1. SAMPLING – SELECTING SAMPLES
By: Zeeshan A. Bhatti

2. Introduction
Whatever your research question(s) and objectives, you will need to collect data to answer them
If you collect and analyze data from every possible case or group member its known as Census
However, for many RQs it is impossible for you to either to collect or to analyze the data because of:
Restrictions of time, money and often access
Population is the full set of cases from which a sample is taken
To discover relative levels of service at burger bars throughout the country, the population would be all the burger bars in the country

3. Sampling provide a range of methods that enable you to reduce the amount of data you need to collect
Considering only data from a sub group rather than all possible cases of element

4. The need to sample
For some RQs it is possible to survey an entire population as is it of a manageable size
Sampling provides an alternative to census when:
It would be impracticable for you to survey the entire population
E.g., you could only get permission for 1 or 2 orgs.
Testing an entire population of products to destruction, such as to establish the crash protection provided by cars

5. It might be theoretically possible to survey whole population but overall cost would prevent it
Cheaper to collect, enter and analyze data in sample
Though the cost/case can be higher

6. You have to save time Fewer data to enter – results are quicker
Collecting data from smaller no. of people means you can ask or more detailed information

7. Sampling Techniques

8. Probability Sampling
The chance or probability of each case being selected from the population is known
Possibility to answer RQs that require you to estimate statistical inference about the populatio
Therefore, Primarily associated with surveys
The process of Probability sampling is divided into 4 categories:

9. 1. Identify a suitable Sampling Frame
Sampling Frame is a complete list of all the cases in the population from which sample will be drawn
E.g., if RQ is concerned with the members of a local video club, Sampling frame will be complete list of membership list of that video club
You then select your sample from that list
The completeness of sampling list is very important
An incomplete list means that some cases will have been excluded and so IT WILL BE IMPOSSIBLE FOR EVERY CASE IN THE POPULATION TO HAVE A CHANCE OF SELECTION
Consequently, Your sample will not be representative of the total population

10. When no suitable list exists – You have to create your own list
E.g., You might decide to use a Telephone Directory of typical Pakistani householders
However, It can be biased because:
It only contains subscribers from a specific geographical area
It is publishes annually – so can be out of date
Some people chose to be ex-directory
THIS MEANS YOU WILL BE SELECTING A SAMPLE PF TELEPHONE SUBSCRIBERS AT THE DATE OF DIRECTORY WAS COMPILED WHO CHOSE NOT TO BE EX-DIRECTORY

11. Recently, some companies specialize in selling lists of names and addresses for surveys:
Wide range
Suitable to be read by word-processors

12. 2. Deciding on a Suitable Sample Size
Generalizations abt populations from data are bases on probability
The larger the sample size– the lowers the likely error in generalizing
Probability sample is a compromise b/w the accuracy of your findings and the amount of money and time you invest in collecting, checking & analyzing data

13. The compromise is governed by:
The confidence you need to have in your data – the level of certainty
Researchers normally work with 95% level of certainty
If you select the sample 100 times, 95 sample should be representative of the population
The types of analyses you undertake
Statistical testing

14. The margin of error you can tolerate – the accuracy
Assumes that data are collected from all cases in the sample
The smaller the sample & the smaller the population – the greater the margin of error

15. The importance of a high response rate
Unfortunately, 100% response rate is not likely in most cases
A perfect representative sample is one that exactly represents the population
E.g., if 60% of your sample are small service sector firms – then you would expect 60% of population to be service sector firms
Therefore, you need to obtain as high response rate as possible to make your sample representative
But this increases your cost of finding extra respondents

16. Non-response is due to 4 problems:
Refusal to respond
Can be minimized by using careful attention to data collection methods
Ineligibility to respond
Some respondents may not meet your research requirements
Inability to locate respondents
Respondent located but unable to make contact

17. Calculating Response Rate

18. Estimating Response Rate and Actual Sample Size Required
It is important that your sample size is large enough
Therefore, your estimate the likely response rate
i.e., the proportion of cases from your sample who will actually respond – and increase the sample size accordingly
Once you have the likely response rate and the adjusted minimum sample size, the actual sample size can be calculated as

20. If collecting data from a secondary source (an organization with access)
A 100% response rate is possible (e.g., employees personnel files)
In contrast, a primary source (survey or questionnaire) is more difficult to have a good response rate
Varies a lot – for postal surveys 30% is reasonable
20-25% for online surveys
90% for face-to-face interviews

21. 3. Selecting the most appropriate Sampling technique and the sample
5 Main techniques of Probability Sampling:
Simple Random
Systematic
Stratified Random
Cluster
Multi-Stage

22. Simple Random Sampling
Selecting the sample at random from the sampling frame
Number each case in your sampling frame with a unique number. The first as 0, the second 1 and so on
Select cases using random numbers until your actual sample size is reached
Note: It is usual to select your first random number at random

23. If a random no. is read off second time, it must be disregarded
You can also use computer programs to generate random numbers (e.g., MS Excel)
Best use when you have an entire sampling frame stored on a computer
Because of the technique's random nature, it is possible that the chance occurrence of such patterns will result in certain parts of population to be over or under-representated

24. This type of sampling is not suitable if the population is spread over a large geographical area
Increases cost and accessibility
Sampling frames for telephone interviewing is often done by random digital dialing

25. Systemic Sampling Selecting the sample at random intervals
Number each of the cases in your sampling frame
Select the first case using random number
Calculate sampling fraction
Select subsequent cases systematically using the sampling fraction

27. Stratified Random Sampling
Modification of random sampling in which population is divided into two or more relevant and significant Strata
A random sample is then drawn from each of the strata
Only possible if you are aware and easily distinguish strata in your sampling frame
Longer, more expensive and difficult to explain

28. Sometime, strata are already made
E.g., membership lists that are ordered by date of joining will automatically result in stratification by length of membership if systemic sampling is used
How to make strata of the previous example??

29. Cluster Sampling
Similar to Stratified Sampling – as you need to divide the population into discrete groups prior to sampling
The groups are termed as clusters
Can be based on any naturally occurring grouping e.g., Geographical area
For cluster sampling, the sampling frame is a complete list of clusters (e.g., US  States  Spanish majority/Asians etc)

30. Multistage Sampling

31. 4. Checking the sample is representative
Compare data you collect from your sample with data from another source of population
E.g., latest national census of population
Within organizations comparisons can also be done with the characteristics of entire population

32. Non Probability Sampling
Quota Sampling
Purposive Sampling
Snowball Sampling
Self-Selection Sampling
Convenience Sampling

33. Quota Sampling Less costly and very quick
Normally used for large populations

36. Purposive Sampling Also called Judgmental Sampling
Select cases on your judgment (e.g., in grounded theory approach – exploration)
When you wish to select cases that are particularly informative

37. Snowball Sampling Make contact with one or two cases in population
Ask these cases to identify further cases
Ask these new cases to identify further new cases (and so on)
Stop when either no new cases are given or if the sample is large enough

38. Self Selection Sampling
When you allow a case to identify their desire to take part in your research
Publicize your need for cases – by advertising through proper media or by asking them to take part (e.g., Online survey)
Collect data from those who respond

39. Convenience Sampling Selecting cases that are easiest to locate
E.g., at random in a shopping center
Continue until required sample size is reached
E.g., managers taking an MBA course as a surrogate for all managers