sampling techniques in data sampling .pptx

sampling techniques

1. Probability/ Random Sampling Technique Random sampling is a part of the sampling technique in which each sample has an equal probability of being chosen. A sample chosen randomly is meant to be an unbiased representation of the total population. Type of Random Sampling Simple Random Sampling . Systematic Sampling. Stratified Sampling. Clustered Sampling. This Photo by Unknown Author is licensed under CC BY-SA-NC

2. Random Sampling Technique Type of Random Sampling Simple Random Sampling. Systematic Sampling. Stratified Sampling. Clustered Sampling. Simple random sampling requires the use of randomly generated numbers to choose a sample. More specifically, it initially requires a sampling frame, which is a list or database of all members of a population. You can then randomly generate a number for each element, using Excel for example, and take the first  n  number of   samples that you require.

3. Random Sampling Technique Type of Random Sampling Simple Random Sampling. Systematic Sampling. Stratified Sampling. Clustered Sampling.

4. Type of Random Sampling Simple Random Sampling. Systematic Sampling. Stratified Sampling. Clustered Sampling. Systematic random sampling   is a common technique in which you sample every  k th element. For example, if you were conducting surveys at a mall, you might survey every 100th person that walks in. If you have a sampling frame, then you would divide the size of the frame,  N , by the desired sample size,  n , to get the index number,  k . You would then choose every  k th element in the frame to create your sample.

5. Random Sampling Technique Type of Random Sampling Simple Random Sampling. Systematic Sampling. Stratified Sampling. Clustered Sampling.

6. Random Sampling Technique Type of Random Sampling Simple Random Sampling. Systematic Sampling. Stratified Sampling. Clustered Sampling. Stratified random sampling involves dividing a population into groups with similar attributes and randomly sampling each group. This method ensures that different segments in a population are equally represented. To give an example, imagine a survey is conducted at a school to determine overall satisfaction. Here, stratified random sampling can equally represent the opinions of students in each department. This Photo by Unknown Author is licensed under CC BY-SA-NC

7. This Photo by Unknown Author is licensed under CC BY-SA-NC Random Sampling Technique Type of Random Sampling Simple Random Sampling. Systematic Sampling. Stratified Sampling. Clustered Sampling.

8. Random Sampling Technique Type of Random Sampling Simple Random Sampling. Systematic Sampling. Stratified Sampling. Clustered Sampling. Cluster sampling starts by dividing a population into groups or clusters.   What makes this different from stratified sampling is that each cluster must be representative of the larger population. Then, you randomly select entire clusters to sample. For example, if a school had five different eighth grade classes, cluster random sampling means any one class would serve as a sample.

9. Random Sampling Technique Type of Random Sampling Simple Random Sampling. Systematic Sampling. Stratified Sampling. Clustered Sampling.

10. SIMPLE RANDOM SAMPLING Simple random sampling requires the use of randomly generated numbers to choose a sample. More specifically, it initially requires a sampling frame, which is a list or database of all members of a population. You can then randomly generate a number for each element, using Excel for example, and take the first  n  number of   samples that you require.

11. SIMPLE RANDOM SAMPLING Stratified random sampling involves dividing a population into groups with similar attributes and randomly sampling each group. This method ensures that different segments in a population are equally represented. To give an example, imagine a survey is conducted at a school to determine overall satisfaction. Here, stratified random sampling can equally represent the opinions of students in each department.

12. CLUSTER SAMPLING Cluster sampling starts by dividing a population into groups or clusters.   What makes this different from stratified sampling is that each cluster must be representative of the larger population. Then, you randomly select entire clusters to sample. For example, if a school had five different eighth grade classes, cluster random sampling means any one class would serve as a sample.

13. SYSTEMATIC RANDOM SAMPLING   Systematic random sampling   is a common technique in which you sample every  k th element. For example, if you were conducting surveys at a mall, you might survey every 100th person that walks in. If you have a sampling frame, then you would divide the size of the frame,  N , by the desired sample size,  n , to get the index number,  k . You would then choose every  k th element in the frame to create your sample.

14. Belief

15. A  belief  is a subjective  attitude  that a  proposition  is true or a  state of affairs  is the case. A subjective attitude is a mental state of having some stance, take, or opinion about something. philosophers use the term "belief" to refer to attitudes about the world which can be either  true or false . To believe something is to take it to be true; for instance, to believe that snow is white is comparable to accepting the truth of the  proposition  "snow is white". However, holding a belief does not require active  introspection . For example, few carefully consider whether or not the sun will rise tomorrow, simply assuming that it will.