多元數據分析

多元數據分析

《多元數據分析》是2011年6月1日機械工業出版社出版的圖書,作者是海爾。

基本信息

作者介紹

海爾(Joseph F Hair,Jr.),於1971獲得佛羅里達大學市場行銷博士學位.現為肯尼索州立大學市場行銷系教授。他出版了四十多本書,包括《Marketing》、《Marketing Essentials》等。他是美國市場行銷協會、市場行銷科學學會、西南市場行銷協會和南方市場行銷學會委員。2004年他被美國市場行銷科學學會授予傑出教育獎,2007年被市場管理協會授予創新性市場行銷人才。

多元數據分析 多元數據分析

William C.Black,於1980年獲得德州大學奧斯汀分校博士學位,現為路易斯安那州立大學工商管理學院市場行銷系教授。他的研究興趣包括多元統計、套用信息技術,以及與電子商務相關的市場原理的進展。他是《Journal of BusinessResearch》編審委員會成員。

Barry J.Babin於1991年獲得路易斯安那州立大學工商管理學博士學位,現為路易斯安那理工大學市場行銷與定量分析學教授、商學院Max P.Watson教授。他主要研究零售的各個方面和服務管理。他還曾被美國市場行銷科學研究院和市場行銷學會評為傑出研究員。

Rolph E.Anderson,擁有佛羅里達大學博士學位,現為Drexei大學工商管理學院R0yal H.Gibson Sr教授。他曾兩次獲得Drexel大學優秀教師獎,並獲得過《Journal of Personal Selling&Sales Management》傑出評論獎、Drexel大學商學院科研成就獎等。

內容介紹

這是一本面向套用的經典多元數據分析教材,自1979年出版第1版至今,深受讀者好評。《多元數據分析(英文版)(第7版)》循序漸進地介紹了各種多元統計分析方法,並通過豐富的實例演示了這些方法的套用。書中不僅涵蓋多元數據分析的基本方法,而且還介紹了一些新方法,如結構方程建模和偏最小二乘法等。

圖書目錄

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

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