# Online International Interdisciplinary Research Journal, {Bi-Monthly}, ISSN , Volume-II, Issue-VI, Nov-Dec 2012

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2 Step 2. Choose between taking a census and sampling: The second step involves choosing between selecting the entire target population taking a census and selecting a subset of the target population (sampling). In making this choice it is important that one has a good understanding of the differences between random sampling error and systematic error. Step 3. Choose nonprobability, probability, or mixed-methods sample design: The next step is that researcher has to decide sampling method, it may be separate or sometimes it may be mixed methods. Once a decision is made to sample, the next step involves choosing between the two major types of sampling, nonprobability sampling and probability sampling. Probability sampling gives every element in the target population a known and nonzero chance of being selected. Nonprobability sampling does not. Step 4. Choose the type of nonprobability, probability, or mixed-method sample design: The next step involves choosing the specific type of nonprobability or probability sample design to be employed. One may opt to utilize a mixed-methods sample procedure combining different types of nonprobability sampling procedures, different types of probability sampling procedures, or combining nonprobability and probability sampling procedures. Step 5. Determine sample size: The next step involves determining of the number of elements to be selected. Two steps are involved in determining survey sample size requirements for a given survey 2 : calculating the number of sample elements required in order to satisfy the measurement requirements for a given indicator, and calculating how many households would have to be contacted in order to find the number of elements needed in the first step. In the first instance, an indicator may be expressed as a proportion of the population, In the second, when they are expressed as mean or total, they may be stated in terms of the amount of a particular commodity, good, or total number of people in given population. The inconsistency in results is due to sampling size. Larger counties have a larger sample so they are less likely to show extreme results. For e.g, a rural county may have a population of 10,000, while an urban county has a population of 1,000,000. Thus, in this example, rural counties are at both extremes. The lesson to be learned from this example is that researcher should not rely on intuitions concerning sample size and intuitions concerning statistics in general, but use statistical methods to determine what the sample size should be?. If the researcher failed to decide the size of population the results will be random meaningless 3. Step 6. Select sample: The final step in sampling involves implementing one s sampling choices. The quality of the resulting sample is dependent substantially on the first step preparing to make sampling choices. Sampling error can be minimized by taking certain precautions: Choose your sample of respondents in an unbiased way. Select a large enough sample for your estimates to be precise. w w w. o i i r j. o r g I S S N Page 184

4 2. This method is used in conjunction with all other probability sampling plans. It therefore serves foundation upon which all types of random samples are based. 3. Simple random sampling method is easiest to apply of all probability plans. 4. It is the simple type of random sampling method. 5. The researcher does not need to know the true composition of population before hand. Random sampling is considered the least biased method of sampling and thus, according to the procedures of critical multiplism, becomes the method of choice. It is the least biased method to generate estimates of population parameters because, by the intrinsic nature of the random choice process, random samples are likely to contain elements which replicate the variations found in the total population. Random sampling permits causal relationships established by the use of random assignment to be generalized beyond the sample to the target population. 7 Disadvantages of Random sampling: Complete accounting population is needed. Cumbersome to provide unique designation to every population member. Stratified sampling 8 : If the population from which a sample is to be drawn does not constitute a homogeneous group, then stratified sampling technique is applied so as to obtain a representative sample. In this technique, the population is stratified into a number of non overlapping subpopulations or strata and sample items are selected from each stratum. If the items selected from each stratum is based on simple random sampling the entire procedure, first stratification and then simple random sampling, is known as stratified random sampling. Proportional stratified random sampling: It is used whenever an investigator possesses some knowledge concerning the population under study eg. He knows the age, sex, distribution of all numbers of population. Such plan is useful for obtaining sample that has specified characteristics in exact proportion to the way in which those same characteristics are distributed in the population. In this method one of the advantage is that, it enhance the representativeness of the sample in relation to the population. Disproportionate stratified random sampling: This method is only marginally different from proportionate stratified random sampling. The basic difference is that the substrata of the resulting sample are not necessarily distributed according to their proportionate weight in the population from which they were drawn. Eg. Research on socio-economic status, the researcher divides population into upper class, lower class and middle class. Instead of drawing proportionate samples in each of these substrata the researcher may either give equal weight to each of the substrata or give greater weight each of the substrata and not enough weight to other w w w. o i i r j. o r g I S S N Page 186

5 substrata. One of the important advantage of this method is that, it is less time consuming compared with proportionate stratified sampling. Systematic sampling: This plan is as opposed to random sampling, has certain characteristics of randomness and at the same time possesses some non-probability traits. It is defined as obtaining a collection of elements by drawing every tenth person from a predetermined list of them. Eg. Drawing every tenth name from the city of practicing lawyers in an area systematic sampling. An element of randomness is usually introduced into this kind of sampling by using random numbers to pick up the unit with which to start. This procedure is useful when sampling frame is available in the form of a list. In such a design the selection process starts by picking some random point in the list and then every 10th element is selected until the desired number is secured. Cluster sampling and area sampling: Cluster sampling involves grouping the population and then selecting the groups or the clusters rather than individual elements for inclusion in the sample. Suppose some departmental store wishes to sample its credit card holders. It has issued its cards to 15,000 customers. The sample size is to be kept say 450. For cluster sampling this list of 15,000 card holders could be formed into 100 clusters of 150 card holders each. Three clusters might then be selected for the sample randomly. The sample size must often be larger than the simple random sample to ensure the same level of accuracy because is cluster sampling procedural potential for order bias and other sources of error are usually accentuated. The clustering approach can, however, make the sampling procedure relatively easier and increase the efficiency of field work, specially in the case of personal interviews. Area sampling is quite close to cluster sampling and is often talked about when the total geographical area of interest happens to be big one. Under area sampling we first divide the total area into a number of smaller non-overlapping areas, generally called geographical clusters, then a number of these smaller areas are randomly selected, and all units in these small areas are included in the sample. Area sampling is specially helpful where we do not have the list of the population concerned. It also makes the field interviewing more efficient since interviewer can do many interviews at each location. Ideally, clusters should meet four criteria. 9 They should have relatively clear physical boundaries to facilitate identification in the field. They should be located somewhat close to one another; otherwise, costs will soar, defeating the major purpose of cluster sampling. Clusters should not include too many people; this will help minimize the amount of sampling frame development that has to be done. Information on the size of the cluster should ideally be available prior to sample selection. Multi-stage sampling: This is a further development of the idea of cluster sampling. This technique is meant for big inquiries extending to a considerably large geographical area like an w w w. o i i r j. o r g I S S N Page 187

6 entire country. Under multi-stage sampling the first stage may be to select large primary sampling units such as states, then districts, then towns and finally certain families within towns. If the technique of random-sampling is applied at all stages, the sampling procedure is described as multi-stage random sampling. Deliberate sampling: Purposive sampling, one of the most common sampling strategies, groups participants according to preselected criteria relevant to a particular research question. For example, HIV-positive women in Capital City. Sample sizes, which may or may not be fixed prior to data collection, depend on the resources and time available, as well as the study s objectives. Purposive sample sizes are often determined on the basis of theoretical saturation (the point in data collection when new data no longer bring additional insights to the research questions). Purposive sampling is therefore most successful when data review and analysis are done in conjunction with data collection. 10 Deliberate sampling is also known as purposive or non-probability sampling. This sampling method involves purposive or deliberate selection of particular units of the universe for constituting a sample which represents the universe. When population elements are selected for inclusion in the sample based on the ease of access, it can be called convenience sampling. If a researcher wishes to secure data from, police station he may select a fixed number of police stations and may conduct interviews at these police stations. This would be an example of convenience sample of police station. At times such a procedure may give very biased results particularly when the population is not homogeneous. Judgment sampling: The researcher s judgment is used for selecting items which he considers as representative of the population. For example, a judgment sample of college students might be taken to secure reactions to a new method of teaching. Judgment sampling is used quite frequently in qualitative research where the desire happens to be to develop hypotheses rather than to generalize to larger populations. Quota sampling: Quota sampling, sometimes considered a type of purposive sampling, is also common. In quota sampling, we decide while designing the study how many people with which characteristics to include as participants. Characteristics might include age, place of residence, gender, class, profession, marital status, use of a particular contraceptive method, etc. The criteria we choose allow us to focus on people we think would be most likely to experience, know about, or have insights into the research topic. Then we go into the community and using recruitment strategies appropriate to the location, culture, and study population find people who fit these criteria, until we meet the prescribed quotas. The size of the quota for each stratum is generally proportionate to the size of that stratum in the population. Quota sampling is thus an important form of non-probability sampling. Quota samples generally happen to be judgment samples rather than random samples. Sequential sampling: This is somewhat a complex sample design where the ultimate size of the sample is not fixed in advance but is determined according to mathematical decisions w w w. o i i r j. o r g I S S N Page 188

7 on the basis of information yielded as survey progresses. This design is usually adopted under acceptance sampling plan in the context of statistical quality control. In practice, several of the methods of sampling described above may well be used in the same study in which case it can be called mixed sampling. It may be pointed out here that normally one should resort to random sampling so that bias can be eliminated and sampling error can be estimated. But purposive sampling is considered desirable when the universe happens to be small and a known characteristic of it is to be studied intensively. Also, there are conditions under which sample designs other than random sampling may be considered better for reasons like convenience and low costs. The sample design to be used must be decided by the researcher taking into consideration the nature of the inquiry and other related factors. Snowball sampling 11 : A third type of sampling, snowballing it also known as chain referral sampling it is considered a type of purposive sampling. In this method, participants or informants with whom contact has already been made use their social networks to refer the researcher to other people who could potentially. Snowball sampling also has the ability to resolve some of the serious problems encountered by descending methodologies, such as in-built validity checks. 12 Thus, whilst recognizing the personal bias and distortion inherent in snowball sampling as a price which must be paid in order to gain an understanding of interpret populations and their particular circumstances, the confidence that develops in a relationship over a period of time is perhaps the best guarantee of sincerity and should increase the validity of the data: Without allowing people to speak freely we will never know what their real intentions are, and what the true meaning of their words might be? 13 Disadvantages of sampling Method: Inadequacy of the samples. Chances for bias. Problems of accuracy. Difficulty of getting the representative sample. Untrained manpower. Absence of the informants. Chances of committing the errors in sampling Conclusion: Sampling methods plays a vital role in the Legal Research and social science research. While doing selection of sampling method there are some important steps which should be kept in mind by the researcher and the research should be done accordingly. On the above discussion it is found that sampling method provides an important tool of research in the field of legal research. The choice of selection of sampling method is given to the researcher. Now a day sampling technique is very widely used in social research. While doing research, researcher has to avoid bias then result of that survey will be treated as accurate one. Each method having some advantages and disadvantages therefore researcher should be very careful while doing research. w w w. o i i r j. o r g I S S N Page 189

8 Reference: 1. Major steps in selection of sample, available at p. 5, visited on Robert Magnani, Sampling Guide, December 1997, p.8 3. Sampling Size Problems available at visited on Choosing the sample available at: visited on visited on S.K. Varma & M.Afzal Wani (eds.), Legal Research and methodology, 319 (ILI Delhi. First Reprint, 2006) 7. Laura Blankertz, American Journal of Evaluation, 1998 the value and practicality of deliberate sampling for heterogeneity: a critical multiplist perspective vol. 19(3), American Journal of Evaluation 309 (1998). 8. Research Methodology: an Introduction, Research Pdf, p Cluster Sampling available at: visited on Natasha Mack, Cynthia Woodsong, Qualitative Research Methods: A Data Collector s Field Guide, 5 (Family health organization 2005). 11. Qualitative Research available at: visited Van Meter K.M. (1990), Methodological and design issues: techniques for assessing the representatives of snowball samples, National Institute on Drug Abuse: Research Monograph Series Mono 98, p Jean Faugier and Mary Sargeant, Sampling hard to reach populations, Journal of Advanced Nursing, 796 (1997). w w w. o i i r j. o r g I S S N Page 190

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