Probability sampling
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This document defines probability sampling and describes four main types: simple random sampling, stratified random sampling, systematic random sampling, and cluster random sampling. Probability sampling involves selecting samples in a way that gives every member of the population an equal and known chance of being chosen. It aims to result in a sample that accurately represents the larger population. The document provides examples of how to select samples for each of the four probability sampling techniques.
Probability sampling
- 2. P R E S E N T E D B Y T A N Z I L I R F A N I N S T I T U T E O F E D U C A T I O N A N D R E S E A R C H , U N I V E R S I T Y O F P E S H A W A R Probability Sampling
- 3. Outlines Definition of sample Classification of Sampling techniques 1. Probability sampling 2. Non-probability sampling Characteristics of probability sampling Classification of probability sampling
- 4. Definition of sample A sample is a smaller collection of units from a population used to determine truths and facts about that population. or A small group of people taken from a larger group and used to represent the larger group is called sample.
- 5. Classification of Sampling Techniques
- 6. Probability Sampling A probability sampling is one that have been selected in such away that every element chosen has a known probability of being included. or Probability sampling involves the selection of elements from the population using random in which each element of the population has an equal and independent chance of being chosen. It is also called random sampling.
- 7. Characteristics of Probability Sampling 1. It refers from the sample as well as the population. 2. Every individual of the population has an equal probability to be taken into the sample. 3. It may be representative of the population.
- 8. Classification of Probability Sampling 1. Simple random sampling 2. Stratified random sampling 3. Systematic random sampling 4. Cluster random sampling
- 9. 1. Simple random sampling It is, in which each element of the population has an equal independent chance of being included in the sample. Thus a sample selected by randomization method is known as simple random sampling. In simple random sampling , most commonly used method is the ‘’lottery method’’.
- 10. Selection process By using the lottery method, there is a need for complete listing of the member of population. The numbers of all members are written on piece of paper and placed in a container. The researcher draws the desired number of sample from container. This process is relatively easy for small population but relatively difficult and time consuming for a large population.
- 11. 2. Stratified random sampling The population is divided into two or more groups called strata, on the basis of some characteristics such as geographic location, age, income or status and sub samples are randomly selected from each strata.
- 12. Selection process Identify and define the population. Determine the desired sample size. Identify the subgroups i.e. (Strata ) for which we want to guarantee appropriate representation. Classify all members of the population as members of one of the identified subgroups.
- 13. 3.Systematic random sampling It is the type of probability sampling, in which one or two items are selected randomly, but other items are selected by adding the average sampling interval to the item selected randomly. It is also called an Nth name selection technique. Selecting every nth subject from a list of the member of the population.
- 14. Selection process Identify and define the population. Determine the desired sample size. Obtain the list of the population. Determine what nth is equal to by dividing the size of population by the desired sample size . Start at some random place in population on list . Take every nth individual on the list.
- 15. 4.Cluster random sampling The process of randomly selecting groups, not individuals, within the define population sharing similar characteristics. Cluster are location with in which group of member of the population can be found. Example: Schools Classrooms etc
- 16. Cluster Random Sampling Cluster random sampling is done when simple random sampling is almost impossible because f the size of the population. Just imagine doing simple random sampling when population in question is the entire population of Asia.
- 17. Selection process Identify and define the population. Determine the desired sample size. Identify and define the cluster. Estimate the average number of population member per cluster. Determine the number of cluster s need by dividing the sample size by the estimated size of a cluster. Randomly select the needed number of clusters. Include in the study all individuals in each selected clusters.
- 18. Thank you!