Influence of Sample Size and Sampling Design on Analysis of Avian Foraging Behavior

The Condor ◽  
1984 ◽  
Vol 86 (2) ◽  
pp. 146 ◽  
Author(s):  
Michael L. Morrison
Keyword(s):  
Sample Size ◽  
Foraging Behavior ◽  
Sampling Design

Optimal design with variable cost and precision requirements

1989 ◽  
Vol 19 (12) ◽  
pp. 1591-1597
Author(s):  
Margaret Penner
Keyword(s):  
Biological Sciences ◽  
Optimal Design ◽  
Sample Size ◽  
Design Theory ◽  
Sampling Design ◽  
Unit Cost ◽  
Evaluation Process ◽  
Variable Cost ◽  
Design Flexibility ◽  
The Cost

A method for incorporating variable costs and differing precision requirements into optimal design theory is developed and discussed. In many studies and experiments, particularly in the biological sciences, the cost of each observation can vary considerably depending on the attributes of the sample. Ignoring observation costs leads to designs that maximize precision for a given sample size. However, by incorporating costs, efficiency is maximized by optimizing precision per unit cost. An example is presented that demonstrates the efficiency of a weighted optimal design in comparison with several alternatives. The weighted optimal design is most efficient at meeting the experimenter's precision objectives. Comparing designs allows the introduction of additional criteria such as design flexibility into the evaluation process. Explicitly incorporating both cost and precision in the search for a sampling design ensures time is wisely spent considering study objectives, including precision requirements.

scholarly journals A comparative study of optimization methods and conventional methods for sampling design in fishery-independent surveys

2009 ◽  
Vol 66 (9) ◽  
pp. 1873-1882 ◽  
Author(s):  
Yong Liu ◽  
Yong Chen ◽  
Jiahua Cheng
Keyword(s):  
Comparative Study ◽  
Sample Size ◽  
Simulation Study ◽  
Sampling Design ◽  
Optimization Methods ◽  
The Other ◽  
Optimal Sampling ◽  
Optimal Sampling Design ◽  
Conventional Methods ◽  
Better Than

Abstract Liu, Y., Chen, Y., and Cheng, J. 2009. A comparative study of optimization methods and conventional methods for sampling design in fishery-independent surveys. – ICES Journal of Marine Science, 66: 1873–1882. We have introduced and evaluated a procedure, the constrained spatial simulated annealing method, for developing an optimal sampling design for fishery-independent surveys. We used two criterion functions, minimization of the mean of the shortest distance (MMSD) and uniform distribution of point pairs for variogram estimation (WM), and three arrangements of the two criteria, all WM, all MMSD, and a combination of MMSD (2/3 of samples) and WM (1/3), to construct three optimized sampling designs (denoted as Designs I, II, and III, respectively). These three designs were compared in a simulation study with systematic sampling (Design IV) and stratified random sampling designs (Design V), commonly used in fishery-independent surveys. Three levels of sample size (small, medium, and large) were considered in the simulation study developed using a geostatistical approach. The results showed that for parameter estimation of the spatial covariance function, Design III was better than the other designs at relatively small sample size and Design II performed better than the other designs at relatively large sample size. For estimating fish stock abundance, the performance of the designs considered in this study can be ranked as follows: Design II > Design IV > Design III > Design V > Design I. It is clearly important to evaluate and improve sampling design based on historical survey data. Such a study allows us to identify an optimal sampling design to balance the quality of the data collected and the costs of the sampling programme, leading to the development and optimization of a sustainable and fishery-independent monitoring programme.

scholarly journals Taxonomic similarity does not predict necessary sample size for ex situ conservation: a comparison among five genera

2020 ◽  
Vol 287 (1926) ◽  
pp. 20200102 ◽  
Author(s):  
Sean Hoban ◽  
Taylor Callicrate ◽  
John Clark ◽  
Susan Deans ◽  
Michael Dosmann ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Sample Size ◽  
Sampling Design ◽  
Ex Situ Conservation ◽  
Threshold Level ◽  
Genetic Conservation ◽  
Ex Situ ◽  
Limited Resources ◽  
Order Of Magnitude ◽  
Genus One

Effectively conserving biodiversity with limited resources requires scientifically informed and efficient strategies. Guidance is particularly needed on how many living plants are necessary to conserve a threshold level of genetic diversity in ex situ collections. We investigated this question for 11 taxa across five genera. In this first study analysing and optimizing ex situ genetic diversity across multiple genera, we found that the percentage of extant genetic diversity currently conserved varies among taxa from 40% to 95%. Most taxa are well below genetic conservation targets. Resampling datasets showed that ideal collection sizes vary widely even within a genus: one taxon typically required at least 50% more individuals than another (though Quercus was an exception). Still, across taxa, the minimum collection size to achieve genetic conservation goals is within one order of magnitude. Current collections are also suboptimal: they could remain the same size yet capture twice the genetic diversity with an improved sampling design. We term this deficiency the ‘genetic conservation gap’. Lastly, we show that minimum collection sizes are influenced by collection priorities regarding the genetic diversity target. In summary, current collections are insufficient (not reaching targets) and suboptimal (not efficiently designed), and we show how improvements can be made.

scholarly journals STOCHASTIC OPTIMIZATION IN MULTIVARIATE STRATIFIED DOUBLE SAMPLING DESIGN

2020 ◽  
Vol 5 (1) ◽  
pp. 115-122
Author(s):  
Ziaul Bakhshi
Keyword(s):  
Stochastic Optimization ◽  
Objective Function ◽  
Sample Size ◽  
Sampling Design ◽  
Computational Procedure ◽  
Optimum Allocation ◽  
Double Sampling ◽  
Numerical Example

This paper deals with optimum allocation of sample size in stratified double sampling when costs are considered as random in the objective function. When costs function are random, by applying modified E. model, objective function is converted into an equivalent deterministic form. A Numerical example is presented to illustrate the computational procedure and the problem is solved by using LINGO Software.

scholarly journals Toward a Meta-Framework for Conducting Mixed Methods Representation Analyses to Optimize Meta-Inferences

2020 ◽  
Author(s):  
Julie Corrigan ◽  
Anthony Onwuegbuzie
Keyword(s):  
Mixed Methods ◽  
Sample Size ◽  
Mixed Methods Research ◽  
Sampling Design ◽  
Sampling Frame ◽  
Time Dimension ◽  
Purposive Sampling ◽  
Equal Status ◽  
Object Of Study ◽  
Selection Of

The purpose of this article is to propose a meta-framework for conducting what we term mixed methods representation analyses (MMRA). We define MMRA as the appropriate selection of sampling design (i.e., the sampling frame [random] or sampling boundary [purposive]; sampling combination, comprising the mixing dimension [partial/fully], time dimension [concurrent/sequential], emphasis dimension [dominant/equal status], and relationship among/between samples [identical/parallel/nested/multilevel]; sample size; and number of sampling units [e.g., of people, cases, words, texts, observations, events, incidents, activities, experiences, or any other object of study]) in order to obtain representation and concomitantly meta-inferences consistent with the study’s generalization goal(s). Thus, the goal of conducting MMRA is to attain representation and interpretive consistency in order to enhance the rigor of mixed methods research studies.

scholarly journals Enhancing sampling design in mist-net bat surveys by accounting for sample size optimization

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0174067 ◽  
Author(s):  
Leonardo Carreira Trevelin ◽  
Roberto Leonan Morim Novaes ◽  
Paul François Colas-Rosas ◽  
Thayse Cristhina Melo Benathar ◽  
Carlos A. Peres
Keyword(s):  
Sample Size ◽  
Sampling Design ◽  
Size Optimization ◽  
Mist Net

scholarly journals Identification of the Effective E-Promotional Tools on Improving Destination Brand Image

2020 ◽  
Vol 28 (3) ◽  
pp. 169-183
Author(s):  
Nader Seyyedamiri ◽  
Ala Khosravani
Keyword(s):  
Sample Size ◽  
Software Reliability ◽  
Sampling Design ◽  
Brand Image ◽  
Reliability And Validity ◽  
Structured Interviews ◽  
Average Value ◽  
Infinite Population ◽  
Spss Software ◽  
Destination Brand

The aim of this research is to evaluate the effect of promotional tools on creating a positive destination brand image and attracting international tourists to Iran. Mixed methods were employed based on semi-structured interviews that conducted with tourism experts. The sample size determined for an infinite population and the interviewees were chosen using the snowball method. The data was analyzed by ATLAS.ti software. Reliability and validity are determined through experts and Cronbach's alpha. The sampling design for the quantitative method is accidental and by using the Cochran formula the sample of an infinite population is obtained. The sample size is 384 tourists. The collected data by questionnaires are analyzed via SPSS software. Quantitative findings indicated the most important promotional tools. Also, the quantitative analysis showed that the internet with an average value of 3/3984 ranked first. Finally, the results indicated that the more focus on using promotional tools on a tourism destination to attract more tourists.

scholarly journals Evaluating Sampling Designs for Assessing the Accuracy of Cropland Extent Maps in Different Cropland Proportion Regions

2019 ◽  
pp. 1-20 ◽  
Author(s):  
Kamini Yadav ◽  
Russell G. Congalton
Keyword(s):  
Sample Size ◽  
Random Sampling ◽  
Sampling Design ◽  
Simulation Analysis ◽  
Accuracy Assessment ◽  
Simple Random Sampling ◽  
Cropland Area ◽  
Global Food Security ◽  
Optimum Sample Size ◽  
Optimum Sample

The GFSAD30m cropland extent map has been recently produced at a spatial resolution of 30m as a part of NASA MEaSUREs’ Program Global Food Security Data Analysis (GFSAD) project. Accuracy assessment of this GFSAD30m cropland extent map was initially performed using an assessment strategy involving a simple random sampling (SRS) design and an optimum sample size of 250 for each of 72 different regions around the world. However, while statistically valid, this sampling design was not effective in regions of low cropland proportion (LCP) of less than 15% cropland area proportion (CAP). The SRS sampling resulted in an insufficient number of samples for the rare cropland class due to low cropland distribution, proportion, and pattern. Therefore, given our objective of effectively assessing the cropland extent map in these LCP regions, the use of an alternate sampling design was necessary. A stratified random sampling design was applied using a predetermined minimum number of samples followed by a proportional distribution (i.e., SMPS) for different cropland proportion regions to achieve sufficient sample size of the rare cropland map class and appropriate accuracy measures. The SRS and SMPS designs were compared at a common optimum sample size of 250 which was determined using a sample simulation analysis in ten different cropland proportion regions. The results demonstrate that the two sampling designs performed differently in the various cropland proportion regions and therefore, must be selected according to the cropland extent maps to be assessed.

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