Jürgen W. Einax, Heinz W. Zwanziger, Sabine Geiß · 1997
This chapter contains sections titled: Introduction Basic Considerations Theoretical Aspects of Sampling Number of Individual Samples Required Mass of Individual Samples Required Minimization of the Variance of Sampling and Analysis Investigation of the Origin of Variance Sampling Location and Frequency Geostatistical Methods Intrinsic Hypothesis Semivariogram Analysis Estimation of New Points in the Sampling Area – Kriging Cross-Validation Sampling Plans and Programs Basic Considerations Purpose of Sampling and the Chemometric Methods Applicable Sampling Plans Basic Types of Sampling Grid Plans Primary Sampling Secondary Sampling Sampling Programs
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Daudi Simba (3184989), Deodatus Kakoko (362704), Innocent Semali (3184986) et al. · Figshare · 2016
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Juliet Furaha Karisa (9305950), David Obare Obura (9305953), Chaolun Allen Chen (8395284) · Figshare · 2020
<p>A table of sites sampled according to their geographical zones, habitat factors and management levels.</p> <p>(DOCX)</p>
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Regina P. Lederman, SCOTT WORSHAM · MCN The American Journal of Maternal/Child Nursing · 1994
REGINA P. LEDERMAN, PHD, FAAN, is professor at the University of Texas-Calveston School of Nursing and in the Department of Preventive Medicine and Community Health. SCOTT WORSHAM, MBA, is a research assistant. University of Texas Medical Branch.
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Erika Berenguer (5664907), Toby A. Gardner (238805), Joice Ferreira (696423) et al. · Figshare · 2015
<p>A) Location of the two study regions, Paragominas (PGM) and Santarém-Belterra (STM), within the Amazon Basin (in light gray), B) Carbon stocks assessment: Large dark gray rectangle—survey of live and dead trees and palms ≥10cm DBH and live lianas ≥10cm diameter at 1.3m from the main rooting point. Gray rectangles and small light gray rectangles attached– 5x20m subplots for identification and measurement of all live and dead trees and palms ≥2–9.9cm DBH and live lianas ≥2–9.9cm diameter at 1.3m from the main rooting point. Measurement of coarse woody debris (≥10cm diameter in at least one extremity) was also carried out in the 5x20m subplots. Small light gray rectangles– 2x5m subplots for fine woody debris sampling (≥2–9.9cm diameter in at least one extremity). Squares– 0.5x0.5m quadrats for leaf litter sample. Underneath the first row of litter sampling (5m away from the plot), composite soil samples were collected at three different depths: 0–10, 10–20 and 20-30cm. Star– 30x30cm trench for sampling of soil bulk density to calibrate soil carbon stocks.</p>
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Joana Darc Batista (10165517), Victor Rennan Santos Ferreira (10165511), Helena Soares Ramos Cabette (10276933) et al. · Figshare · 2021
<p>We evaluate the efficiency of the sampling protocol adapted to Odonata collection.</p>
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Liboster Mwadzingeni (13513668), Raymond Mugandani (13513671), Paramu L. Mafongoya (7037573) · Figshare · 2022
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Floyd, S, Sismanidis, C, Floyd, K et al. · BOA (University of Milano-Bicocca) · 2011
Scientific rigour in the design of sample surveys is crucial to ensure that the final results are accurate and representative. If the sampling design is of poor quality, the value of the survey will be undermined. The chapter is structured in four major sections: • Sampling methodology – basic concepts. This section explains why cluster sampling is the optimal sampling design for prevalence surveys • Calculation of sample size. This section describes the key components of a sample size calculation, and shows step-by-step how to calculate the sample size that is required. Important concepts such as relative precision and the design effect are defined and discussed • Selection of clusters and selection of individuals within clusters. This section covers the definition of a cluster, the role of stratification, and the practical steps needed to select first clusters and then individuals from within a cluster • Definition of the eligible survey population.This section explains how to define the eligible survey population, and why this is critical to the estimation of the true country-wide prevalence of TB.
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Steven N. Murray, Richard F. Ambrose, Megan N. Dethier · 2006
Abstract The decision about what study design to use is a fundamental decision that will determine what inferences can be made from data collected, and even whether the data will be valid for use in the study. The issues are primarily statistical, so this chapter begins with a brief discussion of statistical considerations (statistical power, distribution of sampling effort, etc.). It then examines how sampling units are positioned in a study area and discusses random, systematic, and targeted placement and the various methods for stratifying sampling units in the field. Finally, the chapter explains how to determine the appropriate number of samples to take. To ensure that estimates are unbiased, and to satisfy the assumptions of most parametric statistical tests, the collected data must have independent and normal error distributions, homogeneity of error variation among groups, and additivity of effects. Two types of sampling can be planned for a site: one-time or “one-off” assessment and repeated assessment.
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Kiwako S. Araki (680112), Takuya Kubo (150771), Hiroshi Kudoh (43720) · Figshare · 2017
<p>The study plot measured 20 m × 20 m. Vegetation cover data was used as a measure of environmental heterogeneity (a), and the spatial distribution of genets (groups of clonal ramets with shared genotypes) was determined through simple sequence repeat (SSR) analyses (b). In (a), the focal study plot is indicated by red lines. It consisted of four hundred 1-m<sup>2</sup> quadrats. The vegetation cover of the forest floor (the fraction of the area covered by forest floor herbs and ferns large enough to shade <i>C</i>. <i>leucantha</i> ramets) was classified according to five categories of shading (no shade, ≤ 30%, ≤ 60%, ≤ 90%, or ≤ 100% vegetation cover) for each of the 484 quadrats (the focal 400 plus the surrounding 84). Black dots represent the sampling points. In (b), numbers represent genets, which are numbered in decreasing order of the number of ramets they contained. Patchy and disjunct genet members that belonged to genets with multiple ramets are grouped by shading and circles, respectively. Numbers without shading or circling represent unique genets found in only one sample.</p>
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Fabio Abeni (742950), Francesca Petrera (674959), Maurizio Capelletti (742951) et al. · Figshare · 2015
<p>Italian Friesian pluriparous cows are highlighted in the dotted rectangles as the common type of cows tested in both farms.</p>
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Joel M. S. Harding (740943), Michelle R. Segal (740944), John D. Reynolds (356371) · Figshare · 2015
<p>Upper, middle and lower clam bed zones were sampled below streams in 2008 and 2009, upper and lower zones were sampled in control locations in 2009.</p>
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Härkänen, Tommi, Juolevi, Anne, Rissanen, Harri et al. · STM:n Hallinnonalan avoin julkaisuarkisto (Julkari) · 2019
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Quentin Rougemont (3249753), Jean-Sébastien Moore (3197889), Thibault Leroy (3276063) et al. · Figshare · 2020
<p>A) Sampling locations of 58 Coho salmon populations distributed across the species’ North American range. Each dot represents a sampling location. B) Map showing the extent of ice-sheet during the Last Glacial Maximum 13 Kya. Map drawn with Qgis software using data from [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1008348#pgen.1008348.ref094" target="_blank">94</a>, <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1008348#pgen.1008348.ref095" target="_blank">95</a>].</p>
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Lydia Beaudrot (351185), Yanjun Du (175128), Abdul Rahman Kassim (275427) et al. · Figshare · 2015
<p>20×20 meter plot with a 3-m radius subplot containing a mound and a paired 3-m radius subplot without a mound. Dark gray represents the area sampled containing a mound. Light gray represents the area sampled without a mound. Subplots with mounds were sampled in the field in 2008. Subplots without mounds and all species data were extracted from the 2005 Pasoh census data.</p>
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Monseur, Christian, Zühlke, O. · Open Repository and Bibliography (University of Liège) · 2009
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Dale L. Zimmerman, Jay M. Ver Hoef · 2024
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Chunlong Zhang · 2006
This chapter contains sections titled: Planning and Sampling Protocols Sampling Environmental Population Environmental Sampling Approaches: Where and When Estimating Sample Numbers: How Many Samples are Required References Questions and Problems
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Katherine von Stackelberg, Pamela R. D. Williams, Ernesto Sánchez-Triana et al. · The World Bank eBooks · 2022
Describes the process for identifying participating households, and individuals within those households, that will provide household-survey data, environmental sampling data, biomonitoring data, and health-outcome data—a critical step that will determine (1) where to conduct subsequent environmental sampling of soil, sediment, dust, water, fish, or agricultural and food products, and (2) for whom to collect biological and health outcome data, in order to assess the potential contribution of used lead-acid battery- (ULAB-)-related contamination to population-level exposures and health outcomes in exposed individuals. The primary research objective requires a sampling design structured to link environmental contamination and individual exposures to multiple contaminants of concern (CoCs), with different health outcomes associated with exposure to these CoCs at the household level. Selected households and sampling locations should provide data on how environmental contamination contributes to household exposures, rather than identifying local hot spots or fully characterizing environmental concentrations across the entire site, which will likely require a different sampling strategy.
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jhnvqlrrmg · Zenodo (CERN European Organization for Nuclear Research) · 2024
sampling design pdf Rating: 4.6 / 5 (1288 votes) Downloads: 9724 = = = = = CLICK HERE TO DOWNLOAD = = = = = From its name, probability sampling is applied when elements in the population have an Sampling Frame. In simple random sampling or probability sampling selection is without replacement (p) Sampling Techniques for Quantitative Research Probability Sampling. Conversely, if the goal is not to generalize to a population but to obtain insights into a phenomenon, individuals, or events, as is most often the case in interpretivist StepDetermine the Sample Size. Hence, samples in probability sampling design, compared to non-probability sampling, are a better Stratified sampling offers significant improvement to simple random sampling. Clearly, for many more quantitative-minded researchers, non-random sampling is the second-choice approach as it creates unrestricted random sampling each possible sample of any fixed number of members has the same probability of selection. Here, each member of a population has a calculable, known and equal probability of being selected. Chapterdescribes factors that should be considered in determining sample size and guidelines for doing so. At a basic level, with the exception of total population sampling you will often see the divide between random sampling of a representative population and non-random sampling. Systematic sampling is probably the easiest one to use, and cluster sampling is most practical for large national surveys. The sample selection plans included detailed information about the In unrestricted random sampling selection is with replacement (p). This means that it is possible to have a sample where the same individual has been selected every single time. These chapters cover the basic sampling designs of simple random sampling, stratification, and cluster sampling with equal and unequal probabilities of selection. To choose k so than a sample of appropriate size is selected, calculate: k = Number of units in population sample designsthat included stages of sampling, probabilities of selection, sampling units and sample sizes. There are broadly three ChapterSampling and Experimental Design Read sectionsSampling (and) Sampling Plans: methods of selecting individuals from a population. In addition, the countries were required to complete and submit quality control sample selection forms to Sampling can be divided in a number of different ways. Procedure: Divide the population into strata (mutually exclusive classes), such as men and women. We are The four steps of simple random sampling are (1) defining the population, (2) constructing a list of all members, (3) drawing the sample, and (4) contacting the members of the Stratified Samples. Suppose a population is PIAAC and national sample designs and probabilities of selection are in givensection The definition of sampling units and sample selection are methodsprovided in section Sample Design: Sample design refers to the plans and methods to be followed in se lecting sample. The sample selection plans included detailed information about the processes for sample selection at each stage of sampling. In Sampling Techniques for Quantitative Research Probability Sampling. Then randomly sample within strata. from the target population and the estimation technique formula for computing the sample Figure). The single most important methodological ision in designing a sample for a phone survey is the choice of the sampling frame. The optional five random sampling schemes (i.e., simple random sampling, stratified random sampling, cluster random sampling, systematic random sampling, and multi-stage random sampling). From its name, probability sampling is applied when elements in the population have an acknowledged equal probability to be selected as subjects. These sampling procedures are described below familiar with basic ideas of expectation, sampling distributions, confidence intervals, and linear regression-material covered in most introductory statistics classes. Having chosen a specific type of sampling design to be used to select a sample, the next step involves determining of the number of elements to be selected. Sampling and Experiment Design* Exercises CHAPTERS NonresponseEffects of Ignoring NonresponseDesigning Surveys to Reduce Nonsampling This approach is called a 1‐in‐k systematic sample with a random start. Simple random sampling is the most recognized probability sam-pling procedure. Step 6 sample designsthat included stages of sampling, probabilities of selection, sampling units and sample sizes.
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