Another disadvantage of SyS is that it may be difficult to apply for some finite populations e. SyS typically has less flexibility to change the sample point density or add new points without updating the whole design. If spatial sampling is of interest, random-tessellation stratified RTS study designs help to resolve some of the issues concerning periodic or patterned responses not handled by SyS designs. RTS study designs work by randomly locating a regular grid over the population domain with spacing chosen to provide the required spatial resolution.
Random points from within each random tessellation cell are selected to form the required sample. You need a good descriptive base of background information so that SyS can be both cost-effective and unbiased, and you must document the assumptions and choices made when executing such a sampling regime. A random sample of primary units are taken in the first stage, and sampling units are selected either randomly or systematically from the selected primary units in the second stage. For example, rivers and streams within a subregion may be randomly chosen, and then sites within those selected rivers randomly chosen.
This differs from cluster sampling, where all the sampling units in selected primary units are sampled. Stratified random sampling divides the population into a collection of strata that exhaustively cover the region of interest. In this method, the population is first divided into subgroups or strata who all share a similar characteristic. It is used when we might reasonably expect the measurement of interest to vary between the different subgroups, and we want to ensure representation from all the subgroups.
For example, in a study of stroke outcomes, we may stratify the population by sex, to ensure equal representation of men and women. The study sample is then obtained by taking equal sample sizes from each stratum. In stratified sampling, it may also be appropriate to choose non-equal sample sizes from each stratum. For example, in a study of the health outcomes of nursing staff in a county, if there are three hospitals each with different numbers of nursing staff hospital A has nurses, hospital B has and hospital C has , then it would be appropriate to choose the sample numbers from each hospital proportionally e.
This ensures a more realistic and accurate estimation of the health outcomes of nurses across the county, whereas simple random sampling would over-represent nurses from hospitals A and B.
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The fact that the sample was stratified should be taken into account at the analysis stage. Stratified sampling improves the accuracy and representativeness of the results by reducing sampling bias.
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However, it requires knowledge of the appropriate characteristics of the sampling frame the details of which are not always available , and it can be difficult to decide which characteristic s to stratify by. In a clustered sample, subgroups of the population are used as the sampling unit, rather than individuals.
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The population is divided into subgroups, known as clusters, which are randomly selected to be included in the study. Clusters are usually already defined, for example individual GP practices or towns could be identified as clusters. In single-stage cluster sampling, all members of the chosen clusters are then included in the study. In two-stage cluster sampling, a selection of individuals from each cluster is then randomly selected for inclusion. Clustering should be taken into account in the analysis.
The General Household survey, which is undertaken annually in England, is a good example of a one-stage cluster sample. Cluster sampling can be more efficient that simple random sampling, especially where a study takes place over a wide geographical region.
For instance, it is easier to contact lots of individuals in a few GP practices than a few individuals in many different GP practices. Disadvantages include an increased risk of bias, if the chosen clusters are not representative of the population, resulting in an increased sampling error. Convenience sampling is perhaps the easiest method of sampling, because participants are selected based on availability and willingness to take part.
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Useful results can be obtained, but the results are prone to significant bias, because those who volunteer to take part may be different from those who choose not to volunteer bias , and the sample may not be representative of other characteristics, such as age or sex. Note: volunteer bias is a risk of all non-probability sampling methods.
This method of sampling is often used by market researchers. Interviewers are given a quota of subjects of a specified type to attempt to recruit. For example, an interviewer might be told to go out and select 20 adult men, 20 adult women, 10 teenage girls and 10 teenage boys so that they could interview them about their television viewing. Ideally the quotas chosen would proportionally represent the characteristics of the underlying population. Also known as selective, or subjective, sampling, this technique relies on the judgement of the researcher when choosing who to ask to participate.
Key topics in survey methodology are clearly explained in the book's chapters, with coverage including sampling frame evaluation, sample design, development of questionnaires, evaluation of questions, alternative modes of data collection, interviewing, nonresponse, post-collection processing of survey data, and practices for maintaining scientific integrity.
Now in its third edition, this popular sampling text continues to provide a highly readable, practical treatment of the subject. Keeping the mathematics to a minimum, it walks the reader through real—world sample surveys—from sampling designs to problems of missing data and nonresponse to estimation procedures. This expanded and updated edition reflects the many developments in the field since the publication of the Second Edition, including the latest methods of multistage sampling, analysis of sample survey data, and software manipulation.
Improving Surveys with Paradata: Analytic Uses of Process Information by Frauke Kreuter
Lepkowski, who have made many important contributions in the area of telephone surveys. Replete with examples and exercises, done with excellent organization structure and enhanced by annotated bibliographies, this book should continue to enjoy success as a classroom choice.
Technometrics, August , Vol. PAUL S. He is a Fellow of both the American Statistical Association and the American College of Epidemiology and has been widely published during his long and distinguished career as a statistician and epidemiologist. Most recently he served as section editor for design of experiments and sample surveys of the Encyclopedia of Biostatistics. He is a Fellow of the American Statistical Association and has published numerous articles in statistical and biomedical journals.
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