1. Chapter 5 – Sampling

2. Chapter Objectives Definition of sampling
Understand the difference between probability and non-probability sampling
Sample frame
Strata sampling vs. Quota sampling
Sampling in qualitative research

3. What is sampling? Definition Advantage
The process by which researchers select a representative subset or part of the total population that could be studied for their topic so that they will be able to draw conclusions about the entire population
Advantage
Smaller number of elements (e.g., people, organizations) makes the research more manageable, time efficient, less costly, and more accurate
To cover China, you need 1.3 billion questionnaires

4. Sample? Sample Large Sample
Should be as much as representative of the entire population
Large Sample
Can reduce sampling error
Not always represents entire population
Example
Sample of 2 million failed to predict Landon’s victory over Roosevelt

5. Things to consider… Consideration Example Target Population
General Characteristics
Particular Characteristics
Chinese business travelers
Male, aged between 25-45
Accessible Population
Geographic / Time constraints
Business travellers flying from Shanghai Pudong International Airport
Travellers flying in May and June 2008
Inclusion Criteria
Specific criteria by which participants will be selected for the study
Using Business Class lounges
Exclusion Criteria
Factors that will affect data collection
Ethical issues
Frustrated delayed travellers.
Non-English speaking
Non consenting in tape recording the interview

6. Sampling Frame Sampling Frame Example of Sampling Frame
It is the source material or device from which a sample is drawn
It is a list of all those within a population who can be sampled
Includes individuals, households, institutions, etc.
Example of Sampling Frame
Student lists in a high school
Loyalty program participants in a hotel
Lists of high schools in U.K.

7. Different Types of Sampling
Probability Sampling
Non-probability sampling
Sampling Frame
Yes No

8. I. Probability Sampling
Need to have information about population

9. Probability Sampling Selection bias To minimize selection bias
Certain members of the population under study are underrepresented or overrepresented
To minimize selection bias
Target population has an equal opportunity for selection
Probability sampling

10. 1. Simple Random Sampling #1
All members of the population under study have the same chance (probability) of being selected
You will need
Random number table
Example
You have a sample frame (e.g., detailed contact information) of 1000 customers of a hotel
You are sampling 100 customers to interview

11. 1. Simple Random Sampling #2
Utilization of random number table .
Since you are collecting 100 customers, you should use last three digits (red color)
For instance, suggests you to use 634th customer on the list, suggests to use 349th customer on the list
Repeat this procedure until you reach 100 customers to interview

12. 2. Systematic Sampling Select nth member of the targeted population
Giving out a questionnaire to each 8th passenger to check in for a specific flight
How to determine n (sampling fraction)?
If the passengers in the specific flight is 400, and the airline wishes to collect 50 samples, then the sampling fraction is 8
A researcher should choose a number n between 1 and 8
Give out surveys to nth customer until he collects 50 samples

13. 3. Stratified Sampling #1
Population is divided into homogeneous and mutually exclusive groups called “strata”
Age, gender, market segment
Procedure
Divide population into different strata
For each strata, collect samples by simple random sampling or systematic sampling

14. 3. Stratified Sampling #2 Population Strata #1 Strata #2 Strata #3
Data collection by
Simple random sampling
Systematic sampling
Data collection by
Simple random sampling
Systematic sampling
Strata #3
Strata #4
Data collection by
Simple random sampling
Systematic sampling
Data collection by
Simple random sampling
Systematic sampling

15. 4. Cluster Sampling #1
Frequently used when population is geographically diverse
Divide population into mutually exclusive subsets
Two types of cluster sampling
One stage cluster
Randomly select subsets
Sample entire participants in the selected subset
Two stage cluster
Conduct simple random sampling for participants in the selected subset

16. 4. Cluster Sampling #2 Graphical Illustration of Cluster Sampling
One-stage cluster sampling
Randomly select subsets
(Red Rectangles)
Sample entire participants in the red rectangles

17. 4. Cluster Sampling #3 Graphical Illustration of Cluster Sampling
Two-stage cluster sampling
Randomly select subsets
(Red Rectangles)
Conduct simple random sampling for the participants in each red rectangles

18. II. Non-Probability Sampling
You don’t need information about population

19. Non-Probability Sampling
Definition
Sampling where it is not possible to specify the probability that any person or other unit on which the survey is based will be included in the sample (Smith, 1983)
Advantages
Select samples purposively
Enable researchers to reach difficult-to-identify members of the population
Disadvantages
Difficult to make valid inference about the entire population because the sample selected is not representative

20. 1. Convenience Sampling Definition
Members of the population are chosen based on their relative ease of access
Interviews can be determined by what was convenient, not by random
Also called as haphazard or accidental sampling

21. 2. Judgemental Sampling Definition
Another form of convenience sampling where participants are handpicked from the accessible population
Researcher select participants that are representative of the entire population
Very subjective sampling method (can be biased)

22. 3. Quota Sampling Definition Stratified vs. Quota
Similar to stratified sampling, population is divided into mutually exclusive subsets
Then judgement is used to select the participants from each stratum based on specified proportion
Stratified vs. Quota
Stratified sampling requires random sampling for each strata
Quota sampling does not require random sampling for each quota

23. 4. Snowball Sampling Definition
A non-probability sampling technique where existing study subjects recruit future subjects from among their acquaintances
The sample group grows like a rolling snowball
The first respondent refers a friend
The friend also refers a friend, and so on

24. 5. Self-Selection Sampling
Definition
A non-probability sampling in which individuals identify their wish to participate in the study
Procedure
Recruit participants via advertisement or posting on articles, magazines, journals, s, newsgroups, etc.
People who have interest (volunteers) in the survey will participate

25. III. Other Things to Consider
Sample size
Non-response
Sampling in qualitative research

26. 1. Sample Size #1 Arbitrary in nature
The larger the sample you study, the more likely your findings will be representative of population and the more correct your inferences about data
Sampling Error (Level of Precision)
Range in which the true value of the population is estimated to be
If you find that 60% of hotel guests in a sample agree with the proposed new restaurant concept with a precision rate of ± 5%, then you can conclude that between 55% and 65% of hotel guests agree with the new concept

27. 1. Sample Size #2 Level of Confidence (Risk Level)
Based on the notion that the larger the size of a sample from a population, the closer the average value of the answers will be to the true population value
Researcher prefer 95% confidence level
If the sample was selected 100 times, at least 95 of these times it would represent the entire population

28. 2. Non-Response #1 Response rate
Ratio of number of participants who have actually taken part in your study divided by the number of people in your sample
Low response rate may have negative effect on the credibility of findings, because the sample will be less likely to present the overall population
15% ~ 20% will be considered acceptable response rate

29. 2. Non-Response #2 Two Different Response Rate Total Response Rate
Completes: # of participants who completed survey
Not Qualified: # of participants who do not qualify
Active Response Rate
Not Contacted: # of participants included in sample but cannot be contacted

30. 3. Sampling in Qualitative Research
Goal of Qualitative Research
Gaining in-depth understanding about the topic or phenomenon under study and extrapolate the findings beyond the material in hand
Sampling in Qualitative Research
Must serve the purpose of in-depth understanding
Samples must be selected for the information-rich data
Similar to the judgemental or purpose sampling

31. 3.1. Theoretical Sampling Based on grounded theory
Researcher simultaneously collects, codes, and analyses the date in order to decide what data to collect next
Researcher starts with purposive sampling by selecting the sample seem more appropriate to describe the phenomenon under study
Researcher moves into theoretical sampling, which involves selecting samples that are more appropriate to understand the theories found in earlier stage
Sample size is not defined from the outset of investigation
Sampling terminates when the researcher feels that enough data was collected

32. 3.2. Maximum Variation Sampling
Type of Purposeful Sampling
Central themes that cut across a great deal of participant variation
Trying to achieve representativeness of the total population, not by equal probabilities but by including a wide range of extremes