Relevant bibliographies by topics / Sample size effect

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Sample size effect'

Author: Grafiati
Published: 4 June 2021
Last updated: 22 February 2023

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Journal articles on the topic "Sample size effect"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Sample size effect"

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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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Price, Tonia. "A faster simulation approach to sample size determination for random effect models." Thesis, University of Bristol, 2017. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730872.

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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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Cong, Danni. "The effect of sample size re-estimation on type I error rates when comparing two binomial proportions." Kansas State University, 2016. http://hdl.handle.net/2097/34504.

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Master of Science<br>Department of Statistics<br>Christopher I. Vahl<br>Estimation of sample size is an important and critical procedure in the design of clinical trials. A trial with inadequate sample size may not produce a statistically significant result. On the other hand, having an unnecessarily large sample size will definitely increase the expenditure of resources and may cause a potential ethical problem due to the exposure of unnecessary number of human subjects to an inferior treatment. A poor estimate of the necessary sample size is often due to the limited information at the planning stage. Hence, the adjustment of the sample size mid-trial has become a popular strategy recently. In this work, we introduce two methods for sample size re-estimation for trials with a binary endpoint utilizing the interim information collected from the trial: a blinded method and a partially unblinded method. The blinded method recalculates the sample size based on the first stage’s overall event proportion, while the partially unblinded method performs the calculation based only on the control event proportion from the first stage. We performed simulation studies with different combinations of expected proportions based on fixed ratios of response rates. In this study, equal sample size per group was considered. The study shows that for both methods, the type I error rates were preserved satisfactorily.
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Hadley, Patrick. "The performance of the Mantel-Haenszel and logistic regression dif detection procedures across sample size and effect size: A Monte Carlo study." Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/10019.

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In recent years, public attention has become focused on the issue of test and item bias in standardized tests. Since the 1980's, the Mantel-Haenszel (Holland & Thayer, 1986) and Logistic Regression procedures (Swaminathan & Rogers, 1990) have been developed to detect item bias, or differential item functioning (dif). In this study the effectiveness of the MH and LR procedures was compared under a variety of conditions, using simulated data. The ability of the MH and LR to detect dif was tested at sample sizes of 100/100, 200/200, 400/400, 600/600, and 800/800. The simulated test had 66 items, the first 33 items with item discrimination ("a") set at 0.80, the second 33 items with "a" set at 1.20. The pseudo-guessing parameter ("c") was 0.15 for all items. The item difficulty ("b") parameter ranged from $-$2.00 to 2.00 in increments of 0.125 for the first 33 items, and again for the second 33 items. Both the MH and LRU detected dif with a high degree of success whenever sample size was large (600 or more), especially when effect size, no matter how measured, was also large. The LRU outperformed the MH marginally under almost every condition of the study. However, the LRU also had a higher false-positive rate than the MH, a finding consistent with previous studies (Pang et al., 1994, Tian et al., 1994a, 1994b). Since the "a" and "b" parameters which underly the computation of the three measures of effect size used in the study are not always determinable in data derived from real world test administrations, it may be that the $\Delta\sb{\rm MH}$ is the best available measure of effect size in real world test items. (Abstract shortened by UMI.)
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Lee, Michelle Oi San. "Sample size calculation for testing an interaction effect in a logistic regression under measurement error model /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?MATH%202003%20LEE.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 66-67). Also available in electronic version. Access restricted to campus users.
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Senteney, Michael H. "A Monte Carlo Study to Determine Sample Size for Multiple Comparison Procedures in ANOVA." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou160433478343909.

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Kennedy, Michael. "The influence of sample size, effect size, and percentage of DIF items on the performance of the Mantel-Haenszel and logistic regression DIF identification procedures." Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/6884.

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The frequent use of standardized tests for admission, advancement, and accreditation has increased public awareness of measurement issues, in particular, test and item bias. The logistic regression (LR) and Mantel-Haenszel (MH) procedures are relatively new methods of detecting item bias or differential item functioning (DIF) in tests. In only a few studies has the performance of these two procedures been compared. In the present study, sample size, effect size, and percentage of DIF items in the test were manipulated in order to compare detection rates of uniform DIF by the LR and MH procedures. Simulated data, with known amounts of DIF, were used to evaluate the effects of these variables on DIF detection rates. In detecting uniform DIF, the LR procedure had a slight advantage over the MH procedure at the cost of increased false positive rates. P-value difference was definitely a more accurate measure of the amount of DIF than b value difference. (Abstract shortened by UMI.)
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Schäfer, Thomas, and Marcus A. Schwarz. "The Meaningfulness of Effect Sizes in Psychological Research: Differences Between Sub-Disciplines and the Impact of Potential Biases." Frontiers Media SA, 2019. https://monarch.qucosa.de/id/qucosa%3A33749.

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Effect sizes are the currency of psychological research. They quantify the results of a study to answer the research question and are used to calculate statistical power. The interpretation of effect sizes—when is an effect small, medium, or large?—has been guided by the recommendations Jacob Cohen gave in his pioneering writings starting in 1962: Either compare an effect with the effects found in past research or use certain conventional benchmarks. The present analysis shows that neither of these recommendations is currently applicable. From past publications without pre-registration, 900 effects were randomly drawn and compared with 93 effects from publications with pre-registration, revealing a large difference: Effects from the former (median r = 0.36) were much larger than effects from the latter (median r = 0.16). That is, certain biases, such as publication bias or questionable research practices, have caused a dramatic inflation in published effects, making it difficult to compare an actual effect with the real population effects (as these are unknown). In addition, there were very large differences in the mean effects between psychological sub-disciplines and between different study designs, making it impossible to apply any global benchmarks. Many more pre-registered studies are needed in the future to derive a reliable picture of real population effects.
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Awuor, Risper Akelo. "Effect of Unequal Sample Sizes on the Power of DIF Detection: An IRT-Based Monte Carlo Study with SIBTEST and Mantel-Haenszel Procedures." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/28321.

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This simulation study focused on determining the effect of unequal sample sizes on statistical power of SIBTEST and Mantel-Haenszel procedures for detection of DIF of moderate and large magnitudes. Item parameters were estimated by, and generated with the 2PLM using WinGen2 (Han, 2006). MULTISIM was used to simulate ability estimates and to generate response data that were analyzed by SIBTEST. The SIBTEST procedure with regression correction was used to calculate the DIF statistics, namely the DIF effect size and the statistical significance of the bias. The older SIBTEST was used to calculate the DIF statistics for the M-H procedure. SAS provided the environment in which the ability parameters were simulated; response data generated and DIF analyses conducted. Test items were observed to determine if a priori manipulated items demonstrated DIF. The study results indicated that with unequal samples in any ratio, M-H had better Type I error rate control than SIBTEST. The results also indicated that not only the ratios, but also the sample size and the magnitude of DIF influenced the behavior of SIBTEST and M-H with regard to their error rate behavior. With small samples and moderate DIF magnitude, Type II errors were committed by both M-H and SIBTEST when the reference to focal group sample size ratio was 1:.10 due to low observed statistical power and inflated Type I error rates.<br>Ph. D.
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Books on the topic "Sample size effect"

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

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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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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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Kelley, Ken. Effect Size and Sample Size Planning. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199934874.013.0011.

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Clark-Carter, David. The importance of considering effect size and statistical power in research. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0014.

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This chapter explores why effect size needs to be taken into account when designing and reporting research. It gives an effect size for each of the standard statistical tests which health and clinical psychologists employ, and looks at the need to consider statistical power when choosing a sample size for a study and how statistical power can help to guide the advice which can be given when discussing future research.
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Hubbs, Ann Fraser. The effect of sample size on the precision of strain measurements: an empirical study of Robin's method of analysis. 1985.

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Book chapters on the topic "Sample size effect"

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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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Bruvold, Norman T. "Power and Effect Size in Sample Size Selection for Proportions." In Proceedings of the 1986 Academy of Marketing Science (AMS) Annual Conference. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11101-8_81.

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Ng, Tang-Tat, and Wei Zhou. "Sample Size Effect on DEM Simulations of Binary Mixture." In Springer Proceedings in Physics. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1926-5_9.

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Park, Laurence A. F., and Glenn Stone. "The Effect on Accuracy of Tweet Sample Size for Hashtag Segmentation Dictionary Construction." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31753-3_31.

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Matthews, D. N., and K. H. Muller. "The Effect of Sample Size on the Critical Current Density of Polycrystalline YBa2Cu3O7−x." In Advances in Superconductivity V. Springer Japan, 1993. http://dx.doi.org/10.1007/978-4-431-68305-6_116.

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Lebrun, J. M., J. M. Missiaen, and C. Pascal. "Sintering of Silicon, Effect of the Sample Size on Silica Reduction Kinetics and Densification." In Ceramic Transactions Series. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118486955.ch5.

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Sutrisno. "Financing Scheme and Determinant Factors." In Proceedings of the 19th International Symposium on Management (INSYMA 2022). Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-008-4_4.

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AbstractThis study aims to examine the factors that influence the financing scheme of Islamic Rural Banks (IRBs) in Indonesia. The financing scheme consists of profit margin financing (PMF) and profit-sharing financing (PSF). Factors thought to influence the financing scheme are non-performing financing (NPF), financing to deposit ratio (FDR), capital adequacy ratio (CAR), return on assets (ROA), operating to income ratio (OEIR), and company size (Size). The population in this study was 163 IRBs in Indonesia with a sample of 100 IRBs. The observation period was 4 years, with quarterly data. Hypothesis testing applied multiple regression. The results show that the factors influencing the financing scheme between PMF and PSF are the same, namely, NPF and Size have a significant negative effect, while FDR, CAR, ROA and OEIR have no effect. One variable is OEIR; if the significance level is 10%, then OEIR has a significant and negative effect on the profit-sharing financing scheme, but the profit-margin financing scheme has no effect.
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Riyanti, Novi Dwi, Werner R. Murhadi, and Mudji Utami. "The Influence of Good Corporate Governance through the Gender Diversity on Firm Performance." In Proceedings of the 19th International Symposium on Management (INSYMA 2022). Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-008-4_2.

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AbstractThe objective of this study is to look at how board size, board gender diversity, independent commissioners, leverage, and firm size affect firm performance in manufacturing companies in Indonesia Stock Exchange and the Philippines Stock Exchange. This study used a quantitative approach with two least square regression analysis models and a sample of manufacturing sector companies listed on the Indonesia Stock Exchange and the Philippines Stock Exchange, with a total of 100 business entities and 480 observations. This study used the presence of female commissioners and the proportion of female commissioners to measure board gender diversity. According to the findings of this study, independent commissioners in Indonesia have a significantly positive effect on return on assets, female commissioners in Indonesia have a significant adverse effect on return on assets, and Tobin’s Q, both leverage in Indonesia and firm size in the Philippines have a significant adverse effect on return on assets. Meanwhile, leverage in Indonesia has a significant negative effect on Tobin’s Q, as does board size in Indonesia and firm size in Indonesia and the Philippines.
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Rousing, T., S. H. Møller, and S. W. Hansen. "WelFur-mink: on-farm welfare assessment of mink (Neovision vision) - effect of sample size on animal based measures." In Proceedings of the Xth International Scientific Congress in fur animal production. Wageningen Academic Publishers, 2012. http://dx.doi.org/10.3920/978-90-8686-760-8_61.

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Conference papers on the topic "Sample size effect"

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Rockette, Howard E., Nancy A. Obuchowski, David Gur, and Walter F. Good. "Effect of experimental design on sample size." In Medical Imaging '91, San Jose, CA, edited by R. Gilbert Jost. SPIE, 1991. http://dx.doi.org/10.1117/12.45282.

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Kumar, Kesharpu Kaushal, Kochev Vladislav Dmitrievich, and Grigoriev Pavel Dmitrievich. "Peculiar effect of sample size in layered superconductors." In LOW-DIMENSIONAL MATERIALS: THEORY, MODELING, EXPERIMENT, DUBNA 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0098901.

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Ng, Tang-Tat. "Effect of Sample Size and Shape on Critical State." In GeoCongress 2008. American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40972(311)119.

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Haubold, Alexander, and John R. Kender. "Accommodating sample size effect on similarity measures in speaker clustering." In 2008 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2008. http://dx.doi.org/10.1109/icme.2008.4607737.

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Das, Gita, and Sid Ray. "Effect of Finite Sample Size in Content-Based Image Retrieval." In 2006 IEEE International Conference on Video and Signal Based Surveillance. IEEE, 2006. http://dx.doi.org/10.1109/avss.2006.46.

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R., Hadden, Alkatib A., Reina G., Torero J.L., Singh P., and Phylaktou H.N. "Radiant Ignition of Polyurethane Foam: The Effect of Sample Size." In Sixth International Seminar on Fire and Explosion Hazards. Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7724-8_12-01.

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Han, Kyunghee, Kyee-zu Kim, and Taesung Park. "Unbalanced sample size effect on the genome-wide population differentiation studies." In 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2010. http://dx.doi.org/10.1109/bibmw.2010.5703826.

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Melnik, Ivan S., Sergiy M. Dets, and Tatyana V. Rusina. "Light propagation in tissues: effect of finite size of tissue sample." In BiOS Europe '95, edited by Britton Chance, David T. Delpy, and Gerhard J. Mueller. SPIE, 1995. http://dx.doi.org/10.1117/12.228684.

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Zeng, Minhui, John Douglas Hunt, and Ming Zhong. "Effect of within-sample choice distribution and sample size on the estimation accuracy of logit model." In 2015 International Conference on Transportation Information and Safety (ICTIS). IEEE, 2015. http://dx.doi.org/10.1109/ictis.2015.7232160.

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Skuodis, Šarūnas, Neringa Dirgėlienė, and Ieva Lekstutytė. "Change of soil mechanical properties due to triaxial sample size." In The 13th international scientific conference “Modern Building Materials, Structures and Techniques”. Vilnius Gediminas Technical University, 2019. http://dx.doi.org/10.3846/mbmst.2019.006.

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Triaxial test is widely used to determine the behaviour and strength parameters of soil. Several consolidated drained triaxial compression tests were performed on two specimen sizes of sand and clay. This article investigate and compares the influence of specimen size and scale effect on the soil shear strength. The tests results show that the shear strength parameters are influenced by the clay specimen size. The results indicate that the effect of triaxial clay sample size is more significant with higher confining pressures. The second type of tests carried out on sand samples shows that sample size doesn‘t influence the results of the shear strength. Author’s show that clay analyses can be significantly affected by the choice of the specimen size used to determine shear strength parameters.
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Reports on the topic "Sample size effect"

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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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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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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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Nelson, Gena, Hannah Carter, and Peter Boedeker. Early Math Interventions in Informal Learning Settings Coding Protocol. Boise State University, Albertsons Library, 2021. http://dx.doi.org/10.18122/sped141.boisestate.

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The purpose of document is to provide readers with the coding protocol that authors used to code experimental and quasi-experimental early mathematics intervention studies conducted in informal learning environments. The studies were conducted in homes and in museums with caregivers as intervention agents and included children between the ages of 3,0 and 8,11 years. The coding protocol includes more than 200 variables related to basic study information, participant sample size and demographics, methodological information, intervention information, mathematics content information, the control/comparison condition, outcome measures, and results and effect sizes. The coding protocol was developed for the purpose of conducting a meta-analysis; results of the meta-analysis is pending. The data set associated with this coding protocol will be available to the public at the conclusion of the grant (early 2024).
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Broto, Carmen, Luis Fernández Lafuerza, and Mariya Melnychuk. Do buffer requirements for European systemically important banks make them less systemic? Banco de España, 2022. http://dx.doi.org/10.53479/24876.

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Buffers for systemically important institutions (SIIs) were designed to mitigate the risks posed by these large and complex banks. With a panel data model for a sample of listed European banks, we demonstrate that capital requirements for SIIs effectively reduce the perceived systemic risk of these institutions, which we proxy with the SRISK indicator in Brownlees and Engle (2017). We also study the impact of the adjustment mechanisms that banks use to comply with SII buffer requirements and their contribution to systemic risk. The results show that banks mainly respond to higher SII buffers by increasing their equity, as intended by the regulators. Once we control for the options SIIs employ to fulfil these requirements and SII characteristics (e.g. total asset size), we find a residual effect of having SII status. This result suggests that being an SII provides a positive signal to markets by further decreasing its contribution to systemic risk.
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Nelson, Gena, Angela Crawford, and Jessica Hunt. A Systematic Review of Research Syntheses for Students with Mathematics Learning Disabilities and Difficulties. Boise State University, Albertsons Library, 2022. http://dx.doi.org/10.18122/sped.143.boisestate.

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The purpose of this document is to provide readers with the coding protocol that authors used to code 36 research syntheses (including meta-analyses, evidence-based reviews, and quantitative systematic reviews) focused on mathematics interventions for students with learning disabilities (LD), mathematics learning disabilities (MLD), and mathematics difficulties (MD). The purpose of the systematic review of mathematics intervention syntheses was to identify patterns and gaps in content areas, instructional strategies, effect sizes, and definitions of LD, MLD, and MD. We searched the literature for research syntheses published between 2000 and 2020 and used rigorous inclusion criteria in our literature review process. We evaluated 36 syntheses that included 836 studies with 32,495 participants. We coded each synthesis for variables across seven categories including: publication codes (authors, year, journal), inclusion and exclusion criteria, content area focus, instructional strategy focus, sample size, methodological information, and results. The mean interrater reliability across all codes using this coding protocol was 90.3%. Although each synthesis stated a focus on LD, MLD, or MD, very few students with LD or MLD were included, and authors’ operational definitions of disability and risk varied. Syntheses predominantly focused on word problem solving, fractions, computer- assisted learning, and schema-based instruction. Syntheses reported wide variation in effectiveness, content areas, and instructional strategies. Finally, our results indicate the majority of syntheses report achievement outcomes, but very few syntheses report on other outcomes (e.g., social validity, strategy use). We discuss how the results of this comprehensive review can guide researchers in expanding the knowledge base on mathematics interventions. The systematic review that results from this coding process is accepted for publication and in press at Learning Disabilities Research and Practice.
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Evenson, Kelly R., Ty A. Ridenour, Jacqueline Bagwell, and Robert D. Furberg. Sustaining Physical Activity Following Cardiac Rehabilitation Discharge. RTI Press, 2021. http://dx.doi.org/10.3768/rtipress.2021.rr.0043.2102.

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Because many patients reduce exercise following outpatient cardiac rehabilitation (CR), we developed an intervention to assist with the transition and evaluated its feasibility and preliminary efficacy using a one-group pretest–posttest design. Five CR patients were enrolled ~1 month prior to CR discharge and provided an activity tracker. Each week during CR they received a summary of their physical activity and steps. Following CR discharge, participants received an individualized report that included their physical activity and step history, information on specific features of the activity tracker, and encouraging messages from former CR patients for each of the next 6 weeks. Mixed model trajectory analyses were used to test the intervention effect separately for active minutes and steps modeling three study phases: pre-intervention (day activity tracking began to CR discharge), intervention (day following CR discharge to day when final report sent), and maintenance (day following the final report to ~1 month later). Activity tracking was successfully deployed and, with weekly reports following CR, may offset the usual decline in physical activity. When weekly reports ceased, a decline in steps/day occurred. A scaled-up intervention with a more rigorous study design with sufficient sample size can evaluate this approach further.
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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 &gt;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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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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Allegretto, Sylvia A., and Dave Graham-Squire. Monopsony in Professional Labor Markets: Hospital System Concentration and Nurse Wages. Institute for New Economic Thinking Working Paper Series, 2023. http://dx.doi.org/10.36687/inetwp197.

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Rolling waves of consolidation have significantly decreased the number of hospital systems in the U.S. potentially affecting industry quality, prices, efficiency, wages and more. This research concerns the growth in hospital system consolidation in local labor markets and its effect on registered nurse wages. We first use a nonparametric preprocessing data step via matching methods to define MSA-specific samples of workers analogous to nurses outside of the hospital sector. This step enables an accounting of heterogeneous MSA-specific baseline wage growth, and yields a standardized measure of nurse wage growth across MSAs used to set up a multi-site quasi-experiment. We then run a parsimonious linear model; market size matters, for every 0.1 increase in consolidation in smaller-MSAs, real hourly nurse wage growth decreased by $0.70 (p-value of 0.038). Though not the primary aim of this study, a secondary finding is that real hourly wages for nurses grew less than that of comparable workers by $4.08.
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