Appendix BB - Methods Sampling and weighting NI Report NDNS 2019-2023
Source: data.gov.uk (2025-10-10)

National Diet and Nutrition Survey 2017 to 2023 Report for Northern Ireland. This report presents results from 2017-2023 of the National Diet and Nutrition Survey (NDNS) for Northern Ireland. NDNS is a continuous cross-sectional survey, designed to assess the diet, nutrient intake and nutritional status of the general population aged 18 months and over living in private households in the UK.

National Diet and Nutrition Survey: 2019 to 2023 (years 12 to 15) 
 
Appendix BB  
Sampling and weighting for National 
Diet and Nutrition Survey 2019 to 2023  
 
BB.1 Sample design  
The National Diet and Nutrition Survey (NDNS) sample covers the whole of the UK 
and is made up of a core sample and a Northern Ireland ‘boost’ sample. 
The aim of NDNS is to achieve a representative sample of 500 adults (aged 19+ 
years) and 500 children (aged 1.5 to 18 years) per year from the core UK sample. In 
addition, the Northern Ireland (NI) boost sample is required to yield a sample of 100 
adults and 100 children per year. 
To achieve the even split between adults and children, the sample is divided into 
address types: ‘basic’ addresses, where up to 2 adults (aged 19 years and over) and 
1 child (aged 1.5 to 18 years) are selected; and ‘young person’ addresses, where up 
to 2 children are selected from different NDNS age groups (1.5 to 3 years; 4 to 10 
years; 11 to 18 years). The proportion of the sample assigned as ‘basic’ addresses 
was approximately 28% in years 12 to 14, and 33% in year 15, see table BB.2 
below. 
The participant selection method was adjusted from year 12 onwards to provide a 
more cost-efficient sample design which increased the potential number of sampled 
participants per household. Further changes to the sample design were then 
necessary from year 13 onwards to account for lower response rates due to the 
COVID-19 pandemic. These measures included increasing the number of addresses 
per Primary Sampling Unit (PSU) (from 29 in years 12 to 14 to 33 in year 15) and 
adjusting the address type split. 
BB.1.1 Selecting addresses  
The sample for each year was drawn from the Postcode Address File. Table BB.1 
summarises the number of PSUs for the core, NI boost, reserve and shortfall 
samples. Table BB.2 summarises the number of addresses for the core, NI boost, 
reserve and shortfall samples. 
The core (and NI boost) sample comprised the main, issued sample. The reserve 
sample refers to the PSUs which would be issued in full (all addresses) or in part 
(not all addresses) should the number of achieved (productive) cases and expected 
response rate fall below target in a given fieldwork year. The shortfall sample refers 
to a further set of PSUs and addresses which were drawn in years 14 and 15 to 
make up the shortfall due to the additional impact of COVID-19 on response rates 
and achieved numbers. This ‘shortfall sample’ was issued in England, Scotland and 
Wales between January and March 2022. Note that year 12 fieldwork was 
suspended in March 2020 due to the COVID-19 pandemic and did not resume; a 
total of 2,553 addresses were issued to interviewers prior to the suspension. 
 

National Diet and Nutrition Survey: 2019 to 2023 (years 12 to 15) 
 
 
 
Table BB.1: Summary of selected PSUs 
Sample type 
Year 12 
Year 13 
Year 14 
Year 15 
Core UK sample 
115 PSUs 
115 PSUs 
115 PSUs 
115 PSUs 
NI boost sample 
17 PSUs 
17 PSUs 
17 PSUs 
17 PSUs 
Reserve sample 
18 PSUs 
18 PSUs 
18 PSUs 
20 PSUs 
Shortfall sample 
not applicable 
not applicable 
95 PSUs 
60 PSUs 
 
Table BB.2: Summary of selected addresses 
Sample type 
Year 12 
Year 13 
Year 14 
Year 15 
Core UK sample 
3,335 
addresses  
3,335 
addresses  
3,335 
addresses  
3,795 
addresses  
NI ‘boost’ sample 
493 addresses 
493 addresses 
493 addresses 
561 addresses 
Reserve sample 
522 addresses  522 addresses  522 addresses  
660 addresses  
Shortfall sample 
not applicable 
not applicable 
3,800 
addresses  
1,980 
addresses 
Number of 
addresses per 
PSU 
29 (8 basic, 21 
young person) 
29 (8 basic, 21 
young person) 
29 (8 basic, 21 
young person) 
33 (12 basic, 21 
young person) 
 
In each PSU, the addresses were randomly selected. At each address, the 
interviewer established the number of households and, in cases where there were 2 
or more, selected 1 household at random.  
BB.1.2 Selecting participants  
As only around one-third of households contain children,1 over-sampling of children 
was required to achieve equal numbers of adult (aged 19 years and over) and child 
(aged 18 months to 18 years) participants each fieldwork year. In order to achieve 
equal numbers of adults and children in the sample, the selected addresses in each 
PSU were randomly allocated to 1 of the 2 sample types: a ‘basic’ or ‘young person’ 
address. For years 12 to 14, there was a ‘basic’ sample of 8 addresses where 
interviewers randomly selected up to 2 adults and 1 child. In year 15, 12 addresses 
were randomly assigned to the ‘basic’ sample and the  remaining 21 addresses in 
each year were assigned to the ‘young person’ sample. This involved screening to 
identify households with children and, at each of these addresses, up to 2 children, 
each from a different NDNS age group (18 months to 3 years, 4 to 10 years, 11 to 18 
years), were selected for interview.  
Child selection for households with three NDNS age groups represented changed for 
year 15 to simplify the process for fieldworkers. Through years 12 to 14 two different 
age groups were selected and then children were selected from those. For year 15 
one child was selected at random first and the second child was selected from the 
 
1 
https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/families/bulletins/f
amiliesandhouseholds/2022#:~:text=In%202022%2C%20there%20were%2019.4,dependent%20child
ren%20(2.9%20million) 

National Diet and Nutrition Survey: 2019 to 2023 (years 12 to 15) 
 
remaining (different) age groups. While this only affected a small number of 
households it meant that the selection weight was calculated differently at year 15. 
BB.2 Weighting the survey data  
It is necessary to apply weighting factors to the data collected in the NDNS for 2 
reasons: to remove any bias in the observed results which may be due to differences 
in the probability of households and individuals being selected to take part; and to 
attempt to reduce non-response bias. The survey was designed so that no more 
than 2 adults and up to 2 children were selected to take part from any 1 household. 
This meant that adults living in households with 1 or more other adults, and children 
in households with 1 or more other children were less likely to be selected than were 
adults or children in single adult or single child households.  
In addition, there were a number of stages in the survey where it was possible for 
participants to drop out. If the people who refused to participate at a particular stage 
were systematically different from those who took part, then the sample would be 
biased.  
Weighting factors were used to correct for both these cases. There were 2 stages to 
the weighting scheme: the first was to generate a set of design weights to correct for 
the unequal selection probabilities; and the second was to create a set of weights at 
each stage where dropping out makes a difference to adjust for non-response.  
 
BB.3 Calculation of the general population weights 
BB.3.1 Address selection weights 
The sample design includes an adjustment for the number of addresses selected in 
each country (called add_selwt) as they are not selected in proportion to the UK 
population. All NI addresses, and therefore participants, are weighted down as a 
result due to the use of the NI boost. The applied weights put the 4 countries into 
their correct population proportions in the UK so that, for example, the percentage of 
the NDNS sample in NI is the same as the percentage of the UK population that is in 
NI. These weights are calculated as the number of addresses in each country 
divided by the number of addresses selected in each country.  
BB.3.2 Dwelling unit2 selection weights 
Most addresses selected from the PAF contained a single dwelling unit, with a 
separate entrance. At addresses with more than one dwelling unit, only one was 
selected; interviewers carried out a selection procedure to identify which dwelling 
unit to include in the sample using a Kish grid.3 
 
2 A Dwelling Unit is an address or part of an address, which has its own front door which separates 
one part of the address from other parts (that is, only those who live behind the door have access to 
the area, it is not a communal part of the address). A Dwelling Unit need not be fully self-contained - 
for example, an address may contain four bedsitters, the inhabitants of whom share a bathroom. Each 
bed-sitter would count as a dwelling unit as long as it had its own front door. 
 
3 A Kish grid is a framework to ensure that the dwelling unit is selected without interviewer bias. The 
number of dwelling units is listed across the top of the grid, with a random number below to indicate 
which dwelling unit should be selected. 

National Diet and Nutrition Survey: 2019 to 2023 (years 12 to 15) 
 
The dwelling unit selection weights (wdu) adjust for this selection at addresses with 
more than one dwelling unit. The weights were calculated as the number of dwelling 
units identified at the address. 
The dwelling unit selection weights ensured that addresses containing more than 1 
dwelling unit were not under-represented in the issued sample. 
BB.3.3 Catering unit4 selection weights 
Most addresses selected from the PAF contained a single catering unit. At 
addresses with more than 1 catering unit, only 1 was selected; interviewers carried 
out a selection procedure to identify which catering unit to include in the sample 
using a Kish grid.5 
The catering unit selection weights (wcu) adjust for this selection at addresses with 
more than 1 catering unit. The weights were calculated as the number of catering 
units identified at the address. 
The catering unit selection weights ensured that addresses containing more than 1 
catering unit were not under-represented in the issued sample. 
BB.3.4 Household selection weights 
Overall household selection weights (hh_selwt) were calculated as the product of the 
dwelling unit selection weights (wdu) and catering unit selection weights (wcu). The 
composite selection weights were trimmed at 3 to avoid any large values.  
BB.3.5 Child selection weights 
To ensure that that children in households with 2 or more children were correctly 
represented in the survey estimates, selection weights (wt_ind) were calculated 
reflecting their probability of selection. In basic sample addresses a single child or 
young person aged 18 months to 18 years (hereafter referred to as children for 
simplicity) could be selected for participation, so selection weights were calculated 
as the number of children within the household. In young person sample addresses 
up to 2 children could be selected. In years 12 to 14, these were selected from 
different NDNS age groups (18 months to 3 years; 4 to 10 years; 11 to 18 years). 
Selection weights were thus calculated for years 12 to 14 as the number of children 
within the household in their age group, multiplied by 1.5 if children from all 3 age 
groups were present in the household. For year 15 the selection weights were 
calculated in the same way as for years 12 to 14 unless children from all 3 child age 
groups were present in the household, in which case they were calculated as the 
number of children in the household multiplied by 1 plus the ratios of the number of 
children in the household in each of the other 2 age groups to the number of children 
not in that age group. These weights were trimmed at 4 to avoid any large weights. 
 
4 A Catering unit is a group of people who eat food that is bought and prepared for them (largely) as a 
group.  Occasionally a household will be found to consist of more than one catering unit. Although 
people may share accommodation and even be related, they may not be in the same catering unit. 
For example, young adults sharing a house with their parents may shop, cook and eat by themselves, 
in which case the parents would be in one catering unit and the children in another.  
5 A Kish grid is a framework to ensure that the dwelling unit is selected without interviewer bias. The 
number of dwelling units is listed across the top of the grid, with a random number below to indicate 
which dwelling unit should be selected. 
 

National Diet and Nutrition Survey: 2019 to 2023 (years 12 to 15) 
 
BB.3.6 Adult selection weights 
Adult selection weights (also wt_ind) were simpler. They were calculated as the 
number of adults in the household divided by the number of adults selected. 
Participants from single-adult households therefore had a weight of 1. In practice 
these were made simultaneously with the child selection weights and received the 
same trim at 4 to avoid large weights. 
BB.3.7 Combining the selection weights 
The total person selection weights (ind_selwt) for the general population sample of 
adults and children were then calculated as: 
ind_selwt = hh_selwt x wt_ind 
These were trimmed at 4 to avoid large overall selection weights. 
The final selection weights (Selwt) were calculated as: 
Selwt= add_selwt x ind_selwt 
The selection weights for all responding adults and children were re-scaled so that 
the weighted sample size is the same as the achieved sample size. Therefore, the 
final selection weights adjust for differences in the chance of selection for addresses, 
households and individuals. 
BB.3.8 Individual calibration weighting 
Calibration weighting was used to ensure that the weighted distribution of 
participants matched Office for National Statistics (ONS) 2021 mid-year population 
estimates for sex/age groups and region.  The aim of the calibration weighting is to 
reduce bias resulting from the selection of individuals.  
Calibration was conducted separately for adults and children and separately for each 
country, even being trimmed separately to avoid outlying weights within adults 
versus children. The starting weight was the final selection weight (selwt). The 
separate post-calibration weights were adjusted before combination into a single 
weights variable such that children are weighted down to represent the appropriate 
proportion of the UK population. This overall weight is then rescaled to a mean of 1. 
Thus, the final calibrated individual weight (wti_Y1215) adjusts for address, dwelling 
unit, catering unit and household selection, and for the age,sex and region profiles of 
participating individuals. 
BB.3.9 Biomedical visit weights 
Participants completing at least one dietary recall were asked whether they were 
willing to have a visit from a biomedical fieldworker. The main focus of this visit was 
to take a blood sample, along with other measurements depending on the age of the 
participant. 
Not all those interviewed went on to have a biomedical visit. This may have been the 
source of further non-response bias. For data relating to biomedical visits, a logistic 
regression model was fitted for all individual respondents, weighted by individual 
weight (wti_Y1215). The outcome variable was whether or not a biomedical visit was 
undertaken, and the models were made separately for adults and children.  The 
adult model has the following as covariates: age group by sex, region, household 

National Diet and Nutrition Survey: 2019 to 2023 (years 12 to 15) 
 
size, ethnicity and tenure. The child model has the following as covariates: age 
group by sex, region, household size, ethnicity, tenure and number of children in the 
household. 
 
The weights for non-response to the biomedical visit (w5ad for adults and w5ch for 
children) were calculated as the reciprocal of the predicted probability of a 
biomedical visit being undertaken, estimated from the respective regression model. 
The weights were trimmed by 1% at the tail to remove extremely high weights. The 
weights for the biomedical visit sample were calculated as wt_nurse_ad = wti_Y1215 
x w5ad for adults and wt_nurse_ch = wti_Y1215 x w5ch for children. The weights 
resulting from the adults and child models were then scaled up to the population for 
their respective age groups and combined into one variable, thus weighting children 
down to represent the appropriate proportion of the UK population. These weights 
were re-scaled so that the weighted sample size for the biomedical visit is the same 
as the achieved sample size. They adjust for selection, non-response and population 
profile for the sample that had a biomedical visit. 
 
BB.3.10 Blood weights 
Almost all adults who had a biomedical visit were eligible to have a blood sample 
taken but not all those eligible agreed or were able to do so. A logistic regression 
model was fitted, weighted by the biomedical visit weight (wt_nurse). The outcome 
variable was whether or not a usable blood sample was analysed, and the models 
were made separately for adults and children. The adult model has the following as 
covariates: age group by sex,  region, household size, number of children in the 
household, marital status, academic qualifications and regularity of alcohol 
consumption. The child model has the following as covariates: age group by sex, 
region, household size and number of children in the household. 
The weights for non-participation for the blood sample (w6ad for adults and w6ch for 
children) were calculated as the reciprocal of the predicted probability of a blood 
sample being obtained, estimated from the respective regression model.  
The weights were trimmed by 1% at the tail to remove extremely high weights. The 
weights for the blood sample were calculated as wt_blood_ad = wt_nurse x w6ad for 
adults and wt_blood_ch = wt_nurse x w6ch for children.  The weights resulting from 
the adults and child models were then scaled up to the population for their respective 
age groups and combined into one variable, thus weighting children down to 
represent the appropriate proportion of the UK population. These weights were re-
scaled so that the weighted blood sample size was the same as the achieved 
sample size. 
 
BB.3.11 Spot urine weights 
Those aged 4 years and over who had either a biomedical or face-to-face interviewer 
visit were eligible to provide a spot urine sample, but not all those eligible agreed or 
did so. A logistic regression model was fitted for all NDNS participants, weighted by 
the individual weights. The outcome variable was  whether or not a usable spot urine 
sample was analysed, and the models were made separately for adults and children. 
The adult model has the following as covariates: age group by sex, region, ethnicity 
and tenure. The child model has the following as covariates: age group by sex 

National Diet and Nutrition Survey: 2019 to 2023 (years 12 to 15) 
 
region, household size, number of children in the household and working status of 
representative person. These models are doing more than 1 job – modelling non-
response on face-to-face contact (either by an interviewer or a biomedical 
fieldworker) as well as the urine sample non-response and validity, all in one step. 
 
The weights were trimmed by 1%  at the tail to remove extremely high weights. The 
weights for participation for the urine sample (w7ad for adults and w7ch for children) 
were calculated as the reciprocal of the predicted probability of a urine sample being 
obtained, estimated from the respective regression model. The final spot urine 
weights were calculated as wt_urine_ad = wti_Y1215 x w7ad for adults 19+ and 
wt_urine_ch = wti_Y1215 x w7ch for children. The weights resulting from the adults 
and child models were then scaled up to the population for their respective age 
groups (which are different for spot urine) and combined into 1 variable, thus 
weighting children down to represent the appropriate proportion of the UK 
population. These weights were re-scaled so that the weighted spot urine sample 
size was the same as the achieved sample size. 
 
BB.3.12 Dietary recall weights 
Individuals were asked to recall their dietary intake for 4 separate days. Not all those 
interviewed went on to record dietary recall days. This may have been the source of 
further non-response bias. For data relating to diet recalls, a logistic regression 
model was fitted for all individual respondents, weighted by individual weight 
(wti_Y1215). The outcome variable was whether or not at least 1 recall day was 
recorded, and the models were made for adults and children together. The non-
response model has the following as covariates: age group by sex, region, 
household size, ethnicity, number of children in the household, tenure, working 
status of representative person, survey year, quintiles of population density and 
quintiles of Index of Multiple Deprivation within country. 
The weights for non-response to the dietary recalls (w8) were calculated as the 
reciprocal of the predicted probability of at least one dietary recall being completed, 
estimated from the respective regression model. 
 
The weights were trimmed at the 1% tail to remove extremely high weights. The 
weights for the diet recalls data were calculated as wt_diet = wti_Y1215 x w8. These 
weights were re-scaled so that the weighted blood sample size was the same as the 
achieved sample size. 
 
BB.3.13 Physical Activity Questionnaire weights 
Only those aged 4 years and over with at least 3 dietary recalls were eligible to 
complete the Physical Activity Questionnaire (PAQ), but not all those eligible agreed 
or did so. A logistic regression model was fitted, weighted by the diet weights. The 
outcome variable was whether or not the PAQ was completed. Adults and children 
were given separate weights here, and the threshold for being considered an adult 
for the PAQ decreased from 19 to 16 years.  
 
For those aged 16 years and over, the following were included as covariates: age 
group by sex, region, number of children, tenure and marital status. For children 
aged 4 to 15 years, the following were included as covariates: age group by sex, 
region, household size and tenure.  These models are doing more than 1 job: 

National Diet and Nutrition Survey: 2019 to 2023 (years 12 to 15) 
 
modelling non-response on the number of dietary recalls (as well as non-response to 
the PAQ itself in 1 step. 
 
The PAQ weights (w9) were calculated as the reciprocal of the predicted probability 
of PAQ completion, estimated from the regression model. The weights were trimmed 
by 1% at the tail to remove extremely high weights. The final PAQ weights were 
calculated as wt_RPAQ = wt_diet x w9 for adults and wt_CPAQ = wt_diet x w9 for 
children. 
 
BB.4 Calculation of the Northern Ireland (NI) weights 
Since the UK weights for years 12 to 15 contain all cases who responded to each 
respective section of NDNS, the NI weights are simply the UK weights but for NI 
cases only. Outliers were trimmed, and the weights were rescaled to a mean of 1. 
These weights were checked against the NI mid-year population estimates of age 
and sex and are indeed still representative of the general population. 
Note that weights for NI have also been created for years 10 to 11 in a very similar 
way, rescaled from the weights for years 9 to 11. Years 9 to 11 followed a similar 
approach to years 12 to 15 but with a different way of making the selection weights 
to reflect a different selection process and no need for diet weights as participation in 
that element was a condition to be considered a productive NDNS participant, so for 
years 9 to 11 the individual weight does the same job. 
BB.5 Effect of the weights on the precision of the estimates 
A design factor (DEFT) for each weight has been calculated to provide an 
approximate guide to the effect of the weighting on the precision of estimates. The 
DEFT is calculated as the average squared weight divided by the square of the 
average weight. 
For instance, the DEFT of 1.36 for the individual weight indicates that the standard 
error of estimates is assumed to increase by 36%, with a corresponding loss of 
precision. Consequently, these weighted estimates have the same level of precision 
as an estimate based on a simple random sample, unweighted, of around 54% of the 
size of the actual sample. This is known as the effective sample size. 
BB.5 Selecting the appropriate weight 
The weight to use depends on the part of NDNS to be analysed: 
• For overall analysis and analysis of the original survey, use the individual 
weight (wti_Y1215)   
• For analysis of measurements and questions from the biomedical visit (except 
spot urine and blood samples), use the biomedical weight (wt_nurse)   
• For analysis of the blood samples and associated findings, use the blood 
weight (wt_blood)   
• For analysis of the spot urine samples and associated findings, use the urine 
weight (wt_urine) 
• For analysis of the dietary recalls, use the diet weight (wt_diet) 
• For analysis of the PAQ, use the adult weight for those aged 16 years and 
over (wt_RPAQ) and the child weight for those aged 4 to 15 years (wt_CPAQ) 

National Diet and Nutrition Survey: 2019 to 2023 (years 12 to 15) 
 
The names are self-explanatory. The individual and biomedical weights are unlikely 
to be used unless it is for very specific analyses but they form the basis for 
subsequent weights such as the blood and diet weights.