Sampling Methodologies and Strategies For Ph.D. Dissertation
If you have just stepped into the research world, you’ll inevitably encounter the daunting realm of sampling methods and strategies at some point.
If you’ve landed on this page, it’s likely that you’re currently experiencing some degree of confusion or feeling overwhelmed because you’re clueless about where to get started.
But don’t worry – in this article, we will demystify the concept of sampling in simple words and provide numerous examples to guide you through.
What is Sampling?
At its core, sampling in a research context involves selecting a subgroup of individuals from a larger population.
For instance, if your research revolves around understanding the opinions of U.S. consumers regarding a specific brand of electronics, surveying every electronics user across the nation would be quite a daunting task. Instead, you can opt to gather data from a smaller, manageable subset of this vast group.
In technical terms, the larger group is called the population, while the subset you engage with for research purposes is known as the sample.
To put it differently, think of the population as an entire pie and the sample as a single slice taken from that pie.
Ideally, you’d want your sample to be a perfect representation of the population, enabling you to draw conclusions that apply to the entire population, much like cutting a flawless cross-sectional slice of pie that captures every layer in proportion.
The two primary sampling approaches
Broadly speaking, there are two sampling approaches:
- Probability sampling
- Non-probability sampling
Probability sampling entails the selection of participants on a statistically random basis, earning it the nickname “random sampling.”
In simpler terms, every individual participant is chosen through a predetermined process, not at the researcher’s discretion. Consequently, this approach results in a random sample.
Probability-based sampling methods are predominantly employed in quantitative research, particularly when the goal is to obtain a representative sample that allows the students to generalize their findings.
In non-probability sampling, the participants’ selection is not statistically random. Put differently, the choice of individual participants relies on the researcher’s discretion and judgment rather than a pre-defined process.
Non-probability sampling methods are frequently used in qualitative research, where the depth and richness of data hold more significance than the generalizability of findings.
Now that we have understood the types of sampling let’s learn about different research sampling methods included in the probability sampling method.
Simple random sampling
Simple random sampling entails the completely random selection of participants, ensuring that every participant has an equal probability of being chosen.
For example, if you had a dataset containing 500 individuals, you could employ a random number generator to generate a list of 50 numbers (each corresponding to a participant), and that dataset would constitute your sample.
Moving on to cluster sampling, this approach involves sampling from naturally occurring, distinct clusters within a larger population.
For instance, you might choose to sample specific industries within a region or schools within a school district. After identifying these clusters, a random selection is made of a subset of clusters, followed by the random selection of participants from each chosen cluster.
Stratified random sampling
Stratified random sampling takes the sampling process to a higher level compared to simple random sampling.
Just as the name implies, stratified sampling entails the random selection of participants from specific pre-defined subgroups (known as strata) that exhibit a shared characteristic.
For instance, you could stratify the population based on occupation, income level, geographic location, or political affiliation and then proceed to select individuals randomly from each of these distinct groups.
This method involves the selection of participants at regular intervals, commencing from a random point.
For example, you have a database of employees representing a company’s workforce. To systematically sample this population, you could begin by selecting a random starting point, such as employee number 23, and then proceed to choose individuals at regular intervals, like every 7th employee in the list: 23, 30, 37, and so forth.
Non-probability-based sampling methods
After exploring the probability-based sampling methods, let’s check out the three non-probability methods:
In Purposive sampling, also referred to as judgmental, selective, or subjective sampling, the researcher makes deliberate choices when selecting participants, guided by the study’s specific objectives.
For Instance: Imagine you’re conducting research to explore the preferences of avid users of a niche online forum.
In this scenario, you might apply your discretion to interact with both active and passive forum members, aiming to gain insights into the motivations behind these varying levels of engagement.
Now, let’s explore the snowball sampling technique!
This method hinges on recommendations from initial participants to enlist more individuals.
Essentially, the initial participants initiate the first “seed” group, and each subsequent participant brought in through referrals contributes to expanding this group, much like a snowball gathering size as it rolls.
Snowball sampling frequently finds application in research scenarios where identifying and reaching a specific population poses challenges.
For instance, researching individuals with a unique hobby or those affiliated with an unconventional subculture. It can also prove valuable in situations involving sensitive or taboo subjects, where individuals are more likely to participate if they are referred by someone they have trust in.
Now, let’s discuss convenience sampling. As the name implies, this approach involves choosing participants based on their ease of access or availability. In simpler terms, the sample is determined by how convenient it is for the researcher to reach potential participants rather than following a specific and standardized selection process.
For instance, Imagine a researcher is conducting a survey about smartphone usage in a local coffee shop.
To gather data quickly and conveniently, the researcher approaches customers who happen to be present in the coffee shop at that particular time.
Since these individuals are readily available and accessible, they become the participants in the study. The researcher does not employ any strict criteria or random selection; instead, they opt for convenience by surveying those at hand.
Whether you’re writing a PhD dissertation or research paper, your sampling strategy should be driven by and in sync with your research goals, objectives, and research inquiries.
In particular, you must contemplate whether your research objectives revolve around generating findings that can be broadly applied (in which case, you’re inclined towards a probability-based sampling method) or if your focus is on gaining comprehensive, profound insights (in which case, a non-probability-based approach might be more suitable).