Factors influencing the precision of soil seed bank estimates

1989 ◽  
Vol 67 (10) ◽  
pp. 2833-2840 ◽  
Author(s):  
D. L. Benoit ◽  
N. C. Kenkel ◽  
P. B. Cavers
Keyword(s):  
Sample Size ◽  
Seed Bank ◽  
Random Sampling ◽  
Soil Seed Bank ◽  
Sampling Interval ◽  
Cluster Sampling ◽  
Sampling Variance ◽  
Monte Carlo Techniques ◽  
Repeated Sampling ◽  
Lamb’S Quarters

The dimension of soil augers needed to sample a seed bank of Chenopodium spp. (lamb's-quarters) was determined by randomly sampling a 1.35-ha area within a cornfield in Oxford County, Ontario. Sampling units of three different auger sizes (1.9, 2.7, and 3.3 cm in diameter) were collected. On a per volume basis, there were no significant differences between the three sizes of auger in estimating the number of lamb's-quarters seeds in the soil. Three sampling methods, systematic, stratified random, and cluster, were compared with random sampling in their capacity to minimize the sampling variance. Soil cores of 1.9 cm diameter and 15 cm deep were taken systematically at 3.5-m intervals to form a 32 × 32 matrix. Repeated sampling within the matrix using Monte Carlo techniques indicated that the estimate of sampling variance decreased with increasing sample size, regardless of the sampling method used. No fewer than 60 sampling units should be collected to quantify the seed bank of an abundant weed such as lamb's-quarters. The estimates of sampling variance of systematic and cluster sampling were clearly influenced by the sampling interval and the cluster's shape, respectively. This was attributed to the underlying seed distribution of lamb's-quarters in the soil that was clustered with patterns of high and low seed density parallel to corn rows. There were no significant differences between the estimate of sampling variance of random and stratified random sampling with a fixed sample size of 64 units.

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).

Counting Penguins

1998 ◽  
Vol 91 (2) ◽  
pp. 110-116 ◽  
Author(s):  
Mike Perry ◽  
Gary Kader
Keyword(s):  
Sample Size ◽  
Random Sampling ◽  
Population Distribution ◽  
Statistical Concept ◽  
Specific Objective ◽  
Random Samples ◽  
Repeated Sampling ◽  
Simulated Sampling

The nctm's curriculum standards for statistics give a specific objective for students in grades 9–12: to “understand sampling and recognize its role in statistical claims” (NCTM 1989, 167). The use of random samples for estimation is a fundamental statistical concept. Random sampling and its consequences can be studied through simulated sampling activities. The nature of sampling variability, the influence of sample size on the quality of estimation, and the role of the underlying population distribution are ideas that can be illustrated with repeated sampling.

scholarly journals 745Probability-proportional-to-size cluster sampling as an effective methodology for large-scale participant recruitment

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Suresh Kiran ◽  
Asha Kamath ◽  
Rajashekhar Bellur ◽  
Gopee Krishnan
Keyword(s):  
Sample Size ◽  
Large Scale ◽  
Random Number ◽  
Sampling Interval ◽  
Cluster Sampling ◽  
Sampling Frame ◽  
Participant Recruitment ◽  
Reading Impairment ◽  
Urban Cluster ◽  
Average School

Abstract Background We demonstrate the utility of Probability-Proportional-to-Size Cluster Sampling (PPS-CS) to select participants/sites for large scale surveys. Methods Post ethical and administrative clearance, PPS-CS was carried-out to estimate the prevalence of ‘sublexical dyslexia’, a reading impairment in children from III-VII grades in Udupi district of India. Schools were regarded as clusters. By performing PPS-CS, calculated sample size of 1812 children was systematically recruited. Sampling frame: List of all 1256 schools in Udupi district was retrieved. Kannada medium schools were shortlisted, which yielded 128 schools later classified individually as urban or rural. Results Strength of schools was retrieved and cumulative strength (cs) was derived. With an average school size of 105 for rural and 98 for urban cluster, 18 schools were required to meet the sample size. Owing to a ratio of 3.5:1 of rural-to-urban students, 14 rural and 4 urban schools were selected. Ratio of ‘cs’ to ‘number-of-schools-required’ gave a sampling interval (SI) of 758 for rural, and 662 for urban cluster. A random number (R) was selected between one and SI. First school picked was that with cs > =R. Second was that with cs > =SI+R. The third was with cs > =(SI+R at II school)+SI. Progressively, 18 schools were identified. Conclusions With disproportionate sizes of clusters, PPS-CS ensured that selected participants reflect population estimates accordingly. Prevalence of sublexical dyslexia in Udupi district was therefore accurately estimated using PPS-CS. Key messages Large-scale studies on healthcare or businesses may be effectively carried-out using PPS-CS!

FAKTOR PENGETAHUAN YANG MEMPENGARUHI KETERATURAN IBU BALITA DALAM MENGUNJUNGI POSYANDU DI DESA CIPANGERAN KECAMATAN SAGULING KABUPATEN BANDUNG BARAT TAHUN 2017

2018 ◽  
Vol 5 (2) ◽  
pp. 13-20
Author(s):  
Lia Kamila ◽  
Liawati . ◽  
Suci Lailani Alipah
Keyword(s):  
Public Health ◽  
Data Collection ◽  
Sample Size ◽  
Health Center ◽  
Analytical Method ◽  
Random Sampling ◽  
Research Method ◽  
Cross Sectional ◽  
Public Health Center ◽  
Level Of Knowledge

ABSTRAK Indikator D/S di wilayah kerja Puskesmas Saguling Desa Cipangeran pada tahun 2016 menunjukkan masih rendahnya kunjungan balita dalam kegiatan posyandu dengan rata-rata hanya memcapai 41,5%, sedangkan target standar palayanan kota jumlah D/S yaitu 85%. Tujuan penelitian ini adalah untuk mengetahui keteraturan ibu dalam mengunjungi Posyandu dari faktor pengetahuan di Desa Cipangeran Kecamatan Saguling Kabupaten Bandung Barat tahun 2017. Metode penelitian ini menggunakan metode analitik dengan pendekatan cross sectional. Data yang digunakan adalah data primer. Populasi seluruh balita di wilayah kerja Puskesmas Saguling tahun 2016 sebanyak 424 ibu balita, besar sampel yang diambil 81 ibu balita, pengambilan sampel dengan menggunakan Sampel Random Sampling, pengumpulan data dengan hasil kuesioner berisi pertanyaan untuk mendapatkan data yang berkaitan dengan variabel yang diteliti. Hasil penelitian pengetahuan ibu balita didapatkan hampir setengah berada dikategori cukup yaitu 47 ibu balita (58%), namun masih ada ibu balita yang memiliki pengetahuan baik yaitu 18 ibu balita (22%), dan ibu balita yang memiliki pengetahuan kurang yaitu 16 ibu balita (20%). Kesimpulan dari penelitian didapatkan tingkat pengetahuan ibu balita yang tidak teratur dalam mengunjungi Posyandu di Desa Cipangeran Kecamatan Saguling Kabupaten Bandug Barat hampir setengah ibu balita berpengetahuan cukup. ABSTRACT The D / S indicator in the working area of ​​Saguling Public Health Center of Cipangeran Village in 2016 indicates that the low number of toddler visits in posyandu activities reaches an average of 41.5%, while the standard target for city / city is 85%. The purpose of this study is to determine the regularity of mothers in visiting Posyandu from knowledge factor in Cipangeran Village, Saguling District, West Bandung regency in 2017. This research method using analytical method with cross sectional approach. The data used is primary data.The population of all toddlers in the working area of Saguling Publich Health Center in 2016 were 424 mother, the sample size was 81 mother, using Random Sampling , data collection with questionnaires containing questions to obtain data related to the variables studied. The result of the research of the knowledge of the mother of the toddler is almost sufficient, namely 47 mothers (58%),but there are still mother who have good knowledge that is 18 mother of toddler (22%) and mother with less knowledge that is 16 mother of balita (20%). The conclusion of the research is the level of knowledge of irregular mother in visiting Posyandu in Cipangeran Village, Saguling, of West Bandung district, almost half of the toddler are knowledgeable enough.

Characteristics of Soil Seed Bank of Caragana microphylla Community under the Grazing and Enclosure Conditions in Horqin Sandy Land

2010 ◽  
Vol 26 (5) ◽  
pp. 714-719
Author(s):  
Ming LI ◽  
De-ming JIANG ◽  
Yong-ming LUO ◽  
Xiu-mei WANG ◽  
Bo LIU ◽  
...  
Keyword(s):  
Seed Bank ◽  
Soil Seed Bank ◽  
Horqin Sandy Land ◽  
Sandy Land ◽  
Caragana Microphylla

Community Dynamics and Soil Seed Bank Ecology of Lane Mountain Milkvetch (Astragalus jaegerianus Munz)

2012 ◽  
Author(s):  
T. R. Huggins ◽  
B. A. Prigge ◽  
M. R. Sharifi ◽  
P. W. Rundel
Keyword(s):  
Seed Bank ◽  
Community Dynamics ◽  
Soil Seed Bank

scholarly journals ‘Statistical Irreproducibility’ Does Not Improve with Larger Sample Size: How to Quantify and Address Disease Data Multimodality in Human and Animal Research

2021 ◽  
Vol 11 (3) ◽  
pp. 234
Author(s):  
Abigail R. Basson ◽  
Fabio Cominelli ◽  
Alexander Rodriguez-Palacios
Keyword(s):  
Sample Size ◽  
Data Visualization ◽  
Random Sampling ◽  
Intestinal Inflammation ◽  
Random Number Generation ◽  
Outcome Data ◽  
Multimodal Distributions ◽  
Disease Subtypes ◽  
Study Designs ◽  
Larger Sample

Poor study reproducibility is a concern in translational research. As a solution, it is recommended to increase sample size (N), i.e., add more subjects to experiments. The goal of this study was to examine/visualize data multimodality (data with >1 data peak/mode) as cause of study irreproducibility. To emulate the repetition of studies and random sampling of study subjects, we first used various simulation methods of random number generation based on preclinical published disease outcome data from human gut microbiota-transplantation rodent studies (e.g., intestinal inflammation and univariate/continuous). We first used unimodal distributions (one-mode, Gaussian, and binomial) to generate random numbers. We showed that increasing N does not reproducibly identify statistical differences when group comparisons are repeatedly simulated. We then used multimodal distributions (>1-modes and Markov chain Monte Carlo methods of random sampling) to simulate similar multimodal datasets A and B (t-test-p = 0.95; N = 100,000), and confirmed that increasing N does not improve the ‘reproducibility of statistical results or direction of the effects’. Data visualization with violin plots of categorical random data simulations with five-integer categories/five-groups illustrated how multimodality leads to irreproducibility. Re-analysis of data from a human clinical trial that used maltodextrin as dietary placebo illustrated multimodal responses between human groups, and after placebo consumption. In conclusion, increasing N does not necessarily ensure reproducible statistical findings across repeated simulations due to randomness and multimodality. Herein, we clarify how to quantify, visualize and address disease data multimodality in research. Data visualization could facilitate study designs focused on disease subtypes/modes to help understand person–person differences and personalized medicine.

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