The Application of the GSK Method to the Determination of Minimum Sample Sizes

Biometrics ◽  
1989 ◽  
Vol 45 (1) ◽  
pp. 193 ◽  
Author(s):  
James Rochon
Keyword(s):  
Sample Sizes ◽  
Minimum Sample

scholarly journals Methods and Rule-of-Thumbs in The Determination of Minimum Sample Size When Appling Structural Equation Modelling: A Review

2020 ◽  
Vol 15 ◽  
pp. 102-107
Author(s):  
Hunuwala Malawarage Suranjan Priyanath ◽  
Ranatunga RVSPK ◽  
Megama RGN
Keyword(s):  
Structural Equation Modeling ◽  
Sample Size ◽  
Structural Equation ◽  
Sample Size Determination ◽  
Equation Modeling ◽  
Minimum Sample Size ◽  
Research Project ◽  
Rules Of Thumb ◽  
Minimum Sample

Basic methods and techniques involved in the determination of minimum sample size at the use of Structural Equation Modeling (SEM) in a research project, is one of the crucial problems faced by researchers since there were some controversy among scholars regarding methods and rule-of-thumbs involved in the determination of minimum sample size when applying Structural Equation Modeling (SEM). Therefore, this paper attempts to make a review of the methods and rule-of-thumbs involved in the determination of sample size at the use of SEM in order to identify more suitable methods. The paper collected research articles related to the sample size determination for SEM and review the methods and rules-of-thumb employed by different scholars. The study found that a large number of methods and rules-of-thumb have been employed by different scholars. The paper evaluated the surface mechanism and rules-of-thumb of more than twelve previous methods that contained their own advantages and limitations. Finally, the study identified two methods that are more suitable in methodologically and technically which have identified by non-robust scholars who deeply addressed all the aspects of the techniques in the determination of minimum sample size for SEM analysis and thus, the prepare recommends these two methods to rectify the issue of the determination of minimum sample size when using SEM in a research project.

Use of Ten Gram Samples of Corn for Determination of Mycotoxins

1988 ◽  
Vol 71 (1) ◽  
pp. 41-43
Author(s):  
Octave J Francis ◽  
George M Ware ◽  
Allen S Carman ◽  
Gary P Kirschenheuter ◽  
Shia S Kuan
Keyword(s):  
Statistical Tests ◽  
Automated System ◽  
Sample Sizes ◽  
Concentration Variance ◽  
Assay Procedure ◽  
Coefficients Of Variation ◽  
The Mean ◽  
Aflatoxin B ◽  
Test Result

Abstract Data were gathered, during a study on the development of an automated system for the extraction, cleanup, and quantitation of mycotoxins in corn, to determine if it was scientifically sound to reduce the analytical sample size. Five, 10, and 25 g test portions were analyzed and statistically compared with 50 g test portions of the same composites for aflatoxin concentration variance. Statistical tests used to determine whether the 10 and 50 g sample sizes differed significantly showed a satisfactory observed variance ratio (Fobs) of 2.03 for computations of pooled standard deviations; paired f-test values of 0.952, 1.43, and 0.224 were computed for each of the 3 study samples. The results meet acceptable limits, since each sample’s r-test result is less than the published value of the |t|, which is 1.6909 for the test conditions. The null hypothesis is retained since the sample sizes do not give significantly different values for the mean analyte concentration. The percent coefficients of variation (CVs) for all samples tested were within the expected range. In addition, the variance due to sample mixing was evaluated using radioisotopelabeled materials, yielding an acceptable CV of 22.2%. The variance due to the assay procedure was also evaluated and showed an aflatoxin B, recovery of 78.9% and a CV of 11.4%. Results support the original premise that a sufficiently ground and blended sample would produce an analyte variance for a 10 g sample that was statistically comparable with that for a 50 g sample.

AN EVALUATION OF SEVERAL METHODS OF ESTIMATING SITE INDEX OF IMMATURE STANDS

1952 ◽  
Vol 28 (3) ◽  
pp. 63-74 ◽  
Author(s):  
John W. Ker
Keyword(s):  
Basal Area ◽  
Site Index ◽  
Site Quality ◽  
Average Height ◽  
Office Procedure ◽  
Minimum Sample ◽  
A Site ◽  
The University ◽  
Site Classes

The use of dominant heights for the estimation of site index is compared with the use of the average of dominant and codominant heights. Data collected on the University Research Forest are presented to illustrate the variability in tree heights and diameters within the two upper crown classes in well-stocked stands of immature Douglas fir.HeightIt is shown that the use of dominant heights reduces considerably the number of measurements required for a site index determination of given accuracy. Minimum sample sizes are given for three limits of accuracy for use in different site qualities. A general field and office procedure is outlined for the determination of minimum sample size in stands other than those described.DiameterThe use of diameter in site determination is discussed. Site indices based on the height of the tree of mean diameter, the height of the tree of mean basal area, and mean height are compared.ConclusionsQuick estimates of site quality can best be obtained by the measurement of total height of sample dominant trees, selected at random. For this purpose, tables are presented which list the average height of dominant trees by age and site classes for use in stands of Douglas fir, and western hemlock, respectively.

Sample size for qualitative research

2016 ◽  
Vol 19 (4) ◽  
pp. 426-432 ◽  
Author(s):  
Clive Roland Boddy
Keyword(s):  
Qualitative Research ◽  
Sample Size ◽  
Single Case ◽  
Short Paper ◽  
Sample Sizes ◽  
Homogeneous Population ◽  
Content Type ◽  
Data Saturation ◽  
Theoretical Saturation

Purpose Qualitative researchers have been criticised for not justifying sample size decisions in their research. This short paper addresses the issue of which sample sizes are appropriate and valid within different approaches to qualitative research. Design/methodology/approach The sparse literature on sample sizes in qualitative research is reviewed and discussed. This examination is informed by the personal experience of the author in terms of assessing, as an editor, reviewer comments as they relate to sample size in qualitative research. Also, the discussion is informed by the author’s own experience of undertaking commercial and academic qualitative research over the last 31 years. Findings In qualitative research, the determination of sample size is contextual and partially dependent upon the scientific paradigm under which investigation is taking place. For example, qualitative research which is oriented towards positivism, will require larger samples than in-depth qualitative research does, so that a representative picture of the whole population under review can be gained. Nonetheless, the paper also concludes that sample sizes involving one single case can be highly informative and meaningful as demonstrated in examples from management and medical research. Unique examples of research using a single sample or case but involving new areas or findings that are potentially highly relevant, can be worthy of publication. Theoretical saturation can also be useful as a guide in designing qualitative research, with practical research illustrating that samples of 12 may be cases where data saturation occurs among a relatively homogeneous population. Practical implications Sample sizes as low as one can be justified. Researchers and reviewers may find the discussion in this paper to be a useful guide to determining and critiquing sample size in qualitative research. Originality/value Sample size in qualitative research is always mentioned by reviewers of qualitative papers but discussion tends to be simplistic and relatively uninformed. The current paper draws attention to how sample sizes, at both ends of the size continuum, can be justified by researchers. This will also aid reviewers in their making of comments about the appropriateness of sample sizes in qualitative research.

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