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Relevant bibliographies by topics / Multilevel analysis

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Academic literature on the topic 'Multilevel analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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Journal articles on the topic "Multilevel analysis"

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Augusto Arbugeri, Cesar, Tiago Kommers Jappe, Telles Brunelli Lazzarin, Domingo A. Ruiz-Caballero, Reynaldo R. Astudillo, Luis Martinez, and Samir Ahmad Mussa. "Asymetrical Multilevel Hybrid Inverter - Analysis And Experimentation." Eletrônica de Potência 24, no. 3 (September 30, 2019): 296–305. http://dx.doi.org/10.18618/rep.2019.3.0031.

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Le Borne, Sabine. "Multilevel Hierarchical Matrices." SIAM Journal on Matrix Analysis and Applications 28, no. 3 (January 2006): 871–89. http://dx.doi.org/10.1137/040607964.

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van den Eeden, Pieter, and Joop J. Hox. "Introduction - Multilevel Analysis." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 51, no. 1 (June 1996): 5–9. http://dx.doi.org/10.1177/075910639605100101.

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Zhang, Weihua, Jiaxin Li, Yi Li, and Haibo Chen. "Multilevel Phase Analysis." ACM Transactions on Embedded Computing Systems 14, no. 2 (March 25, 2015): 1–29. http://dx.doi.org/10.1145/2629594.

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Caldwell, Alex B. "Simultaneous multilevel analysis." American Psychologist 49, no. 2 (1994): 144–45. http://dx.doi.org/10.1037/0003-066x.49.2.144.

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HOX, JOOP J., and ITA G. G. KREFT. "Multilevel Analysis Methods." Sociological Methods & Research 22, no. 3 (February 1994): 283–99. http://dx.doi.org/10.1177/0049124194022003001.

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Blau, Peter M. "Multilevel structural analysis." Social Networks 15, no. 2 (June 1993): 201–15. http://dx.doi.org/10.1016/0378-8733(93)90005-6.

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Timmerman, Marieke E. "Multilevel component analysis." British Journal of Mathematical and Statistical Psychology 59, no. 2 (November 2006): 301–20. http://dx.doi.org/10.1348/000711005x67599.

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Kehl, René, Reinhard Nabben, and Daniel B. Szyld. "Adaptive Multilevel Krylov Methods." ETNA - Electronic Transactions on Numerical Analysis 51 (2019): 512–28. http://dx.doi.org/10.1553/etna_vol51s512.

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Kuo, C. C. Jay, Tony F. Chan, and Charles Tong. "Multilevel Filtering Elliptic Preconditioners." SIAM Journal on Matrix Analysis and Applications 11, no. 3 (July 1990): 403–29. http://dx.doi.org/10.1137/0611029.

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Dissertations / Theses on the topic "Multilevel analysis"

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Venkatasubramanian, S. "Illiteracy in India : a multilevel analysis." Thesis, University of Portsmouth, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302445.

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Sy, Oumar Sekou. "Multilevel mediation analysis estimation and applications /." Search for this dissertation online, 2004. http://wwwlib.umi.com/cr/ksu/main.

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Woodhouse, Geoffrey M. "Adjustment for measurement error in multilevel analysis." Thesis, University College London (University of London), 1998. http://discovery.ucl.ac.uk/10019113/.

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Measurements in educational research are often subject to error. Where it is desired to base conclusions on underlying characteristics rather than on the raw measurements of them, it is necessary to adjust for measurement error in the modelling process. In this thesis it is shown how the classical model for measurement error may be extended to model the more complex structures of error variance and covariance that typically occur in multilevel models, particularly multivariate multilevel models, with continuous response. For these models parameter estimators are derived, with adjustment based on prior values of the measurement error variances and covariances among the response and explanatory variables. A straightforward method of specifring these prior values is presented. In simulations using data with known characteristics the new procedure is shown to be effective in reducing the biases in parameter estimates that result from unadjusted estimation. Improved estimates of the standard errors also are demonstrated. In particular, random coefficients of variables with error are successfully estimated. The estimation procedure is then used in a two-level analysis of an educational data set. It is shown how estimates and conclusions can vary, depending on the degree of measurement error that is assumed to exist in explanatory variables at level 1 and level 2. The importance of obtaining satisfactory prior estimates of measurement error variances and covariances, and of correctly adjusting for them during analysis, is demonstrated.

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Wong, Chun-mei May. "Multilevel models for survival analysis in dental research." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B3637216X.

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Pham, Thanh Vinh. "The performance of Multilevel Structural Equation Modeling (MSEM) in comparison to Multilevel Modeling (MLM) in multilevel mediation analysis with non-normal data." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/7077.

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The mediation analysis has been used to test if the effect of one variable on another variable is mediated by the third variable. The mediation analysis answers a question of how a predictor influences an outcome variable. Such information helps to gain understanding of mechanism underlying the variation of the outcome. When the mediation analysis is conducted on hierarchical data, the structure of data needs to be taken into account. Krull and MacKinnon (1999) recommended using Multilevel Modeling (MLM) with nested data and showed that the MLM approach has more power and flexibility over the standard Ordinary Least Squares (OLS) approach in multilevel data. However the MLM mediation model still has some limitations such as incapability of analyzing outcome variables measured at the upper level. Preacher, Zyphur, and Zhang (2010) proposed that the Multilevel Structural Equation Modeling (MSEM) will overcome the limitation of MLM approach in multilevel mediation analysis. The purpose of this study was to examine the performance of the MSEM approach on non-normal hierarchical data. This study also aimed to compare the MSEM method with the MLM method proposed by MacKinnon (2008) and Zhang, Zyphur, and Preacher (2009). The study focused on the null hypothesis testing which were presented by Type I error, statistical power, and convergence rate. Using Monte Carlo method, this study systematically investigates the effect of several factors on the performance of the MSEM and MLM methods. Designed factors considered were: the magnitude of the population indirect effect, the population distribution shape, sample size at level 1 and level 2, and the intra-class correlation (ICC) level. The results of this study showed no significant effect of the degree of non-normality on any performance criteria of either MSEM or MLM models. While the Type I error rates of the MLM model reached the expected alpha level as the group number was 300 or higher, the MSEM model showed very conservative performance in term of controlling for the Type I error with the rejection rates of null conditions were zero or closed to zero across all conditions. It was evident that the MLM model outperformed the MSEM model in term of power for most simulated conditions. Among the simulation factors examined in this dissertation, the mediation effect size emerged as the most important one since it is highly associated with each of the considered performance criteria. This study also supported the finding of previous studies (Preacher, Zhang, & Zyphur, 2011; Zhang, 2005) about the relationship between sample size, especially the number of group, and the performance of either the MLM or MSEM models. The accuracy and precision of the MLM and MSEM methods were also investigated partially in this study in term of relative bias and confidence interval (CI) width. The MSEM model outperformed the MLM model in term of relative bias while the MLM model had better CI width than the MSEM model. Sample size, effect size, and ICC value were the factors that significantly associate with the performance of these methods in term of relative bias and CI width.

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Feng, Yuanjian. "Detection and Characterization of Multilevel Genomic Patterns." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/38577.

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DNA microarray has become a powerful tool in genetics, molecular biology, and biomedical research. DNA microarray can be used for measuring the genotypes, structural changes, and gene expressions of human genomes. Detection and characterization of multilevel, high-throughput microarray genomic data pose new challenges to statistical pattern recognition and machine learning research. In this dissertation, we propose novel computational methods for analyzing DNA copy number changes and learning the trees of phenotypes using DNA microarray data. DNA copy number change is an important form of structural variations in human genomes. The copy number signals measured by high-density DNA microarrays usually have low signal-to-noise ratios and complex patterns due to inhomogeneous composition of tissue samples. We propose a robust detection method for extracting copy number changes in a single signal profile and consensus copy number changes in the signal profiles of a population. We adapt a solution-path algorithm to efficiently solve the optimization problems associated with the proposed method. We tested the proposed method on both simulation and real CGH and SNP microarray datasets, and observed competitively improved performance as compared to several widely-adopted copy number change detection methods. We also propose a chromosome instability measure to summarize the extracted copy number changes for assessing chromosomal instabilities of tumor genomes. The proposed measure demonstrates distinct patterns between different subtypes of ovarian serous carcinomas and normal samples. Among active research on complex human diseases using genomic data, little effort and progress have been made in discovering the relational structural information embedded in the molecular data. We propose two stability analysis based methods to learn stable and highly resolved trees of phenotypes using microarray gene expression data of heterogeneous diseases. In the first method, we use a hierarchical, divisive visualization approach to explore the tree of phenotypes and a leave-one-out cross validation to select stable tree structures. In the second method, we propose a node bandwidth constraint to construct stable trees that can balance the descriptive power and reproducibility of tree structures. Using a top-down merging procedure, we modify the binary tree structures learned by hierarchical group clustering methods to achieve a given node bandwidth. We use a bootstrap based stability analysis to select stable tree structures under different node bandwidth constraints. The experimental results on two microarray gene expression datasets of human diseases show that the proposed methods can discover stable trees of phenotypes that reveal the relationships between multiple diseases with biological plausibility.
Ph. D.

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Neilson, Lisa Anne. "Social capital and political consumerism: a multilevel analysis." Connect to resource, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1156951934.

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Hesari, Saeed Aroni. "A multilevel superelement substructuring for boxlike caisson analysis." Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/6540.

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A multilevel superelement substructuring approach for linear-static analysis of rectangular boxlike caissons is developed. The structure is considered as an assemblage of superelements in three substructural levels. These are panel, layer, and caisson superelements. Each superelement substructure is derived by applying the principle of static condensation. Rectangular 9-node flat shell elements, which are composed of membrane stress and Mindlin plate bending elements, are assembled together to make the stiffness and load matrices of each panel substructure. Every panel substructure is condensed to a panel superelement. Then all of them in a layer are assembled together and condensed to layer superelement. Finally, the later superelement(s) are assembled and condensed to yield to caisson superelement. After the assembling process, equilibrium equations are solved for the caisson superelement. Then a multilevel recovery process, with an inverse sequence of assembling one, gives the internal degrees of freedom for caisson, layer, and panel superelements, respectively. Finding all displacements, one can finally compute internal forces for every individual flat shell element. This special purpose superelement substructure method provides considerable efficiency in practical analysis and design of boxlike caissons. Based on this approach, a computer program is developed which takes the most advantage from the repetition of similar individual elements and panels. Several numerical examples are solved indicating good agreement with other analytical or numerical methods and proper convergency for design purposes.

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Shumka, Ellen. "The social facilitation of bullying : a multilevel analysis." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43517.

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It is well documented that bullying is harmful and relatively common among children and adolescents. Children report understanding that bullying is wrong, yet bullying continues to be a persistent problem in schools. The goal of the present study was to examine whether children’s bullying behaviours were socially facilitated by group norms and beliefs. Children’s justifications and rationalizations for engaging in wrongful behaviour, a phenomenon referred to as moral disengagement (MD), have been linked to bullying behaviour at the individual level. Specifically, children who report engaging in bulling tend to report more MD than those who do not. Only one study to date, however, has examined MD at the group level and results indicated that group levels of MD, over and above individual levels, predicted engagement in bullying. Group level processes, especially group norms supporting aggression and bullying, have also been linked to greater bullying perpetration. The current investigation extended this research by examining how group levels of MD and normative beliefs about deviancy influenced bullying using two unique samples of schoolchildren. The first study examined the influence of two group level variables (MD and normative beliefs about deviancy) on bullying over a school year in a sample of 376 students (surveyed in Grade 5 and 6) from 38 schools in Southern Ontario. The second study examined the influence of group MD on 1128 students across 74 classrooms in Vancouver, British Columbia. Results across both studies did not support the hypothesis that group levels of MD (Study 1 & 2) and normative beliefs about deviancy (Study 1 only) influenced engagement in bullying. The findings suggest that further investigations are required in order to better understand the effects group level MD and normative beliefs on bullying behaviour.

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Wong, Chun-mei May, and 王春美. "Multilevel models for survival analysis in dental research." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B3637216X.

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Books on the topic "Multilevel analysis"

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. Multilevel Analysis. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982.

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Hox, J. J. Applied multilevel analysis. 2nd ed. Amsterdam: TT-Publikaties, 1995.

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J, Bosker R., ed. Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage Publications, 1999.

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Leeuw, Jan de, and Erik Meijer, eds. Handbook of Multilevel Analysis. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-73186-5.

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de, Leeuw Jan, and Meijer Erik 1963-, eds. Handbook of multilevel analysis. New York: Springer, 2008.

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de, Leeuw Jan, and Meijer Erik 1963-, eds. Handbook of multilevel analysis. New York: Springer, 2008.

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de, Leeuw Jan, and Meijer Erik 1963-, eds. Handbook of multilevel analysis. New York: Springer, 2008.

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Hox, J. J. Multilevel analysis: Techniques and applications. 2nd ed. New York: Routledge, 2010.

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Handbook of advanced multilevel analysis. New York: Routledge, 2011.

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Hox, J. J., and J. Kyle Roberts. Handbook of advanced multilevel analysis. New York: Routledge, 2011.

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Book chapters on the topic "Multilevel analysis"

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Introduction to Multilevel Analysis." In Multilevel Analysis, 1–7. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-1.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Multivariate Multilevel Regression Models." In Multilevel Analysis, 173–88. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-10.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "The Multilevel Approach to Meta-Analysis." In Multilevel Analysis, 189–211. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-11.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Sample Sizes and Power Analysis in Multilevel Regression." In Multilevel Analysis, 212–34. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-12.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Assumptions and Robust Estimation Methods." In Multilevel Analysis, 235–68. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-13.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Multilevel Factor Models." In Multilevel Analysis, 269–83. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-14.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Multilevel Path Models." In Multilevel Analysis, 284–93. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-15.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Latent Curve Models." In Multilevel Analysis, 294–304. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-16.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "The Basic Two-Level Regression Model." In Multilevel Analysis, 8–26. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-2.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Estimation and Hypothesis Testing in Multilevel Regression." In Multilevel Analysis, 27–40. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-3.

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Conference papers on the topic "Multilevel analysis"

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Striz, A., and Jaroslaw Sobieszczanski-Sobieski. "Displacement based multilevel structural optimization." In 6th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4098.

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Tianxing, Man, Nataly Zhukova, and Georgi Tsochev. "A Multilevel Intelligent Assistant for Multilevel Social Network Analysis." In 2020 IEEE 10th International Conference on Intelligent Systems (IS). IEEE, 2020. http://dx.doi.org/10.1109/is48319.2020.9199840.

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Nassar, Ahmed, and Ebru Sezer. "Multilevel Sentiment Analysis In Arabic." In 2019 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE). IEEE, 2019. http://dx.doi.org/10.1109/picece.2019.8747209.

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Minai, Ahmad Faiz, and Abu Tariq. "Analysis of Cascaded multilevel inverter." In 2010 India International Conference on Power Electronics (IICPE). IEEE, 2011. http://dx.doi.org/10.1109/iicpe.2011.5728129.

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Ng, Raymond T., and Dominic Tam. "Analysis of multilevel color histograms." In Electronic Imaging '97, edited by Ishwar K. Sethi and Ramesh C. Jain. SPIE, 1997. http://dx.doi.org/10.1117/12.263420.

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Karypis, George, and Vipin Kumar. "Analysis of multilevel graph partitioning." In the 1995 ACM/IEEE conference. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/224170.224229.

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Alexandrov, Natalia. "Multilevel and multiobjective optimization in multidisciplinary design." In 6th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4122.

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Setianingsih, Triseu. "Performance Analysis of Children's Health Program in Indonesia: A Multilevel Analysis." In The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.03.85.

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ABSTRACT Background: Most of the neonatal deaths that occur after 6-48 hours postpartum can be prevented with appropriate newborn care and initiated immediately after delivery through adequate and standardized first neonatal visit. In Indonesia, it is still not in accordance with the expected target. This study aimed to analyze the factors that influence the first neonatal visit from various levels through a multilevel analysis approach. Subjects and Method: This was a cross sectional study with samples of individual level (level 1) was 1014 mother babies, village Level (level 2) as many as 95 village managers, public health center (level 3) was 51 manager of child health programs, and district level (level 4) was 13 managers of health programs in eight provinces Indonesia. The dependent variable was first neonatal visits. The independent variables were birth attendant and pregnancy counselling (level 1), number of village midwives and community control in development (level 2), partnership and resources (level 3), and policy strategy (level 4). Data analysis was performed through univariate, bivariate, multivariate analysis with logistic regression and multilevel modeling using multilevel regression logistic random intercept analysis run on Stata 14.0. Results: At level 1, birth attendants increased first neonatal visits (OR = 3.21; 95% CI 95% = 1,984 to 5,182; p <0.001), pregnancy counselling (OR = 1,705; 95% CI 95% = 1,162 to 2,503; p = 0.007) significantly. At level 2 modelling, the number of village midwives increased the risk of the first neonatal visit (OR = 1,815; 95% CI = 0.950 to 3,467; p = 0.049), community control (OR = 2,659; 95% CI = 1.396 to 5.066; p = 0.009) significantly. At level 3 modelling, partnerships and resources significantly increased the first neonatal visit (OR = 2,131; 95% CI = 1,114 to 4,078; p = 0.012) significantly. At level 4 modelling, birth attendants significantly increased the first neonatal visits (OR = 3.056; 95% CI = 1.901 to 4,914; p = 0.029) significantly. Conclusion: Birth attendants, pregnancy counselling, the number of village midwives, community control, partnerships and resources, birth attendants increase the risk of first neonatal visit. Contextual village areas, public health center, and district health offices have contextual effects on the first neonatal visit. Keywords: first neonatal visit, multilevel analysis Correspondence: Triseu Setianingsih. Drg. Suherman Medical Institut. Jl. Industri Pasirgombong Jababeka Cikarang, Bekasi, West Java. Email: triseu.pantyarso@imds.ac.id. Mobile: +6281299192199. DOI: https://doi.org/10.26911/the7thicph.03.85

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YATES, K., Z. GURDAL, and S. THANGJITHAM. "Multilevel optimization using a continuum model for structures." In 4th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-4786.

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Taouil, Rafik, and Ilyass Belghannou. "Restructuring Iceberg Lattice For Multilevel Analysis." In 2018 6th International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2018. http://dx.doi.org/10.1109/icmcs.2018.8525884.

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Reports on the topic "Multilevel analysis"

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Zeigler-Johnson, Charnita. Multilevel Analysis of Neighborhood Characteristics and Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574693.

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Zeigler-Johnson, Charnita. Multilevel Analysis of Neighborhood Characteristics and Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada554191.

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de Saussure, G., N. M. Larson, J. A. Harvey, and N. W. Hill. Multilevel resonance analysis of sup 59 Co neutron transmission measurements. Office of Scientific and Technical Information (OSTI), May 1991. http://dx.doi.org/10.2172/5697452.

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Manteuffel, T. Development and Analysis of Optimal Multilevel Solvers on Advanced Computers. Office of Scientific and Technical Information (OSTI), November 2022. http://dx.doi.org/10.2172/1898297.

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Stratton, R. C., and D. B. Jarrell. Towards the development of multilevel-multiagent diagnostic aids. [Root-cause analysis]. Office of Scientific and Technical Information (OSTI), September 1991. http://dx.doi.org/10.2172/6128963.

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Adams, Jennifer, and Emily C. Hannum. Under Attack: A Multilevel Analysis of Peer Victimization in Rural Chinese Middle Schools. Unknown, 2013. http://dx.doi.org/10.35648/20.500.12413/11781/ii138.

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Prabaharan, Natarajan, Vijayakumar Arun, Thangam Chinnadurai, Kalyanasundaram Arulkumar, Amalorpavaraj Rini Ann Jerin, and Kaliannan Palanisamy. Analysis of Symmetric Multilevel Inverter Using Unipolar Pulse Width Modulation for Photovoltaic Application. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2018. http://dx.doi.org/10.7546/crabs.2018.02.13.

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Prabaharan, Natarajan, Vijayakumar Arun, Thangam Chinnadurai, Kalyanasundaram Arulkumar, Amalorpavaraj Rini Ann Jerin, and Kaliannan Palanisamy. Analysis of Symmetric Multilevel Inverter Using Unipolar Pulse Width Modulation for Photovoltaic Application. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2018. http://dx.doi.org/10.7546/grabs2018.2.13.

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Kresta, David. Can Churches Change a Neighborhood? A Census Tract, Multilevel Analysis of Churches and Neighborhood Change. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6862.

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Norman, Oliver M., George J. Stukenborg, and Kristen M. Wells. African Americans and Prostate Cancer: A Spatial and Multilevel Analysis of Post-treatment Care and Outcomes. Fort Belvoir, VA: Defense Technical Information Center, August 2008. http://dx.doi.org/10.21236/ada502564.

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