Sampling of contaminated soil: sampling error in relation to sample size and segregation

1993 ◽  
Vol 27 (10) ◽  
pp. 2035-2044 ◽  
Author(s):  
Frank P. J. Lame ◽  
Peter R. Defize
Keyword(s):  
Sample Size ◽  
Contaminated Soil ◽  
Sampling Error ◽  
Soil Sampling

Sample size for foliar analyses of coastal Douglas-fir

1987 ◽  
Vol 17 (10) ◽  
pp. 1240-1245 ◽  
Author(s):  
P. L. Marshall ◽  
K. Jahraus
Keyword(s):  
Sample Size ◽  
Element Concentration ◽  
Sampling Error ◽  
Sample Sizes ◽  
Calculated Concentration ◽  
Forest Region ◽  
Final Sample ◽  
Coefficients Of Variation ◽  
Significance Levels ◽  
Pilot Sample

Coefficients of variation were calculated for 17 foliar elements or element ratios in 10 stands of Pseudotsugamenziesii var. menziesii (Mirb.) Franco in the Vancouver Forest Region of British Columbia. These were found to vary widely between elements and, in some cases, between stands. Sample sizes necessary to produce estimates of mean foliar element concentrations within specified error limits under various α and β significance levels and estimates of coefficient of variation were calculated. Concentration of the least variable foliar elements (N, P, S, and K) could be estimated to within 10% with a sample of 21 trees and within 5% with a sample of 68 trees, assuming average coefficients of variation for each nutrient and α and β significance levels of 0.95. The variability reported in this study can be used to determine sample sizes for operational diagnoses of foliar element concentration if pilot samples are prohibitively expensive. A pilot sample should be used in conjunction with explicit β significance levels when determining foliar sample size for research purposes, especially if the samples are to be composited prior to chemical analysis. Analysis of the interrelationships between final sample size, pilot sample size, maximum desirable sampling error, and significance levels can provide the sample designer with information useful for the design of an efficient sample.

Determining the most efficient method of age determination for estimating the age structure of a fish population

1995 ◽  
Vol 52 (11) ◽  
pp. 2320-2326 ◽  
Author(s):  
D. G. Worthington ◽  
A. J. Fowler ◽  
P. J. Doherty
Keyword(s):  
Sample Size ◽  
Age Structure ◽  
Sampling Error ◽  
Relative Size ◽  
Age Determination ◽  
Fish Population ◽  
Financial Cost ◽  
Economic Methods ◽  
Pomacentrus Moluccensis ◽  
Ageing Methods

Less precise, but economic methods for estimating the age of individual fish can provide better estimates of age structure than precise, but expensive methods. The benefits of using a precise ageing method can be compromised by its cost, which may restrict the size of the sample aged. If sample size is restricted, the effect of sampling error on an age structure may be greater than the effect of ageing error from a less precise ageing method that does not restrict sample size. We used Monte Carlo simulations to assess the relative size of sampling and ageing errors when estimating the age structure of populations of Pomacentrus moluccensis from the southern Great Barrier Reef, Australia. Sampling error associated with ageing less than 200 individuals was, on average, larger than the effects of most commonly reported ageing errors. Other factors that may complicate this comparison of ageing methods involve the financial cost of different methods and the logistics of sampling more fish.

Reliability of Validity Generalization Data Bases

1988 ◽  
Vol 63 (1) ◽  
pp. 131-134 ◽  
Author(s):  
Deborah L. Whetzel ◽  
Michael A. Mc Daniel
Keyword(s):  
Sample Size ◽  
Personnel Selection ◽  
Sampling Error ◽  
Mental Ability ◽  
Data Bases ◽  
General Mental Ability ◽  
Data Record ◽  
Validity Generalization ◽  
Meta Analyses ◽  
Reliability Coefficients

This paper addresses the usefulness of reporting coder reliability in validity generalization studies. The Principles for the Validation and Use of Personnel Selection Instruments of the Society for Industrial and Organizational Psychology state that given the results of meta-analytic studies, validities generalize far more than previously believed; however, users of validity generalization results are required to report the reliability of data entering validity generalization analyses. In response to this concern, reliability coefficients were computed on the validity and sample size between two studies (i.e., data bases) of the Wonderlic Personnel Test and the Otis Test of General Mental Ability. These variables, validity, and sample size, were investigated since these are the crucial components in validity generalization analysis. Results indicated that the correlation between the validities of the two studies was .99 and the correlation between the sample sizes of the two studies was 1.00. To illustrate further the reliability of coding in validity generalization research, separate meta-analyses were conducted on the validity of these tests on each of the two data bases. When correcting only for sampling error, the results indicated that the separate meta-analyses yielded identical results, M = .24, SD = .09. These results show that concerns about the reliability of validity generalization data bases are unwarranted and that independent investigators coding the same data, record the same values and obtain the same results.

Formation of Citizenship of Cossack Youth in Modern Conditions

2020 ◽  
Vol 9 (1) ◽  
pp. 68-71
Author(s):  
D. Solonnikov
Keyword(s):  
Young People ◽  
General Population ◽  
Empirical Study ◽  
Sample Size ◽  
Sampling Error ◽  
Educational Work ◽  
Strong State ◽  
Time Period ◽  
Rank 2 ◽  
Country People

The author proceeds from the position that the idea of patriotism and citizenship should be a serious basis for the ideology in demand, and, consequently, the basis for education. In Russia, people most often associated the idea of service with their attitude to the country, people, and homeland. The desire for justice and security of a person’s self-consciousness in Russia most often connects with a strong state, the person’s ability to serve his country, patriotism, the ability to bring glory and benefit to the motherland. The object of the empirical study of citizenship is student youth aged 17 to 25 years; time period - 2018 - 2020 according to a sample representing the general population by sex and age; the general population is student youth of a specified age. The sample size was 1205 people, the sampling error on one basis was about 5%. Cossack youth was considered in comparison with the main sample. The most important value of citizenship is patriotism. To the question "What does it mean for you to be a patriot?". The answers were as follows. “Love Russia and be proud of your country” - 64.3% (rank 1). Among Cossack youth this indicator is 88.2%. Further: “protect Russia” - 47.8% (rank 2). Among Cossack youth this indicator is 87.3%. Respect the traditions and culture of their country - 35.6% (rank 3). Among Cossack youth this indicator is 67.6%. The results of this study convince us that the system of educational work with Cossack youth gives a more favorable picture of the formation of citizenship of young people.

A Simulation of Factor Analytic Reliability Varying Sample Size and Number of Variables

1979 ◽  
Vol 45 (2) ◽  
pp. 471-478 ◽  
Author(s):  
George E. Manners ◽  
Donald H. Brush
Keyword(s):  
Sample Size ◽  
Factor Structure ◽  
Random Sampling ◽  
Factor Model ◽  
Sampling Error ◽  
Factor Models ◽  
Factor Analytic ◽  
Number Of Factors ◽  
Alpha Factor ◽  
Analytic Models

An examination of four factor analytic models employing random sampling experiments is undertaken using a methodology and hypothetical population factor structure first employed by Browne (2). The factor models are each explored under four separate conditions, varying sample size and number of variables. Under these limited conditions, it is argued that there are no practical differences among the factor models considered with respect to sampling error in the absence of a Heywood variable. However, with respect to the ability of each model to capture, early and at convergence, the number of factors in the population, the alpha factor model is shown to have the greatest reliability.

Reliability of Regression-Corrected Climate Forecasts

2014 ◽  
Vol 27 (9) ◽  
pp. 3393-3404 ◽  
Author(s):  
Michael K. Tippett ◽  
Timothy DelSole ◽  
Anthony G. Barnston
Keyword(s):  
Sample Size ◽  
Climate Model ◽  
Sampling Error ◽  
Small Sample Size ◽  
Small Sample ◽  
Climate Forecast ◽  
Regression Parameters ◽  
Climate Forecasts ◽  
Probability Forecasts ◽  
The Impact

Abstract Regression is often used to calibrate climate model forecasts with observations. Reliability is an aspect of forecast quality that refers to the degree of correspondence between forecast probabilities and observed frequencies of occurrence. While regression-corrected climate forecasts are reliable in principle, the estimated regression parameters used in practice are affected by sampling error. The low skill and small sample sizes typically encountered in climate prediction imply substantial sampling error in the estimated regression parameters. Here the reliability of regression-corrected climate forecasts is analyzed for the case of joint-Gaussian distributed ensemble forecasts and observations with regression parameters estimated by least squares. Hypothesis testing of the regression parameters provides direct information about the skill and reliability of the uncorrected ensemble-based probability forecasts. However, the regression-corrected probability forecasts with estimated parameters are systematically “overconfident” because sampling error causes a positive bias in the regression forecast signal variance, despite the fact that the estimates of the regression parameters are themselves unbiased. An analytical description of the reliability diagram of a generic regression-corrected climate forecast is derived and is shown to depend on sample size and population correlation skill, with small sample size and low skill being factors that increase overconfidence. The analytical reliability estimate is shown to capture the effect of sampling error in synthetic data experiments and in a 29-yr dataset of NOAA Climate Forecast System version 2 predictions of seasonal precipitation totals over the Americas. The impact of sampling error on the reliability of regression-corrected forecast has been previously unrecognized and affects all regression-based forecasts. The use of regression parameters estimated by shrinkage methods such as ridge regression substantially reduces overconfidence.

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