 critical points, table of 459
statistic, null hypothesis testing 157 162
Adjusted goodness-of-fit index, LISREL 159
Akaike's information criteria 324
Alpha factor analysis 109
Analysis of variance (ANOVA) with one dependent/more than one independent variable 7
Analysis of variance (ANOVA), monotonic analysis of variance (MONANOVA) 8—9
Analysis of variance (ANOVA), multivariate analysis of variance (MANOVA) 10
Analysis of variance (ANOVA), situations for use 7
anova see "Analysis of variance (ANOVA)"
Association coefficients, in cluster analysis 220
Assumptions, equality of covariance matrices assumption 383—386
Assumptions, independence assumption 387—388
Assumptions, normality assumptions 375
Average-linkage method, hierarchical clustering method 192—193
Axes, in Cartesian coordinate system 17—19
Backward selection, stepwise discriminant analysis 265
Bartlett's test 76
Bartlett's test, purpose of 123
Bartlett's test, sensitivity of 123
Basis vectors 25 31
Bayesian theory, objective of 256
Bayesian theory, posterior possibilities based on 281
Bernoulli trial 340
Between-group analysis 41—42
Between-group analysis, sum of squares and cross products matrix 42
BIOMED, clustering routines 220
BIOMED, structural model estimation 426
Bootstrap method, discriminant function validation 274
Box' M in multiple-group MANOVA 351 356
Box' M, checking equality of covariance matrices 384—386
Canonical correlation as general technique 409
Canonical correlation with more than one dependent/one or more independent variables 9
Canonical correlation, analytic approach to 397—398
Canonical correlation, canonical variates 401—402 404
Canonical correlation, change in scale, effect of 415
Canonical correlation, computer analysis 398—406
Canonical correlation, examples of use 406—409 412—418
Canonical correlation, external validity of 409
Canonical correlation, geometric view of 391—397
Canonical correlation, practical significance of 404—406
Canonical correlation, situations for use 9 391
Canonical correlation, statistical significance tests for 402—404
Canonical discriminant function 251
Canonical discriminant function, standardized 253—254
Cartesian coordinate system 17—19
Cartesian coordinate system, change in origin and axes 18—19
Cartesian coordinate system, Euclidian distance 19
Cartesian coordinate system, origin and axes in 17—19
Cartesian coordinate system, rectangular Cartesian axes 17
Cartesian coordinate system, representation of points 17—18
Cartesian coordinate system, vectors in 23—25
Central tendency measures, mean 36
Centroid method, hierarchical clustering method 188—191
Chaining effect, in hierarchical clustering methods 211 217
Chi-square difference test 439
Chi-square goodness of fit test 378
Chi-square plot 381—382
Chi-square plot, computer program for 389—390
Classification as independent procedure 242 244
Classification for more than two groups 311—312
Classification in discriminant analysis 242—244 278—284
Classification in logistic regression 326—327
Classification, classification function method 257—258
Classification, classification matrix 255—256
Classification, classification rate, evaluation of 258—260
Classification, computer analysis 256—257 261
Classification, cutoff-value method 255—256
Classification, Mahalanobis distance method 258
Classification, misclassification errors 256—257 261 311—312
Classification, multiple-group discriminant analysis 293 303—304 311—312 313
Classification, multivariate normal distributions, rules for 281—283
Classification, practical significance of 260
Classification, statistical decision theory 256—257 279—281
Classification, statistical tests used 258 260
Classification, total probability of misclassification 280
Cluster analysis, average-linkage method 192—193
Cluster analysis, centroid method 188—191
Cluster analysis, comparison of hierarchical/nonhierarchical methods 211—217
Cluster analysis, complete-linkage or farthest-neighbor method 192
Cluster analysis, computer analysis of 193—202
Cluster analysis, dendrogram in 190—191
Cluster analysis, examples of 221—232
Cluster analysis, external validity of solution 221
Cluster analysis, geometrical view of 186—187
Cluster analysis, hierarchical clustering methods 188—193
Cluster analysis, loss of homogeneity in 200
Cluster analysis, nonhierarchical clustering 202—211
Cluster analysis, objective of 187
Cluster analysis, Q-factor analysis 187
Cluster analysis, R-squared 198 200
Cluster analysis, reliability of solution 221
Cluster analysis, root-mean-square total-sample standard deviation 197 198
Cluster analysis, semipartial R-squared 198 200
Cluster analysis, similarity measures 187—188 218—220
Cluster analysis, single-linkage or nearest-neighbor method 191
Cluster analysis, situations for use 12 185
Cluster analysis, Ward's method 193
Common factors 96 108
Communality 92
Communality estimation problem, and factor analysis 136
Complete-linkage method, hierarchical clustering method 192 217
Computer programs see "BIOMED" "Statistical "Statistical
Concordant pair 325 326
Confirmatory factor analysis 128
Confirmatory factor analysis, LISREL 148—177
Confirmatory factor analysis, objectives of 148
Confirmatory factor analysis, situations for use 144
Confusion matrix 255—256
Conjoint analysis with one dependent/more than one independent variable 8—9
Conjoint analysis, monotonic analysis of variance (MONANOVA) 8—9
Constrained analysis, LISREL 171 173
Contingency table analysis, in logistic regression 327—328
Contrasts, computer analysis 360—366
Contrasts, correlated contrasts 363—366
Contrasts, Helmert contrasts 360—361
Contrasts, multivariate significance tests for 359—360 363
Contrasts, orthogonal contrasts 357—363
Contrasts, univariate significance tests for 357—359 360 362—363
Correlated contrasts, in multiple-group MANOVA 363—366
Correlation coefficient for standardized data 39
Correlation coefficient in cluster analysis 220
Correlation matrix in confirmatory factor analysis 144—145
Correlation matrix, use in 144—145
Correspondence analysis, situations for use 12
Covariance matrix and factor analysis 144—145
Covariance matrix, equality of covariance matrices assumption 383—387
Covariance matrix, one-factor model with 145—147
Cutoff-value method 244 255—256
Data analytic methods, dependence methods 4—10
Data analytic methods, interdependence methods 4 10—12
Data analytic methods, structural models 13—14
Data manipulations, computer procedures for 55—57
Data manipulations, degrees of freedom 36—38
Data manipulations, generalized variance 39
Data manipulations, group analysis 40—42
Data manipulations, mean 36
Data manipulations, mean-corrected data 36
Data manipulations, standardization of data 39
Data manipulations, sum of cross products 39
Data manipulations, sum of squares 38—39
Data manipulations, variance 38
Degrees of freedom 36—38
Degrees of freedom, computation of 37—38
Dendrogram, for clustering process 190—191
Dependence methods 4—10
Dependence methods for more than one dependent/more than one independent variables 9—10
Dependence methods for one dependent/more than one independent variable 5—9
Dependence methods for one dependent/one independent variable 5 6
Dependence methods, analysis of variance 7
| Dependence methods, canonical correlation 9
Dependence methods, conjoint analysis 8—9
Dependence methods, discrete discriminant analysis 8
Dependence methods, discrete multiple-group discriminant analysis 10
Dependence methods, discriminant analysis 7—8
Dependence methods, logistic regression 8
Dependence methods, multiple-group discriminant analysis 10
Dependence methods, multivariate analysis of variance 10
Dependence methods, regression 5
Dependence methods, situations for use of 4
Detrended normal plot 378—379
Dimensional reduction, principal components analysis as 64—65
Direction cosines 25
Discordant pair 325
Discrete discriminant analysis, with one dependent/more than one independent variable 8
Discrete multiple-group discriminant analysis with more than one dependent/one or more independent variables 10
Discrete multiple-group discriminant analysis, situations for use 10
Discriminant analysis see also "Multiple-group discriminant analysis" "Two-group
Discriminant analysis with one dependent/more than one independent variable 7—8
Discriminant analysis, compared to multivariate analysis of variance (MANOVA) 350
Discriminant analysis, compared with logistic regression 332—333
Discriminant analysis, discrete discriminant analysis 8
Discriminant analysis, discrete multiple-group discriminant analysis 10
Discriminant analysis, multiple-group discriminant analysis 10
Discriminant analysis, situations for use 8 237
Discriminant analysis, stepwise discriminant analysis 246 264—273
Discriminant function, multiple-group discriminant analysis 294—303
Discriminant function, multiple-group discriminant analysis, assessment of importance of 303
Discriminant function, multiple-group discriminant analysis, computation options 294 297
Discriminant function, multiple-group discriminant analysis, estimate of 297—299
Discriminant function, multiple-group discriminant analysis, group differences, examination of 307
Discriminant function, multiple-group discriminant analysis, labeling of 307
Discriminant function, multiple-group discriminant analysis, number needed 299—303
Discriminant function, multiple-group discriminant analysis, practical significance of 302—303
Discriminant function, multiple-group discriminant analysis, statistical significance of 299—302
Discriminant function, two-group discriminant analysis 250—254
Discriminant function, two-group discriminant analysis, bootstrap method for validation of 274
Discriminant function, two-group discriminant analysis, canonical discriminant function 251
Discriminant function, two-group discriminant analysis, computation options 250—251
Discriminant function, two-group discriminant analysis, estimate of 251—252
Discriminant function, two-group discriminant analysis, holdout method for validation of 273
Discriminant function, two-group discriminant analysis, linear discriminant function 242
Discriminant function, two-group discriminant analysis, meaning of 242
Discriminant function, two-group discriminant analysis, practical significance of 253
Discriminant function, two-group discriminant analysis, standardized canonical discriminant function 253—254
Discriminant function, two-group discriminant analysis, statistical significance of 252—253
Discriminant function, two-group discriminant analysis, U-method for validation of 273—274
Discriminant score, meaning of 242
Discriminant variables 238
Discriminant variables, assessment of importance of 253—254
Distance measures 218—220
Distance measures, Euclidian distance 19 219
Distance measures, Mahalanobis distance 44—45 220
Distance measures, Minkowski distance 218
Distance measures, statistical distance 42—44
Distinguishability of observations 45
Effect size in MANOVA 348—349
Effect size, multivariate 349
Effect size, univariate 349
Eigenstructure of covariance matrix 84—85
Eigenstructure of covariance matrix, computer analysis 87—89
Equality of covariance matrices assumption 383—387
Equality of covariance matrices assumption, errors and violation of 383—384
Equality of covariance matrices assumption, tests for checking equality 384—387
Equivalent vectors 20
Error sums of squares 193
Euclidian distance 19
Euclidian distance and statistical distance 43—44
Euclidian distance for standardized data 219
Euclidian distance in cluster analysis 219
Euclidian distance in similarity matrix 187—188
Event 325
Exploratory factor analysis 128
Exploratory factor analysis, situations for use 144
F-distribution in multiple-group discriminant analysis 294
F-distribution, table of 460—465
F-ratio in multiple-group discriminant analysis 294 301
F-ratio in multiple-group MANOVA 351
F-ratio in stepwise discriminant analysis 266 271
F-ratio, relationship to Wilks' A 348
F-test, in multiple-group discriminant analysis 293 294
Factor analysis see also "Confirmatory factor analysis"
Factor analysis and communality estimation problem 136
Factor analysis, alpha factor analysis 109
Factor analysis, appropriateness of data for 116 123 125
Factor analysis, choosing technique for 108
Factor analysis, common factors 96 108
Factor analysis, communalities problem, estimation of 100
Factor analysis, compared to principal components analysis 125—128
Factor analysis, computer estimation of 109—115
Factor analysis, concepts/terms related to 90—93
Factor analysis, confirmatory factor analysis 128
Factor analysis, exploratory factor analysis 128
Factor analysis, factor extraction methods 141—142
Factor analysis, factor indeterminacy 97—98 136
Factor analysis, factor rotation problem 97 100—102 136
Factor analysis, factor rotations, types of 137—141
Factor analysis, factor scores 96 142—143
Factor analysis, factor solution 117—118
Factor analysis, fundamental factor analysis equation 136
Factor analysis, geometric view of 99—102
Factor analysis, image analysis 109
Factor analysis, interpretation of factor structure 125
Factor analysis, model with more than two factors 96 102 135—136
Factor analysis, number of factors needed 116—117
Factor analysis, objectives of 99
Factor analysis, one-factor model 93 132—133
Factor analysis, principal axis factoring (PAF) 107 142
Factor analysis, principal components factoring (PCF) 103—107 141—142
Factor analysis, representation of factors 118—119
Factor analysis, situations for use 11 90
Factor analysis, two-factor model 93—96 133—135
Factor extraction methods 141—142
Factor extraction methods, principal axis factoring 142
Factor extraction methods, principal components factoring 141—142
Factor rotation problem 97 100—102 136
Factor rotation problem, basis of 97
Factor rotation problem, geometric view of 100—102
Factor rotations 137—141
Factor rotations, oblique rotation 140—141
Factor rotations, orthogonal rotation 137
Factor rotations, quartimax rotation 120—121 137
Factor rotations, varimax rotation 119—120 138 139—140
Factor scores 96 142—143
Farthest-neighbor method, hierarchical clustering method 192 217
Fisher's linear discriminant function 245 277—278
Fisher's linear discriminant function, computation of 277—278
Fisher's Z transformation 383
Forward selection, stepwise discriminant analysis 265
Fundamental factor analysis equation 136
Generalized variance 39 50—51
Generalized variance, equality to determinant of covariance matrix 54—55
Generalized variance, geometric representation of 50—51
Geometric concepts, Cartesian coordinate system 17—19
Geometric concepts, vectors 19—33
Goodness-of-fit measures in logical regression 324
Goodness-of-fit measures, chi-square 378
Goodness-of-fit measures, LISREL 157—159
Gram — Schmidt orthonormalization procedure 32
group analysis 40—42
Group analysis, between-group analysis 41—42
Group analysis, within-group analysis 40—41
Helmert contrasts 360—361
Heuristic measures, of model fit 157—160
Hierarchical clustering methods 188—193
Hierarchical clustering methods, average-linkage method 192—193
Hierarchical clustering methods, centroid method 188—191
Hierarchical clustering methods, chaining effect in 211 217
Hierarchical clustering methods, complete-linkage or farthest-neighbor method 192
Hierarchical clustering methods, computer analysis 193—202
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