1. SAMPLING

2. Basic Concepts Population: is the entire aggregation of cases that meet a designated set of criteria Population: is the entire aggregation of cases that meet a designated set of criteria Sample: a subset of the units that compose the population Sample: a subset of the units that compose the population Sampling: the process of selecting portion of the population Sampling: the process of selecting portion of the population Representativeness: the key chch of the sample is close to 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 Studying the self esteem and academic achievement among nursing college students Population: all student who are enrolled in any college level of nursing Population: all student who are enrolled in any college level of nursing Sample: nurse college student at the University of Jordan Sample: nurse college student at the University of Jordan

4. Strata: two or more subgroup Strata: two or more subgroup Sampling bias: excluding any subject without any scientific rational. Or not based on the major inclusion and exclusion criteria. 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 To use the more convenient available people or objects Snowballing or networking sampling Snowballing or networking sampling It is the weakest form of sampling It is the weakest form of sampling

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

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

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

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

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