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# Nonprobability Sampling

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### Nonprobability Sampling

1. 1. N O N P R O B A B I L I T Y S A M P L I N G Danilo C. Olaer, Jr., R.N. Graduate Student
2. 2. SOME TERMS TO REMEMBER . . . Sampling The process of selecting a portion of the population to represent the entire population. Element Sample The entire aggregation of cases in which a researcher is interested. The basic unit about which information is collected. A subset of population elements.
3. 3. SOME TERMS TO REMEMBER . . . Probability Sampling Non-probability Sampling Involves random selection of elements in which each element has a chance of being selected. Involves non-random methods in the selection of elements in which not all have equal chances of being selected. Sampling Bias The over-representation or under-representation of some segment of the population in terms of a characteristic relevant to the research question. Sampling Design Used in the selection of a sample within a population intended to yield knowledge, especially for the purposes of making predictions based on statistics.
4. 4. N O N P R O B A B I L I T Y S A M P L I N G
5. 5. <ul><li>The sample is not a proportion of the population. </li></ul><ul><li>The selection depends upon the situation. </li></ul><ul><li>No assurance is given that each item has a chance of being included as a sample. </li></ul><ul><li>Results are of limited value. </li></ul><ul><li>Convenient. </li></ul><ul><li>Samples could be chosen in many ways. </li></ul><ul><li>Far less complicated to set up. </li></ul><ul><li>Considerably less expensive. </li></ul><ul><li>A prelude to the main study. </li></ul>(-) (+) N O N – P R O B A B I L I T Y S A M P L I N G
6. 6. What are the techniques used ??? Snowball Sampling Quota Sampling Convenience Sampling Purposive Sampling
7. 7. <ul><li>Samples most available are chosen. </li></ul><ul><li>Samples are familiar to the researcher. </li></ul><ul><li>Examples are friends, co-workers, or shoppers at a single mall. </li></ul>Convenience/ Accidental/ Incidental Sampling
8. 8. <ul><li>S aves t ime, m oney and e ffort . </li></ul><ul><li>E asy data collection through a known group or class. </li></ul>(+) (-) N o accurate way to assess representativeness of samples. P ossible restrictions of generalization about the study findings. C o n v e n i e n c e S a m p l i n g
9. 9. A group of students in Ateneo de Davao University conduct a study about coping mechanisms of Clinical Instructors. They interview clinical instructors they knew in school, a couple of instructors who handled them in the area and few others who are known to them. C o n v e n i e n c e S a m p l i n g
10. 10. S n o w b a l l S a m p l i n g <ul><li>Is done with the help of study subjects to choose other potential subjects. </li></ul><ul><li>A useful tool for building networks and increasing the number of participants. </li></ul><ul><li>Depends greatly on the initial contacts and the connections made. </li></ul>
11. 11. <ul><li>Very good for locating people of a specific population. </li></ul><ul><li>C an quickly find people who are experts on a field. </li></ul><ul><li>C an help find lead users of a certain product more simply. </li></ul>S n o w b a l l S a m p l i n g (+) (-) H eavily reliant on the skill of the individual. A time consuming process.
12. 12. <ul><li>A researcher is studying environmental engineers but can only name 3 . She asks these engineers if they knew any others. They give her several referrals, who in turn provide additional contacts. In this way, she manages to contact 90 sufficient engineers. </li></ul>S n o w b a l l S a m p l i n g
13. 13. P u r p o s i v e S a m p l i n g Used when you want to access a particular subset of people based on purpose. Method: When taking the sample, reject people who do not fit a particular profile.
14. 14. P u r p o s i v e S a m p l i n g <ul><li>Start with a purpose in mind. </li></ul><ul><li>The sample is selected to include people of interest. </li></ul><ul><li>? ? ? </li></ul><ul><li>Exclude those who do not suit the purpose. </li></ul>
15. 15. <ul><li>Samples are chosen well, based on a certain criterion. </li></ul><ul><li>There is assurance of quality responses. </li></ul>(+) P u r p o s i v e S a m p l i n g (-) Biased selection of samples may occur. Time-consuming process.
16. 16. P u r p o s i v e S a m p l i n g The Graduate Students want to get opinions from non-working mothers. They go around Brgy. Kalunasan knocking on doors during the day when children are likely to be at school. They ask to speak to the “woman” of the house. Their first questions are then about whether there are children and whether the woman has a day job.
17. 17. Q u o t a S a m p l i n g <ul><li>A quota is established. </li></ul><ul><li>There is freedom to choose any respondent as long as the quota is met. </li></ul><ul><li>Subjects are recruited as they arrive and the researcher will assign them to demographic groups based on variables like age, gender or educational background. </li></ul>
18. 18. <ul><li>Each stratum is generally represented. </li></ul><ul><li>U seful when time is limited and the research budget is very tight. </li></ul>Q u o t a S a m p l i n g (-) (+) The result might be at risk that the sample would not be typical of the desired sample quality. Time-consuming.
19. 19. Q u o t a S a m p l i n g A researcher in Colon wants 100 opinions about a new color of cheese. He sets up a stall and canvasses passers-by until she has got 100 people to taste the cheese and complete the questionnaire.
20. 20. Not used when the goal of a study is to draw empirical generalizations about a larger population or group . I n c o n c l u s i o n , Best used in “exploratory” research studies, where the goal is to gain some initial insights… Findings could be viewed as “ suggestive” - A prelude to the main study.
21. 21. N O N – P R O B A B I L I T Y S A M P L I N G Use who is available. Get sampled people to nominate others. Select the samples based on preconceived purpose. Keep going until the sample size is reached. CONVENIENCE SNOWBALL QUOTA PURPOSIVE
22. 22. - E N D - H a p p y – b e r m o n t h s …