Research Methodology

1. Research Methodology Lecture No :15 (Sampling Design / Probability vs Non probility)

2. Recap • Sampling is the process of selecting the right individuals • Sample is used to represent the whole data or population • Sampling process include defining population, sample frame, sampling design, sample size and sampling process

3. Lecture Objectives • Differentiate between probability and non probability sampling • Learn about the types of probability sampling, its advantages and disadvantages • Learn about the types of non probability sampling, its advantages and disadvantages • Issues relevant to sample design and collection

4. Probability Sampling Unrestricted or simple random sampling • Technique which ensures that each element in the population has an equal chance of being selected for the sample. • The simple random sampling is the least bias and offer the most generalizability.

5. Probability Sampling • The major advantage of simple random sampling is its simplicity. • The sampling process could become cumbersome and expensive. Example: Choosing raffle tickets from a drum, computer-generated selections, random-digit telephone dialing.

6. Simple random sampling

7. Probability Sampling Restricted or complex probability sampling: • It is an alternate to simple random sampling design, several complex probability sampling designs can be used. • Efficiency is improved in that more information can be obtained for a given sample size using the complex probability sampling procedures.

8. Probability Sampling The most common complex probability sampling design • Systematic sampling • Stratified sampling • Cluster sampling • Area sampling • Double sampling

9. Probability Sampling Systematic Sampling: • Technique in which an initial starting point is selected by a random process, after which every nth number on the list is selected to constitute part of the sample.

10. Sampling interval (SI) = population list size (N) divided by a pre-determined sample size (n) • How to draw: • 1) calculate SI, say (200/20)=10 • 2) select a number between 1 and SI randomly, i.e. 1-10 • 3) go to this number as the starting point and the item on the list here is the first in the sample, e.g 3 • 4) add SI to the position number of this item and the new position will be the second sampled item, e.g 3+10=13 • 5) continue this process until desired sample size is reached.

11. For systematic sampling to work best, the list should be random in nature and not have some underlying systematic pattern. • E.g: Office directory with the Senior Manager, Middle manager ….names are listed in each department. This can create as systematic problem

12. Probability Sampling Stratified Sampling: • Technique in which simple random subsamples are drawn from within different strata that share some common characteristic. Within the group they are homogenous and among the group they are heterogeneous.

13. Probability Sampling Stratified Sampling Example: The student body of CIIT is divided into two groups (management science, engineering) and from each group, students are selected for a sample using simple random sampling in each of the two groups, whereby the size of the sample for each group is determined by that group’s overall strength.

14. Probability Sampling Cluster Sampling • Technique in which the target population is first divided into clusters. Then, a random sample of clusters is drawn and for each selected cluster either all the elements or a sample of elements are included in the sample. • Cluster samples offer more heterogeneity within groups and more homogeneity among groups

15. Probability Sampling Area sampling Specific type of cluster sampling in which clusters consist of geographic areas such as counties, city blocks, or particular boundaries within a locality. • Area sampling is less expensive than most other sampling designs and it is not dependent on sampling frame. • Key motivation in cluster sampling is cost reduction.

16. Probability Sampling Area sampling Example: A city map showing the blocks of the city is adequate information to allow the researcher to take a sample of the blocks and obtain data from the resident therein. Example: If you wanted to survey the residents of the city, you would get a city map, take a sample of city blocks and select respondents within each city block.

17. Probability Sampling Single stage and multistage cluster sampling • Single stage cluster sampling involves the division of population into convenient clusters, randomly choosing the required number of clusters as sample subjects, and investigating all the elements in each of the randomly chosen clusters • Cluster sampling can also be done in several stages and is then known as multistage cluster sampling.

18. Probability Sampling Example: If we were to do a national survey of the average monthly bank deposits, cluster sampling would be used to select the urban, semi urban and rural geographical location for study. At the next stage particular areas in each of these locations would be chosen. At the third stage, banks within each area would be chosen. Example:

19. Probability Sampling Double sampling: • A sampling design where initially a sample is used in a study to collect some preliminary information of interest, and later a subsample of this primary sample is use to examine the matter in more detail.

20. Probability Sampling Double sampling Example: A structured interview might indicate that a subgroup of respondents has more insight into the problems of the organization. These respondents might be interviewed again and again and asked additional questions.

21. Non-Probability Sampling Convenience Sampling: • Sampling technique which selects those sampling units most conveniently available at a certain point in, or over a period, of time.

22. Non-Probability Sampling Convenience Sampling: • Major advantages of convenience sampling is that is quick, convenient and economical; a major disadvantage is that the sample may not be representative. • Convenience sampling is best used for the purpose of exploratory research and supplemented subsequently with probability sampling.

23. Non-Probability Sampling Judgment (purposive) Sampling: • Sampling technique in which the business researcher selects the sample based on judgment about some appropriate characteristic of the sample members. Example: Selection of certain students who are active in the university activities to inquire about the sports and recreation facilities at the university.

24. Recap • Simple random sampling and restricted sampling are two basic types of probability sampling. • Probability ( Simple Random, Systematic, Cluster, Single stage/multistage, Double sampling) • Non Probability (Convenience, judgment)