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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

An evaluation of parameter estimation when using multilevel structural equation modeling for mediation analysis

Li, Xin 20 June 2011 (has links)
Handling of clustered or nested data structures requires the use of multilevel modeling techniques. One such multilevel modeling technique is multilevel structural equation modeling (MLSEM). While estimation of indirect effect parameters and standard errors based on the conventional multilevel model (MMM) has been assessed, this is not the case for the use of the MLSEM model for estimating indirect effects. This simulation study was designed to investigate the use of the MLSEM for estimating mediated effects for the “upper-level” mediation model as compared with the MMM. The following conditions were manipulated: number of clusters (G), within-cluster sample size (nj ), intra-class correlation, measurement error in the mediator, and the true value of the mediated effect derived from various patterns of true values for a and b. The generating model entailed an upper-level mediation model for a cluster-randomized trial that included a dichotomous level two independent variable, a cluster-level latent mediator and an individual-level latent dependent variable both with four indicators. Relative parameter and standard error bias, obtained using the MLSEM and the MMM were evaluated and compared. Percent coverage was calculated and compared when PRODCLIN was used to calculate the confidence interval estimates of the ab effect. Finally, Type I error rates for conditions when ab = 0 were assessed and compared. In addition, statistical power for detecting a truly non-zero mediated effect was tallied and compared across models. Results showed that use of the MMM provided inaccurate and misleading parameter and standard error estimates for the estimates of the mediated effect, especially when the true values of a, b and ab were not zero and the measurement error for M was large. However, the MLSEM estimates were also unacceptable in some of the conditions with small values for G and nj. Researchers are encouraged to use the MLSEM for assessing the multilevel mediated effects when either or both paths a and b are expected to be non-zero, if G is at least 40 and nj is also greater than 40. Results are presented and discussed along with implications for applied researchers intending to assess mediated effect with clustered data. / text
2

An exploratory study of network marketing as socially embedded exchange.

January 2001 (has links)
Ho Hillbun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 94-100). / Abstracts in English and Chinese. / Chapter 1. --- INTRODUCTION --- p.4 / "Direct Selling, Network Marketing, and Pyramid Scheme" / Chapter 2. --- THE NATURE OF NETWORK MARKETING --- p.12 / Distributor as the End User / Core Product Marketed / Commericalization of Social Relations / Ambivalence in Nature of Exchange / Conflict of Interests / Chapter 3. --- THEORETICAL AND CONCEPTUAL DEVELOPMENT --- p.20 / Exchange Concept / Research on Socially Embedded Exchange / Network Marketing as Socially Embedded Exchange / Sense of Justice / Relational Exchange Norms / Outcome Favorability / Trust and Trustworthiness / Social Value of Exchange / Chapter 4. --- RESEARCH METHODOLOGY --- p.53 / Exploratory Research / Consumer Survey / Scenario Experiment / Research Design / Manipulation / Covariates / Measurement / Manipulation Checks / Sample and Data Collection / Chapter 5. --- RESEARCH FINDINGS --- p.66 / Statistical Analysis / Manipulation Checks / Measurement / Descriptive Statistics / MANCOVA Assumptions / MANCOVA Results / Parameter Estimates / Chapter 6. --- DISCUSSION --- p.79 / Limitations and Future Research / APPENDIX I --- p.88 / APPENDIX II --- p.89 / APPENDIX III --- p.93 / REFERENCE --- p.94
3

Developing a high performing social system within a network marketing business group

Blasko, Christopher D. January 2004 (has links) (PDF)
Thesis, PlanB (M.S.)--University of Wisconsin--Stout, 2004. / Includes bibliographical references.
4

A multilevel search algorithm for feature selection in biomedical data

Oduntan, Idowu Olayinka 10 April 2006 (has links)
The automated analysis of patients’ biomedical data can be used to derive diagnostic and prognostic inferences about the observed patients. Many noninvasive techniques for acquiring biomedical samples generate data that are characterized by a large number of distinct attributes (i.e. features) and a small number of observed patients (i.e. samples). Deriving reliable inferences, such as classifying a given patient as either cancerous or non-cancerous, using these biomedical data requires that the ratio r of the number of samples to the number of features be within the range 5 < r < 10. To satisfy this requirement, the original set of features in the biomedical datasets can be reduced to an ‘optimal’ subset of features that most discriminates the observed patients. Feature selection techniques strategically seek the ‘optimal’ subset. In this thesis, I present a new feature selection technique - multilevel feature selection. The technique seeks the ‘optimal’ feature subset in biomedical datasets using a multilevel search algorithm. This algorithm combines a hierarchical search framework with a search method. The framework, which provides the capability to easily adapt the technique to different forms of biomedical datasets, consists of increasingly coarse forms of the original feature set that are strategically and progressively explored by the search method. Tabu search (a search meta-heuristics) is the search method used in the multilevel feature selection technique. I evaluate the performance of the new technique, in terms of the solution quality, using experiments that compare the classification inferences derived from the result of the technique with those derived from the result of other feature selection techniques such as the basic tabu-search-based feature selection, sequential forward selection, and random feature selection. In the experiments, the same biomedical dataset is used and equivalent amount of computational resource is allocated to the evaluated techniques to provide a common basis for comparison. The empirical results show that the multilevel feature selection technique finds ‘optimal’ subsets that enable more accurate and stable classification than those selected using the other feature selection techniques. Also, a similar comparison of the new technique with a genetic algorithm feature selection technique that selects highly discriminatory regions of consecutive features shows that the multilevel technique finds subsets that enable more stable classification. / February 2006
5

A multilevel search algorithm for feature selection in biomedical data

Oduntan, Idowu Olayinka 10 April 2006 (has links)
The automated analysis of patients’ biomedical data can be used to derive diagnostic and prognostic inferences about the observed patients. Many noninvasive techniques for acquiring biomedical samples generate data that are characterized by a large number of distinct attributes (i.e. features) and a small number of observed patients (i.e. samples). Deriving reliable inferences, such as classifying a given patient as either cancerous or non-cancerous, using these biomedical data requires that the ratio r of the number of samples to the number of features be within the range 5 < r < 10. To satisfy this requirement, the original set of features in the biomedical datasets can be reduced to an ‘optimal’ subset of features that most discriminates the observed patients. Feature selection techniques strategically seek the ‘optimal’ subset. In this thesis, I present a new feature selection technique - multilevel feature selection. The technique seeks the ‘optimal’ feature subset in biomedical datasets using a multilevel search algorithm. This algorithm combines a hierarchical search framework with a search method. The framework, which provides the capability to easily adapt the technique to different forms of biomedical datasets, consists of increasingly coarse forms of the original feature set that are strategically and progressively explored by the search method. Tabu search (a search meta-heuristics) is the search method used in the multilevel feature selection technique. I evaluate the performance of the new technique, in terms of the solution quality, using experiments that compare the classification inferences derived from the result of the technique with those derived from the result of other feature selection techniques such as the basic tabu-search-based feature selection, sequential forward selection, and random feature selection. In the experiments, the same biomedical dataset is used and equivalent amount of computational resource is allocated to the evaluated techniques to provide a common basis for comparison. The empirical results show that the multilevel feature selection technique finds ‘optimal’ subsets that enable more accurate and stable classification than those selected using the other feature selection techniques. Also, a similar comparison of the new technique with a genetic algorithm feature selection technique that selects highly discriminatory regions of consecutive features shows that the multilevel technique finds subsets that enable more stable classification.
6

A multilevel search algorithm for feature selection in biomedical data

Oduntan, Idowu Olayinka 10 April 2006 (has links)
The automated analysis of patients’ biomedical data can be used to derive diagnostic and prognostic inferences about the observed patients. Many noninvasive techniques for acquiring biomedical samples generate data that are characterized by a large number of distinct attributes (i.e. features) and a small number of observed patients (i.e. samples). Deriving reliable inferences, such as classifying a given patient as either cancerous or non-cancerous, using these biomedical data requires that the ratio r of the number of samples to the number of features be within the range 5 < r < 10. To satisfy this requirement, the original set of features in the biomedical datasets can be reduced to an ‘optimal’ subset of features that most discriminates the observed patients. Feature selection techniques strategically seek the ‘optimal’ subset. In this thesis, I present a new feature selection technique - multilevel feature selection. The technique seeks the ‘optimal’ feature subset in biomedical datasets using a multilevel search algorithm. This algorithm combines a hierarchical search framework with a search method. The framework, which provides the capability to easily adapt the technique to different forms of biomedical datasets, consists of increasingly coarse forms of the original feature set that are strategically and progressively explored by the search method. Tabu search (a search meta-heuristics) is the search method used in the multilevel feature selection technique. I evaluate the performance of the new technique, in terms of the solution quality, using experiments that compare the classification inferences derived from the result of the technique with those derived from the result of other feature selection techniques such as the basic tabu-search-based feature selection, sequential forward selection, and random feature selection. In the experiments, the same biomedical dataset is used and equivalent amount of computational resource is allocated to the evaluated techniques to provide a common basis for comparison. The empirical results show that the multilevel feature selection technique finds ‘optimal’ subsets that enable more accurate and stable classification than those selected using the other feature selection techniques. Also, a similar comparison of the new technique with a genetic algorithm feature selection technique that selects highly discriminatory regions of consecutive features shows that the multilevel technique finds subsets that enable more stable classification.
7

Business network: network marketing : analysis of network marketing using business network theories

鄧沛權, Tang, Pui-kuen. January 1997 (has links)
published_or_final_version / Business Administration / Master / Master of Business Administration
8

Use Multilevel Graph Partitioning Scheme to solve traveling salesman problem

KHAN, Muhammad Umair January 2010 (has links)
The traveling salesman problem is although looking very simple problem but it is an important combinatorial problem. In this thesis I have tried to find the shortest distance tour in which each city is visited exactly one time and return to the starting city. I have tried to solve traveling salesman problem using multilevel graph partitioning approach.Although traveling salesman problem itself very difficult as this problem is belong to the NP-Complete problems but I have tried my best to solve this problem using multilevel graph partitioning it also belong to the NP-Complete problems. I have solved this thesis by using the k-mean partitioning algorithm which divides the problem into multiple partitions and solving each partition separately and its solution is used to improve the overall tour by applying Lin Kernighan algorithm on it. Through all this I got optimal solution which proofs that solving traveling salesman problem through graph partition scheme is good for this NP-Problem and through this we can solved this intractable problem within few minutes.Keywords: Graph Partitioning Scheme, Traveling Salesman Problem.
9

A Monte Carlo investigation of robustness to nonnormal incomplete data of multilevel modeling

Zhang, Duan 30 October 2006 (has links)
Due to its increasing popularity, hierarchical linear modeling (HLM) has been used along with structural equation modeling (SEM) to analyze data with nested structure. In spite of the extensive research on commonly encountered problems such as violation of normality and missing data treatment within the framework of SEM, these areas have been much less explored in HLM. The present study compared HLM and multilevel SEM through a Monte Carlo study from the perspectives of the influence of nonnormality and performance of multiple imputation based on the expectationmaximization (EM) algorithm under various combinations of sample sizes at two levels. The statistical power, parameter estimates, standard errors, and estimation bias for the main effects and cross-level interaction in a two- level model were compared across the four design factors: analysis method, normality condition, missing data proportion, and sample size. HLM and multilevel SEM appeared to have similar power detecting the main effect, while HLM had better power for the cross- level interaction. Neither seemed to be sensitive to violation of the normality assumption. A higher proportion of missing data resulted in larger standard errors and estimation bias. Sample sizes at both the individual and cluster levels played a role in the statistical power for parameter estimates. The two-way interactions for the four factors were generally nonzero. Overall, both HLM and multilevel SEM were quite robust to violation of normality. SEM appears more useful in more complex path models while HLM is superior in detecting main effects. Multiple imputation based on the EM algorithm performed well in producing stable parameter estimates for up to 30% missing data. Sample size design should take into account the level at which the research is most focused.
10

Sufficient sample sizes for the multivariate multilevel regression model

Chang, Wanchen 08 September 2015 (has links)
The three-level multivariate multilevel model (MVMM) is a multivariate extension of the conventional univariate two-level hierarchical linear model (HLM) and is used for estimating and testing the effects of explanatory variables on a set of correlated continuous outcome measures. Two simulation studies were conducted to investigate the sample size requirements for restricted maximum likelihood (REML) estimation of three-level MVMMs, the effects of sample sizes and other design characteristics on estimation, and the performance of the MVMMs compared to corresponding two-level HLMs. The model for the first study was a random-intercept MVMM, and the model for the second study was a fully-conditional MVMM. Study conditions included number of clusters, cluster size, intraclass correlation coefficient, number of outcomes, and correlations between pairs of outcomes. The accuracy and precision of estimates were assessed with parameter bias, relative parameter bias, relative standard error bias, and 95% confidence interval coverage. Empirical power and type I error rates were also calculated. Implications of the results for applied researchers and suggestions for future methodological studies are discussed. / text

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