Signaler

Partager

•11 j'aime•7,140 vues

•11 j'aime•7,140 vues

Signaler

Partager

Télécharger pour lire hors ligne

A powerpoint presentation on the characteristics of sampling, its techniques, and errors.

- 1. SAMPLING Presented By- Prof. Madhu Gupta, Department of Education, M.D.U. Rohtak
- 2. Introduction In a simple sense, sampling refers to the method used to select a given number of people (or things) from a population. It may be termed as the process of selecting a few (a sample) from a bigger group (the sampling population) to become the basis for predicting the prevalence of an unknown piece of information, situation, or outcome regarding the bigger group. - Kumar, 2005
- 3. “ ” The simplest rationale for sampling is that it may not be feasible because of time or financial constraints, or even physically possible, to collect data from everyone involved in an evaluation. Sampling strategies provide systematic, transparent processes for choosing who will actually be asked to provide data. - Mertens and Wilson, 2012
- 4. Sampling Terminology Population is any group of individuals that has one or more characteristics in common and that are of interest to the researcher. Sampling Units are non-overlapping collections of elements from the population that cover the entire population . A Sampling Frame is a list of sampling units. A Sample is a small proportion of the population that is selected for observation and analysis. Inferential Statistics refers to a statistical technique that is used for drawing conclusions about population from samples.
- 5. Cont.… Statistics is known as the measurements of the characteristics of the elements included in the research sample. When these measurements are related to the parent population, these are referred to as parameters. Sampling Errors are those errors which arise on account of sampling. This occurs due to certain amount of inaccuracy in the collection of information. It is inversely proportional.
- 6. Advantages of sampling Accuracy & quality control Economy in terms of cost Economy in terms of time Economy in terms of labour & efforts
- 7. Sample Size A sample should be of optimum size which fulfils the requirements of efficiency, representativeness, reliability and flexibility. It should be small enough to avoid unnecessary expenditure. And should be large enough to avoid sample errors. Borg and gall (1979) suggest that, as a general rule, sample size should be large where: There are many variables. Only small differences or small relationships are expected or predicted. The sample will be broken down into subgroups. The sample is heterogenous in terms of variables under study. Reliable measures of the dependent variables are unavailable.
- 8. Factors affecting size of sample The nature of population Number of strata required Style of research Kind of sample used Method of data used Kind of data analysis required Number of variables
- 9. Some Findings About Sample Size The larger the sample, the smaller the magnitude of sampling error. Survey studies typically should have larger samples than are needed in experimental studies because the returns from surveys are from who, in sense, are volunteers. Researcher should select large enough samples so that the subgroups are of adequate size. Subject availability and cost factors are legitimate considerations in determining appropriate sample size.
- 10. Characteristics of good sample Should be chosen randomly Representativ e of the population According to the research objectives Should be flexible Large enough to give sufficient precision Should be unbiased
- 11. Steps in Sampling Procedure According to W. C. Cochran Formulation of the objectives of the study Clear definition of population to be selected Use of appropriate tools No overlapping in the choice of a sampling unit Selection of adequate size of the samples Tabulation and analysis of samples
- 12. Types of Sampling Strategies Probability or Random Sampling Strategies Non- Probability or Non- Random Sampling Strategies
- 13. 1) Simple Random Sampling Strategy • 2) Systematic Random Sampling Strategy 3) Multi-Stage Random Sampling 4) Stratified Random Sampling Strategy • 5) Cluster Random Sampling Strategy Non-Probability or Non- Random Sampling Probability or Random Sampling Strategies 1) Convenience Sampling Strategy • 2) Purposive Sampling Strategy 3) Quota Sampling Strategy • 4) Snowball Sampling Strategy
- 14. 1) Probability or Random Sampling Strategies The word ‘probability’ refers to the study of likelihood of events, e.g., what are the chances of winning a toss? In probability sampling, the sample is selected in such a way that each unit within the population or universe has an equal and independent chance of being included in the sample. It deliberately attempts to seek proper representation of the wider population instead of seeking representation of a particular group or section of the wider population .
- 15. a) Simple Random Sampling Strategy A sample selected by randomization method is known as simple random sample. Methods for selection of sample:- Lottery method The use of table of random numbers The use of computer assisted technology Steps 1) Define the population 2) List population from 1-N 3) Decide the sample size 4) Selection of sample
- 16. Advantages Simple to apply Analysis of data is reasonable easy Analysis of data has a sound mathematical basis Disadvantages A complete list of the population might not be available . It is also possible that the subpopulations of interest might not be represented in the population.
- 17. b) Systematic Random Sampling Strategy It is a commonly used strategy, in which units of population are arranged in some systematic manner. This strategy can be used if the complete and up to date list of the sampling units are available. The mechanics of taking a systematic sample is simple. This consists of selecting only the first unit at random, the rest being automatically selected according to some pre- determined pattern involved.
- 18. Cont….. Make a list of all participants and give them number 1-100. Use any method of simple randomization (lottery or random method) to select the first participant. For example participant no. 5. For selection of other participants, select every nth participant. n = 100/ 20 = 5 Therefore, select every 5th individual after participant 6 till you obtain desired number of par Steps for Systematic Sampling list of sampling units 1 6 11 1621 26 100 Start from here Sample every 5th number
- 19. Advantages Easier to draw, without mistakes More precise than simple random sampling as more evenly spread over population. Disadvantages If the units are arranged in a specific pattern, that could result in choosing a biased sample. E.g. Files kept in alphabetical order.
- 20. c)Multistage Random Sampling This method consists of a combination of sampling strategies. It is choosing a sample from the random sampling schemes in multiple / various stages (K.M.T. Collins, 2010). Here the population is regarded as made of a number of primary units each of which is further composed of a number of secondary stage units and so on, till the researcher ultimately reaches the desired sampling unit in which he is interested.
- 21. For example, to get a sample of crop fields growing wheat in Punjab: 1) Select a State (Punjab) 2) Get a sample of districts 3) Sample of villages from each selected district 4) Finally a sample of crop fields from each selected village.
- 22. Advantages It is convenient, less time-consuming and less expensive method of sampling. This kind of sampling is more flexible and enables existing divisions and subdivisions to be taken into account.
- 23. 4) Stratified Random Sampling Strategy This type of sampling requires dividing the population into homogenous groups, each group containing subjects with similar characteristics. These subgroups are known as strata. The strata are the partition of the population , which is more homogenous than the complete population. The members of a stratum are similar to each other and are different from the members of another stratum in the characteristics that are to be measured.
- 24. Advantages of stratified Random Sampling by Mendenhall, Ott and Schaeffer (1971) It produces more homogenous data within each stratum The logistics of collecting data within the specified strata reduce the cost of the sample. Since data are collected within each of the separately defined strata, it is possible to obtain separate estimates of population characteristics from each stratum.
- 25. 5) Cluster Random Sampling Strategy When the population is large and widely dispersed gathering a simple random sample poses administrative problems. So, the subjects are selected in groups or clusters. For example, to study the fitness level of students it would be impractical to select students randomly. By cluster sampling, the researcher can select a specific number of schools and test all the students in those selected schools, i.e. a geographically close cluster is sampled.
- 26. Cont….. Cluster random sampling technique emphasizes first on random selection of clusters from the large population of clusters and then on including all the population members of a selected cluster in the study sample.
- 27. 2) Non-Probability Random Sampling Strategy In this sampling, the sample is selected in such a way that the chance of being selected for each unit within the population or universe is unknown. It does not require random selection of sample. The sample is selected in such a way that the chance of being selected for each unit within the population or universe is unknown. Because the selection of sample is subjective, there are no statistical techniques that allow for the measurement of sampling error.
- 28. a) Convenience Sampling Strategy In this sampling strategy, the selection of units from the population is based on easy availability or accessibility. The researcher may try to include the people in their research samples who are willingly available or conveniently approached by them, e.g. members of their family. The major disadvantage of this technique is that the researcher has no idea how much representative the sample will be.
- 29. b) Purposive Sampling Strategy When the desired population is rare or vey difficult to locate, purposive sampling May be used as it targets a particular group of people. E.g. studying juvenile delinquents. Such sampling may satisfy researcher's need but it does not pretend to represent the wider population; it is deliberately and unashamedly selective and biased (Cohen et al., 2007)
- 30. Variants of Purposive Sampling To include those in a sample who otherwise may be excluded from, or under represented because there are few of them (Gorard, 2003). 1) Boosted Sample Here the researcher deliberately seeks those people who disconfirm the theories being advanced, thereby the theory if it survives such disconfirming cases. 2) Negative Case Sampling Selecting sample from as diverse a population as possible in order to ensure strength and richness to the data, their applicability and their interpretation. 3) Maximum Variation Sampling
- 31. c) Quota Sampling Strategy It may be defined as a non – probability equivalent of stratified sampling in which one attempts to represent significant strata of the wider population without resorting to randomization (Bailey, 1978). Like stratified sample, a quota sample strives to represent significant characteristics of a wider population; unlike stratified sampling it sets out to represent these in proportions in which they can be found in the wider population. Example- sup[pose the wider population is were composed of 55 % females and 45 % male, then the sample would have to contain 55 % females and 45% males.
- 32. Steps of Quota Sampling Strategy 1) Identify those characteristics which appear in the wider population which must also appear in the sample, i.e. divide the wider into homogenous groups, e.g. male & female, rural & urban etc. 2) Identify the proportions in which the selected characteristics appear in the wider population, expressed as percentage. 3) Ensure that the percentaged proportions of the characteristics selected from the wider population appear in the sample.
- 33. d) Snowball Sampling Strategy This type of sampling is basically sociometric. It is defined as obtaining a sample by having initially identified subjects who can refer you to other subjects with like or similar characteristics (Eckhardt & Ermann, 1977). A researcher a required to start with the identification of a small number of participants who have the characteristics in which he is interested. These people are then used as informants to identify others who qualify for inclusion ( Cohen et al., 2007).
- 34. Snowball Sampling is applied in the cases where: The population is unknown. The access to the population of the study is difficult. One is to conduct ethnographic studies. The topic of the research study is too sensitive ( e.g. to study the teenage drug addicts)
- 35. Errors in Sample Research (Fienberg, 2003) Errors 1) Sampling Errors Chance or Random Error Errors of Bias 2) Non- Sampling Errors Chance or Random Error Errors of Bias
- 36. 1) Sampling Errors (SE) Sampling errors results from the luck of the draw when choosing a sample: one gets a few too many units of one kind, and not enough of another. With the probability samples, the SE can be estimated using (i) the sample design and (ii) the sample data. As the sample size increases, the SE goes down. If the population is relatively homogenous, the SE will be small. The errors arising in the outcomes of the research studies on account of using samples instead of the total population are named as sampling errors.
- 37. Types of Sampling Errors Chance Errors The errors happen by chance, in carving sample from the parent population, are named as the chance errors or random errors. Unusual units in a population do exist and there is always a possibility that a one or two abnormally large number of them will be chosen . This issue can be resolved by using large sample Errors of Bias in Sampling It is a tendency to favour the selection of units that have particular characteristics. The errors due to this tendency cannot be corrected.
- 38. 2) Non-Sampling Errors The bias associated with non-sampling errors is not dependent o the size of the sample; rather, it is associated with differences between those who respond to the questions and those who do not. Main factors of errors (Kothari,1990): Errors due to observation. Errors due to non-response and measurement errors. Errors in processing & analysis of data. Errors in preparation of reports. Errors in coverage by using defective frame.
- 39. Types of Sampling Errors Chance Errors The effect of these errors approximately cancel out if fairly large samples are used. Errors of Bias in Sampling These are brought about intentionally by measuring devices, the observer, and the respondent of the study through involvement of their own bias. The effects of these errors persist and not get cancelled in the long run.
- 40. Conclusion Sampling is the procedure a researcher uses to gather people, places or things to study. The representative proportion of the population is called a ‘sample’. Population refers to the large group from which the sample is taken. Probability and Non-Probability sampling are two strategies of sampling. Sampling and Non-Sampling Errors may occurs due to biased sample and wrong measurement.