scholarly journals  Efficiency of adaptive cluster sampling and traditional sampling for coastal mangrove in Hainan of China

2012 ◽  
Vol 58 (No. 9) ◽  
pp. 381-390 ◽  
Author(s):  
Y. Lei ◽  
J. Shi ◽  
T. Zhao
Keyword(s):  
Sampling Method ◽  
Sampling Methods ◽  
Unit Area ◽  
Simple Random Sampling ◽  
Systematic Sampling ◽  
Cluster Sampling ◽  
Density Estimator ◽  
Adaptive Cluster Sampling ◽  
Initial Sample ◽  
Initial Sampling

Based on two species of Coastal Mangrove in Hainan of China, Sonneratia Apetala Buch-Ham and Sonneratia caseoli, we estimated the density of the two species to evaluate the efficiency of adaptive cluster sampling (ACS), simple random sampling (SRS) and traditional systematic sampling (SYS). Our initial experimental designs for ACS consisted of 5 unit areas, 6 initial sampling proportions, 4 initial sample sizes and 5 criterion values in 1,000&nbsp;repetitions. From the aspect of factors influencing efficiency, we analysed the efficiency of ACS in various designs. We also compared the efficiencies of the three methods on the indexes of the relative error, the variance of density estimator and the relative sampling efficiencies. We found that ACS yielded smaller variance than the traditional sampling methods. ACS was a powerful sampling method when a population was spatially aggregated. We also determined the optimum unit area for the two species studied using the two estimators (HT and HH) of adaptive cluster sampling. They were 20&nbsp;m<sup>2 </sup>(2 &times; 10 m), 15 m<sup>2 </sup>(3 &times; 5 m) for S. Apetala Buch-Ham and 25 m<sup>2 </sup>(5 &times; 5 m), 15 m<sup>2 </sup>(3 &times; 5 m) for S. caseolari, respectively.

A beginner's Guide to Sampling Methods in Medical Research

2021 ◽  
Author(s):  
Moslem Basti ◽  
Farzan Madadizadeh
Keyword(s):  
Random Sampling ◽  
Sampling Method ◽  
Sampling Methods ◽  
Simple Random Sampling ◽  
Cluster Sampling ◽  
Stratified Random Sampling ◽  
Systematic Random Sampling ◽  
Probabilistic Sampling ◽  
Multi Stage ◽  
Main Components

Background: Sampling methods are one of the main components of each research. Familiarity with a variety of sampling methods is essential for researchers. Objective: The main purpose of this study was to teach different probabilistic and non-probabilistic sampling methods to improve the knowledge of researchers in conducting more accurate research. Methods: In this tutorial article, useful information about each sampling method, as well as how to properly use each method and its strengths and weaknesses are provided. Results: Five cases of probabilistic sampling methods and four cases of non-probabilistic sampling methods that are common are mentioned. Probabilistic sampling included simple random sampling, stratified random sampling, cluster sampling, systematic random sampling, and multi-stage random sampling. In addition to introducing each method, its strengths and weaknesses are also mentioned. Conclusion: Probabilistic sampling methods despite limiting assumptions provide more reliable results. Therefore, if it is possible, researchers should use probabilistic sampling methods to increase the accuracy of the study.

scholarly journals Efficiency of Adaptive Cluster Sampling for Estimating Plant Disease Incidence

2010 ◽  
Vol 100 (7) ◽  
pp. 663-670 ◽  
Author(s):  
P. S. Ojiambo ◽  
H. Scherm
Keyword(s):  
Sample Size ◽  
Random Sampling ◽  
Disease Incidence ◽  
Simple Random Sampling ◽  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Initial Sample ◽  
Animal Populations ◽  
Final Sample ◽  
Minimum Number

Conventional sampling designs such as simple random sampling (SRS) tend to be inefficient when assessing rare and highly clustered populations because most of the time is spent evaluating empty quadrats, leading to high error variances and high cost. In previous studies with rare plant and animal populations, adaptive cluster sampling, where sampling occurs preferentially in the neighborhood of quadrats in which the species of interest is detected during the sampling bout, has been shown to estimate population parameters with greater precision at an effort comparable to SRS. Here, we use computer simulations to evaluate the efficiency of adaptive cluster sampling for estimating low levels of disease incidence (0.1, 0.5, 1.0, and 5.0%) at various levels of aggregation of infected plants having variance-to-mean ratios (V/M) of ≈1, 3, 5, and 10. For each simulation, an initial sample size of 50, 100, and 150 quadrats was evaluated, and the condition to adapt neighborhood sampling (CA), i.e., the minimum number of infected plants per quadrat that triggers a switch from random sampling to sampling in neighboring quadrats, was varied from 1 to 4 (corresponding to 7.7 to 30.8% incidence of infected plants per quadrat). The simulations showed that cluster sampling was consistently more precise than SRS at a field-level disease incidence of 0.1 and 0.5%, especially when diseased plants were highly aggregated (V/M = 5 or 10) and when the most liberal condition to adapt (CA = 1) was used. One drawback of adaptive cluster sampling is that the final sample size is unknown at the beginning of the sampling bout because it depends on how often neighborhood sampling is triggered. In our simulations, the final sample size was close to the initial sample size for disease incidence up to 1.0%, especially when a more conservative condition to adapt (CA > 1) was used. For these conditions, the effect of disease aggregation was minor. In summary, both precision and the sample size required with adaptive cluster sampling responded similarly to disease incidence and aggregation, i.e., both were most favorable at the lowest disease incidence with the highest levels of clustering. However, whereas relative precision was optimized with the most liberal condition to adapt, the ratio of final to initial sample size was best for more conservative CA values, indicating a tradeoff. In our simulations, precision and final sample size were both simultaneously favorable for disease incidence of up to 1.0%, but only when infected plants were most aggregated (V/M = 10).

scholarly journals On Using Adaptive Cluster Sampling Design in Forest Inventory: It's Implication to Forest Biodiversity Status Reporting

2009 ◽  
Vol 9 (1) ◽  
pp. 56-66
Author(s):  
Dennis Peque ◽  
Keyword(s):  
Sampling Design ◽  
Systematic Sampling ◽  
Cluster Sampling ◽  
Forest Biodiversity ◽  
Adaptive Cluster Sampling ◽  
State University ◽  
Statistical Efficiency ◽  
Forest Reserve ◽  
Sparse Population ◽  
Combined Data

This paper presents adaptive cluster sampling (ACS) as a method of assessing forest biodiversity. In this study, ACS was used to estimate the abundance of ecologically sparse population of Diospyros philippinensis (Desrousseaux) within the Visayas State University Forest Reserve. Its statistical efficiency were analyzed by comparing them to the conventional systematic sampling (Syst) estimator. Results indicated that adaptive cluster sampling (ACS) plots captured more trees into the sample compared to systematic sampling (Syst) plots. In addition, ACS estimates for mean and total numbers of individuals per ha was higher than systematic sampling estimates and in terms of variance ACS gave substantially lower variance than systematic sampling. However, the ratio of the adjusted SE of ACS to the adjusted SE of systematic sampling for each species and the combined data of the two species was generally lesser than 1 which means that ACS was not a better design than systematic sampling.

Analyses of Data from Complex Survey Sampling

2018 ◽  
Author(s):  
Peter Miksza ◽  
Kenneth Elpus
Keyword(s):  
Survey Data ◽  
Random Sampling ◽  
Sampling Methods ◽  
Simple Random Sampling ◽  
Survey Sampling ◽  
Cluster Sampling ◽  
Complex Survey ◽  
Complex Survey Data ◽  
Survey Sample ◽  
Proper Analysis

This chapter introduces the specialized techniques necessary for analyzing data that have been gathered in a complex or multistage survey sample. The chapter details the methods most commonly used to collect complex survey data and then explains the specific statistical tools that must be employed to correctly analyze complex survey data. First, an overview of the various types of sampling methods is presented, beginning with simple random sampling and moving through other methods to finally discuss the commonly employed research techniques of cluster sampling. The chapter continues with a discussion of survey weights—what they mean and how they are derived. The chapter concludes with software-based suggestions on the proper analysis of survey data.

scholarly journals SAMPLING METHODS AND NUMBER OF ROUNDWOODS FOR DETERMINING THE MOISTURE CONTENT

2018 ◽  
Vol 42 (4) ◽  
Author(s):  
Danilo Barros Donato ◽  
Renato Vinícius Oliveira Castro ◽  
Angélica de Cássia Oliveira Carneiro ◽  
Ana Márcia Macedo Ladeira Carvalho ◽  
Benedito Rocha Vital ◽  
...  
Keyword(s):  
Moisture Content ◽  
Random Sampling ◽  
Sampling Method ◽  
Optimal Allocation ◽  
Sampling Methods ◽  
Simple Random Sampling ◽  
Optimum Number ◽  
Average Standard Deviation ◽  
Proportional Allocation ◽  
Allowable Error

ABSTRACT This study had the objective of comparing two methodologies of sampling, Simple Random Sampling (ACS) and Stratified Random Sampling (ACE) to determine the optimum number of roundwood samples to obtain the moisture content of the population. In order to achieve this goal, different percentages of allowable error (5,10,15 and 20%) were considered for each sampling methodology. In the conduction of this study, the samples were randomly taken from a lot of 250 steres of wood, 144 roundwood of three meters of length and distributed in four classes of diameter. Later, the moisture content of these samples was determined. And, from these values, the population estimates (average, standard deviation, variance, coefficient of variation, and standard error) by ACS and ACE methods, helped to determine the optimum number of roundwood (n) to be sampled from different percentages of allowable error adopted in this study at 95% probability. According to the results, the amount of roundwood to be sampled from ACS for each allowable error 5, 10, 15 and 20% was respectively 214, 55, 25 and 14. For the ACE (proportional allocation) the amount of roundwood was 141, 35, 16 and 9 for ACE (optimal allocation) this number was 136, 34, 15 and 8. It was concluded that the most indicated sampling method for this study, considering the allowable error, was the ACE method.

scholarly journals Field sampling methods for investigating forest-floor bryophytes: Microcoenose vs. random sampling

2018 ◽  
Vol 70 (3) ◽  
pp. 589-598
Author(s):  
Milos Ilic ◽  
Ruzica Igic ◽  
Mirjana Cuk ◽  
Dragana Vukov
Keyword(s):  
Species Richness ◽  
Random Sampling ◽  
Forest Floor ◽  
Sampling Method ◽  
Sampling Methods ◽  
Diversity Index ◽  
Systematic Sampling ◽  
Field Sampling ◽  
Beech Forests ◽  
Quadrat Size

Because of the high importance of bryophytes in forest ecosystems, it is necessary to develop standardized field sampling methodologies. The quadrat method is commonly used for bryophyte diversity and distribution pattern surveys. Quadrat size and the position of quadrats within the studied area have a significant influence on different analyses. The aim of the present study was to define the minimum quadrat size appropriate for sampling ground bryophytes in temperate beech forests, to compare two different field sampling methods for research on ground bryophytes, the random and microcoenose methods; and to test the adequacy of the microcoenose sampling method in temperate beech forests. Research was carried out on Fruska Gora mountain (Serbia) at four different sites. All sites contained temperate broadleaf forest vegetation, predominantly Fagus sylvatica, but also included various other tree species. Systematic sampling based on nested quadrats was used to determine the minimum sampling area. Random sampling was performed using 10 or 20 microplots (minimum area quadrat), randomly located within 10x10 m plots. Microcoenose sampling is a systematic sampling method based on the fact that every bryophyte fragment on the forest floor is a separate microcoenose. These methods were compared using the following criteria: species richness; Shannon?s diversity index and evenness measure; coverage of dominant species, and the time needed for sampling. The microcoenose sampling method has proven to be highly applicable in temperate beech forests in terms of species richness and diversity, in contrast to random sampling, which was not suitable for bryophyte flora with a patchy distribution.

scholarly journals A performance comparison of sampling methods in the assessment of species composition patterns and environment–vegetation relationships in species-rich grasslands

2017 ◽  
Vol 86 (4) ◽  
Author(s):  
Grzegorz Swacha ◽  
Zoltán Botta-Dukát ◽  
Zygmunt Kącki ◽  
Daniel Pruchniewicz ◽  
Ludwik Żołnierz
Keyword(s):  
Species Composition ◽  
Sampling Method ◽  
Sampling Methods ◽  
Vegetation Type ◽  
Habitat Type ◽  
Spatial Arrangement ◽  
Performance Comparison ◽  
Systematic Sampling ◽  
Soil Parameters ◽  
Preferential Sampling

The influence that different sampling methods have on the results and the interpretation of vegetation analysis has been much debated, but little is yet known about how the spatial arrangement of samples affect patterns of species composition and environment–vegetation relationships within the same vegetation type. We compared three data sets of the same sample size obtained by three standard sampling methods: preferential, random, and systematic. These different sampling methods were applied to a study area comprising of 36 ha of intermittently wet <em>Molinia</em> meadows. We compared the performance of the three methods under two management categories: managed (extensively mown) and unmanaged (abandoned for 10 years). A total of 285 vegetation-plots were sampled, with 95 plots recorded per sampling method. In preferential sampling, we sampled only patches of vegetation with an abundance of indicator species of the habitat type, while random and systematic plots were positioned independently from the researcher by using GIS. The effect of each sampling method on the patterns of species composition and species–environment relationships was explored by redundancy analysis and the significance of effects was tested by the randomization test. Preferential sampling revealed different patterns of species composition than random and systematic sampling methods. Random and systematic sampling methods have resulted in broader vegetation variability than with preferential sampling method. Preferential sampling revealed different relationship between soil parameters and species composition in contrast to random and systematic sampling methods. Although we have not found significant differences in vegetation–environment relationships between random and systematic sampling methods, random sampling revealed a more robust correlation of species data to soil factors than preferential and systematic sampling methods. Intentional restriction of vegetation variation sampled preferentially may be detrimental to statistical inference in studies of species composition patterns and vegetation–environment relationships.

scholarly journals Domestic injuries among geriatric population: a study in a rural community of Bankura district

2019 ◽  
Vol 6 (12) ◽  
pp. 5100
Author(s):  
Arindam De ◽  
Indu Padmey ◽  
Debakar Halder ◽  
Eashin Gazi ◽  
Aditya Prasad Sarkar ◽  
...  
Keyword(s):  
Random Sampling ◽  
Sampling Method ◽  
Longitudinal Design ◽  
Public Health Problem ◽  
Positive Association ◽  
Simple Random Sampling ◽  
Common Type ◽  
Cluster Sampling ◽  
Geriatric Population ◽  
Cross Sectional

Background: Domestic injury is an injury, which takes place in the home or in its immediate surroundings and more generally, all injury not connected with traffic, vehicles or sport. It is a worldwide public health problem. Geriatric population is more vulnerable to domestic injury. Objectives of this study are to estimate the incidence and to identify the correlates, if any, of domestic injuries among geriatric population and to study the consequences of domestic injuries among study subjects.Methods: Community-based descriptive study with longitudinal design. Multistage random sampling was adopted in the study. One block was selected by simple random sampling method then cluster sampling method (30/7) was used considering village as cluster. Three cross-sectional surveys were conducted in study subjects. Data was collected with the help of pre-designed, pre-tested, semi-structured schedule by paying house-to-house visits and review of records.Results: The subjects under study comprised of 210 elderly individuals, out of which 27 faced domestic injuries and three study subjects faced injury twice in study period. So, total number of injured was 30. Incidence rate was calculated to be 142.85 injuries per thousand persons per year. Fall was most common type of domestic injury. According to the consequence of injury, impairment was found in 13 cases out of them two injured cases were suffered from permanent disability.Conclusions: Incidence was estimated to be higher than what was found in other studies. Fall was the most common type of domestic injury. Marital status, use of central nervous system depressant drugs and co-morbidities were found to have positive association with injury. 

scholarly journals Assessing network structure with practical sampling methods

2018 ◽  
Author(s):  
Georgiy Bobashev ◽  
R. Joey Morris ◽  
Elizabeth Costenbader ◽  
Kyle Vincent
Keyword(s):  
Network Structure ◽  
Sampling Method ◽  
Sampling Design ◽  
Sampling Methods ◽  
Simple Random Sampling ◽  
Underlying Structure ◽  
Snowball Sampling ◽  
Using Data ◽  
Network Metrics ◽  
Selection Of

Using data from an enumerated network of worldwide flight connections between airports, we examine how sampling designs and sample size influence network metrics. Specifically, we apply three types of sampling designs: simple random sampling, nonrandom strategic sampling (i.e., selection of the largest airports), and a variation of snowball sampling. For the latter sampling method, we design what we refer to as a controlled snowball sampling design, which selects nodes in a manner analogous to a respondent-driven sampling design. For each design, we evaluate five commonly used measures of network structure and examine the percentage of total air traffic accounted for by each design. The empirical application shows that (1) the random and controlled snowball sampling designs give rise to more efficient estimates of the true underlying structure, and (2) the strategic sampling method can account for a greater proportion of the total number of passenger movements occurring in the network.

scholarly journals Adaptive cluster sampling based on ranked sets

2011 ◽  
Vol 8 (1) ◽  
Author(s):  
Girish Chandra ◽  
Neeraj Tiwari ◽  
Hukum Chandra
Keyword(s):  
Hot Spot ◽  
Simple Random Sampling ◽  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Rare And Endangered Species ◽  
Rare And Endangered ◽  
Mean And Variance ◽  
Unequal Allocation ◽  
Pollution Concentrations ◽  
Better Than

In many surveys, characteristic of interest is sparsely distributed but highly aggregated; in such situations the adaptive cluster sampling is very useful. Examples of such populations can be found in fisheries, mineral investigations (unevenly distributed ore concentrations), animal and plant populations (rare and endangered species), pollution concentrations and hot spot investigations, and epidemiology of rare diseases. Ranked Set Sampling (RSS) is another useful technique for improving the estimates of mean and variance when the sampling units in a study can be more easily ranked than measured. Under equal and unequal allocation, RSS is found to be more precise than simple random sampling, as it contains information about each order statistics. This paper deal with the problem in which the value of the characteristic under study on the sampled places is low or negligible but the neighbourhoods of these places may have a few scattered pockets of the same. We proposed an adaptive cluster sampling theory based on ranked sets. Different estimators of the population mean are considered and the proposed design is demonstrated with the help of one simple example of small populations. The proposed procedure appears to perform better than the existing procedures of adaptive cluster sampling.

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