Six Selecting Samples from methods of business research.pptx

Selecting Samples

1. Selecting Samples Imran Omer

2. Population, Element , Population Frame, Sample and Subject Population refers to the entire group of people, events, or things of interest that the researcher wishes to investigate. Example : If a banker is interested in investigating the savings habits of blue-collar workers in the mining industry in the Pakistan, then all blue-collar workers in that industry throughout the country will form the population . An element is a single member of the population . Example : If 1,000 blue-collar workers in a particular organization happen to be the population of interest to a researcher , each blue-collar worker therein is an element.

3. Population, Element , Population Frame, Sample and Subject The population frame is a listing of all the elements in the population from which the sample is drawn. Examples : The payroll of an organization would serve as the population frame if its members are to be studied. University registry containing a listing of all students, faculty, administrators, and support staff in the university during a particular academic year or semester could serve as the population frame for a study of the university population. A roster of class students could be the population frame for the study of students in a class.

4. Population, Element , Population Frame, Sample and Subject A sample is a subset of the population. It comprises some members selected from it . A sample is a “part of a whole to show what the rest is like”. Examples : If 200 members are drawn from a population of 1,000 blue-collar workers , these 200 members form the sample for the study . A subject is a single member of the sample. Examples: If 200 members from the total population of 1,000 blue-collar workers formed the sample for the study, then each blue-collar worker in the sample is a subject.

5. Need of Sampling Sampling - a valid alternative to a census when; A survey of the entire population is impracticable, Budget constraints restrict data collection, Time constraints restrict data collection, Results from data collection are needed quickly.

6. Benefits of Sampling Save Costs : Less expensive to study the sample than the population . Save Time : Less time needed to study the sample than the population . Accuracy : Since sampling is done with care and studies are conducted by skilled and qualified interviewers, the results are expected to be accurate. Destructive Nature of Elements : For some elements, sampling is the way to test, since tests destroy the element itself.

7. Limitations of Sampling Demands more rigid control in undertaking sample operation. Minority and smallness in number of sub-groups often render study to be suspected. Accuracy level may be affected when data is subjected to weighing. Sample results are good approximations at best.

8. Sampling Process Step 1: Defining the Universe Universe or population is the whole mass under study. Step 2: Establishing the Sampling Frame A sample frame is the list of all elements in the population (such as telephone directories, electoral registers, club membership etc.) from which the samples are drawn.

9. Sampling Process Step 3: Determining Sample Size Sample size may be determined by using: Subjective methods ( less sophisticated methods) The rule of thumb approach: e.g.. 5% of population Conventional approach: e.g.. Average of sample sizes of similar other studies; Cost basis approach: The number that can be studied with the available funds; Statistical formulae ( more sophisticated methods) Confidence interval approach.

10. Sampling Process Sample size determination using statistical formulae: The confidence interval approach To determine sample sizes using statistical formulae, researchers use the confidence interval approach based on the following factors: Desired level of data precision or accuracy; Amount of variability in the population (homogeneity); Level of confidence required in the estimates of population values. Availability of resources such as money, manpower and time may prompt the researcher to modify the computed sample size. Students are encouraged to consult any standard marketing research textbook to have an understanding of these formulae.

11. Sampling Process

12. Sampling Process

13. Sampling Process Step 4: Specification of Sampling Method There are two major types of sampling designs: Probability Non-probability sampling. In probability sampling , the elements in the population have some known chance or probability of being selected as sample subjects . In non-probability sampling , the elements do not have a known or predetermined chance of being selected as subjects.

14. Probability Sampling

15. Probability Sampling Five main techniques used for a probability sample: 1.Simple random 2.Stratified random 3.Systematic 4.Cluster 5.Multi-stage

16. Simple Random (Random Sampling): Involves you selecting at random frame using either random number tables, a computer or an online random number generator such as Research Randomizer . Stratified Random Sampling: Stratified random sampling is a modification of random sampling in which you divide the population into two or more relevant and significant strata (groups) based on a one or a number of attributes. Sampling frame is divided into a number of subsets. A random sample (simple or systematic) is then drawn from each of the strata. Consequently stratified sampling shares many of the advantages and disadvantages of simple random or systematic sampling. Three Approaches a)Proportional Allocation b)Disproportional Allocation c) Neyman’s Allocation

17. Systematic sampling Systematic sampling involves you selecting the sample at regular intervals from the sampling frame . 1.Number each of the cases in your sampling frame with a unique number . The first is numbered 0, the second 1 and so on. 2.Select the first case using a random number. 3.Calculate the sample fraction. 4.Select subsequent cases systematically using the sample fraction to determine the frequency of selection

18. Cluster Sampling Similar to stratified as you need to divide the population into discrete groups prior to sampling. The groups are termed clusters in this form of sampling and can be based in any naturally occurring grouping. For example, you could group your data by type of manufacturing firm or geographical area. For cluster sampling your sampling frame is the complete list of clusters rather than complete list of individual cases within population, you then select a few cluster normally using simple random sampling. Data are then collected from every case within the selected clusters.

19. Multi-stage sampling (multi-stage cluster sampling) It is a development of cluster sampling. It is normally used to overcome problems associated with a geographically dispersed population when face to face contact is needed or where it is expensive and time consuming to construct a sampling frame for a large geographical area. However , like cluster sampling you can use it for any discrete groups, including those not are geographically based. The technique involves taking a series of cluster samples, each involving some form of random sampling method.

20. NON-Probability Sampling

21. Non-Probability Sampling Quota Sampling Purposive Sampling Extreme Case Sampling Heterogeneous /Maximum Variation Homogeneous Sampling Critical Case Sampling Typical Case Sampling Snowball Sampling Self-selection Sampling Convenience Sampling

22. Quota Sampling It is entirely non random and it is normally used for interview surveys. It is based on the premise that your sample will represent the population as the variability in your sample for various quota variables is the same as that in population. Quota sampling is therefore a type of stratified sample in which selection of cases within strata is entirely non-random. Divide the population into specific groups. Calculate a quota for each group based on relevant and available data. Give each interviewer an ‘assignment', which states the number of cases in each quota from which they must collect data. Combine the data collected by interviewers to provide the full sample.

23. Purposive Sampling Purposive or judgmental sampling enables you to use your judgment to select cases that will best enable you to answer your research question(s) and to meet your objectives. This form of sample is often used when working with very small samples such as in case research and when you wish to select cases that are particularly informative. Purposive sampling can also be used by researchers adopting the grounded theory strategy. For such research, findings from data collected from your initial sample inform the way you extend your sample into subsequent cases. Such samples, however can not be considered to be statistically representative of the total population. The logic on which you base your strategy for selecting cases for a purposive sample should be dependent on your research question(s)and objectives. Select information-rich cases in purposive sampling in contrast to need to be statistically representative in probability sampling.

24. Extreme case or deviant sampling Extreme case or deviant sampling focuses on unusual or special cases You will learn the most to answer your research question(s) and to meet your objects more effectively . Heterogeneous or maximum variation sampling Heterogeneous or maximum variation sampling enables you to collect data to explain and describe the key themes that can be observed. To ensure maximum variation within a sample it is suggested to identify diverse characteristics (sample selection criteria) prior to selecting your sample.

25. Homogenous Sampling In direct contrast to heterogeneous sampling, homogenous sampling focuses on one particular sub-group in which all the sample members are similar. This enables you to study the group in great depth. Critical Case Sampling Critical case sampling selects critical cases on the bases that they can make a point dramatically or because they are important. The focus of data collections to understand what is happening in each critical case so that logical generalizations can be made. A number of clues that suggest critical cases can be summarized by the questions such as: If it happens there, will it happen everywhere? If they are having problems, can you be sure that everyone will have problems ? If they cannot understand the process, is it likely that no one will be able to understand the process?

26. Typical case sampling In contrast of critical case sampling, typical case sampling is usually used as a part of a research project to provide an illustrative profile using a representative case. Such a sample enables you to provide an illustration of what is ‘typical’ to those who will be reading your research report and may be unfamiliar with the subject matter.

27. Snowball Sampling Is commonly used when it is difficult to identify members of desired population. For example people who are working while claiming unemployment benefit you therefore, need to: Make contact with one or two cases in the population. Ask these cases to identify further cases. Ask theses new cases to identify further new cases (and so on) Stop when either no new cases are given or the sample is as large as manageable

28. Self Selecting Sampling It occurs when you allow each case usually individuals, to identify their desire to take part in the research you therefore; Publicize your need for cases, either by advertising through appropriate media or by asking them to take part. Collect data from those who respond.

29. Convenience sampling Convenience sampling (or haphazard sampling) involves selecting haphazardly those cases that are easiest to obtain for your sample, such as the person interviewed at random in a shopping centre for a television program or the book about entrepreneurship you find at the airport. The sample selection process is continued until your required sample size has been reached. Although this technique of sampling is used widely, it is prone to bias and influences that are beyond your control, as the cases appear in the sample only because of the ease of obtaining them.

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