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  1. SAMPLING TECHNIQUES Prof. Jobi Jacob Vice Principal KNN College of Nursing
  2. KEYWORDS  Population : It is a set of people with common characteristics  Target Population : The entire group of people to which the Researcher wishes to generalize the findings of the study  Accessible Population : The available group from which the Researcher selects the sample 2
  3. KEYWORDS  Sample : A unit from which necessary data is collected  Sampling Frame : It is a physical representation of objects or individuals important to the development of the final study sample  Sampling Design : The method used to select the sample 3
  4. SAMPLING DESIGN PROCESS Define the Population Determine the Sampling Frame Select Sampling Technique(s) Determine the Sample Size Execute the Sampling Process 4
  5. CLASSIFICATION OF SAMPLING TECHNIQUES Sampling Techniques Non Probability Sampling Techniques • Simple Random Sampling • Stratified Random Sampling • Cluster Random Sampling • Systematic Random Sampling Probability Sampling Techniques • Convenience Sampling • Quota Sampling • Snowball Sampling • Purposive Sampling 5
  7. SIMPLE RANDOM SAMPLING  Each element in the population has an equal probability of selection  Enumerate the list of elements on a sampling frame  Slips of paper representing each element is folded and kept in a bowl  Pick out number of slips needed(samples) 7
  8. RANDOM NUMBERS 21 71 89 96 82 59 22 78 12 17 76 93 79 28 20 60 70 34 51 93 68 36 96 19 21 99 18 32 8
  9. STRATIFIED RANDOM SAMPLING  A two-step process in which the population is partitioned into subpopulations, or strata  The strata should be mutually exclusive and collectively exhaustive in that every population element should be assigned to one and only one stratum and no population elements should be omitted  The Elements within the stratum should be homogeneous as possible  Elements are selected from each stratum by a random procedure  A major objective of stratified sampling is to increase precision without increasing cost 9
  10. CLUSTER RANDOM SAMPLING  The target population is first divided into mutually exclusive and collectively exhaustive subpopulations or clusters  Then a random sample of clusters is selected, based on simple random sampling  For each selected cluster, either all the elements are included in the sample (one-stage) or a sample of elements is drawn probabilistically (two-stage)  Elements within a cluster should be as heterogeneous as possible, but clusters themselves should be as homogeneous as possible  Ideally, each cluster should be a small-scale representation of the population  This is also called Multi-stage Sampling 10
  12. SYSTEMATIC RANDOM SAMPLING  Selecting every kth element of population Step 1: Obtain a list of total population Step 2 : Determine sample size Step 3 : Find out k Step 4 : A random number between 1 – k is selected. For ex. 23 Then the samples will be 23, 123, 223, 323, … till required samples are needed. needed sample total population total  k Example 100 1000 100000   k 12
  14. CONVENIENCE SAMPLING It attempts to obtain a sample of convenient elements OR it involves the samples which are readily available Often, respondents are selected because they happen to be in the right place at the right time. Ex.  Students in a class  People at malls  People on the street 14
  15. QUOTA SAMPLING  It is similar to stratified random sampling  Selecting the sample from each strata is by convenient sampling method and not by simple random sampling  The strata is based on variables of study Ex - Age, Gender, Educational qualification 15
  16. SNOWBALL SAMPLING  Here an initial group of respondents is selected  After being interviewed, these respondents are asked to identify other samples who could belong to the target population  Subsequent respondents are selected based on the referrals 16
  17. PURPOSIVE SAMPLING  It is a form of convenience sampling in which the samples are selected based on the judgement of the researcher or whom the researcher feels is a typical representative of the accessible population Ex. – Cancer patients who have problem with port- a-cath used for chemotherapy 17
  18. SAMPLING DESIGN CHOICE CONSIDERATIONS Probability Non-probability Cost More costly Less costly Accuracy More accurate Less accurate Time More time Less time Acceptance Universal Reasonable acceptance Generalisability Good Poor 18
  19. FACTORS AFFECTING SAMPLE SIZE  Homogeneity of sampling units  Confidence  Precision  Statistical power  Analytic procedures  Costs, Time & Personnel 19
  20. THANK YOU 20