Mastering Survey Sampling Methods for Consumer Intelligence

Mastering Survey Sampling Methods for Consumer Intelligence

Survey sampling methods are a crucial part of the survey research process, as the aspect of sampling is more than just a data collection practice. 

This is because in order to glean any valuable insight from surveying, the respondents must be as representative of the study’s target population as can be. The correct survey sampling method can make this possible. 

When conducting survey research, there are several sampling methods researchers can leverage. In order to apply the correct method, there are certain things you need to establish. This article delves into survey sampling methods, including the considerations to take before settling on a sampling method for your research needs.

Defining Survey Sampling Methods

Survey sampling methods denote the types of techniques used to select participants from a target market (or any target population) to take part in a survey sampling pool. 

In survey research, the sampling pool is the group, or “pool” of targeted respondents who participate in a survey study. This sampling pool must accurately represent the targeted subject population. 

It is important to have a group of people who will participate in the survey and be able to represent the whole target population. This group is called a “sample". 

Settling on the proper sampling pool is known as sampling, which is critical to surveys, as it makes up the foundation of the survey campaign. 

Why Survey Sampling Methods are Necessary

The main goal of surveys is to gather accurate information about a particular population. As such, they would be futile if they insufficiently accounted for the participants that they’re set on studying.  

Survey sampling is necessary, as sampling provides a potent means of extracting and analyzing a targeted subset of a population. Even when researchers zero in on a subset, it is still virtually impossible to study the entire population of a targeted group.

The reason is twofold and fairly straightforward: not all members of a particular population will be exposed to a survey, and out of those that do, most will not be willing to spend time filling out a survey. 

As such, researchers turn to survey sampling methods, so that their sampling pool best represents the population of researchers’ interest. With the right method, researchers can make well-informed inferences about their targeted population.  

Sampling reduces the sampled respondents, which lessens both the workload and costs associated with a particular survey study. However, researchers have to find the correct balance of participant involvement to accurately ascertain associations between variables. 

Determining Your Target Population: The Precursor to Survey Sampling

Effective survey sampling occurs when the researchers have established the population subset which they intend to study. As such, you should begin any survey sampling campaign by defining your targeted population. 

If you need to conduct surveys for a business, you should always aim your studies on your target market (when not observing your competitors). This is because the target market is the group of individuals most interested in your sector and most likely to buy from you.

In this regard, it is also important to conduct market segmentation of your target market, as your target market is made up of several consumer segments. Surveys are a powerful tool for segmenting your target market.

But again, you must properly sample your population before conducting any survey research. There are several ways to approach survey sampling.

Probability (Random) Sampling Vs. Non-Probability Sampling

There are various kinds of survey sampling methods, which fall under two main classifications: probability and non-probability sampling. Businesses, governments and other entities can apply either one or both of these methods for their research needs. 

Before navigating the multitude of survey sampling methods, it is key to be able to differentiate the two main categories of sampling. This will put the subcategories, i.e., the specific sampling methods into sharper perspective. 

The following explains the core aspects of the main types of sampling methods.

Probability Sampling: 

Also called random sampling, this category initiates with a full sampling of all the individuals qualified to be in your sample. This main method grants all eligible participants the chance to be used in the sample. In this way, your sample will be able to allow you to make generalizations from your survey results. 

The methods that fall under probability sampling can be more expensive and take up more time than their non-probability sampling counterparts. 

The main advantage of using probability, or random sampling is that the chosen sample is more representative of the target population. As such, this kind of sampling fosters credible statistical conclusions. 

There are five main types of probability sampling methods: simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling. 

1. Simple Random Sampling: The most common form of probability sampling, random device sampling involves each member of the population receiving an equal chance of being selected in the sample pool. True to its name, the respondent is chosen by chance. This method reduces selection bias and allows you to calculate the sampling error. 
2. Stratified Sampling: This method involves dividing the population into subgroups. Known as strata, these groups are divided based on a shared characteristic. This method is used when there is reason to believe the variables will differ between each subgroup. Populations can be stratified by gender, age, location, interests, habits, etc. The study sample is acquired by taking either equal or unequal sample sizes from each stratum. This method enables all categories within the population to be represented in the sample. 
3. Cluster Sampling: This form of sampling assigns every member of the population to a single group called a cluster. Then, a sample of clusters is chosen, typically via simple random sampling. Contrary to stratified sampling, which includes elements from each stratum in the sample, cluster sampling uses a sample with elements only from the sampled clusters. As such, it is more exclusive. This method can be efficient when it comes to studying a wide geographical area, as it’s easier to contact many members of one area than a few members of various regions. The disadvantage includes an increased risk bias when chosen respondents are not representative of a population, which yields sampling errors. 
4. Multistage Sampling: This technique relies on selecting a sample by way of combining different sampling methods. As such, this method involves different stages, wherein Stage 1 may use random sampling, while Stage 2 may use stratified sampling. This method allows researchers to merge different styles of sampling, as a means to study various variables and draw conclusions through different focuses. 
5. Systematic Random Sampling: This method is used when a given population is logically homogenous. It involves enumerating all members of the given population on a list. When all members of the list are compiled, the researchers select the first sample element from the first several elements on the list at regular intervals. The advantage in using this method is its relative ease of use, in comparison to simple random sampling. Also, since simple random sampling may involve clusters, systematic random sampling offers a contrast: evenly sampling the population. 

Non-Probability Sampling: 

Also referred to as non-random sampling, this chief method does not start with a complete sampling pool, as some participants will not have a chance to be selected in the sample. Instead, it relies on the researcher's judgment.

As such, researchers can’t assess the effect of the sampling error. Additionally, there is a higher risk of using an unrepresentative sample, which harms the chances of reaping generalized results. 

On the other hand, non-random sampling methods are less costly and are easier to conduct, making them conducive for exploratory research and formulating hypotheses.

There are four main types of non-probability sampling methods: convenience sampling, quota sampling, judgment (purposeful) sampling and snowball sampling. 

1. 1. Convenience sampling: Known as the simpler non-random sampling method, convenience sampling selects respondents based on their own availability and willingness to participate in the sample. Although researchers can amass valuable information, this method carries a greater risk of volunteer bias, as those who wish to take part may be significantly different from those who don’t. Thus, the sample may not be representative of certain characteristics, such as habits, age or geographical location.
   2. Quota sampling: The most pertinent non-probability sampling method for market research, as respondents are chosen based on quotas. For example, a survey study may require 100 adult men, 100 adult women and 200 children. The quotas used would need to represent the characteristics of the studied population. The benefit in this method is the potential to be highly representative. However, respondents may not be representative of characteristics that were not considered, which is one of the general drawbacks in non-random sampling. 
   3. Judgment Sampling: Also referred to as purposeful, selective or subjective sampling, this method involves exercising the researcher’s own judgment when choosing sample participants. Therefore, they may decide on a representative sample, one that exhibits certain characteristics. Oftentimes, media outlets use this method when surveying the public on qualitative research.  
   4. Snowball sampling: This method is named based on the analogy its methodology puts into practice. Typically used in surveying groups that are difficult to reach, respondents are tasked with calling on more respondents (the ones they know) to take part in the sample. This is how the sample of an otherwise hard-to-recruit group increases, or snowballs, in size. This method is productive for bringing on individuals that can be difficult to study, but it risks selection bias, as is the case when choosing a large group of people with similar traits to the original respondent).
      

      

Survey Sampling Size, Bias & Other Considerations

When undertaking survey research, aside from understanding your target population and what kind of data to derive from them for your survey research, researchers need to decide on a sample size. This does not necessarily need to occur before deciding on the best survey sampling methods for an investigation.

Instead, it is apt to start with an approximate number of respondents in your sample, while identifying an exact size after you settled on a sampling method. This is because researchers may come upon factors that change the proper sample size for their studies. Additionally, facets such as budget and availability come into play.

Researchers also ought to note that both probability and non-probability sampling methods run the risk of developing a survey bias. These biases take place in various situations. These include omitting respondents from hard-to-recruit groups, straying from sampling rules, replacing already opted-in individuals with others, low response rates and others.

Another critical issue to consider is that your survey vehicle, the tool you will use to design and deploy your survey. A strong online survey platform can help you avoid biases and will offer a modern survey sampling method. One of the latest and most potent techniques is RDE (Random Device Engagement), which can reach a massive sample and incorporate several of the methods listed above.  

Businesses and researchers in other industries should therefore consider using this survey sampling method.

by lorraine