Sampling Techniques – Concepts, Types, and Applications

This presentation provides a comprehensive overview of sampling techniques in research methodology, emphasizing their importance in deriving representative and unbiased conclusions from large populations. It explains fundamental concepts such as population, sample, sampling unit, sampling frame, and sample size.

The slides detail two major categories of sampling:

Probability Sampling, including simple random, systematic, stratified, cluster, and multistage sampling methods, each illustrated with practical examples, advantages, and limitations.

Non-Probability Sampling, covering convenience, judgmental, purposive, quota, and snowball sampling, with emphasis on their application in exploratory and field research.


Through clear definitions, diagrams, and examples, the presentation helps learners understand how to choose appropriate sampling methods based on research objectives, population structure, and available resources. It also highlights potential sources of bias, the importance of representativeness, and introduces advanced concepts like sampling error, design effect, and bias reduction strategies.

Learning Outcomes:
By the end of this presentation, learners will be able to:

Explain the concept and importance of sampling in research.

Differentiate between probability and non-probability sampling techniques.

Identify suitable methods for different research designs.

Recognize advantages, disadvantages, and practical applications of each technique.

Keywords: Sampling, Probability Sampling, Non-Probability Sampling, Stratified Sampling, Cluster Sampling, Purposive Sampling, Biostatistics, Research Methodology

1. Sampling Techniques Presenter: Dr. Saurabh Krishna Verma (JR-3) Moderator: Dr. Pranshu Pandit (SR) Peer Support: Dr. Surya K (JR-3) Department of Pharmacology and Therapeutics King George’s Medical University, Lucknow (UP) E-mail: [email protected] 31-07-2025 Dr. Saurabh Krishna Verma 1

2. Contents Introduction Types of sampling techniques Methods used in Probability sampling Methods used in Non-probability sampling Summary References 31-07-2025 Dr. Saurabh Krishna Verma 2

3. Specific learning objectives : At the end of this teaching learning session, co-learners shall be able to: Explain the sample, and why it is important in research? Differentiate between various types of sampling techniques? Enumerate the methods used in probability sampling? Enumerate the methods used in non-probability sampling? 31-07-2025 Dr. Saurabh Krishna Verma 3

4. Introduction Population: is the term statisticians use to describe a large set or collection of items that have something in common Sample is a subset of a population, selected in such a way that it is representative of the larger population Sampling unit: is the subject on which information is obtained Sample size: The number of units sampled for inclusion in the study is called the sample size 31-07-2025 Dr. Saurabh Krishna Verma 4

5. Sampling It is the process of selecting a subset of individuals, items, or observations from a larger population, in such a way that the selected subset represents the characteristics of the whole population It allows researchers to draw conclusions or make inferences about the population without studying every member The sample needs to be representative of the population in terms of time, place, and person 31-07-2025 Dr. Saurabh Krishna Verma 5

6. Sampling frame: This is the complete list of sampling units in the target population to be subjected to the sampling procedure Completeness and accuracy of this list are essential for the success of the study Sampling scheme: It is a structured plan that outlines which sampling technique will be used to select a sample from the population 31-07-2025 Dr. Saurabh Krishna Verma 6

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8. 31-07-2025 Dr. Saurabh Krishna Verma 8 Statistic Parameter Population: Complete collection to be studied Sampling process Sample: part of population Characteristic of a sample Characteristic of a population Inference

9. Sampling in Epidemiology 31-07-2025 Dr. Saurabh Krishna Verma 9 Why Sample? Unable to study all members of a population Reduce bias Less resources (time, money) Feasibility Measurements are better in a sample than in the entire population

10. 31-07-2025 Dr. Saurabh Krishna Verma 10 PROBABILITY SAMPLING NON-PROBABILITY SAMPLING DEFINITION Subjects of the population get equal opportunity to be selected as a representative sample Subjects of the population not get equal opportunity to be selected as a representative sample SELECTION Random sampling Non randomly ( Arbitrarily ) RESULTS Unbiases ( less sampling error ) Bias ( more sampling error ) REQUIREMENT RESOURCES More in terms of time, cost, efforts less Types of Sampling

11. Sampling methods 31-07-2025 Dr. Saurabh Krishna Verma 11 PROBABILITY SAMPLING Simple random sampling Systematic random sampling Stratified random sampling Cluster random sampling Multistage random sampling NON-PROBABILITY SAMPLING Convenience sampling Subjective/ Judgmental sampling Purposive sampling Snowball sampling Quota sampling

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13. Simple random sampling Equal probability of selection of units for inclusion in the study Requires a list of all sampling units Each individual is chosen randomly Techniques: Lottery method Random number tables Software that generates random numbers 31-07-2025 Dr. Saurabh Krishna Verma 13

14. 31-07-2025 Dr. Saurabh Krishna Verma 14 Lottery Method Software

15. 31-07-2025 Dr. Saurabh Krishna Verma 15 Random number tables

16. Advantages Very scientific method Equal chance of all subjects for selection Reduce bias Disadvantage Requires a list of sampling units 31-07-2025 Dr. Saurabh Krishna Verma 16

17. Systematic sampling Systematic sampling is a commonly employed technique when a complete and up-to-date list of sampling units is available A systematic random sample is obtained by- Selecting the first unit on a random basis Then others are included based on the sampling interval I = N/n 31-07-2025 Dr. Saurabh Krishna Verma 17

18. For example , if there are 100 patients (N) in a hospital and we want to select a sample of 20 patients (n) by a systematic random sampling procedure, Step 1: Write the names of 100 patients in alphabetical order or their roll numbers one below the other Step 2: Sampling fraction: divide N by n to get the sampling fraction (k). example, k=100/20=5 Step 3: Randomly select any number between 1 to k i.e., between 1 to 5 . Suppose the number we select is 4 31-07-2025 Dr. Saurabh Krishna Verma 18

19. Step 4: Patient number 4 is selected in the sample Step 5: Thereafter, every 4th patient is selected in the sample until we reach the last one 31-07-2025 Dr. Saurabh Krishna Verma 19 N= 100 want n= 20 N/n=5 select a random number from 1 to 5: Choose 4 start with #4 and take every 5 th unit

20. Advantages Easy to draw Assurance that the population will be evenly sampled Disadvantage Requires sampling frame 31-07-2025 Dr. Saurabh Krishna Verma 20

21. Stratified sampling Preferred method, when the population is heterogeneous concerning the characteristics under study Population is divided into groups or strata based on certain characteristics A simple random sample is selected from each strata Can be done by selecting individuals from different strata in certain fixed, predetermined proportions 31-07-2025 Dr. Saurabh Krishna Verma 21

22. For example, if we draw a simple random sample from a population, a sample of 100 may contain- 10 to 15 from a high socioeconomic group 20 to 25 from the middle socioeconomic group 70 to 75 from a low socioeconomic group   To get an adequately large representation for all three socioeconomic structures, we can stratify on socioeconomic class and select simple random samples from each of the three strata 31-07-2025 Dr. Saurabh Krishna Verma 22

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24. Advantages: All groups, however small, are equally represented To sample the rare extremes of the given population Higher statistical precision compared to simple random sampling. So less time and money Disadvantages: Requires a sampling frame for each stratum separately Requires accurate information on proportions of each stratum 31-07-2025 Dr. Saurabh Krishna Verma 24

25. Cluster sampling The population is divided into subgroups (clusters) like families. A simple random sample is taken of the subgroups, and then all members of the cluster selected are surveyed Cluster sampling is used when the population is heterogeneous Clusters are formed by grouping units based on their geographical locations Cluster sampling is a very useful method for field epidemiological research and health administrators 31-07-2025 Dr. Saurabh Krishna Verma 25

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27. Types: One stage — when all units in the selected cluster are selected Two-stage — Only some units from a selected cluster are taken using simple random or systematic random sampling Advantages : Simple as a complete list of sampling units within the population is not required Can estimate characteristics of both cluster and population Less travel/resources required 31-07-2025 Dr. Saurabh Krishna Verma 27

28. Disadvantages: Cluster members are more likely to be alike than those in another cluster (homogenous) Each stage in cluster sampling introduces sampling error 31-07-2025 Dr. Saurabh Krishna Verma 28

29. Multistage sampling  In this method, the whole population is divided into first-stage sampling units from which a random sample is selected The selected first stage is then subdivided into second-stage units from which another sample is selected Third and fourth stage sampling is done in the same manner if necessary 31-07-2025 Dr. Saurabh Krishna Verma 29

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31. Advantages: Cost-Effective Flexibility Disadvantages: Time-Consuming Requires expertise 31-07-2025 Dr. Saurabh Krishna Verma 31

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33. Convenient sampling Convenient sampling is a matter of taking what you can get An accidental sample is considered easiest, cheapest, and least time-consuming Principle : Provides a quick and easy way to gather data Example : To collect feedback from Junior Residents (JRs) who participated in the ROLEPLAY IN EXTRACURRICULAR ACTIVITIES 31-07-2025 Dr. Saurabh Krishna Verma 33

34. 31-07-2025 Dr. Saurabh Krishna Verma 34 Population Convenient sampling

35. Advantages: Easy and quick to administer Useful for preliminary or exploratory research Disadvantage: Highly prone to bias 31-07-2025 Dr. Saurabh Krishna Verma 35

36. Subjective / Judgmental Sampling Definition : Samples are selected based on researcher knowledge rather than specific criteria Example : Selecting Junior Residents based on recommendations from Senior Residents who are familiar with them 31-07-2025 Dr. Saurabh Krishna Verma 36

37. 31-07-2025 Dr. Saurabh Krishna Verma 37 Advantages: Easy to implement Disadvantages: Highly prone to researcher bias

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39. Purposive Sampling : Focuses on selecting participants that specifically meet the purpose of the study criteria Example : Selecting JRs who have published research papers in the past year for a study on research Advantages : Cost-effective Disadvantages : Bias , limited scope ( small sample size) 31-07-2025 Dr. Saurabh Krishna Verma 39

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41. Quota sampling Researcher ensures that specific characteristics (such as age, gender, education level, etc.) of the population are represented proportionally within the sample Dividing the population into subgroups (quotas) and then selecting a specific number of individuals from each subgroup Advantages: Ensures representation of specific characteristics Disadvantages: Potential for bias 31-07-2025 Dr. Saurabh Krishna Verma 41

42. Example of quota sampling : In a study on the effects of a new drug, the researcher ensures that the sample includes a specific proportion of male and female participants, as well as different age groups (18 to 35 yrs & 36 to 59 yrs), based on their proportions in the general population 31-07-2025 Dr. Saurabh Krishna Verma 42

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44. Snowball sampling Used to identify hidden/ hard to reach populations in the absence of a sampling frame Identifies an initial participant based on certain characteristics of interest Initial participants then recruit other participants from their same criteria This process continues, expanding like a snowball rolling downhill 31-07-2025 Dr. Saurabh Krishna Verma 44

45. 31-07-2025 Dr. Saurabh Krishna Verma 45 Example : Investigating experiences of patients with a rare genetic disorder. Begin with a few patients found through clinics who refer others with the same condition Advantages: Useful for studying hard-to-reach or hidden populations Disadvantages: Prone to bias as it depends on initial subjects to recruit others

46. 31-07-2025 Dr. Saurabh Krishna Verma 46 Snowball sampling

47. Summary Sampling is the process of selecting a subset from a population that represents the whole Types of Sampling: Probability and Non-Probability Sampling Probability Sampling: simple random, systematic, stratified, cluster, and multistage sampling Non-Probability: convenience, judgmental, purposive, snowball, and quota sampling USES : Probability: Research, surveys ; Non-Probability: Exploratory studies 31-07-2025 Dr. Saurabh Krishna Verma 47

48. Specific learning objectives achieved: At the end of this teaching learning session, I hope co-learners are now able to: Explain the sample, and why it is important in research? Differentiate between various types of sampling techniques? Enumerate the methods used in probability sampling? Enumerate the methods used in non-probability sampling? 31-07-2025 Dr. Saurabh Krishna Verma 48

49. Further readings What is sampling error vs. non-sampling error? What is design effect in sample size calculation? What are the types of sampling bias, and how to reduce them? 31-07-2025 Dr. Saurabh Krishna Verma 49

50. References Indian Council of Medical Research – National Institute of Epidemiology. Basic Course in Biomedical Research Course Material. Chennai: ICMR-NIE, https://nptel.ac.in/courses/127/106/127106134/ Mehta, T. Analytical study designs . Basic Course in Biomedical Research Handbook. 1st edition 2021. Pg.180 – 197 Banerjee B. Mahajan’s Methods in Biostatistics for Medical Students and Research Workers. 9th ed. New Delhi: Jaypee Brothers; 2018. p. 1–10. Dakhale GN, Hiware SK, Shinde AT, Mahatme MS. Basic biostatistics for post-graduate students. Indian J Pharmacol2012;44(4):435–42 Arora PN, Malhan PK. Biostatistics . New Delhi: Global Media; 2009. p. 1–3 31-07-2025 Dr. Saurabh Krishna Verma 50

51. Audience questions 7/30/2025 Dr Ranjan Awana 51

52. QUESTIONS Difference between probability and non-probability sampling? How does multistage sampling differ from cluster sampling? Types of Non-probability methods? Define convenience sampling and discuss one potential drawback of using this method? What are the advantages and disadvantages of using snowball sampling? 31-07-2025 Dr. Saurabh Krishna Verma 52

53. 31-07-2025 Dr. Saurabh Krishna Verma 53 THANK YOU