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Relevant bibliographies by topics / Sample size effects

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Academic literature on the topic 'Sample size effects'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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Journal articles on the topic "Sample size effects"

1

Büntgen, U., W. Tegel, KU Heussner, et al. "Effects of sample size in dendroclimatology." Climate Research 53, no. 3 (2012): 263–69. http://dx.doi.org/10.3354/cr01107.

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Shiffler, Ronald E., and Arthur J. Adams. "A Correction for Biasing Effects of Pilot Sample Size on Sample Size Determination." Journal of Marketing Research 24, no. 3 (1987): 319. http://dx.doi.org/10.2307/3151643.

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Shiffler, Ronald E., and Arthur J. Adams. "A Correction for Biasing Effects of Pilot Sample Size on Sample Size Determination." Journal of Marketing Research 24, no. 3 (1987): 319–21. http://dx.doi.org/10.1177/002224378702400309.

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When a pilot study variance is used to estimate σ2 in the sample size formula, the resulting [Formula: see text] is a random variable. The authors investigate the theoretical behavior of [Formula: see text]. Though [Formula: see text] is more likely to underachieve than overachieve the unbiased n, correction factors to balance the bias are provided.

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Barzola-Quiquia, J., J. L. Yao, P. Rödiger, K. Schindler, and P. Esquinazi. "Sample size effects on the transport characteristics of mesoscopic graphite samples." physica status solidi (a) 205, no. 12 (2008): 2924–33. http://dx.doi.org/10.1002/pssa.200824288.

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Gong, Jicheng, and Angus J. Wilkinson. "Sample size effects on grain boundary sliding." Scripta Materialia 114 (March 2016): 17–20. http://dx.doi.org/10.1016/j.scriptamat.2015.11.029.

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Weichenthal, Scott, Jill Baumgartner, and James A. Hanley. "Sample Size Estimation for Random-effects Models." Epidemiology 28, no. 6 (2017): 817–26. http://dx.doi.org/10.1097/ede.0000000000000727.

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Fukunaga, K., and R. R. Hayes. "Effects of sample size in classifier design." IEEE Transactions on Pattern Analysis and Machine Intelligence 11, no. 8 (1989): 873–85. http://dx.doi.org/10.1109/34.31448.

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Weinberg, Michael C. "Finite sample size effects in transformation kinetics." Journal of Non-Crystalline Solids 72, no. 2-3 (1985): 301–14. http://dx.doi.org/10.1016/0022-3093(85)90186-3.

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Gaston, Kevin J., Rachel M. Quinn, Simon Wood, and Henry R. Arnold. "Measures of geographic range size: the effects of sample size." Ecography 19, no. 3 (1996): 259–68. http://dx.doi.org/10.1111/j.1600-0587.1996.tb00235.x.

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Lindemuth, J., J. Krause, and B. Dodrill. "Finite sample size effects on the calibration of vibrating sample magnetometer." IEEE Transactions on Magnetics 37, no. 4 (2001): 2752–54. http://dx.doi.org/10.1109/20.951296.

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Dissertations / Theses on the topic "Sample size effects"

1

Tse, Kwok Ho. "Sample size calculation : influence of confounding and interaction effects /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?MATH%202006%20TSE.

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Norfleet, David M. "Sample size effects related to nickel, titanium and nickel-titanium at the micron size scale." The Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1187038020.

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Gilkey, Justin Michael. "The Effects of Sample Size on Measures of Subjective Correlation." Bowling Green State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1211901739.

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Norfleet, David Matthew. "Sample size effects related to nickel, titanium and nickel-titanium at the micron size scale." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1187038020.

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Janse, Sarah A. "INFERENCE USING BHATTACHARYYA DISTANCE TO MODEL INTERACTION EFFECTS WHEN THE NUMBER OF PREDICTORS FAR EXCEEDS THE SAMPLE SIZE." UKnowledge, 2017. https://uknowledge.uky.edu/statistics_etds/30.

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In recent years, statistical analyses, algorithms, and modeling of big data have been constrained due to computational complexity. Further, the added complexity of relationships among response and explanatory variables, such as higher-order interaction effects, make identifying predictors using standard statistical techniques difficult. These difficulties are only exacerbated in the case of small sample sizes in some studies. Recent analyses have targeted the identification of interaction effects in big data, but the development of methods to identify higher-order interaction effects has been limited by computational concerns. One recently studied method is the Feasible Solutions Algorithm (FSA), a fast, flexible method that aims to find a set of statistically optimal models via a stochastic search algorithm. Although FSA has shown promise, its current limits include that the user must choose the number of times to run the algorithm. Here, statistical guidance is provided for this number iterations by deriving a lower bound on the probability of obtaining the statistically optimal model in a number of iterations of FSA. Moreover, logistic regression is severely limited when two predictors can perfectly separate the two outcomes. In the case of small sample sizes, this occurs quite often by chance, especially in the case of a large number of predictors. Bhattacharyya distance is proposed as an alternative method to address this limitation. However, little is known about the theoretical properties or distribution of B-distance. Thus, properties and the distribution of this distance measure are derived here. A hypothesis test and confidence interval are developed and tested on both simulated and real data.

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Lynch, Edward. "The Effects of Irrelevant Information and Minor Errors in Client Documents on Assessments of Misstatement Risk and Sample Size." VCU Scholars Compass, 2017. http://scholarscompass.vcu.edu/etd/4959.

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This dissertation consists of three studies. The first study conducts a 2 by 2 experiment to examine how auditors are influenced by the presence of irrelevant information and minor errors (i.e., “dirty documents”) when reviewing audit evidence produced by the client. This study tasks 97 public accountants to review audit evidence and finds some evidence that dirty documents influence an auditor’s assessment of the likelihood of account misstatement and the appropriate sample size. In order to demonstrate the usefulness of eye-tracking and to help generate potential research topics, the second study reviews extant literature in other disciplines where eye-tracking technology is applied to various judgment and decision-making contexts. This study suggests how eye-tracking can enhance extant accounting research. Illustrative examples of promising research opportunities (extending extant research) are provided. In addition, this study identifies how eye-tracking can be applied to more contemporary decision making and educational circumstances. The third study extends the first experiment through the use of eye-tracking technology. This study utilizes the same 2 by 2 experiment as the first study, but in this case records the eye movements of 43 auditing students while they review the audit evidence. The eye-tracking technology provides additional detail as to the specific evidence participants’ focus on during their review. This study finds that participants focus their attention differently depending on whether irrelevant information or minor errors were present.

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Liv, Per. "Efficient strategies for collecting posture data using observation and direct measurement." Doctoral thesis, Umeå universitet, Yrkes- och miljömedicin, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-59132.

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Relationships between occupational physical exposures and risks of contracting musculoskeletal disorders are still not well understood; exposure-response relationships are scarce in the musculoskeletal epidemiology literature, and many epidemiological studies, including intervention studies, fail to reach conclusive results. Insufficient exposure assessment has been pointed out as a possible explanation for this deficiency. One important aspect of assessing exposure is the selected measurement strategy; this includes issues related to the necessary number of data required to give sufficient information, and to allocation of measurement efforts, both over time and between subjects in order to achieve precise and accurate exposure estimates. These issues have been discussed mainly in the occupational hygiene literature considering chemical exposures, while the corresponding literature on biomechanical exposure is sparse. The overall aim of the present thesis was to increase knowledge on the relationship between data collection design and the resulting precision and accuracy of biomechanical exposure assessments, represented in this thesis by upper arm postures during work, data which have been shown to be relevant to disorder risk. Four papers are included in the thesis. In papers I and II, non-parametric bootstrapping was used to investigate the statistical efficiency of different strategies for distributing upper arm elevation measurements between and within working days into different numbers of measurement periods of differing durations. Paper I compared the different measurement strategies with respect to the eventual precision of estimated mean exposure level. The results showed that it was more efficient to use a higher number of shorter measurement periods spread across a working day than to use a smaller number for longer uninterrupted measurement periods, in particular if the total sample covered only a small part of the working day. Paper II evaluated sampling strategies for the purpose of determining posture variance components with respect to the accuracy and precision of the eventual variance component estimators. The paper showed that variance component estimators may be both biased and imprecise when based on sampling from small parts of working days, and that errors were larger with continuous sampling periods. The results suggest that larger posture samples than are conventionally used in ergonomics research and practice may be needed to achieve trustworthy estimates of variance components. Papers III and IV focused on method development. Paper III examined procedures for estimating statistical power when testing for a group difference in postures assessed by observation. Power determination was based either on a traditional analytical power analysis or on parametric bootstrapping, both of which accounted for methodological variance introduced by the observers to the exposure data. The study showed that repeated observations of the same video recordings may be an efficient way of increasing the power in an observation-based study, and that observations can be distributed between several observers without loss in power, provided that all observers contribute data to both of the compared groups, and that the statistical analysis model acknowledges observer variability. Paper IV discussed calibration of an inferior exposure assessment method against a superior “golden standard” method, with a particular emphasis on calibration of observed posture data against postures determined by inclinometry. The paper developed equations for bias correction of results obtained using the inferior instrument through calibration, as well as for determining the additional uncertainty of the eventual exposure value introduced through calibration. In conclusion, the results of the present thesis emphasize the importance of carefully selecting a measurement strategy on the basis of statistically well informed decisions. It is common in the literature that postural exposure is assessed from one continuous measurement collected over only a small part of a working day. In paper I, this was shown to be highly inefficient compared to spreading out the corresponding sample time across the entire working day, and the inefficiency was also obvious when assessing variance components, as shown in paper II. The thesis also shows how a well thought-out strategy for observation-based exposure assessment can reduce the effects of measurement error, both for random methodological variance (paper III) and systematic observation errors (bias) (paper IV).

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Guan, Tianyuan. "Sample Size Calculations in Simple Linear Regression: A New Approach." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627667392849137.

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Wang, Xiangrong. "Effect of Sample Size on Irt Equating of Uni-Dimensional Tests in Common Item Non-Equivalent Group Design: a Monte Carlo Simulation Study." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/37555.

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Test equating is important to large-scale testing programs because of the following two reasons: strict test security is a key concern for high-stakes tests and fairness of test equating is important for test takers. The question of adequacy of sample size often arises in test equating. However, most recommendations in the existing literature are based on classical test equating. Very few research studies systematically investigated the minimal sample size which leads to reasonably accurate equating results based on item response theory (IRT). The main purpose of this study was to examine the minimal sample size for desired IRT equating accuracy for the common-item nonequivalent groups design under various conditions. Accuracy was determined by examining the relative magnitude of six accuracy statistics. Two IRT equating methods were carried out on simulated tests with combinations of test length, test format, group ability difference, similarity of the form difficulty, and parameter estimation methods for 14 sample sizes using Monte Carlo simulations with 1,000 replications per cell. Observed score equating and true score equating were compared to the criterion equating to obtain the accuracy statistics. The results suggest that different sample size requirements exist for different test lengths, test formats and parameter estimation methods. Additionally, the results show the following: first, the results for true score equating and observed score equating are very similar. Second, the longer test has less accurate equating than the shorter one at the same sample size level and as the sample size decreases, the gap is greater. Third, concurrent parameter estimation method produced less equating error than separate estimation at the same sample size level and as the sample size reduces, the difference increases. Fourth, the cases with different group ability have larger and less stable error comparing to the base case and the cases with different test difficulty, especially when using separate parameter estimation method with sample size less than 750. Last, the mixed formatted test is more accurate than the single formatted one at the same sample size level.<br>Ph. D.

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Silva, Wandermon CorrÃa. "Sorte versus habilidade na anÃlise de desempenho de fundos de investimento em aÃÃes no Brasil." Universidade Federal do CearÃ, 2012. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=10979.

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nÃo hÃ<br>Esta dissertaÃÃo visa contribuir ao mainstream da Teoria de ApreÃamento de Ativos, ao analisar o desempenho dos fundos de investimento em aÃÃes no Brasil, a partir de um painel composto por 75 fundos do tipo ANBIMA Ibovespa Ativo, sobreviventes no perÃodo de janeiro de 1998 a dezembro de 2008, identificando aqueles cujo resultado se deve simplesmente Ã sorte ou ao azar e aqueles cujo resultado se deve Ã habilidade ou Ã falta de habilidade dos seus gestores. Seguindo a metodologia desenvolvida em Fama & French (1992, 1993) e o trabalho elaborado por Matos e Silva (2010), construÃram-se fatores, os quais consistem em zero cost equal weighted portfolios compostos apenas por fundos, capazes de captar os efeitos tamanho e ganho acumulado destes ativos, sendo os mesmos usados em diversas aplicaÃÃes em uma versÃo estendida do Capital Asset Pricing Model (CAPM). Os efeitos tamanho e ganho acumulado, evidenciados pela inadequaÃÃo do CAPM em modelar fundos com maior patrimÃnio lÃquido e ganhos acumulados muito altos ou baixos, parecem ser muito bem acomodados quando da incorporaÃÃo dos fatores, os quais se mostraram significativos conjuntamente em 50% dos 75 fundos analisados. As principais evidÃncias obtidas a partir de regressÃes temporais individuais sÃo corroboradas quando do teste em painel com efeitos aleatÃrios em que ambos os efeitos sÃo indispensÃveis na explicaÃÃo dos retornos dos fundos de investimento em aÃÃes no Brasil. Para a anÃlise de performance dos fundos, seguiu-se a metodologia proposta por Fama & French (2010), na qual, por meio de tÃcnicas de bootstrap, modela-se o estudo transversal do desempenho dos fundos de investimento. Para a maioria dos fundos que apresentaram outperformance significativa, com base nos alfas estimados nas regressÃes individuais, identificou-se desempenho devido ao acaso. No modelo de fatores proposto, somente trÃs fundos apresentaram real desempenho superior devido Ã habilidade de seus gestores, todos esses vinculados a instituiÃÃes financeiras privadas. O modelo de fatores se mostrou mais criterioso na caracterizaÃÃo da aleatoriedade de performance.<br>This dissertation aims to contribute to the mainstream in Asset Pricing Theory, to analyze the performance of stock mutual funds in Brazil, for a panel with 75 mutual funds type ANBIMA Active Ibovespa which have survived during the period between Jan-1998 and Dec-2008, identifying those whose result is simply due to good luck or bad luck and those whose result is due to the skill or lack of skill of their managers. Following the methodology developed in Fama and French (1992, 1993), we built two factors, mutual funds zero cost equal weighted portfolios, able to accommodate the size and performance effects observed for these assets, which are used in some applications in an extended version of Capital Asset Pricing Model (CAPM). Both effects, which seem to play a relevant role due to the inefficiency of the CAPM model to price big funds with huge relative performance (very high or very low), are partially accommodated when one adds factors, which are significant jointly in 50% of the 75 funds analyzed. The main evidences obtained running individual time series regressions are corroborated if one uses the panel technique estimation with random effects, where both factors seem to be vital if one intends to better understand the returns of the mutual funds in Brazil. To analyze the performance of the funds, the methodology developed in Fama and French (2010) was used, in which, by bootstrap techniques, the cross-section of the performance of investment funds are modeled. For most of the funds that had significant outperformance, based on the estimated alphas in individual regressions, performance due to chance was identified. In the factors model proposed, only three funds really outperformed due to the ability of their managers, all those linked to private financial institutions. The factor model proved to be more accurate in characterizing the randomness of performance with the appropriate criteria.

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Books on the topic "Sample size effects"

1

Coggins, Lewis G. Effects of sample size and ageing error on estimates of sustained yield. State of Alaska, Dept. of Fish and Game, 1997.

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Chen, Son-Nan. The effects of the sample size, the investment horizon, and market conditions on the validity of composite performance measures: A generalization. University of Illinois at Urbana-Champaign, College of Commerce and Business Administration, 1985.

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Tang, K. Linda. The effect of small calibration sample sizes on TOEFL IRT-based equating. Educational Testing Service, 1993.

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Zheng, Youxin. The effect of sample size on laboratory measurement of shear strength in terms of effective stress. typescript, 1996.

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Simpson, J. C. Uncertainty in North American wet deposition isopleth maps: Effect of site selection and valid sample criteria. U.S. Environmental Protection Agency, Atmospheric Research and Exposure Assessment Laboratory, 1990.

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Simpson, J. C. Uncertainty in North American wet deposition isopleth maps: Effect of site selection and valid sample criteria. U.S. Environmental Protection Agency, Atmospheric Research and Exposure Assessment Laboratory, 1990.

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Marlowe, Christopher. The effect of decreasing sample size on the precision of GSI stock composition estimates for chinook salmon (Onchorhynchus tshawytscha) using data from the Washington Coastal and Strait of Juan de Fuca troll fisheries in 1989-1990. Washington Dept. of Fish and Wildlife, 1995.

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An Investigation of the Effects of Correlation, Autocorrelation, and Sample Size in Classifier Fusion. Storming Media, 2004.

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Fitzpatrick, Anne R. Issues in linking scores on alternative assessments : Effects of test length and sample size on test reliability and test equating. Council of Chief State School Officers, 1999.

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Robertson, Rob. Effects of collinearity, sample size, multiple correlation, and predictor-criterion correlation salience on the order of variable entry in stepwise regression. 1997.

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Book chapters on the topic "Sample size effects"

1

Jahanshad, Neda, Agatha D. Lee, Natasha Leporé, et al. "Genetics of Anisotropy Asymmetry: Registration and Sample Size Effects." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04271-3_61.

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Payne, John F. B., and Nassia Tzelepi. "Theoretical Investigations into Sample Size Effects on Ultrasonic Measurements of Elastic Moduli." In Modelling and Measuring Reactor Core Graphite Properties and Performance. Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849735179-00052.

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Tzelepi, Nassia. "Sample Size Effects on Ultrasonic Measurements of Elastic Moduli—Experimental and Theoretical Investigations." In Graphite Testing for Nuclear Applications: The Significance of Test Specimen Volume and Geometry and the Statistical Significance of Test Specimen Population. ASTM International, 2014. http://dx.doi.org/10.1520/stp157820130130.

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Garbaruk, A., S. Leicher, C. Mockett, P. Spalart, M. Strelets, and F. Thiele. "Evaluation of Time Sample and Span Size Effects in DES of Nominally 2D Airfoils beyond Stall." In Progress in Hybrid RANS-LES Modelling. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14168-3_7.

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Skurichina, Marina, Liudmila I. Kuncheva, and Robert P. W. Duin. "Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45428-4_6.

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Gnecco, Giorgio, and Federico Nutarelli. "Optimal Trade-Off Between Sample Size and Precision of Supervision for the Fixed Effects Panel Data Model." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37599-7_44.

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Chen, Xinmei, and Yuancai Lei. "Effects of Sample Size on Accuracy and Stability of Species Distribution Models: A Comparison of GARP and Maxent." In Recent Advances in Computer Science and Information Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25789-6_80.

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Vilares, Manuel J., and Pedro S. Coelho. "Likelihood and PLS Estimators for Structural Equation Modeling: An Assessment of Sample Size, Skewness and Model Misspecification Effects." In Studies in Theoretical and Applied Statistics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34904-1_2.

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Valliant, Richard, Jill A. Dever, and Frauke Kreuter. "Outcome Rates and Effect on Sample Size." In Statistics for Social and Behavioral Sciences. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93632-1_6.

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Valliant, Richard, Jill A. Dever, and Frauke Kreuter. "Outcome Rates and Effect on Sample Size." In Practical Tools for Designing and Weighting Survey Samples. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6449-5_6.

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Conference papers on the topic "Sample size effects"

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Dass, Sarat C., and Anil K. Jain. "Effects of user correlation on sample size requirements." In Defense and Security, edited by Anil K. Jain and Nalini K. Ratha. SPIE, 2005. http://dx.doi.org/10.1117/12.603441.

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Elragi, Ahmed, Dawit Negussey, and George Kyanka. "Sample Size Effects on the Behavior of EPS Geofoam." In Soft Ground Technology Conference. American Society of Civil Engineers, 2001. http://dx.doi.org/10.1061/40552(301)22.

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Chan, Heang-Ping, Berkman Sahiner, Robert F. Wagner, and Nicholas Petrick. "Effects of sample size on classifier design for computer-aided diagnosis." In Medical Imaging '98, edited by Kenneth M. Hanson. SPIE, 1998. http://dx.doi.org/10.1117/12.310895.

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Xue, L. "The effects of sample size on ranging precision for space debris." In International Conference on Quality, Reliability, Risk, Maintenance and Safety Engineering, edited by M. Li, L. L. Wang, G. L. He, and Y. Yin. WIT Press, 2015. http://dx.doi.org/10.2495/qr2mse140801.

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Suzuki, Norikazu, Shengchuan Zhao, and Nobuhiro Akimoto. "Effects of Sample Size and Collection Methods on Stated Preference Models." In International Conference on Traffic and Transportation Studies (ICTTS) 2002. American Society of Civil Engineers, 2002. http://dx.doi.org/10.1061/40630(255)103.

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Pillitteri Gotusso, A. M. "Combine Effects of Frequency Dependence, Sample Size and Heterogeneities on Acoustic Velocity." In 63rd EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2001. http://dx.doi.org/10.3997/2214-4609-pdb.15.n-19.

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Chan, Heang-Ping, Berkman Sahiner, Robert F. Wagner, Nicholas Petrick, and Joseph T. Mossoba. "Effects of sample size on classifier design: quadratic and neural network classifiers." In Medical Imaging 1997, edited by Kenneth M. Hanson. SPIE, 1997. http://dx.doi.org/10.1117/12.274091.

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Fernandez-Carrillo, Angel, Mihai A. Tanase, Miguel A. Belenguer-Plomer, and Emilio Chuvieco. "Effects of sample size on burned areas accuracy estimates in the Amazon Basin." In Earth Resources and Environmental Remote Sensing/GIS Applications, edited by Ulrich Michel and Karsten Schulz. SPIE, 2018. http://dx.doi.org/10.1117/12.2325686.

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Dabbaghchian, S., A. Aghagolzadeh, and M. S. Moin. "Reducing the effects of small sample size in DCT domain for face recognition." In 2008 International Symposium on Telecommunications (IST). IEEE, 2008. http://dx.doi.org/10.1109/istel.2008.4651378.

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"Effects of climate, objective function and sample size on global sensitivity in a SWAT Model." In 21st International Congress on Modelling and Simulation (MODSIM2015). Modelling and Simulation Society of Australia and New Zealand, 2015. http://dx.doi.org/10.36334/modsim.2015.a1.seo.

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Reports on the topic "Sample size effects"

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Axelrod, M. Using Ancillary Information to Reduce Sample Size in Discovery Sampling and the Effects of Measurement Error. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/877925.

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Garino, Terry J. The effects of composition, temperature and sample size on the sintering of chem-prep high field varistors. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/933217.

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Yang, Dali, Robin A. Montoya, and Kevin C. Henderson. Effect of Sample Size on the Mechanical Properties of SX 358 Foam. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1055754.

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Clausen, Jay, Samuel Beal, Thomas Georgian, Kevin Gardner, Thomas Douglas, and Ashley Mossell. Effects of milling on the metals analysis of soil samples containing metallic residues. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41241.

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Metallic residues are distributed heterogeneously onto small-arms range soils from projectile fragmentation upon impact with a target or berm backstop. Incremental Sampling Methodology (ISM) can address the spatially heterogeneous contamination of surface soils on small-arms ranges, but representative kilogram-sized ISM subsamples are affected by the range of metallic residue particle sizes in the sample. This study compares the precision and concentrations of metals in a small-arms range soil sample processed by a puck mill, ring and puck mill, ball mill, and mortar and pestle prior to analysis. The ball mill, puck mill, and puck and ring mill produced acceptable relative standard deviations of less than 15% for the anthropogenic metals of interest (Lead (Pb), Antimony (Sb), Copper (Cu), and Zinc (Zn)), with the ball mill exhibiting the greatest precision for Pb, Cu, and Zn. Precision by mortar and pestle, without milling, was considerably higher (40% to >100%) for anthropogenic metals. Media anthropogenic metal concentrations varied by more than 40% between milling methods, with the greatest concentrations produced by the puck mill, followed by the puck and ring mill and then the ball mill. Metal concentrations were also dependent on milling time, with concentrations stabilizing for the puck mill by 300 s but still increasing for the ball mill over 20 h. Differences in metal concentrations were not directly related to the surface area of the milled sample. Overall, the tested milling methods were successful in producing reproducible data for soils containing metallic residues. However, the effects of milling type and time on concentrations require consideration in environmental investigations.

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W Djimeu, Eric, and Deo-Gracias Houndolo. Power calculation for causal inference in social science: sample size and minimum detectable effect determination. International Initiative for Impact Evaluation (3ie), 2015. http://dx.doi.org/10.23846/wp0026.

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Ahumada, Hildegart, Eduardo A. Cavallo, Santos Espina-Mairal, and Fernando Navajas. Sectoral Productivity Growth, COVID-19 Shocks, and Infrastructure. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003411.

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This paper examines sectoral productivity shocks of the COVID-19 pandemic, their aggregate impact, and the possible compensatory effects of improving productivity in infrastructure-related sectors. We employ the KLEMS annual dataset for a group of OECD and Latin America and the Caribbean countries, complemented with high-frequency data for 2020. First, we estimate a panel vector autoregression of growth rates in sector level labor productivity to specify the nature and size of sectoral shocks using the historical data. We then run impulse-response simulations of one standard deviation shocks in the sectors that were most affected by COVID 19. We estimate that the pandemic cut economy-wide labor productivity by 4.9 percent in Latin America, and by 3.5 percent for the entire sample. Finally, by modeling the long-run relationship between productivity shocks in the sectors most affected by COVID 19, we find that large productivity improvements in infrastructure--equivalent to at least three times the historical rates of productivity gains--may be needed to fully compensate for the negative productivity losses traceable to COVID 19.

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Simpson, J. C., and A. R. Olsen. Uncertainty in North America wet deposition isopleth maps: Effect of site selection and valid sample criteria. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/7000106.

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Beverinotti, Javier, Gustavo Canavire-Bacarreza, and Alejandro Puerta. Understanding the Growth of the Middle Class in Bolivia. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003407.

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In this paper we aim to disentangle how sectoral economic growth affects the growth of the middle class size using state-level data of Bolivia from 2000 to 2017, a country with limited data, breaking the three main economic activities into subsectors aiming for more specific results. By means of a Bayesian hierarchical longitudinal model for small samples, we find that the commerce and services sectors have the biggest impact, even though mining and agriculture also have a positive effect on the increase of the middle class in Bolivia. Our results also suggest that both formality and public social investment have a significant, yet smaller, effect.

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Shahin, Mohamed Y., Chad Stock, Lisa Beckberger, Chao-Ming Wang, and M. R. Crrovetti. Effect of Sample Unit Size and Number of Survey Distress Types on the Pavement Condition Index (PCI) for Asphalt-Surfaced Roads. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada310687.

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Roschelle, Jeremy, Britte Haugan Cheng, Nicola Hodkowski, Julie Neisler, and Lina Haldar. Evaluation of an Online Tutoring Program in Elementary Mathematics. Digital Promise, 2020. http://dx.doi.org/10.51388/20.500.12265/94.

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Many students struggle with mathematics in late elementary school, particularly on the topic of fractions. In a best evidence syntheses of research on increasing achievement in elementary school mathematics, Pelligrini et al. (2018) highlighted tutoring as a way to help students. Online tutoring is attractive because costs may be lower and logistics easier than with face-to-face tutoring. Cignition developed an approach that combines online 1:1 tutoring with a fractions game, called FogStone Isle. The game provides students with additional learning opportunities and provides tutors with information that they can use to plan tutoring sessions. A randomized controlled trial investigated the research question: Do students who participate in online tutoring and a related mathematical game learn more about fractions than students who only have access to the game? Participants were 144 students from four schools, all serving low-income students with low prior mathematics achievement. In the Treatment condition, students received 20-25 minute tutoring sessions twice per week for an average of 18 sessions and also played the FogStone Isle game. In the Control condition, students had access to the game, but did not play it often. Control students did not receive tutoring. Students were randomly assigned to condition after being matched on pre-test scores. The same diagnostic assessment was used as a pre-test and as a post-test. The planned analysis looked for differences in gain scores ( post-test minus pre-test scores) between conditions. We conducted a t-test on the aggregate gain scores, comparing conditions; the results were statistically significant (t = 4.0545, df = 132.66, p-value < .001). To determine an effect size, we treated each site as a study in a meta-analysis. Using gain scores, the effect size was g=+.66. A more sophisticated treatment of the pooled standard deviation resulted in a corrected effect size of g=.46 with a 95% confidence interval of [+.23,+.70]. Students who received online tutoring and played the related Fog Stone Isle game learned more; our research found the approach to be efficacious. The Pelligrini et al. (2018) meta-analysis of elementary math tutoring programs found g = .26 and was based largely on face-to-face tutoring studies. Thus, this study compares favorably to prior research on face-to-face mathematics tutoring with elementary students. Limitations are discussed; in particular, this is an initial study of an intervention under development. Effects could increase or decrease as development continues and the program scales. Although this study was planned long before the current pandemic, results are particularly timely now that many students are at home under shelter-in-place orders due to COVID-19. The approach taken here is feasible for students at home, with tutors supporting them from a distance. It is also feasible in many other situations where equity could be addressed directly by supporting students via online tutors.

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