Hasil untuk "Analysis"

Anastasiya Zavyalova

Background & Aim: Content analysis was used first in communication sciences. Today, it is frequently used in media analysis. In other sciences such as nursing, researchers apply this method in their studies. Material & Method: In spite of the importance of this method in nursing research, there was not enough Persian material on the subject. Therefore, this review study was conducted to clarify and describe definitions, classifications, principles and conceptual bases of content analysis. Persian and Enghlish foreign articles and books were used in this review study. Results: Most scholars believe that content analysis is a research tool used to determine the presence of certain words or concepts within texts or sets of texts. Some categorize it as a data analysis technique. Texts can be defined broadly as books, book chapters, essays, interviews, discussions, newspaper headlines and articles, and historical documents. Using content analysis, researchers analyze the presence of meanings and relationships of such words and concepts, then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of which these are a part. Conclusion: Content analysis can be used in both quantitative and qualitative researches.

John Geweke, Gianni Amisano

Introduction to Mixed ModellingExperimental Design and the Analysis of VariancePrimer of Applied Regression & Analysis of VarianceAnalysis of Variance and CovarianceExperiments in EcologyLearning Statistics with RAnalysis of Variance Via Confidence IntervalsMultivariate Analysis of Variance (MANOVA)Statistical Methods for GeographyMultivariate Analysis of Variance and Repeated MeasuresThe Analysis of VarianceApplied Analysis of Variance in Behavioral ScienceLevine's Guide to SPSS for Analysis of VarianceSequential AnalysisAnalysis of Variance, Design, and RegressionStatistics for Health Care ProfessionalsThe Analysis of VarianceThe SAGE Dictionary of Social Research MethodsAnalysis of Variance for Functional DataTwo-Way Analysis of VarianceAnalysis of Variance DesignsAnalysis of Variance for Random ModelsLinear ModelsOnline Statistics EducationAnalysis of VarianceA Student's Guide to Analysis of VarianceApplied Statistics in Agricultural, Biological, and Environmental SciencesData Analysis Using SASEncyclopedia of Survey Research MethodsThe Analysis of VarianceEncyclopedia of Research DesignAnalysis of Variance for Sensory DataStatistics Using Technology, Second EditionAdvanced Analysis of VarianceAnalysis of VarianceA Practical Approach to Using Statistics in Health ResearchThe SAGE Encyclopedia of Communication Research MethodsAnalysis of Variance, Design, and RegressionStatistical Analysis Quick Reference GuidebookIntroduction to Analysis of Variance

J. Cappelleri, Robert A. Gerber

PCA and SVD are considered simple forms of exploratory factor analysis. The term ‘factor analysis’ is a bit confusing and you will find a variety of definitions out there–some people assert that PCA is not factor analysis, and others might use PCA but call it factor analysis. PCA involves a complete redescription of the covariance or correlation matrix along a set of independent dimensions, until it completely redescribes the data, and then you pick off the most important components and ignore the rest. EFA is typically attempting to do the same thing, but trying to maximize the variance accounted for by a fixed number of ‘latent’ factors, so that we have the model + error. Furthermore, most of the time factor analysis lets us pick orthogonal dimensions based on criteria other than maximum variance, and permits factors that are not orthogonal, allowing for latent factors that are somewhat related.

L. Furst

L. Snyder, Tiffany Barnes, Daniel D. Garcia et al.

L. DeCamp, Julie S Byerley, Nipa Doshi et al.

A. Gionis

The first part of this book is devoted to methods seeking relevant dimensions of data. The variables thus obtained provide a synthetic description which often results in a graphical representation of the data. After a general presentation of the discriminating analysis, the second part is devoted to clustering methods which constitute another method, often complementary to the methods described in the first part, to synthesize and to analyze the data. The book concludes by examining the links existing between data mining and data analysis.

L. Smircich

G. Box, G. Tiao

D. Kleinbaum

Yochi Cohen-Charash, Paul E. Spector

R. Tesch

S. Rice

A. Demers, S. Keshav, S. Shenker

We discuss gateway queueing algorithms and their role in controlling congestion in datagram networks. A fair queueing algorithm, based on an earlier suggestion by Nagle, is proposed. Analysis and simulations are used to compare this algorithm to other congestion control schemes. We find that fair queueing provides several important advantages over the usual first-come-first-serve queueing algorithm: fair allocation of bandwidth, lower delay for sources using less than their full share of bandwidth, and protection from ill-behaved sources.

Tadao Murata

L. Cavalli-Sforza, A. Edwards

T. J. Breen, J. H. Zar

R. Melchers

G. McLachlan