Sampling Methods in Survey Research: Definition, Types, Methods, and More

What Is Sampling? Why Is Sampling Important?

Sampling is the backbone for primary data collection and analysis in marketing research and the social sciences. It involves selecting a subset of individuals from a larger population to participate in a study, with the aim of making valid inferences about the entire population without needing to survey every individual within that population. Sampling makes research both practical (cost and time effective) and feasible.

The key to effective sampling lies in the concept of a representative sample—a subset that fairly mirrors the diverse characteristics of the population it is drawn from. Achieving this ensures the data collection and data analysis phases yield accurate and meaningful insights, minimizing the risk of biased (systematically wrong) or irrelevant findings.

“Dewey Beats Truman” USA Presidential Election 1948 Example

The classic example showing how poor sampling procedures can lead to erroneous conclusions about the population is had in the oft-cited “Dewey Beats Truman” false headline newspaper article on November 3, 1948, the day after Truman had actually beaten Dewey to win the presidency of the United States of America. 

How did the newspaper get it wrong? Conventional wisdom, based on various polls with large sample sizes, predicted that Dewey would win. Facing the printing deadline and anxious to be able to report the winner of the election the next morning, they decided to print the expected results prior to the election results being finalized. “Dewey Defeats Truman” the headline trumpeted; but they had gotten it wrong.

(President Harry Truman gleefully showing the erroneous headline) Image Credit: Byron Rollins, The Washington Post

There were multiple reasons that the polls didn’t get it right. Prominent among those was that telephone polls in 1948 led to a biased sample of respondents who tended to favor Dewey, because telephones were something more commonly found in well-to-do households. Well-to-do households were more likely to vote for Dewey. The sample used to project that Dewey would beat Truman, though substantial, was nonetheless biased. 

Key Sampling Definitions: Population vs. Sample

Understanding the distinction between a population and sample is crucial in sampling:

• Sampling: The process of selecting a sample from a population.
• Population: The entire group of individuals or entities relevant to a particular research study, from which a sample is drawn.
• Sample: A subset of the population selected for participation in the research study.

• Sample Frame: A list or database from which the sample is drawn, ideally encompassing the entire population.

These foundational concepts lay the groundwork for understanding the various sampling methods and their applications in research, guiding the choice of method based on the research objectives and constraints.

Types of Sampling Methods in Research

Sampling methods are broadly categorized into probability and non-probability sampling, each with distinct approaches and implications for research accuracy and validity.

Probability Sampling Method

Probability sampling, the first of two primary sampling methods, ensures every member of the population has a known and non-zero chance of being selected. This method fosters objectivity and minimizes sampling bias, enhancing the representativeness of the sample.

1. Simple Random Sampling (SRS) Method

Simple random sampling (SRS) epitomizes equal chance selection, where each population member has an identical probability of being chosen. Its advantages include simplicity and reduced bias, while disadvantages may involve logistical challenges in large populations. Theory-based classroom examples involve drawing balls of different colors out of a cylinder where the balls have been thoroughly mixed. A real-world application is selecting respondents for a customer satisfaction survey from a database of all customers.

2. Systematic Sampling Method

Systemic sampling selection is made at regular intervals from an ordered list, combining efficiency with a reduced risk of selection bias. However, its systematic nature can introduce bias if the list has an underlying pattern. An application example is selecting every 100th theme park visitor coming through the turnstiles to provide feedback.

3. Stratified Sampling Method

Stratified sampling involves dividing the population into subgroups based on identifiable characteristics and randomly sampling from each. It enhances representation but is more complex to implement. A practical use case is conducting a health survey across different age groups to ensure all are adequately represented.

Stratified sampling reduces the likelihood of obtaining an unlucky sample that is not representative. For example, if we sampled 30 respondents, it’s possible you’ll obtain only a very few male respondents. If we divided the sample frame into males and females and drew 15 females and 15 males, we could ensure good representation with respect to gender. In practice, stratified sampling typically involves consideration of more than one demographic (or otherwise known) characteristic of the sample elements.

4. Cluster Sampling Method

Cluster sampling selection occurs by dividing the population into clusters and randomly selecting entire clusters. This approach is cost-effective, especially for geographically dispersed populations, but can increase sampling error. It's sometimes used in field surveys where researchers select specific neighborhoods or schools to study.

Non-Probability Sampling Method

Non-probability sampling, the second primary sampling method, does not provide every individual with a known and non-zero chance of selection, often used when probability sampling is impractical or unnecessary.

1. Convenience Sampling Method

Convenience sampling selection is based on ease of access, favoring quick and easy data collection despite inherent bias risks. Examples related to survey research involve placing a link to take a survey on social media, or using Mechanical Turk (Amazon’s crowdsourcing marketplace) where people go to do a variety of tasks, such as completing surveys, to make money. 

Although we would like to think otherwise, many sample sources we rely on for marketing and social survey research purposes, including online panel samples, are in reality convenience samples.

2. Quota Sampling Method

Quota sampling involves selecting individuals to fill a "quota" such as 500 smokers in a non-random manner, such as through convenience sampling approaches.

3. Judgement Sampling Method

In judgement sampling, researchers select participants based on specific criteria and judgment, useful for targeted studies but susceptible to subjective bias. For example, selecting experts in a field for in-depth interviews.

4. Snowball Sampling Method

For hard-to-reach populations, snowball sampling relies on referrals from initial subjects to recruit further participants. While it can be effective for niche studies, it can lead to bias. A case in point is researching a rare medical condition where patients might know and might be able to refer other patients. (Snowball sampling is rarely used in practice.)

Each sampling method has its context of application, influenced by the study's objectives, the population's nature, and logistical considerations. Choosing the appropriate method is important for research validity and reliability.

Differences Between Probability and Non-Probability Sampling Methods

Each sampling method has its own set of advantages and limitations, influencing the accuracy, applicability, and generalizability of research findings. Here's a summary exploration of the differences:

Criterion	Probability Sampling	Non-Probability Sampling
Basis of Selection	Random selection, every member has a known and non-zero chance of being selected.	Non-random selection, not all members have a known and non-zero chance of being selected.
Types of Methods	Simple random sampling, systematic sampling, stratified sampling, cluster sampling.	Convenience sampling, quota sampling, judgement sampling, snowball sampling.
Representativeness	Generally results in a representative sample.	Usually does not produce a representative sample.
Applications	Appropriate for inferential statistics and studies requiring generalization.	Used in exploratory research, qualitative studies, or specific subgroup focuses.
Advantages	Reduces sampling bias, allows calculation of sampling error, more reliable.	Easier and less expensive to implement, useful for inaccessible populations.
Limitations	More time-consuming and costly, requires comprehensive population list.	May introduce bias, limits generalization of findings to the entire population.

Choosing the Right Sampling Method in Research

Selecting the appropriate sampling method can significantly affect the validity of your research findings. This choice should be guided by a structured decision-making process, taking into account several key considerations:

1. Research Goals: Begin by clarifying whether your study seeks to gain general insights applicable to the broader population or if it focuses on specific segments or behaviors. This determination will influence whether a probability or non-probability sampling method is more appropriate.
2. Nature of the Population: Consider the diversity, geographic distribution, and accessibility of your target population. Probability sampling methods are preferable for a diverse and widespread population to ensure representativeness, while non-probability methods might suffice for more homogeneous or accessible groups.
3. Constraints: Practical constraints such as time, budget, and resources available for your study can significantly affect your sampling strategy. Non-probability methods often require less time and resources, making them suitable for studies with tight constraints.
4. Reach of Findings: Decide on the importance of the study’s findings being representative of the broader population. Studies aiming for broad applicability should lean towards probability methods to ensure a representative sample.
5. Feedback: Engaging with peers or experts in your field can provide valuable insights and feedback on your chosen sampling method. Pilot tests or preliminary studies can also help validate your approach before full-scale implementation.

When interviewing human subjects for market or social survey research, we can do our best to try to follow sound sampling procedures, but non-response bias (when the people who don’t complete the survey systematically differ from those that do) can still be a significant problem, leading to incorrect inferences about the population.

Sampling Methods Theory vs. Practice

This article mainly focuses on the theory of sampling. The theory and science of sampling leads to well-established formulas that lead to probability-based inferences we can make about the population (such as a mean with its accompanying confidence interval). But, in the practice of marketing research and social sciences, we’re dealing with humans, rather than colored balls being drawn from buckets or samples of widgets being pulled off the line at a factory. 

Humans self-select themselves out of the sample, by refusing to complete surveys or by having filters on their email servers that block our emailed invitations to complete a survey. The formulas we rely on typically assume simple random sampling (SRS), whereas we rarely ever achieve SRS when conducting survey research with human respondents. To the degree that our samples are biased, the measurements and confidence intervals we obtain in surveys are also systematically incorrect. No amount of sample size (short of interviewing every human in the population) can erase the negative effects of biased sampling procedures.

Avoiding Sampling Errors and Bias

The integrity of research findings hinges on the ability to control and minimize sampling errors and biases. Sampling error refers to the natural variation that occurs by chance because a sample, rather than the entire population, is surveyed. Non-sampling error, on the other hand, encompasses all other errors in the research process, from data collection to analysis. Bias, a systematic error, can significantly alter the results (e.g., too high, too low, not variable enough, or too variable), leading to inaccurate conclusions.

Sampling Error and Bias Mitigation Strategies

• Careful Design and Planning: Developing a robust sampling plan that considers the objectives and scope of the study can help in selecting the most suitable sampling method to minimize errors and bias.
• Randomization: Employing random selection methods where possible to ensure each population member has an equal chance of being included, thereby reducing selection bias.
• Stratification: Using stratified sampling to ensure that important subgroups within the population are adequately represented can minimize sampling bias related to specific characteristics.

By proactively addressing these areas, researchers can enhance the reliability and validity of their study outcomes, ensuring that conclusions drawn are reflective of the true population characteristics.

A Note on Online Panel Providers

Whereas phone and snail mail surveys were popular in the 1940s through the 1980s for sampling from the general population, online panel providers are the most commonly used approach today. Although panel providers may report that their samples are balanced to represent the demographic characteristics of the population as a whole, the samples are not unbiased. The kinds of people who participate in panels and self-select to take surveys may be systematically different on the characteristics that are the subject of your study than those who do not participate in your survey. 

Figuring Out Sample Size

Figuring out (determining) the appropriate sample size is crucial for ensuring that your study results are statistically significant and representative of the population. An inadequately small sample size may lead to unreliable results, while an unnecessarily large sample can waste resources and time.

Factors to consider when determining sample size include:

• Population Size: Larger populations may require a larger sample to accurately reflect the population's characteristics, although the relationship is not linear.
• Confidence Level: This reflects how sure you can be that the population’s true mean falls within a certain range. 95% confidence is a common threshold. A higher confidence level requires a larger sample size.
• Margin of Error: Also known as the confidence interval, it indicates the range within which the true value is likely to lie (given a specified degree of confidence, such as 95%). A smaller margin of error requires a larger sample size.
• Power: The ability to detect true differences or relationships in statistics/variables if they in actuality exist.

Combining the elements of confidence level and margin of error, it’s common to read that a poll found that 30% of respondents said they would vote for candidate A with a margin of error of +/- 3% at the 95% confidence level. (If it isn’t explicitly stated, it is generally assumed that the research organization has assumed a 95% confidence level when calculating the margin of error.)

Several sample size calculators and formulas are available to assist researchers in selecting the sample size for determining margins of error at given confidence levels. Consulting with statistical experts or utilizing specialized software can provide additional accuracy and confidence in these calculations, especially regarding power.

False Positives (Type 1 error) vs. False Negatives (Type 2 error)

Most common sample size calculators used for survey research focus on achieving reasonably good precision, meaning tight confidence intervals (such as the common +/-3% confidence interval for proportions at 95% confidence). 

Most researchers applying tests for significance (of effects or parameters) want a likelihood of 5% or lower of signaling that a difference or impact (effect) is non-zero when in reality it is not. This is called a False Positive, or a Type 1 error, and 5% “alpha” threshold for Type 1 errors is typical. 

Most researchers don’t think about the danger of False Negatives (Type 2 error) when calculating needed sample size. Setting the likelihood of Type 1 errors at 5% means that you are only 50% likely to find a significant effect that actually exists for hypothesis testing purposes. To increase the power that you will be able to detect significant effects if they exist requires additional sample size. Our consultants at Sawtooth Analytics can help you with decisions regarding precision and power related to selecting an appropriate sample size.

Conclusion

Understanding and correctly applying sampling methods are pivotal for the success of market and survey research. The choice of sampling method directly impacts the accuracy, reliability, and applicability of the research findings. By carefully considering the research objectives, population characteristics, practical constraints, and by diligently working to avoid errors and biases, researchers can select the most appropriate sampling strategy for their studies.

Furthermore, determining the right sample size is essential for achieving statistically significant results without overextending resources. Through thoughtful planning and execution, researchers can ensure their studies contribute valuable insights that lead to good policy, decision-making, and inferences about the population.