| 词条 | 多元数据分析 |
| 释义 | 图书信息书 名: 多元数据分析 作 者：海尔 出版社： 机械工业出版社 出版时间： 2011年6月1日 ISBN: 9787111341987 开本： 16开 定价: 109.00元 内容简介这是一本面向应用的经典多元数据分析教材，自1979年出版第1版至今，深受读者好评。《多元数据分析(英文版)(第7版)》循序渐进地介绍了各种多元统计分析方法，并通过丰富的实例演示了这些方法的应用。书中不仅涵盖多元数据分析的基本方法，而且还介绍了一些新方法，如结构方程建模和偏最小二乘法等。 作者简介海尔（Joseph F Hair，Jr.），于1971获得佛罗里达大学市场营销博士学位.现为肯尼索州立大学市场营销系教授。他出版了四十多本书，包括《Marketing》、《Marketing Essentials》等。他是美国市场营销协会、市场营销科学学会、西南市场营销协会和南方市场营销学会委员。2004年他被美国市场营销科学学会授予杰出教育奖，2007年被市场管理协会授予创新性市场营销人才。 William C.Black，于1980年获得德州大学奥斯汀分校博士学位，现为路易斯安那州立大学工商管理学院市场营销系教授。他的研究兴趣包括多元统计、应用信息技术，以及与电子商务相关的市场原理的进展。他是《Journal of BusinessResearch》编审委员会成员。 Barry J.Babin于1991年获得路易斯安那州立大学工商管理学博士学位，现为路易斯安那理工大学市场营销与定量分析学教授、商学院Max P.Watson教授。他主要研究零售的各个方面和服务管理。他还曾被美国市场营销科学研究院和市场营销学会评为杰出研究员。 Rolph E.Anderson，拥有佛罗里达大学博士学位，现为Drexei大学工商管理学院R0yal H.Gibson Sr教授。他曾两次获得Drexel大学优秀教师奖，并获得过《Journal of Personal Selling&Sales Management》杰出评论奖、Drexel大学商学院科研成就奖等。 图书目录preface iii about the authors v chapter 1 introduction: methods and model building what is multivariate analysis? multivariate analysis in statistical terms some basic concepts of multivariate analysisthe variate measurement scales measurement error and multivariate measurement statistical significance versus statistical power types of statistical error and statistical power impacts on statistical power using power with multivariate techniques a classification of multivariate techniques dependence techniques interdependence techniques types of multivariate techniques principal components and common factor analysis multiple regression multiple discriminant analysis and logistic regression canonical correlation multivariate analysis of variance and covariance conjoint analysis cluster analysis perceptual mapping correspondence analysis structural equation modeling and confirmatory factor analysis guidelines for multivariate analyses and interpretation establish practical significance as well as statistical significance recognize that sample size affects all results know your data strive for model parsimony look at your errors validate your results a structured approach to multivariate model building stage 1: define the research problem, objectives, and multivariate technique to be used stage 2: develop the analysis plan stage 3: evaluate the assumptions underlying the multivariate technique stage 4: estimate the multivariate model and assess overall model fit stage 5: interpret the variate(s) stage 6: validate the multivariate model a decision flowchart databases primary database other databases organization of the remaining chapters section i: understanding and preparing for multivariate analysis section ii: analysis using dependence techniques section iii: interdependence techniques section iv: structural equations modeling summary 28 . questions 30 . suggested readings references section i understanding and preparing for multivariate analysis chapter 2 cleaning and transforming data introduction graphical examination of the data univariate profiling: examining the shape of the distribution bivariate profiling: examining the relationship between variables bivariate profiling: examining group differences multivariate profiles missing data the impact of missing data a simple example of a missing data analysis a four-step process for identifying missing data and applying remedies an illustration of missing data diagnosis with the four-step process outliers detecting and handling outliers an illustrative example of analyzing outliers testing the assumptions of multivariate analysis assessing individual variables versus the variate four important statistical assumptions data transformations an illustration of testing the assumptions underlying multivariate analysis incorporating nonmetric data with dummy variables summary 88 . questions 89 . suggested readings references chapter 3 factor analysis what is factor analysis? a hypothetical example of factor analysis factor analysis decision process stage 1: objectives of factor analysis specifying the unit of analysis achieving data summarization versus data reduction variable selection using factor analysis with other multivariate techniques stage 2: designing a factor analysis correlations among variables or respondents variable selection and measurement issues sample size summary stage 3: assumptions in factor analysis conceptual issues statistical issues summary stage 4: deriving factors and assessing overall fit selecting the factor extraction method criteria for the number of factors to extract stage 5: interpreting the factors the three processes of factor interpretation rotation of factors judging the significance of factor loadings interpreting a factor matrix stage 6: validation of factor analysis use of a confirmatory perspective assessing factor structure stability detecting influential observations stage 7: additional uses of factor analysis results selecting surrogate variables for subsequent analysis creating summated scales computing factor scores selecting among the three methods an illustrative example stage 1: objectives of factor analysis stage 2: designing a factor analysis stage 3: assumptions in factor analysis component factor analysis: stages 4 through 7 common factor analysis: stages 4 and 5 a managerial overview of the results summary 148 . questions 150 . suggested readings references section ii analysis using dependence techniques chapter 4 simple and multiple regression what is multiple regression analysis? an example of simple and multiple regression prediction using a single independent variable: simple regression prediction using several independent variables: multiple regression summary a decision process for multiple regression analysis stage 1: objectives of multiple regression research problems appropriate for multiple regression specifying a statistical relationship selection of dependent and independent variables stage 2: research design of a multiple regression analysis sample size creating additional variables stage 3: assumptions in multiple regression analysis assessing individual variables versus the variate methods of diagnosis linearity of the phenomenon constant variance of the error term independence of the error terms normality of the error term distribution summary stage 4: estimating the regression model and assessing overall model fit selecting an estimation technique testing the regression variate for meeting the regression assumptions examining the statistical significance of our model identifying influential observations stage 5: interpreting the regression variate using the regression coefficients assessing multicollinearity stage 6: validation of the results additional or split samples calculating the press statistic comparing regression models forecasting with the model illustration of a regression analysis stage 1: objectives of multiple regression stage 2: research design of a multiple regression analysis stage 3: assumptions in multiple regression analysis stage 4: estimating the regression model and assessing overall model fit stage 5: interpreting the regression variate stage 6: validating the results evaluating alternative regression models a managerial overview of the results summary 231 . questions 234 . suggested readings references chapter 5 canonical correlation what is canonical correlation? hypothetical example of canonical correlation developing a variate of dependent variables estimating the first canonical function estimating a second canonical function relationships of canonical correlation analysis to other multivariate techniques stage 1: objectives of canonical correlation analysis selection of variable sets evaluating research objectives stage 2: designing a canonical correlation analysis sample size variables and their conceptual linkage missing data and outliers stage 3: assumptions in canonical correlation linearity normality homoscedasticity and multicollinearity stage 4: deriving the canonical functions and assessing overall fit deriving canonical functions which canonical functions should be interpreted? stage 5: interpreting the canonical variate canonical weights canonical loadings canonical cross-loadings which interpretation approach to use stage 6: validation and diagnosis an illustrative example stage 1: objectives of canonical correlation analysis stages 2 and 3: designing a canonical correlation analysis and testing the assumptions stage 4: deriving the canonical functions and assessing overall fit stage 5: interpreting the canonical variates stage 6: validation and diagnosis a managerial overview of the results summary 258 . questions 259 . references chapter 6 conjoint analysis what is conjoint analysis? hypothetical example of conjoint analysis specifying utility, factors, levels, and profiles gathering preferences from respondents estimating part-worths determining attribute importance assessing predictive accuracy the managerial uses of conjoint analysis comparing conjoint analysis with other multivariate methods compositional versus decompositional techniques specifying the conjoint variate separate models for each individual flexibility in types of relationships designing a conjoint analysis experiment stage 1: the objectives of conjoint analysis defining the total utility of the object specifying the determinant factors stage 2: the design of a conjoint analysis selecting a conjoint analysis methodology designing profiles: selecting and defining factors and levels specifying the basic model form data collection stage 3: assumptions of conjoint analysis stage 4: estimating the conjoint model and assessing overall fit selecting an estimation technique estimated part-worths evaluating model goodness-of-fit stage 5: interpreting the results examining the estimated part-worths assessing the relative importance of attributes stage 6: validation of the conjoint results managerial applications of conjoint analysis segmentation profitability analysis conjoint simulators alternative conjoint methodologies adaptive/self-explicated conjoint: conjoint with a large number of factors choice-based conjoint: adding another touch of realism overview of the three conjoint methodologies an illustration of conjoint analysis stage 1: objectives of the conjoint analysis stage 2: design of the conjoint analysis stage 3: assumptions in conjoint analysis stage 4: estimating the conjoint model and assessing overall model fit stage 5: interpreting the results stage 6: validation of the results a managerial application: use of a choice simulator summary 327 . questions 330 . suggested readings references chapter 7 multiple discriminant analysis and logistic regression what are discriminant analysis and logistic regression? discriminant analysis logistic regression analogy with regression and manova hypothetical example of discriminant analysis a two-group discriminant analysis: purchasers versus nonpurchasers a geometric representation of the two-group discriminant function a three-group example of discriminant analysis: switching intentions the decision process for discriminant analysis stage 1: objectives of discriminant analysis stage 2: research design for discriminant analysis selecting dependent and independent variables sample size division of the sample stage 3: assumptions of discriminant analysis impacts on estimation and classification impacts on interpretation stage 4: estimation of the discriminant model and assessing overall fit selecting an estimation method statistical significance assessing overall model fit casewise diagnostics stage 5: interpretation of the results discriminant weights discriminant loadings partial f values interpretation of two or more functions which interpretive method to use? stage 6: validation of the results validation procedures profiling group differences a two-group illustrative example stage 1: objectives of discriminant analysis stage 2: research design for discriminant analysis stage 3: assumptions of discriminant analysis stage 4: estimation of the discriminant model and assessing overall fit stage 5: interpretation of the results stage 6: validation of the results a managerial overview a three-group illustrative example stage 1: objectives of discriminant analysis stage 2: research design for discriminant analysis stage 3: assumptions of discriminant analysis stage 4: estimation of the discriminant model and assessing overall fit stage 5: interpretation of three-group discriminant analysis results stage 6: validation of the discriminant results a managerial overview logistic regression: regression with a binary dependent variable representation of the binary dependent variable sample size estimating the logistic regression model assessing the goodness-of-fit of the estimation model testing for significance of the coefficients interpreting the coefficients calculating probabilities for a specific value of the independent variable overview of interpreting coefficients summary an illustrative example of logistic regression stages 1, 2, and 3: research objectives, research design, and statistical assumptions stage 4: estimation of the logistic regression model and assessing overall fit stage 5: interpretation of the results stage 6: validation of the results a managerial overview summary 434 . questions 437 . suggested readings references chapter 8 anova and manova manova: extending univariate methods for assessing group differences multivariate procedures for assessing group differences a hypothetical illustration of manova analysis design differences from discriminant analysis forming the variate and assessing differences a decision process for manova stage 1: objectives of manova when should we use manova? types of multivariate questions suitable for manova selecting the dependent measures stage 2: issues in the research design of manova sample size requirements-overall and by group factorial designs-two or more treatments using covariates-ancova and mancova manova counterparts of other anova designs a special case of manova: repeated measures stage 3: assumptions of anova and manova independence equality of variance-covariance matrices normality linearity and multicollinearity among the dependent variables sensitivity to outliers stage 4: estimation of the manova model and assessing overall fit estimation with the general linear model criteria for significance testing statistical power of the multivariate tests stage 5: interpretation of the manova results evaluating covariates assessing effects on the dependent variate identifying differences between individual groups assessing significance for individual dependent variables stage 6: validation of the results summary illustration of a manova analysis example 1: difference between two independent groups stage 1: objectives of the analysis stage 2: research design of the manova stage 3: assumptions in manova stage 4: estimation of the manova model and assessing the overall fit stage 5: interpretation of the results example 2: difference between k independent groups stage 1: objectives of the manova stage 2: research design of manova stage 3: assumptions in manova stage 4: estimation of the manova model and assessing overall fit stage 5: interpretation of the results example 3: a factorial design for manova with two independent variables stage 1: objectives of the manova stage 2: research design of the manova stage 3: assumptions in manova stage 4: estimation of the manova model and assessing overall fit stage 5: interpretation of the results summary a managerial overview of the results summary 498 . questions 500 . suggested readings references section iii analysis using interdependence techniques chapter 9 grouping data with cluster analysis what is cluster analysis? cluster analysis as a multivariate technique conceptual development with cluster analysis necessity of conceptual support in cluster analysis how does cluster analysis work? a simple example objective versus subjective considerations cluster analysis decision process stage 1: objectives of cluster analysis stage 2: research design in cluster analysis stage 3: assumptions in cluster analysis stage 4: deriving clusters and assessing overall fit stage 5: interpretation of the clusters stage 6: validation and profiling of the clusters an illustrative example stage 1: objectives of the cluster analysis stage 2: research design of the cluster analysis stage 3: assumptions in cluster analysis employing hierarchical and nonhierarchical methods step 1: hierarchical cluster analysis (stage 4) step 2: nonhierarchical cluster analysis (stages 4, 5, and 6) summary 561 . questions 563 . suggested readings references chapter 10 mds and correspondence analysis what is multidimensional scaling? comparing objects dimensions: the basis for comparison a simplified look at how mds works gathering similarity judgments creating a perceptual map interpreting the axes comparing mds to other interdependence techniques individual as the unit of analysis lack of a variate a decision framework for perceptual mapping stage 1: objectives of mds key decisions in setting objectives stage 2: research design of mds selection of either a decompositional (attribute-free) or compositional (attribute-based) approach objects: their number and selection nonmetric versus metric methods collection of similarity or preference data stage 3: assumptions of mds analysis stage 4: deriving the mds solution and assessing overall fit determining an object's position in the perceptual map selecting the dimensionality of the perceptual map incorporating preferences into mds stage 5: interpreting the mds results identifying the dimensions stage 6: validating the mds results issues in validation approaches to validation overview of multidimensional scaling correspondence analysis distinguishing characteristics differences from other multivariate techniques a simple example of ca a decision framework for correspondence analysis stage 1: objectives of ca stage 2: research design of ca stage 3: assumptions in ca stage 4: deriving ca results and assessing overall fit stage 5: interpretation of the results stage 6: validation of the results overview of correspondence analysis illustrations of mds and correspondence analysis stage 1: objectives of perceptual mapping identifying objects for inclusion basing the analysis on similarity or preference data using a disaggregate or aggregate analysis stage 2: research design of the perceptual mapping study selecting decompositional or compositional methods selecting firms for analysis nonmetric versus metric methods collecting data for mds collecting data for correspondence analysis stage 3: assumptions in perceptual mapping multidimensional scaling: stages 4 and 5 stage 4: deriving mds results and assessing overall fit stage 5: interpretation of the results overview of the decompositional results correspondence analysis: stages 4 and 5 stage 4: estimating a correspondence analysis stage 5: interpreting ca results overview of ca stage 6: validation of the results a managerial overview of mds results summary 623 . questions 625 . suggested readings references section iv structural equations modeling chapter 11 sem: an introduction what is structural equation modeling? estimation of multiple interrelated dependence relationships incorporating latent variables not measured directly defining a model sem and other multivariate techniques similarity to dependence techniques similarity to interdependence techniques the emergence of sem the role of theory in structural equation modeling specifying relationships establishing causation developing a modeling strategy a simple example of sem the research question setting up the structural equation model for path analysis the basics of sem estimation and assessment six stages in structural equation modeling stage 1: defining individual constructs operationalizing the construct pretesting stage 2: developing and specifying the measurement model sem notation creating the measurement model stage 3: designing a study to produce empirical results issues in research design issues in model estimation stage 4: assessing measurement model validity the basics of goodness-of-fit absolute fit indices incremental fit indices parsimony fit indices problems associated with using fit indices unacceptable model specification to achieve fit guidelines for establishing acceptable and unacceptable fit stage 5: specifying the structural model stage 6: assessing the structural model validity structural model gof competitive fit comparison to the measurement model testing structural relationships summary 678 . questions 680 . suggested readings appendix 11a: estimating relationships using path analysis appendix 11b: sem abbreviations appendix 11c: detail on selected gof indices references chapter 12 applications of sem part 1: confirmatory factor analysis cfa and exploratory factor analysis a simple example of cfa and sem a visual diagram sem stages for testing measurement theory validation with cfa stage 1: defining individual constructs stage 2: developing the overall measurement model unidimensionality congeneric measurement model items per construct reflective versus formative constructs stage 3: designing a study to produce empirical results measurement scales in cfa sem and sampling specifying the model issues in identification avoiding identification problems problems in estimation stage 4: assessing measurement model validity assessing fit path estimates construct validity model diagnostics summary example cfa illustration stage 1: defining individual constructs stage 2: developing the overall measurement model stage 3: designing a study to produce empirical results stage 4: assessing measurement model validity hbat cfa summary part 2: what is a structural model? a simple example of a structural model an overview of theory testing with sem stages in testing structural theory one-step versus two-step approaches stage 5: specifying the structural model unit of analysis model specification using a path diagram designing the study stage 6: assessing the structural model validity understanding structural model fit from cfa fit examine the model diagnostics sem illustration stage 5: specifying the structural model stage 6: assessing the structural model validity part 3: extensions and applications of sem reflective versus formative measures reflective versus formative measurement theory operationalizing a formative construct distinguishing reflective from formative constructs which to use-reflective or formative? higher-order factor analysis empirical concerns theoretical concerns using second-order measurement theories when to use higher-order factor analysis multiple groups analysis measurement model comparisons structural model comparisons measurement bias model specification model interpretation relationship types: mediation and moderation mediation moderation longitudinal data additional covariance sources: timing using error covariances to represent added covariance partial least squares characteristics of pls advantages and disadvantages of pls choosing pls versus sem summary 778 . questions 781 . suggested readings references index |
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