Getting data on tenure security that is comparable between countries requires a consistent sampling approach. The Prindex team works with all the data collection vendors to ensure the sampling requirements are met and the data is robust. Because all countries do not have recent or accurate census data and some surveys are carried out face-to-face and by telephone, sample frames need to be constructed in different ways and sampling methods adapted. The following paragraphs set out the key features of the sampling approach we used in Wave 1 & 2. It is very similar to the approach used in the Gallup World Poll (GWP) wave of data collection.

We use a 3-stage cluster sampling design.

Our target sample population is adults (i.e. 18 years and above). As we aim to interview a representative sample of the adult population, not the head of household or the most knowledgeable person about their home or land, we use a randomisation process to select which household adult is interviewed. We use a different sampling approach to select respondents for face-to-face and telephone interviews.

Face-to-face interviews

The first stage of sampling involves the identification of clusters (sampling units) of households. The ratio of interviews to clusters is typically 10:1 to minimize problems associated with intra-cluster correlation. Sampling units are stratified by population size and/or geography. Where population information is available, selection is based on probabilities proportional to population size; otherwise simple random sampling is used. This population strata selection methodology is in line with that taken by leading polling organisations such as Gallup.

In the second stage, we select households through randomising selection procedures. Unless an outright refusal occurs, interviewers make up to three attempts (separated by two hours within the same day) to survey the sampled household. If an interviewer cannot obtain an interview at the initial household, a simple substitution method is employed whereby an effort is made to make contact at the neighbouring (right) household, and if unsuccessful, at the left, thereafter alternating households.

In the third stage, respondents are randomly selected within the nominated households. Interviewers list all eligible household members and their ages and birthdays. Gender matching is undertaken in some Middle Eastern and Asian countries where cultural restrictions so dictate. The respondent is selected by means of the Kish grid (see https://surveymethods.com/blog/what-is-the-kish-selection-procedure/ for an accessible explanation). The interviewer does not inform the person who answers the door of the selection criteria until after the respondent has been identified.

Telephone interviews

In countries where telephone penetration is over 80%, we explore the use of telephone interviews rather than a face-to-face method. In the Wave 1 & 2 (2018) data collection, this is only relevant for the UK. It was used more extensively in the GWP data collection. When used, telephone numbers are selected by either random digit dial (RDD) or from a nationally representative list of phone numbers. In countries where cell phone penetration is high, a dual sampling frame is adopted. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day. Appointments for call-backs that fall within the survey data collection period are made.

Data Preparation & Weighting

The dataset goes through a rigorous quality assurance process before being publicly released. In Wave 1 & 2, this included quality checks once 10% and 50% of the data was collected to test results for completeness, suspicious patterns that might suggest interviewer misbehaviour, and improper sampling. New data is collected as necessary. The GWP uses an equally robust quality assurance procedure.

Data weighting is necessary to ensure the data are representative at the national level. Base sampling weights are constructed to account for unequal probability of selection, for example due to oversamples and household size. Population statistics are used to adjust the base weights so the data are as representative as possible for other socio-demographic characteristics, including age and gender and, where possible, location (e.g. urban vs. rural).

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