Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampling and Non Probability Sampling)

1. Alam Nuzhathalam1 Associate Professor Nuzhath Alam

2. Alam Nuzhathalam 2  Sampling  Introduction, terminologies  Definition, characteristics of good sample  Sampling criteria, factors influencing sampling process  Sampling Steps/Design/Process  Sampling Methods/Techniques/Types  Probability Sampling  Non-Probability Sampling  Sampling Errors and Non-Sampling Errors

3. Alam Nuzhathalam3

4.  Population: The entire set of individuals or objects having some common characteristics selected for a research study  Target Population: Entire group of people or objects to which the researcher wishes to generalize the findings of the study  Sample: A subset of the population study population target population sample  Study Population (Sampling): The population to be studied/to which the investigator wants to generalize his results 4 Alam Nuzhathalam

5.  Sampling Unit: Smallest unit from which sample can be selected  Sampling Frame: List of all the sampling units from which sample is drawn  Sampling Scheme: Method of selecting sampling units from sampling frame  Sampling Fraction: Ratio between sample size and population size 5 Alam Nuzhathalam

6. 6 Alam Nuzhathalam

7. Definition:  Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen  The process by which researchers select a representative subset or part of the total population that could be studied for their topic so that they will be able to draw conclusions about the entire population 7 Alam Nuzhathalam

8.  A sample should be reliable  A sample should be economical  A sample should be proportional  A sample should be goal oriented  A sample should be appropriate in size  A sample should be selected at randomly  A sample should be free from bias and errors  A sample should be true representation of population 8 Alam Nuzhathalam

9.  Sampling criteria refers to the essential characteristics of a subject or respondent such as ability to read and write responses on the data collection instruments  For example, these criteria could include:  Age (elderly, children, etc)  Gender (male/female)  Marital status  Ability to understand English/Hindi, etc  Ability to write 9 Alam Nuzhathalam

10. Factors that influence sampling process:  Element Type  Research Type  Population size  Available Resources  Constraints/ limitations  Participation (response)  When might you sample the entire population?  When your population is very small, When you have extensive resources, When you don’t expect a very high response 10 Alam Nuzhathalam

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12. SSCSM: Soft Skill Computer Shaksharta Mission  S - Simple  S - Stratified  C - Cluster  S - Systematic  M - Multi Stage CQPSS: Continuous Quality Process Software & Systems  C - Convenience  Q - Quota  P - Purposive  S - Self Selection  S - Snow Ball 12 Alam Nuzhathalam Probability Sampling Non-Probability Sampling

14.  Probability sampling is defined as a sampling technique in which the researcher chooses samples from a larger population using a method based on the theory of probability. For a participant to be considered as a probability sample, he/she must be selected using a random selection  Probability sampling uses statistical theory to randomly select a small group of people (sample) from an existing large population and then predict that all their responses will match the overall population 14 Alam Nuzhathalam

17. 17 Alam Nuzhathalam  Simple random sampling is a sampling technique where every item in the population has an even chance and likelihood of being selected in the sample. Here the selection of items completely depends on chance and therefore this sampling technique is also sometimes known as a method of chances (i.e., Lottery method, random table)  Example: A simple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees. In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen

18. Advantages:  It is a fair method of sampling and if applied appropriately it helps to reduce any bias involved as compared to any other sampling method involved  Since it involves a large sample frame it is usually easy to pick smaller sample size from the existing larger population Disadvantages:  It is a costlier method of sampling as it requires a complete list of all potential respondents to be available beforehand  This sampling method is not suitable for studies involving face- to-face interviews as covering large geographical areas have cost and time constraints  18 Alam Nuzhathalam

20.  Stratified sampling is a probability sampling technique wherein the researcher divides the entire population into different subgroups or strata, then randomly selects the final subjects proportionally from the different strata  A stratified sample is one that ensures that subgroups (strata) of a given population are each adequately represented within the whole sample population of a research study. Example: one might divide a sample of adults into subgroups by age, like 15–19, 20–24, 25–29, 30–34, and 35 and above 20 Alam Nuzhathalam

21. Advantages:  Assures representation of all groups in sample population  Characteristics of all groups in same population Disadvantages:  Requires accurate information on proportions of each stratum  Stratified lists costly to prepare 21 Alam Nuzhathalam

23.  Cluster sampling refers to a type of sampling method. With cluster sampling, the researcher divides the population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. The researcher conducts his analysis on data from the sampled clusters  For example, a researcher wants to survey academic performance of nursing students in India. He can divide the entire population into different cities. Then the researcher selects a number of clusters depending on his research through simple or systematic random sampling 23 Alam Nuzhathalam

24. Advantages:  Requires fewer resources; Since cluster sampling selects only certain groups from the entire population, the method requires fewer resources for the sampling process  More feasible; The division of the entire population into homogenous groups increases the feasibility of the sampling Disadvantages:  The cost to reach an element to sample is very high  Each stage in cluster sampling introduces sample error. The more stages there are more error tends to be 24 Alam Nuzhathalam

26.  Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size  For example, if a researcher is seeking to form a systematic sample of 50 volunteers from a nursing students of 500, they can select every 10th student to build a sample systematically 26 Alam Nuzhathalam

27. Advantages:  It’s extremely simple and convenient for the researchers to create, conduct, analyze samples  As there’s no need to number each member of a sample, it is better for representing a population in a faster and simpler manner Disadvantages:  Periodic ordering required 27 Alam Nuzhathalam

29.  Multistage sampling is defined as a sampling method that divides the population into groups (or clusters) for conducting research. During this sampling method, significant clusters of the selected people are split into sub-groups at various stages to make it simpler for primary data collection  Example: Stratify the population by region of the country. For each region, stratify by urban, suburban, and rural and take a random sample of communities within those strata. Divide the selected communities into city blocks as clusters, and sample some blocks. Everyone on the block or within the fixed area may then be sampled 29 Alam Nuzhathalam

30. Advantages:  More accurate than the cluster sampling for same size population  Less time consuming Disadvantages:  Costly  Not as accurate as simple random sampling  Each stage in sampling introduces sample error. The more stages there are more error tends to be 30 Alam Nuzhathalam

31.  Population divided into few groups  Homogeneity within sub- groups  Heterogeneity between sub-groups  Choice of elements from within sub-groups  Population divided into many groups  Heterogeneity within sub- groups  Homogeneity between sub-groups  Random choice of sub- groups 31 Alam Nuzhathalam

32.  Non-probability sampling is a sampling method in which not all members of the population have an equal chance of participating in the study  It is most useful for exploratory studies like a pilot survey. Researchers use this method in studies where it is not possible to draw random probability sampling due to time or cost considerations  It is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. It is a less stringent method 32 Alam Nuzhathalam

35. 35 Alam Nuzhathalam  Convenience sampling (grab sampling, accidental sampling, or opportunity sampling) is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand. This type of sampling is most useful for pilot testing. Convenience sampling involves choosing respondents all the convenience of the researcher  A convenience sample is a type of non-probability sampling method where the sample is taken from a group of people easy to contact or to reach. Example is using subjects that are selected from a clinic, a class or an institution that is easily accessible to the researcher

36. 36 Alam Nuzhathalam Advantages:  Quick  Convenient  Economical  Extensivelyused Disadvantages:  Restriction of Generalization  Sample may not be representative  Projecting data beyond sample not justified  Variability and bias cannot be measured or controlled

38. 38 Alam Nuzhathalam  Quota sampling is a method for selecting survey participants that is a non-probabilistic version of stratified sampling. The population is first segmented into mutually exclusive sub-groups, just asin stratified sampling  In quota sampling, a population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgment is used to select the subjects or units from each segment based on a specified proportion  Example, interviewers might be tempted to interview those people in the street who look most helpful, or may choose to use accidental sampling to question those closest to them, to save time

39. 39 Alam Nuzhathalam Advantages:  Used when research budget is limited  No need for list of population elements  Easy to carry out than stratifies sampling Disadvantages:  Time consuming  Selection of sample upon accessibility, prone to bias

41. 41 Alam Nuzhathalam  Purposive sampling, also known as judgmental, selective, or subjective sampling, is a form of non- probability sampling in which researchers rely on their own judgment when choosing members of the population to participate in their study  Participants are selected according to the needs of the study. Applicants who do not meet the profile are rejected. For example, Conducting a study on why high school students choose community college over university

42. 42 Alam Nuzhathalam Advantages:  Low cost  Less time involved  Meet the specific objective  There is a assurance of quality response Disadvantages:  Time consuming process  Bias selection of sample may occur

44. 44 Alam Nuzhathalam  Self-selection sampling (volunteer sampling) is a non- probability technique, that is based on the judgement of the researcher. This is a useful tool for researchers, who want people or organizations, to participate as part of a study on their own accord  As a sampling strategy, self section sampling can be used with a wide range of research designs and research methods  Example, Survey researchers may put a questionnaire online and subsequently invite anyone within a particular organisation to take part

45. 45 Alam Nuzhathalam Advantages:  More accurate  Access to a variety of participants  Useful in specific circumstances to serve the purpose Disadvantages:  Massare left  Volunteer based  More costly due to Advertizing

47. 47 Alam Nuzhathalam  Snowball sampling is also known as chain sampling, chain-referral sampling, referral sampling. It is a non- probability sampling technique where existing study subjects recruit future subjects from among their acquaintances  The research starts with a key person and introduce the next one to becomeachain

48. 48 Alam Nuzhathalam Advantages:  Lowcost  Locate hidden population  Useful in specific circumstances & for locating rare populations Disadvantages:  Not Random  Not independent  Projecting data beyond sample not justified

50. 50 Alam Nuzhathalam  Error caused by act of taking sample  Sampling error is the error caused by observing a sample instead of the whole population. The sampling error is the difference between a sample statistic used to estimate a population parameter and the actual but unknown value of the parameter Twotypes of samplingerrors  Biased Errors: Due to selection of sampling technique; size of the sample  Unbiased Errors/Random sampling errors: Differences between the members of the population included or notincluded

51. 51 Alam Nuzhathalam Sampling errors can be reduced by the following methods:  Increase the sample size. A larger sample size leads to a more precise result because the study gets closer to the actual population size  Randomize selection to eliminate bias  Divide the population into groups  Perform an external record check  Know your population  Train your team

52. 52 Alam Nuzhathalam  Any error or inaccuracies caused by factors other than sampling error  Non-sampling errors to biases and mistakes in selection of sample Examples of non-sampling errors are:  Selection bias  Interviewer error  Population mis-specification error  Respondent error, non-response error  Sampling frame error, processing error

53. Alam Nuzhathalam 53 Causes for non-sampling error:  Sample size  Processing error  Loaded questions  Lack of knowledge  Sampling operations  Inadequate of response  Concealment of the truth  Misunderstanding the concept

54. Alam Nuzhathalam 54  Probability sampling can generalize to population  Probability sampling should be attempted as it has lowest bias and more importantly significance of the result  Non-probability sampling can be generalize to the institution or place where the sample was studied  Using a sample in research saves mainly on money and time, if a suitable sample strategy is used appropriate size selected and necessary precautions to reduce on sampling and measurement errors, then a sample should yield valid and reliable information

Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampling and Non Probability Sampling)

Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampling and Non Probability Sampling)

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