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Journal articles on the topic 'Sample size effect'

Author: Grafiati

Published: 4 June 2021

Last updated: 22 February 2023

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1

C. O, Osueke, Uguru-Okorie Daniel, and Aondoyila Kuhe. "Sample Size Effect on Combustion Analysis." International Journal of Scientific Engineering and Technology 4, no. 7 (2015): 401–6. http://dx.doi.org/10.17950/ijset/v4s7/704.

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2

McShane, Blakeley B., and Ulf Böckenholt. "Planning sample sizes when effect sizes are uncertain: The power-calibrated effect size approach." Psychological Methods 21, no. 1 (2016): 47–60. http://dx.doi.org/10.1037/met0000036.

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3

Jacka, T. H. "Investigations of discrepancies between laboratory studies of the flow of ice: density, sample shape and size, and grain-size." Annals of Glaciology 19 (1994): 146–54. http://dx.doi.org/10.3189/1994aog19-1-146-154.

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Laboratory results are presented concerning ice creep at minimum creep rate (at ~1% strain) for fine-grained, initially isotropic, polycrystalline samples. The effect on the creep rate of ice density, sample shape (aspect ratio) and size, grain-size and ratio of grain-size to sample size is examined. Provided sample density is above ~0.83 Mg m−3 (i.e. the close-off density), there is no effect of density on ice-creep rate. Results provide no evidence of a creep rate dependence on test sample length for cylindrical samples. Sample diameter, however, does affect creep rate. Over the range of sample diameters studied (16.2 to 90 mm) creep rate decreases monotonically by a factor of ~4. This effect is independent of sample aspect ratio. Experiments examining size effects in simple shear indicate no dependence of minimum flow rate on shape or size in this stress configuration. Two grain-sizes were represented within the samples tested for the effect of sample size. As expected from earlier work, no grain-size effect on minimum creep rate is evident. In addition, there was no evidence of an effect on creep rate of the ratio of grain-size to sample size.

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4

Jacka, T. H. "Investigations of discrepancies between laboratory studies of the flow of ice: density, sample shape and size, and grain-size." Annals of Glaciology 19 (1994): 146–54. http://dx.doi.org/10.1017/s0260305500011137.

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Laboratory results are presented concerning ice creep at minimum creep rate (at ~1% strain) for fine-grained, initially isotropic, polycrystalline samples. The effect on the creep rate of ice density, sample shape (aspect ratio) and size, grain-size and ratio of grain-size to sample size is examined. Provided sample density is above ~0.83 Mg m−3 (i.e. the close-off density), there is no effect of density on ice-creep rate. Results provide no evidence of a creep rate dependence on test sample length for cylindrical samples. Sample diameter, however, does affect creep rate. Over the range of sample diameters studied (16.2 to 90 mm) creep rate decreases monotonically by a factor of ~4. This effect is independent of sample aspect ratio. Experiments examining size effects in simple shear indicate no dependence of minimum flow rate on shape or size in this stress configuration. Two grain-sizes were represented within the samples tested for the effect of sample size. As expected from earlier work, no grain-size effect on minimum creep rate is evident. In addition, there was no evidence of an effect on creep rate of the ratio of grain-size to sample size.

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5

Hueftle, MG, and Haaga. "Effect of suction on biopsy sample size." American Journal of Roentgenology 147, no. 5 (1986): 1014–16. http://dx.doi.org/10.2214/ajr.147.5.1014.

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6

Norouzian, Reza. "SAMPLE SIZE PLANNING IN QUANTITATIVE L2 RESEARCH." Studies in Second Language Acquisition 42, no. 4 (2020): 849–70. http://dx.doi.org/10.1017/s0272263120000017.

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AbstractResearchers are traditionally advised to plan for their required sample size such that achieving a sufficient level of statistical power is ensured (Cohen, 1988). While this method helps distinguishing statistically significant effects from the nonsignificant ones, it does not help achieving the higher goal of accurately estimating the actual size of those effects in an intended study. Adopting an open-science approach, this article presents an alternative approach, accuracy in effect size estimation (AESE), to sample size planning that ensures that researchers obtain adequately narrow confidence intervals (CI) for their effect sizes of interest thereby ensuring accuracy in estimating the actual size of those effects. Specifically, I (a) compare the underpinnings of power-analytic and AESE methods, (b) provide a practical definition of narrow CIs, (c) apply the AESE method to various research studies from L2 literature, and (d) offer several flexible R programs to implement the methods discussed in this article.

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7

Nagoshi, Takashi, Masahide Mutoh, Tso-Fu Mark Chang, Tatsuo Sato, and Masato Sone. "Sample size effect of electrodeposited nickel with sub-10nm grain size." Materials Letters 117 (February 2014): 256–59. http://dx.doi.org/10.1016/j.matlet.2013.12.017.

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8

Kühberger, Anton, Thomas Scherndl, and Astrid Fritz. "On the correlation between effect size and sample size: A reply." Theory & Psychology 23, no. 6 (2013): 801–5. http://dx.doi.org/10.1177/0959354313500863.

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9

Kaufmann, Martina, and Tilmann Betsch. "Origins of the Sample-Size Effect in Explicit Evaluative Judgment." Experimental Psychology 56, no. 5 (2009): 344–53. http://dx.doi.org/10.1027/1618-3169.56.5.344.

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This research considers situations in which individuals explicitly form attitude judgments toward a target object after considering a sample of information. Previous research shows sample-size effects under such conditions: Increasing sample size can produce more extreme judgments. Commonly, these effects are attributed to summative processes in information integration. Alternatively, this research proposes that sample size affects perceived reliability of information, which in turn affects the extremity of the subsequent judgment. Three experiments were conducted to empirically substantiate this alternative account. Experiment 1 provides evidence that participants perceive larger samples as more reliable than smaller samples. Experiment 2 demonstrates that perceived reliability mediates the sample-size effect on judgments. Experiment 3 shows that other variables, such as variability, which lowers the perceived reliability, attenuate sample-size effects. The results are explained with reference to the value account model of attitude formation, stating that implicit and explicit modes of attitude formation are guided by different principles of information integration.

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10

Fritz, Matthew S., and David P. MacKinnon. "Required Sample Size to Detect the Mediated Effect." Psychological Science 18, no. 3 (2007): 233–39. http://dx.doi.org/10.1111/j.1467-9280.2007.01882.x.

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11

XU, Z., and X. LI. "Sample size effect on nanoindentation of micro-/nanostructures." Acta Materialia 54, no. 6 (2006): 1699–703. http://dx.doi.org/10.1016/j.actamat.2005.11.043.

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12

Vandekar, Simon, Ran Tao, and Jeffrey Blume. "A Robust Effect Size Index." Psychometrika 85, no. 1 (2020): 232–46. http://dx.doi.org/10.1007/s11336-020-09698-2.

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AbstractEffect size indices are useful tools in study design and reporting because they are unitless measures of association strength that do not depend on sample size. Existing effect size indices are developed for particular parametric models or population parameters. Here, we propose a robust effect size index based on M-estimators. This approach yields an index that is very generalizable because it is unitless across a wide range of models. We demonstrate that the new index is a function of Cohen’s d, $$R^2$$ R 2 , and standardized log odds ratio when each of the parametric models is correctly specified. We show that existing effect size estimators are biased when the parametric models are incorrect (e.g., under unknown heteroskedasticity). We provide simple formulas to compute power and sample size and use simulations to assess the bias and standard error of the effect size estimator in finite samples. Because the new index is invariant across models, it has the potential to make communication and comprehension of effect size uniform across the behavioral sciences.

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13

Slavin, Robert, and Dewi Smith. "The Relationship Between Sample Sizes and Effect Sizes in Systematic Reviews in Education." Educational Evaluation and Policy Analysis 31, no. 4 (2009): 500–506. http://dx.doi.org/10.3102/0162373709352369.

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Research in fields other than education has found that studies with small sample sizes tend to have larger effect sizes than those with large samples. This article examines the relationship between sample size and effect size in education. It analyzes data from 185 studies of elementary and secondary mathematics programs that met the standards of the Best Evidence Encyclopedia. As predicted, there was a significant negative correlation between sample size and effect size. The differences in effect sizes between small and large experiments were much greater than those between randomized and matched experiments. Explanations for the effects of sample size on effect size are discussed.

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14

Rhode, David. "Measurement of Archaeological Diversity and the Sample-Size Effect." American Antiquity 53, no. 4 (1988): 708–16. http://dx.doi.org/10.2307/281114.

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Assemblage diversity is an important part of the structure of the archaeological record, but measuring this parameter often is difficult if samples of assemblages differ in size. Two methods, here called the sampling approach and regression approach, currently are used to assess the sample-size effect. The approaches differ in method and in results. The sampling approach is better suited to analysis of assemblage diversity among samples when the underlying population structure is well known, while the regression approach is more useful for examination of the sample-size effect when the underlying population structure is known poorly.

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15

Anderson, Samantha F., Ken Kelley, and Scott E. Maxwell. "Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty." Psychological Science 28, no. 11 (2017): 1547–62. http://dx.doi.org/10.1177/0956797617723724.

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The sample size necessary to obtain a desired level of statistical power depends in part on the population value of the effect size, which is, by definition, unknown. A common approach to sample-size planning uses the sample effect size from a prior study as an estimate of the population value of the effect to be detected in the future study. Although this strategy is intuitively appealing, effect-size estimates, taken at face value, are typically not accurate estimates of the population effect size because of publication bias and uncertainty. We show that the use of this approach often results in underpowered studies, sometimes to an alarming degree. We present an alternative approach that adjusts sample effect sizes for bias and uncertainty, and we demonstrate its effectiveness for several experimental designs. Furthermore, we discuss an open-source R package, BUCSS, and user-friendly Web applications that we have made available to researchers so that they can easily implement our suggested methods.

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16

Heckmann, T., K. Gegg, A. Gegg, and M. Becht. "Sample size matters: investigating the effect of sample size on a logistic regression debris flow susceptibility model." Natural Hazards and Earth System Sciences Discussions 1, no. 3 (2013): 2731–79. http://dx.doi.org/10.5194/nhessd-1-2731-2013.

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Abstract. Predictive spatial modelling is an important task in natural hazard assessment and regionalisation of geomorphic processes or landforms. Logistic regression is a multivariate statistical approach frequently used in predictive modelling; it can be conducted stepwise in order to select from a number of candidate independent variables those that lead to the best model. In our case study on a debris flow susceptibility model, we investigate the sensitivity of model selection and quality to different sample sizes in light of the following problem: on the one hand, a sample has to be large enough to cover the variability of geofactors within the study area, and to yield stable results; on the other hand, the sample must not be too large, because a large sample is likely to violate the assumption of independent observations due to spatial autocorrelation. Using stepwise model selection with 1000 random samples for a number of sample sizes between n = 50 and n = 5000, we investigate the inclusion and exclusion of geofactors and the diversity of the resulting models as a function of sample size; the multiplicity of different models is assessed using numerical indices borrowed from information theory and biodiversity research. Model diversity decreases with increasing sample size and reaches either a local minimum or a plateau; even larger sample sizes do not further reduce it, and approach the upper limit of sample size given, in this study, by the autocorrelation range of the spatial datasets. In this way, an optimised sample size can be derived from an exploratory analysis. Model uncertainty due to sampling and model selection, and its predictive ability, are explored statistically and spatially through the example of 100 models estimated in one study area and validated in a neighbouring area: depending on the study area and on sample size, the predicted probabilities for debris flow release differed, on average, by 7 to 23 percentage points. In view of these results, we argue that researchers applying model selection should explore the behaviour of the model selection for different sample sizes, and that consensus models created from a number of random samples should be given preference over models relying on a single sample.

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17

Peterson, Sarah J., and Sharon Foley. "Clinician's Guide to Understanding Effect Size, Alpha Level, Power, and Sample Size." Nutrition in Clinical Practice 36, no. 3 (2021): 598–605. http://dx.doi.org/10.1002/ncp.10674.

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18

Bates, B. T., J. S. Dulek, and H. P. Davis. "1098 RELATIONSHIPS BETWEEN SAMPLE SIZE, DATA RELIABILITY, EFFECT SIZE AND SUBJECT VARIABILITY." Medicine & Science in Sports & Exercise 25, Supplement (1993): S195. http://dx.doi.org/10.1249/00005768-199305001-01102.

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19

Zhu, Yaxin, Zhenhuan Li, and Minsheng Huang. "Coupled effect of sample size and grain size in polycrystalline Al nanowires." Scripta Materialia 68, no. 9 (2013): 663–66. http://dx.doi.org/10.1016/j.scriptamat.2013.01.029.

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20

Hudson, Zoe. "Sample size, power and effect size – What all researchers need to know." Physical Therapy in Sport 10, no. 2 (2009): 43–44. http://dx.doi.org/10.1016/j.ptsp.2009.03.002.

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21

Gibertini, Michael, Kari R. Nations, and John A. Whitaker. "Obtained effect size as a function of sample size in approved antidepressants." International Clinical Psychopharmacology 27, no. 2 (2012): 100–106. http://dx.doi.org/10.1097/yic.0b013e32834f504f.

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22

Hidalgo, M., I. Rodriguez, J. Dorado, J. Sanz, and C. Soler. "Effect of sample size and staining methods on stallion sperm morphometry by the Sperm Class Analyzer ." Veterinární Medicína 50, No. 1 (2012): 24–32. http://dx.doi.org/10.17221/5593-vetmed.

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Computer-assisted sperm morphometry analysis has improved the assessment of sperm morphology, but the results depend on the use of adequate evaluation and staining procedures of spermatozoa from individual species. In this study, the morphological module of the Sperm Class Analyzer&reg;was used for the morphometric analysis of stallion sperm heads and midpieces. Semen samples were obtained from six fertile stallions in order to evaluate the influence of three staining procedures (Diff-Quik, Hemacolor and Harris&rsquo; Haematoxylin) on the accuracy of image processing and sperm morphometry, and the effect of the sample size on sperm morphometric measurements. Harris&rsquo; Haematoxylin was the staining technique of choice on the accuracy of the image processing with an optimum contrast of sperm cells with the surrounding background that allows an efficient boundary detection and segmentation which results in the highest proportion of sperm heads and midpieces assessed (80.47%). The results indicate that the staining methods affected significantly the sperm dimensions with increased values from Diff-Quik than Hemacolor and Harris&rsquo; Haematoxylin respectively (Diff-Quik &gt; Hemacolor &gt; Harris&rsquo; Haematoxylin). No differences in morphometric parameters were found when 100, 150, 175 or 200 spermatozoa were analysed. In conclusion, to obtain objective and accurate sperm morphometric measurements by the Sperm Class Analyzer&reg; system in the stallion, it&rsquo;s recommended the analysis of 100 spermatozoa from slides which have been previously stained with Harris&rsquo; Haematoxylin.

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23

Heidel, R. Eric. "Causality in Statistical Power: Isomorphic Properties of Measurement, Research Design, Effect Size, and Sample Size." Scientifica 2016 (2016): 1–5. http://dx.doi.org/10.1155/2016/8920418.

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Statistical power is the ability to detect a significant effect, given that the effect actually exists in a population. Like most statistical concepts, statistical power tends to induce cognitive dissonance in hepatology researchers. However, planning for statistical power by ana priorisample size calculation is of paramount importance when designing a research study. There are five specific empirical components that make up ana priorisample size calculation: the scale of measurement of the outcome, the research design, the magnitude of the effect size, the variance of the effect size, and the sample size. A framework grounded in the phenomenon of isomorphism, or interdependencies amongst different constructs with similar forms, will be presented to understand the isomorphic effects of decisions made on each of the five aforementioned components of statistical power.

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24

Heckmann, T., K. Gegg, A. Gegg, and M. Becht. "Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows." Natural Hazards and Earth System Sciences 14, no. 2 (2014): 259–78. http://dx.doi.org/10.5194/nhess-14-259-2014.

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Abstract. Predictive spatial modelling is an important task in natural hazard assessment and regionalisation of geomorphic processes or landforms. Logistic regression is a multivariate statistical approach frequently used in predictive modelling; it can be conducted stepwise in order to select from a number of candidate independent variables those that lead to the best model. In our case study on a debris flow susceptibility model, we investigate the sensitivity of model selection and quality to different sample sizes in light of the following problem: on the one hand, a sample has to be large enough to cover the variability of geofactors within the study area, and to yield stable and reproducible results; on the other hand, the sample must not be too large, because a large sample is likely to violate the assumption of independent observations due to spatial autocorrelation. Using stepwise model selection with 1000 random samples for a number of sample sizes between n = 50 and n = 5000, we investigate the inclusion and exclusion of geofactors and the diversity of the resulting models as a function of sample size; the multiplicity of different models is assessed using numerical indices borrowed from information theory and biodiversity research. Model diversity decreases with increasing sample size and reaches either a local minimum or a plateau; even larger sample sizes do not further reduce it, and they approach the upper limit of sample size given, in this study, by the autocorrelation range of the spatial data sets. In this way, an optimised sample size can be derived from an exploratory analysis. Model uncertainty due to sampling and model selection, and its predictive ability, are explored statistically and spatially through the example of 100 models estimated in one study area and validated in a neighbouring area: depending on the study area and on sample size, the predicted probabilities for debris flow release differed, on average, by 7 to 23 percentage points. In view of these results, we argue that researchers applying model selection should explore the behaviour of the model selection for different sample sizes, and that consensus models created from a number of random samples should be given preference over models relying on a single sample.

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25

Lee, C. J., J. C. Huang, and T. G. Nieh. "Sample size effect and microcompression of Mg65Cu25Gd10 metallic glass." Applied Physics Letters 91, no. 16 (2007): 161913. http://dx.doi.org/10.1063/1.2800313.

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26

Volkert, C. A., A. Donohue, and F. Spaepen. "Effect of sample size on deformation in amorphous metals." Journal of Applied Physics 103, no. 8 (2008): 083539. http://dx.doi.org/10.1063/1.2884584.

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27

Wilkerson, Matt, and Mary R. Olson. "Misconceptions About Sample Size, Statistical Significance, and Treatment Effect." Journal of Psychology 131, no. 6 (1997): 627–31. http://dx.doi.org/10.1080/00223989709603844.

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28

Lincoln, Tania Marie, and Winfried Rief. "How much do sample characteristics affect the effect size?" Journal of Anxiety Disorders 18, no. 4 (2004): 515–29. http://dx.doi.org/10.1016/s0887-6185(03)00040-9.

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29

Qu, R. T., S. G. Wang, X. D. Wang, S. J. Wu, and Z. F. Zhang. "Shear band fracture in metallic glass: Sample size effect." Materials Science and Engineering: A 739 (January 2019): 377–82. http://dx.doi.org/10.1016/j.msea.2018.10.078.

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30

Dhar, Ajay, C. Jagadish, and D. L. Atherton. "The effect of sample size on magneto-acoustic emission." NDT & E International 24, no. 1 (1991): 15–19. http://dx.doi.org/10.1016/0963-8695(91)90677-u.

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31

Wu, F. F., Z. F. Zhang, S. X. Mao, and J. Eckert. "Effect of sample size on ductility of metallic glass." Philosophical Magazine Letters 89, no. 3 (2009): 178–84. http://dx.doi.org/10.1080/09500830902720917.

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32

Ma, Qing, Nicolaie Moldovan, Derrick C. Mancini, and Richard A. Rosenberg. "Sample size effect in photoelectrochemical etching of n-GaAs." Applied Physics Letters 77, no. 9 (2000): 1319–21. http://dx.doi.org/10.1063/1.1289908.

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33

Engstrom, John W., and Richard K. Olney. "Quantitative motor unit analysis: The effect of sample size." Muscle & Nerve 15, no. 3 (1992): 277–81. http://dx.doi.org/10.1002/mus.880150304.

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34

Han, Qiang, Yaohui Gao, and Yan Zhang. "Experimental Study of Size Effects on the Deformation Strength and Failure Characteristics of Hard Rocks under True Triaxial Compression." Advances in Civil Engineering 2021 (October 4, 2021): 1–15. http://dx.doi.org/10.1155/2021/6832775.

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Size effect has always been the focus of rock mechanics as a bridge between laboratory test and engineering site. Previously, the research conditions and objects of the rock size effect have mostly focused on cylindrical rock samples with different height-to-diameter ratios (H/Ds) under uniaxial or conventional triaxial compression, while there has been little research on the rock size effect under true triaxial compression (TTC), especially rectangular rock samples with different sizes and the same length-to-width-to-height ratio. Based on this, the deformation, strength, and failure characteristics of Beishan (BS) granite and Baihetan (BHT) basalt with different sample sizes under TTC were studied by a comparative analysis method. The size effect of deformation and failure characteristics under TTC are not obvious, including stress-strain curves, Young’s modulus, peak strains, failure angles, and macrofailure mode. However, the damage stress (σcd) and peak strength (σp) have obvious size effect; that is, the smaller the sample size is, the higher the strength is. Additionally, the relationship among the peak strength, sample size, and intermediate principal stress (σ2) is power function. In addition, by comparing the peak strength increment caused by the sample size of the two types of rocks, the σp of the fine-grained BHT basalt is more sensitive to sample size than that of the coarse-grained BS granite. Finally, by analyzing the relationship between the size of the mineral grains or clusters in the two types of hard rocks and the complexity of crack propagation in the fracture surface under TTC, it is suggested that the minimum side length of rock samples should not be less than 10 times the maximum mineral clusters (such as feldspar phenocrysts in BHT basalt). In addition, the method of estimating elastic strain is established by analyzing the relationship between the size of the rock sample σ2 and the elastic strain under TTC.

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35

Luo, Jia, and BJ Fox. "A Review of the Mantel Test in Dietary Studies: Effect of Sample Size and Inequality of Sample Sizes." Wildlife Research 23, no. 3 (1996): 267. http://dx.doi.org/10.1071/wr9960267.

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The Mantel test has been widely used in many areas of research in biological science since its publication in 1967 and is particularly well suited to use in dietary studies. It is a non-parametric test that has been suggested as appropriate for comparisons when sample sizes are small. The methodology is reviewed, benefits to be gained are examined, and effects of features that have considerable impact (sample-size dependence and sensitivity to inequality of sample size) are considered.

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36

Hittner, James B. "Effects of Population Distribution, Sample Size and Correlation Structure on Huberty’s Effect Size R." Journal of Modern Applied Statistical Methods 8, no. 1 (2009): 95–99. http://dx.doi.org/10.22237/jmasm/1241136420.

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37

Huang, Xin, Kevin J. Hanley, Catherine O'Sullivan, and Fiona C. Y. Kwok. "Effect of sample size on the response of DEM samples with a realistic grading." Particuology 15 (August 2014): 107–15. http://dx.doi.org/10.1016/j.partic.2013.07.006.

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38

Shieh, Gwowen. "Sample Size Calculations for Precise Interval Estimation of the Eta-Squared Effect Size." Journal of Experimental Education 83, no. 2 (2014): 203–17. http://dx.doi.org/10.1080/00220973.2014.907227.

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39

Nelson, Matthew S., Alese Wooditch, and Lisa M. Dario. "Sample size, effect size, and statistical power: a replication study of Weisburd’s paradox." Journal of Experimental Criminology 11, no. 1 (2014): 141–63. http://dx.doi.org/10.1007/s11292-014-9212-9.

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40

De Martini, Daniele. "Robustness and Corrections for Sample Size Adaptation Strategies Based on Effect Size Estimation." Communications in Statistics - Simulation and Computation 40, no. 9 (2011): 1263–77. http://dx.doi.org/10.1080/03610918.2011.568152.

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41

Bradley, M. T., D. Smith, and G. Stoica. "A Monte-Carlo Estimation of Effect Size Distortion Due to Significance Testing." Perceptual and Motor Skills 95, no. 3 (2002): 837–42. http://dx.doi.org/10.2466/pms.2002.95.3.837.

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A Monte-Carlo study was done with true effect sizes in deviation units ranging from 0 to 2 and a variety of sample sizes. The purpose was to assess the amount of bias created by considering only effect sizes that passed a statistical cut-off criterion of α = .05. The deviation values obtained at the .05 level jointly determined by the set effect sizes and sample sizes are presented. This table is useful when summarizing sets of studies to judge whether published results reflect an accurate appraisal of an underlying effect or a distorted estimate expected because significant studies are published and nonsignificant results are not. The table shows that the magnitudes of error are substantial with small sample sizes and inherently small effect sizes. Thus, reviews based on published literature could be misleading and especially so if true effect sizes were close to zero. A researcher should be particularly cautious of small sample sizes showing large effect sizes when larger samples indicate diminishing smaller effects.

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42

Posch, Martin, Florian Klinglmueller, Franz König, and Frank Miller. "Estimation after blinded sample size reassessment." Statistical Methods in Medical Research 27, no. 6 (2016): 1830–46. http://dx.doi.org/10.1177/0962280216670424.

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Blinded sample size reassessment is a popular means to control the power in clinical trials if no reliable information on nuisance parameters is available in the planning phase. We investigate how sample size reassessment based on blinded interim data affects the properties of point estimates and confidence intervals for parallel group superiority trials comparing the means of a normal endpoint. We evaluate the properties of two standard reassessment rules that are based on the sample size formula of the z-test, derive the worst case reassessment rule that maximizes the absolute mean bias and obtain an upper bound for the mean bias of the treatment effect estimate.

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43

Garga, Vinod K. "Effect of sample size on consolidation of a fissured clay." Canadian Geotechnical Journal 25, no. 1 (1988): 76–84. http://dx.doi.org/10.1139/t88-009.

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This paper describes an experimental investigation on the effect of sample size on consolidation characteristics of fissured London Clay. Pore pressure dissipation tests on 38, 100, and 300 mm diameter samples were undertaken in the laboratory. Constant-head in situ permeability tests were conducted in four boreholes at different depths in the clay. Conventional oedometer tests on 76 mm diameter samples recovered from the same depths at which in situ permeability tests were carried out were also undertaken. The results show that both the coefficient of compressibility mv and the coefficient of consolidation determined in the laboratory are not significantly affected by sample size. It is concluded that estimate of in situ coefficient of consolidation can best be made from mv determined in the laboratory, and from in situ permeability measurements. Key words: consolidation, compressibility, fissured clay, permeability, size effect, testing.

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Liang, Xin Yu, and Fa Ning Dang. "Numeric Analysis of Size Effect on Mesol Concrete Random Aggregate Model." Applied Mechanics and Materials 226-228 (November 2012): 1780–84. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.1780.

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In order to research that statics properties of concrete cylinder sample are influenced by micro-concrete material heterogeneity, by random aggregate models generated by different random number were established. By fixed aggregate size and constantly changing of the sample size, the concrete numerical model was simulated and Strength change of concrete samples was analyzed .So that strength influence of the aggregate location of the concrete random sample was study. Calculation shows that: the strength of concrete has been little effect by the aggregate random location, the size effect on concrete has been changed regularly, with the size effect ratio coefficient of aggregate and sample gradually increasing, the error square sum of strain was reduced and the brittlness of the samples becomes obvious.

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Owens, Max M., Alexandra Potter, Courtland S. Hyatt, et al. "Recalibrating expectations about effect size: A multi-method survey of effect sizes in the ABCD study." PLOS ONE 16, no. 9 (2021): e0257535. http://dx.doi.org/10.1371/journal.pone.0257535.

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Effect sizes are commonly interpreted using heuristics established by Cohen (e.g., small: r = .1, medium r = .3, large r = .5), despite mounting evidence that these guidelines are mis-calibrated to the effects typically found in psychological research. This study’s aims were to 1) describe the distribution of effect sizes across multiple instruments, 2) consider factors qualifying the effect size distribution, and 3) identify examples as benchmarks for various effect sizes. For aim one, effect size distributions were illustrated from a large, diverse sample of 9/10-year-old children. This was done by conducting Pearson’s correlations among 161 variables representing constructs from all questionnaires and tasks from the Adolescent Brain and Cognitive Development Study® baseline data. To achieve aim two, factors qualifying this distribution were tested by comparing the distributions of effect size among various modifications of the aim one analyses. These modified analytic strategies included comparisons of effect size distributions for different types of variables, for analyses using statistical thresholds, and for analyses using several covariate strategies. In aim one analyses, the median in-sample effect size was .03, and values at the first and third quartiles were .01 and .07. In aim two analyses, effects were smaller for associations across instruments, content domains, and reporters, as well as when covarying for sociodemographic factors. Effect sizes were larger when thresholding for statistical significance. In analyses intended to mimic conditions used in “real-world” analysis of ABCD data, the median in-sample effect size was .05, and values at the first and third quartiles were .03 and .09. To achieve aim three, examples for varying effect sizes are reported from the ABCD dataset as benchmarks for future work in the dataset. In summary, this report finds that empirically determined effect sizes from a notably large dataset are smaller than would be expected based on existing heuristics.

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Garga, Vinod K. "Effect of sample size on shear strength of basaltic residual soils." Canadian Geotechnical Journal 25, no. 3 (1988): 478–87. http://dx.doi.org/10.1139/t88-053.

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This paper first provides a brief review of the very limited data available on the size effect on strength of soils. Then it presents the results of an investigation of this effect on the drained strength of two residual soils derived from basalt. The dense basaltic soil, derived from weathering of columnar basalt, is fissured, whereas the vesicular basaltic soil, product of weathering of amygdaloidal basalt, is remarkably free of discontinuities. The results of tests on 500 mm square, 100 mm square, and 63.5 mm diameter direct shear tests, as well as on 36 mm diameter triaxial samples were obtained. The data clearly indicate the significant effect of fissures on the strength of dense basaltic soil, whereas the effect is absent in the vesicular soil. The reduction in strength with size in the former can be attributed almost totally to a loss of the cohesive component of shear strength. In the absence of tests on large-sized samples, a method is suggested to estimate the mass strength of such soils from results of tests on small-sized samples. Key words: fissures, residual soil, size effect, shear, strength, testing.

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Álvarez, Lidia N., Sara García-Sanz, Néstor E. Bosch, Rodrigo Riera, and Fernando Tuya. "Optimizing Costs to Collect Local Infauna through Grabs: Effect of Sampling Size and Replication." Diversity 12, no. 11 (2020): 410. http://dx.doi.org/10.3390/d12110410.

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Most ecological studies require a cost-effective collection of multi-species samples. A literature review unravelled that (1) large-sized grabs to collect infauna have been used at greater depths, despite no consistent relationship between grab size and replication across studies; and (2) the total number of taxa and individuals is largely determined by the replication. Then, infauna from a sedimentary (sandy) seabed at Gran Canaria Island was collected through van Veen grabs of three sizes: 0.018, 0.042 and 0.087 m2 to optimize, on a simple cost-benefit basis, sample size and replication. Specifically, (1) the degree of representativeness in the composition of assemblages, and (2) accuracy of three univariate metrics (species richness, total infaunal abundances and the Shannon-Wiener index), was compared according to replication. Then, by considering mean times (a surrogate of costs) to process a sample by each grab, (3) their cost-efficiency was estimated. Representativeness increased with grab size. Irrespective of the grab size, accuracy of univariate metrics considerably increased when n > 10 replicates. Costs associated with the 0.087 m2 grab were consistently lower than costs by the other grabs. In conclusion, because of high representativeness and low cost, a 6.87 L grab appears to be the optimal sample size to assess infauna at our local site.

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Shi, Dexin, Taehun Lee, and Alberto Maydeu-Olivares. "Understanding the Model Size Effect on SEM Fit Indices." Educational and Psychological Measurement 79, no. 2 (2018): 310–34. http://dx.doi.org/10.1177/0013164418783530.

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This study investigated the effect the number of observed variables ( p) has on three structural equation modeling indices: the comparative fit index (CFI), the Tucker–Lewis index (TLI), and the root mean square error of approximation (RMSEA). The behaviors of the population fit indices and their sample estimates were compared under various conditions created by manipulating the number of observed variables, the types of model misspecification, the sample size, and the magnitude of factor loadings. The results showed that the effect of p on the population CFI and TLI depended on the type of specification error, whereas a higher p was associated with lower values of the population RMSEA regardless of the type of model misspecification. In finite samples, all three fit indices tended to yield estimates that suggested a worse fit than their population counterparts, which was more pronounced with a smaller sample size, higher p, and lower factor loading.

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Teng, Yun, and Zhen-Dong Sha. "Uncovering the Inherent Size Dependence of Yield Strength and Failure Mechanism in Micron-Sized Metallic Glass." Materials 15, no. 18 (2022): 6362. http://dx.doi.org/10.3390/ma15186362.

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The sample size effect on the deformation behavior of metallic glasses (MGs) has recently become research of intense interest. An inverse sample size effect is observed in previous experimental studies; where the yield strength decreases with decreasing sample size, rather than increasing. We propose a theoretical analysis based on the shear banding process to rationalize the inherent size dependence of yield strength, showing an excellent agreement with experimental results. Our model reveals that the anomalous inverse size effect is, in fact, caused by a transition in failure mode; from a rapid shear banding process with a shear band (SB) traversing the entire sample in bulk MGs, to an immature shear banding process with propagated SBs only at the surface in micron-sized MGs. Our results fill the gap in the current understanding of size effects in the strength and failure mechanism of MGs at different length scales.

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Zedaker, S. M., T. G. Gregoire, and J. H. Miller. "Sample-size needs for forestry herbicide trials." Canadian Journal of Forest Research 23, no. 10 (1993): 2153–58. http://dx.doi.org/10.1139/x93-268.

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Forest herbicide experiments are increasingly being designed to evaluate smaller treatment differences when comparing existing effective treatments, tank mix ratios, surfactants, and new low-rate products. The ability to detect small differences in efficacy is dependent upon the relationship among sample size, type I and II error probabilities, and the coefficients of variation of the efficacy data. The common sources of variation in efficacy measurements and design considerations for controlling variation are reviewed, while current shortcomings are clarified. A summary of selected trials estimates that coefficients of variation often range between 25 and 100%, making the number of observations necessary to detect small differences very large, especially when the power of the test (1–β) is considered. Very often the power of the test has been ignored when designing experiments because of the difficulty in calculating β. An available program for microcomputers is introduced that allows researchers to examine relationships among sample size, effect size, and coefficients of variation for specified designs, α and β. This program should aid investigators in planning studies that optimize experimental power to detect anticipated effect sizes within resource constraints.

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