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M-3 sampling Design .pptx by Prof Raman
- 1. SAMPLING Sampling may bedefined as the selection of some part of an aggregate or totality, on the basis of which a judgment or inference about the aggregate or totality is made. In other words it is process of obtaining information about an entire population by examining only a part of it. 1
- 2. POPULATION Based on Numbers Finite Infinite Basedon subjects Real Hypothetical
- 3. Need for Sampling Samplingis used in practice for a variety of reasons such as:- 1. Reduces the time and cost 2. saves labor 3. Quality of a study is often better with sampling than with a complete 4.Coverage 5. Provides much better results 6. Only procedure possible, if the population is infinite. 3
- 4. Main steps ofsample design • Objective • Population • Sampling unit and Frame • Size of Sample • Parameters of Interest • Data collection • Non-respondents • Selection of proper sampling design • Organizing field work • Pilot survey or Pre-test • Budgetary constraints 4
- 5. There are manytypes of sampling, most sampling types can be categorized as: a) Probability sampling and b) Non-probability sampling 5
- 6. a) Probability sampling:- isone in which every unit in the population • has a chance ( greater than Zero) of being selected in the sample, and this probability can be accurately determined. The combinations of these traits make it possible to produce unbiased estimates of population totals, by weighing sampled units according to their probability of selection. 6
- 7. Probability Sampling isof the following types: 1.Simple Random sampling – URESTRICTED SAMPLING 2.Stratified Random sampling-A) Proportionate and b) disproportionate 3.Systematic Random sampling 4. Cluster/ Area sampling 5.Multi stage sampling 6.Random sampling with probability proportional to size (PPS) 7.Double sampling and Multiphase sampling 8.Replicated or interpenetrating sampling. 7
- 8. Non-Probability sampling: • Nonprobability sampling plans are those that provide no basis for estimating how closely the sample characteristics approximate the parameters of the population from which the sample was obtained. In fact the investigator is generally unable to identify the parent population. • Characteristics: 1. Non-representative sample 2. Biased views 3. Freedom of selection sample 4. Sample size 5. Level of Errors and accuracy in conclusion 6. Convenient and money saving method 8
- 9. Non-Probability sampling maybe classified into:- 1.Convenience or Accidental sampling 2.Purposive or judgment sampling 3.Quota sampling 4.Snow – ball sampling 9
- 10. Simple Random sampling:- A simple random sample is one in which each element of the population has an equal and independent chance of being included in the sample i.e. a sample selected by randomization method is known as simple random sample and this technique is simple random- sampling. Randomization is a method and is done by using a number of techniques as:- a)Tossing a coin b)Throwing a disc c)Lottery method d)Blind folded method e)by using random table of Tipett’s Table 10
- 11. The Fish BowlDraw: 11 The simplest and most familiar type of sample selection consists of putting numbers on slips of paper or marbles and depositing them in a large container. The numbers identify and stand for specific elements in the populations and presumably the entire population of elements has been numbered and is represented in the bowl. After mixing the thoroughly, the investigator selects one number at a time, blindfolded until the desired sample size is obtained. This is called a random sample.
- 12. Systematic Sampling: Systematic samplingrelies on arranging the target population according to some ordering scheme and then selecting elements at regular start and then proceeds with the selection of every Kth element from the onwards. In this case K= (population size). It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the Kth element in the list. 12
- 13. Example A simple eg:-would be to select every 10th name from the telephone directory (an every 10th sample, also referred to as sampling with a skip of 10). 13
- 14. Stratified Sampling It isan improvement over the earlier method, when employing this techniques, the researcher divides his population in strata on the basis of some characteristics and from each of these smaller homogenous groups (strata) drawn at random a pre-determined number of Units. Researcher should choose that characteristic or criterion which seems to be more relevant in his research work. 14
- 15. - a) Dispropationate StratifiedSampling Means that the size of the sample in each Unit is not proportionate to the size of the unit but depends upon considerations involving personal judgment and convenience. b) Proportionate sampling: - Refers to the selection from each sampling unit of a sample that is proportionate to the size of the unit. c) Optimum allocation stratified sampling:- Is representative as well as comprehensive than other stratified samples. It refers to selecting units from each stratum should be in proportion to the corresponding stratum the population. Thus sample obtained is known as optimum allocation stratified sample. 15
- 16. 16 Levels Disproportionate stratified sampling Proportionate stratified sampling Optimum allocation stratifiedsampling Population Sample HG 35 25 250 25 AG 43 50 400 40 L.G 22 25 350 35 Sample 100 100 1000 100 These three are clear from the following table as given below
- 17. Cluster sampling: To selectthe intact group as a whole is known as a cluster sampling. In cluster sampling the sample units contain groups of elements (clusters) instead of individual members or items in the population.eg:- Rather than listing all elementary school children in a given city and random selecting 15 per cent these students for the sample, a researcher lists all of the elementary schools in the city, selects at random 15 percent of these clusters of units, and uses all of the children in the selected schools as the sample. 17
- 18. Non- probability sampling methods: 1.Convenience or accidental sampling: It means selecting sample units in a ‘1 hit and miss fashion’. Example: interviewing people whom we happen to meet. This sampling also means selecting whatever sampling units are conveniently available. Example A teacher may select students in his class. This method is also known as accidental sampling because the respondents whom the researcher meets accidentally are included in the sample. 18
- 19. Purposive or Judgment Sampling: •This method means deliberate selection of sample units that conform to some pre-determined criteria. This is also known as judgment sampling. This involves selection of cases which we judge as the most appropriate ones for the given study. It is based on the judgment of the researcher or some expert. It does not aim at searching a cross section of a population. 19
- 20. Quota Sampling: • Thisis a form of convenient sampling involving selection of quota groups of accessible sampling units by traits such as Sex, Social class etc. In specific proportions, each investigator may be given an assignment of quota groups specified by the pre-determined traits in specific proportions. He can then select accessible persons belonging to those groups in the area assigned to him. • Quota sampling is therefore, a method of stratified sampling in which the selection within strata is non-random. Quota sampling is used in studies like marketing survey, opinion polls, and readership survey which do not aim at precision but to get quickly some crude results. 20
- 21. 21 Snow ball sampling:Is a technique of building up a list or a sample of a special population by using an initial set of its members as informants. For example a researcher wants to study the problem faced by Indians in another country, Say, he may identify an initial group of Indians through some source like Indian Embassy, Then he can ask each one of them to supply names of other Indians known to them and continue this procedure until he gets an exhaustive list from which he can draw a sample or make a census survey. This sampling technique may also be used in socio-metric studies. For example, the members of a social group may be asked to name the persons with whom they have social contacts, each one of the persons so named may also be asked to do so, and so on. The researcher may thus get a constellation of associates and analyze it.
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