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)

Chapter 7 (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)

(to p2) Figure 7.1 3

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)

Figure 7.2 (p4) 5

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)

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)

Figure 7.4 (p7) 8

Figure 7.3 (p7) 9

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)

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)

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)

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)

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)

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)

(p4) (p10) Figure 6.5 16

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)

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)

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)