Back to Top

 Skip navigation

Background Notes

Open in Excel:

Purpose of the Survey and Reference Period

A questionnaire for the second round of the Social Impact of COVID-19 survey was conducted by the CSO from 12th-18th November.  Individuals selected received an email from the CSO and were asked to complete the questionnaire online.  The questionnaire asked for information on the following topics:

  • Life satisfaction levels, in general terms as well as in terms of financial satisfaction and satisfaction with personal relationships
  • Personal well-being
  • Concerns about the impact of COVID-19
  • Changes in consumption since the introduction of COVID-19 restrictions
  • Working arrangements through COVID-19
  • Chance of infection and impact of infection
  • Aspects in life that have changed for the better
  • Compliance levels with official COVID-19 advice
  • Changes in expected Christmas expenditure
  • Worries in relation to the impact of COVID-19 on Christmas
  • Attitude towards international travel and travel restrictions

Sample Selection

This survey is the fourth in a series on the Social Impact of COVID-19.  The sample was generated from Labour Force Survey (LFS) respondents 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 see the Labour Force Survey.

This Social Impact of COVID-19 survey is fourth such survey conducted by the CSO.  The sample selection methodology resulted in a sample of 5,105 people.

Data Collection

All potential respondents were contacted by email and were asked to complete an online questionnaire.  Data collection was closed on Wednesday 18th November 2020, at which point the achieved sample was 1,585 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, Q2 2020 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 distributions matched the Q2 2020 benchmark distributions for a number of key characteristics such as gender, age, education level, region, urban/rural location, household composition.

As outlined above, non-response adjustment has been used to address some of the imbalances between the original sample design and the achieved sample distribution as much as possible, and the subsequent calibration adjusts to key population totals to try and match current population distributions.  However, given the non-random nature of the final sample selected, it is unlikely that we can fully account fully for bias inherent in the final sample. For this reason, caution should be taken when attempting to make inferences to the entire population from these results.


The questionnaire focused on the impact that COVID-19 has had on personal well-being, working conditions, health and lifestyle.  It also covered topics such as levels of compliance with government guidelines, the impact of COVID-19 on Christmas celebrations and attitudes around international travel and regulations.

Data analysis

Some key analysis variables that may be included in the publication:


In 2018, the Survey on Income and Living Conditions (SILC) carried out an ad-hoc module on “Material deprivation, well-being and housing difficulties”, which itself provides comparisons with the SILC 2013 “Well-being” module.  These surveys provided an interesting reference point for the Social Impact of COVID-19 surveys conducted in April and August 2020.  The well-being questions in this November survey aim to provide some further insight into feelings of wellbeing in Ireland.  While the methodologies across all surveys differ and care should be taken in the interpretation of trends over time, nonetheless the findings from these surveys present an important perspective on the impact of COVID19 in Ireland.

SILC Module on Well-being, 2018

Social Impact of COVID-19 Survey, April 2020

Social Impact of COVID-19 Survey, August 2020: The Reopening of Schools

Household composition

For the purposes of deriving household composition, a child was defined as any member of the household aged 17 or under.  Household were then categorised as:

  • 1 adult households
  • 2+ adult households
  • Households with children

Marital status

Marital status refers to the current marital status of the respondent. In order to achieve appropriate sample sizes for each group, the responses were grouped as:

  • Single - never married
  • Married
  • Separated, divorced, or widowed

Highest level of education attained

This classification is derived from a single question and refers to educational standards that have been attained and can be compared in some measurable way and it is included in the core LFS on an ongoing basis.

The question is phrased as follows:

What is the highest level of education or training you have ever successfully completed?

For the purposes of this publication these have been classified as follows:

  • Higher secondary education or lower
  • Post-secondary or Short-cycle tertiary
  • Third-level bachelor degree or higher

Tenure status

Tenure status refers to the nature of the accommodation in which the individual resides. The status is provided by the respondent of the household questionnaire during the interview and responses are classified into the following two categories:

  • Owner-occupied
  • Rented

Urban/rural location

Areas are classified as Urban or Rural based on the following population densities derived from Census of Population 2016:


Population density >100,000

Population density 50,000 – 99,999

Population density 20,000 – 49,999

Population density 10,000 – 19,999

Population density 5,000 – 9,999

Population density 1,000 – 4,999


Population density <199 – 999

Rural areas in counties


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.

Go to next chapter >>> Contact Details 

Why you can Trust the CSO

Learn about our data and confidentiality safeguards, and the steps we take to produce statistics that can be trusted by all.