1. Session Six Jeff Driskell, MSW, PhD
Research in Social Work Practice Salem State University School of Social Work
Session Six
Jeff Driskell, MSW, PhD

2. Today’s class
Check/Announcements
Lecture-
Sampling

3. Follow-up: Article Search
This is a follow up the exercise in our last class.

4. Sampling Design

5. What Do We Mean by Sampling?
It is NOT going to Costco on a Saturday morning and sampling all the free food.
It is NOT taking a bit out of all the chocolates in the candy box to see which one tastes the best.

6. Sampling Defined
“A process of selecting a group of subjects from a larger population in the hope that studying this smaller group (the sample) will reveal important things about the larger group (the population) from which it is drawn”.

7. Wedding Cake Scenario

8. Why is Sampling Important in Research?

9. Population vs. Sample
Sample
Infers population characteristics from a subset of the population
Saves money
Saves time
Can be more accurate – don’t need whole population
Population
A set of people or events in which a sample is drawn.

10. What is the sampling frame?
Sampling Frame Examples?

11. Sampling Error Not representative Non-response error
Sample error can be reduced:
The larger the sample, the less error
Homogenous samples have less error compared to heterogeneous samples

12. Sampling Approaches

13. Sampling Process Definition of target population
Selection of a sampling frame (list)
Probability or Non-probability sampling
Inclusion/exclusion criteria
Determine sample size (beyond scope of this class)
Execute the sampling process

14. Non-Probability Sampling
Types of Sampling
Simple Random
Systematic
Stratified
Cluster
Convenience
Quota
Purposive
Snowball
Probability Sampling
Non-Probability Sampling

15. Probability Sampling
Every member of the population has an equal chance of (non-zero) of being selected. RANDOM SELECTION
Allows the researcher to make few observations and generalize to larger population
Selection of elements occurs in a way that portrays the characteristics of the total population.

16. Random Selection/SamplingVs
Random Assignment

17. Public Opinion Polls What sampling frame was identified in this clip?
How is random sampling described?
ceac-4582-bee9-5f195951a01a/science-behind- news-opinion-polls-random-sampling

18. Application:
Study aim: To assess grades and social adjustment of students in a U.S. 4th grade public school suburban classroom
You walk into the classroom to pick a random sample, you choose the first two rows of students.
What is the probability that you have a representative group?
What will this do to your results?

19. Web Sampling Activity
ngines/index.htm

20. Activity- Applying Research Design and Sampling
See WORD handout for instructions

21. Simple Random Sampling
Simplest to implement and understand
By luck of the draw, may not have a good representation of the population
Several random methods

22. Simple Random Sampling

23. Systematic Random Sampling
Identical to simple but with a more organized selection system
More precise than simple sampling
Every nth number is selected (i.e. every third or tenth)
Begin with simple random number selection

24. Systematic Random Sampling

25. Stratified Random Sampling
Also known as proportional or quota sampling
Divide population into sub-groups then take a simple random sample in each group.
Greater degree of representativeness than simple random sampling
Groups are homogenous

26. Stratified Random Sampling

27. Cluster Sampling Also known as multi-stage
Often used when your population is dispersed over a large geographic area
Drawing a sample in two or more stages
Divide population into clusters
Randomly select clusters
Measure all elements within those clusters

28. Cluster Example

29. Non-Probability Sampling
Does not involve random selection
May be able to generalize but does not follow rules of probability theory
More cost effective for agencies to implement
Types
Convenience
Purposive
Quota
Snowball

30. Convenience Once of the most common methods
Obtaining cases based on convenience
Increase in sampling error due to researcher bias

31. Purposive
Selecting a sample based on one’s knowledge of a population OR based on predetermined characteristics
Often used in qualitative research

32. Quota Selecting a stratified non-random sample
Divide population into categories and select a certain number (quota) of subjects from each category

33. Snowball
Start with one member of a group and use them to assist you in gaining access to other members of the same group
Think of it as a referral system
Often used with hard to reach populations

34. Case Example- M’LANA
Community based Participatory Research
(CBPR)

35. Limitations to Non-probability
Less representative of your study population compared to using probability sampling
Common uses of non-probability sampling:
Pilot study
Agency based research
Qualitative design

36. Strengths and Weaknesses of Basic Sampling Techniques
Nonprobability Sampling
Least expensive, least
Selection bias, sample not
Convenience sampling
time-consuming, most
representative, not recommended for
convenient
descriptive or causal research
Judgmental sampling
Low cost, convenient,
Does not allow generalization,
not time-consuming
subjective
Quota sampling
Sample can be controlled
Selection bias, no assurance of
for certain characteristics
representativeness
Snowball sampling
Can estimate rare
Time-consuming
characteristics
Probability sampling
Easily understood,
Difficult to construct sampling
Simple random sampling
results
projectable
frame, expensive,
lower precision,
(SRS)
no assurance of
representativeness.
Systematic sampling
Can increase
Can decrease
representativeness
representativeness,
easier to implement than
SRS, sampling frame not
necessary
Stratified sampling
Include all important
Difficult to select relevant
subpopulations,
stratification variables, not feasible to
precision
stratify on many variables, expensive
Cluster sampling
Easy to implement, cost
Imprecise, difficult to compute and
effective
interpret results

37. Sample Size
Common question How large does my sample need to be?

38. Group Discussion
Use the article you selected for class today and answer the following questions:
Who is the study population?
What is the sampling frame?
Probability or Non-probability?
Name the sample strategy (i.e. Snowball)
Strengths/limitations of the design

39. Determining Your Study Sampling Method and Procedures
Define the study population
Determine the sampling frame
Determine inclusion criteria
Determine appropriate sampling method/strategy (random, non-random, snowball)
Determine how you will recruit the study population
Determine size of your sample

40. Qualitative Sampling

41. Qualitative Sampling Differs from quantitative sampling methods
Sample can include schools, agencies, or people.
Not concerned with representativeness but more so the depth and quality of the data.
Non-probability approach (not randomized)
Typically a purposive sampling strategy is implemented (eligibility criteria).
Sample size varies (i.e attempt saturation)

42. Other Sampling Types/Techniques (Patton, 2002)
Extreme or deviant case sampling- “outer edges” of a phenomena
Intensity sampling-
Maximum variation sampling-
Homogeneous sampling- opposite maximum variation.
Typical case sampling- recruits average members of a population