Survey Sampling in Technicolor: Types of Sampling Methods
If sampling spanned just a single approach called “random sampling,” mastery would be simple. Instead, researchers face a veritable rainbow of options mixing random and non-random techniques tailored to varying study demands. Like a golfer selecting clubs, academics must first match designs to desired sampling goals.
On the broadest level, two overarching sampling camps exist:
1. Probability Sampling
Packs in all sampling modes involving random selection where each population unit carries calculable selection probability. Every person has an equal chance of being selected.
2. Non-Probability Sampling
Encompasses all nonrandom approaches, allowing unequal selection probability. Selection in survey research stems from factors like availability or subjective researcher discretion rather than random chance to be included in the sample.
From here, academics drill down into more nuanced sampling species.
When to Use
Probability Sampling Methods
When needing completely random and generalizable data from large populations
When having an ordered population list; simpler than random sampling
When wanting to guarantee subgroups in a sample; improves the representation
When the population spread across wide geographic areas; cost-efficient
Non-Probability Sampling Methods
Fast and affordable; when random sampling is unfeasible
When wanting to select a sample to serve specific needs; risks bias
Attempts to balance samples on traits like demographics
leverages networks for hard-to-reach groups
Stratified sampling aims for representative demographic splits
Probability sampling prioritizes representation and random selection
Non-probability sampling focuses on speed, cost efficiency, or specific needs
What is Random Sampling?
Random sampling refers to selection processes that give all members of a target population equal chances of inclusion within research samples. Just like rolling dice or drawing lottery balls, no population units receive advantageous or disadvantaged odds of selection. This guards against systematic biases and lends greater credibility.
Simple Random Sampling (SRS)
The “gold standard” uses pure random selection where each member of the population holds an equal likelihood of inclusion. Analogues include lotteries or dice rolls. Easy to explain but labor-intensive in practice.
Selection of every kth unit from population lists after random starting points. Easier to implement than SRS but risks periodic biases within ordering cycles.
Dividing populations into non-overlapping “strata” by shared traits (i.e. gender, income brackets) and sampling within a stratum. Attempts accurate subgroup representation.
Grouping population units within “clusters” (i.e. towns) and selecting some clusters. Each cluster unit then included. Cost-effective for widely spread populations.
What is Non-Random Sampling?
Non-random sampling makes no claims towards equal probability of selection. Instead, factors like convenience, availability, costs, or subjective judgments determine inclusion likelihood for certain population components over others. While sometimes pragmatically necessary, non-random risks biased samples misrepresentative of total populations.
Selecting readily available or conveniently accessed units. Speedy and affordable but risks missing population segments lacking convenient representation (like the homeless).
Purposive or Judgment Sampling
Researchers handpick supposedly “typical” or divergent units based on experience and desired sample traits. But lacks safeguards against bias.
Attempt to balance samples on traits like age, income, and gender. But within quotas, selection remains nonrandom and biases loom.
This simplified typology equips academics with starting mental models to deploy and assess sampling approaches fit for purpose. Now let’s spotlight key considerations when applying methodologies.
Mastering Quota Sampling Method
for Precision in Representing Your Target Market!
Sampling Scientificamente: Key Implementation Insights
While core sampling vocabulary and typologies provide the map, successfully navigating research journeys requires understanding principles guiding on-the-ground decision points:
1. Sampling Error vs. Non-Sampling Error
Sampling error stems from natural variation when subsets attempt to model populations. It's manageable through sufficient sample sizes. Non-sampling errors from poor selection designs or biases distort the samples' reliability.
2. Sample Size - How Many is Enough for My Survey?
Required samples depend on desired precision levels and confidence intervals ranging from 90% to 99%. Typical parameters for +/- 3% margins of error at 95% confidence level demand sample sizes exceeding 1,000 units.
3. Stratified Samples & Pooling
When sampling from groups with substantially different characteristics (i.e. income brackets), stratified sampling improves precision by allowing correct subgroup proportions. Stratifications can also be leveraged by pooling subgroups differently based on goals.
4. Cluster Sampling Efficiency
Cluster sampling proves cost-efficient for dispersed groups by sampling entire clusters (i.e. blocks). But wide intra-cluster homogeneity is vital to avoid masking inter-cluster differences lost by focusing only on sampled clusters.
5. Weighting Samples
Sample weighting assigns individual observations differential impact on survey conclusions to correct demographic skews. For instance, weighting can prevent groups like the elderly or younger population from excessive influence if samples overrepresent their population proportion.
6. Deliberately Non-Random Sampling
For some research goals like case studies, non-random processes best serve needs despite ceding generalizability. However, the methodology must explicitly frame projects as exploratory investigations rather than generalized inferences.
Mixing & Matching: Sampling for Quantitative vs. Qualitative Research
Sampling modes remain crucial for both quantitative hypothesis testing focused on generalizable models and qualitative studies providing nuanced situated insights. However specific applications differ based on the distinct needs of quantitative vs. qualitative projects.
In quantitative realms, probability sampling to achieve representative randomness takes center stage for most survey-based statistical research. Simple random and stratified techniques allow unbiased model inputs that best leverage quantitative analytics. Sample sizes also skew larger.
By contrast, qualitative work may focus less on air-tight numerical representations of target groups than illuminating lived experiences or meaning-making narratives accessible through small samples. Here, non-probability judgment sampling often proves effective when researchers leverage personal expertise to extract data-rich cases.
But qualitative sampling still requires meticulous planning to fulfill standards like data saturation across themes where additional data provides negligible additional interpretive utility. Regardless of methods, the bedrock goals of minimizing bias while capturing adequate diversity remain unchanged.
Wrapping Up: Springboard to Survey Sampling Success
Like any formidable academic journey, navigating the world of sampling remains far easier said than done. However, equipping dissertations and research projects with scientific sampling principles provides a springboard for the methodology that leaps past common credibility pitfalls.
So breathe easy: The population of intimidating sampling terminology and theory has now been tamed into a manageable methodological toolbox ready for academic application. And with survey sampling mastery fused with skillful research design, your projects won’t just withstand scrutiny—they’ll blaze trails through domains desperate for actionable insights.