Probability sampling

1. Probability sampling Is the random selection of elements from the population. Simple random sampling A- Identify the accessible population. B- The development of the sampling frame C- Enumeration of the all elements. D- Selection of the sample elements.

2. Advantages: • Little knowledge of population is needed. • Most unbiased of probability methods. • Easy to analyze data and compute errors.

3. Disadvantages: • A complete listing of population is necessary. • Time consuming. • Expensive. 2. Stratified random sampling: -The population is divided into homogeneous subgroups, or strata, according to some variable or variables of importance to research study, then a simple random sample is taken from each of these subgroups.

4. Proportional stratified sampling: Involves obtaining a sample from each stratum that is in proportion to the size of that stratum in the total population. Disproportional stratified sampling: used whenever comparisons are sought between strata of greatly unequal membership size.

5. Advantages: • Increase the precision and representiveness of the sample. • Assures adequate number of cases for subgroups. • Disadvantages: • Requites accurate knowledge of population. • Costly. • Statistics more complicated.

6. 3. Cluster sampling: -Is a random sampling of units “multistage sampling” • Advantages: • Saves time and money. • Arrangements made with small number of sampling units.

7. Disadvantages: • Larger sampling errors than other probability samples. • Statistics are more complicated. 4. Systematic sampling: The selection of every kth case from some list or group. K=N n N: the size of the population. n: the size of the desired sample. K: the sampling interval width.

8. Evaluation of probability sampling: • The superiority of probability sampling lies in its avoidance of conscious or unconscious biases. • The great drawbacks of probability sampling are its expense and inconvenience. • The larger the sample, the more representative of the population it is likely to be. • The larger the sample, the smaller the sampling error.

9. Guidelines for critiquing sampling plans. • Is the target or accessible population identified and described? Are the eligibility criteria clearly specified? • Given the research problem and resource limitations, is the target population appropriately designated? Would a more limited population specification have controlled for important sources of extraneous variation not covered by thy research design?

10. Are the sample selection procedures clearly described? Does the report make clear whether probability or nonprobalitiy sampling was used? • How were subjects recruited into the sample? Does the method suggest potential biases? • Is the sampling plan one that is likely to have produced a representative sample?

11. 6. Did some factor other than the sampling plan itself (such as a low rate of response) affect the representative ness of the sample? • If the sampling plan is relatively weak (such as in the case of a convenience sample), are potential sample biases identified? • Are the size and key characteristics of the sample described?

12. 9. Is the sample sufficiently large? 10. Was the sample size justified on the basis of a power analysis? Is another rationale for the sample size presented? 11. If the sampling plan is relatively weak (e.g., use of a small nonprobaility sample), can the use of such a design be justified on the basis of homogeneity of the population the key variables?

13. 12. To whom can the study results be generalized? Can the results of the study reasonably be generalized to a broader population than the one from which the subjects were sampled? Does the report discuss limitations on the study’s generalizability?