What type of sampling is the group comprising the sample is chosen in a way that such group is liable to subdivision during the data analysis stage?
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marvinderegino16
marvinderegino16 03.02.2021 answered 10. This is a method that describe the group comprising the sample is chosen in a way that such group is late to subdivision during the data analysis stage.A. Availability C. Simple randomB. Cluster D. StratifiedAdvertisement Advertisement
briannariafayemendez briannariafayemendez Question: This is a method that describe the group comprising the sample is chosen in a way that such group is late to subdivision during the data analysis stage. Answer: I think b. Explanation: sana po makatulong:) #Answerfortrees#CarryOnLearningAdvertisement Advertisement New questions in EnglishContruction a paragraph Answer the question what Is your Expection in your English Subject 1) How do I manage myself in times of stress Short Opening and Closing Prayer please para sa f2f class. Short lang sana, slmt! construction a paragraph answer the question what is your expectations in your English subject?please pasagot If poverty is eliminate do you think crime will not exist? 4. Which of the following expresses permission? A. Could I interview the city mayor? B. I must call the police now. C. You have to consult the doctor … immediately. D. You can't interview the governor. Read the situations and write sentences using the words in parenthesis to complete the idea 1. You went to Jill's house, but she wasn't there. (she / … go / out) 2. Luis started reading a book two hours ago. He is still reading it, and now he is on page 53. (He/read/53 pages so far) 3. John goes to work every day. After breakfast, he leaves home at 8:00 and arrives at work at 9:30. Tomorrow will be no exception. Tomorrow, (John / leave) the house at 8:15. what is meaning of analogy Short Opening and Closing Prayer please para sa f2f class. Short lang sana, slmt! how are you similar with the kite?
Previous Next Advertisement SAMPLE DESIGN Introduction
The following discussion will give a brief introduction to some basic terms and ideas in sampling and an outline of sample designs commonly used. The main focus of the discussion will be on determining an appropriate sampling method. PROBABILITY AND NON-PROBABILITY Non-Probability
Samples However, it is not possible to accurately evaluate the precision (ie. closeness of estimates under repeated sampling of the same size) of estimates from non-probability samples since there is no control over the representativeness of the sample. If a non-probability sample is carried out carefully, then the bias in the results can be reduced. As it is dangerous to make inferences about the target population on the basis of a non-probability sample, non-probability methodology is often used to test aspects of a survey such as questionnaire design, processing systems etc. rather than make inferences about the target population. Different types of non-probability samples are discussed below. Quota Sampling This is the method of sampling commonly used by market researchers and political pollsters as it can produce fairly good estimates if it is properly conducted. When top up units are selected randomly to fill a quota, and no element of judgment is used by the researcher for unit selection, it is very similar to a probability sample. However, when non-response is significant (which is almost always the case for voluntary surveys), quota sampling can under-represent those portions of the population that are unwilling to respond or hard to contact. This is of particular concern when the data items collected influence the likelihood of response. See also the section on Non-Response in Errors in Statistical Data for further details. Convenience and Haphazard Sampling Street corner interviews can be biased depending on the timing and the placement of the interviewer. There is no control over selecting the sample of respondents in any of these methods, however they are very cheap and easy to administer.
Judgement or Purposive Sampling Probability
Samples A probability sample allows inferences about the target population to be made. By knowing the selection probability for each unit, objective selections can then be made which should produce a more representative sample. Known probabilities also allow the measurement of the precision of the survey estimates in terms of standard errors and confidence intervals. Probability samples require a frame for selection purposes and thus are relatively expensive in terms of operational costs and frame maintenance. The most common sampling techniques, such as simple random, systematic, stratified, multi-stage and cluster sampling, are all examples of probability samples. These will be looked at later in this chapter. Choosing Between Probability and Non-Probability Samples
Probability sampling is normally preferred when conducting major surveys, especially when a population frame is available ensuring that we are able to select and contact each unit in the (frame) population. However, where time and financial constraints make probability sampling infeasible, or where knowing the level of accuracy in the results is not an important consideration, non-probability samples do have a role to play since they are inexpensive, easy to run and no frame is required. For this reason, when conducting qualitative (investigative), rather than quantitative research, non-probability samples & techniques such as case studies are generally superior to probability samples & quantitative estimation. Non-probability sampling can also be useful when pilot testing surveys. If a non-probability sample is carried out carefully, then the bias in the results can be reduced. Note that with non-probability methods it is dangerous to make inferences about the whole population. Quota sampling may be appropriate when response rates are expected to be low. True probability sampling would be more expensive and may require top up units to be selected. If quota sampling is used, selection of units should be as random as possible and care should be taken to avoid introducing a bias. Unlike certain non-probability samples, probability sampling involves a random selection of units. This allows us to quantify the standard error of estimates and hence allow confidence intervals to be formed and hypotheses to be formally tested. The main disadvantages with probability sampling involve cost, such as the costs involved with frame maintenance and surveying units which are difficult to contact. SIMPLE RANDOM SAMPLING With (SRSWR) and Without (SRSWOR) Replacement Advantages Disadvantages In practice, simple random sampling is rarely used because there is almost always a more efficient method of designing the sample (in terms of producing accurate results for a given cost). Nevertheless, simple random sampling forms the basis of a number of the more complex methods of sample design, and is used as a benchmark to which other designs are compared. USE OF AUXILIARY INFORMATION There is no point in using any other kind of sample selection method if one knows no more about the population to be sampled than the existence of each of the units in the population. However, some further information is often available about each of the population units from simple observation or data from a previous study (for example, a census). This information can be in the form of demographic variables such as age, sex and income or geographical/business types such as industry, employment, state, region and sector (private or public). This further information about the population units is called auxiliary information. Such information can be used in the selection and the estimation process to obtain more accurate estimates or reduce costs. Sampling techniques using this information include systematic sampling, stratified sampling (including post-stratification) and cluster sampling. Auxiliary information is also used in estimation techniques such as ratio and regression estimations. One of the major aspects of sample design is the efficient use of auxiliary or supplementary information. As there is often a cost involved in obtaining auxiliary information, it is necessary to quantify the gains that are obtained through using auxiliary information and balance it against the cost of acquisition. SYSTEMATIC
SAMPLING
Method
r, r+k, r+2*k, r+3*k,..., r+(n-1)*k. The value of k is usually not an integer. In this case we either
Example: Calculating the Skip Interval Order the population units in some way and number them from 1 to 37.
n = 5 k = 37/5 = 7.4 1. Round k to the nearest integer k = 7 r = 4 Then the sample units are : 2. Round r+ik to the nearest integer k = 7.4 r = 4.2 sample = ( 4.2, 11.6, 19, 26.4, 33.8) = ( 4, 12, 19, 26, 34) Features of Systematic Random Sampling Advantages
Disadvantages
Stratified Sampling Stratified sampling involves
Stratification almost always improves the accuracy of estimates. This is because the population variability can be thought of as having components within strata and between strata. By independently sampling within each stratum we ensure each stratum is appropriately reflected in the sample, so between stratum variability is eliminated and we are left only with the within stratum component. With this factor in mind we see that the most efficient way to stratify is to have strata which are as different from each other as possible (to maximise the variance which is being eliminated) while being internally as homogeneous as possible (to minimise the variance remaining). Practical Considerations
Example of Stratification If on the other hand, we were interested in the level of education (PhD, Masters, Bachelor) rather than the background we should stratify the faculty by level (Professor, Senior Lecturer, Lecturer) rather than by the department. Using this stratification we are more likely to find uniformity of educational standards within a level rather than an area of work, and we are also more likely to separate the better qualified from the less qualified. Advantages
Disadvantages
Number of Strata ABS Surveys Allocation of Sample
Another method of sample selection is to have a completely enumerated stratum in our sample. This is where the units that contribute significantly to our estimates are placed in a single stratum and every unit within the stratum is then selected. Estimation Similarly, standard errors or variances (measures of sample variability) are calculated for each stratum and then all strata specific variances are added up to obtain the overall variance. The addition of variances is possible because the sample is selected independently from each strata. This overall variance can then be used to calculate an overall standard error. Post Stratification Post-stratification is a method used when stratification is not possible before the survey. The stratification variable can then be used after the survey is conducted, to improve the efficiency of estimates or, to obtain estimates corresponding to different categories of that variable (eg. sex) by stratifying the sample as if the benchmark information had been available previously. CLUSTER AND MULTI-STAGE SAMPLING Cluster Sampling Practical Considerations
Advantages
Disadvantages For example, if we take a simple random sample of 10,000 households across the whole of Australia then we are more likely to cover the population more evenly, but it is more expensive than sampling 50 clusters of 200 households. Multi-stage Sampling At the second stage, units are sampled from the selected clusters to derive the final sample. If more than two stages are used, the process of selecting "sub-clusters" within clusters continues until the final sample is achieved. The same practical considerations apply to multi-stage sampling as to the cluster sampling. Example: A Three-Stage Sample
Uses of Multi-stage Sampling Advantages and Disadvantages SAMPLE SIZE ISSUES AND DETERMINATION
Once these issues have been addressed, you are in a better position to decide on the size of the sample.
Variability When the characteristic being measured is comparatively rare, a larger sample size will be required to ensure that sufficient units having that characteristic are included in the sample. Population Size Resources and Accuracy When planning a survey, you might wish to minimise the size of the standard error to maximise the accuracy of the estimates. This can be done by choosing as large a sample as resources permit. Alternatively, you might specify the size of the standard error to be achieved and choose a sample size designed to achieve that. In some cases it will cost too much to take the sample size required to achieve a certain level of accuracy. Decisions then need to be made on whether to relax the accuracy levels, reduce data requirements, increase the budget or reduce the cost of other areas in the survey process. Level of Detail Required Likely level of Non-response The second problem with non-respondents is that the characteristics of non-respondents may differ markedly from those of respondents. The survey results will still be biased even with an increase in sample size (ie. increasing the sample size will have no effect on the non-response bias). The lower the response rate, the less representative the final sample will be of the total population, and the bigger the bias of sample estimates. Non-response bias can sometimes be reduced by post-stratification as well as through intensive follow up of non-respondents, particularly in strata with poor response rates. Sampling Method Relative importance of the variables of interest Calculation of
sample size In practice, cost is a major consideration. Many surveys opt to maximise the accuracy of population estimates by choosing as large a sample as resources permit. In complex surveys, where estimates are required for population subgroups, enough units must be sampled from each subgroup to ensure reliable estimates at these levels. To select a sample in this case, you might specify the size of the standard error to be achieved within each subgroup and choose a sample size to produce that level of accuracy. The total sample is then formed by aggregating this sample over the subgroups. Sample size should also take into account the expected level of non-response from surveyed units. When the characteristic being measured is comparatively rare, a larger sample size will be required to ensure that sufficient units having that characteristic are included in the sample. Sample Size Formulae
where n = sample size, p = sample proportion, SE(p) = required standard error of the sample proportion However, to be able to use this formula, the proportion being estimated needs to be roughly known from supplementary information or a similar study conducted elsewhere. For example, suppose a survey seeks to estimate the proportion of Richmond residents in favour of Sunday night football at the MCG. The standard error (SE) desired is 0.04, while the proportion (p) in favour of the proposal is thought to be about 0.40. The size of the sample would need to be n=150. If this survey was then completed with a sample size of n=150 and it was a found that the sample proportion (p) in favour of the proposal was 0.8 (not 0.4 as guessed), then the standard error of this sample proportion of 0.8 would be 0.033 not 0.04 as originally planned for. A proportion of 0.5 gives the highest standard error for a fixed sample size or, requires the highest sample size for a fixed standard error, hence p=0.5 is the worst case scenario. It is for this reason that an estimate of p=0.5 is often used when calculating sample sizes when there is no information on the proportion to be estimated. Example: Gains From Sampling
The gains from employing sampling are greatest when working with large populations. What kind of sampling is used when participants are grouped according to a set of criteria specified in your research?Stratified sampling: in this type of sampling, the target population is first divided into separate strata. Then, samples are selected within each stratum, either through simple or systematic sampling.
What sampling is group by group selection of sample?Cluster sampling
Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample.
Which sampling procedure is considered when all the members of the sample have the same characteristic or traits?Quota sampling: In Quota sampling, the selection of members in this sampling technique happens based on a pre-set standard. In this case, as a sample is formed based on specific attributes, the created sample will have the same qualities found in the total population.
Which sampling technique is done by choosing people from whom you are sure to correspond to the objectives of your study?Snowball Sampling
In this technique, you rely on your initial respondents to refer you to the next respondents whom you may connect with for the purpose of your survey.
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