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Understanding Sampling: Key Concepts and Types in Research Methodology

This comprehensive overview explores the critical concepts of sampling in research, distinguishing between probability and non-probability samples. It delves into essential terms such as element, population, sample, and sampling frame, and explains different sampling techniques including Simple Random Sampling, Systematic Random Sampling, Stratified Random Sampling, and Cluster Sampling. Understanding these sampling types is vital for ensuring representative samples and accurate data interpretation. Additionally, the text highlights sample size considerations and practical tools for conducting random sampling in statistical analysis.

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Understanding Sampling: Key Concepts and Types in Research Methodology

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  1. The Logic of Sampling

  2. Key Sampling Concepts • Sampling (two types) • Element • Population • Sample • Sampling Frame • Representative Sample

  3. Application of Sampling Terms

  4. Types of Samples • Probability -- Strictly following two rules. • Non Probability -- Failing to follow the two rules

  5. Types of Probability Samples • Simple Random Sample (SRS) • Systematic Random Sample • Stratified Random Sample • Cluster Sample

  6. Simple Random Sample • Every element has an equal chance of selection • No element can be selected more than once

  7. Example of a good random sample outcome

  8. Simple Random Sample • Every element has an equal chance of selection • No element can be selected more than once

  9. Systematic Random Sample Sampling Frame Nth Element A simple random sample employing a sample frame.

  10. Stratified Random Sample

  11. Cluster Sample

  12. Non probability samples • Convenience (available to researcher) • Snowball (available connections) • Quota (stratified without randomness) • Informant (case study/social history) • Focus Groups

  13. What’s the difference?How important is the difference? Probability samples can be generalized to a population; while non-probability samples cannot. Non-probability offer an in depth understanding and are most often: “I don’t know what I am seeking until after I find it.” Following is an illustration:

  14. Overview of Sample Problems

  15. Sample Size Selection The problem with the following formula: It is calibrated for dichotomous data. The sample size will increase with the number of options given to the subject.

  16. Do NOT Forget!!!! Regardless of what formula one uses, always increase the sample size by 20%.

  17. Excel • RANDBETWEEN – Returns a random number between the numbers you specify. • RAND – Returns a random number greater than or equal to 0 and less than 1, evenly distributed (changes on recalculation).

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