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:
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 https://www.cso.ie/en/methods/labourmarket/labourforcesurvey/
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.
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 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.
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:
The sample is then grouped into strata based on propensity score, for which non-response adjustments were calculated and applied to each respondent.
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, 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|
|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%|
|Single - never married||36.9%||23.2%||36.9%|
|Separated or divorced||5.4%||4.0%||3.2%|
|Working for payment or profit||59.2%||63.2%||59.3%|
|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%|
|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%|
|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%|
|Deprivation Index Stratum|
|Nuts 3 region|
|Northern and Western||17.6%||13.3%||17.6%|
|Eastern and Midland||49.4%||52.1%||49.4%|
|Degree of urbanisation|
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.
 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