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SAMPLING

Understanding the key concepts of sampling in research, including population, representativeness, and sample size, is crucial for conducting valid studies. Learn about different sampling methods and considerations to ensure the reliability of research findings.

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SAMPLING

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  1. SAMPLING

  2. Basic Concepts • Population: is the entire aggregation of cases that meet a designated set of criteria • Sample: a subset of the units that compose the population • Sampling: the process of selecting portion of the population • Representativeness: the key chch of the sample is close to the population

  3. Example • Studying the self esteem and academic achievement among nursing college students • Population: all student who are enrolled in any college level of nursing • Sample: nurse college student at the University of Jordan

  4. Strata: two or more subgroup • Sampling bias: excluding any subject without any scientific rational. Or not based on the major inclusion and exclusion criteria.

  5. Non-probability A. Convenient sampling or accidental sampling. • To use the more convenient available people or objects • Snowballing or networking sampling • It is the weakest form of sampling

  6. Non-Probability B. Quota sampling • The researcher defines the population and then set a proportion form which he will choose form each segment of the population

  7. Non-Probability C. purposeful and theoretical sampling • The researcher’s knowledge about the population used to select the sample • Theoretical sampling used as of the progression of the study. More subject can be added as a preliminary analysis may induce.

  8. Probability Sampling • Simple random sampling. E.g., tables • Stratified random sampling. Gender, age • Cluster sampling. City, neighborhood, block, house • Systematic sampling. Selecting every kth case.

  9. Sample Size • The larger the sample size the better the Representativeness • The techniques of power analysis • Computer program such as PASS, NCSS

  10. Considerations • Homogeneity vs. heterogeneity • Effect size: r/s between I & D variables • Attrition: N of subjects decline the study

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