Random digit dialing or RDD is commonly used to conduct general population studies, as it allows researchers to create a sample frame that represents everyone with access to a phone in a population, rather than only those who are listed in a phonebook or have shared their phone number with another source. As random digit dialing does not require researchers to gain access to existing lists of phone numbers, it is one of the fastest and simplest ways to create sample for researchers who do not have an existing sample source. At GeoPoll, we have access to a large database of mobile subscribers in most of the countries we work in, however, we utilize Random Digit Dialing as a sample source for certain projects or in countries where we do not have existing sample.

Pros and Cons of Random Digit Dialing Sample

While random digit dialing is a popular technique, there are some pros and cons to using RDD over a provided sample source, such as a list of specific phone numbers. Some of the pros and cons of random digit dialing include:

Pros of Random Digit Dialing

• Allows those without their own sample sources to conduct general population studies via telephone numbers
• Generating sample is fast and cost-effective, and large sample sizes can be achieved as additional telephone numbers can be easily generated
• It is a highly regarded method of sample creation and is commonly used by research organizations around the globe

Cons of Random Digit Dialing

• May be difficult to reach more targeted respondents, such as those with specific professions, as you do not have any prior information about the characteristics of each respondent, which other sample sources may provide. You also do not have information about those who do not respond to your survey (known as nonresponse error) for the same reason.
• Depending on the phone number format within a country and use of mobile phones versus landlines, targeting respondents by location can also be a challenge
• Not all generated numbers may be valid, which can lead to lower response rates than with a pre-verified list of numbers

Overall, when administered through an experienced research firm, RDD is an excellent way to gather high-quality sample, especially for projects aiming to gather a nationally representative sample.

GeoPoll’s Random Digit Dialing Process

GeoPoll has our own database of respondents in many of the countries we operate in who are profiled by demographics include age, gender, and location, but in certain circumstances, we may turn to RDD to gather sample. In these cases, GeoPoll uses our extensive knowledge of telephone samples to intelligently generate RDD sample that has response rates in-line with those found from the GeoPoll respondent database. GeoPoll’s random digit dialing has three main steps for generating and testing phone numbers:

1. Mobile number generation: Using public information, GeoPoll’s team will identify the most common prefixes for each mobile network operator operating in a market, as well as the percentage share that each mobile network operator represents. We then randomly generate lists of unique numbers that include numbers from each telecom network. This ensures that every mobile number within a country has an equal opportunity of being selected and reduces risks of selection bias.
2. Mobile number validation and testing: Once the initial files are generated, GeoPoll conducts a validation process that identifies likely active and inactive numbers, allowing us to remove numbers that are inactive before proceeding with live testing. GeoPoll’s call center teams then conduct testing with the final list of numbers. If the response rates during testing are in line with expectations based on previous work, we proceed with a survey. If we encounter high numbers of disconnected numbers, we may generate additional numbers.
3. Survey administration: Once GeoPoll has finalized the list of mobile numbers, it is handed off to our trained survey interviewers for full survey administration. During this stage, each interviewer is given a unique list of mobile numbers and is equipped with the GeoPoll Computer Assisted Telephone Interviewing (CATI) Application, which tracks the outcome of each call. The CATI application tracks the percent of phone numbers that are invalid, and the percent of respondents who refuse to take a survey. For those respondents who agree to take a survey, GeoPoll’s system securely stores demographic information along with their telephone number so they can participate in future research.

Random digit dialing is a useful method for conducting surveys via telephone calls in almost any country around the globe. To learn more about GeoPoll’s experience with RDD surveys or to get a quote for your own project, please contact us.

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In this series of blog posts, GeoPoll will outline the various aspects that make up a sample and why each one is important. First, we will examine how sample is selected and the differences between a probability sample and a non-probability sample.

Probability Sample vs Non-Probability Sample

There are two main methods of sampling: Probability sampling and non-probability sampling. In probability sampling, respondents are randomly selected to take part in a survey or other mode of research. For a sample to qualify as a probability sample, each person in a population must have an equal chance of being selected for a study, and the researcher must know the probability that an individual will be selected. Probability sampling is the most common form of sampling for public opinion studies, election polling, and other studies in which results will be applied to a wider population. This is the case whether or not the wider population is very large, such as the population of an entire country, or small, such as young females living in a specific town.

Non-probability sampling is when a sample is created through a non-random process. This could include a researcher sending a survey link to their friends or stopping people on the street. This type of sampling would also include any targeted research that intentionally samples from specific lists such as aid beneficiaries, or participants in a specific training course. Non-probability samples are often used during the exploratory stage of a research project, and in qualitative research, which is more subjective than quantitative research, but are also used for research with specific target populations in mind, such as farmers that grow maize.

Generally speaking, non-probability sampling can be a more cost-effective and faster approach than probability sampling, but this depends on a number of variables including the target population being studied. Certain types of non-probability sampling can also introduce bias into the sample and results. For general population studies intended to represent the entire population of a country or state, probability sampling is usually the preferred method.

Types of Probability Sampling

There are several sampling methods that fall under probability sampling. In each method, those who are within the sample frame have some chance of being selected to participate in a study. Four of the common types of probability sampling are:

Simple Random Sample: The most basic form of probability sampling, in a simple random sample each member of a population is assigned an identifier such as a number, and those selected to be within the sample are picked at random, often using an automated software program.

Stratified Random Sample: A stratified random sample is a step up from complexity from a simple random sample. In this method, the population is divided into sub-groups, such as male and female, and within those sub-groups a simple random sample is performed. This enables a random sample that is representative of a larger population and its specific makeup, such as a country’s population. 

Cluster Sample: In cluster sampling, a population is divided into clusters which are unique, yet represent a diverse group – for example, cities are often used as clusters. From the list of clusters, a select number are randomly selected to take part in a study.

Systematic Sample: Using a systematic sample, participants are selected to be part of a sample using a fixed interval. For example, if using an interval of 5, the sample may consist of the fifth, 10^th, 15^th, and 20^th, and so forth person on a list.

Types of Non-Probability Sample

In non-probability sampling, those who participate in a research study are selected not by random, but due to some factor that gives them the chance of participating in a study that others in the population do not have. Types of non-probability sample include:

Convenience Sample: As its name implies, this method uses people who are convenient to access to complete a study. This could include friends, people walking down a street, or those enrolled in a university course. Convenience sampling is quick and easy, but will not yield results that can be applied to a broader population.

Snowball Sample: A snowball sample works by recruiting some sample members who in turn recruit people they know to join a sample. This method works well for reaching very specific populations who are likely to know others who meet the selection criteria.

Quota Sample: In quota sampling, a population is divided into subgroups by characteristics such as age or location and targets are set for the number of respondents needed from each subgroup. The main difference between quota sampling and stratified random sampling is that a random sampling technique is not used in quota sampling; For example, a researcher could conduct a convenience sample with specific quotas to ensure an equal number of males and females are included, but this technique would still not give every member of the population a chance of being selected and thus would not be a probability sample.

Purposive or Judgmental Sample: Using a purposive or judgmental sampling technique, the sample selection is left up to the researcher and their knowledge of who will fit the study criteria. For example, a purposive sample may include only PhD candidates in a specific subject matter. When studying specific characteristics this selection method may be used, however as the researcher can influence those who are selected to take place in the study, bias may be introduced.

GeoPoll Sampling Methods

GeoPoll uses all of the sampling approaches described above based on the needs and can use probability-based methods for our sample selection, including stratified random sampling, to build nationally representative samples. To learn more, please contact us.

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