This document discusses different sampling methods used in qualitative research. It explains that qualitative researchers are interested in understanding social phenomena through the eyes of respondents and capturing rich context rather than making generalized predictions. As such, non-probability sampling methods are preferred over probability sampling as they are more flexible and able to change based on emerging insights. Several non-probability sampling techniques are then described in detail, including convenience sampling, snowball sampling, quota sampling, theoretical sampling, and accidental sampling. The document contrasts the goals of qualitative and quantitative researchers when determining sample sizes, with qualitative researchers seeking saturation and understanding over numbers and quantitative researchers concerned with statistical validity and generalization.

1. SAMPLING METHODS

2. What exactly IS a “sample”?

3. What do qualitative
researchers worry about?
I want to see the
world through
the eyes of my
respondents.
I want to
describe the
context in a lot
of detail.
I want to show
how social
change occurs.
I’m interested in
how things come
to be. I really want my
research
approach to be
flexible and able
to change.

4. Social actors are not
predictable like
objects.
Randomized events are
irrelevant to social life.
Probability sampling is
expensive and inefficient.
Non-probability sampling is
the best approach.

5. Types of samples

6. Simple Random Sample
 Get a list or “sampling frame”
a. This is the hard part! It must not systematically exclude
anyone.
 Generate random numbers
 Select one person per random numbers

7. Systematic Random Sample
 Select a random number, which will be known as k
 Get a list of people, or observe a flow of people
(e.g., pedestrians on a corner)
 Select every kth person
 Careful that there is no systematic rhythm to the flow or
list of people.
 If every 4th person on the list is, say, “rich” or “senior” or
some other consistent pattern, avoid this method

8. Stratified Random Sample
1. Separate your population into groups or
“strata”
2. Do either a simple random sample or
systematic random sample from there
a. Note you must know easily what the
“strata” are before attempting this
b. If your sampling frame is sorted by,
say, school district, then you’re able to
use this method

9. Multi-stage Cluster Sample
 Get a list of “clusters,” e.g., branches of a company
 Randomly sample clusters from that list
 Have a list of, say, 10 branches
 Randomly sample people within those branches
 This method is complex and expensive

10. The Convenience Sample
Find some people that are easy to find

11. The Snowball Sample
 Find a few people that are relevant to your topic.
 Ask them to refer you to more of them.

12. The Quota Sample
 Determine what the population looks like in
terms of specific qualities.
 Create “quotas” based on those qualities.
 Select people for each quota.

13. The Theoretical Sample

14. Accidental sampling
• A type of nonprobability sampling which involves the
sample being drawn from that part of the population
which is close to hand
• The researcher using such a sample cannot
scientifically make generalizations about the total
population
• In social science research, snowball sampling is a
similar technique

15. Panel sampling
• The method of first selecting a group of participants
through a random sampling
• Period of data collection is called a "wave“
• Panel sampling can also be used to inform
researchers about within-person health changes due
to age

16. How many?
Qualitative researchers seek “saturation”
• “How many” isn’t the issue. Do you understand the
phenomenon? Have you learned enough?
• Mere numbers are irrelevant. You want “verstehn” or deep
understanding
Quantitative researchers seek statistical validity
• Can you safely generalize to the population? Have you
systematically excluded anyone? (See the “famous sampling
mistake”)

17. Improving Response Rates
• Personalize the invitation
• Offer money -- no strings attached

18. Thank You