LESSON 7 ILLUSTRATING RANDOM SAMPLING G11
- 2. POPULATION •includes all of its elements from a set of data. The size of the population is the number of observations in the population.
- 3. SAMPLE •consists of one or more data drawn from the population. It is a subset or an incomplete set taken from a population of objects or observations. Taking samples instead of the population is less time- consuming and cost-effective.
- 4. SAMPLE •In research, collecting data can either be done in the entire population or the subset of this population
- 6. RANDOM SAMPLING •is a sampling method of choosing representatives from the population wherein every sample has an equal chance of being selected. Accurate data can be collected using random sampling techniques.
- 8. Use your understanding of the previous activity to identify whether the following situations illustrate simple, systematic, stratified or cluster random sampling. 1. A researcher writes the name of each student on a piece of paper, mixes the papers in a bowl, and draws 7 pieces of paper.
- 9. 2. A researcher selects every 7th student from a random list. Use your understanding of the previous activity to identify whether the following situations illustrate simple, systematic, stratified or cluster random sampling.
- 10. 3. A researcher tells the class to count off and then selects those students who count a multiple of 7 numbers. Use your understanding of the previous activity to identify whether the following situations illustrate simple, systematic, stratified or cluster random sampling.
- 11. 4. A researcher separates the list of boys and girls, then draws 7 names by Gender. Use your understanding of the previous activity to identify whether the following situations illustrate simple, systematic, stratified or cluster random sampling.
- 12. 5. A researcher surveys all students from 3 randomly selected classes out of 7 classes. Use your understanding of the previous activity to identify whether the following situations illustrate simple, systematic, stratified or cluster random sampling.
- 14. SIMPLE RANDOM SAMPLING TECHNIQUE •is the most basic random sampling wherein each element in the population has an equal probability of being selected. They are usually represented by a unique identification number that is written on equal-sized and shaped papers and then selection of samples is possible through the lottery method.
- 16. SYSTEMATIC RANDOM SAMPLING •is a random sampling that uses a list of all the elements in the population and then elements are being selected based on the kth consistent intervals. To get the kth interval, divide the population size by the sample size.
- 18. STRATIFIED RANDOM SAMPLING •is a random sampling wherein the population is divided into different strata or divisions. The number of samples will be proportionately picked in each stratum that is why all strata are represented in the samples.
- 20. CLUSTER SAMPLING •is a random sampling wherein population is divided into clusters or groups and then the clusters are randomly selected. All elements of the clusters randomly selected are considered the samples of the study.
- 22. •The sampling techniques that involve random selection are called probability sampling. Likewise, simple random, systematic, and stratified and cluster sampling are all probability sampling techniques.
- 23. •There are also sampling techniques that do not involve random selection of data. They are called non- probability sampling.
- 24. •Purposive sampling is also not considered a random sampling since the respondents are being selected based on the goal of the studies of the researcher.
Editor's Notes
- #3: For example, if ABS-CBN network has 11,000 employees having the required blood type in a certain study, then we have a population of size 11,000
- #5: If a researcher opts to use a sample rather than a population, he must take considerations on the number of samples and how these samples can be chosen out of the target population.
- #7: .
- #8: B A D C
- #9: Situation 1 illustrates simple random sampling. The pieces of papers correspond to each student as elements of the population. All of them have an equal chance of being selected as a sample by randomly picking 7 pieces of paper in a bowl
- #10: Situations 2 and 3 illustrate systematic random sampling because samples are being selected based on the kth consistent intervals. Selecting every 7th student on the random list of names creates an equal chance for all of the students to become samples.
- #11: The same thing happened in selecting students who count multiples of 7 or 14, 21, and so on.
- #12: Situation 4 illustrates stratified random sampling because the students are divided into two different strata or groups, boys and girls. With a proportional number for each group, samples are then selected at random from these two groups.
- #13: Situation 5 illustrates cluster sampling since all students are divided into clusters or classes, then 3 classes are selected at random out of the 7 classes. All of the students of these three classes comprised the samples of the study. Take note that each cluster is mutually homogeneous yet internally heterogeneous
- #15: . Random numbers are selected to decide which elements are included as the sample. The number of papers to be drawn is based on the desired number of samples.
- #24: An example of this is convenience sampling wherein the researcher gathers data from nearby sources of information exerting minimal effort. Convenience is being used by persons giving questionnaires on the streets to ask the passers-by.
- #25: If the study is about the students who are children of OFW, the researcher will get samples who are children of OFW. This excludes other students from being a sample.