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Gatignon H. — Statistical analysis of management data
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.


Язык: en

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

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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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$\circledR$ market simulation program      314
MARKSTRAT$\circledR$ market simulation program, Environment      314—318
Matrix algebra, rules in      309
Matrix, $\Gamma$ matrix, structure of      91—92
Matrix, $\Sigma$ 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 $\Sigma$      14
Multivariate test withunknown $\Sigma$      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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