Common Approaches to Sampling Data

Data Science
  • 8 Videos | 47m 2s
  • Includes Assessment
  • Earns a Badge
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Data science is an interdisciplinary field that seeks to find interesting generalizable insights within data and then puts those insights to monetizable use. In this 8-video Skillsoft Aspire course, learners can explore the first step in obtaining a representative sample from which meaningful generalizable insights can be obtained. Examine basic concepts and tools in statistical theory, including the two most important approaches to sampling—probability and nonprobability sampling—and common sampling techniques used for both approaches. Learn about simple random sampling, systematic random sampling, and stratified random sampling, including their advantages and disadvantages. Next, explore sampling bias. Then consider what is probably the most popular type of nonprobability sampling technique—the case study, used in medical education, business education, and other fields. A concluding exercise on efficient sampling invites learners to review their new knowledge by defining the two properties of all probability sampling techniques; enumerating the three types of probability sampling techniques; and listing two types of nonprobability sampling.

WHAT YOU WILL LEARN

  • describe important terms associated with the sampling process
    define sampling bias and describe problems caused by this phenomenon
    define simple random sampling and enumerate its properties
    define systematic random sampling and differentiate it from simple random sampling
  • define stratified random sampling and differentiate it from simple and systematic random sampling
    define non-probability sampling and enumerate some non-probability sampling techniques
    define the two properties of probability sampling, enumerate three types of probability sampling, and list two types of non-probability sampling

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 42s
    UP NEXT
  • Playable
    2. 
    Terms in Sampling
    9m 3s
  • Locked
    3. 
    Sampling Bias
    8m 39s
  • Locked
    4. 
    Simple Random Sampling
    4m 26s
  • Locked
    5. 
    Systematic Random Sampling
    4m 41s
  • Locked
    6. 
    Stratified Sampling
    4m 55s
  • Locked
    7. 
    Non-Probability Sampling
    6m 49s
  • Locked
    8. 
    Exercise: Efficient and Correct Sampling
    5m 48s

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