RANDOM SAMPLING and SAMPLING TECHNIQUE.pptx

SAMPLING TECHNIQUE

1. RANDOM SAMPLING

2. SAMPLING Sampling means selecting a particular group or sample to represent the entire population. In research it is a process used in statistical analysis in which a predetermined number of observations ( sample) are taken from a larger population.

3. SAMPLING

4. SAMPLING TECHNIQUES SAMPLING TECHNIQUES PROBABILITY SAMPLING Simple Random Sampling Systematic sampling Stratified sampling Cluster sampling Multi- Stage Sampling - NON- PROBABILITY SAMPLING Purposive Sampling Incidental/Accidental Sampling Quota Sampling

5. PROBABILITY SAMPLING TECHNIQUES Probability Sampling Technique gives every member of the population equal chance to be selected as part of the study.

6. PROBABILITY SAMPLING TECHNIQUES Simple random sampling is made when all the members of the population are given a chance to be selected. Selection is done by draw lot or the use of the table of random numbers.

7. PROBABILITY SAMPLING SIMPLE RANDOM SAMPLING (conducting a survey) imagine you are a teacher, and you want to conduct a survey to understand the opinion of the students in your school regarding a new extra- curricular activity. The total students population in the school is 500.

8. PROBABILITY SAMPLING Steps in Simple Random Identify the population: in this case the population is 500. 2. Assign a number to each individual: assign a unique number to each student in the school. number them from 1- 500. 3. Determine the sample size: decide on the sample size you want to survey. Example 50 students. 4. Use a random selecting method: use number generator or draw names from the hat. Generate 50 random number from 1-500. these 50 numbers represent the students on your survey. 5. Select the chosen individuals: identify the students corresponding to the randomly generated numbers. 6. Invite the selected individuals to participate: reach out to the selected students and invite them to participate.

9. PROBABILITY SAMPLING TECHNIQUE SYSTEMATIC SAMPLING selecting every nth individual from the population after a random start.

10. PROBABILITY SAMPLING TECHNIQUE SYSTEMATIC SAMPLING (Surveying customer in a mall) imagine you are conducting a survey to gather feedback from customer in a busy shopping mall. The mall has a total of 500 customers, and you want to systematically survey a representative sample.

11. PROBABILITY SAMPLING Steps in systematic Identify the population: in this case the population is 500. 2. Determine the sample size: decide on the overall sample size you want. Let’s say you want to survey 50 customer. 3. Calculate the sampling interval: determine the sampling interval (k) by dividing the total population by the desired sample size. 500/50 = 10 In this case every 10 th individual will be surveyed.   4. Randomly start: choose a random starting point with the first k individuals. Example selecting the 3 rd customer as your starting point. 5. Select the chosen individuals: survey every 10 th customer from a randomly chosen starting point until you reach your desired sample size.

12. PROBABILITY SAMPLING STRATIFIED SAMPLING Dividing the population into subgroups(strata) and then randomly sampling from each subgroup.

13. PROBABILITY SAMPLING STRATIFIED SAMPLING (Assessing Academic Performance) suppose you are a researcher interested in understanding students’ academic performance in JHS. The school has a total of 800 students, and you want to ensure that your sample is a representative across different grade levels (grade 7, 8, 9, 10).

14. PROBABILITY SAMPLING Steps in stratified Identify the population: in this case the population is 800. 2. Define the strata: divide the population into strata based on the characteristics interest. In the example the strata are: grade7, 8, 9, 10. 3. Determine the sample size: decide on the overall sample size you want and the proportion of the sample from each stratum. If you want to survey 1oo. Grade 7- 25 students Grade 8- 25 students Grade 9- 25 students Grade 10- 25 students 4. Randomly select with strata: use random sampling with each stratum to select the specified number of students. 5. Select the chosen individuals: identify the students corresponding to the randomly generated numbers within each stratum. The students makes your final sample.

15. PROBABILITY SAMPLING Cluster sampling is a design that uses a group as sample rather than an individual.

16. PROBABILITY SAMPLING For example, the population may be the parents in one school district. The parents may be grouped by barangay within the district or by those in the east, west, north and south of the district. From these groupings, the sample cluster is chosen randomly or systematically. This differs from stratified sampling that includes all the strata in the sampling process.

17. PROBABILITY SAMPLING Multi-stage sampling is done by stages: two, three, four as the case may be depending on the number of stages sampling is made. Here the population is grouped by hierarchy from which sampling is done in each stage.

18. PROBABILITY SAMPLING For example, the population to be studied consists of the personnel in the public elementary schools in the country. So samples have to be taken from the national, regional, provincial, district, and school levels Example: 1 st level, 3 municipalities/province 2 nd level, 2 districts/municipality 3 rd level, 4 barangays/districts 4 th level, 100 respondents/barangay

19. NON- PROBABILITY SAMPLING Purposive Sampling In this design, the samples are chosen based on the judgment of the researcher who determines an individual as sample for possessing special characteristics of some sort.

20. NON- PROBABILITY SAMPLING Incidental/Accidental Sampling As the term implies, this design is used to take samples who may be the most available or the nearest at the time of data gathering.

21. NON- PROBABILITY SAMPLING Quota Sampling A design popular for opinion research, this sampling is made by looking for individuals that possess the required characteristics or prescribed criteria of the research.

22. SAMPLE SIZE ( carmolins ) Ss- sample size N- number of population V- standard value (2.58) of 1% level of probability Se- sampling error P- largest possible proportion (0.50)  

23. NON- PROBABILITY SAMPLING The total population is 500 has a standard value of 2.58 at 1 percent level of probability and 99 percent reliability. The sampling error is 1 percent (0.01) and the proportion of a target population is 50 percent (0.50).

24. SAMPLE SIZE ( Slovin’s Formula) n- is the number of samples N- population E- margin of errors  

25. NON- PROBABILITY SAMPLING A researcher wants to study the academic performance in Mathematics of students in a certain school. The school has a population of 12,000 students. If the researcher allows a margin of error of 5%, how many students must he include in his sample?