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Gatignon H. — Statistical analysis of management data
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Название: Statistical analysis of management data
Автор: Gatignon H.
Аннотация: This book covers multivariate statistical analyses that are important for researchers in all fields of management whether finance, production, accounting, marketing, strategy, technology or human resources management. Although multivariate statistical techniques such as those described in this book play key roles in fundamental disciplines of the social sciences (e.g., economics and econometrics or psychology and psychometrics), the methodologies particularly relevant and typically used in management research are the center of focus of this study. Statistical Analysis of Management Data is especially designed to provide doctoral students with a theoretical knowledge of the basic concepts underlying the most important multivariate techniques and with an overview of actual applications in various fields. The content herein addresses both the underlying mathematics and problems of application. As such, a reasonable level of competence in both statistics and mathematics is needed. This book is not intended as a first introduction to statistics and statistical analysis. Instead it assumes that the student is familiar with basic statistical techniques. The techniques are presented in a fundamental way but in a format accessible to students in a doctoral program, to practicing academicians, and to data analysts.
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Рубрика: Математика /Вероятность /Статистика и приложения /
Статус предметного указателя: Готов указатель с номерами страниц
ed2k: ed2k stats
Год издания: 2002
Количество страниц: 335
Добавлена в каталог: 03.06.2005
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Предметный указатель
"Saturated" model 169
2SLS see two stage least squares
3SLS see three stage least squares
Adjusted Goodness of Fit Index (AGFI) 170
Advertising 53 317
Advertising, expenditures, marketing example 89
Aitken estimator see Generalized Least Squares (GLS) Estimator
AMOS 171
AMOS, two factor confirmatory factor model 220—221
Analysis of three variables, data example for 19
Analysis, types of 29
Axis rotation 33—34
Bagozzi, Yi and Phillips (1991) procedure 179
Bartlett, test of 92
Bartlett, V of 18 24
Belgium 21
Bentler and Bonnet (1980) goodness-of-fit index (GFT) 169
Bernoulli process 113
Best linear estimator 61—65
Best linear unbiased estimator (BLUE) 62 81
Bivariate normal distribution 9—10
blue see Best Linear Unbiased Estimator
Brand attitude 165
Brand awareness 317
Brand cognitions 166
Brand evaluations of consumers 315
Bretton — Clark 143
Business schools, ranking of 154
Categorical dependent variables 6 105
Channels of distribution 316
Chi-square distribution 14
Chi-Square for Independence Model 186 200 232
Chi-square test 120 168
Chi-Square variate 12
Choice modeling 5
Classification table 120
Coding principle 137
Coefficient of orthogonal polynomials 142
Coefficient, alpha 31—33
Cognitive responses to advertising 165
Commonalities 40
Commonalities, estimating 41
Competition and market structure 315—316
Complementary data analysis 258
Completely restricted residual sum of squares (CRSS) 69—70
Completely unrestricted residual sum of squares (CUSS) 69—72
Composite measurement with К components, generalization of 33
Composite scales 30—33
Concentric ellipses 11
Conditional logit 119
Conditional logit, discrete choice in LIMDEP 118—119
Conditional logit, model 117—119
Configuration improvement 259
Confirmatory factor analysis 29 39 42—44
Confirmatory Factor Analysis, example of 172—178
Confirmatory Factor Analysis, model, path diagram of 178
Conjoint Analysis 137
Conjoint Analysis, using SAS, example 151
Conjoint Designer 143
Constitutive indices 38
Consumer, durable goods 315
Consumer, information 318
Consumer, segments 315—317
Consurv 143
Contemporaneous covariances, matrix of 80—84
Contemporaneously correlated disturbances 79—81
Covariance matrix 83 86
Covariance matrix, estimation 165—168
Covariance matrix, test of 92
Covariance matrix, variances in 40
Covariance model 68
Covariance model, covariance structure analysis 6 29 159 221
Covariance model, description of model 163—165
Covariance model, examples of 171
Covariance model, model fit 168—170
Covariance structure model, analysis of 171
Criterion variable 137 139
Cronbach's alpha 29
Cumulative density functions 146
Data base, INDUP 25—26
Data base, PANEL 25—26
Data scan.dat 129
Data sets, description of 313
Data, metric versus non-metric 253
Data, unconditional versus conditional 254
demographics 318
Design programs 142—143
Developing a composite scale 53
Dimensionality 258
Discriminant analysis 105—112 120
Discriminant analysis, classification and fit 109—112
Discriminant analysis, using SAS, example 121—127
Discriminant coefficients 127
Discriminant criterion 105 107
Discriminant function 108—110
Discriminant function, fit measures 110—112
Dissimilarity data 256
Dissimilarity, measure of 253
Distribution coverage 19
Distribution structure 316
Distribution, chi-squared 311—312
Distribution, cumulative normal 310
Distribution, outlets 71
Disturbance-related set of equations 79
Dual mediation hypothesis model (DMH) 165
Dual mediation hypothesis model (DMH) theory 166
Dummy coding 141—142
Dummy variable 141
Dummy variable, coding 137
Econometric theory elements 55
Effect coding 137 139—142
Eigenvalues and eigenvectors 34—37
Eigenvalues and eigenvectors, properties of 36—37
Electric appliance stores 316
Electronic entertainment products 315
Ellipse with centroid 9
Endogeneity in system 99
Endogenous variables in system 83
England 21
Error, component model 67
Error, structure 56—57
Errors-in-variables, effect of 159
Estimated Generalized Least Square (EGLS) Estimator 82 93 101 115
Estimators, properties of 61—65
Excel 19
Exogenous variables in system 83
Expected Cross-Validation Index (ECVI) 199 212 232 245
Exploratory factor analysis 29 33 39—43
Exploratory Factor Analysis, calculation of eigenvalues and eigenvectors 35
Exploratory Factor Analysis, difference from Confirmatory Factor Analysis 38
F distribution 313
Factor analysis 5—6 33—43
Factor analysis, application examples 44—52
Factor analysis, structure 171
Factorial design, two by two 137
Factors, determining number of 41
Fader, Lattin, and Little A992) procedure 326
Firm factors 93
Firm sales force 316
FORTRAN 262
FORTRAN conventions 148
Fortran-style format 275
France 21
General linear models (GLM) estimator 62
General linear models (GLM) procedure 144 152—153
Generalized Least Squares (GLS) Estimator 58—60 62 81—82 115—116
Goodness of fit or stress values 258
Goodness of fit, measures 169—170
Goodness of fit, statistics 178 185 199 212 231 245
Goodness-of-fit index (GFI) 170 178
Grand mean rating 138
Graphical representation of measurement model for exogenous and endogenous constructs 222
Graphical representation of measures 31
Graphical representation of multiple measures with a confirmatory factor structure 39
Graphicalrepresentation of full structural model 237
Guadagni and Little (1983) MNL model of brand choice 326
Heterogeneity of coefficients, issue of 55
Hierarchy of effects 104
Hotelling'sT 17
Ideal Point model of preference 260—261
Identity matrix 92 99
Imperfect measures, impact of 159
IMS 143
Independent factor model 192
Index of fit of model 256
Individual Differences Scaling (INDSCAL) 261 268 275 284 292
Individual Differences Scaling (INDSCAL), algorithm 260
Individual Differences Scaling (INDSCAL), analysis 293
Individual Differences Scaling (INDSCAL), example of PC-MDS for 266—274
INDUP.CSV 103 324
INDUP.CSV, data set 75—76
Industry characteristics 93
Industry dataset 324
Industry market segments 75
Initial factors, extracting 41
Innovations, characteristics of 93
Interval scale 3—4
Isodensity contour 9
Iterative Seemingly Unrelated Regression (ITSUR) 93 99
Key learning experiences 6
Kronecker products 81 309
KYST 261
KYST algorithm 255 259
KYST analysis 275
KYST example of 262—266
KYST Multidimensional Scaling 263—266
Lagrangian multiplier problem 64
Lawley's approximation 93
Lilinear restrictions 65—67
LIMDEP 2 118 128—129 151—156 326
Linear effect coding 140—141
Linear model 139
Linear model, estimation with SAS, examples of 71—74
Linear statistical model 55—59
LISREL 2 6
LISREL, Estimates (Maximum Likelihood) 196
LISREL8 for Windows 172—178 205 222
LISREL8 for Windows for full structural model 236—250
LISREL8 for Windows for measurement model for exogenous and endogenous constructs 221
LISREL8 for Windows in model with single factor 193—205
LISREL8 for Windows in model with two factors 179—220
LISREL8 for Windows in model with two independent factors 205—219
Logit models of choice 112
Logit type model 151
LOGIT.EXE 132
LOGIT.PRM 131
LOGIT.RES 131—132
Loyalty variable of Guadagny and Little 326
MacKenzie, Lutz and Belch's Model (1986) of role of attitude towards ad 165—167
Mail survey 318—323
Main effect model 139
Management research, measuring variables in 3
Market, behavioral responses 25
Market, response function 89 104
Market, share 19 25 71
Marketing decision functions 89 104
Marketing mix 25 316—318
Marketing strategy 25 316
MARKSTRAT market simulation program 314
MARKSTRAT market simulation program, Environment 314—318
Matrix algebra, rules in 309
Matrix, matrix, structure of 91—92
Matrix, matrix, structure of 92
Matrix, derivation rule 107
Maximum chance criterion 111
Maximum Likelihood Estimation 43 59—60
Maximum likelihood estimator 60
Maximum r procedure 258
MBA program 154
MDPREF see multidimensional analysis of preference data
MDS see Multidimensional Scaling
Mean vectors, test of difference between several, K-sample problem 21
Mean vectors, test of difference between two, one-sample problem 19
Means, tests about 12
Measure, definition of a 29
Measurement errors, problems associated with 162
Measurement model or confirmatory factor analysis 221
Measurement model, parameters 167 171
Measurement model, results, LISREL8 222—235
Measurement theory, notions of 29—33
Media habits 321
Mental factor analysis 53
Minimum Fit Function, Chi-Square 199 231 245
Minimum Fit Function, Value 245
Minimum variance estimator 116
Minkowski p-metric 257
Model parameters, test of significance of 170
Model specification and estimation methods 91
Model to test discriminant validity between two constructs 178—221
Modification indices 170 191 204 217 232 246—248
Monotone analysis of variance (MONANOVA) 137 143—141
Monotone analysis of variance (MONANOVA) using PC-MDS, example 147—151
Monotone multiple regressions 258
Multidimensional analysis of preference data (MDPREF) example of 261 285—291
Multidimensional Scaling (MDS) 253
Multidimensional Scaling (MDS) via a Generalization of Coombs Unfolding Model 294
Multidimensional Scaling (MDS), solution, interpretation of 258
Multinomial logit analysis using LIMDEP 128—130 133—135
Multinomial logit model 117
Multinomial logit model, analysis using LOGIT.EXE 130—132
Multiple independent variables, case with 162
Multiple measures, graphical representation of 39
Multiple regression with a single dependent variable 55
Multivariate analysis of variance 5
Multivariate case, generalization to 11
Multivariate logit program LOGIT.EXE 130
Multivariate normal distribution 9
Multivariate test with known 14
Multivariate test withunknown 14—15
Nested models 169
Nominal scale 3—4-
Non-linear effect coding 141
Non-linear parameters in MNL models 326
Non-metric dissimilarity measures 255
Non-Normed Fit Index (NNFT) 232
Nonsymmetric matrix, diagonal present 255
Nonsymmetric matrix, missing diagonal 255
Normed Fit Index (NFI) 232
Norway 19
Objective function 256
Order and rank conditions 89—91 171
Ordered model 147
Ordered Probit Analysis using LIMDEP, example 151—156
Ordered probit model 144—147
Ordinal scale 3—4
Ordinary Least Square (OLS) 62
Ordinary Least Square (OLS), estimates 99
Ordinary Least Square (OLS), estimation 144
Ordinary Least Square (OLS), estimator 57 64 82 85—86 115 160
Orthogonal rotation 34
Panel data 3
Panel dataset 324—325
PANEL.CSV 103
PANEL.CSV, data set 75—76
Parallel measurements 29
Part-worth coefficients, estimation of 143—141
Partially restricted residual sum of squares model (PRSS) 68 70 72
PC-MDS 143
PC-MDS software 261
Perceptions, underlying dimensions of 260
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