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Sampling methods

  1. SAMPLING METHODS Prepared by: Dr. Satish Kumar Yadav MPH, 1st Sem (2021 Batch) SPH & CM, BPKIHS
  2. Population: is the entire group that we want to draw conclusions about. Sample: is the specific group that we will collect data from. It represents the characteristics of population. The size of the sample is always less than the total size of the population. ● In research, a population doesn’t always refer to people. ● It can mean a group containing elements of anything we want to study, such as objects, events, organizations, countries, species, organisms, etc.
  3. Sampling:- is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population. When the population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sample. Two advantages of sampling are: 1. Lower cost and 2. faster data collection (than measuring the entire population.)
  4. Types of Sampling Random Sampling Non-Random Sampling Also k/a Probability sampling 1. Simple Random Sampling 2. Systemic Random Sampling 3. Stratified Random Sampling 4. Multistage Random Sampling 5. Multiphase Random Sampling 6. Cluster Random Sampling Also k/a Non-Probability sampling 1. Convenience Sampling 2. Quota Sampling 3. Snow-ball Sampling 4. Judgemental Sampling
  5. Probability Sampling Methods Non-Probability Sampling Methods Definition Probability Sampling is a sampling technique in which samples from a larger population are chosen using a method based on the theory of probability. Non-probability sampling is a sampling technique in which the researcher selects samples based on the researcher’s subjective judgment rather than random selection. Nature The research is conclusive. The research is exploratory. Sample Since there is a method for deciding the sample, the population demographics are conclusively represented. Since the sampling method is arbitrary, the population demographics representation is almost always skewed. Time Taken Takes longer to conduct since the research design defines the selection parameters before the research study begins. This type of sampling method is quick since neither the sample or selection criteria of the sample are undefined. Results This type of sampling is entirely unbiased and hence the results are unbiased too and conclusive. This type of sampling is entirely biased and hence the results are biased too, rendering the research speculative. Hypothesi In probability sampling, there is an underlying hypothesis before the study In non-probability sampling, the hypothesis is derived after conducting the research
  6. SIMPLE RANDOM SAMPLING - Every unit of population has equal and known chance of being selected. - Is also known as “unrestricted random sampling” - Applicable for small, homogenous and readily available populations - Used in clinical trials - Methods of simple random sampling: - lottery method - random number tables - computer softwares
  7. SYSTEMIC RANDOM SAMPLING - Based on sampling fraction: every ‘K’ th unit is chosen in the population list, where K is chosen by sampling interval. - Sampling interval (K) Q = Total no. of units in population / Total no. of units in sample - For example, if there is a population of 1000 from which sample of 20 is to be chosen, then K=1000/20 = 50; thus EVERY 50TH UNIT WILL BE INCLUDED IN THE SAMPLE (1st, 51st, 101st, so on..) First unit among the 50 is chosen by simple random sampling. - Applicable for large, non-homogenous populations where complete list of individual is available.
  8. STRATIFIED RANDOM SAMPLING - Non-homogenous population is converted to homogenous groups/classes (stata); sample is drawn from each strata at random, in proportion to its size For example, in a population of 1000, sample of 100 is to be drawn for hemoglobin estimation; first non-homogenous population is converted to homogenous strata (i.e. 700 males and 300 females), then draw 70 males and 30 females randomly respectively. - Applicable for large non- homogenous population. - Gives more representative sample than simple random sampling. - None of the categories is under or over represented
  9. Cluster Sampling  Cluster sampling is an example of two stage sampling (first stage sample of area is chosen, second area a sample or respondent within the area)  A cluster sample is obtained by selecting clusters from the population on the basis of simple random sampling  The sample comprises a census of each random cluster selected, sample unit is group rather than individual  This method is used when the unit of population are natural groups or clusters such as villages, wards, blocks, children of school etc. in which every unit of cluster is taken.  Used to study large populations, particularly those that are widely geographically dispersed. BPKIHS; MPH 2021 9
  10. Multistage Sampling  Complex form of cluster sampling in which two or more levels of units are embedded one in the other  Sampling procedures carried out in several stages using random sampling techniques  Done for large county surveys  First stage, random number of districts chosen in all province.  Followed by random number of municipalities/rural municipalities .  Then third stage units will be houses.  At the last step, households and individuals within household are randomly selected  Part of the information collected from whole sample & part from sub-sample.  In TB survey MT in all cases – Phase I  X –Ray chest in MT +ve cases – Phase II  Sputum examination in X-Ray +ve cases - Phase III Survey by such procedure is less costly, less laborious & more purposeful BPKIHS; MPH 2021 10
  11. Non-Probability Sampling  Each element of population has no equal chance of being selected of the item from the population  If the sampling frame is not available, non-probability sampling technique is used.  Using this technique, we may or may not represent the population well.  Within the probability sampling we are using in between. BPKIHS; MPH 2021 11
  12. Advantages  Useful when the sample size is small  Applied when the number of elements in the population is unknown. Limitations  There are high chances of selection bias  It is not a scientific method BPKIHS; MPH 2021 12
  13. Convenience Sampling  Convenience sampling entails using most conveniently available people as participants. Example, a researcher selects a sample as to those who appear in the hospital.  Advantages - It is useful for pre-testing questionnaires - It is useful for pilot studies  Limitations - Selected samples might be a typical to the population - There are high chances of selection bias BPKIHS; MPH 2021 13
  14. Snowball Sampling  Snowball sampling is used to reach target population where the sampling units are difficult to identify.  Under snowball sampling, each identified member of the target population is asked to identify other sampling units who belong to the same target population.  Snowball sampling would be used to identify successive sampling units, for example drug addicts, sex workers, etc.  The issues under investigation are usually confidential or sensitive in nature. BPKIHS; MPH 2021 14
  15. Quota Sampling  Is more suitable under the condition when sample quotas have been fixed according to some specified characteristics ( e.g; age, income, education, sex, colors etc.)  This technique is similar to stratified random sampling, however instead of randomly sampling from each stratum, the researcher uses a non-random sampling method to gather data from one stratum until the desired quota of samples is filled.  Thank you BPKIHS; MPH 2021 15