Sampling

Sampling is the process of selecting participants from a population for inclusion in a research study. How participants are selected determines the External Validity of findings, that is, whether results can be generalised beyond the study's specific sample.

Sampling Methods

Probability Sampling

Every member of the population has a known, non-zero chance of selection. Enables statistical generalisation.

Method	Procedure	Strength
Simple random	Every individual has an equal chance of selection	Unbiased; basis for inferential statistics
Stratified random	Population divided into subgroups (strata), then random selection within each	Ensures representation of key subgroups (e.g., L1 background, proficiency level)
Cluster	Random selection of groups (e.g., classes, schools), then all members included	Practical when individual random selection is impossible
Systematic	Every nth individual selected from a list	Simple to implement; approximates random sampling

Non-probability Sampling

Selection is not random. Common in applied linguistics because researchers rarely have access to complete population lists.

Method	Procedure	When used
Convenience	Whoever is available and willing	The default in most classroom SLA research: researchers use their own classes or accessible institutions
Purposive	Participants selected for specific characteristics	Qualitative Research: selecting information-rich cases (e.g., a teacher known for innovative practice)
Snowball	Participants recruit other participants	Hard-to-reach populations (e.g., undocumented immigrants learning English)
Quota	Non-random selection ensuring specified proportions	Ensuring balanced representation without randomisation

The Convenience Sampling Problem in SLA

The vast majority of SLA research uses convenience sampling, typically intact university classes, accessible schools, or volunteer participants. This has significant consequences:

• Population bias: university students (typically young, educated, motivated) are dramatically overrepresented; children, older adults, migrant workers, and low-literacy learners are underrepresented
• Geographic bias: most published SLA research comes from North American, European, and East Asian universities
• Limited generalisability: findings from one institutional context may not transfer to others
• WEIRD problem: samples are disproportionately from Western, Educated, Industrialised, Rich, Democratic societies

Sample Size

Sample size affects statistical power, the ability to detect a real effect. In SLA classroom research:

• Individual classes typically contain 15-30 students
• Many studies compare just two classes (total N = 30-60)
• Plonsky (2013) showed that the median sample size in SLA research is too small to detect medium effect sizes reliably
• Power analysis (determining the required sample size before data collection) is still uncommon but increasingly recommended

Sampling in Qualitative Research

Qualitative Research uses purposive rather than random sampling. The goal is not statistical representativeness but information richness: selecting cases, participants, or sites that can provide the deepest insight into the research question. Sample size is determined by data saturation (the point at which new data no longer generate new themes), not by statistical power calculations.

References

• Dörnyei (2007): sampling in applied linguistics research
• Plonsky (2013): sample size and power in SLA research
• Patton (2015): purposive sampling strategies in qualitative inquiry
• Mackey & Gass (2005): participant selection in SLA research design