MELC 2 - Sampling Procedure and Sample

• Sampling


  It is a word that refers to your method or process of selecting respondents or people to answer questions meant to yield data for research study. 
• Population


  It is a group of persons or objects that possess some common characteristics that are of interest to the researcher and about what the researcher seeks to learn more.
• The bigger group where you choose the sample is called

  population
• There are two groups of population: the target population and the accessible population.
• The target population is composed of the entire group of people and objects to which the researcher wishes to generalize the findings of the study.
• The accessible population is a portion of the population to which the researcher has reasonable access.
• The individual participants in the study are often referred to as subjects or respondents. They may also be referred to as elements.
• Subjects 


  These are individuals or entities which serve as the focus of the study.
• Respondents


  These are individuals or groups of people who actively serve as sources of information during data collection.
• Statistic


  It is a number describing a property of a sample.
• Parameter


  It is a number describing a property of a population.
• A statistic can be used to estimate the parameter in what is called a statistical inference.
• Sampling frame

  It is the term used to mean the list of the members of such population from where you will get the sample.
• Sampling has been categorized into two classes; Probability sampling and Non-Probability sampling.
• Simple Random Sampling


  This is the best type of probability sampling in which all the members has the opportunity to be chosen.
• Simple Random Sampling


  This type of sampling happens through any of these two

  methods:

  • Have a list of all members of the population; write each name on a card, and choose cards through a pure-chance selection.
  • Have a list of all members; give a number to member and then use randomized or unordered numbers in
• Systematic Sampling


  For this kind of sampling chance and system are the ones to determine who should compose the sample.
• Stratified Sampling


  In this type of sampling, the group comprising the sample is chosen in a way that such group is liable to subdivision during the data analysis stage.
• A study needing group by group analysis finds stratified sampling the right probability sampling to use.
• Cluster Sampling


  This is a probability sampling that makes you isolate a set of persons instead of individual members to serve as sample members.
• The types of probability sampling are: simple random sampling, systematic sampling, stratified sampling, and cluster sampling.
• Quota Sampling 


  To use this kind of sampling means you definitely know the characteristics of the target sample.
• With quota sampling, you directly choose that set of persons for you already believe that their characteristics is suited or closely related

  to your study.
• Voluntary Sampling


  In this type of sampling, election process is not needed since your target sample are the ones who willingly present themselves to participate in the study.
• Purposive Sample or Judgmental Sample


  In this type of sampling, selection is based in the judgment of the researcher. They are the people with interest in the study, and possess capability and experiences in the said topic.
• Availability Sampling


  The willingness of each person is the measurement of this sampling. 
• In availability sampling, you can choose people walking along the street by approaching them or any person as long as they are willing to respond in your questions.
• Snowball Sampling 


  This type of sampling means rolling or it could be strolling anywhere.
• Snowball Sampling


  In this type of sampling, there is no specific set of sample and data could freely obtained to various group of people like vendors, street children, call center workers, drug dependents and etc. 
• In snowball sampling, you have the freedom to choose and increase your sample.
• The following are the acceptable sizes for different types of research:

  • Descriptive research - 10% to 20% may be required
  • Comparative research - 15 subjects or groups
• When the total population is equal to or less than 100, this same number may serve as the sample size. This is called universal sampling.
• True


  True or False: The higher the degree of homogeneity the population, the smaller the sample size that can be utilized.
• True


  True or False: The larger the sample size, the higher the precision or accuracy of the results will be.
• True


  True of False: Probability sampling uses smaller sample sizes than non-probability sampling.
• Slovin's formula is used to compute for sample size.
• Sample sizes as small as 30 are generally adequate to ensure that the sampling distribution of the mean will approximate the normal curve.