Sample
Introduction
Sampling- the process of selecting a number of participants for a study in such a way that they represent the larger group from which they were selected.Sample comprises the individuals, items, or events selected from a larger group referred to as a population. Population is the group of interest to the researcher, the group to which the results of the study will ideally generalize.

Four basic random sampling techniques:

  • simple random sampling
  • stratified sampling
  • cluster sampling
  • systematic sampling
  • Simple Random Sampling
    Random sampling involves defining the population, identifying each member of the population, and selecting participants for the sample on a completely chance basis.Use a table of random numbers (table of random digits)- selects the sample for you on a purely random, chance basis.

    Selecting a sample involves the following steps:

  • Identify and define the population
  • Determine the desired sample size
  • List all members of the population
  • Assign all individuals on the list a consecutive number form zero; each individual must have the same amount of digits
  • Select an arbitrary number in the table (close eyes and point)
  • Look at only the number of digits assigned.
  • If a number corresponds to an individual then individual is in the sample
  • Go to the next number in the column and repeat steps 6 and 7.
  • Stratified Sampling
    Stratified Sampling is the process of selecting a sample in such a way that identified subgroups in the population are represented in the sample in the same proportion that they exist in the population.Subgroup or strata is a variable that can be divided into groups.Proportional stratified sampling- would be appropriate if you were going to take a survey prior to a national election to predict the winner.

    Selecting a sample involves the following steps:

  • Identify and define the population
  • Determine the desired sample size
  • Identify the variable and subgroups, you want to guarantee appropriate equal representation
  • Classify all members of the population as members of one of the identified subgroups.
  • Randomly select an appropriate number of people from each subgroup.
  • Cluster Sampling
    Cluster sampling is randomly selects groups, not individuals; all members of group have similar characteristics; most useful when population is very large or spread out over a wide geographic area.Cluster is any population where we find an intact group of similar characteristics .

    Steps in Cluster Sampling:

  • Identify and define the population
  • Determine the desired sample size
  • Identify and define a logical cluster
  • List all clusters (or obtain list) that make up the population of clusters.
  • Estimate the average number of population members per cluster
  • Determine the number of clusters by dividing the sample size by the estimated size of cluster
  • Randomly select the needed number of clusters
  • Include in your study all population members in each selected cluster
  • Systematic Sampling
    Not used often. It is sampling in which individuals are selected from a list taking every kth name. Where k=size of the population divided by the desired sample size. For example 5000 500=10.

    Steps in Cluster Sampling:

  • Identify and define the population
  • Determine the desired sample size
  • Obtain a list of the population
  • Determine what k is equal to
  • Start at some random place in the population list. Close your eyes and stick your finger on a name
  • Starting at that point, take every kth name on the list until the desired sample size is reached.
  • If the end is reached before the desired sample is reached, go back to the top of the list.
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