TILDA collects information on all aspects of health, economic and social circumstances from adults aged 50 years and over resident in Ireland. Waves of data collection take place every two years. TILDA provides a comprehensive and accurate picture of the characteristics, needs and contributions of older persons in Ireland to inform and support improvements in policy and practice; advancements in technology and innovation; tailored education and training through an enhanced ageing research infrastructure; harmonisation with leading international research to ensure adoption of best policy and practice and comparability of results. TILDA is necessary to act as the foundation on which we can plan appropriate health, medical, social and economic policies for our older adults. Wave 5 dataset: v5.1 The version 5.1 anonymised dataset includes data from the 4,980 TILDA respondents who completed a fifth interview during the Wave 5 fieldwork. Data from any respondents who were new at Wave 4, respondents who had passed away between the waves and data from returning respondents who required a proxy interview have been removed (n=262) to protect anonymity. The dataset includes data from the home interview, the self-completion questionnaire and certain other variables derived from this data. More information on these derived variables can be found in the ‘Wave 5 Derived Variable Codebook’.

Probability: Stratified, Probability: Cluster, Probability: Multistage. An initial multi-stage sample of addresses was chosen by means of the RANSAM sampling procedure, which was developed by the Economic and Social Research Institute (ESRI) based on the Irish Geodirectory, a comprehensive listing/mapping of residential addresses in Ireland compiled by the Ordnance Survey Office. Stage 1: RANSAM groups the residential addresses in the country into 3,155 first stage units or clusters. These clusters are townlands or aggregations of townlands and range in size from 500 to 1180 addresses. It was decided to select 640 of these clusters, with implicit proportionate stratification of clusters by socio-economic group (3 categories) and geography. Characteristics of the clusters can be inferred from the District Electoral Divisions of which they are a part, on the basis of the Small Area Population Statistics compiled by the Central Statistics Office. Stratification was achieved by pre-sorting all addresses in the country by socio-economic group (three equal groups on the basis of percentage of the population in the professional/managerial category) and within socio-economic group by RANSAM’s geographical “snake” pattern which orders clusters within county based on a north/south pattern which preserves contiguity. Clusters were selected randomly with a probability of selection proportional to the estimated number of persons aged 50 or over in each cluster. Stage 2: This stage involved the selection of a probability sample of 50 addresses within each cluster (10 to be held in reserve). The combination of selection probabilities used at the two stages produces an equal probability (“epsem“) sample of addresses. All persons aged 50 or over in the selected households (and their spouses or partners of any age) were asked to participate. The addresses were partitioned into two groups: an initial sample list of 25,600 addresses (40 randomly selected from each of the 640 clusters) for immediate issue to the field force and 6,400 addresses (10 randomly selected from each of the 640 clusters) for retention as a reserve list. The reserve list would only be utilised later in the fieldwork process if it appeared unlikely that the target sample size would be achieved however this was not the case and the reserve list was not used. As described, the sample design incorporates stratification, clustering and multi-stage selection. The design results in an equal probability sample of both households containing members of the target population and of persons in the target group. This means that the resulting sample is “epsem” and self-weighting, except for biases caused by non-random variations in response rates. Such biases were dealt with at analysis stage by means of calibration weights.

Face-to-face interview: CAPI/CAMI

Self-administered questionnaire: Paper