A clinical researcher collecting data from two respondents after clinical research training

4 Common Sampling Strategies To Keep In Mind After Clinical Research Training

As you shift from academic clinical research training to hands-on research, you’ll dive deep into the complexities of sampling. In this field, “population” refers to individuals with common traits or conditions, usually linked to a specific disease. 

The large size and inaccessibility of a typical population make it impractical to study every affected individual. As a result, researchers usually focus on a “sample population,” a subset of the broader “target population.” The essence of sampling is to ensure that this chosen subset accurately reflects the larger group, minimizing discrepancies and gaps. Sampling is the foundational approach to selecting a group that exemplifies the broader population, which is essential in all clinical studies.

There are various sampling strategies that a researcher can employ, each with its advantages and disadvantages. Here, we’ll delve into four common sampling strategies that every budding clinical researcher should be well-acquainted with.

1. Random or Probability Sampling

Random sampling is the most straightforward and unbiased method. In this technique, every member of the population has an equal chance of being selected. This method reduces selection bias, and if done correctly, the sample should be representative of the entire population. As you may have learned in clinical research training, researchers often use tools like random number generators to facilitate this kind of selection. However, the catch is that random sampling requires a comprehensive list of all population members, which is only sometimes feasible in large populations.

A clinical researcher collating results of random sampling test after clinical research training

As you’ll discover in clinical research training, the random sampling method is the most common.

2. Stratified Sampling After Clinical Research Training

When specific subgroups within a population are known to be distinct, stratified sampling can be more appropriate. Here, the population is divided into non-overlapping subgroups, or ‘strata,’ usually based on a particular characteristic (like age, gender, or ethnicity). A random sample is then taken from each stratum. This ensures that each subgroup is adequately represented in the overall sample. As clinical research courses often emphasize, it’s beneficial when differences between strata are significant. However, the challenge lies in identifying all potential strata and ensuring that the divisions are non-overlapping.

A clinical researcher interviewing a member of a research subgroup after clinical research training

As learned in pharmaceutical courses, stratified sampling divides the population into subgroups.

3. Cluster Sampling

Cluster sampling becomes handy when it’s challenging or costly to study individual population members directly. This method divides the population into clusters, often based on geographic regions or other natural groupings. Then, a random sample of clusters is selected, and all members (or a random sample of members) within those chosen clusters are studied. This technique is often used in large-scale national surveys or when population members are widespread geographically. However, remember that more variability within clusters may lead to larger sampling errors.

4. Convenience Sampling

As the name suggests, convenience sampling involves selecting participants who are readily available or easy to reach. It’s a non-probability method, meaning that not every member of the population has a known or equal chance of being included in the sample.

A clinical researcher interacting with a member of her population after clinical research training

Convenience sampling is done at the researcher’s convenience.

For instance, a researcher might sample individuals who happen to be at a clinic on a particular day. While this method is cost-effective and straightforward, it’s prone to significant biases. The sample might not reflect the true characteristics of the population, and any observed differences or trends may be due to chance rather than real relationships. It’s essential to exercise caution when generalizing results from convenience samples to the broader population.

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