look without the use of administrative data

Wave 1 Wave 2 Wave 3 Wave 4 

GCSE attainment 

None ? 52 47 42 

1-4 D-G ? 50 41 44 

5+ D-G ? 41 39 46 

1-4 A*-C ? 39 34 39 

5-7 A*-C ? 32 33 36 

8+ A*-C ? 23 25 33 

Female ? 28 29 33 

Male ? 34 31 40 

Non FSM ? ? ? ? 

FSM ? ? ? ? 

Asian ? 29 29 32 

Black ? 32 32 41 

Other ethnic group ? 33 28 33 

Unclassified ? 31 34 41 

White ? 30 30 36 

Total 54 30 30 36 

Source Youth Cohort Study, Cohort 12, waves 1-4, 2004-2007 

It is clear that the availability and use of administrative data adds greatly to our understanding of what is going on. Even 

if administrative data are not used to correct the dataset through weighting, it is vital in being able to check any 

correction carried out. 

Theory into practice – weighting in LSYPE 

The use of administrative data in LSYPE has always been a major part of its design and strength. Initially administrative 

data were used as a sampling frame and have been used since to apply weighting to the data. As with the approach in 

YCS, a matrix approach was used at wave 1, comparing survey totals against actual population totals. Variables used 

were ethnicity, region (primarily whether in London or not), gender and attainment. Some additional calculations were 

needed to deal with the small numbers of young people who were not in maintained schools – since the full range of 

administrative data are not available for those in independent schools. In these cases, the only variables that could be 

used for weighting were region (London or not London) and type of school, depending on whether it was single sex or 

mixed. The weights from the independent and maintained schools then had to be combined and reweighted to reflect the 

proper proportion of young people in independent schools; around 7 per cent, compared to 4 per cent of the sample. 

At wave 2, there is again the question of whether the attrition between waves 1 and 2 is similar in characteristics to the 

non response at wave 1. Although at the time of writing, this weighting process for wave 2 is yet to be finalised, early 

indications are that, for certain variables, there is actually more bias in wave 1 to 2 attrition than there was in initial non 

response. The following table presents some of the odds ratios and shows how although there is less drop out at wave 2 

than wave 1, we should not assume that it is less of a problem. In these cases below the odds ratios change markedly – 

there is more differential attrition than there was differential non response at wave 1. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 7: Comparison of drop out odds ratios in waves 1 and 2 of LSYPE 

Wave 1 Wave 2 

Reached targets at 14 in Maths (yes vs. no) 1.28 1.71 

Reached targets at 14 in English (yes vs. no) 1.34 1.80 

Reached targets at 14 in Science (yes vs. no) 1.34 1.82 

Eligible for FSM (yes vs. no) 0.86 0.63 

School with high levels of FSM (yes vs. no) 0.80 0.64 

note: odds ratios measured as chance of response / chance of non response

Source: provisional analysis of LSYPE responses at waves 1 and 2 

It is clear that the presence of administrative data enables us to take account of these sorts of differences at different 

waves in order to apply corrective weighting. As shown in table 6, in the absence of administrative data, the best option 

is simply to assume that patterns of initial non response mirror the patterns of attrition seen later on. However, as the 

above practical example shows, this assumption is not always valid. 

Conclusions 

The availability of administrative data can enable significant improvements to many stages of the survey process: 

• Initial samples can be defined more precisely 

• Fieldwork can be influenced by more detailed data 

• Samples can easily be refreshed if necessary 

• Questionnaire length can be reduced 

• Data can be better weighted at every wave 

All of these improvements are potentially valuable and although such comprehensive administrative data are not always 

available, it is well worth considering potential sources early on in the planning process. This involves finding out what 

sources may be available, considering how they might be used and, perhaps most importantly of all, ensuring that the 

study is legally able to use such data. In the UK, data protection laws are quite strict and it is important to know what 

requirements have to be met.