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Background Notes

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Purpose of the Survey and Reference Period

A questionnaire on the Social Impact of COVID-19 survey was conducted by the CSO between Thursday 23 April and Friday 1st May. Most individuals selected received a letter and/or an email from the CSO and were asked to complete the questionnaire online, while some were contacted via telephone. The questionnaire asked for information on the following topics:

  • Personal well-being
  • Concerns about the impact of COVID-19
  • Changes in consumption since the introduction of COVID-19 restrictions
  • Labour market activity, working arrangements and financial impact of COVID-19
  • Compliance with official advice and other social topics

Sample Selection

The sample for the Social Impact of COVID-19 survey was generated from Labour Force Survey (LFS) respondents in Q1 2019 that agreed to be contacted for further research and provided an email address and phone number. The Labour Force Survey is a 2-stage sample design stratified using Administrative County and the Pobal HP Deprivation Index. For further information on the Labour Force Survey see

Data Collection

The sample selection methodology resulted in a sample of 4,033 people. 3,033 of these were issued the questionnaire via email on Thursday 23rd April, while 500 were issued with letters requesting their participation in the online survey. A further 500 were scheduled to be contacted via CATI (Computer Assisted Telephone Interviewing). For the purposes of this analysis, data collection was closed on Friday 1st May 2020, at which point the achieved sample was 1,362 individuals.

Sample Design

Timeliness was a key priority in this survey and therefore the sample and subsequent weighting process is one of convenience to some extent. Some consideration needs to be given to the potential impact of sample design on response rates and achieved sample:

  • The original LFS sample from which this sample was selected was based on Census 2011 data and designed to represent the population then, so new additions to the population may not be fully represented.
  • The sampling frame excludes people who do not live in private households, so may not cover some of those more likely to be negatively affected by the spread of the disease.
  • The sampling frame was composed of individuals who had responded through 5 waves of the LFS survey, which could have introduced bias into the sample, as their characteristics may differ in some way to people more inclined to drop out of a longitudinal sample over time.
  • The sample did not include people that did not provide an email address, which means members of the population less likely to have an email address, such as older people or people without internet access, were likely to be underrepresented.
  • There is a mode effect, whereby the method of administration of the questionnaire can impact responses. For example, the presence of an interviewer in CATI can encourage a higher response rate compared to self-administered web questionnaires. On the other hand, sensitive questions may be more honestly answered without an interviewer present.
  • The achieved sample distribution could also have been impacted by non-response bias. This is caused by the fact that some respondents might be more inclined to respond than others, and people who respond to these surveys often have different characteristics compared to non-respondents.

The weighting procedure outlined below was designed to adjust for possible bias in the achieved sample as much as possible.


The following weighting process was devised to counteract some of the potential bias within the sample, and to make the final weighted sample distribution as representative as possible of the population.

Stage 1: Non-response

In the first stage of the weighting process, each person in the sample was given a weight of 1. We utilised the current LFS non-response adjustment process, in which a stepwise logistic regression was conducted based on census household-level data, to generate response propensities based upon the following characteristics:

  • Personal characteristics (of head of household) – Sex, Marital status, Education, Nationality, PES, Age, Social class, Ethnicity
  • Household characteristics – Area type, Dwelling type, Tenure, Number of persons in household, Number of cars owned by household, Number of rooms, Household PC, Urban/Rural, County

The sample is then grouped into strata based on propensity score, for which non-response adjustments were calculated and applied to each respondent.

Stage 2: Calibration

In stage 2, Q4 2019 LFS population estimates were used to benchmark the dataset across key characteristics for calibration. The non-response adjustments were inflated match overall the population total and then calibrated using CALMAR[1], to ensure that weighted sample totals matched the Q4 2019 benchmark totals for a number of key characteristics such as gender, age, education level, region, urban/rural location, household composition.

  LFS Q42019 Achieved SICS Sample Final Weighted Distribution
State 100.0% 100.0% 100.0%
Male 49.0% 40.2% 48.8%
Female 51.0% 59.8% 51.3%
18 to 34 27.8% 7.7% 27.4%
35 to 44 20.8% 26.2% 20.9%
45 to 54 17.9% 24.5% 18.0%
55 to 69 20.5% 32.2% 20.6%
70+ 13.0% 9.4% 13.1%
Marital Status      
Single - never married 36.9% 23.2% 36.9%
Married 52.7% 65.1% 52.7%
Separated or divorced 5.4% 4.0% 3.2%
Widowed 5.1% 7.8% 7.3%
Working for payment or profit 59.2% 63.2% 59.3%
Unemployed 4.0% 3.0% 4.0%
Student or pupil 6.7% 1.9% 6.7%
Retired from employment 15.9% 21.3% 15.9%
Unable to work due to permanent sickness or disability 4.8% 2.6% 4.1%
Engaged on home duties 8.9% 7.4% 8.9%
Other 0.5% 0.6% 1.1%
Irish 85.2% 93.0% 85.2%
Non-Irish 14.8% 7.0% 14.8%
Highest educational level      
Higher secondary education or lower 47.9% 22.0% 47.9%
Post-secondary or Short cycle tertiary 18.4% 23.4% 18.4%
Third level bachelor or higher 33.7% 54.6% 33.7%
Household Composition      
1 adult,no children 13.2% 15.3% 13.2%
2+ adults, no children 48.3% 44.3% 48.3%
Households with children 38.5% 40.5% 38.5%
Urban 66.3% 68.4% 66.3%
Rural 33.7% 31.6% 33.7%
Detached House 42.7% 45.2% 42.7%
Semi-detached house 30.5% 32.2% 30.5%
Terraced house 16.1% 14.4% 16.1%
Apartment/Flat/Bedsitter 10.7% 8.3% 10.8%
Other 0.1% 0.0% 0.0%
Deprivation Index Stratum      
Very disadvantaged 19.3% 10.1% 19.3%
Disadvantaged 18.9% 16.5% 18.9%
Average 19.6% 20.6% 19.6%
Affluent 21.3% 23.6% 21.3%
Very affluent 21.0% 29.2% 21.0%
Nuts 3 region      
Northern and Western 17.6% 13.3% 17.6%
Southern 33.0% 34.7% 33.0%
Eastern and Midland 49.4% 52.1% 49.4%
Degree of urbanisation      
Densely-populated area 36.0% 39.7% 36.0%
Intermediate area 22.3% 23.4% 22.3%
Thinly-populated area 41.7% 36.9% 41.7%
Tenure Status      
Owner-occupied 72.5% 83.3% 72.5%
Rented 27.5% 16.7% 27.5%


The Central Statistics Office wishes to thank the participants for their co-operation in agreeing to take part in the Social Impact of COVID-19 Survey and for facilitating the collection of the relevant data.

[1] CALMAR is the statistical software developed by INSEE. Calmar is a SAS macro program that implements the calibration approach and adjusts weights assigned to individuals using auxiliary variables.

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