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Dissertations / Theses on the topic 'Intraclass correlation coefficient'

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Published: 4 June 2021

Last updated: 6 February 2022

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1

Yu, Jianhui. "On Intraclass Correlation Coefficients." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/75.

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This paper uses Maximum likelihood estimation method to estimate the common correlation coefficients for multivariate datasets. We discuss a graphical tool, Q-Q plot, to test equality of the common intraclass correlation coefficients. Kolmogorov-Smirnov test and Cramér-von Mises test are used to check if the intraclass correlation coefficients are the same among populations. Bootstrap and empirical likelihood methods are applied to construct the confidence interval of the common intraclass correlation coefficients.

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2

Liu, Huayu. "Modified Profile Likelihood Approach for Certain Intraclass Correlation Coefficient." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_theses/96.

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In this paper we consider the problem of constructing confidence intervals and lower bounds forthe intraclass correlation coefficient in an interrater reliability study where the raters are randomly selected from a population of raters.The likelihood function of the interrater reliability is derived and simplified, and the profile likelihood based approach is readily available for computing the confidence intervals of the interrater reliability. Unfortunately, the confidence intervals computed by using the profile likelihood function are in general too narrow to have the desired coverage probabilities. From the point view of practice, a conservative approach, if is at least as precise as any existing method, is preferred sinceit gives the correct results with a probability higher than claimed. Under this rationale, we propose the so-called modified likelihood approach in this paper. Simulation study shows that, the proposed method in general has better performance than currently used methods.

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3

Ukoumunne, Obioha Chukwunyere. "Confidence intervals for the intraclass correlation coefficient in cluster randomised trials." Thesis, King's College London (University of London), 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418293.

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4

Wu, Baohua. "Data Driven Approaches to Testing Homogeneity of Intraclass Correlation Coefficients." Digital Archive @ GSU, 2010. http://digitalarchive.gsu.edu/math_theses/92.

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The test of homogeneity for intraclass correlation coefficients has been one of the active topics in statistical research. Several chi-square tests have been proposed to test the homogeneity of intraclass correlations in the past few decades. The big concern for them is that these methods are seriously biased when sample sizes are not large. In this thesis, data driven approaches are proposed to testing the homogeneity of intraclass correlation coefficients of several populations. Through simulation study, data driven methods have been proved to be less biased and accurate than some commonly used chi-square tests.

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5

Bai, Shasha. "Inference on Intraclass Correlation Coefficients arising in a General Clustered Repeated-Measures Design." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1395614770.

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6

Perrett, Jamis J. "Using prior information on the intraclass correlation coefficient to analyze data from unreplicated and under-replicated experiments." Diss., Manhattan, Kan. : Kansas State University, 2004. http://hdl.handle.net/2097/45.

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7

Rodrigue, Natalie. "A comparison of the performance of Generalized Procrustes analysis and the intraclass coefficient of correlation to estimate interrater reliability." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0022/MQ50868.pdf.

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8

Wang, Luqiang. "Contributions to estimation of measures for assessing rater reliability." Diss., Temple University Libraries, 2009. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/44053.

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Statistics<br>Ph.D.<br>Reliability measures have been well studied over many years, beginning with an entire chapter devoted to intraclass correlation in the first edition of Fisher (1925). Such measures have been thoroughly studied for two factor models. This dissertation, motivated by a medical research problem, extends point and confidence interval estimation of both intraclass correlation coefficient and interater reliability coefficient to models containing three crossed random factors -- subjects, raters and occasions. The intraclass correlation coefficient is used when decision is made on an absolute basis with rater's scores, while the interater reliability coefficient is defined for decisions made on a relative basis. The estimation is conducted using both ANOVA and MCMC methods. The results from the two methods are compared. The MCMC method is preferred for analyses of small data sets when ICC values are high. Besides, the bias of estimator of intraclass correlation coefficient in one-way random effects model is evaluated.<br>Temple University--Theses

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9

Smoljanovic, Lada. "The estimation of intraclass correlation coefficients." Thesis, University of Hull, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301491.

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10

Giraudeau, Bruno. "Sensibilite du coefficient de correlation intraclasse a une observation extreme." Paris 7, 1997. http://www.theses.fr/1997PA077112.

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Le coefficient de correlation intraclasse, defini comme la proportion de la variabilite totale due a la variabilite inter-sujets, est classiquement utilise pour estimer la reproductibilite d'un instrument de mesure. L'objet de ce travail est d'etudier l'influence d'observations extremes sur ce coefficient. Une observation (i. E. Un vecteur de p mesures) est extreme soit parce que la moyenne des p mesures s'ecarte de facon anormale de la moyenne globale, soit parce que la variance des p mesures est anormalement elevee. Deux approches permettant le traitement de ces observations ont ete envisagees. D'une part, une fonction d'influence d'une observation a ete analytiquement etablie. Elle dissocie l'influence positive associee a la moyenne des p mesures de l'influence negative associee a la variance de ces mesures. Deux tests en sont derives, ce qui permet d'exclure une observation extreme de l'echantillon. D'autre part, une etude de simulation a ete conduite pour comparer la robustesse de quatre estimateurs : l'estimateur du maximum de vraisemblance, deux estimateurs bootstrap et un estimateur defini a partir de quartiles. Le reechantillonnage ne permet pas de gagner en robustesse, par contre, l'estimateur defini a partir des quartiles est robuste aux observations extremes, mais necessite une distribution normale des donnees. L'utilisation du coefficient de correlation intraclasse est ensuite illustree par deux etudes en rhumatologie. Ainsi, on a estime la reproductibilite de quatre instruments utilises pour l'evaluation radiologique de la gonarthrose, puis on a etudie la reproductibilite et la sensibilite au changement de plusieurs methodes d'evaluation de la progression de la gonarthrose. Cette derniere etude clinique a, en outre, conduit a une investigation analytique des relations existant entre la reproductibilite et la sensibilite au changement, ce qui nous a permis de montrer que la qualite metrologique primordiale reste la reproductibilite.

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11

Bergamaschi, Denise Pimentel. "Correlação intraclasse de Pearson para pares repetidos: comparação entre dois estimadores." Universidade de São Paulo, 1999. http://www.teses.usp.br/teses/disponiveis/6/6132/tde-01102014-105050/.

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Objetivo. Comparar, teórica e empiricamente, dois estimadores do coeficiente de correlação intraclasse momento-produto de Pearson para pares repetidos Pi. O primeiro é o estimador \"natural\", obtido mediante a correlação momento-produto de Pearson para membros de uma mesma classe (rI) e o segundo, obtido como função de componentes de variância (icc). Métodos. Comparação teórica e empírica dos parâmetros e estimadores. A comparação teórica envolve duas definições do coeficiente de correlação intraclasse PI como medida de confiabilidade (*), para o caso de duas réplicas, assim como uma apresentação da técnica de análise de variância e a definição e interpretação dos estimadores ri e icc. A comparação empírica é realizada mediante um estudo de simulação Monte Carlo com a geração de pares de valores correlacionados segundo o coeficiente de correlação intraclasse, momento-produto de Pearson para pares repetidos. Os pares de valores são distribuídos segundo uma distribuição Normal bivariada, com valores do tamanho da amostra e da correlação intraclasse previamente fixados em: n= 15, 30 e 45 e pI = {O; 0,15; 0,30; 0,45; 0,60; 0,75; 0,9}. Resultados. Comparando-se o vício e o erro quadrático médio dos estimadores, bem como as amplitudes dos intervalos de confiança, tem-se como resultado que o vício de icc foi sempre menor que o vício de rI, mesmo ocorrendo com o erro quadrático médio. Conclusões. O icc é um estimador melhor, principalmente para n pequeno (por exemplo 15). Para valores maiores de n (30 ou mais), os estimadores produzem resultados iguais até a segunda casa decimal.<br>Objective. This thesis presents and compares, theoretically and empirically, two estimators of the intraclass correlation coefficient pI, defined as Pearson\'s pairwise intraclass correlation coefficient. The first is the \"natural\" estimator, obtained by Pearson\'s moment-product correlation for members of one class (rI) while the second was obtained as a function of components of variance (icc). Methods. Theoretical and empirical comparison of the parameters and estimators are performed. The theoretical comparison involves two definitions of the intrac1ass correlation coefficient pI as a measure of reliability (*) for two repeated measurements in the same class and the presentation of the technique of analysis of variance, as well as for the definition and interpretation of the estimators ri and icc. The empirical comparison was carried out by means of a Monte Carlo simulation study of pairs of correlated values according Pearson\'s pairwise correlation. The pairs of values follow a normal bivariate distribution, with correlation values and sample size previously fixed: n= 15, 30 e 45 and Pl = . Results. Bias and mean square error for the estimators were compared as well as the range of the intervals of confidence. The comparison shows that the bias of icc is always smaller than of rI This also applies to the mean square error. Conclusions. The icc is a better estimator, especially for n less than or equal to 15. For larger samples sízes (n 30 or more), the estimators produce results that are equal to the second decimal place. (*) Fórmula

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12

Lee, Kyung Ah. "Analysis of the Total Food Folate Intake Data from the National Health and Nutrition Exa-amination Survey (Nhanes) Using Generalized Linear Model." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/80.

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The National health and nutrition examination survey (NHANES) is a respected nation-wide program in charge of assessing the health and nutritional status of adults and children in United States. Recent cal research found that folic acid play an important role in preventing baby birth defects. In this paper, we use the generalized estimating equation (GEE) method to study the generalized linear model (GLM) with compound symmetric correlation matrix for the NHANES data and investigate significant factors to ence the intake of food folic acid.

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13

Chen, Chia-Cheng. "Assessing agreement with intraclass correlation coefficient and concordance correlation coefficient." 2009. http://www.lib.ncsu.edu/theses/available/etd-05202009-163011/unrestricted/etd.pdf.

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14

Wong, Chung Fai Paddison. "Composite estimators of small-area means and the intraclass correlation coefficient." 2004. http://hdl.handle.net/1993/17916.

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15

Cheng, Yu Chun, and 鄭宇君. "Estimation of intraclass correlation coefficient and Kappa statistic in categorical data." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/93387061533488183894.

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16

Tai, Chu-Chun, and 戴竹君. "Sample Size Calculations for Precise Interval Estimation of Intraclass Correlation Coefficient (2)." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/4mm6rh.

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碩士<br>國立交通大學<br>管理科學系所<br>103<br>Whether in the natural sciences or the social sciences, intraclass correlation coefficient is always used to measure the degree of the correlation between the interclass variation and the intraclass variation. This index reflects the differences between groups of data. It’s always regard as a way to inspect variation of variable is interpreted by variable between groups for evaluating whether integrating individual data to group information. Also, intraclass correlation coefficient is used to inspect the consistency of retest measured results. In this dissertation, the SAS/IML software is used to construct the model, and there are two simulation situations, interval width and interval coverage rate, to find the optimal sample size in conditions of conditional level, measurement and intraclass correlation coefficient. These calculated sample size are analyzed and compared.

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17

葉家豪. "nterval Estimation and Sample Size Calculation for Fisher Transformation of Intraclass Correlation Coefficient." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/7d84zz.

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碩士<br>國立交通大學<br>管理科學系所<br>101<br>ICCs is an indispensable part of social science research, it has to use the F-distribution through the computing process, it seems pretty hard to find the optimal sample size in some specific situations, so the domestic and foreign researchers have debated on the ICCs estimator for many years. Mostly, researchers choose to use the normal distribution because the normal distribution is relatively easy to use to find the confidence interval and optimal sample size. This article is based on the theory which proposed by Bonett (2002) , he used the formula to compute the sample size, however, he didn't show the confidence interval as a proof, so this research use two method to compute the optimal sample size and further compare to the original method. I also use the simulation to develop the confidence interval coverage to help me discuss the optimal sample size and the timing of using these two methods. I hope that I can provide more information for those researchers who use the ICCs as the research meth-od.

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18

Tsai, Lin-Su, and 蔡麗淑. "Using Intraclass Correlation Coefficient to Assess Effects of the Hospital Global Budget Payment System in Taiwan." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/5599r6.

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碩士<br>嘉南藥理科技大學<br>醫務管理系<br>100<br>Objectives: Using a common indicator to assess effects on hospital global budget payment system implemented in 2002 in Taiwan is urgently required. We present the intraclass correlation coefficient (ICC) to assess how well Taiwan constrained hospital-provided medical services in such a system, and compare what difference existed in the two sections of inpatient and outpatient in Taiwan’s hospitals Methods: Data from 1999 to 2009 regarding 421 hospital inpatient and outpatient medical reimbursements to Taiwan’s Bureau of National Health Insurance (BNHI) were used to examine the extent of monthly unexpected values in a range from -4 to 4 standard deviations applying the statistical process control chart to the nation-based 120 months’ reimbursement datasets in the past 10 years. Intraclass correlation coefficients (ICC) were hence annually obtained to inspect whether global budgets were well controlled among hospitals in different sections. A bubble chart of SDs for a specific month was generated to present the effects of using control charts in a national healthcare system. Results: ICCs were generated for Taiwan''s year-based convergent power to constrain its medical services from 2000 to 2009. All hospital groups showed a gradually well-controlled supply of services that decreased from 0.77 to 0.42 and from 0.88 to 0.61 in inpatient and outpatient sections, respectively. We found that 1) the effect on hospital global budget payment system was gradually improved in past 10 years; 2) inpatient sector showed significantly more well-controlled than its outpatient counterpart. The bubble chart identified outlier hospitals that required investigation of possible excessive reimbursements in a specific time period. Conclusions: ICC could be the indicator used for annually assessing effects on hospital global budgeting. We suggest the committee in charge of monitoring hospital global budgeting reveal the ICC to report annual effects on carrying out the hospital global budgeting in a specific controlled limit range

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19

Hsieh, Ming-Yu, and 謝茗伃. "Point Estimation analysis for Intraclass Correlation Coefficients." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/37854929522545263793.

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碩士<br>國立交通大學<br>管理科學系所<br>104<br>Intraclass correlation coefficients are used extensively to measure the reliability or degree of resemblance among group members in multilevel research. The study compares the behavior of several intraclass correlation estimators in terms of bias, bias ratio, and mean square error. Monte Carlo simulation results revealed that the mutual dominance relationship among the indices. The numerical investigations provide guidelines to help researchers choose between the competing measures.

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20

Yang, Nai-Chen, and 楊乃蓁. "Sample size Calculation for Hypothesis Testing of Intraclass Correlation Coefficients (2)." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/59526552985122107636.

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碩士<br>國立交通大學<br>管理科學系所<br>104<br>In the practical applications, Intraclass correlation coefficients is the model in random effects model. The main contribution in this research is how to determine the optimum number of samples based on ICC(2). For the begging we learn to use the tools of measurement is the Pearson correlation coefficient. Although, we can know the correlation between variables, but satisfy the assumption is difficult in the reality. The reason is that the two variables of Pearson correlation coefficient t must be assumed independent. Therefore, in practical application is not easy. Most of the research data have similarities and dependent. Due to the reason above, Using the correlation coefficient ICC(2) to measure the relationship between variables is more appropriate. This article will focus on research on One-way random effects model, using statistical software SAS / IML program do the simulation. Through the hypothesis testing method to set the power, and employ the F ratio to look for the optimistic sample size.

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21

Tsai, Hsin-Ni, and 蔡忻妮. "Approximate Bayesian approaches for assessing intraclass correlation coefficients in dependent binary data." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/07158093177833882557.

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碩士<br>國立彰化師範大學<br>統計資訊研究所<br>99<br>Medical data in clinical studies are commonly carried out in clustered settings, where the subjects are correlated within clusters. When observations approximately follow a normal distribution, the intraclass correlation coefficient (ICC) is frequently used to assess the similarity within clusters with respect to the particular biological or environmental characteristics. However, for correlated binary data, it is difficult to obtain directly the ICCs by the definition of the proportion of the total variation explained by variation between clusters. In this article, we propose two approximate Bayesian statistical approaches, approximate Taylor Bayesian and empirical Bayesian approaches, to estimate ICCs in multilevel logit models for dependent binary data. To compare with a frequentist approach, we make a comparison between the approximate Taylor Bayesian, the empirical Bayesian, and the ANOVA approaches in simulation studies. The results of comparison studies reveal that the proposed approximate Bayesian approaches provide reliable and stable approaches in estimating ICCs, and parameters of fixed effects and variance components simultaneously. Furthermore, the results indicate that the approximate Bayesian approaches are robust in model misspecification.

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22

Yu, Chia-Chun, and 余嘉淳. "The effects of intraclass correlation coefficients on regression analyses with clustered binary data." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/99909520707197232422.

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碩士<br>國立陽明大學<br>公共衛生研究所<br>102<br>Binary clustered data are often encountered in public health and clinical research. Difference analyses methods can obtain different results of parameter estimates. Intraclass correlation coefficient (ICC) usually is used to quantify the degree of relation in outcomes within cluster. The ICCs are not necessarily substantial in longitudinal or clustered data after adjusting for related variables. The effects of ICC, prevalence of outcome variable, number of clusters and cluster size on parameter estimates remain to be explored. The present study carried out a simulation study with different scenarios with different ICC, prevalences of outcome, numbers of clusters and clusters size for both binary and continuous predictors . The simulations considered four values of ICC: 0, 0.1, 0.3, 0.5, four prevalence of outcome: 0.3, 0.1, 0.05, 0.01, conditional on the total number of observations 1000 with five combinations of the number of clusters (m) and cluster size (n): (m,n)=(500,2), (200,5), (100,10), (10,100), (5,200), independent variables for continue and dichotomy with multicollinearity or not. Based on the context of logistic regression models, the methods of parameter estimation included standard method of maximum-likelihood which assume data were independent, generalized estimating equation (GEE) that using exchangeable correlation coefficient and odds ratio (alternate logistic regression), and generalized mixed linear models (GLMMs) using adaptive guass-hermite quadrature (GLMM_FL) and pseudo-likelihood approximation (GLMM_PL). The results of simulation showed that the prevalence of outcome did not have substantial effect on parameter estimates, on the other hand, the estimates were different with different ICC. When the ICC was close to zero(ICC=0), the performance of standard logistic regression is the best, GEE was second best when m>n and GLMMs in general obtain invalid estimates of parameter. When ICC was low (ICC=0.1) and the number of clusters was bigger than cluster size (m>n), the estimates by standard logistic regression, GEE and GLMMs_FL were valid. When ICC is low (ICC=0.1) and the number of clusters was smaller than cluster size (m<n), the performance of standard logistic regression was the best. When ICC is medium to high (ICC≧0.3), the parameter estimates of standard logistic regression and GEE were not valid. The performance of GLMMs_FL was on the best in this situation. The GLMMs_FL usually obtained more conservative estimates (wider confidence intervals) when m < n. In general, the GLMMs_PL had a worse performance than other methods.

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