1. Chapter 7
When we conduct a research project , it is desired to draw observations from the selected population
However, we cannot observe all pop because of (p151 of text):
Impractical to survey the entire pop
Budget constraints
Time constraints
Need result quickly
(p2)

2. Chapter (cont)
Typically, we draw a sample from the population as shown in Figure 7.1
Of course, the choice of sampling is dependent upon the feasibility and sensibility of collecting data to answer a research questionnaire from the calling population
How do we select of a sampling technique?
(p3)
(p4)

3. (to p2)
Figure 7.1 3

4. Sample Techniques
Choice of sampling technique or techniques is dependent upon the research question(s) and objective(s):
Two choices: (Table 7.2)
Probability sampling technique,
Research question(s) and objective(s) that necessitate a statistical estimate of the characteristics of the pop
non-probability sampling technique
Research question(s) and objective(s) that do not require such generalizations
Which one do we adopt the most? (See Figure 7.5 as well)
(p5)
(p6)
(p10)
(p16)

5. Figure 7.2
(p4) 5

6. Probability sampling technique
Process divided into four stages (p151):
Identify a suitable framed
That is calling population that suits for your project, such as entire pop for video tapes company etc
Decide on a suitable sample size
Depend of the statistical technique, such as sample 30 for t test etc., and determine the error of tolerance (see Table 6.1 on p156)
Select the most appropriate technique/sample
Five main techniques (p159)
Check that the sample is representative
To place a number of questions in your questionnaire to ensure that your participants are really what you are anticipated, such all managers, more than 5 years working experience etc (see p169)
(p7)
(p4)

7. Five main techniques (p159)
Simple random
Numbering your each case of your samples, and then select them in according to a random table until sample size is reached (p162)
Systematic
Calculate a fraction and then apply it as a random selection seed by starting with a selected number on your sampling (see p 164)
Stratified random
Same as systematic, but divide pop into series of blocks and applied fraction principle accordingly
Cluster
A combine of 1 and 3; by firstly stratify samples into respective group and then use simple random to select sample from each group
Mutli-stage (Figure 7.4)
A different version of cluster; so to over come geographically dispersed pop
How do select them (Figure 7.3)?
(p17)
(p18)
(p8)
(p6)
(p9)

8. Figure 7.4
(p7) 8

9. Figure 7.3
(p7) 9

10. Non-probability sampling technique
When to use this sampling?
When it is not possible to construct a sample frame
Techniques include:
Quota
Purposive
Snowball
Self-selection
Convenience sampling
How to select them? (Figure 7.5)
(p11)
(p12)
(p13)
(p14)
(p15)
(p16)
(p4)

11. Quota (refer to p228 or example on p228) Use when pop is large
Overcome variations between groups
Divide pop into specific group
Select a desired percentage, calculate quote for each group
Use each quota for each group for data collection
Combine all data
(p10)

12. Purposive Enables you to use your judgment to selected cases
Concentrate on:
Extreme case, ie focuses on unusual/special cases
Heterogeneous or maximum variation
Homogenous, only one group in depth
Critical case so to make a point dramatically
Illustrative profiling using a representative case
Such as public house case in HK
(p10)

13. Snowball
Adopted when difficulty in identifying members of the desired pop
It works like a snowball effect (p 232)
Make contact with one to two cases in the pop
Ask them to identify (refer) further cases
Repeat step 2
Stop if a) no more case or b) enough of samples
(p10)

14. Self-selection (refer to p233)
Publicizing it in a media at which it allows the case to identify individual desrie to take part in the research
Collect data from those who respond
Example, use of a web site for collecting data
(p10)

15. Convenience sampling (refer to p233)
Involves selecting haphazardly those cases that are easier to obtain
Such as data collection at a shopping mall, or at the street
(p10)

16. (p4)
(p10)
Figure 6.5 16

17. For example, the researcher has a population total of 100 individuals and need 12 subjects. He first picks his starting number, 5.
Then the researcher picks his interval, 8. The members of his sample will be individuals 5, 13, 21, 29, 37, 45, 53, 61, 69, 77, 85, 93.
(p7)

18. Practical example
In general the size of the sample in each stratum is taken in proportion to the size of the stratum. This is called proportional allocation. Suppose that in a company there are the following staff:
male, full time: 90
male, part time: 18
female, full time: 9
female, part time: 63
Total: 180
and we are asked to take a sample of 40 staff, stratified according to the above categories.
The first step is to find the total number of staff (180) and calculate the percentage in each group.
% male, full time = 90 / 180 = 50%
% male, part time = 18 / 180 = 10%
% female, full time = 9 / 180 = 5%
% female, part time = 63 / 180 = 35%
This tells us that of our sample of 40,
50% should be male, full time.
10% should be male, part time.
5% should be female, full time.
35% should be female, part time.
(p7)

19. The most common cluster used in research is a geographical cluster
The most common cluster used in research is a geographical cluster. For example, a researcher wants to survey academic performance of high school students in Spain.
He can divide the entire population (population of Spain) into different clusters (cities).
Then the researcher selects a number of clusters depending on his research through simple or systematic random sampling.
Then, from the selected clusters (randomly selected cities) the researcher can either include all the high school students as subjects or he can select a number of subjects from each cluster through simple or systematic random sampling.
The important thing to remember about this sampling technique is to give all the clusters equal chances of being selected.
(p7)