Urval
Vill man studera ett fåtal variabler kvantitativt i en stor population med en enkät- eller intervjuundersökning är det lämpligt att använda statistiska urvalsmetoder. Om urvalet görs på korrekt sätt kan nämligen information från urvalet generaliseras till populationen. Man talar om statistisk generalisering. Opinionsundersökningar är ett välkänt exempel. För att göra ett statistisk urval ska det vara slumpmässigt. Vill man däremot studera många aspekter av en liten population är det olämpligt att använda ett slumpmässigt urval. Man talar istället om kvalitativa studier där urvalet är målstyrt.
Patton (1990, sid. 182-183) listar ett antal urvalsprinciper där avsnitt (A) används i statistiska undersökningar medan avsnitt (B) används i kvalitativa studier.
A. Random probability sampling
1. simple random sample
2. stratified random and cluster sampling
   
Representativeness: Sample size a function of population size and desired confidence level.
Permits generalization from sample to the population it represents.
Increases confidence in making generalizations to particular subgroups or areas.
  • Purposeful sampling
1. extreme or deviant case sampling
2. intensity sampling
3. maximum variation sampling-purposefully picking a wide range of variation on dimensions of interest
4. homogeneous sampling
5. typical case sampling
6. stratified purposeful sampling
7. critical case sampling
8. snowball or chain sampling
9. criterion sampling
10. theory-based or operational construct sampling
11. confirming and disconfirming cases
12. opportunistic sampling
13. random purposeful sampling (still small sample size)
14. sampling politically important cases
15. convenience sampling
16. combination or mixed purposeful sampling
Selects information-rich cases for in-depth study. Size and specific cases depend on study purpose.
Learning from highly unusual manifestations of the phenomenon of interest, such as outstanding successes/notable failures, top of the class/ dropouts, exotic events, crises.
Information-rich cases that manifest the phenomenon intensely, but not extremely, such as good students/poor students, above average/below average.
Documents unique or diverse variations that have emerged in adapting to different conditions. Identifies important common patterns that cut across variations.
Focuses, reduces variation, simplifies analysis, facilitates group interviewing.
Illustrates or highlights what is typical, normal, average.
Illustrates characteristics of particular subgroups of interest; facilitates comparisons.
Permits logical generalization and maximum application of information to other cases because if it's true of this one case it's likely to be true of all other cases.
Identifies cases of interest from people who know people who know people who know what cases are information-rich, that is, good examples for study, good interview subjects.
Picking all cases that meet some criterion, such as all children abused in a treatment facility. Quality assurance.
Finding manifestations of a theoretical construct of interest so as to elaborate and examine the construct.
Elaborating and deepening initial analysis, seeking exceptions, testing variation.
Following new leads during fieldwork, taking advantage of the unexpected, flexibility.
Adds credibility to sample when potential purposeful sample is larger than one can handle. Reduces judgment within a purposeful category. (Not for generalizations or representativeness.)
Attracts attention to the study (or avoids attracting undesired attention by purposefully eliminating from the sample politically sensitive cases).
Saves time, money, and effort. Poorest rationale; lowest credibility. Yields information-poor cases.
Triangulation, flexibility, meets multiple interests and needs.
Tabell 1 Urvalsstrategier (Patton, 1990, sid. 182-183)
Patton (1990, sid. 184-185) utvecklar nedan förhållandet mellan slumpmässigt och målstyrt urval. Vilket alternativ som väljs beror påomständigheterna. Det kan uppstå missförstånd om kvalitativa studiers kvalitet om de utvärderas med kriterier för kvantitativa studier.
“To understand the problem of small samples in qualitative inquiry, it's necessary to place these small samples in the context of probability sampling. A qualitative inquiry sample only seems small in comparison with the sample size needed for representativeness when the purpose is generalizing from a sample to the population of which it is a part. …
The logic of purposeful sampling is quite different from the logic of probability sampling. The problem is, however, that the utility and credibility of small purposeful samples are often judged on the basis of the logic, purpose, and recommended sample sizes of probability sampling. What should happen is that purposeful samples be judged on the basis of the purpose and rationale of each study and the sampling strategy used to achieve the study's purpose. The sample, like all other aspects of qualitative inquiry, must be judged in context- the same principle that undergirds analysis and presentation of qualitative data. Random probability samples cannot accomplish what in-depth, purposeful samples accomplish, and vice versa.
Piaget contributed a major breakthrough to our understanding of how children think by observing his own two children at length and in great depth. Freud established the field of psychoanalysis based on fewer than ten client cases. Bandler and Grinder (1975a, 1975b) founded neurolinguistic programming (NLP) by studying three renowned and highly effective therapists: Milton Erickson, Fritz Perls, and Virginia Satir. Peters and Waterman (1982) formulated their widely followed eight principles for organizational excellence by studying 62 companies, a very small sample of the thousands of companies one might study.
The validity, meaningfulness, and insights generated from qualitative inquiry have more to do with the information-richness of the cases selected and the observational/analytical capabilities of the researcher than with sample size.”
 
Urvalsfrågor är centrala i alla typer av undersökningar. I akademiska uppsatser där empiriskt material insamlas bör urvalet planeras noggrant samt eventuella effekter av urvalet på resultatet diskuteras. Om småföretag har undersökts t ex, gäller resultaten för medelstora företag?
 
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