Sampling & Population Normazhazlin Alzahari 2009506635 Azlyn Sarafina A Hamid 2009126421

1. Sampling & Population Normazhazlin Alzahari 2009506635  Azlyn Sarafina A Hamid 2009126421   Hanim Othman 2009394453

2. What is sample & population? A sample is a finite part of a statistical population whose properties are studied to gain information about the whole (Webster, 1985). When dealing with people, it can be defined as a set of respondents (people) selected from a larger population for the purpose of a survey. A population is a group of individuals, persons, objects, or items from which samples are taken for measurement for example a population of presidents or professors, books or students.

3. What is sampling? Sampling is the act, process, or technique of selecting a suitable sample, or a representative part of a population for the purpose of determining parameters or characteristics of the whole population.

4. What is the purpose of sampling? To draw conclusions about populations from samples, we must use inferential statistics which enables us to determine a population’s characteristics by directly observing only a portion (or sample) of the population. We obtain a sample rather than a complete enumeration (a census ) of the population for many reasons. Obviously, it is cheaper to observe a part rather than the whole, but we should prepare ourselves to cope with the dangers of using samples.

5. Defining the population First task in selecting a sample: to define the populations of interest(5 wives 1 Husband) Some examples of populations: - All high school principals in Selangor - All students in Mrs Nadarajah’s form 4 class at STAR Klang

6. Defining the population In educational research, the population of interest is usually a group of persons (students, teachers) who possess certain characteristics. In some cases, the population may be defined as a group of classrooms, schools or even facilities. Example: All Form 5 students in Kolej Islam Klg (The hypothesis might be that classrooms in which teachers / students display a greater number of achievements)

7. Target Versus Accessible Populations Unfortunately, the actual populations a.k.a target populations are rarely available. The population to which a research is able to generalize is called accessible population.

8. Target Versus Accessible Populations Research problem to be investigated: The effects of Sponge Bob Square standard 1 students in Selangor. Target Population: All standard 1 students in Selangor. Accessible population: Sekolah Kebangsaan Kg Jawa, Klang

9. Target Versus Accessible Populations The more narrowly research define the population, the more they save on time, effort and money but, the more they limit their generalizability. Failure to define in detail the population of interest, and the sample studied, is one of the common weaknesses of published research reports. VERY important: to describe the characteristics of the actual sample studied in some detail.

10. Random Versus Nonrandom Sampling There 2 main types of sampling. Random Sampling A personnel from the Ministry’s office wishes to find out how many secondary school principals in Selangor is keen to have Mandarin taught in as one of their subject. So she places all 100 names of secondary school, mix them thoroughly, and then draw out the names of 25 names of schools to interview.

11. Random Versus Nonrandom Sampling Nonrandom sampling: The Ministry wishes to sent its personnel to find out how many secondary school principals in Selangor is keen to have Mandarin taught in as one of their subject. There are 5 jr. personnel's in the department. They need to select only 3. Thus, 3 personnel’s were selected based on some criteria. Example: Must be permanent staff At least 5 years serving the Ministry etc

12. Random Sampling methods 4 types of random sampling methods Simple Random Sampling Stratified Random Sampling Cluster Random Sampling Two-stage Random Sampling

13. Random Sampling methods Simple Random Sampling Each and every member of the population has an equal and independent chance of being selected. Best way to use when the sample is large – devised to obtain a sample representative of the population of interest. The key to obtaining a random sample is to ensure that each number of population has an equal chance of being selected.

14. Random Sampling methods This can be done by using the table of random numbers.

15. Random Sampling methods Advantage: If large enough, it is likely to produce a representative sample. Disadvantage: Not easy to do. Each member of the population must be identified. A B C D Q R N M LT W E JK IS ZR YU A D R M L W J I Z Y SIMPLE RANDOM

16. Stratified Random Sampling Stratified Random Sampling is a process where certain subgroups (strata) are selected for a sample in a same proportion as they exist in the population. AB CD 25% QR NM LT WE JK IS 50% ZR YU 25% A D 25% R M L W J I 50% Z Y 25% STRATIFIED RANDOM

17. Cluster Random Sampling A subject rather than individuals is known as cluster random sampling. Is more effective with large numbers of clusters. AB CD QR NM LT WE JK IS ZR YU QR LT YU CLUSTER RANDOM

18. Two-stage Random Sampling Often useful to combine cluster random sampling with individual random sampling. AB CD QR NM LT WE JK IS ZR YU Random sample of clusters Random sample of individuals CD WE C, E, R ZR TWO-STAGE RANDOM

19. Nonrandom Sampling The purpose of this method is to make an explicit choice based on our own judgement about exactly whom to include in the sample. When random sampling is not possible, then we may choose the following :- Systematic Sampling Convenience Sampling Purposive Sampling

20. Systematic Sampling Every nth individual in the population list is selected for inclusion in the sample; when n is the use number. The number may be chosen by the researcher to avoid from any bias in the sample. eg. in a population of 3,000 staff of a company, the researcher would chose the 3rd name in the list of a sample of 300 staff.

21. Systematic Sampling

22. Sampling Interval The distance in the list between each of the individuals selected for the sample. The interval can be determined using the following formula:- Population size___ Desired sample size

23. Sampling Interval eg. the ABC Bank wants to know how its staff perceive their new tools used for dissemination of information amongst the staff. Total number of staff : 9,000 Sample size : 1,000 Sampling Interval Population size___ Desired sample size 9,000 1,000 = 9

24. Sampling Ratio The proportion of individuals in the population that is selected for the sample. The ratio can be determined using the following formula:- Sample Size____ Population Size

25. Sampling Ratio Total number of staff : 9,000 Sample size : 1,000 Sampling Interval Sample Size____ Population size 1,000 9,000 = 0.11

26. Convenience Sampling A group of individuals who are available at any point of time and are ready for the study for the research. eg. a nurse of an aesthetic clinic interviews the patients in the clinic on their opinion about the effectiveness of the radiofrequency machine in the clinic. However, they hardly could be considered in representing any population.

27. Convenience Sampling

28. Purposive Sampling Selection of samples are based on the researchers’ judgment that the samples could contribute to the research in providing the information for the data better as they are the involved party in the research matter itself. The power of purposive sampling lies in selecting information rich-cases for in-depth analysis related to the central issues being studied

29. Purposive Sampling c eg. researcher interviewed the old man to find out how he feels staying in old folks home managed and administered by the government.

30. SAMPLE SIZE The sample should be as large as the researcher can obtain with a reasonable expenditure of time and energy. It means that, the researcher should try to obtain as large as a sample they reasonably can. Here is the suggestion:

31. EXTERNAL VALIDITY POPULATION GENERALIZABILITY ECOLOGICAL GENERALIZABILITY

32. WHAT IS EXTERNAL VALIDITY? • This is the term used in research, refers to the extent that the results of the study can be generalized from a sample to a population. • Most researchers wish to generalize their findings to appropriate populations. • When is generalizing warranted? • When can researchers say with confidence that what they have learned about sample is also true of the population?

33. Both the nature of the sample and the environmental conditions- the setting-within which a study takes place must be considered in thinking about generalizability.

34. POPULATION GENERALIZABILITY • The term population generalizability refers to the extent to which the result of the study ca be generalized to the intended population. • Ex: a study that randomly selects students but not teachers is only entitled to generalized the outcomes to the population of the students. • In research, it is very important to obtain a representative sample. • Representativeness referring only to the essential, or relevant, characteristics of a population. (what is relevant?)

35. Example: • To study the effects of reading method on students achievement. • Characteristics such as height, eye color, jumping ability would be irrelevant. • Characteristics such as age, gender or visual acuity (might logically) have an effects and hence should be appropriately represented in the sample.

36. When random sampling is not feasible? • There is another possibility when a random sample is impossible to obtain. • It is called replication. • The researcher/ other researchers repeat the study using different group of subjects in different situation. • If the study is repeated several times, a researcher may have additional confidence about generalizing the findings.

37. Reasons for why random samples have not been used? • 1st: The educational researchers may be unaware of the hazards involved in generalizing when one does not have a random sample. • 2nd In many studies, it is simply not feasible for a researcher to invest the time, money or other resources necessary to obtain a random sample.

38. ECOLOGICAL GENERALIZABILITY • Refers to the extent to which the results of a study can be generalized to conditions or setting other than those that prevailed in a particular study. • The researchers must make clear the nature of the environmental conditions-the setting-under which study takes place (must be the same). • Research results from urban school environments may not be apply to suburban or rural schools environments. • What holds true for one subject, or with certain materials, or under certain conditions, or at certain times may not be generalized to other subjects, material, condition, or times. • Therefore, researchers must be cautious about generalizing the results from anyone study.

39. Thank you