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050 sampling theory
 

050 sampling theory

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    050 sampling theory 050 sampling theory Presentation Transcript

    •  
      • A process of selecting units from a population
      • A process of selecting a sample to determine certain characteristics of a population
      • A sample is a subset of a larger population of objects individuals, households, businesses, organizations and so forth.
      Concept of sampling
      • Sampling enables researchers to make estimates of some unknown characteristics of the population in question
      • A finite group is called population whereas a non-finite (infinite) group is called universe
      • A census is a investigation of all the individual elements of a population
    • 29 Population Sample A sample is a subset of a larger population of objects individuals, households, businesses, organizations and so forth. Sampling enables researchers to make estimates of some unknown characteristics of the population in question A finite group is called population whereas a non-finite (infinite) group is called universe A census is a investigation of all the individual elements of a population
      • Get information about large populations
      • Less costs
      • Less field time
      • More accuracy i.e. Can Do A Better Job of Data Collection
      • When it’s impossible to study the whole population
      Why sampling
    • Classification of Sampling Techniques Probability Sampling Techniques Stratified Sampling Cluster Sampling Simple random Sampling Sampling Techniques Non-probability Sampling Techniques Convenience Sampling Judgment Samples Quota Sampling Snowball Sampling Systematic Sampling
      • Probability Sampling: utilizes some form of random selection. A probability sample is a sample in which every element of the population has a known and equal probability of being selected into the sample.
      • Non-probability sampling : does not involve random selection
      • Simple random
      • Stratified random
      • Systematic random
      • Cluster/area random
      • Multi-stage random
      • Non-probability Sampling are of following types
      • Convenience Sampling Judgment Sampling
      • Quota Sampling Snow ball Sampling
      • Probability selected = n i /N
      • When population is rather uniform (e.g. school/college students, low-cost houses)
      • Simplest, fastest, cheapest
      • Could be unreliable, why?
      A T Y W B P G E S C K L G N Q B T G K Population Sample Population not uniform Wrong procedure ?
      • Pick any “element”
      • Use random table
      • Break population into “meaningful” strata and take random sample from each stratum
      • Can be proportionate or disproportionate within strata
      • When:
      • * population is not very uniform (e.g. shoppers, houses)
      • * key sub-groups need to be represented -> more
      • precision
      • * variability within group affects research results
      1 4 8 12 3 6 13 2 10 20 15 7 14 11 16 3 7 10 16 Population Sample Stratum 2 = even no. Stratum 1 = odd no.
      • Simple or stratified in nature
      • Systematic in the “picking-up” of element. E.g. every 5 th . visitor, every 10 th . House, every 15 th . minute
      • Steps:
      • * Number the population (1,…,N)
      • * Decide on the sample size, n
      • * Decide on the interval size, k = N/n
      • * Select an integer between 1 and k
      • * Take case for every k th . unit
      • Research involves spatial issues ( e.g. do prices vary according to neighbourhood’s level of crime? )
      • Sampling involves analysis of geographic units
      • Sampling involves extensive travelling -> try to minimise logistic and resources
      • Steps:
      • * Divide population into “clusters” (localities)
      • * Choose clusters randomly (simple random,
      • stratified, etc.)
      • * Take all cases from each cluster
      • Efficient from administrative perspective
    • Section 5 Section 2 Section 1
      • Convenience Samples
        • Non-probability samples used primarily because they are easy to collect.
      • Judgment Samples
        • Non-probability samples in which the selection criteria are based on personal judgment that the element is representative of the population under study.
      • Quota Samples
        • Non-probability samples in which population subgroups are classified on the basis of researcher judgment.
      • Snowball Samples
        • Non-probability samples in which selection of additional respondents is based on referrals from the initial respondents.
    •  
      • Thank you!