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Academic literature on the topic 'Sample size'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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Journal articles on the topic "Sample size"

1

Muralidharan, K. "On Sample Size Determination." MATHEMATICAL JOURNAL OF INTERDISCIPLINARY SCIENCES 3, no. 1 (2014): 55–64. http://dx.doi.org/10.15415/mjis.2014.31005.

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G, Ajithakumari. "Sample Size Determination and Sampling Technique." International Journal of Science and Research (IJSR) 13, no. 9 (2024): 1432–40. http://dx.doi.org/10.21275/es24924103353.

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Byrne, Enda M., Anjali K. Henders, Ian B. Hickie, Christel M. Middeldorp, and Naomi R. Wray. "Nick Martin and the Genetics of Depression: Sample Size, Sample Size, Sample Size." Twin Research and Human Genetics 23, no. 2 (2020): 109–11. http://dx.doi.org/10.1017/thg.2020.13.

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AbstractNick Martin is a pioneer in recognizing the need for large sample size to study the complex, heterogeneous and polygenic disorders of common mental disorders. In the predigital era, questionnaires were mailed to thousands of twin pairs around Australia. Always quick to adopt new technology, Nick’s studies progressed to phone interviews and then online. Moreover, Nick was early to recognize the value of collecting DNA samples. As genotyping technologies improved over the years, these twin and family cohorts were used for linkage, candidate gene and genome-wide association studies. These cohorts have underpinned many analyses to disentangle the complex web of genetic and lifestyle factors associated with mental health. With characteristic foresight, Nick is chief investigator of our Australian Genetics of Depression Study, which has recruited 16,000 people with self-reported depression (plus DNA samples) over a time frame of a few months — analyses are currently ongoing. The mantra of sample size, sample size, sample size has guided Nick’s research over the last 30 years and continues to do so.

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Parker, Robert A., and Nancy G. Berman. "Sample Size." American Statistician 57, no. 3 (2003): 166–70. http://dx.doi.org/10.1198/0003130031919.

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Hill-Smith, I. "Sample size." BMJ 308, no. 6939 (1994): 1304. http://dx.doi.org/10.1136/bmj.308.6939.1304.

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Carlin, JB, and LW Doyle. "Sample size." Journal of Paediatrics and Child Health 38, no. 3 (2002): 300–304. http://dx.doi.org/10.1046/j.1440-1754.2002.00855.x.

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Brown, George W. "Sample Size." Archives of Pediatrics & Adolescent Medicine 142, no. 11 (1988): 1213. http://dx.doi.org/10.1001/archpedi.1988.02150110091026.

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Elston, Dirk M. "Sample size." Journal of the American Academy of Dermatology 79, no. 4 (2018): 635. http://dx.doi.org/10.1016/j.jaad.2017.11.006.

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Cuoco, Daniel J. "Sample Size." JEMS: Journal of Emergency Medical Services 33, no. 6 (2008): 20. https://doi.org/10.1016/s0197-2510(08)70212-2.

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KILIC, Selim. "Sample size, power concepts and sample size calculation." Journal of Mood Disorders 2, no. 3 (2012): 140. http://dx.doi.org/10.5455/jmood.20120921043306.

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Dissertations / Theses on the topic "Sample size"

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Salar, Kemal. "Sample size for correlation estimates." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/27248.

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Denne, Jonathan S. "Sequential procedures for sample size estimation." Thesis, University of Bath, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320460.

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Jinks, R. C. "Sample size for multivariable prognostic models." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1354112/.

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Prognosis is one of the central principles of medical practice; useful prognostic models are vital if clinicians wish to predict patient outcomes with any success. However, prognostic studies are often performed retrospectively, which can result in poorly validated models that do not become valuable clinical tools. One obstacle to planning prospective studies is the lack of sample size calculations for developing or validating multivariable models. The often used 5 or 10 events per variable (EPV) rule (Peduzzi and Concato, 1995) can result in small sample sizes which may lead to overfitting and optimism. This thesis investigates the issue of sample size in prognostic modelling, and develops calculations and recommendations which may improve prognostic study design. In order to develop multivariable prediction models, their prognostic value must be measurable and comparable. This thesis focuses on time-to-event data analysed with the Cox proportional hazards model, for which there are many proposed measures of prognostic ability. A measure of discrimination, the D statistic (Royston and Sauerbrei, 2004), is chosen for use in this work, as it has an appealing interpretation and direct relationship with a measure of explained variation. Real datasets are used to investigate how estimates of D vary with number of events. Seeking a better alternative to EPV rules, two sample size calculations are developed and tested for use where a target value of D is estimated: one based on significance testing and one on confidence interval width. The calculations are illustrated using real datasets; in general the sample sizes required are quite large. Finally, the usability of the new calculations is considered. To use the sample size calculations, researchers must estimate a target value of D, but this can be difficult if no previous study is available. To aid this, published D values from prognostic studies are collated into a ‘library’, which could be used to obtain plausible values of D to use in the calculations. To expand the library further an empirical conversion is developed to transform values of the more widely-used C-index (Harrell et al., 1984) to D.

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Callan, Peggy Ann. "Developmental sentence scoring sample size comparison." PDXScholar, 1990. https://pdxscholar.library.pdx.edu/open_access_etds/4170.

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In 1971, Lee and Canter developed a systematic tool for assessing children's expressive language: Developmental Sentence Scoring (DSS). It provides normative data against which a child's delayed or disordered language development can be compared with the normal language of children the same age. A specific scoring system is used to analyze children's use of standard English grammatical rules from a tape-recorded sample of their spontaneous speech during conversation with a clinician. The corpus of sentences for the DSS is obtained from a sample of 50 complete, different, consecutive, intelligible, non-echolalic sentences elicited from a child in conversation with an adult using stimulus materials in which the child is interested. There is limited research on the reliability of language samples smaller and larger than 50 utterances for DSS analysis. The purpose of this study was to determine if there is a significant difference among the scores obtained from language samples of 25, 50, and 75 utterances when using the DSS procedure for children aged 6.0 to 6.6 years. Twelve children, selected on the basis of chronological age, normal receptive vocabulary skills, normal hearing, and a monolingual background, were chosen as subjects.

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Cámara, Hagen Luis Tomás. "A consensus based Bayesian sample size criterion." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ64329.pdf.

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Ahn, Jeongyoun Marron James Stephen. "High dimension, low sample size data analysis." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2006. http://dc.lib.unc.edu/u?/etd,375.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2006.<br>Title from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.

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Nataša, Krklec Jerinkić. "Line search methods with variable sample size." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2014. http://dx.doi.org/10.2298/NS20140117KRKLEC.

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The problem under consideration is an unconstrained optimization problem with the objective function in the form of mathematical ex-pectation. The expectation is with respect to the random variable that represents the uncertainty. Therefore, the objective  function is in fact deterministic. However, nding the analytical form of that objective function can be very dicult or even impossible. This is the reason why the sample average approximation is often used. In order to obtain reasonable good approximation of the objective function, we have to use relatively large sample size. We assume that the sample is generated at the beginning of the optimization process and therefore we can consider this sample average objective function as the deterministic one. However, applying some deterministic method on that sample average function from the start can be very costly. The number of evaluations of the function under expectation is a common way of measuring the cost of an algorithm. Therefore, methods that vary the sample size throughout the optimization process are developed. Most of them are trying to determine the optimal dynamics of increasing the sample size.The main goal of this thesis is to develop the clas of methods that can decrease the cost of an algorithm by decreasing the number of function evaluations. The idea is to decrease the sample size whenever it seems to be reasonable - roughly speaking, we do not want to impose a large precision, i.e. a large sample size when we are far away from the solution we search for. The detailed description of the new methods is presented in Chapter 4 together with the convergence analysis. It is shown that the approximate solution is of the same quality as the one obtained by dealing with the full sample from the start.Another important characteristic of the methods that are proposed here is the line search technique which is used for obtaining the sub-sequent iterates. The idea is to nd a suitable direction and to search along it until we obtain a sucient decrease in the  function value. The sucient decrease is determined throughout the line search rule. In Chapter 4, that rule is supposed to be monotone, i.e. we are imposing strict decrease of the function value. In order to decrease the cost of the algorithm even more and to enlarge the set of suitable search directions, we use nonmonotone line search rules in Chapter 5. Within that chapter, these rules are modied to t the variable sample size framework. Moreover, the conditions for the global convergence and the R-linear rate are presented. In Chapter 6, numerical results are presented. The test problems are various - some of them are academic and some of them are real world problems. The academic problems are here to give us more insight into the behavior of the algorithms. On the other hand, data that comes from the real world problems are here to test the real applicability of the proposed algorithms. In the rst part of that chapter, the focus is on the variable sample size techniques. Different implementations of the proposed algorithm are compared to each other and to the other sample schemes as well. The second part is mostly devoted to the comparison of the various line search rules combined with dierent search directions in the variable sample size framework. The overall numerical results show that using the variable sample size can improve the performance of the algorithms signicantly, especially when the nonmonotone line search rules are used.The rst chapter of this thesis provides the background material for the subsequent chapters. In Chapter 2, basics of the nonlinear optimization are presented and the focus is on the line search, while Chapter 3 deals with the stochastic framework. These chapters are here to provide the review of the relevant known results, while the rest of the thesis represents the original contribution. <br>U okviru ove teze posmatra se problem optimizacije bez ograničenja pri čcemu je funkcija cilja u formi matematičkog očekivanja. Očekivanje se odnosi na slučajnu promenljivu koja predstavlja neizvesnost. Zbog toga je funkcija cilja, u stvari, deterministička veličina. Ipak, odredjivanje analitičkog oblika te funkcije cilja može biti vrlo komplikovano pa čak i nemoguće. Zbog toga se za aproksimaciju često koristi uzoračko očcekivanje. Da bi se postigla dobra aproksimacija, obično je neophodan obiman uzorak. Ako pretpostavimo da se uzorak realizuje pre početka procesa optimizacije, možemo posmatrati uzoračko očekivanje kao determinističku funkciju. Medjutim, primena nekog od determinističkih metoda direktno na tu funkciju  moze biti veoma skupa jer evaluacija funkcije pod ocekivanjem često predstavlja veliki tro&scaron;ak i uobičajeno je da se ukupan tro&scaron;ak optimizacije meri po broju izračcunavanja funkcije pod očekivanjem. Zbog toga su razvijeni metodi sa promenljivom veličinom uzorka. Većcina njih je bazirana na odredjivanju optimalne dinamike uvećanja uzorka.Glavni cilj ove teze je razvoj algoritma koji, kroz smanjenje broja izračcunavanja funkcije, smanjuje ukupne tro&scaron;skove optimizacije. Ideja je da se veličina uzorka smanji kad god je to moguće. Grubo rečeno, izbegava se koriscenje velike preciznosti  (velikog uzorka) kada smo daleko od re&scaron;senja. U čcetvrtom poglavlju ove teze opisana je nova klasa metoda i predstavljena je analiza konvergencije. Dokazano je da je aproksimacija re&scaron;enja koju dobijamo bar toliko dobra koliko i za metod koji radi sa celim uzorkom sve vreme.Jo&scaron; jedna bitna karakteristika metoda koji su ovde razmatrani je primena linijskog pretražzivanja u cilju odredjivanja naredne iteracije. Osnovna ideja je da se nadje odgovarajući pravac i da se duž njega vr&scaron;si pretraga za dužzinom koraka koja će dovoljno smanjiti vrednost funkcije. Dovoljno smanjenje je odredjeno pravilom linijskog pretraživanja. U čcetvrtom poglavlju to pravilo je monotono &scaron;to znači da zahtevamo striktno smanjenje vrednosti funkcije. U cilju jos većeg smanjenja tro&scaron;kova optimizacije kao i pro&scaron;irenja skupa pogodnih pravaca, u petom poglavlju koristimo nemonotona pravila linijskog pretraživanja koja su modifikovana zbog promenljive velicine uzorka. Takodje, razmatrani su uslovi za globalnu konvergenciju i R-linearnu brzinu konvergencije.Numerički rezultati su predstavljeni u &scaron;estom poglavlju. Test problemi su razliciti - neki od njih su akademski, a neki su realni. Akademski problemi su tu da nam daju bolji uvid u pona&scaron;anje algoritama. Sa druge strane, podaci koji poticu od stvarnih problema služe kao pravi test za primenljivost pomenutih algoritama. U prvom delu tog poglavlja akcenat je na načinu ažuriranja veličine uzorka. Različite varijante metoda koji su ovde predloženi porede se medjusobno kao i sa drugim &scaron;emama za ažuriranje veličine uzorka. Drugi deo poglavlja pretežno je posvećen poredjenju različitih pravila linijskog pretraživanja sa različitim pravcima pretraživanja u okviru promenljive veličine uzorka. Uzimajuci sve postignute rezultate u obzir dolazi se do zaključcka da variranje veličine uzorka može značajno popraviti učinak algoritma, posebno ako se koriste nemonotone metode linijskog pretraživanja.U prvom poglavlju ove teze opisana je motivacija kao i osnovni pojmovi potrebni za praćenje preostalih poglavlja. U drugom poglavlju je iznet pregled osnova nelinearne optimizacije sa akcentom na metode linijskog pretraživanja, dok su u trećem poglavlju predstavljene osnove stohastičke optimizacije. Pomenuta poglavlja su tu radi pregleda dosada&scaron;njih relevantnih rezultata dok je originalni doprinos ove teze predstavljen u poglavljima 4-6.

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Serra, Puertas Jorge. "Shrinkage corrections of sample linear estimators in the small sample size regime." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/404386.

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We are living in a data deluge era where the dimensionality of the data gathered by inexpensive sensors is growing at a fast pace, whereas the availability of independent samples of the observed data is limited. Thus, classical statistical inference methods relying on the assumption that the sample size is large, compared to the observation dimension, are suffering a severe performance degradation. Within this context, this thesis focus on a popular problem in signal processing, the estimation of a parameter, observed through a linear model. This inference is commonly based on a linear filtering of the data. For instance, beamforming in array signal processing, where a spatial filter steers the beampattern of the antenna array towards a direction to obtain the signal of interest (SOI). In signal processing the design of the optimal filters relies on the optimization of performance measures such as the Mean Square Error (MSE) and the Signal to Interference plus Noise Ratio (SINR). When the first two moments of the SOI are known, the optimization of the MSE leads to the Linear Minimum Mean Square Error (LMMSE). When such statistical information is not available one may force a no distortion constraint towards the SOI in the optimization of the MSE, which is equivalent to maximize the SINR. This leads to the Minimum Variance Distortionless Response (MVDR) method. The LMMSE and MVDR are optimal, though unrealizable in general, since they depend on the inverse of the data correlation, which is not known. The common approach to circumvent this problem is to substitute it for the inverse of the sample correlation matrix (SCM), leading to the sample LMMSE and sample MVDR. This approach is optimal when the number of available statistical samples tends to infinity for a fixed observation dimension. This large sample size scenario hardly holds in practice and the sample methods undergo large performance degradations in the small sample size regime, which may be due to short stationarity constraints or to a system with a high observation dimension. The aim of this thesis is to propose corrections of sample estimators, such as the sample LMMSE and MVDR, to circumvent their performance degradation in the small sample size regime. To this end, two powerful tools are used, shrinkage estimation and random matrix theory (RMT). Shrinkage estimation introduces a structure on the filters that forces some corrections in small sample size situations. They improve sample based estimators by optimizing a bias variance tradeoff. As direct optimization of these shrinkage methods leads to unrealizable estimators, then a consistent estimate of these optimal shrinkage estimators is obtained, within the general asymptotics where both the observation dimension and the sample size tend to infinity, but at a fixed rate. That is, RMT is used to obtain consistent estimates within an asymptotic regime that deals naturally with the small sample size. This RMT approach does not require any assumptions about the distribution of the observations. The proposed filters deal directly with the estimation of the SOI, which leads to performance gains compared to related work methods based on optimizing a metric related to the data covariance estimate or proposing rather ad-hoc regularizations of the SCM. Compared to related work methods which also treat directly the estimation of the SOI and which are based on a shrinkage of the SCM, the proposed filter structure is more general. It contemplates corrections of the inverse of the SCM and considers the related work methods as particular cases. This leads to performance gains which are notable when there is a mismatch in the signature vector of the SOI. This mismatch and the small sample size are the main sources of degradation of the sample LMMSE and MVDR. Thus, in the last part of this thesis, unlike the previous proposed filters and the related work, we propose a filter which treats directly both sources of degradation.<br>Estamos viviendo en una era en la que la dimensión de los datos, recogidos por sensores de bajo precio, está creciendo a un ritmo elevado, pero la disponibilidad de muestras estadísticamente independientes de los datos es limitada. Así, los métodos clásicos de inferencia estadística sufren una degradación importante, ya que asumen un tamaño muestral grande comparado con la dimensión de los datos. En este contexto, esta tesis se centra en un problema popular en procesado de señal, la estimación lineal de un parámetro observado mediante un modelo lineal. Por ejemplo, la conformación de haz en procesado de agrupaciones de antenas, donde un filtro enfoca el haz hacia una dirección para obtener la señal asociada a una fuente de interés (SOI). El diseño de los filtros óptimos se basa en optimizar una medida de prestación como el error cuadrático medio (MSE) o la relación señal a ruido más interferente (SINR). Cuando hay información sobre los momentos de segundo orden de la SOI, la optimización del MSE lleva a obtener el estimador lineal de mínimo error cuadrático medio (LMMSE). Cuando esa información no está disponible, se puede forzar la restricción de no distorsión de la SOI en la optimización del MSE, que es equivalente a maximizar la SINR. Esto conduce al estimador de Capon (MVDR). El LMMSE y MVDR son óptimos, pero no son realizables, ya que dependen de la inversa de la matriz de correlación de los datos, que no es conocida. El procedimiento habitual para solventar este problema es sustituirla por la inversa de la correlación muestral (SCM), esto lleva al LMMSE y MVDR muestral. Este procedimiento es óptimo cuando el tamaño muestral tiende a infinito y la dimensión de los datos es fija. En la práctica este tamaño muestral elevado no suele producirse y los métodos LMMSE y MVDR muestrales sufren una degradación importante en este régimen de tamaño muestral pequeño. Éste se puede deber a periodos cortos de estacionariedad estadística o a sistemas cuya dimensión sea elevada. El objetivo de esta tesis es proponer correcciones de los estimadores LMMSE y MVDR muestrales que permitan combatir su degradación en el régimen de tamaño muestral pequeño. Para ello se usan dos herramientas potentes, la estimación shrinkage y la teoría de matrices aleatorias (RMT). La estimación shrinkage introduce una estructura de los estimadores que mejora los estimadores muestrales mediante la optimización del compromiso entre media y varianza del estimador. La optimización directa de los métodos shrinkage lleva a métodos no realizables. Por eso luego se propone obtener una estimación consistente de ellos en el régimen asintótico en el que tanto la dimensión de los datos como el tamaño muestral tienden a infinito, pero manteniendo un ratio constante. Es decir RMT se usa para obtener estimaciones consistentes en un régimen asintótico que trata naturalmente las situaciones de tamaño muestral pequeño. Esta metodología basada en RMT no requiere suposiciones sobre el tipo de distribución de los datos. Los filtros propuestos tratan directamente la estimación de la SOI, esto lleva a ganancias de prestaciones en comparación a otros métodos basados en optimizar una métrica relacionada con la estimación de la covarianza de los datos o regularizaciones ad hoc de la SCM. La estructura de filtro propuesta es más general que otros métodos que también tratan directamente la estimación de la SOI y que se basan en un shrinkage de la SCM. Contemplamos correcciones de la inversa de la SCM y los métodos del estado del arte son casos particulares. Esto lleva a ganancias de prestaciones que son notables cuando hay una incertidumbre en el vector de firma asociado a la SOI. Esa incertidumbre y el tamaño muestral pequeño son las fuentes de degradación de los LMMSE y MVDR muestrales. Así, en la última parte de la tesis, a diferencia de métodos propuestos previamente en la tesis y en la literatura, se propone un filtro que trata de forma directa ambas fuentes de degradación.

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Banton, Dwaine Stephen. "A BAYESIAN DECISION THEORETIC APPROACH TO FIXED SAMPLE SIZE DETERMINATION AND BLINDED SAMPLE SIZE RE-ESTIMATION FOR HYPOTHESIS TESTING." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/369007.

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Statistics<br>Ph.D.<br>This thesis considers two related problems that has application in the field of experimental design for clinical trials: • fixed sample size determination for parallel arm, double-blind survival data analysis to test the hypothesis of no difference in survival functions, and • blinded sample size re-estimation for the same. For the first problem of fixed sample size determination, a method is developed generally for testing of hypothesis, then applied particularly to survival analysis; for the second problem of blinded sample size re-estimation, a method is developed specifically for survival analysis. In both problems, the exponential survival model is assumed. The approach we propose for sample size determination is Bayesian decision theoretical, using explicitly a loss function and a prior distribution. The loss function used is the intrinsic discrepancy loss function introduced by Bernardo and Rueda (2002), and further expounded upon in Bernardo (2011). We use a conjugate prior, and investigate the sensitivity of the calculated sample sizes to specification of the hyper-parameters. For the second problem of blinded sample size re-estimation, we use prior predictive distributions to facilitate calculation of the interim test statistic in a blinded manner while controlling the Type I error. The determination of the test statistic in a blinded manner continues to be nettling problem for researchers. The first problem is typical of traditional experimental designs, while the second problem extends into the realm of adaptive designs. To the best of our knowledge, the approaches we suggest for both problems have never been done hitherto, and extend the current research on both topics. The advantages of our approach, as far as we see it, are unity and coherence of statistical procedures, systematic and methodical incorporation of prior knowledge, and ease of calculation and interpretation.<br>Temple University--Theses

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Timberlake, Allison M. "Sample Size in Ordinal Logistic Hierarchical Linear Modeling." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/eps_diss/72.

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Most quantitative research is conducted by randomly selecting members of a population on which to conduct a study. When statistics are run on a sample, and not the entire population of interest, they are subject to a certain amount of error. Many factors can impact the amount of error, or bias, in statistical estimates. One important factor is sample size; larger samples are more likely to minimize bias than smaller samples. Therefore, determining the necessary sample size to obtain accurate statistical estimates is a critical component of designing a quantitative study. Much research has been conducted on the impact of sample size on simple statistical techniques such as group mean comparisons and ordinary least squares regression. Less sample size research, however, has been conducted on complex techniques such as hierarchical linear modeling (HLM). HLM, also known as multilevel modeling, is used to explain and predict an outcome based on knowledge of other variables in nested populations. Ordinal logistic HLM (OLHLM) is used when the outcome variable has three or more ordered categories. While there is a growing body of research on sample size for two-level HLM utilizing a continuous outcome, there is no existing research exploring sample size for OLHLM. The purpose of this study was to determine the impact of sample size on statistical estimates for ordinal logistic hierarchical linear modeling. A Monte Carlo simulation study was used to investigate this research query. Four variables were manipulated: level-one sample size, level-two sample size, sample outcome category allocation, and predictor-criterion correlation. Statistical estimates explored include bias in level-one and level-two parameters, power, and prediction accuracy. Results indicate that, in general, holding other conditions constant, bias decreases as level-one sample size increases. However, bias increases or remains unchanged as level-two sample size increases, holding other conditions constant. Power to detect the independent variable coefficients increased as both level-one and level-two sample size increased, holding other conditions constant. Overall, prediction accuracy is extremely poor. The overall prediction accuracy rate across conditions was 47.7%, with little variance across conditions. Furthermore, there is a strong tendency to over-predict the middle outcome category.

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Books on the topic "Sample size"

1

Desu, M. M. Sample size methodology. Academic Press, 1990.

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Salar, Kemal. Sample size for correlation estimates. Naval Postgraduate School, 1989.

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1955-, Chow Shein-Chung, Shao Jun, and Wang Hansheng 1977-, eds. Sample size calculations in clinical research. Marcel Dekker, 2003.

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Chow, Shein-Chung. Sample size calculations in clinical research. 2nd ed. Chapman & Hall/CRC, 2007.

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Daplyn, M. G. Sample size determination for formal surveys. Pakistan Agricultural Research Council, 1994.

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David, Machin, ed. Sample size tables for clinical studies. 2nd ed. Blackwell Science, 1997.

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1955-, Chow Shein-Chung, Shao Jun, and Wang Hansheng 1977-, eds. Sample size calculations in clinical research. 2nd ed. Taylor & Francis, 2008.

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1939-, Machin David, ed. Sample size tables for clinical studies. 3rd ed. Wiley-Blackwell, 2008.

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9

Stanley, Lemeshow, and World Health Organization, eds. Adequacy of sample size in health studies. Published on behalf of the World Health Organization by Wiley, 1990.

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Zarnoch, Stanley J. Determining sample size for tree utilization surveys. U.S. Dept. of Agriculture, Forest Service, Southern Research Station, 2004.

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Book chapters on the topic "Sample size"

1

Friedman, Lawrence M., Curt D. Furberg, and David L. DeMets. "Sample Size." In Fundamentals of Clinical Trials. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-1586-3_8.

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Friedman, Lawrence M., Curt D. Furberg, and David L. DeMets. "Sample Size." In Fundamentals of Clinical Trials. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2915-3_7.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Sample Size." In Statistical Analysis of Clinical Data on a Pocket Calculator. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1211-9_8.

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Heppner, John B., David B. Richman, Steven E. Naranjo, et al. "Sample Size." In Encyclopedia of Entomology. Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_4011.

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Patten, Mildred L., and Melisa C. Galvan. "Sample Size." In Proposing Empirical Research. Routledge, 2019. http://dx.doi.org/10.4324/9780429463013-38.

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Mikyo Oh, Deborah, and Fred Pyrczak. "Sample Size." In Making Sense of Statistics, 8th ed. Routledge, 2023. http://dx.doi.org/10.4324/9781003299356-9.

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Armstrong, Terry W. "Sample Size." In Introduction to Experimental Methods. CRC Press, 2023. http://dx.doi.org/10.1201/9781003329237-5.

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Friedman, Lawrence M., Curt D. Furberg, David L. DeMets, David M. Reboussin, and Christopher B. Granger. "Sample Size." In Fundamentals of Clinical Trials. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18539-2_8.

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Vieira, Edward T. "Sample Size." In Introduction to Real World Statistics. Routledge, 2017. http://dx.doi.org/10.4324/9781315233024-6.

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Vieira, Edward T. "Sample Size." In An Introduction to Applied Statistics, 2nd ed. Routledge, 2025. https://doi.org/10.4324/9781003441991-8.

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Conference papers on the topic "Sample size"

1

Gou, Juan-Juan, Long Wu, Bo-An Ying, Jing Qi, and Yue Wang. "Breast Feature Size Based on Personalized Bra Sample Design." In 16th Textile Bioengineering and Informatics Symposium. Textile Bioengineering and Informatics Society Limited (TBIS), 2023. https://doi.org/10.52202/070821-0058.

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Bober, Gregory D. "Method for Reducing Validation Test Sample Size Through Sample Manipulation." In SAE 2005 World Congress & Exhibition. SAE International, 2005. http://dx.doi.org/10.4271/2005-01-1059.

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Ming Lei, Barend J. van Wyk, and Guoyuan Qi. "A variable sample size particle filter." In IEEE International Conference on Automation and Logistics (ICAL). IEEE, 2008. http://dx.doi.org/10.1109/ical.2008.4636206.

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Feng Gao, A. H. Strahler, W. Lucht, Zong-Guo Xia, and Xiaowen Li. "Retrieving albedo in small sample size." In IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174). IEEE, 1998. http://dx.doi.org/10.1109/igarss.1998.702230.

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Karim, Musa, Shumaila Furnaz, Ahmed Raheem Buksh, Muhammad Asim Beg, Muhammad Shahbaz Khan, and Bushra Moiz. "Sample Size Calculation In Medical Research." In 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2019. http://dx.doi.org/10.1109/macs48846.2019.9024807.

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Dharmarajan, Sai, Satabdi Saha, Xinying Fang, and Jaejoon Song. "Sample Size Determination for Electronic Phenotyping." In 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI). IEEE, 2023. http://dx.doi.org/10.1109/ichi57859.2023.00084.

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Esmen, N. A., G. A. Day, and T. A. Hall. "179. Sample Size Based Indication of Normality in Log-Normally Distributed Samples." In AIHce 1997 - Taking Responsibility...Building Tomorrow's Profession Papers. AIHA, 1999. http://dx.doi.org/10.3320/1.2765301.

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Mindrila, Diana. "Bayesian Latent Class Analysis: Sample Size, Model Size, and Classification Precision." In 2023 AERA Annual Meeting. AERA, 2023. http://dx.doi.org/10.3102/2007141.

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Rahman, Foyzur, Daryl Posnett, Israel Herraiz, and Premkumar Devanbu. "Sample size vs. bias in defect prediction." In the 2013 9th Joint Meeting. ACM Press, 2013. http://dx.doi.org/10.1145/2491411.2491418.

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NG, HS Raymond, and KP LAM. "How does Sample Size Affect GARCH Models?" In 9th Joint Conference on Information Sciences. Atlantis Press, 2006. http://dx.doi.org/10.2991/jcis.2006.139.

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Reports on the topic "Sample size"

1

Hansen, Clifford. Sample size for PV lifetime project. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1367408.

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Valenciano, Marilyn. Developmental sentence scoring sample size comparison. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.3108.

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Callan, Peggy. Developmental sentence scoring sample size comparison. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6053.

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Vera-Hernandez, Marcos. Sample size calculations for impact evaluations. The IFS, 2014. http://dx.doi.org/10.1920/ps.ifs.2024.0725.

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Zarnoch, Stanley J., James W. Bentley, and Tony G. Johnson. Determining sample size for tree utilization surveys. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2004. http://dx.doi.org/10.2737/srs-rp-34.

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6

Zarnoch, Stanley J., James W. Bentley, and Tony G. Johnson. Determining sample size for tree utilization surveys. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2004. http://dx.doi.org/10.2737/srs-rp-34.

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Royset, Johannes O. On Sample Size Control in Sample Average Approximations for Solving Smooth Stochastic Programs. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada513136.

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McConnell, Brendon, and Marcos Vera-Hernandez. Going beyond simple sample size calculations: a practitioner's guide. Institute for Fiscal Studies, 2015. http://dx.doi.org/10.1920/wp.ifs.2015.1517.

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Davidson, J. R. Verification of the Accuracy of Sample-Size Equation Calculations for Visual Sample Plan Version 0.9C. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/786780.

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Davidson, James R. Verification of the Accuracy of Sample-Size Equation Calculations for Visual Sample Plan Version 0.9C. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/965663.

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