In your research, the way you collect your sample plays an important role. Your sampling method can exert a significant effect on the accuracy and generalizability of your results. In this article, we delve into the different sampling methods and provide insights into selecting the most suitable approach for a study.
Types of Sampling Methods
Picture yourself selecting participants entirely by chance, giving each individual in the population an equal shot at being chosen. This method wipes out bias and paves the way for generalizing findings to the entire population. Nirmani et al. (2024) used simple random sampling to assess OTC drug usage among pregnant people.
2. Stratified Sampling
Here, you divide your population into distinct subgroups based on relevant characteristics like age or gender, and then randomly pick samples from each subgroup. This ensures representation from all corners of the population. Yarroo and Rathebe (2024) used stratified random sampling to examine the respiratory effects of solvents among workers in the Mauritius paint industry.
3. Convenience Sampling
If you’re using this method, you’re selecting participants based on their easy accessibility or proximity to you (e.g., your students or the patients at the hospital you work at). This method is convenient and budget-friendly but could introduce bias and compromise sample representativeness. Legros and Boyraz (2023) used convenience sampling to examine college students’ perceived mental health and help-seeking behaviors during the COVID-19 pandemic.
4. Snowball Sampling
When you use snowball sampling, you rely on referrals from initial participants to rope in more participants with similar characteristics or experiences. This method shines when studying hard-to-reach or marginalized groups. Nolan-Isles et al. (2021) used snowball sampling to examine barriers and enablers to health care services among Aboriginal people in Australia.
