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Issues in Probability & Non-Probability Sampling

Instructions:

Choose an answer and hit 'next'. You will receive your score and answers at the end.

question 1 of 3

Which of the following explains the difference between probability and non-probability sampling?

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1. Mattie is worried that the difference between the results of her study and what is actually true for the population may be too large. Which of the following would best define what is Mattie worried about?

2. Which of the following type of error will always happen no matter how you design your study?

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About This Quiz & Worksheet

Probability and non-probability sampling refers to the process of selecting subjects for studies and this quiz/worksheet combination will help you improve your understanding of this process. The quiz questions will help you test your understanding of vocabulary terms associated with probability and non-probability sampling.

Quiz & Worksheet Goals

These assessments will test your knowledge of the vocabulary associated with sampling, including:

  • Probability sampling
  • Non-probability sampling
  • Random error
  • Systematic error
  • Nonresponse error

Skills Practiced

This quiz and worksheet will allow you to test your skills in the following areas:

  • Reading comprehension - ensure that you draw the most important information from the related lesson on probability and non-probability sampling
  • Interpreting information - verify that you can read information regarding types of sampling and interpret it correctly
  • Information recall - access the knowledge you've gained regarding the errors associated with sampling

Additional Learning

To learn more about sampling, take a look at the lesson called Issues in Probability and Non-Probability Sampling. This research methods lesson covers these objectives:

  • Define sampling
  • Differentiate between probability and non-probability sampling
  • Understand when sampling issues occur
  • Identify the three types of error
  • Know what type or error is caused by things like underrepresentation, researcher bias, and inadequate sample size
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