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Journal articles on the topic 'Sample size estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 June 2025

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1

Chander, NGopi. "Sample size estimation." Journal of Indian Prosthodontic Society 17, no. 3 (2017): 217. http://dx.doi.org/10.4103/jips.jips_169_17.

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2

Likhvantsev, V. V., M. Ya Yadgarov, L. B. Berikashvili, K. K. Kadantseva, and A. N. Kuzovlev. "Sample size estimation." Anesteziologiya i reanimatologiya, no. 6 (2020): 77. http://dx.doi.org/10.17116/anaesthesiology202006177.

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3

Weller, Susan C. "Sample Size Estimation." Field Methods 27, no. 4 (2014): 333–47. http://dx.doi.org/10.1177/1525822x14530086.

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4

Sivasamy, Shyam. "Sample size considerations in research." Endodontology 35, no. 4 (2023): 304–8. http://dx.doi.org/10.4103/endo.endo_235_23.

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ABSTRACT “What should be the sample size for my study?” is a common question in the minds of every research at some point of the research cycle. Answering this question with confident is tough even for a seasoned researcher. Sample size determination, an important aspect of sampling design consideration of a study, is a factor which directly influences the internal and external validity of the study. Unless the sample size is of adequate size, the results of the study cannot be justified. Conducting a study in too small sample size or too large sample size have ethical, scientific, practical, and economic strings attached to it and have detrimental effects in the research outcomes. A myriad of factors including the study design, type of power analysis, sampling technique employed, and acceptable limits of error fixed play a decisive role in estimating the sample size. However, the advent of free to use software and websites for sample size estimation has actually diluted or sometimes complicated the whole process of sample size estimation as important factors or assumptions related to sample size are overlooked. Engaging a professional biostatistician from the very beginning of the research process would be a wise decision while conducting research. This article highlights the important concepts related to sample size estimation with emphasis on factors which influences it.
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Sharma, Suresh K., Shiv Kumar Mudgal, Rakhi Gaur, Jitender Chaturvedi, Satyaveer Rulaniya, and Priya Sharma. "Navigating Sample Size Estimation for Qualitative Research." Journal of Medical Evidence 5, no. 2 (2024): 133–39. http://dx.doi.org/10.4103/jme.jme_59_24.

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Abstract There are well-established rules and methods about sample size estimation in quantitative research approaches. However, qualitative research approaches justify very little about sample size estimation principles and largely depend on subjective judgements and arbitrariness. Contrarily, an adequate sample size is essential for a study to address the core elements of validity and credibility in qualitative research too such as rigor, trustworthiness, conformability and acceptance. Therefore, this review was carried out to explain the available methods to estimate sample size for qualitative studies. After conducting a thorough literature review, we discovered related articles that explore the estimation of sample size for qualitative studies. By examining these findings and integrating the information with our personal experience for estimation of sample size in the field of qualitative studies, we have produced an all-encompassing narrative review. After an in-depth literature search, four different approaches were described in this paper to answer the question of how to estimate sample size in qualitative studies. The four approaches described in this paper are (a) rules of thumb, (b) conceptual models, (c) concept of saturation and (d) statistics-based methods for sample size estimation in qualitative research. The paper presents four methods for estimating sample size in qualitative studies and simplifies the statistical approach for saturation calculation in qualitative studies. Yet, it is vital to responsibly integrate these methods, acknowledging their limitations and maintaining the importance of sample size estimation in qualitative studies.
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6

McCormick, Joshua L., and Kevin A. Meyer. "Sample Size Estimation for On-Site Creel Surveys." North American Journal of Fisheries Management 37, no. 5 (2017): 970–80. http://dx.doi.org/10.1080/02755947.2017.1342723.

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7

Kitikidou, K., and G. Chatzilazarou. "Estimating the sample size for fitting taper equations." Journal of Forest Science 54, No. 4 (2008): 176–82. http://dx.doi.org/10.17221/789-jfs.

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Much work has been done fitting taper equations to describe tree bole shapes, but few researchers have investigated how large the sample size should be. In this paper, a method that requires two variables that are linearly correlated was applied to determine the sample size for fitting taper equations. Two cases of sample size estimation were tested, based on the method mentioned above. In the first case, the sample size required is referred to the total number of diameters estimated in the sampled trees. In the second case, the sample size required is referred to the number of sampled trees. The analysis showed that both methods are efficient from a validity standpoint but the first method has the advantage of decreased cost, since it costs much more to incrementally sample another tree than it does to make another diameter measurement on an already sampled tree.
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8

Streiner, David L. "Sample-Size Formulae for Parameter Estimation." Perceptual and Motor Skills 78, no. 1 (1994): 275–84. http://dx.doi.org/10.2466/pms.1994.78.1.275.

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Formulae are presented for calculating sample-size requirements when the purpose of the study is to estimate the magnitude of a parameter rather than to test an hypothesis. Formulae are given for the mean, a proportion, and correlation, for the slope, intercept, value of Ȳ, and Y for a given value of X in multiple regression, and for the odds and risk ratios.
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9

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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10

De Martini, Daniele. "Conservative Sample Size Estimation in Nonparametrics." Journal of Biopharmaceutical Statistics 21, no. 1 (2010): 24–41. http://dx.doi.org/10.1080/10543400903453343.

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11

Govindarajulu, Z. "Sample Size Re-Estimation: Nonparametric Approach." Journal of Statistical Theory and Practice 1, no. 2 (2007): 253–64. http://dx.doi.org/10.1080/15598608.2007.10411837.

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12

Ingram, Richard. "Power analysis and sample size estimation." NT Research 3, no. 2 (1998): 132–39. http://dx.doi.org/10.1177/174498719800300210.

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13

Sobel, Marc, and Ibrahim Turkoz. "Bayesian blinded sample size re-estimation." Communications in Statistics - Theory and Methods 47, no. 24 (2017): 5916–33. http://dx.doi.org/10.1080/03610926.2017.1404097.

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14

Lerman, Jerrold. "Sample size estimation for nominal data." Canadian Journal of Anaesthesia 44, no. 8 (1997): 901. http://dx.doi.org/10.1007/bf03013172.

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15

Wang, Xiaofeng, and Xinge Ji. "Sample Size Estimation in Clinical Research." Chest 158, no. 1 (2020): S12—S20. http://dx.doi.org/10.1016/j.chest.2020.03.010.

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16

Arya, Ravindra, Belavendra Antonisamy, and Sushil Kumar. "Sample Size Estimation in Prevalence Studies." Indian Journal of Pediatrics 79, no. 11 (2012): 1482–88. http://dx.doi.org/10.1007/s12098-012-0763-3.

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17

Sakpal, TusharVijay. "Sample size estimation in clinical trial." Perspectives in Clinical Research 1, no. 2 (2010): 67. http://dx.doi.org/10.4103/2229-3485.71856.

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18

Patel, Divyangkumar. "Sample Size Estimation in Clinical Trials." National Journal of Community Medicine 15, no. 06 (2024): 503–8. http://dx.doi.org/10.55489/njcm.150620243815.

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Sample size estimation remains as a cornerstone in the meticulous planning and execution of clinical trials, pivotal for ensuring studies possess the requisite statistical power to discern meaningful treatment effects. Insufficient sample sizes compromise the robustness of findings, whereas excessively large samples inflate costs and compromise data integrity. This article meticulously explains the multifaceted factors that outline sample size determination, encompassing various factors such as research design, types of hypotheses, error thresholds, effect size considerations, validity and precision. It investigates into the scope of methodologies available for sample size computation, spanning from intricate statistical formulas to pragmatic tabular approaches. Moreover, it underscores the significance of post-hoc power analysis in retrospectively evaluating completed studies, shedding light on their statistical robustness. This literature review furnishes a nuanced understanding of the sample size estimation landscape in clinical trials, delineating their strengths, limitations, and real-world applications. Anticipating participant attrition assumes paramount importance for proactively adjusting sample sizes, ensuring studies remain methodologically sound. Equipped with a profound grasp of these principles, researchers are empowered to conduct scientifically rigorous and impactful clinical trials, furnishing compelling evidence to inform judicious decision-making in healthcare interventions.
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19

Zanella, Pablo Giliard, Carlos Augusto Brandão de Carvalho, Everton Teixeira Ribeiro, Afrânio Silva Madeiro, and Raphael Dos Santos Gomes. "Optimal quadrat area and sample size to estimate the forage mass of stargrass." Semina: Ciências Agrárias 38, no. 5 (2017): 3165. http://dx.doi.org/10.5433/1679-0359.2017v38n5p3165.

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The objective of this study was to evaluate the sample size and area of the quadrats necessary to accurately estimate the forage mass (FM) of a fenced pasture of stargrass (Cynodon nlemfuensis cv. Florico) during the winter. Five metal quadrats were used: a 0.09 m² square (0.30 m side), a 0.25 m2 square (0.50 m side), a 0.25 m2 circle (0.28 m diameter), a 0.5 m2 rectangle (0.5 x 1.0 m), and a 1 m2 square (1.0 m side), each with eight replicates. The size and shape of the quadrats were determined based on cumulative variances to identify combinations that minimized the coefficient of variation (CV). The minimum sample size required to estimate the FM, morphological components and height was established by the CV maximum curvature method. The 0.25 m2 square quadrat (0.5 m side) presented the lowest cumulative CV in estimating the FM and the dry mass of dead material. However, for the estimation of the leaf and stem dry mass, the 1.00 m2 square quadrat (1.00 m side) presented the lowest CV. Using the 0.25 m2 square quadrat, a minimum number of six samples were required for the FM estimation, and eight samples were required for estimating the mean height of the stargrass pasture. Therefore, at least eight samples are recommended to obtain accurate results for the estimation of both variables.
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20

Kumar, A. "The Sample Size." Journal of Universal College of Medical Sciences 2, no. 1 (2014): 45–47. http://dx.doi.org/10.3126/jucms.v2i1.10493.

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Finding an "appropriate sample size" has been the most basic and foremost problem; a research worker is always faced with, in all sampling based analytical researches. This is so, since a very large sized sample results to unnecessary wastage of resources, while a very small sized sample may affect adversely the accuracy of sample estimates and thus in turn losing the very efficacy of selected sampling plan. The present paper attempts to highlight the main determinant factors and the analytical approach towards estimation ofrequired sample size, along with a few illustrations. DOI: http://dx.doi.org/10.3126/jucms.v2i1.10493 Journal of Universal College of Medical Sciences (2014) Vol.2(1): 45-47
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21

Djidu, Hasan, Heri Retnawati, and Haryanto Haryanto. "Ensuring Parameter Estimation Accuracy in 3PL IRT Modeling: The Role of Test Length and Sample Size." JP3I (Jurnal Pengukuran Psikologi dan Pendidikan Indonesia) 12, no. 2 (2023): 177–90. http://dx.doi.org/10.15408/jp3i.v12i2.34130.

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The objective of this simulation study was to evaluate the accuracy of item parameters estimation when employing the 3PL IRT model, mainly focusing on sample size and the length of the test (number of test items). The investigation used six datasets produced by WinGen, each comprising 5000 responses and varying test lengths within 10 to 40 items. For each dataset, the study conducted simulations and re-analyzed the data 15 times, generating a total of 2025 data subsets and estimating 225 parameters for each item. The results revealed that smaller sample sizes led to more pronounced biases, emphasizing a recommended minimum sample size of 3000 for precise parameter estimation. Additionally, the study found that a limited number of items (short test) yielded biased estimations and proposed a minimum of 25 or 40 test items for accurate estimation using the 3PL IRT model. These findings offer valuable insights for test developers in making informed decisions regarding sample sizes and test length, ultimately ensuring reliable and accurate parameter estimates.
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22

Jain, Sandhya, Alpana Gupta, and Deshraj Jain. "Estimation of sample size in dental research." International Dental & Medical Journal of Advanced Research - VOLUME 2015 1, no. 1 (2015): 1–6. http://dx.doi.org/10.15713/ins.idmjar.9.

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23

Lan, K. K. Gordon, and Zhenming Shun. "A Short Note on Sample Size Estimation." Statistics in Biopharmaceutical Research 1, no. 4 (2009): 356–61. http://dx.doi.org/10.1198/sbr.2009.0057.

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24

Lawrence Gould, A. "Issues in blinded sample size re-estimation." Communications in Statistics - Simulation and Computation 26, no. 3 (1997): 1229–39. http://dx.doi.org/10.1080/03610919708813436.

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25

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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26

Lee, Gyoung-Ah. "Taphonomy and sample size estimation in paleoethnobotany." Journal of Archaeological Science 39, no. 3 (2012): 648–55. http://dx.doi.org/10.1016/j.jas.2011.10.025.

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27

Blumenthal, Saul, and Ram C. Dahiya. "Estimation of sample size with grouped data." Journal of Statistical Planning and Inference 44, no. 1 (1995): 95–115. http://dx.doi.org/10.1016/0378-3758(94)00041-s.

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28

Kenkel, N. "Sample size requirements for fractal dimension estimation." Community Ecology 14, no. 2 (2013): 144–52. http://dx.doi.org/10.1556/comec.14.2013.2.4.

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29

Proschan, Michael A. "Sample size re-estimation in clinical trials." Biometrical Journal 51, no. 2 (2009): 348–57. http://dx.doi.org/10.1002/bimj.200800266.

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30

Dharmarajan, Sai, Joo-Yeon Lee, and Rima Izem. "Sample size estimation for case-crossover studies." Statistics in Medicine 38, no. 6 (2018): 956–68. http://dx.doi.org/10.1002/sim.8030.

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31

Slawnych, Michael, Charles Laszlo, and Cecil Hershler. "Motor unit number estimation: Sample size considerations." Muscle & Nerve 20, no. 1 (1997): 22–28. http://dx.doi.org/10.1002/(sici)1097-4598(199701)20:1<22::aid-mus3>3.0.co;2-j.

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32

Schreuder, H. T., H. G. Li, and C. T. Scott. "Jackknife and Bootstrap Estimation for Sampling with Partial Replacement." Forest Science 33, no. 3 (1987): 676–89. http://dx.doi.org/10.1093/forestscience/33.3.676.

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Abstract Jackknife and bootstrap estimators and variance estimators were compared with a classical estimator and variance estimator for sampling with partial replacement (SPR) on two occasions. One hundred twenty plots were sampled at time 1. At time 2, 10, 20, or 30 plots were remeasured, and a new sample size of size 20 was also selected. The samples were drawn from three large samples of forest plots from the northeastern United States, which were treated as populations. Although variables are correlated on the two occasions (r = 0.648 - 0.891), the assumptions of linearity and homogeneity of variance are questionable. The classical estimator is generally preferable to the jackknife and bootstrap estimators when both estimation bias and efficiency are important in SPR sampling. The jackknife variance estimator is generally preferable if variance estimation bias and confidence limit coverage rates are taken into consideration, particularly for skewed populations with small sample sizes. Generally, these jackknife variance estimates are less stable than the classical variance estimates. For. Sci. 33(3):676-689.
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33

Ganapathy, Sachit, Deepthy MS, and Akash Mishra. "Contextual Role of Absolute and Relative Precision in Estimation of Sample Size for Single Proportion in Health Research." International Journal of Health Sciences and Research 13, no. 2 (2023): 63–68. http://dx.doi.org/10.52403/ijhsr.20230210.

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Background: Sample size estimation is one of the key components in the initial stage of any health research. The validity of an observational study in estimating prevalence of an outcome of interest is primarily determined by the precision of the estimate. This is generally motivated by the method of sample size estimation. The choice of incorporating absolute precision or relative precision in estimating the sample size of proportions has been a grey area for many years. The objectives of the study were to investigate the role of relative and absolute precision in sample size estimation of proportion and also to provide an easy guide for estimation of sample size using relative and absolute precision using real life examples. Materials and Methods: Sample sizes for different proportions using varying levels of relative and absolute precision were estimated and the variations in the sample size using both methods were graphically plotted. Results: Sample size decreases exponentially with increase in anticipated prevalence in the case of relative precision whereas for absolute precision, it follows a bell-shaped curve. Conclusions: The current study provides scenarios where and how absolute and relative precision can be used. Also, the relation between absolute and relative precision is provided. Key words: sample size, prevalence, health research, relative precision, absolute precision
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34

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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35

Xiaoyu, Cai, Tsong Yi, and Shen Meiyu. "A Critical Review on Adaptive Sample Size Re-estimation (SSR) Designs for Superiority Trials with Continuous Endpoints." Open Journal of Pharmaceutical Science and Research 1, no. 1 (2019): 01–13. https://doi.org/10.36811/ojpsr.2019.110001.

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Adaptive sample size re-estimation (SSR) methods have been widely used for designing clinical trials, especially during the past two decades. We give a critical review for several commonly used two-stage adaptive SSR designs for superiority trials with continuous endpoints. The objective, design and some of our suggestions and concerns of each design will be discussed in this paper. Keywords: Adaptive Design; Sample Size Re-estimation; Review
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36

Johnson, Roger W., and Donna V. Kliche. "Large Sample Comparison of Parameter Estimates in Gamma Raindrop Distributions." Atmosphere 11, no. 4 (2020): 333. http://dx.doi.org/10.3390/atmos11040333.

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Raindrop size distributions have been characterized through the gamma family. Over the years, quite a few estimates of these gamma parameters have been proposed. The natural question for the practitioner, then, is what estimation procedure should be used. We provide guidance in answering this question when a large sample size (&gt;2000 drops) of accurately measured drops is available. Seven estimation procedures from the literature: five method of moments procedures, maximum likelihood, and a pseudo maximum likelihood procedure, were examined. We show that the two maximum likelihood procedures provide the best precision (lowest variance) in estimating the gamma parameters. Method of moments procedures involving higher-order moments, on the other hand, give rise to poor precision (high variance) in estimating these parameters. A technique called the delta method assisted in our comparison of these various estimation procedures.
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37

Juhkam, Mihhail, and Kalev Pärna. "Estimation of the sample size required for obtaining given sample coverage." Acta et Commentationes Universitatis Tartuensis de Mathematica 12 (December 31, 2008): 89–99. http://dx.doi.org/10.12697/acutm.2008.12.08.

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We consider sampling from populations with large number of classes. The problem is to disclose a sufficiently big number of classes,which represent a dominating part of population (e.g. 99%). In many applications, e.g. in genetics, disclosure of all classes is not necessary, since it can require a very large sample and, hence, is too costly. In this paper we propose a method for estimation of the sample size, necessary to achieve a given sample coverage. We apply the method to populations where the class probabilities are the members of a geometric sequence. A Monte Carlo study demonstrates that the method we propose gives good results for values if the common ratio of the sequence is not too close to 1.
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38

Omair, Aamir. "Sample size estimation and sampling techniques for selecting a representative sample." Journal of Health Specialties 2, no. 4 (2014): 142. http://dx.doi.org/10.4103/1658-600x.142783.

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39

Jansen, Marieke S., Rolf H. H. Groenwold, and Olaf M. Dekkers. "The power of sample size calculations." European Journal of Endocrinology 191, no. 5 (2024): E5—E9. http://dx.doi.org/10.1093/ejendo/lvae129.

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Abstract Researchers frequently come across sample size calculations in the scientific literature they read, in projects undertaken by their peers, and likely within their own work. However, despite its ubiquity, calculating a sample size is often perceived as a hurdle and not fully understood. This paper provides a brief overview of sample size estimation to guide readers, researchers, and reviewers through its fundamentals.
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40

Smith, Andrew R., and Paul C. Price. "Sample size bias in the estimation of means." Psychonomic Bulletin & Review 17, no. 4 (2010): 499–503. http://dx.doi.org/10.3758/pbr.17.4.499.

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41

Brakenhoff, TB, KCB Roes, and S. Nikolakopoulos. "Bayesian sample size re-estimation using power priors." Statistical Methods in Medical Research 28, no. 6 (2018): 1664–75. http://dx.doi.org/10.1177/0962280218772315.

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The sample size of a randomized controlled trial is typically chosen in order for frequentist operational characteristics to be retained. For normally distributed outcomes, an assumption for the variance needs to be made which is usually based on limited prior information. Especially in the case of small populations, the prior information might consist of only one small pilot study. A Bayesian approach formalizes the aggregation of prior information on the variance with newly collected data. The uncertainty surrounding prior estimates can be appropriately modelled by means of prior distributions. Furthermore, within the Bayesian paradigm, quantities such as the probability of a conclusive trial are directly calculated. However, if the postulated prior is not in accordance with the true variance, such calculations are not trustworthy. In this work we adapt previously suggested methodology to facilitate sample size re-estimation. In addition, we suggest the employment of power priors in order for operational characteristics to be controlled.
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42

Jones, S. R. "An introduction to power and sample size estimation." Emergency Medicine Journal 20, no. 5 (2003): 453–58. http://dx.doi.org/10.1136/emj.20.5.453.

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43

Lin, Wei-Jiun, Huey-Miin Hsueh, and James J. Chen. "Power and sample size estimation in microarray studies." BMC Bioinformatics 11, no. 1 (2010): 48. http://dx.doi.org/10.1186/1471-2105-11-48.

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44

Eng, John. "Sample Size Estimation: A Glimpse beyond Simple Formulas." Radiology 230, no. 3 (2004): 606–12. http://dx.doi.org/10.1148/radiol.2303030297.

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45

Kim, Ki-Yeol. "Sample size estimation using nomogram in dental research." Journal of The Korean Dental Association 54, no. 8 (2016): 630–38. http://dx.doi.org/10.22974/jkda.2016.54.8.004.

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The appropriate sample size calculation in dental research is important to achieve the study purpose at the first step in study design. However, it cannot be easy to calculate sample size using standard formulas, because the several factors must be considered for calculation. This study introduced the graphic method for sample size calculation, which is called nomogram. The purpose of this study is to show the effectiveness of the nomogram using examples, expecting the researchers can easily use nomogram for sample size determination.
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46

Zellner, Dietmar, Günter E. Zellner, and Frieder Keller. "A SAS macro for sample size re-estimation." Computer Methods and Programs in Biomedicine 65, no. 3 (2001): 183–90. http://dx.doi.org/10.1016/s0169-2607(00)00119-x.

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47

Ribeiro, Daniel Cury, Stephan Milosavljevic, and J. Haxby Abbott. "Sample size estimation for cluster randomized controlled trials." Musculoskeletal Science and Practice 34 (April 2018): 108–11. http://dx.doi.org/10.1016/j.msksp.2017.10.002.

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48

Cox, T. A., E. Gemmen, M. Nixon, et al. "PRM61 Sample Size Estimation for Prospective Observational Studies." Value in Health 14, no. 7 (2011): A432. http://dx.doi.org/10.1016/j.jval.2011.08.1092.

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49

Lin, Ruitao, Zhao Yang, Ying Yuan, and Guosheng Yin. "Sample size re-estimation in adaptive enrichment design." Contemporary Clinical Trials 100 (January 2021): 106216. http://dx.doi.org/10.1016/j.cct.2020.106216.

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50

Ramírez, Pepa, and Brani Vidakovic. "Wavelet density estimation for stratified size-biased sample." Journal of Statistical Planning and Inference 140, no. 2 (2010): 419–32. http://dx.doi.org/10.1016/j.jspi.2009.07.021.

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