Sampling

Sampling is one of the fundamental building blocks of the research process. In scientific studies, it is often impractical, costly, or time-consuming to examine the entire population. Therefore, researchers prefer to work with a smaller group called a "sample" that represents the population. The methods and techniques used in this process are referred to as "sampling."

The research population is the collective group of individuals related to a specific problem. A smaller group selected from this population and structured to reflect its characteristics is defined as the sample. An ideal sample must have a representative structure that mirrors the population’s features and allows findings from the sample to be generalized to the entire population. To enhance the representativeness of the sample, various sampling techniques are employed.

Sampling methods are broadly divided into two main categories: probability-based (random) and non-probability-based (purposive or judgmental) methods.

In this type, every individual in the population has an equal chance of being selected for the sample. It produces highly representative results with strong generalizability. The main types include:

• Simple Random Sampling: This method ensures that all individuals have an equal probability of selection. While easy to implement, it requires a complete and accurate sampling frame.
• Systematic Sampling: This method involves selecting individuals at regular intervals. The ordering of the population may introduce bias into the sample.
• Stratified Sampling: The population is divided into subgroups with similar characteristics, and samples are drawn from each subgroup in proportion to their size. This method yields more accurate results, especially in heterogeneous populations.
• Cluster Sampling: Used when the population is spread across wide geographic areas. First, clusters are selected, and then individuals within those clusters are included in the sample. While it improves accessibility, it may reduce representativeness.

^{Probability-Based (Random) Sampling Methods (Generated by Artificial Intelligence)}

In this type of sampling, individuals do not have an equal chance of selection. Participants are chosen based on the research objective, favoring those who can provide rich informational value. It is especially common in qualitative research. The main types include:

• Maximum Variation Sampling: Involves including individuals with diverse characteristics to capture multiple dimensions of the research topic.
• Homogeneous Sampling: Focuses on an in-depth examination of a group composed of individuals with similar characteristics.
• Snowball Sampling: Used for hard-to-reach populations; new participants are recruited through referrals from existing participants.
• Convenience Sampling: Involves including individuals who are easily accessible. It is highly practical but has low representativeness.
• Quota Sampling: Specific quotas are set for certain groups, and a predetermined number of individuals are selected from each group. It is often fast and cost-effective.
• Theoretical Sampling: Aims to develop theory through qualitative data. It is commonly used in grounded theory research.

In qualitative research, sample size is determined based on data saturation, which refers to the point at which no new information is obtained and the study can be concluded. This approach is applicable when calculating sample size using classical statistical formulas is not feasible. At the same time, validity (the accuracy of measurement) and reliability (the consistency of measurement) are key criteria to consider when selecting a sample.

Non-representative samples can compromise the ability to generalize research findings to the broader population. For example, data collected through home visits during working hours may exclude individuals who are not at home, introducing systematic bias into the sample. If such biases are not corrected through appropriate statistical adjustments, they can negatively affect the validity of the analysis.