collection of sample of research methodology.pptx

sampling of research methodolog

1. Sampling Dr.Taima Moqbel

2. Definition s Sampling : is the process of selecting a few (a sample) from a bigger group, the ( sampling population ) to become the basis for predicting the prevalence of an unknown information, situation or outcome regarding the bigger group. Sampling frame The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population

3. Adv. & Disad. Of Sampling Process • Advantages 1- Saves time 2- Saves financial and human resources

• Disadvantages 1- Unable to find out the information about the population’s characteristics of interest to you but you only estimate or predict them 2- The possibility of an error in your estimation exists

4. What is Good Sample ? The sample must be: 1. R epresentative of the population;
. 2 .A ppropriately sized (the larger the better);
. 3 .U nbiased;
. 4 .R andom (selections occur by chance);

5. Types of Sampling Probability sample – a method of sampling that uses of random selection so that all units/ cases in the population have an equal probability of being chosen. • Non-probability sample – does not involve random selection and methods are not based on the rationale of probability theory.

6. Probability (Random) Samples - Simple random sample
-Systematic random sample
-Stratified random sample
-Cluster sample

7. Non-Probability Samples - Convenience samples (ease of access )
-Purposive sample (Judgmental Sampling)
- Quota Sampling
- Snowball Sampling (friend of friend....etc.)

8. SIMPLE RANDOM SAMPLING Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population. Each member of the population has an equal chance of being selected

9. example: You want to select a simple random sample of 1000 employees of a social media marketing company. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.

10. Syst e m at ic Sampling Systematic sampling is a probability  sampling  method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval. This sampling interval is calculated by dividing the population size by the desired sample size. •

11. Example All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people .

12. Stratified Random Sample stratified random sampling is a  method of selecting a sample  in which researchers first divide a population into smaller subgroups, or strata, based on shared characteristics of the members and then randomly select among each stratum to form the final sample. These shared characteristics can include gender, age, sex, race, education level, or income.

13. Example: company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.

14. CLUSTER SAMPLING Cluster sampling is an example of ‘two-stage sampling’
First stage a sample of areas is chosen;
Second stage a sample of respondents within those areas is selected.
Population divided into clusters of homogeneous units, usually based on geographical contiguity.   They then randomly select among these clusters to form a sample. Cluster sampling is a method that is often used to study large populations, 

15. Example: company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters.

16. Non Probability CONVENIENCE SAMPLING Some times known as grab or opportunity sampling or accidental or haphazard sampling.
Selection of whichever individuals are easiest to reach

It is done at the "convenience" of the researcher

17. Example you are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a  survey  on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university.

18. QUOTA SAMPLING The population is first segmented into mutually exclusive sub- groups, just as in stratified sampling.

Then judgment used to select subjects or units from each segment based on a specified proportion.

19. Example:

20. Purposive sampling   This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

21. example An example of purposive sampling might be a researcher studying the experiences of individuals living with chronic pain, and therefore selecting a sample of individuals who have been diagnosed with chronic pain.

22. Snowball Sampling Useful when a population is hidden or difficult to gain access to. The contact with an initial group is used to make contact with others Example: are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city, probability sampling isn’t possible. You meet one person who agrees to participate in the research, and she puts you in contact with other homeless people that she knows in the area

23. Types of Sampling Errors 1-Population-Specific Error A population-specific error occurs when a researcher doesn't understand who to survey. 2-Selection Error Selection error occurs when the survey is self-selected, or when only those participants who are interested in the survey respond to the questions. Researchers can attempt to overcome selection error by finding ways to encourage participation. 3-Sample Frame Error A sample frame error occurs when a sample is selected from the wrong population data. 4-Non-response Error A non-response error occurs when a useful response is not obtained from the surveys because researchers were unable to contact potential respondents (or potential respondents refused to respond).

24. THANK YOU