SAMPLING PROCESS OF SELECTING A PORTION OF THE POPULATION .pptx

SAMPLING:REFERS TO THE PROCESS OF SELECTING A PORTION OF THE POPULATION TO REPRESENT THE ENTIRE POPULATION.

1. SAMPLING Mrs. AROCKIA MARY ASSOCIATE PROFESSOR PADMASHREE COLLEGE OF NURSING BANGALORE

2. TERMINOLOGIES SAMPLING: REFERS TO THE PROCESS OF SELECTING A PORTION OF THE POPULATION TO REPRESENT THE ENTIRE POPULATION .

3. TERMINOLOGIES A POPULATION IS THE GROUP IN WHICH YOU ARE INTERESTED IN STUDYING. A POPULATION MUST HAVE A WELL DEFINED SET OF CHARACTERISTICS THAT THE RESEARCHER IS INTERESTED IN STUDYING. IT IS OFTEN DESIGNATED LAY SPECIFIC CRITERIA SUCH AS AGE, SEX AND ILLNESS STATE.

4. TERMINOLOGIES TYPES OF POPULATION: TARGET POPULATION: IT IS THE AGGREGATE OF CASES ABOUT WHICH THE RESEARCHER WOULD LIKE TO MAKE GENERALIZATION. FOR EXAMPLE: ALL INSTITUTIONALIZED OLDER ADULTS WITH DEMENTIA IN INDIA.

5. TERMINOLOGIES TYPES OF POPULATION: ACCESSIBLE POPULATION: THE PEOPLE WHO MEET YOUR CRITERIA AND WHO YOU ACTUALLY HAVE ACCESS TO WORK.

6. SAMPLING CRITERIA INCLUSION CRITERIA: THESE ARE CHARACTERISTICS THAT MUST BE PRESENT FOR THE ELEMENT TO BE INCLUDED IN THE SAMPLE. EXCLUSION CRITERIA: THESE ARE CHARACTERISTICS THAT THE ELEMENT MUST NOT POSSESS OR WHO IS NOT ELIGIBLE TO BE IN THE STUDY.

7. TERMINOLOGIES SAMPLE: THE SAMPLE IS A SUB SET OF THE POPULATION SELECTED BY THE INVESTIGATOR TO PARTICIPATE IN A RESEARCH PROJECT.

8. TERMINOLOGIES TYPES OF POPULATION: TARGET POPULATION: IT IS THE AGGREGATE OF CASES ABOUT WHICH THE RESEARCHER WOULD LIKE TO MAKE GENERALIZATION. FOR EXAMPLE: ALL INSTITUTIONALIZED OLDER ADULTS WITH DEMENTIA IN INDIA.

9. ELEMENT / SUBJECT / PARTICIPANT IT IS A SINGLE MEMBER OF THE POPULATION UNDER STUDY ABOUT WHICH INFORMATION IS COLLECTED. EG. PATIENTS, INDIVIDUALS, OBJECT, EVENT OR GROUP.

10. SAMPLING UNIT IT IS THE ELEMENT OR SET OF ELEMENT USED FOR SELECTING THE SAMPLE OR THE OVER ALL ENTITY USED FOR SAMPLE SELECTION. IT CAN BE A SPECIFIC SITE OR SETTING. EG. GYNECOLOGIC OPD OF KCG HOSPITAL, BANGALORE.

11. SAMPLING SIZE THIS REFERS TO THE NUMBER OF ITEMS TO BE SELECTED FROM THE UNIVERSE TO CONSTITUTE THE SAMPLE. THE SIZE OF SAMPLE SHOULD NEITHER BE EXCESSIVELY LARGE, NOR TOO SMALL. IT SHOULD BE OPTIMUM. AN OPTIMUM SAMPLE IS ONE WHICH FULFILLS THE REQUIREMENT OF EFFICIENCY, REPRESENTATIVENESS, RELIABILITY AND FLEXIBILITY.

12. SAMPLING BIAS REFERS TO THE SYSTEMATIC OVER REPRESENTATION OR UNDER REPRESENTATION OF SOME SEGMENT OF THE POPULATION IN TERMS OF A CHARACTERISTIC RELEVANT TO THE RESEARCH QUESTION.

13. SAMPLING FRAME THE ELEMENTARY UNITS OR THE GROUP OR CLUSTER OF SUCH UNITS MAY FORM THE BASIS OF SAMPLING PROCESS IN WHICH CASE THEY ARE CALLED AS SAMPLING UNITS. A LIST CONTAINING ALL SUCH SAMPLING UNITS IS KNOWN AS SAMPLING FRAME. FOR INSTANCE, ONE CAN USE TELEPHONE DIRECTORY AS A FRAME FOR CONDUCTING OPINION SURVEY IN A CITY

14. SAMPLING DESIGN A SAMPLE DESIGN IS A DEFINITE PLAN FOR OBTAINING A SAMPLE FROM THE SAMPLING FRAME. IT REFERS TO THE TECHNIQUE OR THE PROCEDURE THE RESEARCHER WOULD ADOPT IN SELECTING SOME SAMPLING UNITS FROM WHICH INFERENCES ABOUT THE POPULATION IS DRAWN. SAMPLING DESIGN IS DETERMINED BEFORE ANY DATA ARE COLLECTED.

15. SAMPLING ERROR

16. SAMPLING ERROR SAMPLE SURVEYS DO IMPLY THE STUDY OF A SMALL PORTION OF THE POPULATION AND AS SUCH THERE WOULD NATURALLY BE A CERTAIN AMOUNT OF INACCURACY IN THE INFORMATION COLLECTED. SAMPLING ERROR=FRAME ERROR + CHANCE ERROR + RESPONSE ERROR

17. TERMINOLOGIES TYPES OF POPULATION: ACCESSIBLE POPULATION: THE PEOPLE WHO MEET YOUR CRITERIA AND WHO YOU ACTUALLY HAVE ACCESS TO WORK.

18. ADVANTAGES IT IS MORE ECONOMICAL AND EFFICIENT TO WORK WITH A SMALL GROUP OF ELEMENTS THAN WITH AN ENTIRE SET OF ELEMENTS. IT IS FEASIBLE AND LOGICAL WAY OF MAKING STATEMENTS ABOUT A LARGE GROUP BASED ON SMALLER GROUP. STUDYING A SAMPLE IS MUCH LESS EXPENSIVE AND MUCH LESS TIME CONSUMING STUDYING AN ENTIRE POPULATION.

19. ADVANTAGES IN DEALING WITH A SAMPLE, THE VOLUME OF WORK IS REDUCED. THEREFORE CAREFUL EXECUTION OF FIELD WORK IS POSSIBLE. SAMPLING IS PRACTICAL AND EFFICIENT MEANS OF COLLECTING DATA.

20. DISADVANTAGES SAMPLING MAY INVOLVE BIASES SELECTION OF SAMPLE AND LEADING TO DRAW ERRONEOUS CONCLUSIONS. BIASNESS IN SELECTION MAY BE CONSCIOUS OR UNCONSCIOUS. DIFFICULTY IN GETTING REPRESENTATIVE SAMPLE (REPRESENTATIVE SAMPLE IS ONE WHOSE KEY CHARACTERISTICS CLOSELY APPROXIMATE THOSE OF THE POPULATION). NEED FOR SPECIALIZED KNOWLEDGE OF SAMPLING TECHNIQUE. THE SAMPLE MAY BE WIDELY DISPERSED, SINCE MANY REFUSE TO CO OPERATE AND SOME MAY BE IN ACCESSIBLE. ALL THESE INTRODUCE A CHANGE IN THE STIPULATED SUBJECTS TO BE STUDIED.

21. SAMPLING PROCESS

22. TYPES OF SAMPLING METHOD PROBABILITY SAMPLING NON-PROBABILITY SAMPLING :

23. PROBABILITY SAMPLING IT IS ANY METHOD OF SAMPLING THAT UTILIZES SOME FORM OF RANDOM SELECTION THE PROCEDURE SHOULD ASSURE THAT THE DIFFERENT UNITS IN THE POPULATION HAVE EQUAL PROBABILITIES OF BEING CHOSEN. THE MAIN FEATURE IS “RANDOMNESS”. “RANDOMNESS” IS ONE IN WHICH EACH ELEMENT IN THE POPULATION HAS EQUAL AND INDEPENDENT CHANCE OF BEING SELECTED OR INCLUDED IN THE STUDY.

24. NONPROBABILITY SAMPLING IT DOES NOT INVOLVE RANDOM SELECTION MAY OR MAY NOT REPRESENT THE POPULATION WELL USED WHEN RESEARCHER LACKS A SAMPLING FRAME FOR THE POPULATION WITH THE NON PROBABILITY APPROACH, THE RESULT MAY NOT BE REPRESENTATIVE OF THE LARGE POPULATION. THEREFORE, GENERALIZING STUDY FINDINGS IS DIFFICULT.  

25. PROBABILITY SAMPLING SIMPLE RANDOM SAMPLING STRATIFIED RANDOM SAMPLING MULTISTAGE OR CLUSTER SAMPLING SYSTEMIC RANDOM SAMPLING

26. SIMPLE RANDOM SAMPLING A SAMPLE SELECTED SUCH THAT EACH POSSIBLE SAMPLE COMBINATION HAS EQUAL PROBABILITY OF BEING CHOSEN. ALSO CALLED UNRESTRICTED RANDOM SAMPLING

27. SIMPLE RANDOM SAMPLING RESEARCHERS ESTABLISH A SAMPLING FRAME , THE TECHNICAL NAME FOR THE LIST OF THE ELEMENTS FROM WHICH THE SAMPLE WILL BE CHOSEN. FOR EXAMPLE IF THE SAMPLING UNIT WERE 500 BED HOSPITAL IN KARNATAKA, THEN A LIST OF ALL SUCH HOSPITALS WOULD BE THE SAMPLING FRAME

28. METHODS OF SELECTION OF A SIMPLE RANDOM SAMPLING I. LOTTERY METHOD

29. METHODS OF SELECTION OF A SIMPLE RANDOM SAMPLING II. TABLE OF RANDOM NUMBERS TO SELECT THE SAMPLE THE RESEARCHER CAN ASSIGN A NUMBER TO EACH MEMBER OF POPULATION AND UTILIZE A TABLE OF RANDOM NUMBER SUCH AS TABLE. WITH CLOSED EYES, USE A PENCIL TO POINT TO A NUMBER ON THE TABLE. MOVE IN A SYSTEMATIC WAY – UP DOWN OR DIAGONALLY CHOOSING THE SAMPLE BY PICKING THOSE SUBJECTS WHOSE NUMBER CORRESPONDS TO THE TABLE OF RANDOM NUMBERS. STOP WHEN THE DESIRED SAMPLE SIZE OF OBTAINED.

30. SIMPLE RANDOM SAMPLING MERITS: Since the selection of items in the sample depends entirely on chance, there is no possibility of personal bias affecting the results. The investigator can easily assess the access of the estimate because sampling errors follow the principle of chance. The compared to judgment, sampling, a random sampling represents the universe in a better way.

31. SIMPLE RANDOM SAMPLING LIMITATIONS: The use of simple random sampling necessitate a completely calatogued universe from which to draw the sample. But it is often difficult for the investigator to have up to date lists of all the items of the population to be sampled. From the point of view of field survey, it has been claimed that cases selected by random sampling tend to be to widely dispersed geographically and that the time and cost of collecting data become too large.

32. STRATIFIED RANDOM SAMPLING

33. STRATIFIED RANDOM SAMPLING Stratification means division into groups. In this method the population is divided into a number of subgroups or strata From each stratum a simple random sample is selected and combined together to form the required sample from the population

34. STRATIFIED RANDOM SAMPLING The population is stratified according to any number of attributes such as age, gender, religion, soicio -economic status, level of education, ethnicity, geographic location etc ., A stratified sample may be either proportional or disproportionate.

35. PROPOTIONATE STRATIFIED SAMPLING IN THIS RESEARCHER STRATIFIES THE POPULATION ACCORDING TO THE KNOWN CHARACTERISTICS OF THE POPULATION AND SUBSEQUENTLY DRAWS THE ELEMENT RANDOMLY IN A SIMILAR PROPORTION FROM EACH STRATUM OF POPULATION. 

36. DISPROPOTIONATE STRATIFIED SAMPLING In some instance in which the distribution of cases within in the population under study provides too few cases to draw meaningful conclusions, a disproportionate number of cases may be selected, when the number of member chosen from stratum is not in proportion to the size of stratum in the total population it is called disproportional stratified sampling.

37. STRATIFIED RANDOM SAMPLING MERITS: Since the population is first divided into various strata and then a sample is drawn from each stratum there is little possibility of any essential group of the population being completely excluded. A more representative sample is secured . Stratified sampling is frequently regarded as the most efficient system of sampling.

38. Cont.. Stratified sampling ensures greater accuracy, the accuracy is maximum if each stratum is so formed that is consists of uniform or homogeneous items. As compared to random sample, stratified samples can be more concentrated geographically. Thus the time and expense of interviewing may be considerably reduced.

39. STRATIFIED RANDOM SAMPLING LIMITATIONS: Utmost care must be exercised in dividing the population into various strata. Each stratum must contain, as far as possible, homogeneous items as otherwise the results may not be reliable. However, this is a very difficult task and may involve considerable time and expense.

40. STRATIFIED RANDOM SAMPLING The items from each stratum should be selected at random. But this may be difficult to achieve in the absence of skilled sampling supervisors and a random selection within each stratum may not be ensured.

41. CLUSTER SAMPLING

42. CLUSTER SAMPLING Each sampling unit is a collection or cluster of elements Used when units of population are natural groups or clusters like wards, villages etc The group is taken as a sampling unit

43. CLUSTER SAMPLING In the first stage, certain groups or clusters called primary sampling units are selected from the population. In the second stage, individual items called elementary sampling units are drawn from each of these clusters. This second step in the sampling process is called sub – sampling. A single subject can appear in only one clusters.  

44. CLUSTER SAMPLING If investigator wishes to study all cancer patients across the country, they may identify groups (clusters) of cancer hospitals in various regions of the country. A sample of cancer hospitals could then be drawn, from which a sample of cancer patients could ultimately be selected.

45. CLUSTER SAMPLING The main advantage of cluster sampling is that it minimizes the cost per elementary sampling unit. It is extremely costly to choose items at random from a population, or large stratum there of. The use of cluster sampling permits grouping of observation for easier coverage. Lists of names need be prepared only for the sample clusters. Interviewers can also concentrate on these few clusters ( eg . Blocks) and thus save travel time and expense.

46. CLUSTER SAMPLING On the other hand, the results of a cluster sample are usually not as precise as those of a random sample of the same size. They can be made equally or more precise only by taking a large sample. Concentrating interviews in a restricted number of clusters rather than having the same number of interviews more widely scattered over the universe will tend to increase sampling error.

47. SYSTEMIC RANDOM SAMPLING Also called Quasi-random sampling Divide the population size by the sample size, to get sampling fraction Select a random number b/w 1 and sampling fraction, which is the first sampling unit Systematically select the remaining sample units, by adding sampling faction

48. SYSTEMIC RANDOM SAMPLING EXAMPLE: Selection of study participants such that every ‘ kth ’ person in a sampling frame or list is chosen & every 5 th person who enters the hall.

49. SYSTEMIC RANDOM SAMPLING First , element is chosen at random from sampling frame and then every kth sampling frame is selected . K=N/n K= sampling interval N= accessible population n= sample size. e.g., 5000 population, sample size 50 K=5000/500=10 Ie every 10 th element in sampling frame.

50. SYSTEMIC RANDOM SAMPLING MERITS: It is simple and convenient to adopt. The time and the work involved in sampling by this method are relatively smaller. The results obtained one also found to be generally satisfactory.