Sampling design. types, methods, merits and demerits

Sampling design

1. Sampling Design Presented by- Dr. Kapil Kumar PG 1 st year Deptt . of Community Medicine MMIMSR,Mullana

2. A sample design is a definite plan for obtaining a sample from a given population. It refers to the technique or the procedure the researcher would adopt in selecting items for the sample. Sample design may as well lay down the number of items to be included in the sample i.e., the size of the sample.

3. Sample design is determined before data are collected. There are many sample designs from which a researcher can choose. Some designs are relatively more precise and easier to apply than others. Researcher must select/prepare a sample design which should be reliable and appropriate for his research study.

4. Objective : The first step of sampling design is to define the objectives of survey in clear and concrete terms. The sponsors or the researchers of the survey should confirm that the objectives are commensurate with the money, manpower and time limit available for the Survey. Population : In order to meet the objectives of the survey, what should be the population? This question should be answered in the second step. The population should be clearly defined.

5. Sampling units and Frame A decision has to be taken concerning a sampling unit before selecting sample. Sampling unit may be a geographical one such as state, district, village, etc., or a construction unit such as house, flat, etc., or it may be a social unit such as family, club, school, etc., or it may be an individual. The researcher will have to decide one or more of such units that he has to select for his study. The list of sampling units is called as 'frame or sampling frame. Sampling frame contains the names of all items of a universe (in case of finite universe only).

6. Size of sample This refers to the number of items to be selected from the universe to constitute a sample. This is a major problem before a researcher. The size of sample should neither be excessively large, nor too small. It should be optimum. An optimum sample is one which full fills the requirements of efficiency, representativeness, reliability and flexibility.

7. Parameters of interest: Statistical constants of the population are called as parameters, e.g., population mean, population proportion etc. When we do census survey we get the actual value of parameters. On the other hand, when we do sample survey we get the estimates of unknown population parameters in place of their actual values. Data collection: No irrelevant information should be collected and no e ssential information should be discarded. The objectives of the survey should be very much clear in the mind of surveyor.

8. Non-respondents: Because of practical difficulties, data may not be collected for all the sampled units. This non-response tends to change the results. The reasons for non-response should be recorded by the investigator. Selection of proper sampling design: he must select that design which, for a given sample size and for a given cost, has a smaller sampling error.

9. Organizing field work: The success of a survey depends on the reliable field work. There should be efficient supervisory staff and trained personals for the field work. Pilot survey: It is always helpful to try out the research design on a small scale before going to the field. This is called as 'pilot survey' or ‘pre-test'. It might give the better idea of practical problems and troubles.

10. Budgetary constraint: Cost considerations, from practical point of view, have a major impact upon decisions relating to not only the size of the sample but also to the type of sample. This fact can even lead to the use of a non-probability sample. SAMPLING AND NON-SAMPLING ERRORS The errors involved in the collection of data are classified into sampling and non-sampling errors.

11. Sampling Errors Sampling errors arise due to the fact that only a part of the population has been used to estimate population parameters and to draw inferences about the population. Sampling errors are absent in a census survey. Sampling error can be measured for a given sample design and size. The measurement of sampling error is usually called the 'precision of the sampling plan’. If we increase the samples size, the precision can be improved.

12. Non-sampling Errors Non-sampling errors arise at the stage of collection and preparation of data and both thus are present in the sample survey as well as the census survey. Thus the data obtained in census survey is free from sampling errors, however subjected to non-sampling errors. Non-sampling errors can be reduced by defining the sampling units, frame and the population correctly and by e mploying e fficient people in the investigations.

13. Non-sampling errors arise due to a number of factors such as inefficiency of field workers, non-response, bias due to interviewers, etc. These errors are likely to grow when the number of units inspected increases. TYPES OF SAMPLING DESIGNS ‘ non-probability sampling’ and 'probability sampling ,'.

14. Non-probability Sampling Non-probability sampling is that sampling procedure which does not afford any basis for estimating the probability that each item in the population has of being included in the sample. Also known as... deliberate sampling, purposive sampling and judgement sampling . In this type of sampling, items for the sample are selected deliberately by the researcher; his choice concerning the items remain supreme.

15. CONVENIEN C E S AMP L ING Convenience samp l ing is a process of select i ng subjects or units for exam i nat i on and analys i s that i s b ased on access i b i l i t y , eas e , spee d , and low cost. Uni ts are not purposefully or strategically selected. It a ttempts to obtain a s a m p l e of convenient e l ement s . Often, re s po n de n ts are s el ected bec ause they ha p pen to be in the right place at right time. use of stude n ts, and members of s oci al organiz a tions. mall i n ter c ept interviews without t qualifying the respondent. depart n 1 ent stor e s using charge ac c ount l i sts. " peo p l e on the s tre e t" intervie w s.

16. In such a design, personal element has a great chance of entering into the selection of the sample. The investigator may select a sample which shall yield results favourable to his point of view. I n small inquiries and research by individuals, this design may be adopted because of the relative advantage of time and money inherent in this method of sampling.

17. T he samples so selected certainly do not possess the characteristic of random samples. Quota sampling is also an example of non probability sampling. This type of sampling is very convenient and is relatively inexpensive.

18. Snow ball sampling In snow ball sampling, an initial group of respondent is selected, usually at random. After being interviewed, these respondent are asked to identify others who belong to the target population of interest. Subsequent participants are selected based on the referrals.

19. P u rpo s i ve (Ju d g m e n ta l) sampling Pur p o s i ve s ampli n g, a l s o k n o w n a s judgmental , s e l e ct i v e or subj e ct i v e s amp l in g , r e f l ects a g r o u p o f s ampli n g t e c hniq u e s that r e l y on the judgment o f the r e s e arch e r; w h e n it c o m e s t o s e l e c ti n g t h e uni t s that ar e t o b e s t u d i e d . For examples … S p e c if i c P e o p l e , S p e c ifi c c a s e s/ o r g anizat i o n s , S p e c i f i c ev e n t s , S p e c if i c pi e c e s o f dat a .

20. Quota sampling In this method, the population is divided into different groups or classes according to different characteristics of the population and some percentage of different groups in same population is fixed.

21. Probability Sampling Probability sampling is also known as 'random sampling' or 'chance sampling. Under this sampling design, every item of the universe has an equal chance of inclusion in the sample. we can measure the errors of estimation or the significance of results obtained from a random sample, and this fact brings out the superiority of random sampling design over the deliberate sampling design.

22. Random sampling ensures the law of Statistical Regularity which states that if on an average the sample chosen is a random one, the sample will have the same composition and characteristics as the universe. R andom sampling is considered as the best technique of selecting a representative sample.

23. Simple Random Sampling Simple random sampling from a finite population refers to that method of sample selection which gives each possible sample combination an equal probability of being picked up and each item in the entire population to have an equal chance of being included in the sample. This applies to sampling without replacement. T his relatively easy method of obtaining a random sample can be simplified in actual practice by the use of random number tables.

24. Complex Random Sampling Designs Some complex random sampling designs, which are the mixture of probability and non-probability sampling methods. Systematic Sampling T he most practical way of sampling is to select every n th item on a list. Sampling of this type is known as systematic sampling. An element of randomness is introduced into this kind of sampling by using random numbers to pick up the unit with which to start. For instance, if a 4 percent sample is desired, the first item would be selected randomly from the first twenty-five and thereafter every 25th item would automatically be included in the sample.

25. Thus, in systematic sampling only the first unit is selected randomly and the remaining units of the sample are selected at fixed intervals. It random can be taken as an improvement over a simple sample in as much as the systematic sample is spread more e venly over the entire population. If there is a hidden periodicity in the population, systematic sampling will prove to be an inefficient method of sampling. In practice, systematic sampling is used when lists of population are available and they are of considerable length.

26. Stratified Sampling Under stratified sampling the population is divided into several sub-populations that are individually more homogeneous than the total population (the different sub-populations are called 'strata') and then we select items from each stratum to constitute a sample. Since each stratum is more homogeneous than the total population, we are able to get more precise estimates for each stratum and by estimating more accurately each of the component parts, we get a better estimate of the whole. In brief, stratified sampling results in more reliable and detailed information.

27. For example, a system-wide survey designed to determine the attitude of students towards a new teaching plan, a state college system with 20 colleges might stratify the students with respect to class, section and college. Stratification of this type is known as cross-stratification, and up to a point such stratification increases the reliability of estimates and is much used in opinion surveys.

28. From what has been stated above in respect of stratified sampling, we can say that the sample so co nstituted is the result of successive application of purposive (involved in stratification of items) and random sampling methods. As such it is an example of mixed sampling. The procedure wherein we first have stratification and then simple random sampling is known as stratified random sampling.

29. Cluster Sampling If the total area of interest happens to be a big one, a convenient way in which a sample can be take is to divide the area into a number of smaller non-overlapping areas and then to randomly select number of these smaller areas (usually called clusters), with the ultimate sample consisting of all or samples of) units in these small areas or clusters. I n cluster sampling the total population is divided into a number of relatively small sub divisions which are themselves clusters of still smaller units and then some of these clusters are randomly selected for inclusion in the overall sample.

30. Cluster sampling reduces cost by concentrating surveys in selected clusters. But certainly it is less precise than random sampling. There is also not as much information in n' observations within a cluster as there happens to be in 'n' randomly drawn observations. Cluster sampling is used only because of the economic advantage it possesses; estimates based on cluster samples are usually more reliable per unit cost. cluster designs, where the primary sampling unit represents a cluster of units based on geographic area, are distinguished as area sampling.

31. Multi-stage Sampling Multistage sampling is defined as a sampling method that divides the population into groups (or clusters) for conducting research. It is a complex form of c luster sampling, sometimes, also known as multi-stage cluster sampling . During this sampling method, significant clusters of the selected people are split into sub-groups at various stages to make it simpler for primary data collection.

32. There are four multistage steps to conduct multistage sampling Step one: Choose a sampling frame, Considering the population of interest. The researcher allocates a number to every group and selects a small sample of population. Step two: Select a sampling frame of relevant separate sub-groups. Do this from related, different discrete groups selected in the previous stage. Step three: Repeat the second step if necessary. Step four: Using some variation of probability sampling, choose the members of the sample group from the sub-groups.

33. Applications of multistage sampling Applied to a multistage design where the population is too vast and researching every individual is impossible. To gather student perceptions from students belonging to various colleges, studying different courses and located throughout the country. To survey employees of a multinational company belonging to multiple locations in multiple countries Government bureaus use this method all the time to draw inferences from the population. Multiphase sampling reduces the time taken to research an area. It also keeps a tab on the cost of the research. The information collected from the samples is used to draw inferences from the population as a whole.

34. A dvantages of multistage sampling It allows researchers to apply cluster or random sampling after determining the groups. Researchers can apply multistage sampling to make clusters and sub-clusters until the researcher reaches the desired size or type of group. Researchers can divide the population into groups without restrictions. It allows flexibility to the researchers to choose the sample carefully. It is useful while collecting primary data from a geographically dispersed population.

35. Cost-effective and time-effective because this method helps cut down the population into smaller groups. Finding the right survey sample becomes very convenient for researchers. The researcher mindfully chooses the audience. It decreases the issues faced during random sampling. It does not need a complete list of all the members of the target population, dramatically reducing sample preparation cost.

36. T ypes of multi-stage sampling There are two types of multistage sampling - Multi-stage cluster sampling and multi-stage random sampling. In market research, multistage sampling is the choosing of samples at stages and choosing smaller sampling units at every step.

37. Multi-stage cluster sampling Multistage cluster sampling is a complex type of cluster sampling. The researcher divides the population into groups at various stages for better data collection, management, and interpretation. These groups are called clusters . For example, a researcher wants to know the different eating habits in western Europe. It is practically impossible to collect data from every household. The researcher will first choose the co untries of interest. From these countries, he/she chooses the regions or states to….

38. survey And from these regions, he/she further narrows down his research by choosing specific cities and towns that represent the region. The researcher does not interview all the residents of the city or town. He/she further chooses particular respondents from the selected cities to participate in research. Here we see that clusters are selected at various stages until the researcher narrows down to the sample required .

39. Multi-stage random sampling I n this case, the researcher chooses the samples randomly at each stage. Here, the researcher does not create clusters, but he/she narrows down the sample by applying random sampling. For example, a researcher wants to understand pet feeding habits among people living in the USA. For this, he/she requires a sample size of 200 respondents. The researcher selects 10 states out of 50 at random. Further, he/she randomly picks out 5 districts per state. From these 50 randomly selected states, he/she then chooses 4 pet-owning households to conduct his research.

40. Tips for efficient multi-stage sampling Think cautiously- Its good practice to brainstorm about a way to implement the multistage approach. Keep in mind that as there's no exact definition of multiphase sampling, there is no conventional method on a route to mix the sampling methods (such as cluster, stratified, and simple random). The process design must be in a way that is both cost-effective and time-effective. Retaining its randomness and its sample si ze is a must. Consult an experienced and skilled expert when you apply this method for the first time .

41. Sequential Sampling In this case the decision rests on the basis of more than two samples but the number of samples is certain and decided in advance, the sampling is known as multiple sampling. But when the number of samples is more than two but it is neither certain nor decided in advance, this type of system is often referred to as sequential sampling, Thus, in brief, we can say that in sequential sampling, one can go on taking samples one after another as long as one desires to do so.